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A still young prize which with it results confirms what I seemed to guess at the first time of my participation: a vocation for a continued growth with the right ambition to be a point of reference in the Italian panorama of the contemporary artists awards. Giornalista, curatore indipendente, insegnante di pianoforte.

Giornalista e direttrice responsabile del portale Teknemedia. Autore di numerose pubblicazioni e cataloghi, collabora con testate giornalistiche di settore e non. He is a journalist, independent curator and teacher of piano.

Also he is the curator of the fourth edition of the festival, Tina B in Prague. She has been working at the Teknemedia. He works on the contemporary aspect of culture as curator and cultural planner. He is author of numerous publications and catalogs, working with general and specialized newspapers. He is columnist for Nordest Europa and Exibart. He is director and founder of the Fuoribiennale and Innov e tion Valley, the first Italian network of creative companies.

Giornalista, lavora al Sole 24 Ore dal Con Mursia ha pubblicato Il dito sulla piaga. Togliatti e il Pci nella rottura fra Stalin e Tito, She is a journalist and she has been working at the Sole 24 Ore since She has taught Art and Technology at the Bocconi University of Milan Cleacc - Bachelor of economics for the arts, culture and communication. She is currently president of ActionAid Italy. She is professor of history of contemporary art at the Fine Art Academy in Venice, she is also an expert of iconography and semiotics of art.

With Mursia he has published Il dito sulla piaga. In September he released his first novel, Cenere, with the same publishing house. Siamo lusingati di essere parte di questo vivace scambio artistico e interpersonale. Abbiamo conosciuto il Premio Arte Laguna grazie a Guido Airoldi, nostro artista che fu finalista nella scorsa edizione e vinse anche dei premi.

We are very grateful to be part of this vivid artistic and interpersonal exchange. We know the Arte Laguna Prize thanks to Guido Airoldi, our artist that he was a finalist in last edition and also he won some prizes. Visiting the exhibition in the Arsenale, we immediately knew we wanted to participate in this project. Find and develop new talents is part of our work.

Spacing from figurative-imaginative art to illustration, from deviant art to pop-surrealism, with a special touch of dark humor, Cell63 is an art gallery promoting exciting and imaginative Emerging Talents of the international art circuit, perfect site for hosting the solo exhibition of one the talented artists selected by the well renown competion Premio Arte Laguna.

I consider the Laguna Prize a stimulating opportunity for further developments offered to artists and I consider the organization very efficient. An event that makes known the work of artists and gallery owners, thanks to prizes and selections it gives the opportunity to increase their artistic experiences.

It collaborate with the Arte Laguna Prize as a worthwhile tool for the support and promotion of Italian and international art. Its increasing initiatives including this one, to exhibit the finalists in the various galleries, represent an important platform of comparison between the artists and the art market. We decided to participate in the competition with both venues for the quality of the organization, the jury and the proposals.

We believe that a contemporary art gallery should pay attention to young artists and that the opportunity to host a personal exhibition of one of the finalists is a great way to discover new ones. I have always worked to promote young artists, often at their debut; it is in this sense that I consider the Arte Laguna Prize as an excellent initiative for the arts; it is a prize wellstructured and organized. The adhesion of Fabbrica Eos is because I strongly believe in the opportunities that arises from the encounter.

The Fu Xin Gallery is a new significant voice in the large chorus of galleries active in the Shanghai and international art scene, pays special attention to women artists and is a meeting point for Chinese and Western intellectuals. When we were invited to participate at Premio Arte Laguna, we were honored and we accepted because we see the opportunity in this collaboration in two ways, first to support the young talents at the photographic field and second in this international exchange of the art which enriches our work.

Through our activities we promote ideas, arts and culture, with a focus on the work of young artists working in Venice and the Adriatic basin. The Galleria Bianconi, that from many years is doing a selective work within the second half of the XX century, with the opening of new venue it founded the b. I think that Arte Laguna Prize has achieved the deserved success thanks to the passion for the art shown in last years and the constant commitment of all those who have collaborated to this realization.

Arte Laguna is doing everything of it. The Prize Arte Laguna is currently one of the most interesting contests ables to select young artists of the national and international scene. The multiplicity and the International offer in all disciplines are fundamental to this prize. The artists chosen by a jury of experts ensures the high quality of the selection.

From here the validity and the pleasure of being the award for the favorite artist. Photo by: Carlo Casella. Sono entusiasta di collaborare col Premio Arte Laguna sia come gallerista specializzata in giovani artisti che come veneziana adottiva. I am excited to collaborate with the Arte Laguna Prize and as a gallery owner specializing in young artists and as Venice foster daughter.

For many years my family has owned an apartment in the Salute area for me it is as a second home. The evocative location of the Nappe of the Arsenale adds a special flavor to the initiative that offers an international stage to those talents who often struggle to be known even if they are deserving form a quality perspective.

Fiorella Pieri Gallery is a benchmark for contemporary art in the heart of Cesena: it proposes an interesting alternation of solo and group exhibitions dedicated to already established and emerging national and international artists, for this reason, it continues to collaborate with Arte Laguna Prize. Gagliardi Gallery in its twenty years of experience, has always searched for reliable partners in order to share with them good organizational criterias, continuity of the promotion and responsibility of judgment.

We consider Premio Arte Laguna a rare and perfect synthesis of the expression of an international prize in the sign of quality and professionalism. To build a virtuous circuit of Italian and foreigner galleries selected by Laguna Art Prize is a useful idea of national and international involvement and contamination the gives to our art space the chance of visibility and to meet new gifted artists. For a gallery that always presents proposals and opportunities to meet new artists, the participation in the Arte Laguna Prize since the beginning was a gamble on the future of a courageous and wider idea.

We are proud of the appreciation we have received from the artists that we awarded and with whom we are still working today with. I think the Arte Laguna Prize is an excellent opportunity to know more about contemporary artists of quality from around the world. Leo Galleries nasce a Monza nel marzo del Leo Galleries started in Monza in March It is mainly concerned with experimenting and promoting young art through exhibitions in their Monza and Lugano spaces and with prestigious events in some public places.

It collaborates with national and international institutions for organizing cultural exchanges through residencies for young artists. Il supports the art that arises as a means of comparing the social and cultural awareness. It is an instrument of defense and offense against the enemy. The Arte Laguna Prize has become an important event for the young Italian art and a great stage and test that gives the opportunity for the artists to propose themselves and get in touch with galleries.

Il Premio Arte Laguna incarna alla perfezione la ricerca artistica che io stessa, attraverso la mia galleria, perseguo da molti anni. Also we appreciate the interest and the attention that the prize give to emerging artists, as well as for years our gallery aims to do. The Arte Laguna Prize is an important platform to give birth to new young contemporary art, because it gathers in the same contest new realities and established art scene of today.

The contribution of our gallery is directed mainly to enhance and develop the talents of young artists. The Arte Laguna Prize represents to perfection the artistic research that, through my gallery, I pursue for many years. Contemporary art has to be done by young people and this Prize allows the institutional art channels to interact directly with some of the most innovative Italian artists. Grazie a Igor Zanti per avermi proposto questa collaborazione.

Spazio Thetis supports all those who are dealing seriously with contemporary art in Venice. It is for this reason that we welcome one of the finalists of the Arte Laguna Prize, continuing a collaboration begun last year through the site-specific installation of Francesca Pasquali. I think a community should support their artists, especially if they are young; leave to future generations a real heritage, dreams of value and a constructive example, is a mission to which each cultural operator should join.

It is a great honour for me to participate in the Arte Laguna Prize. Since I am part of a joung gallery, I think it is fundamental to represent new artists and to work together in such an important initiative. I thank Igor Zanti for proposing me this collaboration. Il Premio Arte Laguna sviluppa un progetto annuale e coinvolge il pubblico in proposte stimolanti e artisticamente rappresentative della nostra epoca.

The Promenade Gallery has chosen to collaborate because it has noted the reliability and the constant work of the Organizazion. The venue in Albania is available to host art-works of one of the artists. The collaboration between the selected international operators in this field is vital for artists, for galleries and for supporting the contemporary art. The Arte Laguna Prize develops an annual project, involving the public in stimulating proposals and artistically representative of our era.

I think the synergies are crucial in this bizarre world of contemporary art and this is why I am happy to share this experience, opening the doors of my gallery. Io ammiro corraggio e iniziative, di conseguenza non ho avuto nessun dubbio quando le organizzatrici mi hanno chiesto di partecipare a questa edizione con il Festival TinB, mi sono sentita a casa….

The intent is to create a platform for exchanges and relationships. This partnership will extend to the next editions of OPEN International Exhibition of Sculptures and Installations and the Arte Laguna Prize, two international platforms for giving space to new talents in contemporary art.

I followed Arte Laguna for several years with curiosity and admiration. I am convinced that for creating a Prize Arte Laguna in Venice where there is the Important Biennale of art you should prove a great courage.

I admire the courage and the initiative and so I had no doubt when it was asked me to become a juror, immediately I felt at home In order to offer ever more opportunities for artists, the Organization of Arte Laguna Prize wanted to create, for his fifth edition, four Art Residences, in collaboration with four lively Institutions in the artistic reality, as concrete opportunities for professional development of artists careers.

From our point of view, what gives a special value to Arte Laguna Prize, is the fact that it is open to artists of any age, form of art and from all over the world along with its connection, through exhibition prizes and art residencies, with important realities of different nature.

A network of relations that, from the prestigious venue of the Arsenale, extendes ideally to national and international interesting and important contexts. All this, and our feeling of shearing the adventure and the enthusiasm of the Prize organizers, is the fundamental aspect that has given us the will to develop a collaboration for the project Artist in Residence. Nowadays, the school encloses both the past and the future of the art of glass.

Here is a wise combination of purposes. There is a strong will to stress the indissoluble link between the multiple facets of the glass art scenario - and their ties with the modern world of the design, culture and art - and, at the same time, the necessity of preserving a secular tradition and the technical capabilities proper of an island such as Murano. In Brasile da 20 anni e oggi ricca di esperienze e incontri importanti, Reil vanta consolidati legami col tessuto socio- economico di questo grande Paese.

The Arte Laguna Prize has become an important platform for contemporary art. It is thanks to the support of international partners based all over the world as the Arte Laguna that Art Stays is now the key point of international and intercultural exchange of ideas, thoughts and artistic processes. Created in in memory of the founder of Fashion Box-Replay, the Fondazione Claudio Buziol opened its Venice headquarters in , quickly becoming an important meeting point for young artists.

The Foundation is active in the organisation of exhibitions, events and lectures, in collaboration with other national and international institutions and supporting personal artistic projects and researches.

In this context, the partnership with Premio Internazionale Arte Laguna represents the will to renovate the attention towards emerging talents, trying to trace an interesting map of contemporary art. In Brazil since the s, Reil is today privileged with the many connections and business relations established over the many years of hard work in this great Country. Brazil has now come onto the world limelight and is growing tremendously fast.

Nevertheless, in the Brazilian government strategic marketing plan for the period we read that at the centre of this extraordinary growth there are the environment, the traditions, the Country history and therefore the people and their aspiration to have a good life quality.

We very much thank Arte Laguna for having given us the opportunity to contribute to this exciting process. Come mai? Proprio come dei bulimici ci abbuffiamo di immagini, di informazioni, di spunti, per poi rimetterli senza averne recepito nessun nutrimento. Sometimes, not often, but just sometimes I wonder why so many artists choose to enroll in an art prize.

Perhaps it is a question that I should do as one of the creators and the curator of the International Arte Laguna Prize, but without false hypocrisies, I confess that year after year I am surprised for the growing numbers of people who set their hopes, expectations, and energies in an art prize. From my point of view the prize, and in particular the selection of the works, it is an opportunity that is not to be missed.

When I would have another the opportunity, in only one time, to see and learn about the production of so many artists around this huge country which is called the world? Where else, as a young curator, I would have the opportunity to work closely with the excellent and renowned professionals that are members of the jury? The Art, as I like to remind IED students, is a very specialized language, a form - perhaps the best, perhaps the most complex — of communication, but also for many people it represent a need, a necessity.

I never believed, or rather, I give up to believe, during university years as an art historian, that the artists are alienated beings alternating with torment and ecstasy moments. I leave to literature and film fictions the representation of violent crypto erotic passion of Jackson Pollock during the realization of his intricate dripping, or to American interpreters of Mr.

Buonarroti to suppose Michelangelo wraths against bearded popes, or in the darkness of a stage the pleasure to assist the imaginary staging of bohemian existence in the company of green fairies with anise taste. This is not a requirement or need to make art, which I think is the true essence of the artist, but a pantomime version of operetta or, in a more contemporary way, a soap opera of being an artist.

We all, of any socio-cultural level, have a preferred communication form: some people like me have chosen or has been condemned to have to express himself only written or verbal forms; who communicates through the results of his work, who through his faith, who through music, or who, like artists, through thousands expressive possibilities of visual arts. This communication feature is a innate need of the human being as social animal and if was developed many different communication forms or languages where the art is one of the most complex and interesting, without any doubt.

The problem that occurs talking about art is that, as all other languages, it is necessary there should be some interlocutors. Surely, the possibility of spreading and the amount of these proposals create a situation that could be called bulimic, introducing an inevitable risk that is represented by overexposure. In fact there are a lot of opportunities to find a work of art: the number of galleries is growing, more or less valid, which are dedicated to contemporary art; the amount of art fair is increasing, even small and remote, dedicated to contemporary art and it is also growing the flourish - on the basis of what Adriana Polveroni defines Bilbao-effect - of opening projects of institutional spaces dedicated to exhibit contemporary art works.

The contemporary society is a bulimic society, there is a constant binge of proposals, almost without reason; it is a liquid society, as Bauman calls it, where the speed of transformation and evolution does not allow any kind of assimilation. As bulimic persons we binge with images, information, ideas, and we put them back without having implemented any nourishment.

Art is perhaps one of the realities for its complexity that is affected most of the disadvantage of being in a bulimic dimension and it do not have the necessary and proper times for being assimilated, understood, transformed. In front of the amount of proposals and their evolution speed, even the most refined and interested experts sinking in a sea which tends to down in it, through increasingly violent currents, and to make lose his reference points.

I believe, in this frenetic dimension, that art prizes can represent a craved and necessary pause and reflection moment, for who is behind a table as juror and for those who, with great difficulty, is trying to find suitable partners that will understand their work. But we are not faced with an ideal situation, we are faced with a possibility, an attempt to overcome the inevitable superficiality of the opinion that comes from the ordinary dimension of ultra-communicating and ultra-communicated.

The responsibility of the jurors is great because you have to give to every artist the possibility to use this opportunity in the best way, you have to try to get through the means at our disposal over the work and to understand the complexity of a path, richness of a research, the roots on which an act was formed. Perhaps in last years, the Arte Laguna Prize has represented an opportunity for many artists, and it has become an oasis of attention within a context where, inevitably, everything goes fast, everything becomes liquid under the baumanian perspective.

Also this edition of Arte Laguna Prize continues to be a workshop and a privileged point of view of what is the real context of contemporary art in an international level. The choices of the jury tried to be representative of the contemporary art evolution and that tried — as and where it is possible - to witness the versatility of the particular historical moment in which we live with difficulty but with great intellectual honesty and a great respect for the works that has been applied for the prize.

I wish a good edition to everyone. Eva Armental UK Centro Cultural Bancaja, Alicante Spain. Selected work. Selected works. Juried by Manon Sloan 3. Espacio 8 — Madrid. Finalista, Premio Celeste, Fabbrica Borroni 2. Premio Nazionale Canon Giovani Fotografi Celeste Prize International selezionato 2. Yicca finalista 3.

Casona de Reinosa, Cantabria, Casimiro Sainz art prize, London International Creative Competition , shortlist winner 2. Group exhibitions curated by Professor Dr. C, Buttrio, Udine Italy. Dal Vincitore del primo premio sezione pittura al Premio Arte Mondatori 2. Vincitore del Premio Celeste 3. Finalista al premio Terna Ermanno Tedeschi Gallery, , Milano 2. Contemporanea mente art gallery, , Parma Italy 3. Secondo classificato, Premio Celeste , esposizione dei finalisti alla Fondazione Brodbeck, Catania 2.

Opera selezionata per il catalogo, Premio Celeste Aufhebung, , spazio projects, Roma. Principali Mostre Personali Most important solo exhibitions: 1. Torino — Roma in arte, Cortile del maglio, Torino Italy 3. Premio Cupra 3. Spychalski Competition, Poznan Poland 2.

Principali Mostre Collettive Group Exhibitions: 1. Piercing ,via farini Gallery Milano 2. New York Expectations, Opera segnalata categoria pittura. Dependtendency, Plot rt. Artwave West, Dorset UK. National Lottery Award, busery for travel and education. Pollock-Krasner Grant 2. Corrado Abate Italy Senza titolo tecnica mista, , Bolzano Italy 3.

Die perfekte Ausstellung — Oder warum eine solche nicht existiert, Heidelberger Kunstverein, Heidelberg 2. Qui vive? Italy 2. Premio Arte Laguna , finalista 2. Premio Terna 02, , secondo premio Gigawatt 3.

Premio Celeste , finalista. Gli illustri sconosciuti, Galleria 18, Bologna 2. Collettiva di arte contemporanea, Circolo Artistico Iterarte, Bologna. OpenArt 08, , Trii Bellinzona , Svizzera. Selezionate per Celeste Prize international Videocards, C. Celeste Prize selection , 2. Talent Prize selection , 3. Utopia, Officina 64, Firenze 2. Premio Arte Targa oro primo premio sezione scultura. Lectures, curated by A. Progetti per Milano, Palazzo della Permanente, Milano 2.

Gemine Muse, Battistero del Duomo, Cremona. Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis ICA , which is a powerful method to reconstruct latent source signals from their linear mixtures.

In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA hc-ICA model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks.

Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks , we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion.

We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder PTSD. Curvilinear component analysis : a self-organizing neural network for nonlinear mapping of data sets. We present a new strategy called "curvilinear component analysis " CCA for dimensionality reduction and representation of multidimensional data sets.

The principle of CCA is a self-organized neural network performing two tasks: vector quantization VQ of the submanifold in the data set input space ; and nonlinear projection P of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold.

After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space. This paper proposes a probabilistic neural network NN developed on the basis of time-series discriminant component analysis TSDCA that can be used to classify high-dimensional time-series patterns.

TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space.

The analysis can be incorporated into an NN, which is named a time-series discriminant component network TSDCN , so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method.

The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis PCA. However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing.

An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features.

Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Concurrent white matter bundles and grey matter networks using independent component analysis. Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging.

Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts.

We show how this framework can be used to investigate individual variations in brain networks in terms of both nodes and edges as well as their associations with individual differences in behaviour and anatomy. Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Published by Elsevier Inc.

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis. Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind ToM. However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization.

Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis ICA , is a data-driven technique used to isolate intrinsic connectivity networks , and this approach provides insight into network -level regional recruitment. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions.

The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated. Identifying apple surface defects using principal components analysis and artifical neural networks.

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths nm and nm.

Extracting intrinsic functional networks with feature-based group independent component analysis. There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in functional MRI data.

While networks are typically estimated based on the temporal similarity between regions based on temporal correlation, clustering methods, or independent component analysis [ICA] , some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies.

Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks spatial correlations as high as 0. In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature.

In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro. Online signature recognition using principal component analysis and artificial neural network.

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space.

Artificial neural network is adopted to solve the complex signature classification problem. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components. Chinese medicine is known to treat complex diseases with multiple components and multiple targets.

However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula LQF. The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work.

In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target PT network. An influenza-related protein-protein interaction PPI network was constructed and integrated with the PT network. Then the compound-effective target CET network and compound-ineffective target network CIT were extracted, respectively. As a result, 15 main effective components were identified along with 61 corresponding targets.

The main effective component -target MECT network was further constructed with main effective components and their key targets. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions. In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network.

Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis , most commonly to compare control and disease groups through the average curves in each group.

Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis FPCA is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level.

This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings.

To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores. Rodent models have opened the door to a better understanding of the neurobiology of brain disorders and increased our ability to evaluate novel treatments.

Resting-state functional magnetic resonance imaging rs-fMRI allows for in vivo exploration of large-scale brain networks with high spatial resolution. We present a comprehensive protocol to evaluate resting-state brain networks using Independent Component Analysis ICA in rodent model. Specifically, we begin with a brief review of the physiological basis for rs-fMRI technique and overview of rs-fMRI studies in rodents to date, following which we provide a robust step-by-step approach for rs-fMRI investigation including data collection, computational preprocessing, and brain network analysis.

Pipelines are interwoven with underlying theory behind each step and summarized methodological considerations, such as alternative methods available and current consensus in the literature for optimal results. The presented protocol is designed in such a way that investigators without previous knowledge in the field can implement the analysis and obtain viable results that reliably detect significant differences in functional connectivity between experimental groups.

Our goal is to empower researchers to implement rs-fMRI in their respective fields by incorporating technical considerations to date into a workable methodological framework. Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals.

The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases.

A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis PCA and the Karhunen-Loeve expansion KLE both decompose network time series into a set of temporally varying modes and their spatial responses.

Therefore they provide a mathematical framework to perform spatiotemporal filtering. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components , which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California.

Furthermore, the common mode errors exhibit temporally nonrandom patterns. Long-term intensive gymnastic training induced changes in intra- and inter- network functional connectivity: an independent component analysis. Long-term intensive gymnastic training can induce brain structural and functional reorganization.

Previous studies have identified structural and functional network differences between world class gymnasts WCGs and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training.

We examined both intra- and inter- network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI R-fMRI. Group-independent component analysis ICA was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks RSNs. We interpret this generally weaker intra- and inter- network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis. A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context.

To look into this issue, we conducted a functional magnetic resonance imaging fMRI study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis ICA and graph theoretical analysis GTA.

The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area SMA , left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending.

Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs. Reliability analysis of C turboprop engine components using artificial neural network.

In this study, we predict the failure rate of Lockheed C Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including feed-forward back-propagation, radial basis neural network , and multilayer perceptron neural network model ; will be utilized to perform this study.

For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network ANN model utilizing the feed-forward back-propagation algorithm as a learning rule.

The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box.

In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model.

By the end of the study, we forecast the general failure rate of the Lockheed C Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network MLP model on DTREG commercial software. The results also give an insight into the reliability of the engine. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis. Memory function is likely subserved by multiple distributed neural networks , which are disrupted by the pathophysiological process of Alzheimer's disease AD.

In this study, we used multivariate analytic techniques to investigate memory-related functional magnetic resonance imaging fMRI activity in 52 individuals across the continuum of normal aging, mild cognitive impairment MCI , and mild AD. Independent component analyses revealed specific memory-related networks that activated or deactivated during an associative memory paradigm. Across all subjects, hippocampal activation and parietal deactivation demonstrated a strong reciprocal relationship.

Furthermore, we found evidence of a nonlinear trajectory of fMRI activation across the continuum of impairment. Less impaired MCI subjects showed paradoxical hyperactivation in the hippocampus compared with controls, whereas more impaired MCI subjects demonstrated significant hypoactivation, similar to the levels observed in the mild AD subjects.

We found a remarkably parallel curve in the pattern of memory-related deactivation in medial and lateral parietal regions with greater deactivation in less-impaired MCI and loss of deactivation in more impaired MCI and mild AD subjects. Interestingly, the failure of deactivation in these regions was also associated with increased positive activity in a neocortical attentional network in MCI and AD.

Our findings suggest that loss of functional integrity of the hippocampal-based memory systems is directly related to alterations of neural activity in parietal regions seen over the course of MCI and AD. These data may also provide functional evidence of the interaction between neocortical and medial temporal lobe pathology in early AD. With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis PCA using pollution data from different cities.

This paper describes how students can obtain data on air quality and process such data for additional information…. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network , in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months.

We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis , we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron.

In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon. Increase of posterior connectivity in aging within the Ventral Attention Network : A functional connectivity analysis using independent component analysis.

Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks. However, little is known about age-related changes within the Ventral Attention Network VAN , which underlies selective attention. The aim of this study is to examine age-related changes within the VAN, focusing on connectivity between its regions. Here we report our findings on the analysis of 27 participants' 13 younger and 14 older healthy adults BOLD signals as well as their performance on a letter-matching task.

We identified the VAN independently for both groups using spatial independent component analysis. Three main findings emerged: First, younger adults were faster and more accurate on the task. Second, older adults had greater connectivity among posterior regions right temporoparietal junction, right superior parietal lobule, right middle temporal gyrus and left cerebellum crus I than younger adults but lower connectivity among anterior regions right anterior insula, right medial superior frontal gyrus and right middle frontal gyrus.

Older adults also had more connectivity between anterior and posterior regions than younger adults. Finally, correlations between connectivity and response time on the task showed a trend toward connectivity in posterior regions for the older group and in anterior regions for the younger group. Thus, this study shows that intrahemispheric neurofunctional changes in aging also affect the VAN.

The results suggest that, in contexts of selective attention, posterior regions increased in importance for older adults, while anterior regions had reduced centrality. This work presents a non-parametric method based on a principal component analysis PCA and a parametric one based on artificial neural networks ANN to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix.

The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing synthetic composed spectra at different signal-to-baseline ratio SBR , signal-to-noise ratio SNR , and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used.

Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose e-nose combined with Independent Component Analysis ICA as a dimensionality reduction technique, Partial Least Squares PLS to predict sensorial descriptors and Artificial Neural Networks ANNs for classification purpose.

A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. Ad hoc Laser networks component technology for modular spacecraft. Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization.

Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network.

Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft. Ad hoc laser networks component technology for modular spacecraft. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested.

Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways in isolation is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network.

The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed i. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis.

As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in….

Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis. This paper discusses spatially diverse optical vector network analysis for space division multiplexing SDM component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems.

Characterization of a channel photonic lantern spatial multiplexer, coupled to a core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss IL and mode-dependent loss MDL data.

Moreover, the mode-mixing behavior of fiber splices in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis. Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects 41 health, and 26 hyperviscosity according to blood sample analysis results participate. Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity.

Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with times Hold-out method and 67 times Leave-one-out method are Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. We present an automatic method, termed as the principal component analysis network with composite kernel PCANet-CK , for the classification of three-dimensional 3-D retinal optical coherence tomography OCT images.

Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images.

Finally, the fused composite kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT SD-OCT datasets of normal subjects and subjects with the macular edema and age-related macular degeneration , which demonstrated its effectiveness. Wireless sensor networks WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained.

Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric.

Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Generalized Structured Component Analysis. We propose an alternative method to partial least squares for path analysis with components , called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables.

It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…. Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task. The present study identified the neural mechanism of risky decision-making in Internet gaming disorder IGD under a probability discounting task.

Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network DMN and less engagement in the executive control network ECN than HC when making the risky decisions.

Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences.

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained.

Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis. This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis PCA to reduce the dimensionality of a data set and artificial neural networks ANNs for forecasting purposes was used.

The results obtained were compared with those obtained by using discrete Fourier transform DFT. The proposed methodology was applied to four absorbance time series data sets composed by a total number of UV-Vis spectra. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. Cost component analysis.

In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors FAPPs have been suggested to overcome this problem. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis ICA. In turn, i CCA intends to partition the original problem into subproblems and ii separating partitioning the original optimization problem into subproblems may serve interpretation.

Most importantly, iii CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm. Factor Analysis via Components Analysis. When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa.

This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…. Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes.

Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia AD has also been observed. Methods: We implemented independent- component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment MCI not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups.

Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

Decoding the encoding of functional brain networks : An fMRI classification comparison of non-negative matrix factorization NMF , independent component analysis ICA , and sparse coding algorithms. Brain networks in fMRI are typically identified using spatial independent component analysis ICA , yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks.

Spatial sparse coding algorithms L1 Regularized Learning and K-SVD would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity.

These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in fMRI scans from 51 subjects.

Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks containing more zero-valued voxels had higher classification accuracy p Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks. The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI -based resting-state functional connectivity is a matter of ongoing debate.

The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network -specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network -specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks.

Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component PC that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.

Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network -specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model.

After regressing this PC-based estimator out of the original time-series, we observed robust anti. Abstract The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI —based resting-state functional connectivity is a matter of ongoing debate.

After regressing this PC-based estimator out of the original time-series, we observed. Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks. Artificial neural network ANN and a hybrid principal component analysis -artificial neural network PCA-ANN classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry ToF-SIMS mass spectra collected from complex Cu-Fe sulphides chalcopyrite, bornite, chalcocite and pyrite at different flotation conditions.

ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, 2D 2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness.

The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis PCA and independent component analysis ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. Independent component analysis ICA and self-organizing map SOM approach to multidetection system for network intruders.

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System IDS is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies.

However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA.

Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model GLM approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach independent component analysis ; ICA.

Participants' 18 musicians and 18 controls functional magnetic resonance imaging fMRI responses were acquired while listening to music. A targeted region of interest ROI related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.

The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory perception and motor action areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders.

Results revealed a a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and b group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing. An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of MV mutation on HIV reverse transcriptase resistance to lamivudine.

The emergence of different drug resistant strains of HIV-1 reverse transcriptase HIV RT remains of prime interest in relation to viral pathogenesis as well as drug development. Amongst those mutations, MV was found to cause a complete loss of ligand fitness. This involved molecular dynamics simulation, binding free energy analysis , principle component analysis PCA and residue interaction networks RINs.

The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains. Data has been collected which will permit users to identify and analyze the current network of interactions between organizations within the community of practice, harvest research results fixed to those interactions, and identify potential collaborative opportunities to further research streams.

The PNKB will assemble information on funded research institutions and categorize the research emphasis of each as it relates to NASA's six major science focus areas and 12 national applications. To further the utility of the PNKB, relational links have been integrated into the RPKB - which will contain data about projects awarded from NASA research solicitations, project investigator information, research publications, NASA data products employed, and model or decision support tools used or developed as well as new data product information.

In this paper we propose a methodology consisting of specific computational intelligence methods, i. We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities.

Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks multi-layer perceptron models. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting.

Regularized Generalized Structured Component Analysis. Generalized structured component analysis GSCA has been proposed as a component -based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge…. The chief expense for dedicated links today is that of leasing a fiber pair, especially in a city like Wash.

But, at least in the US, the intercity cable overbuild of the telecom boom has now, in the bust, made in-the-ground dark fiber pairs buyable for networking components ,with corresponding opportunities for e-VLBI. A budget for LOFAR networking requirements, assumed here to be built from scratch in the WA desert where the dominant cable laying costs are minimized, is considered in some detail.

Differentiating malignant from benign breast tumors on acoustic radiation force impulse imaging using fuzzy-based neural networks with principle component analysis. Many modalities have been developed as screening tools for breast cancer. A new screening method called acoustic radiation force impulse ARFI imaging was created for distinguishing breast lesions based on localized tissue displacement. This displacement was quantitated by virtual touch tissue imaging VTI. However, VTIs sometimes express reverse results to intensity information in clinical observation.

In the study, a fuzzy-based neural network with principle component analysis PCA was proposed to differentiate texture patterns of malignant breast from benign tumors. Eighty VTIs were randomly retrospected. Twenty four quantitative parameters deriving from first-order statistics FOS , fractal dimension and gray level co-occurrence matrix GLCM were utilized to analyze the texture pattern of breast tumors on VTIs. PCA was employed to reduce the dimension of features.

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Using our approach we are able to reduce the resources required to properly identify misbehaving hosts, protocols, or networks , by dedicating system resources to only those metrics that actually matter in detecting network deviations. Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis. Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging fMRI have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown.

Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis ICA to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: i a dorsal Auditory-Premotor network ; ii a dorsal Sensory-Motor network ; iii a dorsal Fronto-Parietal network ; and iv a ventral Fronto-Temporal network.

The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: i word learning relies on segregated connectivity patterns involving dorsal and ventral networks ; and ii specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance.

All rights reserved. Drug target identification using network analysis : Taking active components in Sini decoction as an example. PubMed Central. Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction SND simultaneously against heart failure.

To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. At last, network analysis was conducted to identify most possible targets of components in SND. We envisage that network analysis will also be useful in target identification of a bioactive compound.

In order to evaluate the development level of the low-voltage distribution network objectively and scientifically, chromatography analysis method is utilized to construct evaluation index model of low-voltage distribution network. Based on the analysis of principal component and the characteristic of logarithmic distribution of the index data, a logarithmic centralization method is adopted to improve the principal component analysis algorithm. The algorithm can decorrelate and reduce the dimensions of the evaluation model and the comprehensive score has a better dispersion degree.

The clustering method is adopted to analyse the comprehensive score because the comprehensive score of the courts is concentrated. Then the stratification evaluation of the courts is realized. An example is given to verify the objectivity and scientificity of the evaluation method. Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis ICA , which is a powerful method to reconstruct latent source signals from their linear mixtures.

In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA hc-ICA model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks.

Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy.

To test the differences in functional networks , we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder PTSD.

Curvilinear component analysis : a self-organizing neural network for nonlinear mapping of data sets. We present a new strategy called "curvilinear component analysis " CCA for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization VQ of the submanifold in the data set input space ; and nonlinear projection P of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold.

After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space. This paper proposes a probabilistic neural network NN developed on the basis of time-series discriminant component analysis TSDCA that can be used to classify high-dimensional time-series patterns.

TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network TSDCN , so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method.

The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis PCA. However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing.

An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted.

Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine.

We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. Concurrent white matter bundles and grey matter networks using independent component analysis.

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging. Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways.

This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts. We show how this framework can be used to investigate individual variations in brain networks in terms of both nodes and edges as well as their associations with individual differences in behaviour and anatomy.

Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Published by Elsevier Inc. Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind ToM. However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization.

Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis ICA , is a data-driven technique used to isolate intrinsic connectivity networks , and this approach provides insight into network -level regional recruitment. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions.

The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing.

Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated. Identifying apple surface defects using principal components analysis and artifical neural networks. Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images.

Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths nm and nm. Extracting intrinsic functional networks with feature-based group independent component analysis. There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in functional MRI data.

While networks are typically estimated based on the temporal similarity between regions based on temporal correlation, clustering methods, or independent component analysis [ICA] , some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies.

Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks spatial correlations as high as 0. In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature.

In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro. Online signature recognition using principal component analysis and artificial neural network.

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space.

Artificial neural network is adopted to solve the complex signature classification problem. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components. Chinese medicine is known to treat complex diseases with multiple components and multiple targets.

However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula LQF. The LQF is commonly used for the prevention and treatment of viral influenza in China.

It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target PT network. An influenza-related protein-protein interaction PPI network was constructed and integrated with the PT network. Then the compound-effective target CET network and compound-ineffective target network CIT were extracted, respectively.

As a result, 15 main effective components were identified along with 61 corresponding targets. The main effective component -target MECT network was further constructed with main effective components and their key targets. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions. In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network.

Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis , most commonly to compare control and disease groups through the average curves in each group.

Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis FPCA is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level.

This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings.

To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Rodent models have opened the door to a better understanding of the neurobiology of brain disorders and increased our ability to evaluate novel treatments. Resting-state functional magnetic resonance imaging rs-fMRI allows for in vivo exploration of large-scale brain networks with high spatial resolution. We present a comprehensive protocol to evaluate resting-state brain networks using Independent Component Analysis ICA in rodent model.

Specifically, we begin with a brief review of the physiological basis for rs-fMRI technique and overview of rs-fMRI studies in rodents to date, following which we provide a robust step-by-step approach for rs-fMRI investigation including data collection, computational preprocessing, and brain network analysis. Pipelines are interwoven with underlying theory behind each step and summarized methodological considerations, such as alternative methods available and current consensus in the literature for optimal results.

The presented protocol is designed in such a way that investigators without previous knowledge in the field can implement the analysis and obtain viable results that reliably detect significant differences in functional connectivity between experimental groups. Our goal is to empower researchers to implement rs-fMRI in their respective fields by incorporating technical considerations to date into a workable methodological framework.

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases.

A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis PCA and the Karhunen-Loeve expansion KLE both decompose network time series into a set of temporally varying modes and their spatial responses.

Therefore they provide a mathematical framework to perform spatiotemporal filtering. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components , which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Long-term intensive gymnastic training induced changes in intra- and inter- network functional connectivity: an independent component analysis. Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts WCGs and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training.

We examined both intra- and inter- network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI R-fMRI. Group-independent component analysis ICA was adopted to decompose the R-fMRI data into spatial independent components and associated time courses.

An automatic component identification method was used to identify components of interest associated with resting-state networks RSNs. We interpret this generally weaker intra- and inter- network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis. A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging fMRI study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis ICA and graph theoretical analysis GTA.

The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area SMA , left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs.

Reliability analysis of C turboprop engine components using artificial neural network. In this study, we predict the failure rate of Lockheed C Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including feed-forward back-propagation, radial basis neural network , and multilayer perceptron neural network model ; will be utilized to perform this study.

For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network ANN model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance.

In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model.

By the end of the study, we forecast the general failure rate of the Lockheed C Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network MLP model on DTREG commercial software. The results also give an insight into the reliability of the engine. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis.

Memory function is likely subserved by multiple distributed neural networks , which are disrupted by the pathophysiological process of Alzheimer's disease AD. In this study, we used multivariate analytic techniques to investigate memory-related functional magnetic resonance imaging fMRI activity in 52 individuals across the continuum of normal aging, mild cognitive impairment MCI , and mild AD.

Independent component analyses revealed specific memory-related networks that activated or deactivated during an associative memory paradigm. Across all subjects, hippocampal activation and parietal deactivation demonstrated a strong reciprocal relationship. Furthermore, we found evidence of a nonlinear trajectory of fMRI activation across the continuum of impairment. Less impaired MCI subjects showed paradoxical hyperactivation in the hippocampus compared with controls, whereas more impaired MCI subjects demonstrated significant hypoactivation, similar to the levels observed in the mild AD subjects.

We found a remarkably parallel curve in the pattern of memory-related deactivation in medial and lateral parietal regions with greater deactivation in less-impaired MCI and loss of deactivation in more impaired MCI and mild AD subjects. Interestingly, the failure of deactivation in these regions was also associated with increased positive activity in a neocortical attentional network in MCI and AD.

Our findings suggest that loss of functional integrity of the hippocampal-based memory systems is directly related to alterations of neural activity in parietal regions seen over the course of MCI and AD. These data may also provide functional evidence of the interaction between neocortical and medial temporal lobe pathology in early AD.

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis PCA using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network , in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months.

We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis , we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron.

In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Increase of posterior connectivity in aging within the Ventral Attention Network : A functional connectivity analysis using independent component analysis. Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks. However, little is known about age-related changes within the Ventral Attention Network VAN , which underlies selective attention.

The aim of this study is to examine age-related changes within the VAN, focusing on connectivity between its regions. Here we report our findings on the analysis of 27 participants' 13 younger and 14 older healthy adults BOLD signals as well as their performance on a letter-matching task.

We identified the VAN independently for both groups using spatial independent component analysis. Three main findings emerged: First, younger adults were faster and more accurate on the task. Second, older adults had greater connectivity among posterior regions right temporoparietal junction, right superior parietal lobule, right middle temporal gyrus and left cerebellum crus I than younger adults but lower connectivity among anterior regions right anterior insula, right medial superior frontal gyrus and right middle frontal gyrus.

Older adults also had more connectivity between anterior and posterior regions than younger adults. Finally, correlations between connectivity and response time on the task showed a trend toward connectivity in posterior regions for the older group and in anterior regions for the younger group.

Thus, this study shows that intrahemispheric neurofunctional changes in aging also affect the VAN. The results suggest that, in contexts of selective attention, posterior regions increased in importance for older adults, while anterior regions had reduced centrality. This work presents a non-parametric method based on a principal component analysis PCA and a parametric one based on artificial neural networks ANN to remove continuous baseline features from spectra.

The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline.

Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing synthetic composed spectra at different signal-to-baseline ratio SBR , signal-to-noise ratio SNR , and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used.

Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity.

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose e-nose combined with Independent Component Analysis ICA as a dimensionality reduction technique, Partial Least Squares PLS to predict sensorial descriptors and Artificial Neural Networks ANNs for classification purpose.

A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. Ad hoc Laser networks component technology for modular spacecraft. Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization.

Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.

Ad hoc laser networks component technology for modular spacecraft. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested.

Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways in isolation is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network.

We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network.

The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed i. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis.

As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in….

Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis. This paper discusses spatially diverse optical vector network analysis for space division multiplexing SDM component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems.

Characterization of a channel photonic lantern spatial multiplexer, coupled to a core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss IL and mode-dependent loss MDL data. Moreover, the mode-mixing behavior of fiber splices in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis.

Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network. With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects 41 health, and 26 hyperviscosity according to blood sample analysis results participate.

Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation.

To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with times Hold-out method and 67 times Leave-one-out method are Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. We present an automatic method, termed as the principal component analysis network with composite kernel PCANet-CK , for the classification of three-dimensional 3-D retinal optical coherence tomography OCT images.

Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused composite kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT SD-OCT datasets of normal subjects and subjects with the macular edema and age-related macular degeneration , which demonstrated its effectiveness.

Wireless sensor networks WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs.

In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy.

Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms. Generalized Structured Component Analysis. We propose an alternative method to partial least squares for path analysis with components , called generalized structured component analysis.

The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…. Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task. The present study identified the neural mechanism of risky decision-making in Internet gaming disorder IGD under a probability discounting task.

Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network DMN and less engagement in the executive control network ECN than HC when making the risky decisions.

Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences. Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.

An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained. Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis.

This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis PCA to reduce the dimensionality of a data set and artificial neural networks ANNs for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform DFT.

The proposed methodology was applied to four absorbance time series data sets composed by a total number of UV-Vis spectra. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined.

Cost component analysis. In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors FAPPs have been suggested to overcome this problem. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape.

That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis ICA. In turn, i CCA intends to partition the original problem into subproblems and ii separating partitioning the original optimization problem into subproblems may serve interpretation. Most importantly, iii CCA may give rise to high gains in optimization time.

Numerical simulations illustrate the working of the algorithm. Factor Analysis via Components Analysis. When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa.

This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…. Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia AD has also been observed.

Methods: We implemented independent- component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment MCI not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups.

Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

Decoding the encoding of functional brain networks : An fMRI classification comparison of non-negative matrix factorization NMF , independent component analysis ICA , and sparse coding algorithms. Brain networks in fMRI are typically identified using spatial independent component analysis ICA , yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks.

Spatial sparse coding algorithms L1 Regularized Learning and K-SVD would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity.

These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in fMRI scans from 51 subjects. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks containing more zero-valued voxels had higher classification accuracy p Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI -based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity.

Hence, removal of the GAS is a common practice for facilitating the observation of network -specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network -specific fluctuations are super-positioned.

However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS.

Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component PC that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.

Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network -specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model.

After regressing this PC-based estimator out of the original time-series, we observed robust anti. Abstract The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI —based resting-state functional connectivity is a matter of ongoing debate. After regressing this PC-based estimator out of the original time-series, we observed. Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

Artificial neural network ANN and a hybrid principal component analysis -artificial neural network PCA-ANN classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry ToF-SIMS mass spectra collected from complex Cu-Fe sulphides chalcopyrite, bornite, chalcocite and pyrite at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism.

PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model.

Next, 2D 2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis PCA and independent component analysis ICA.

The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. Independent component analysis ICA and self-organizing map SOM approach to multidetection system for network intruders. With the growing rate of interconnection among computer systems, network security is becoming a real challenge.

Intrusion Detection System IDS is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure.

This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA.

Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology. Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures.

Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model GLM approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach independent component analysis ; ICA. Participants' 18 musicians and 18 controls functional magnetic resonance imaging fMRI responses were acquired while listening to music.

A targeted region of interest ROI related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified.

Their corresponding spatial maps uncovered a network of auditory perception and motor action areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed a a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and b group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of MV mutation on HIV reverse transcriptase resistance to lamivudine. The emergence of different drug resistant strains of HIV-1 reverse transcriptase HIV RT remains of prime interest in relation to viral pathogenesis as well as drug development.

Amongst those mutations, MV was found to cause a complete loss of ligand fitness. This involved molecular dynamics simulation, binding free energy analysis , principle component analysis PCA and residue interaction networks RINs. The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains.

Data has been collected which will permit users to identify and analyze the current network of interactions between organizations within the community of practice, harvest research results fixed to those interactions, and identify potential collaborative opportunities to further research streams. The Journal of cell biology 5 , , Journal of Biological Chemistry 8 , , Journal of Biological Chemistry 18 , , Journal of Biological Chemistry 39 , , Journal of Biological Chemistry 22 , , Biochemistry and Cell Biology 85 4 , , The Journal of clinical investigation 3 , , Molecular biology of the cell 15 8 , , Molecular and cellular biology 26 12 , , Articoli 1—20 Mostra altri.

Guida Privacy Termini. Indice H. Nature cell biology 4 1 , , Molecular cell 25 5 , , Oncogene 14 6 , , Interference with p53 protein inhibits hematopoietic and muscle differentiation. The Journal of Cell Biology 1 , , FEBS letters 3 , , Journal of Cell Biology 6 , ,

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The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance.

These findings suggest that: i word learning relies on segregated connectivity patterns involving dorsal and ventral networks ; and ii specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. All rights reserved. Drug target identification using network analysis : Taking active components in Sini decoction as an example.

PubMed Central. Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction SND simultaneously against heart failure.

To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. At last, network analysis was conducted to identify most possible targets of components in SND. We envisage that network analysis will also be useful in target identification of a bioactive compound. In order to evaluate the development level of the low-voltage distribution network objectively and scientifically, chromatography analysis method is utilized to construct evaluation index model of low-voltage distribution network.

Based on the analysis of principal component and the characteristic of logarithmic distribution of the index data, a logarithmic centralization method is adopted to improve the principal component analysis algorithm. The algorithm can decorrelate and reduce the dimensions of the evaluation model and the comprehensive score has a better dispersion degree. The clustering method is adopted to analyse the comprehensive score because the comprehensive score of the courts is concentrated.

Then the stratification evaluation of the courts is realized. An example is given to verify the objectivity and scientificity of the evaluation method. Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis ICA , which is a powerful method to reconstruct latent source signals from their linear mixtures.

In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA hc-ICA model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks.

Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model.

We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks , we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies.

We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder PTSD. Curvilinear component analysis : a self-organizing neural network for nonlinear mapping of data sets.

We present a new strategy called "curvilinear component analysis " CCA for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization VQ of the submanifold in the data set input space ; and nonlinear projection P of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold.

After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space. This paper proposes a probabilistic neural network NN developed on the basis of time-series discriminant component analysis TSDCA that can be used to classify high-dimensional time-series patterns.

TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network TSDCN , so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method.

The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis PCA. However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing.

An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted.

Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data.

Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. Concurrent white matter bundles and grey matter networks using independent component analysis.

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging.

Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter 'nodes' and white matter 'edges', and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts.

We show how this framework can be used to investigate individual variations in brain networks in terms of both nodes and edges as well as their associations with individual differences in behaviour and anatomy.

Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI. Published by Elsevier Inc. Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

Social neuroscience research has focused on an identified network of brain regions primarily associated with processing Theory of Mind ToM. However, ToM is a broad cognitive process, which encompasses several sub-processes, such as mental state detection and intentional attribution, and the connectivity of brain regions underlying the broader ToM network in response to paradigms assessing these sub-processes requires further characterization.

Standard fMRI analyses which focus only on brain activity cannot capture information about ToM processing at a network level. An alternative method, independent component analysis ICA , is a data-driven technique used to isolate intrinsic connectivity networks , and this approach provides insight into network -level regional recruitment. Based on visual comparison of the derived networks for each task, the spatiotemporal network patterns were similar between the RMIE and RMIV tasks, which elicited mentalizing about the mental states of others, and these networks differed from the network derived for the Causality task, which elicited mentalizing about goal-directed actions.

The medial prefrontal cortex, precuneus, and right inferior frontal gyrus were seen in the components with the highest correlation with the task condition for each of the tasks highlighting the role of these regions in general ToM processing. Using a data-driven approach, the current study captured the differences in task-related brain response to ToM in three distinct ToM paradigms. The findings of this study further elucidate the neural mechanisms associated.

Identifying apple surface defects using principal components analysis and artifical neural networks. Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths nm and nm. Extracting intrinsic functional networks with feature-based group independent component analysis.

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions based on temporal correlation, clustering methods, or independent component analysis [ICA] , some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies.

Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks spatial correlations as high as 0. In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks.

We believe it will become a useful and important approach in the study of the macro. Online signature recognition using principal component analysis and artificial neural network. In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space.

Artificial neural network is adopted to solve the complex signature classification problem. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components.

Chinese medicine is known to treat complex diseases with multiple components and multiple targets. However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula LQF.

The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target PT network.

An influenza-related protein-protein interaction PPI network was constructed and integrated with the PT network. Then the compound-effective target CET network and compound-ineffective target network CIT were extracted, respectively. As a result, 15 main effective components were identified along with 61 corresponding targets. The main effective component -target MECT network was further constructed with main effective components and their key targets.

In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions. In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network.

Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual.

This has led to the adoption of basic tools of functional data analysis , most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis FPCA is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference.

Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated.

The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Rodent models have opened the door to a better understanding of the neurobiology of brain disorders and increased our ability to evaluate novel treatments. Resting-state functional magnetic resonance imaging rs-fMRI allows for in vivo exploration of large-scale brain networks with high spatial resolution. We present a comprehensive protocol to evaluate resting-state brain networks using Independent Component Analysis ICA in rodent model.

Specifically, we begin with a brief review of the physiological basis for rs-fMRI technique and overview of rs-fMRI studies in rodents to date, following which we provide a robust step-by-step approach for rs-fMRI investigation including data collection, computational preprocessing, and brain network analysis. Pipelines are interwoven with underlying theory behind each step and summarized methodological considerations, such as alternative methods available and current consensus in the literature for optimal results.

The presented protocol is designed in such a way that investigators without previous knowledge in the field can implement the analysis and obtain viable results that reliably detect significant differences in functional connectivity between experimental groups.

Our goal is to empower researchers to implement rs-fMRI in their respective fields by incorporating technical considerations to date into a workable methodological framework. Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals.

The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis PCA and the Karhunen-Loeve expansion KLE both decompose network time series into a set of temporally varying modes and their spatial responses.

Therefore they provide a mathematical framework to perform spatiotemporal filtering. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components , which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California.

Furthermore, the common mode errors exhibit temporally nonrandom patterns. Long-term intensive gymnastic training induced changes in intra- and inter- network functional connectivity: an independent component analysis.

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts WCGs and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training.

We examined both intra- and inter- network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI R-fMRI. Group-independent component analysis ICA was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks RSNs.

We interpret this generally weaker intra- and inter- network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training. Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

A set of cortical and sub-cortical brain structures has been linked with sentence-level semantic processes. However, it remains unclear how these brain regions are organized to support the semantic integration of a word into sentential context. To look into this issue, we conducted a functional magnetic resonance imaging fMRI study that required participants to silently read sentences with semantically congruent or incongruent endings and analyzed the network properties of the brain with two approaches, independent component analysis ICA and graph theoretical analysis GTA.

The GTA suggested that the whole-brain network is topologically stable across conditions. The ICA revealed a network comprising the supplementary motor area SMA , left inferior frontal gyrus, left middle temporal gyrus, left caudate nucleus, and left angular gyrus, which was modulated by the incongruity of sentence ending. Furthermore, the GTA specified that the connections between the left SMA and left caudate nucleus as well as that between the left caudate nucleus and right thalamus were stronger in response to incongruent vs.

Reliability analysis of C turboprop engine components using artificial neural network. In this study, we predict the failure rate of Lockheed C Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation.

The Weibull regression model and the Artificial Neural Network model including feed-forward back-propagation, radial basis neural network , and multilayer perceptron neural network model ; will be utilized to perform this study.

For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network ANN model utilizing the feed-forward back-propagation algorithm as a learning rule.

The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box.

In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model.

By the end of the study, we forecast the general failure rate of the Lockheed C Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network MLP model on DTREG commercial software.

The results also give an insight into the reliability of the engine. Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis. Memory function is likely subserved by multiple distributed neural networks , which are disrupted by the pathophysiological process of Alzheimer's disease AD. In this study, we used multivariate analytic techniques to investigate memory-related functional magnetic resonance imaging fMRI activity in 52 individuals across the continuum of normal aging, mild cognitive impairment MCI , and mild AD.

Independent component analyses revealed specific memory-related networks that activated or deactivated during an associative memory paradigm. Across all subjects, hippocampal activation and parietal deactivation demonstrated a strong reciprocal relationship. Furthermore, we found evidence of a nonlinear trajectory of fMRI activation across the continuum of impairment. Less impaired MCI subjects showed paradoxical hyperactivation in the hippocampus compared with controls, whereas more impaired MCI subjects demonstrated significant hypoactivation, similar to the levels observed in the mild AD subjects.

We found a remarkably parallel curve in the pattern of memory-related deactivation in medial and lateral parietal regions with greater deactivation in less-impaired MCI and loss of deactivation in more impaired MCI and mild AD subjects. Interestingly, the failure of deactivation in these regions was also associated with increased positive activity in a neocortical attentional network in MCI and AD.

Our findings suggest that loss of functional integrity of the hippocampal-based memory systems is directly related to alterations of neural activity in parietal regions seen over the course of MCI and AD. These data may also provide functional evidence of the interaction between neocortical and medial temporal lobe pathology in early AD. With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis PCA using pollution data from different cities.

This paper describes how students can obtain data on air quality and process such data for additional information…. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network , in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months.

We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis , we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron.

In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Increase of posterior connectivity in aging within the Ventral Attention Network : A functional connectivity analysis using independent component analysis. Multiple studies have found neurofunctional changes in normal aging in a context of selective attention. Furthermore, many articles report intrahemispheric alteration in functional networks.

However, little is known about age-related changes within the Ventral Attention Network VAN , which underlies selective attention. The aim of this study is to examine age-related changes within the VAN, focusing on connectivity between its regions. Here we report our findings on the analysis of 27 participants' 13 younger and 14 older healthy adults BOLD signals as well as their performance on a letter-matching task. We identified the VAN independently for both groups using spatial independent component analysis.

Three main findings emerged: First, younger adults were faster and more accurate on the task. Second, older adults had greater connectivity among posterior regions right temporoparietal junction, right superior parietal lobule, right middle temporal gyrus and left cerebellum crus I than younger adults but lower connectivity among anterior regions right anterior insula, right medial superior frontal gyrus and right middle frontal gyrus.

Older adults also had more connectivity between anterior and posterior regions than younger adults. Finally, correlations between connectivity and response time on the task showed a trend toward connectivity in posterior regions for the older group and in anterior regions for the younger group. Thus, this study shows that intrahemispheric neurofunctional changes in aging also affect the VAN. The results suggest that, in contexts of selective attention, posterior regions increased in importance for older adults, while anterior regions had reduced centrality.

This work presents a non-parametric method based on a principal component analysis PCA and a parametric one based on artificial neural networks ANN to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix.

The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing synthetic composed spectra at different signal-to-baseline ratio SBR , signal-to-noise ratio SNR , and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used.

Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose e-nose combined with Independent Component Analysis ICA as a dimensionality reduction technique, Partial Least Squares PLS to predict sensorial descriptors and Artificial Neural Networks ANNs for classification purpose.

A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. Ad hoc Laser networks component technology for modular spacecraft. Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization.

Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment.

The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft. Ad hoc laser networks component technology for modular spacecraft. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic.

The creation of sets from individual pathways in isolation is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets.

Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance.

We show that our method finds significant aspects of biology that would be missed i. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible.

The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. Of particular interest is the structure and function of intrinsic networks regions exhibiting temporally coherent activity both at rest and while a task is being performed , which account for a significant portion of the variance in….

Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis. This paper discusses spatially diverse optical vector network analysis for space division multiplexing SDM component and system characterization, which is becoming essential as SDM is widely considered to increase the capacity of optical communication systems.

Characterization of a channel photonic lantern spatial multiplexer, coupled to a core 3-mode fiber, is experimentally demonstrated, extracting the full impulse response and complex transfer function matrices as well as insertion loss IL and mode-dependent loss MDL data. Moreover, the mode-mixing behavior of fiber splices in the few-mode multi-core fiber and their impact on system IL and MDL are analyzed, finding splices to cause significant mode-mixing and to be non-negligible in system capacity analysis.

Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network. With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects 41 health, and 26 hyperviscosity according to blood sample analysis results participate.

Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation.

To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with times Hold-out method and 67 times Leave-one-out method are Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. We present an automatic method, termed as the principal component analysis network with composite kernel PCANet-CK , for the classification of three-dimensional 3-D retinal optical coherence tomography OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images.

Finally, the fused composite kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT SD-OCT datasets of normal subjects and subjects with the macular edema and age-related macular degeneration , which demonstrated its effectiveness.

Wireless sensor networks WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis PCA.

First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy.

Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms. Generalized Structured Component Analysis. We propose an alternative method to partial least squares for path analysis with components , called generalized structured component analysis.

The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…. Dysfunctional default mode network and executive control network in people with Internet gaming disorder: Independent component analysis under a probability discounting task. The present study identified the neural mechanism of risky decision-making in Internet gaming disorder IGD under a probability discounting task.

Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network DMN and less engagement in the executive control network ECN than HC when making the risky decisions. Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates.

The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences.

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained.

Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis. This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis PCA to reduce the dimensionality of a data set and artificial neural networks ANNs for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform DFT.

The proposed methodology was applied to four absorbance time series data sets composed by a total number of UV-Vis spectra. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined.

Cost component analysis. In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors FAPPs have been suggested to overcome this problem. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis ICA. In turn, i CCA intends to partition the original problem into subproblems and ii separating partitioning the original optimization problem into subproblems may serve interpretation.

Most importantly, iii CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm. Factor Analysis via Components Analysis. When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa. This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis.

Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…. Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia AD has also been observed.

Methods: We implemented independent- component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment MCI not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups.

Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

Decoding the encoding of functional brain networks : An fMRI classification comparison of non-negative matrix factorization NMF , independent component analysis ICA , and sparse coding algorithms. Brain networks in fMRI are typically identified using spatial independent component analysis ICA , yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks.

Spatial sparse coding algorithms L1 Regularized Learning and K-SVD would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity.

These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in fMRI scans from 51 subjects. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks containing more zero-valued voxels had higher classification accuracy p Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI -based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity.

Hence, removal of the GAS is a common practice for facilitating the observation of network -specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network -specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks.

Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component PC that correlated best with the GAS.

The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network -specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model.

After regressing this PC-based estimator out of the original time-series, we observed robust anti. Abstract The influence of the global average signal GAS on functional-magnetic resonance imaging fMRI —based resting-state functional connectivity is a matter of ongoing debate. After regressing this PC-based estimator out of the original time-series, we observed. Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

Artificial neural network ANN and a hybrid principal component analysis -artificial neural network PCA-ANN classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry ToF-SIMS mass spectra collected from complex Cu-Fe sulphides chalcopyrite, bornite, chalcocite and pyrite at different flotation conditions.

ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, 2D 2PCA is utilized to reduce the dimension of the data and extract its intrinsic features.

The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis PCA and independent component analysis ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. Independent component analysis ICA and self-organizing map SOM approach to multidetection system for network intruders.

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System IDS is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks.

Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA.

Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model GLM approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach independent component analysis ; ICA.

Participants' 18 musicians and 18 controls functional magnetic resonance imaging fMRI responses were acquired while listening to music. A targeted region of interest ROI related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas.

The ICA decomposition was performed under different model orders, i. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory perception and motor action areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders.

Results revealed a a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and b group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing. An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of MV mutation on HIV reverse transcriptase resistance to lamivudine.

The emergence of different drug resistant strains of HIV-1 reverse transcriptase HIV RT remains of prime interest in relation to viral pathogenesis as well as drug development. Amongst those mutations, MV was found to cause a complete loss of ligand fitness.

This involved molecular dynamics simulation, binding free energy analysis , principle component analysis PCA and residue interaction networks RINs. The comprehensive molecular insight gained from this study should be of great importance in understanding drug resistance against HIV RT as well as assisting in the design of novel reverse transcriptase inhibitors with high ligand efficacy on resistant strains. Data has been collected which will permit users to identify and analyze the current network of interactions between organizations within the community of practice, harvest research results fixed to those interactions, and identify potential collaborative opportunities to further research streams.

The PNKB will assemble information on funded research institutions and categorize the research emphasis of each as it relates to NASA's six major science focus areas and 12 national applications. To further the utility of the PNKB, relational links have been integrated into the RPKB - which will contain data about projects awarded from NASA research solicitations, project investigator information, research publications, NASA data products employed, and model or decision support tools used or developed as well as new data product information.

In this paper we propose a methodology consisting of specific computational intelligence methods, i. We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities.

Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks multi-layer perceptron models. Titolo Ordina Ordina per citazioni Ordina per anno Ordina per titolo. The Journal of cell biology 5 , , Journal of Biological Chemistry 8 , , Journal of Biological Chemistry 18 , , Journal of Biological Chemistry 39 , , Journal of Biological Chemistry 22 , , Biochemistry and Cell Biology 85 4 , , The Journal of clinical investigation 3 , , Molecular biology of the cell 15 8 , , Molecular and cellular biology 26 12 , , Articoli 1—20 Mostra altri.

Guida Privacy Termini. Indice H. Nature cell biology 4 1 , , Molecular cell 25 5 , , Oncogene 14 6 , , Interference with p53 protein inhibits hematopoietic and muscle differentiation. The Journal of Cell Biology 1 , , FEBS letters 3 , ,

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Silvia Soddu. Regina Elena Cancer Institute I Cordone, S Masi, FR Mauro, S Soddu, O Morsilli, T Valentini, ML Vegna, Blood, The Journal of the American. Laboratorio di Oncologia; Dipartimento di Oncologia Sperimentale; Istituti Fisioterapici Ospitalieri Centro Ricerca Sperimentale; Via delle Messi d'Oro Silvia SODDU, laboratory Head of Istituto Regina Elena - Istituti Fisioterapici Ospitalieri, Rome | Read publications | Contact Silvia SODDU.