Integrative Transcriptomic Analysis Uncovers Novel Gene Modules That Underlie the Sulfate Response in Arabidopsis thaliana

PubMed Central

Henríquez-Valencia, Carlos; Arenas-M, Anita; Medina, Joaquín; Canales, Javier

2018-01-01

Sulfur is an essential nutrient for plant growth and development. Sulfur is a constituent of proteins, the plasma membrane and cell walls, among other important cellular components. To obtain new insights into the gene regulatory networks underlying the sulfate response, we performed an integrative meta-analysis of transcriptomic data from five different sulfate experiments available in public databases. This bioinformatic approach allowed us to identify a robust set of genes whose expression depends only on sulfate availability, indicating that those genes play an important role in the sulfate response. In relation to sulfate metabolism, the biological function of approximately 45% of these genes is currently unknown. Moreover, we found several consistent Gene Ontology terms related to biological processes that have not been extensively studied in the context of the sulfate response; these processes include cell wall organization, carbohydrate metabolism, nitrogen compound transport, and the regulation of proteolysis. Gene co-expression network analyses revealed relationships between the sulfate-responsive genes that were distributed among seven function-specific co-expression modules. The most connected genes in the sulfate co-expression network belong to a module related to the carbon response, suggesting that this biological function plays an important role in the control of the sulfate response. Temporal analyses of the network suggest that sulfate starvation generates a biphasic response, which involves that major changes in gene expression occur during both the early and late responses. Network analyses predicted that the sulfate response is regulated by a limited number of transcription factors, including MYBs, bZIPs, and NF-YAs. In conclusion, our analysis identified new candidate genes and provided new hypotheses to advance our understanding of the transcriptional regulation of sulfate metabolism in plants. PMID:29692794

Integrative Transcriptomic Analysis Uncovers Novel Gene Modules That Underlie the Sulfate Response in Arabidopsis thaliana.

PubMed

Henríquez-Valencia, Carlos; Arenas-M, Anita; Medina, Joaquín; Canales, Javier

2018-01-01

Sulfur is an essential nutrient for plant growth and development. Sulfur is a constituent of proteins, the plasma membrane and cell walls, among other important cellular components. To obtain new insights into the gene regulatory networks underlying the sulfate response, we performed an integrative meta-analysis of transcriptomic data from five different sulfate experiments available in public databases. This bioinformatic approach allowed us to identify a robust set of genes whose expression depends only on sulfate availability, indicating that those genes play an important role in the sulfate response. In relation to sulfate metabolism, the biological function of approximately 45% of these genes is currently unknown. Moreover, we found several consistent Gene Ontology terms related to biological processes that have not been extensively studied in the context of the sulfate response; these processes include cell wall organization, carbohydrate metabolism, nitrogen compound transport, and the regulation of proteolysis. Gene co-expression network analyses revealed relationships between the sulfate-responsive genes that were distributed among seven function-specific co-expression modules. The most connected genes in the sulfate co-expression network belong to a module related to the carbon response, suggesting that this biological function plays an important role in the control of the sulfate response. Temporal analyses of the network suggest that sulfate starvation generates a biphasic response, which involves that major changes in gene expression occur during both the early and late responses. Network analyses predicted that the sulfate response is regulated by a limited number of transcription factors, including MYBs, bZIPs, and NF-YAs. In conclusion, our analysis identified new candidate genes and provided new hypotheses to advance our understanding of the transcriptional regulation of sulfate metabolism in plants.

Analysis of Gene Regulatory Networks of Maize in Response to Nitrogen.

PubMed

Jiang, Lu; Ball, Graham; Hodgman, Charlie; Coules, Anne; Zhao, Han; Lu, Chungui

2018-03-08

Nitrogen (N) fertilizer has a major influence on the yield and quality. Understanding and optimising the response of crop plants to nitrogen fertilizer usage is of central importance in enhancing food security and agricultural sustainability. In this study, the analysis of gene regulatory networks reveals multiple genes and biological processes in response to N. Two microarray studies have been used to infer components of the nitrogen-response network. Since they used different array technologies, a map linking the two probe sets to the maize B73 reference genome has been generated to allow comparison. Putative Arabidopsis homologues of maize genes were used to query the Biological General Repository for Interaction Datasets (BioGRID) network, which yielded the potential involvement of three transcription factors (TFs) (GLK5, MADS64 and bZIP108) and a Calcium-dependent protein kinase. An Artificial Neural Network was used to identify influential genes and retrieved bZIP108 and WRKY36 as significant TFs in both microarray studies, along with genes for Asparagine Synthetase, a dual-specific protein kinase and a protein phosphatase. The output from one study also suggested roles for microRNA (miRNA) 399b and Nin-like Protein 15 (NLP15). Co-expression-network analysis of TFs with closely related profiles to known Nitrate-responsive genes identified GLK5, GLK8 and NLP15 as candidate regulators of genes repressed under low Nitrogen conditions, while bZIP108 might play a role in gene activation.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder

PubMed Central

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-01-01

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery. PMID:25646593

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

PubMed

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-02-03

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

Using Module Analysis for Multiple Choice Responses: A New Method Applied to Force Concept Inventory Data

ERIC Educational Resources Information Center

Brewe, Eric; Bruun, Jesper; Bearden, Ian G.

2016-01-01

We describe "Module Analysis for Multiple Choice Responses" (MAMCR), a new methodology for carrying out network analysis on responses to multiple choice assessments. This method is used to identify modules of non-normative responses which can then be interpreted as an alternative to factor analysis. MAMCR allows us to identify conceptual…

Analysis of the SOS response of Vibrio and other bacteria with multiple chromosomes

PubMed Central

2012-01-01

Background The SOS response is a well-known regulatory network present in most bacteria and aimed at addressing DNA damage. It has also been linked extensively to stress-induced mutagenesis, virulence and the emergence and dissemination of antibiotic resistance determinants. Recently, the SOS response has been shown to regulate the activity of integrases in the chromosomal superintegrons of the Vibrionaceae, which encompasses a wide range of pathogenic species harboring multiple chromosomes. Here we combine in silico and in vitro techniques to perform a comparative genomics analysis of the SOS regulon in the Vibrionaceae, and we extend the methodology to map this transcriptional network in other bacterial species harboring multiple chromosomes. Results Our analysis provides the first comprehensive description of the SOS response in a family (Vibrionaceae) that includes major human pathogens. It also identifies several previously unreported members of the SOS transcriptional network, including two proteins of unknown function. The analysis of the SOS response in other bacterial species with multiple chromosomes uncovers additional regulon members and reveals that there is a conserved core of SOS genes, and that specialized additions to this basic network take place in different phylogenetic groups. Our results also indicate that across all groups the main elements of the SOS response are always found in the large chromosome, whereas specialized additions are found in the smaller chromosomes and plasmids. Conclusions Our findings confirm that the SOS response of the Vibrionaceae is strongly linked with pathogenicity and dissemination of antibiotic resistance, and suggest that the characterization of the newly identified members of this regulon could provide key insights into the pathogenesis of Vibrio. The persistent location of key SOS genes in the large chromosome across several bacterial groups confirms that the SOS response plays an essential role in these organisms and sheds light into the mechanisms of evolution of global transcriptional networks involved in adaptability and rapid response to environmental changes, suggesting that small chromosomes may act as evolutionary test beds for the rewiring of transcriptional networks. PMID:22305460

Pharmacologic therapies for severe steroid refractory hospitalized ulcerative colitis: A network meta-analysis.

PubMed

Komaki, Yuga; Komaki, Fukiko; Micic, Dejan; Yamada, Akihiro; Suzuki, Yasuo; Sakuraba, Atsushi

2017-06-01

A limited option of therapies is available for hospitalized patients with severe steroid refractory ulcerative colitis (UC). Furthermore, there exists a paucity of direct comparisons between them. To provide a comparative evaluation of the efficacy and safety of pharmacologic therapies, we conducted a network meta-analysis combined with a benefit-risk analysis of randomized controlled trials (RCTs) performed in hospitalized patients with severe steroid refractory UC. Electronic databases were searched through November 2015 for RCTs evaluating the efficacy of therapies for severe steroid refractory hospitalized UC. The outcomes were clinical response, colectomy free rate, and severe adverse events leading to discontinuation of therapy. The primary endpoints were the rank of therapies based on network meta-analysis combined with benefit-risk analysis between clinical response and severe adverse events as well as colectomy free rate and severe adverse events. Eight RCTs of 421 patients were identified. Cyclosporine, infliximab, and tacrolimus as well as placebo were included in our analysis. Network meta-analysis with benefit-risk analysis simultaneously assessing clinical response and severe adverse events demonstrated the rank order of efficacy as infliximab, cyclosporine, tacrolimus, and placebo. Similar analysis for colectomy-free rate and severe adverse events demonstrated the same rank order of efficacy. The differences among infliximab, cyclosporine, and tacrolimus were small in all analyses. The results of the present comprehensive benefit-risk assessment using network meta-analysis provide RCT-based evidence on efficacy and safety of infliximab, cyclosporine, and tacrolimus for hospitalized patients with severe steroid refractory UC. © 2016 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections.

PubMed

Jiang, Zhenhong; Dong, Xiaobao; Zhang, Ziding

2016-01-11

A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity. Here, we compared the Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea through integrative network analysis. Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction (PPI) network to construct a G. orontii conditional PPI sub-network (gCPIN) and a B. cinerea conditional PPI sub-network (bCPIN). We found that hubs in gCPIN and bCPIN played important roles in disease resistance. Hubs in bCPIN evolved faster than hubs in gCPIN, indicating the different selection pressures imposed on plants by different pathogens. By analyzing the common network from gCPIN and bCPIN, we identified two network components in which the genes were heavily involved in defense and development, respectively. The co-expression relationships between interacting proteins connecting the two components were different under G. orontii and B. cinerea infection conditions. Closer inspection revealed that auxin-related genes were overrepresented in the interactions connecting these two components, suggesting a critical role of auxin signaling in regulating the different co-expression relationships. Our work may provide new insights into plant defense responses against pathogens with different lifestyles.

Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses.

PubMed

Tian, Ye; Wang, Sean S; Zhang, Zhen; Rodriguez, Olga C; Petricoin, Emanuel; Shih, Ie-Ming; Chan, Daniel; Avantaggiati, Maria; Yu, Guoqiang; Ye, Shaozhen; Clarke, Robert; Wang, Chao; Zhang, Bai; Wang, Yue; Albanese, Chris

2014-01-01

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

PubMed Central

Waters, Katrina M.; Liu, Tao; Quesenberry, Ryan D.; Willse, Alan R.; Bandyopadhyay, Somnath; Kathmann, Loel E.; Weber, Thomas J.; Smith, Richard D.; Wiley, H. Steven; Thrall, Brian D.

2012-01-01

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response. PMID:22479638

s-core network decomposition: A generalization of k-core analysis to weighted networks

NASA Astrophysics Data System (ADS)

Eidsaa, Marius; Almaas, Eivind

2013-12-01

A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.

The Colorado Crisis Education and Response Network: an analysis of policy and practices.

PubMed

Crepeau-Hobson, Franci; Drennen, Curt

2011-01-01

The Federal government has recognized the importance of including behavioral health in disaster response plans and policies. Many states have responded to these directives with the development and implementation of disaster behavioral health response teams. The Colorado Crisis Education and Response Network (CoCERN) is a statewide asset that is based in community partnerships formed to deliver effective, efficient, and professional disaster behavioral health services to communities impacted by a disaster Using the K. McInnis-Dittrich model of policy analysis, this paper analyzes this approach to disaster behavioral health response. Strengths and weaknesses of the program, as well as implications for practice are discussed.

Filling the gap between disaster preparedness and response networks of urban emergency management: Following the 2013 Seoul Floods.

PubMed

Song, Minsun; Jung, Kyujin

2015-01-01

To examine the gap between disaster preparedness and response networks following the 2013 Seoul Floods in which the rapid transmission of disaster information and resources was impeded by severe changes of interorganizational collaboration networks. This research uses the 2013 Seoul Emergency Management Survey data that were collected before and after the floods, and total 94 organizations involving in coping with the floods were analyzed in bootstrap independent-sample t-test and social network analysis through UCINET 6 and STATA 12. The findings show that despite the primary network form that is more hierarchical, horizontal collaboration has been relatively invigorated in actual response. Also, interorganizational collaboration networks for response operations seem to be more flexible grounded on improvisation to coping with unexpected victims and damages. Local organizations under urban emergency management are recommended to tightly build a strong commitment for joint response operations through full-size exercises at the metropolitan level before a catastrophic event. Also, interorganizational emergency management networks need to be restructured by reflecting the actual response networks to reduce collaboration risk during a disaster. This research presents a critical insight into inverse thinking of the view designing urban emergency management networks and provides original evidences for filling the gap between previously coordinated networks for disaster preparedness and practical response operations after a disaster.

Analysis And Augmentation Of Timing Advance Based Geolocation In Lte Cellular Networks

DTIC Science & Technology

2016-12-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA DISSERTATION ANALYSIS AND AUGMENTATION OF TIMING ADVANCE-BASED GEOLOCATION IN LTE CELLULAR NETWORKS by...estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the...AND SUBTITLE ANALYSIS AND AUGMENTATION OF TIMING ADVANCE-BASED GEOLOCA- TION IN LTE CELLULAR NETWORKS 5. FUNDING NUMBERS 6. AUTHOR(S) John D. Roth 7

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

NASA Astrophysics Data System (ADS)

White, Forest M.; Wolf-Yadlin, Alejandro

2016-06-01

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics.

PubMed

Somvanshi, Pramod Rajaram; Venkatesh, K V

2014-03-01

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

Management of the Space Physics Analysis Network (SPAN)

NASA Technical Reports Server (NTRS)

Green, James L.; Thomas, Valerie L.; Butler, Todd F.; Peters, David J.; Sisson, Patricia L.

1990-01-01

Here, the purpose is to define the operational management structure and to delineate the responsibilities of key Space Physics Analysis Network (SPAN) individuals. The management structure must take into account the large NASA and ESA science research community by giving them a major voice in the operation of the system. Appropriate NASA and ESA interfaces must be provided so that there will be adequate communications facilities available when needed. Responsibilities are delineated for the Advisory Committee, the Steering Committee, the Project Scientist, the Project Manager, the SPAN Security Manager, the Internetwork Manager, the Network Operations Manager, the Remote Site Manager, and others.

Comparison of gene co-networks reveals the molecular mechanisms of the rice (Oryza sativa L.) response to Rhizoctonia solani AG1 IA infection.

PubMed

Zhang, Jinfeng; Zhao, Wenjuan; Fu, Rong; Fu, Chenglin; Wang, Lingxia; Liu, Huainian; Li, Shuangcheng; Deng, Qiming; Wang, Shiquan; Zhu, Jun; Liang, Yueyang; Li, Ping; Zheng, Aiping

2018-05-05

Rhizoctonia solani causes rice sheath blight, an important disease affecting the growth of rice (Oryza sativa L.). Attempts to control the disease have met with little success. Based on transcriptional profiling, we previously identified more than 11,947 common differentially expressed genes (TPM > 10) between the rice genotypes TeQing and Lemont. In the current study, we extended these findings by focusing on an analysis of gene co-expression in response to R. solani AG1 IA and identified gene modules within the networks through weighted gene co-expression network analysis (WGCNA). We compared the different genes assigned to each module and the biological interpretations of gene co-expression networks at early and later modules in the two rice genotypes to reveal differential responses to AG1 IA. Our results show that different changes occurred in the two rice genotypes and that the modules in the two groups contain a number of candidate genes possibly involved in pathogenesis, such as the VQ protein. Furthermore, these gene co-expression networks provide comprehensive transcriptional information regarding gene expression in rice in response to AG1 IA. The co-expression networks derived from our data offer ideas for follow-up experimentation that will help advance our understanding of the translational regulation of rice gene expression changes in response to AG1 IA.

Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pei, Guangsheng; Chen, Lei; Wang, Jiangxin

2014-11-03

Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap inmore » the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.« less

Differential reward responses during competition against in- and out-of-network others.

PubMed

Fareri, Dominic S; Delgado, Mauricio R

2014-04-01

Social interactions occur within a variety of different contexts--cooperative/competitive--and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one's friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations.

Differential reward responses during competition against in- and out-of-network others

PubMed Central

Fareri, Dominic S.

2014-01-01

Social interactions occur within a variety of different contexts––cooperative/competitive––and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one’s friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations. PMID:23314007

Development of visible-light responsive and mechanically enhanced "smart" UCST interpenetrating network hydrogels.

PubMed

Xu, Yifei; Ghag, Onkar; Reimann, Morgan; Sitterle, Philip; Chatterjee, Prithwish; Nofen, Elizabeth; Yu, Hongyu; Jiang, Hanqing; Dai, Lenore L

2017-12-20

An interpenetrating polymer network (IPN), chlorophyllin-incorporated environmentally responsive hydrogel was synthesized and exhibited the following features: enhanced mechanical properties, upper critical solution temperature (UCST) swelling behavior, and promising visible-light responsiveness. Poor mechanical properties are known challenges for hydrogel-based materials. By forming an interpenetrating network between polyacrylamide (PAAm) and poly(acrylic acid) (PAAc) polymer networks, the mechanical properties of the synthesized IPN hydrogels were significantly improved compared to hydrogels made of a single network of each polymer. The formation of the interpenetrating network was confirmed by Fourier Transform Infrared Spectroscopy (FTIR), the analysis of glass transition temperature, and a unique UCST responsive swelling behavior, which is in contrast to the more prevalent lower critical solution temperature (LCST) behaviour of environmentally responsive hydrogels. The visible-light responsiveness of the synthesized hydrogel also demonstrated a positive swelling behavior, and the effect of incorporating chlorophyllin as the chromophore unit was observed to reduce the average pore size and further enhance the mechanical properties of the hydrogel. This interpenetrating network system shows potential to serve as a new route in developing "smart" hydrogels using visible-light as a simple, inexpensive, and remotely controllable stimulus.

Spike-train spectra and network response functions for non-linear integrate-and-fire neurons.

PubMed

Richardson, Magnus J E

2008-11-01

Reduced models have long been used as a tool for the analysis of the complex activity taking place in neurons and their coupled networks. Recent advances in experimental and theoretical techniques have further demonstrated the usefulness of this approach. Despite the often gross simplification of the underlying biophysical properties, reduced models can still present significant difficulties in their analysis, with the majority of exact and perturbative results available only for the leaky integrate-and-fire model. Here an elementary numerical scheme is demonstrated which can be used to calculate a number of biologically important properties of the general class of non-linear integrate-and-fire models. Exact results for the first-passage-time density and spike-train spectrum are derived, as well as the linear response properties and emergent states of recurrent networks. Given that the exponential integrate-fire model has recently been shown to agree closely with the experimentally measured response of pyramidal cells, the methodology presented here promises to provide a convenient tool to facilitate the analysis of cortical-network dynamics.

Characterization of the SOS meta-regulon in the human gut microbiome.

PubMed

Cornish, Joseph P; Sanchez-Alberola, Neus; O'Neill, Patrick K; O'Keefe, Ronald; Gheba, Jameel; Erill, Ivan

2014-05-01

Data from metagenomics projects remain largely untapped for the analysis of transcriptional regulatory networks. Here, we provide proof-of-concept that metagenomic data can be effectively leveraged to analyze regulatory networks by characterizing the SOS meta-regulon in the human gut microbiome. We combine well-established in silico and in vitro techniques to mine the human gut microbiome data and determine the relative composition of the SOS network in a natural setting. Our analysis highlights the importance of translesion synthesis as a primary function of the SOS response. We predict the association of this network with three novel protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification, and we confirm binding of the SOS response transcriptional repressor to sites in the promoter of a cell wall biogenesis enzyme, a phage integrase and a death-on-curing protein. We discuss the implications of these findings and the potential for this approach for metagenome analysis.

A Network Biology Approach to Decipher Stress Response in Bacteria Using Escherichia coli As a Model.

PubMed

Nagar, Shashwat Deepali; Aggarwal, Bhavye; Joon, Shikha; Bhatnagar, Rakesh; Bhatnagar, Sonika

2016-05-01

The development of drug-resistant pathogenic bacteria poses challenges to global health for their treatment and control. In this context, stress response enables bacterial populations to survive extreme perturbations in the environment but remains poorly understood. Specific modules are activated for unique stressors with few recognized global regulators. The phenomenon of cross-stress protection strongly suggests the presence of central proteins that control the diverse stress responses. In this work, Escherichia coli was used to model the bacterial stress response. A Protein-Protein Interaction Network was generated by integrating differentially expressed genes in eight stress conditions of pH, temperature, and antibiotics with relevant gene ontology terms. Topological analysis identified 24 central proteins. The well-documented role of 16 central proteins in stress indicates central control of the response, while the remaining eight proteins may have a novel role in stress response. Cluster analysis of the generated network implicated RNA binding, flagellar assembly, ABC transporters, and DNA repair as important processes during response to stress. Pathway analysis showed crosstalk of Two Component Systems with metabolic processes, oxidative phosphorylation, and ABC transporters. The results were further validated by analysis of an independent cross-stress protection dataset. This study also reports on the ways in which bacterial stress response can progress to biofilm formation. In conclusion, we suggest that drug targets or pathways disrupting bacterial stress responses can potentially be exploited to combat antibiotic tolerance and multidrug resistance in the future.

Disaster Response on September 11, 2001 Through the Lens of Statistical Network Analysis.

PubMed

Schweinberger, Michael; Petrescu-Prahova, Miruna; Vu, Duy Quang

2014-05-01

The rescue and relief operations triggered by the September 11, 2001 attacks on the World Trade Center in New York City demanded collaboration among hundreds of organisations. To shed light on the response to the September 11, 2001 attacks and help to plan and prepare the response to future disasters, we study the inter-organisational network that emerged in response to the attacks. Studying the inter-organisational network can help to shed light on (1) whether some organisations dominated the inter-organisational network and facilitated communication and coordination of the disaster response; (2) whether the dominating organisations were supposed to coordinate disaster response or emerged as coordinators in the wake of the disaster; and (3) the degree of network redundancy and sensitivity of the inter-organisational network to disturbances following the initial disaster. We introduce a Bayesian framework which can answer the substantive questions of interest while being as simple and parsimonious as possible. The framework allows organisations to have varying propensities to collaborate, while taking covariates into account, and allows to assess whether the inter-organisational network had network redundancy-in the form of transitivity-by using a test which may be regarded as a Bayesian score test. We discuss implications in terms of disaster management.

Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.)

PubMed Central

2011-01-01

Background Global transcriptional analysis of loblolly pine (Pinus taeda L.) is challenging due to limited molecular tools. PtGen2, a 26,496 feature cDNA microarray, was fabricated and used to assess drought-induced gene expression in loblolly pine propagule roots. Statistical analysis of differential expression and weighted gene correlation network analysis were used to identify drought-responsive genes and further characterize the molecular basis of drought tolerance in loblolly pine. Results Microarrays were used to interrogate root cDNA populations obtained from 12 genotype × treatment combinations (four genotypes, three watering regimes). Comparison of drought-stressed roots with roots from the control treatment identified 2445 genes displaying at least a 1.5-fold expression difference (false discovery rate = 0.01). Genes commonly associated with drought response in pine and other plant species, as well as a number of abiotic and biotic stress-related genes, were up-regulated in drought-stressed roots. Only 76 genes were identified as differentially expressed in drought-recovered roots, indicating that the transcript population can return to the pre-drought state within 48 hours. Gene correlation analysis predicts a scale-free network topology and identifies eleven co-expression modules that ranged in size from 34 to 938 members. Network topological parameters identified a number of central nodes (hubs) including those with significant homology (E-values ≤ 2 × 10-30) to 9-cis-epoxycarotenoid dioxygenase, zeatin O-glucosyltransferase, and ABA-responsive protein. Identified hubs also include genes that have been associated previously with osmotic stress, phytohormones, enzymes that detoxify reactive oxygen species, and several genes of unknown function. Conclusion PtGen2 was used to evaluate transcriptome responses in loblolly pine and was leveraged to identify 2445 differentially expressed genes responding to severe drought stress in roots. Many of the genes identified are known to be up-regulated in response to osmotic stress in pine and other plant species and encode proteins involved in both signal transduction and stress tolerance. Gene expression levels returned to control values within a 48-hour recovery period in all but 76 transcripts. Correlation network analysis indicates a scale-free network topology for the pine root transcriptome and identifies central nodes that may serve as drivers of drought-responsive transcriptome dynamics in the roots of loblolly pine. PMID:21609476

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus

PubMed Central

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S.

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites. PMID:27588023

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

PubMed

Pathania, Shivalika; Bagler, Ganesh; Ahuja, Paramvir S

2016-01-01

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites.

Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

PubMed

Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

2017-09-05

Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

Frequency Domain Analysis of Narx Neural Networks

NASA Astrophysics Data System (ADS)

Chance, J. E.; Worden, K.; Tomlinson, G. R.

1998-06-01

A method is proposed for interpreting the behaviour of NARX neural networks. The correspondence between time-delay neural networks and Volterra series is extended to the NARX class of networks. The Volterra kernels, or rather, their Fourier transforms, are obtained via harmonic probing. In the same way that the Volterra kernels generalize the impulse response to non-linear systems, the Volterra kernel transforms can be viewed as higher-order analogues of the Frequency Response Functions commonly used in Engineering dynamics; they can be interpreted in much the same way.

Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks.

PubMed

Takemoto, Kazuhiro; Kajihara, Kosuke

2016-01-01

Theoretical studies have indicated that nestedness and modularity-non-random structural patterns of ecological networks-influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.

Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses.

PubMed

Li, Hong; Zhou, Yuan; Zhang, Ziding

2017-01-01

Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

TimeXNet Web: Identifying cellular response networks from diverse omics time-course data.

PubMed

Tan, Phit Ling; López, Yosvany; Nakai, Kenta; Patil, Ashwini

2018-05-14

Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. http://txnet.hgc.jp/. ashwini@hgc.jp. Supplementary data are available at Bioinformatics online.

Social Networks and High Healthcare Utilization: Building Resilience Through Analysis

DTIC Science & Technology

2016-09-01

of Social Network Analysis Patients Developing targeted intervention programs based on the individual’s needs may potentially help improve the...network structure is found in the patterns of interconnection that develop between nodes. It is this linking through common nodes, “the AB link shares...transitivity is responsible for the clustering of nodes that form “communities” of people based on geography, common interests, or other group

Performance Analysis of Hierarchical Group Key Management Integrated with Adaptive Intrusion Detection in Mobile ad hoc Networks

DTIC Science & Technology

2016-04-05

applications in wireless networks such as military battlefields, emergency response, mobile commerce , online gaming, and collaborative work are based on the...www.elsevier.com/locate/peva Performance analysis of hierarchical group key management integrated with adaptive intrusion detection in mobile ad hoc...Accepted 19 September 2010 Available online 26 September 2010 Keywords: Mobile ad hoc networks Intrusion detection Group communication systems Group

Mapping Creativity: Creativity Measurements Network Analysis

ERIC Educational Resources Information Center

Pinheiro, Igor Reszka; Cruz, Roberto Moraes

2014-01-01

This article borrowed network analysis tools to discover how the construct formed by the set of all measures of creativity configures itself. To this end, using a variant of the meta-analytical method, a database was compiled simulating 42,381 responses to 974 variables centered on 64 creativity measures. Results, although preliminary, indicate…

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice.

PubMed

Smita, Shuchi; Katiyar, Amit; Chinnusamy, Viswanathan; Pandey, Dev M; Bansal, Kailash C

2015-01-01

MYB transcription factor (TF) is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by "top-down" and "guide-gene" approaches. More than 50% of OsMYBs were strongly correlated under 50 experimental conditions with 51 hub genes via "top-down" approach. Further, clusters were identified using Markov Clustering (MCL). To maximize the clustering performance, parameter evaluation of the MCL inflation score (I) was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by "guide-gene" approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought response in rice. Thus, the co-regulatory network analysis facilitated the identification of complex OsMYB regulatory networks, and candidate target regulon genes of selected guide MYB genes. The results contribute to the candidate gene screening, and experimentally testable hypotheses for potential regulatory MYB TFs, and their targets under stress conditions.

Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa

PubMed Central

Gehan, Malia A; Mockler, Todd C; Weinig, Cynthia; Ewers, Brent E

2017-01-01

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought. PMID:28826479

Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production.

PubMed

Beaty, Roger E; Christensen, Alexander P; Benedek, Mathias; Silvia, Paul J; Schacter, Daniel L

2017-03-01

Functional neuroimaging research has recently revealed brain network interactions during performance on creative thinking tasks-particularly among regions of the default and executive control networks-but the cognitive mechanisms related to these interactions remain poorly understood. Here we test the hypothesis that the executive control network can interact with the default network to inhibit salient conceptual knowledge (i.e., pre-potent responses) elicited from memory during creative idea production. Participants studied common noun-verb pairs and were given a cued-recall test with corrective feedback to strengthen the paired association in memory. They then completed a verb generation task that presented either a previously studied noun (high-constraint) or an unstudied noun (low-constraint), and were asked to "think creatively" while searching for a novel verb to relate to the presented noun. Latent Semantic Analysis of verbal responses showed decreased semantic distance values in the high-constraint (i.e., interference) condition, which corresponded to increased neural activity within regions of the default (posterior cingulate cortex and bilateral angular gyri), salience (right anterior insula), and executive control (left dorsolateral prefrontal cortex) networks. Independent component analysis of intrinsic functional connectivity networks extended this finding by revealing differential interactions among these large-scale networks across the task conditions. The results suggest that interactions between the default and executive control networks underlie response inhibition during constrained idea production, providing insight into specific neurocognitive mechanisms supporting creative cognition. Copyright © 2017 Elsevier Inc. All rights reserved.

An Exploratory Investigation of Functional Network Connectivity of Empathy and Default Mode Networks in a Free-Viewing Task.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-08-01

This study reports dynamic functional network connectivity (dFNC) analysis on time courses of putative empathy networks-cognitive, emotional, and motor-and the default mode network (DMN) identified from independent components (ICs) derived by the group independent component analysis (ICA) method. The functional magnetic resonance imaging (fMRI) data were collected from 15 subjects watching movies of three genres, an animation (S1), Indian Hindi (S2), and a Hollywood English (S3) movie. The hypothesis of the study is that empathic engagement in a movie narrative would modulate the activation with the DMN. The clippings were individually rated for emotional expressions, context, and empathy self-response by the fMRI subjects post scanning and by 40 participants in an independent survey who rated at four time intervals in each clipping. The analysis illustrates the following: (a) the ICA method separated ICs with areas reported for empathy response and anterior/posterior DMNs. An IC indicating insula region activation reported to be crucial for the emotional empathy network was separated for S2 and S3 movies only, but not for S1, (b) the dFNC between DMN and ICs corresponding to cognitive empathy network showed higher positive periodical fluctuating correlations for all three movies, while ICs with areas crucial to motor or emotional empathy display lower positive or negative correlation values with no distinct periodicity. A possible explanation for the lower values and anticorrelation between the DMN and emotional empathy networks could possibly be inhibition due to internal self-reflections, attributed to DMN, while processing and preparing a response to external emotional content. The positive higher correlation values for cognitive empathy networks may reflect a functional overlap with DMN for enhanced internal self-reflections, inferring beliefs and intentions about the 'other', all triggered by the external stimuli. The findings are useful in the study of deviations in functional synergies of large complex networks associated with empathy responses and DMN in clinical applications like autism and schizophrenia.

NEAT: an efficient network enrichment analysis test.

PubMed

Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

2016-09-05

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

PubMed

Hu, Chelsea Y; Takahashi, Melissa K; Zhang, Yan; Lucks, Julius B

2018-05-22

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

A catalyst for system change: a case study of child health network formation, evolution and sustainability in Canada.

PubMed

McPherson, Charmaine; Ploeg, Jenny; Edwards, Nancy; Ciliska, Donna; Sword, Wendy

2017-02-01

The purpose of this study was to examine key processes and supportive and inhibiting factors involved in the development, evolution, and sustainability of a child health network in rural Canada. This study contributes to a relatively new research agenda aimed at understanding inter-organizational and cross-sectoral health networks. These networks encourage collaboration focusing on complex issues impacting health - issues that individual agencies cannot effectively address alone. This paper presents an overview of the study findings. An explanatory qualitative case study approach examined the Network's 13-year lifespan. Data sources were documents and Network members, including regional and 71 provincial senior managers from 11 child and youth service sectors. Data were collected through 34 individual interviews and a review of 127 documents. Interview data were analyzed using framework analysis methods; Prior's approach guided document analysis. Three themes related to network development, evolution and sustainability were identified: (a) Network relationships as system triggers, (b) Network-mediated system responsiveness, and (c) Network practice as political. Study findings have important implications for network organizational development, collaborative practice, interprofessional education, public policy, and public system responsiveness research. Findings suggest it is important to explicitly focus on relationships and multi-level socio-political contexts, such as supportive policy environments, in understanding health networks. The dynamic interplay among the Network members; central supportive and inhibiting factors; and micro-, meso-, and macro-organizational contexts was identified.

Comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults: a network meta-analysis.

PubMed

Thorlund, Kristian; Druyts, Eric; Wu, Ping; Balijepalli, Chakrapani; Keohane, Denis; Mills, Edward

2015-05-01

To establish the comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults using the network meta-analysis approach. Systematic review and network meta-analysis. Individuals aged 60 and older. Data on partial response (defined as at least 50% reduction in depression score from baseline) and safety (dizziness, vertigo, syncope, falls, loss of consciousness) were extracted. A Bayesian network meta-analysis was performed on the efficacy and safety outcomes, and relative risks (RRs) with 95% credible intervals (CrIs) were produced. Fifteen randomized controlled trials were eligible for inclusion in the analysis. Citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, fluoxetine, and sertraline were represented. Reporting on partial response and dizziness was sufficient to conduct a network meta-analysis. Reporting on other outcomes was sparse. For partial response, sertraline (RR=1.28), paroxetine (RR=1.48), and duloxetine (RR=1.62) were significantly better than placebo. The remaining interventions yielded RRs lower than 1.20. For dizziness, duloxetine (RR=3.18) and venlafaxine (RR=2.94) were statistically significantly worse than placebo. Compared with placebo, sertraline had the lowest RR for dizziness (1.14) and fluoxetine the second lowest (1.31). Citalopram, escitalopram, and paroxetine all had RRs between 1.4 and 1.7. There was clear evidence of the effectiveness of sertraline, paroxetine, and duloxetine. There also appears to be a hierarchy of safety associated with the different antidepressants, although there appears to be a dearth of reporting of safety outcomes. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

Ictal connectivity in childhood absence epilepsy: Associations with outcome.

PubMed

Tenney, Jeffrey R; Kadis, Darren S; Agler, William; Rozhkov, Leonid; Altaye, Mekibib; Xiang, Jing; Vannest, Jennifer; Glauser, Tracy A

2018-05-01

The understanding of childhood absence epilepsy (CAE) has been revolutionized over the past decade, but the biological mechanisms responsible for variable treatment outcomes are unknown. Our purpose in this prospective observational study was to determine how pretreatment ictal network pathways, defined using a combined electroencephalography (EEG)-functional magnetic resonance imaging (EEG-fMRI) and magnetoencephalography (MEG) effective connectivity analysis, were related to treatment response. Sixteen children with newly diagnosed and drug-naive CAE had 31 typical absence seizures during EEG-fMRI and 74 during MEG. The spatial extent of the pretreatment ictal network was defined using fMRI hemodynamic response with an event-related independent component analysis (eICA). This spatially defined pretreatment ictal network supplied prior information for MEG-effective connectivity analysis calculated using phase slope index (PSI). Treatment outcome was assessed 2 years following diagnosis and dichotomized to ethosuximide (ETX)-treatment responders (N = 11) or nonresponders (N = 5). Effective connectivity of the pretreatment ictal network was compared to the treatment response. Patterns of pretreatment connectivity demonstrated strongest connections in the thalamus and posterior brain regions (parietal, posterior cingulate, angular gyrus, precuneus, and occipital) at delta frequencies and the frontal cortices at gamma frequencies (P < .05). ETX treatment nonresponders had pretreatment connectivity, which was decreased in the precuneus region and increased in the frontal cortex compared to ETX responders (P < .05). Pretreatment ictal connectivity differences in children with CAE were associated with response to antiepileptic treatment. This is a possible mechanism for the variable treatment response seen in patients sharing the same epilepsy syndrome. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Modelling and analysis of gene regulatory network using feedback control theory

NASA Astrophysics Data System (ADS)

El-Samad, H.; Khammash, M.

2010-01-01

Molecular pathways are a part of a remarkable hierarchy of regulatory networks that operate at all levels of organisation. These regulatory networks are responsible for much of the biological complexity within the cell. The dynamic character of these pathways and the prevalence of feedback regulation strategies in their operation make them amenable to systematic mathematical analysis using the same tools that have been used with success in analysing and designing engineering control systems. In this article, we aim at establishing this strong connection through various examples where the behaviour exhibited by gene networks is explained in terms of their underlying control strategies. We complement our analysis by a survey of mathematical techniques commonly used to model gene regulatory networks and analyse their dynamic behaviour.

Research on two-port network of wavelet transform processor using surface acoustic wavelet devices and its application.

PubMed

Liu, Shoubing; Lu, Wenke; Zhu, Changchun

2017-11-01

The goal of this research is to study two-port network of wavelet transform processor (WTP) using surface acoustic wave (SAW) devices and its application. The motive was prompted by the inconvenience of the long research and design cycle and the huge research funding involved with traditional method in this field, which were caused by the lack of the simulation and emulation method of WTP using SAW devices. For this reason, we introduce the two-port network analysis tool, which has been widely used in the design and analysis of SAW devices with uniform interdigital transducers (IDTs). Because the admittance parameters calculation formula of the two-port network can only be used for the SAW devices with uniform IDTs, this analysis tool cannot be directly applied into the design and analysis of the processor using SAW devices, whose input interdigital transducer (IDT) is apodized weighting. Therefore, in this paper, we propose the channel segmentation method, which can convert the WTP using SAW devices into parallel channels, and also provide with the calculation formula of the number of channels, the number of finger pairs and the static capacitance of an interdigital period in each parallel channel firstly. From the parameters given above, we can calculate the admittance parameters of the two port network for each channel, so that we can obtain the admittance parameter of the two-port network of the WTP using SAW devices on the basis of the simplification rule of parallel two-port network. Through this analysis tool, not only can we get the impulse response function of the WTP using SAW devices but we can also get the matching circuit of it. Large numbers of studies show that the parameters of the two-port network obtained by this paper are consistent with those measured by network analyzer E5061A, and the impulse response function obtained by the two-port network analysis tool is also consistent with that measured by network analyzer E5061A, which can meet the accuracy requirements of the analysis of the WTP using SAW devices. Therefore the two-port network analysis tool discussed in this paper has comparatively higher theoretical and practical value. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

NASA Astrophysics Data System (ADS)

Pietrowski, Wojciech; Górny, Konrad

2017-12-01

Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

Genomic analysis of regulatory network dynamics reveals large topological changes

NASA Astrophysics Data System (ADS)

Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

2004-09-01

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Transcriptome Profiling of the Pineapple under Low Temperature to Facilitate Its Breeding for Cold Tolerance

PubMed Central

Chen, Chengjie; Zhang, Yafeng; Xu, Zhiqiang; Luan, Aiping; Mao, Qi; Feng, Junting; Xie, Tao; Gong, Xue; Wang, Xiaoshuang; Chen, Hao; He, Yehua

2016-01-01

The pineapple (Ananas comosus) is cold sensitive. Most cultivars are injured during winter periods, especially in sub-tropical regions. There is a lack of molecular information on the pineapple’s response to cold stress. In this study, high-throughput transcriptome sequencing and gene expression analysis were performed on plantlets of a cold-tolerant genotype of the pineapple cultivar ‘Shenwan’ before and after cold treatment. A total of 1,186 candidate cold responsive genes were identified, and their credibility was confirmed by RT-qPCR. Gene set functional enrichment analysis indicated that genes related to cell wall properties, stomatal closure and ABA and ROS signal transduction play important roles in pineapple cold tolerance. In addition, a protein association network of CORs (cold responsive genes) was predicted, which could serve as an entry point to dissect the complex cold response network. Our study found a series of candidate genes and their association network, which will be helpful to cold stress response studies and pineapple breeding for cold tolerance. PMID:27656892

Pervasive and opposing effects of Unpredictable Chronic Mild Stress (UCMS) on hippocampal gene expression in BALB/cJ and C57BL/6J mouse strains.

PubMed

Malki, Karim; Mineur, Yann S; Tosto, Maria Grazia; Campbell, James; Karia, Priya; Jumabhoy, Irfan; Sluyter, Frans; Crusio, Wim E; Schalkwyk, Leonard C

2015-04-03

BALB/cJ is a strain susceptible to stress and extremely susceptible to a defective hedonic impact in response to chronic stressors. The strain offers much promise as an animal model for the study of stress related disorders. We present a comparative hippocampal gene expression study on the effects of unpredictable chronic mild stress on BALB/cJ and C57BL/6J mice. Affymetrix MOE 430 was used to measure hippocampal gene expression from 16 animals of two different strains (BALB/cJ and C57BL/6J) of both sexes and subjected to either unpredictable chronic mild stress (UCMS) or no stress. Differences were statistically evaluated through supervised and unsupervised linear modelling and using Weighted Gene Coexpression Network Analysis (WGCNA). In order to gain further understanding into mechanisms related to stress response, we cross-validated our results with a parallel study from the GENDEP project using WGCNA in a meta-analysis design. The effects of UCMS are visible through Principal Component Analysis which highlights the stress sensitivity of the BALB/cJ strain. A number of genes and gene networks related to stress response were uncovered including the Creb1 gene. WGCNA and pathway analysis revealed a gene network centered on Nfkb1. Results from the meta-analysis revealed a highly significant gene pathway centred on the Ubiquitin C (Ubc) gene. All pathways uncovered are associated with inflammation and immune response. The study investigated the molecular mechanisms underlying the response to adverse environment in an animal model using a GxE design. Stress-related differences were visible at the genomic level through PCA analysis highlighting the high sensitivity of BALB/cJ animals to environmental stressors. Several candidate genes and gene networks reported are associated with inflammation and neurogenesis and could serve to inform candidate gene selection in human studies and provide additional insight into the pathology of Major Depressive Disorder.

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Humanitarian Information Management Network Effectiveness: An Analysis at the Organizational and Network Levels

ERIC Educational Resources Information Center

Ngamassi Tchouakeu, Louis-Marie

2011-01-01

Massive international response to humanitarian crises such as the South Asian Tsunami in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the importance of humanitarian inter-organizational collaboration networks, especially in information management and exchange. Despite more than a decade old call for more research…

Bootstrapping disaster: The challenge of growing and maintaining a cross-sector collaborative network.

PubMed

Wachhaus, Aaron

This article examines the interaction of nonprofit and private actors with the traditional bureaucratic structures of government in central Pennsylvania&s recovery from hurricane Irene and tropical storm Lee. That effort relied heavily on private and nonprofit organizations as drivers of the response and recovery. The author maps the organizations involved in the recovery effort and explores the impact of the recovery effort on those organizations. A social network analysis was conducted and complemented with follow-up interviews with key actors. The network analysis reveals weak communication between sectors and a reliance on nonprofits to deliver services; interviews uncover the challenges of intersectoral collaboration. The author addresses the successes and limitations of the means by which a network of nonprofit efforts were coordinated with federal and state relief efforts and draw lessons for improving future practices. The author finds that this case deviates from theory in several ways that complicated community response and recovery. In particular, the challenges of developing and maintaining a recovery network while simultaneously delivering services placed great strain on several organizations, as well as on the fledgling network as a whole.

Working at Community Boundaries: A Micro-Analysis of the Activist's Role in Participatory Learning Networks

ERIC Educational Resources Information Center

Thorpe, Mary; Kubiak, Chris

2005-01-01

The interaction of agency and context in workplace learning is explored through a micro-analysis of the implementation of networked learning communities in schools in England. Interviews with local activists show evidence of co-participation between individuals' responses and their workplace roles and experience as they take up the opportunity to…

Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs

USGS Publications Warehouse

Irvine, Kathryn M.; Rodhouse, Thomas J.

2014-01-01

As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially different results and/or computational instability. However, when only fixed effects are of interest, the survey package (svyglm and svyolr) may be suitable for a model-assisted analysis for trend. We provide possible directions for future research into combined analysis for ordinal and continuous vital sign indictors.

How time delay and network design shape response patterns in biochemical negative feedback systems.

PubMed

Börsch, Anastasiya; Schaber, Jörg

2016-08-24

Negative feedback in combination with time delay can bring about both sustained oscillations and adaptive behaviour in cellular networks. Here, we study which design features of systems with delayed negative feedback shape characteristic response patterns with special emphasis on the role of time delay. To this end, we analyse generic two-dimensional delay differential equations describing the dynamics of biochemical signal-response networks. We investigate the influence of several design features on the stability of the model equilibrium, i.e., presence of auto-inhibition and/or mass conservation and the kind and/or strength of the delayed negative feedback. We show that auto-inhibition and mass conservation have a stabilizing effect, whereas increasing abruptness and decreasing feedback threshold have a de-stabilizing effect on the model equilibrium. Moreover, applying our theoretical analysis to the mammalian p53 system we show that an auto-inhibitory feedback can decouple period and amplitude of an oscillatory response, whereas the delayed feedback can not. Our theoretical framework provides insight into how time delay and design features of biochemical networks act together to elicit specific characteristic response patterns. Such insight is useful for constructing synthetic networks and controlling their behaviour in response to external stimulation.

Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

PubMed

Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

2016-01-01

A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. Copyright © 2015 Elsevier Inc. All rights reserved.

Acute pharmacologically induced shifts in serotonin availability abolish emotion-selective responses to negative face emotions in distinct brain networks.

PubMed

Grady, Cheryl L; Siebner, Hartwig R; Hornboll, Bettina; Macoveanu, Julian; Paulson, Olaf B; Knudsen, Gitte M

2013-05-01

Pharmacological manipulation of serotonin availability can alter the processing of facial expressions of emotion. Using a within-subject design, we measured the effect of serotonin on the brain's response to aversive face emotions with functional MRI while 20 participants judged the gender of neutral, fearful and angry faces. In three separate and counterbalanced sessions, participants received citalopram (CIT) to raise serotonin levels, underwent acute tryptophan depletion (ATD) to lower serotonin, or were studied without pharmacological challenge (Control). An analysis designed to identify distributed brain responses identified two brain networks with modulations of activity related to face emotion and serotonin level. The first network included the left amygdala, bilateral striatum, and fusiform gyri. During the Control session this network responded only to fearful faces; increasing serotonin decreased this response to fear, whereas reducing serotonin enhanced the response of this network to angry faces. The second network involved bilateral amygdala and ventrolateral prefrontal cortex, and these regions also showed increased activity to fear during the Control session. Both drug challenges enhanced the neural response of this set of regions to angry faces, relative to Control, and CIT also enhanced activity for neutral faces. The net effect of these changes in both networks was to abolish the selective response to fearful expressions. These results suggest that a normal level of serotonin is critical for maintaining a differentiated brain response to threatening face emotions. Lower serotonin leads to a broadening of a normally fear-specific response to anger, and higher levels reduce the differentiated brain response to aversive face emotions. Copyright © 2012 Elsevier B.V. and ECNP. All rights reserved.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

PubMed Central

Luo, Jie; Xu, Pei; Cao, Peijian; Wan, Hongjian; Lv, Xiaonan; Xu, Shengchun; Wang, Gangjun; Cook, Melloni N.; Jones, Byron C.; Lu, Lu; Wang, Xusheng

2018-01-01

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses. PMID:29674951

Experimental Modal Analysis and Dynaic Strain Fiber Bragg Gratings for Structural Health Monitoring of Composite Aerospace Structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Fransen, S.; Gomez Molinero, V.; Kostopoulos, V.

2012-07-01

The objective of this work is to develop a new structural health monitoring system for composite aerospace structures based on dynamic response strain measurements and experimental modal analysis techniques. Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of vibration testing. The hypothesis of all vibration tests was that actual damage in composites reduces their stiffness and produces the same result as mass increase produces. Thus, damage was simulated by slightly varying locally the mass of the structure at different zones. Experimental modal analysis based on the strain responses was conducted and the extracted strain mode shapes were the input for the damage detection expert system. A feed-forward back propagation neural network was the core of the damage detection system. The features-input to the neural network consisted of the strain mode shapes, extracted from the experimental modal analysis. Dedicated training and validation activities were carried out based on the experimental results. The system showed high reliability, confirmed by the ability of the neural network to recognize the size and the position of damage on the structure. The experiments were performed on a real structure i.e. a lightweight antenna sub- reflector, manufactured and tested at EADS CASA ESPACIO. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on the structure with optimum topology. Numerical simulation of both structures was used as a support tool at all the steps of the work. Potential applications for the proposed system are during ground qualification extensive tests of space structures and during the mission as modal analysis tool on board, being able via the FBG responses to identify a potential failure.

Differential Coexpression Analysis Reveals Extensive Rewiring of Arabidopsis Gene Coexpression in Response to Pseudomonas syringae Infection

PubMed Central

Jiang, Zhenhong; Dong, Xiaobao; Li, Zhi-Gang; He, Fei; Zhang, Ziding

2016-01-01

Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity. Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways. Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions. PMID:27721457

Exploring Mechanisms Underlying Impaired Brain Function in Gulf War Illness through Advanced Network Analysis

DTIC Science & Technology

2017-10-01

networks of the brain responsible for visual processing, mood regulation, motor coordination, sensory processing, and language command, but increased...4    For each subject, the rsFMRI voxel time-series were temporally shifted to account for differences in slice acquisition times...responsible for visual processing, mood regulation, motor coordination, sensory processing, and language command, but increased connectivity in

A Formal Analysis of Cytokine Networks in Chronic Fatigue Syndrome

PubMed Central

Broderick, Gordon; Fuite, Jim; Kreitz, Andrea; Vernon, Suzanne D; Klimas, Nancy; Fletcher, Mary Ann

2010-01-01

Chronic Fatigue Syndrome (CFS) is a complex illness affecting 4 million Americans for which no characteristic lesion has been identified. Instead of searching for a deficiency in any single marker, we propose that CFS is associated with a profound imbalance in the regulation of immune function forcing a departure from standard preprogrammed responses. To identify these imbalances we apply network analysis to the co-expression of 16 cytokines in CFS subjects and healthy controls. Concentrations of IL-1a, 1b, 2, 4, 5, 6, 8, 10, 12, 13, 15, 17 and 23, IFN-γ, lymphotoxin-α (LT-α) and TNF-α were measured in the plasma of 40 female CFS and 59 case-matched controls. Cytokine co-expression networks were constructed from the pair-wise mutual information (MI) patterns found within each subject group. These networks differed in topology significantly more than expected by chance with the CFS network being more hub-like in design. Analysis of local modularity isolated statistically distinct cytokine communities recognizable as pre-programmed immune functional components. These showed highly attenuated Th1 and Th17 immune responses in CFS. High Th2 marker expression but weak interaction patterns pointed to an established Th2 inflammatory milieu. Similarly, altered associations in CFS provided indirect evidence of diminished NK cell responsiveness to IL-12 and LTα stimulus. These observations are consistent with several processes active in latent viral infection and would not have been uncovered by assessing marker expression alone. Furthermore this analysis identifies key subnetworks such as IL-2:IFNγ:TNFα that might be targeted in restoring normal immune function. PMID:20447453

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

PubMed

Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

PubMed

Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

2015-08-20

Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

Analysis of global gene expression in Brachypodium distachyon reveals extensive network plasticity in response to abiotic stress.

PubMed

Priest, Henry D; Fox, Samuel E; Rowley, Erik R; Murray, Jessica R; Michael, Todd P; Mockler, Todd C

2014-01-01

Brachypodium distachyon is a close relative of many important cereal crops. Abiotic stress tolerance has a significant impact on productivity of agriculturally important food and feedstock crops. Analysis of the transcriptome of Brachypodium after chilling, high-salinity, drought, and heat stresses revealed diverse differential expression of many transcripts. Weighted Gene Co-Expression Network Analysis revealed 22 distinct gene modules with specific profiles of expression under each stress. Promoter analysis implicated short DNA sequences directly upstream of module members in the regulation of 21 of 22 modules. Functional analysis of module members revealed enrichment in functional terms for 10 of 22 network modules. Analysis of condition-specific correlations between differentially expressed gene pairs revealed extensive plasticity in the expression relationships of gene pairs. Photosynthesis, cell cycle, and cell wall expression modules were down-regulated by all abiotic stresses. Modules which were up-regulated by each abiotic stress fell into diverse and unique gene ontology GO categories. This study provides genomics resources and improves our understanding of abiotic stress responses of Brachypodium.

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.

PubMed

Zhang, Chaoyang; Peng, Li; Zhang, Yaqin; Liu, Zhaoyang; Li, Wenling; Chen, Shilian; Li, Guancheng

2017-06-01

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein-protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM-receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets.

Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

PubMed

Poudel, R; Jumpponen, A; Schlatter, D C; Paulitz, T C; Gardener, B B McSpadden; Kinkel, L L; Garrett, K A

2016-10-01

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

NASA Technical Reports Server (NTRS)

Harrington, Peter DEB.; Zheng, Peng

1995-01-01

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Stream network analysis and geomorphic flood plain mapping from orbital and suborbital remote sensing imagery application to flood hazard studies in central Texas

NASA Technical Reports Server (NTRS)

Baker, V. R. (Principal Investigator); Holz, R. K.; Hulke, S. D.; Patton, P. C.; Penteado, M. M.

1975-01-01

The author has identified the following significant results. Development of a quantitative hydrogeomorphic approach to flood hazard evaluation was hindered by (1) problems of resolution and definition of the morphometric parameters which have hydrologic significance, and (2) mechanical difficulties in creating the necessary volume of data for meaningful analysis. Measures of network resolution such as drainage density and basin Shreve magnitude indicated that large scale topographic maps offered greater resolution than small scale suborbital imagery and orbital imagery. The disparity in network resolution capabilities between orbital and suborbital imagery formats depends on factors such as rock type, vegetation, and land use. The problem of morphometric data analysis was approached by developing a computer-assisted method for network analysis. The system allows rapid identification of network properties which can then be related to measures of flood response.

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks.

PubMed

Van Landeghem, Sofie; Van Parys, Thomas; Dubois, Marieke; Inzé, Dirk; Van de Peer, Yves

2016-01-05

Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/.

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Lavelle, Thomas M.; Patnaik, Surya

2003-01-01

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Network Analysis of Rodent Transcriptomes in Spaceflight

NASA Technical Reports Server (NTRS)

Ramachandran, Maya; Fogle, Homer; Costes, Sylvain

2017-01-01

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

Efficacy and safety of TNF-α inhibitors for active ankylosing spondylitis patients: Multiple treatment comparisons in a network meta-analysis

NASA Astrophysics Data System (ADS)

Liu, Wei; Wu, Yuan-Hao; Zhang, Lei; Liu, Xiao-Ya; Bin Xue; Bin Liu; Wang, Yi; Ji, Yang

2016-09-01

Ankylosing spondylitis (AS) is an inflammatory rheumatic disease with impact on axial skeleton, peripheral joints and enthuses, and it may result in severe disabilities of those parts. Tumor necrosis factor-α (TNF-α) inhibitors are considered as an effective treatment for patients with active AS. In this study, we conducted a network meta-analysis to compare the clinical outcomes of active AS patients treated with TNF-α inhibitors. Randomized controlled trials (RCTs) evaluating the efficacy and safety of TNF-α inhibitors were retrieved in literature search and selected for meta-analysis. Changes in ASAS20 response, ASAS40 response and BASDAI 50% response were regarded as efficacy outcomes; serious adverse events (SAE) and all cause withdrawals were regarded as safety outcomes. Both traditional pairwise meta-analysis and network meta-analysis were performed. The results showed that adalimumab and infliximab had better clinical outcomes. Infliximab consistently appeared to be the most effective TNF-α inhibitors with a high risk of adverse events for patients with active AS; meanwhile, adalimumab ranked highest with respect to adverse effects with efficacy secondary to infliximab. As a result, we were unable to conclude the optimal TNF-α inhibitor and this issue should be solved by future researchers.

How to become a superhero

NASA Astrophysics Data System (ADS)

Gleiser, Pablo M.

2007-09-01

We analyze a collaboration network based on the Marvel Universe comic books. First, we consider the system as a binary network, where two characters are connected if they appear in the same publication. The analysis of degree correlations reveals that, in contrast to most real social networks, the Marvel Universe presents a disassortative mixing on the degree. Then, we use a weight measure to study the system as a weighted network. This allows us to find and characterize well defined communities. Through the analysis of the community structure and the clustering as a function of the degree we show that the network presents a hierarchical structure. Finally, we comment on possible mechanisms responsible for the particular motifs observed.

Process-based network decomposition reveals backbone motif structure

PubMed Central

Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

2010-01-01

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated). PMID:20498084

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments[OPEN

PubMed Central

Hafemeister, Christoph; Nicotra, Adrienne B.; Jagadish, S.V. Krishna; Bonneau, Richard; Purugganan, Michael

2016-01-01

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference. PMID:27655842

Abscisic acid regulates root growth under osmotic stress conditions via an interacting hormonal network with cytokinin, ethylene and auxin.

PubMed

Rowe, James H; Topping, Jennifer F; Liu, Junli; Lindsey, Keith

2016-07-01

Understanding the mechanisms regulating root development under drought conditions is an important question for plant biology and world agriculture. We examine the effect of osmotic stress on abscisic acid (ABA), cytokinin and ethylene responses and how they mediate auxin transport, distribution and root growth through effects on PIN proteins. We integrate experimental data to construct hormonal crosstalk networks to formulate a systems view of root growth regulation by multiple hormones. Experimental analysis shows: that ABA-dependent and ABA-independent stress responses increase under osmotic stress, but cytokinin responses are only slightly reduced; inhibition of root growth under osmotic stress does not require ethylene signalling, but auxin can rescue root growth and meristem size; osmotic stress modulates auxin transporter levels and localization, reducing root auxin concentrations; PIN1 levels are reduced under stress in an ABA-dependent manner, overriding ethylene effects; and the interplay among ABA, ethylene, cytokinin and auxin is tissue-specific, as evidenced by differential responses of PIN1 and PIN2 to osmotic stress. Combining experimental analysis with network construction reveals that ABA regulates root growth under osmotic stress conditions via an interacting hormonal network with cytokinin, ethylene and auxin. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Co-expression Network Approach to Studying the Effects of Botulinum Neurotoxin-A.

PubMed

Mukund, Kavitha; Ward, Samuel R; Lieber, Richard L; Subramaniam, Shankar

2017-10-16

Botulinum Neurotoxin A (BoNT-A) is a potent neurotoxin with several clinical applications.The goal of this study was to utilize co-expression network theory to analyze temporal transcriptional data from skeletal muscle after BoNT-A treatment. Expression data for 2000 genes (extracted using a ranking heuristic) served as the basis for this analysis. Using weighted gene co-expression network analysis (WGCNA), we identified 19 co-expressed modules, further hierarchically clustered into 5 groups. Quantifying average expression and co-expression patterns across these groups revealed temporal aspects of muscle's response to BoNT-A. Functional analysis revealed enrichment of group 1 with metabolism; group 5 with contradictory functions of atrophy and cellular recovery; and groups 2 and 3 with extracellular matrix (ECM) and non-fast fiber isoforms. Topological positioning of two highly ranked, significantly expressed genes- Dclk1 and Ostalpha within group 5 suggested possible mechanistic roles in recovery from BoNT-A induced atrophy. Phenotypic correlations of groups with titin and myosin protein content further emphasized the effect of BoNT-A on the sarcomeric contraction machinery in early phase of chemodenervation. In summary, our approach revealed a hierarchical functional response to BoNT-A induced paralysis with early metabolic and later ECM responses and identified putative biomarkers associated with chemodenervation. Additionally, our results provide an unbiased validation of the response documented in our previous workBotulinum Neurotoxin A (BoNT-A) is a potent neurotoxin with several clinical applications.The goal of this study was to utilize co-expression network theory to analyze temporal transcriptional data from skeletal muscle after BoNT-A treatment. Expression data for 2000 genes (extracted using a ranking heuristic) served as the basis for this analysis. Using weighted gene co-expression network analysis (WGCNA), we identified 19 co-expressed modules, further hierarchically clustered into 5 groups. Quantifying average expression and co-expression patterns across these groups revealed temporal aspects of muscle's response to BoNT-A. Functional analysis revealed enrichment of group 1 with metabolism; group 5 with contradictory functions of atrophy and cellular recovery; and groups 2 and 3 with extracellular matrix (ECM) and non-fast fiber isoforms. Topological positioning of two highly ranked, significantly expressed genes- Dclk1 and Ostalpha within group 5 suggested possible mechanistic roles in recovery from BoNT-A induced atrophy. Phenotypic correlations of groups with titin and myosin protein content further emphasized the effect of BoNT-A on the sarcomeric contraction machinery in early phase of chemodenervation. In summary, our approach revealed a hierarchical functional response to BoNT-A induced paralysis with early metabolic and later ECM responses and identified putative biomarkers associated with chemodenervation. Additionally, our results provide an unbiased validation of the response documented in our previous work.

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

PubMed Central

2011-01-01

Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga. PMID:22035155

[Prediction of the molecular response to pertubations from single cell measurements].

PubMed

Remacle, Françoise; Levine, Raphael D

2014-12-01

The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure. © 2014 médecine/sciences – Inserm.

Description of a method to support public health information management: organizational network analysis

PubMed Central

Merrill, Jacqueline; Bakken, Suzanne; Rockoff, Maxine; Gebbie, Kristine; Carley, Kathleen

2007-01-01

In this case study we describe a method that has potential to provide systematic support for public health information management. Public health agencies depend on specialized information that travels throughout an organization via communication networks among employees. Interactions that occur within these networks are poorly understood and are generally unmanaged. We applied organizational network analysis, a method for studying communication networks, to assess the method’s utility to support decision making for public health managers, and to determine what links existed between information use and agency processes. Data on communication links among a health department’s staff was obtained via survey with a 93% response rate, and analyzed using Organizational Risk Analyzer (ORA) software. The findings described the structure of information flow in the department’s communication networks. The analysis succeeded in providing insights into organizational processes which informed public health managers’ strategies to address problems and to take advantage of network strengths. PMID:17098480

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions

NASA Astrophysics Data System (ADS)

Roy, Raktim; Phani Shilpa, P.; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level.

Passive and Active Analysis in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)

Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.

An Analysis of the Differences between Student Age and Social Networking Utilization within a School of Business

ERIC Educational Resources Information Center

Zula, Ken; Yarrish, Karen K.; Pawelzik, Walter

2011-01-01

Social networking sites such as Facebook, LinkedIn, and Twitter are widely regarded as an exciting opportunity to communicate with friends, especially for college students. The overall response to social networking tends to be one of trust regarding a generation that, supposedly has many friends but little sense of privacy. Employers use social…

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

PubMed Central

2013-01-01

Background Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. Results Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer. Conclusions Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics. PMID:24153255

Mid-latitude response to geomagnetic storms observed in 630nm airglow over continental United States

NASA Astrophysics Data System (ADS)

Bhatt, A.; Kendall, E. A.

2016-12-01

We present analysis of mid-latitude response observed to geomagnetic storms using the MANGO network consisting of all-sky cameras imaging 630nm emission over the continental United States. The response largely falls in two categories: Stable Auroral Red (SAR) arc and Large-scale traveling ionospheric disturbances (LSTIDs). However, outside of these phenomena, less often observed response include anomalous airglow brightening, bright swirls, and frozen in traveling structures. We will present an analysis of various events observed over 3 years of MANGO network operation, which started with two imagers in the western US with addition of new imagers in the last year. We will also present unusual north and northeastward propagating waves often observed in conjunction with diffuse aurora. Wherever possible, we will compare with observations from Boston University imagers located in Massachusetts and Texas.

Genome-wide characterization of differentially expressed genes provides insights into regulatory network of heat stress response in radish (Raphanus sativus L.).

PubMed

Wang, Ronghua; Mei, Yi; Xu, Liang; Zhu, Xianwen; Wang, Yan; Guo, Jun; Liu, Liwang

2018-03-01

Heat stress (HS) causes detrimental effects on plant morphology, physiology, and biochemistry that lead to drastic reduction in plant biomass production and economic yield worldwide. To date, little is known about HS-responsive genes involved in thermotolerance mechanism in radish. In this study, a total of 6600 differentially expressed genes (DEGs) from the control and Heat24 cDNA libraries of radish were isolated by high-throughput sequencing. With Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, some genes including MAPK, DREB, ERF, AP2, GST, Hsf, and Hsp were predominantly assigned in signal transductions, metabolic pathways, and biosynthesis and abiotic stress-responsive pathways. These pathways played significant roles in reducing stress-induced damages and enhancing heat tolerance in radish. Expression patterns of 24 candidate genes were validated by reverse-transcription quantitative PCR (RT-qPCR). Based mainly on the analysis of DEGs combining with the previous miRNAs analysis, the schematic model of HS-responsive regulatory network was proposed. To counter the effects of HS, a rapid response of the plasma membrane leads to the opening of specific calcium channels and cytoskeletal reorganization, after which HS-responsive genes are activated to repair damaged proteins and ultimately facilitate further enhancement of thermotolerance in radish. These results could provide fundamental insight into the regulatory network underlying heat tolerance in radish and facilitate further genetic manipulation of thermotolerance in root vegetable crops.

Molecular ecological network analyses.

PubMed

Deng, Ye; Jiang, Yi-Huei; Yang, Yunfeng; He, Zhili; Luo, Feng; Zhou, Jizhong

2012-05-30

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data. Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA). The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Deduction and Analysis of the Interacting Stress Response Pathways of Metal/Radionuclide-reducing Bacteria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Jizhong; He, Zhili

2010-02-28

Project Title: Deduction and Analysis of the Interacting Stress Response Pathways of Metal/Radionuclide-reducing Bacteria DOE Grant Number: DE-FG02-06ER64205 Principal Investigator: Jizhong (Joe) Zhou (University of Oklahoma) Key members: Zhili He, Aifen Zhou, Christopher Hemme, Joy Van Nostrand, Ye Deng, and Qichao Tu Collaborators: Terry Hazen, Judy Wall, Adam Arkin, Matthew Fields, Aindrila Mukhopadhyay, and David Stahl Summary Three major objectives have been conducted in the Zhou group at the University of Oklahoma (OU): (i) understanding of gene function, regulation, network and evolution of Desulfovibrio vugaris Hildenborough in response to environmental stresses, (ii) development of metagenomics technologies for microbial community analysis,more » and (iii) functional characterization of microbial communities with metagenomic approaches. In the past a few years, we characterized four CRP/FNR regulators, sequenced ancestor and evolved D. vulgaris strains, and functionally analyzed those mutated genes identified in salt-adapted strains. Also, a new version of GeoChip 4.0 has been developed, which also includes stress response genes (StressChip), and a random matrix theory-based conceptual framework for identifying functional molecular ecological networks has been developed with the high throughput functional gene array hybridization data as well as pyrosequencing data from 16S rRNA genes. In addition, GeoChip and sequencing technologies as well as network analysis approaches have been used to analyze microbial communities from different habitats. Those studies provide a comprehensive understanding of gene function, regulation, network, and evolution in D. vulgaris, and microbial community diversity, composition and structure as well as their linkages with environmental factors and ecosystem functioning, which has resulted in more than 60 publications.« less

State-level legal preparedness for nuclear and radiological emergencies in the U.S.: a network analysis of state laws and regulations.

PubMed

Guclu, Hasan; Ferrell Bjerke, Elizabeth; Galvan, Jared; Sweeney, Patricia; Potter, Margaret A

2014-01-01

This study explored if and to what extent the laws of U.S. states mirrored the U.S. federal laws for responding to nuclear-radiological emergencies (NREs). Emergency laws from a 12-state sample and the federal government were retrieved and translated into numeric codes representing acting agents, their partner agents, and the purposes of activity in terms of preparedness, response, and recovery. We used network analysis to explore the relationships among agents in terms of legally directed NRE activities. States' legal networks for NREs appear as not highly inclusive, involving an average of 28% of agents among those specified in the federal laws. Certain agents are highly central in NRE networks, so that their capacity and effectiveness might strongly influence an NRE response. State-level lawmakers and planners might consider whether or not greater inclusion of agents, modeled on the federal government laws, would enhance their NRE laws and if more agents should be engaged in planning and policy-making for NRE incidents. Further research should explore if and to what extent legislated NRE directives impose constraints on practical response activities including emergency planning.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Evaluation of the annual Canadian biodosimetry network intercomparisons

PubMed Central

Wilkins, Ruth C.; Beaton-Green, Lindsay A.; Lachapelle, Sylvie; Kutzner, Barbara C.; Ferrarotto, Catherine; Chauhan, Vinita; Marro, Leonora; Livingston, Gordon K.; Boulay Greene, Hillary; Flegal, Farrah N.

2015-01-01

Abstract Purpose: To evaluate the importance of annual intercomparisons for maintaining the capacity and capabilities of a well-established biodosimetry network in conjunction with assessing efficient and effective analysis methods for emergency response. Materials and methods: Annual intercomparisons were conducted between laboratories in the Canadian National Biological Dosimetry Response Plan. Intercomparisons were performed over a six-year period and comprised of the shipment of 10–12 irradiated, blinded blood samples for analysis by each of the participating laboratories. Dose estimates were determined by each laboratory using the dicentric chromosome assay (conventional and QuickScan scoring) and where possible the cytokinesis block micronucleus (CBMN) assay. Dose estimates were returned to the lead laboratory for evaluation and comparison. Results: Individual laboratories performed comparably from year to year with only slight fluctuations in performance. Dose estimates using the dicentric chromosome assay were accurate about 80% of the time and the QuickScan method for scoring the dicentric chromosome assay was proven to reduce the time of analysis without having a significant effect on the dose estimates. Although analysis with the CBMN assay was comparable to QuickScan scoring with respect to speed, the accuracy of the dose estimates was greatly reduced. Conclusions: Annual intercomparisons are necessary to maintain a network of laboratories for emergency response biodosimetry as they evoke confidence in their capabilities. PMID:25670072

An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury

PubMed Central

2013-01-01

Background The regenerative response of Schwann cells after peripheral nerve injury is a critical process directly related to the pathophysiology of a number of neurodegenerative diseases. This SC injury response is dependent on an intricate gene regulatory program coordinated by a number of transcription factors and microRNAs, but the interactions among them remain largely unknown. Uncovering the transcriptional and post-transcriptional regulatory networks governing the Schwann cell injury response is a key step towards a better understanding of Schwann cell biology and may help develop novel therapies for related diseases. Performing such comprehensive network analysis requires systematic bioinformatics methods to integrate multiple genomic datasets. Results In this study we present a computational pipeline to infer transcription factor and microRNA regulatory networks. Our approach combined mRNA and microRNA expression profiling data, ChIP-Seq data of transcription factors, and computational transcription factor and microRNA target prediction. Using mRNA and microRNA expression data collected in a Schwann cell injury model, we constructed a regulatory network and studied regulatory pathways involved in Schwann cell response to injury. Furthermore, we analyzed network motifs and obtained insights on cooperative regulation of transcription factors and microRNAs in Schwann cell injury recovery. Conclusions This work demonstrates a systematic method for gene regulatory network inference that may be used to gain new information on gene regulation by transcription factors and microRNAs. PMID:23387820

Electroconvulsive Therapy Response in Major Depressive Disorder: A Pilot Functional Network Connectivity Resting State fMRI Investigation

PubMed Central

Abbott, Christopher C.; Lemke, Nicholas T.; Gopal, Shruti; Thoma, Robert J.; Bustillo, Juan; Calhoun, Vince D.; Turner, Jessica A.

2013-01-01

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case–control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode. PMID:23459749

Electroconvulsive therapy response in major depressive disorder: a pilot functional network connectivity resting state FMRI investigation.

PubMed

Abbott, Christopher C; Lemke, Nicholas T; Gopal, Shruti; Thoma, Robert J; Bustillo, Juan; Calhoun, Vince D; Turner, Jessica A

2013-01-01

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case-control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode.

Adverse outcome pathway networks II: Network analytics.

PubMed

Villeneuve, Daniel L; Angrish, Michelle M; Fortin, Marie C; Katsiadaki, Ioanna; Leonard, Marc; Margiotta-Casaluci, Luigi; Munn, Sharon; O'Brien, Jason M; Pollesch, Nathan L; Smith, L Cody; Zhang, Xiaowei; Knapen, Dries

2018-06-01

Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses (or previously undefined emergent patterns of response) are introduced. Along with a companion article (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

DTIC Science & Technology

2009-03-01

of biological networks. Nature Biotechnology, 23(9):961–966, 2005. [18] A. Ma’ayan, S. L Jenkins, S. Neves, A. Hasseldine, E. Grace, B . Dubin-Thaler...functions of biochemical networks. Trends Biochemical Sci 31: 284–291. 56. Blinov ML, Faeder JR, Goldstein B , Hlavacek WS (2006) A network model of early...mean intensity value, red - increased intensity of signal and green - decreased intensity of signal. Lap- Lapatinib, Das- Dasatinib, C-control, A& B

Differential proteomic analysis reveals sequential heat stress-responsive regulatory network in radish (Raphanus sativus L.) taproot.

PubMed

Wang, Ronghua; Mei, Yi; Xu, Liang; Zhu, Xianwen; Wang, Yan; Guo, Jun; Liu, Liwang

2018-05-01

Differential abundance protein species (DAPS) involved in reducing damage and enhancing thermotolerance in radish were firstly identified. Proteomic analysis and omics association analysis revealed a HS-responsive regulatory network in radish. Heat stress (HS) is a major destructive factor influencing radish production and supply in summer, for radish is a cool season vegetable crop being susceptible to high temperature. In this study, the proteome changes of radish taproots under 40 °C treatment at 0 h (Control), 12 h (Heat12) and 24 h (Heat24) were analyzed using iTRAQ (Isobaric Tag for Relative and Absolute Quantification) approach. In total, 2258 DAPS representing 1542 differentially accumulated uniprotein species which respond to HS were identified. A total of 604, 910 and 744 DAPS was detected in comparison of Control vs. Heat12, Control vs. Heat24, and Heat12 vs. Heat24, respectively. Gene ontology and pathway analysis showed that annexin, ubiquitin-conjugating enzyme, ATP synthase, heat shock protein (HSP) and other stress-related proteins were predominately enriched in signal transduction, stress and defense pathways, photosynthesis and energy metabolic pathways, working cooperatively to reduce stress-induced damage in radish. Based on iTRAQ combined with the transcriptomics analysis, a schematic model of a sequential HS-responsive regulatory network was proposed. The initial sensing of HS occurred at the plasma membrane, and then key components of stress signal transduction triggered heat-responsive genes in the plant protective metabolism to re-establish homeostasis and enhance thermotolerance. These results provide new insights into characteristics of HS-responsive DAPS and facilitate dissecting the molecular mechanisms underlying heat tolerance in radish and other root crops.

Graph Theory-Based Analysis of the Lymph Node Fibroblastic Reticular Cell Network.

PubMed

Novkovic, Mario; Onder, Lucas; Bocharov, Gennady; Ludewig, Burkhard

2017-01-01

Secondary lymphoid organs have developed segregated niches that are able to initiate and maintain effective immune responses. Such global organization requires tight control of diverse cellular components, specifically those that regulate lymphocyte trafficking. Fibroblastic reticular cells (FRCs) form a densely interconnected network in lymph nodes and provide key factors necessary for T cell migration and retention, and foster subsequent interactions between T cells and dendritic cells. Development of integrative systems biology approaches has made it possible to elucidate this multilevel complexity of the immune system. Here, we present a graph theory-based analysis of the FRC network in murine lymph nodes, where generation of the network topology is performed using high-resolution confocal microscopy and 3D reconstruction. This approach facilitates the analysis of physical cell-to-cell connectivity, and estimation of topological robustness and global behavior of the network when it is subjected to perturbation in silico.

Common neural correlates of intertemporal choices and intelligence in adolescents.

PubMed

Ripke, Stephan; Hübner, Thomas; Mennigen, Eva; Müller, Kathrin U; Li, Shu-Chen; Smolka, Michael N

2015-02-01

Converging behavioral evidence indicates that temporal discounting, measured by intertemporal choice tasks, is inversely related to intelligence. At the neural level, the parieto-frontal network is pivotal for complex, higher-order cognitive processes. Relatedly, underrecruitment of the pFC during a working memory task has been found to be associated with steeper temporal discounting. Furthermore, this network has also been shown to be related to the consistency of intertemporal choices. Here we report an fMRI study that directly investigated the association of neural correlates of intertemporal choice behavior with intelligence in an adolescent sample (n = 206; age 13.7-15.5 years). After identifying brain regions where the BOLD response during intertemporal choice was correlated with individual differences in intelligence, we further tested whether BOLD responses in these areas would mediate the associations between intelligence, the discounting rate, and choice consistency. We found positive correlations between BOLD response in a value-independent decision network (i.e., dorsolateral pFC, precuneus, and occipital areas) and intelligence. Furthermore, BOLD response in a value-dependent decision network (i.e., perigenual ACC, inferior frontal gyrus, ventromedial pFC, ventral striatum) was positively correlated with intelligence. The mediation analysis revealed that BOLD responses in the value-independent network mediated the association between intelligence and choice consistency, whereas BOLD responses in the value-dependent network mediated the association between intelligence and the discounting rate. In summary, our findings provide evidence for common neural correlates of intertemporal choice and intelligence, possibly linked by valuation as well as executive functions.

Brain network response underlying decisions about abstract reinforcers.

PubMed

Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

2014-12-01

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

Model-based analysis of pattern motion processing in mouse primary visual cortex

PubMed Central

Muir, Dylan R.; Roth, Morgane M.; Helmchen, Fritjof; Kampa, Björn M.

2015-01-01

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features. PMID:26300738

Genome-wide gene phylogeny of CIPK family in cassava and expression analysis of partial drought-induced genes

PubMed Central

Hu, Wei; Xia, Zhiqiang; Yan, Yan; Ding, Zehong; Tie, Weiwei; Wang, Lianzhe; Zou, Meiling; Wei, Yunxie; Lu, Cheng; Hou, Xiaowan; Wang, Wenquan; Peng, Ming

2015-01-01

Cassava is an important food and potential biofuel crop that is tolerant to multiple abiotic stressors. The mechanisms underlying these tolerances are currently less known. CBL-interacting protein kinases (CIPKs) have been shown to play crucial roles in plant developmental processes, hormone signaling transduction, and in the response to abiotic stress. However, no data is currently available about the CPK family in cassava. In this study, a total of 25 CIPK genes were identified from cassava genome based on our previous genome sequencing data. Phylogenetic analysis suggested that 25 MeCIPKs could be classified into four subfamilies, which was supported by exon-intron organizations and the architectures of conserved protein motifs. Transcriptomic analysis of a wild subspecies and two cultivated varieties showed that most MeCIPKs had different expression patterns between wild subspecies and cultivatars in different tissues or in response to drought stress. Some orthologous genes involved in CIPK interaction networks were identified between Arabidopsis and cassava. The interaction networks and co-expression patterns of these orthologous genes revealed that the crucial pathways controlled by CIPK networks may be involved in the differential response to drought stress in different accessions of cassava. Nine MeCIPK genes were selected to investigate their transcriptional response to various stimuli and the results showed the comprehensive response of the tested MeCIPK genes to osmotic, salt, cold, oxidative stressors, and ABA signaling. The identification and expression analysis of CIPK family suggested that CIPK genes are important components of development and multiple signal transduction pathways in cassava. The findings of this study will help lay a foundation for the functional characterization of the CIPK gene family and provide an improved understanding of abiotic stress responses and signaling transduction in cassava. PMID:26579161

Towards systems biology of the gravity response of higher plants -multiscale analysis of Arabidopsis thaliana root growth

NASA Astrophysics Data System (ADS)

Palme, Klaus; Aubry, D.; Bensch, M.; Schmidt, T.; Ronneberger, O.; Neu, C.; Li, X.; Wang, H.; Santos, F.; Wang, B.; Paponov, I.; Ditengou, F. A.; Teale, W. T.; Volkmann, D.; Baluska, F.; Nonis, A.; Trevisan, S.; Ruperti, B.; Dovzhenko, A.

Gravity plays a fundamental role in plant growth and development. Up to now, little is known about the molecular organisation of the signal transduction cascades and networks which co-ordinate gravity perception and response. By using an integrated systems biological approach, a systems analysis of gravity perception and the subsequent tightly-regulated growth response is planned in the model plant Arabidopsis thaliana. This approach will address questions such as: (i) what are the components of gravity signal transduction pathways? (ii) what are the dynamics of these components? (iii) what is their spatio-temporal regulation in different tis-sues? Using Arabidopsis thaliana as a model-we use root growth to obtain insights in the gravity response. New techniques enable identification of the individual genes affected by grav-ity and further integration of transcriptomics and proteomics data into interaction networks and cell communication events that operate during gravitropic curvature. Using systematic multiscale analysis we have identified regulatory networks consisting of transcription factors, the protein degradation machinery, vesicle trafficking and cellular signalling during the gravire-sponse. We developed approach allowing to incorporate key features of the root system across all relevant spatial and temporal scales to describe gene-expression patterns and correlate them with individual gene and protein functions. Combination of high-resolution microscopy and novel computational tools resulted in development of the root 3D model in which quantitative descriptions of cellular network properties and of multicellular interactions important in root growth and gravitropism can be integrated for the first time.

Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

NASA Astrophysics Data System (ADS)

Kamal, Hassan; Kanhirodan, Rajan; Srinivas, Kalyan V.; Sikdar, Sujit K.

2010-04-01

We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level.

Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

PubMed Central

Huang, Jie; Sun, Zhandong; Yan, Wenying; Zhu, Yujie; Lin, Yuxin; Chen, Jiajai; Shen, Bairong

2014-01-01

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers. PMID:24809055

Predicting musically induced emotions from physiological inputs: linear and neural network models.

PubMed

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Real-Time Visualization of Network Behaviors for Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

Comparison of functional network connectivity for passive-listening and active-response narrative comprehension in adolescents.

PubMed

Wang, Yingying; Holland, Scott K

2014-05-01

Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14-18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task.

Generalised Transfer Functions of Neural Networks

NASA Astrophysics Data System (ADS)

Fung, C. F.; Billings, S. A.; Zhang, H.

1997-11-01

When artificial neural networks are used to model non-linear dynamical systems, the system structure which can be extremely useful for analysis and design, is buried within the network architecture. In this paper, explicit expressions for the frequency response or generalised transfer functions of both feedforward and recurrent neural networks are derived in terms of the network weights. The derivation of the algorithm is established on the basis of the Taylor series expansion of the activation functions used in a particular neural network. This leads to a representation which is equivalent to the non-linear recursive polynomial model and enables the derivation of the transfer functions to be based on the harmonic expansion method. By mapping the neural network into the frequency domain information about the structure of the underlying non-linear system can be recovered. Numerical examples are included to demonstrate the application of the new algorithm. These examples show that the frequency response functions appear to be highly sensitive to the network topology and training, and that the time domain properties fail to reveal deficiencies in the trained network structure.

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis.

PubMed

Banks, Victoria A; Stanton, Neville A

2015-01-01

Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect. This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.

Application of Network Analysis to Identify and Map Relationships between Information Systems in the context of Arctic Sustainability

NASA Astrophysics Data System (ADS)

Kontar, Y. Y.

2017-12-01

The Arctic Council is an intergovernmental forum promoting cooperation, coordination and interaction among the Arctic States and indigenous communities on issues of sustainable development and environmental protection in the North. The work of the Council is primarily carried out by six Working Groups: Arctic Contaminants Action Program, Arctic Monitoring and Assessment Programme, Conservation of Arctic Flora and Fauna, Emergency Prevention, Preparedness and Response, Protection of the Arctic Marine Environment, and Sustainable Development Working Group. The Working Groups are composed of researchers and representatives from government agencies. Each Working Group issues numerous scientific assessments and reports on a broad field of subjects, from climate change to emergency response in the Arctic. A key goal of these publications is to contribute to policy-making in the Arctic. Complex networks of information systems and the connections between the diverse elements within the systems have been identified via network analysis. This allowed to distinguish data sources that were used in the composition of the primary publications of the Working Groups. Next step is to implement network analysis to identify and map the relationships between the Working Groups and policy makers in the Arctic.

Proteomic analysis of intestinal tissues from mice fed with Lentinula edodes-derived polysaccharides.

PubMed

Xu, Xiaofei; Yang, Jiguo; Ning, Zhengxiang; Zhang, Xuewu

2016-01-01

Lentinula edodes-derived polysaccharides are well known for their immunomodulation and antitumor activities. However, the mechanisms of action have not been fully elucidated. This study presents proteomic analysis of the colon and small intestine from mice fed with an immunostimulating heteropolysaccharide L2 from the fruit body of L. edodes. Two-dimensional gel electrophoresis (2-DE) and MALDI-TOF-TOF MS/MS were employed to characterize the protein profiles. Twenty nine gel spots representing 20 proteins in colon tissues and 38 gel spots in small intestine tissues representing 23 proteins were identified as showing significant changes in abundance. These differential proteins in abundance are mainly involved in metabolism, binding, structural components, and response to stimulus. Protein-protein interaction network analysis demonstrated mapping of the 20 colon proteins to a 7-protein and a 3-protein sub-network, and mapping of the 23 small intestine proteins to a 9-protein and a 5-protein sub-network. All the 40 altered proteins were integrated into a unified network containing 25 proteins, suggesting the existence of a concerted mechanism, although acting on the colon and small intestine separately. These findings facilitate the understanding of the regulatory mechanism in response to L2 treatment.

Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gearhart, Jared Lee; Kurtz, Nolan Scot

2014-09-01

The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance aremore » investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.« less

Connectivity, excitability and activity patterns in neuronal networks

NASA Astrophysics Data System (ADS)

le Feber, Joost; Stoyanova, Irina I.; Chiappalone, Michela

2014-06-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFPi,j) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFPi,j with the autocorrelation of i (i.e. CFPi,i), to obtain the single pulse response (SPRi,j)—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression.

Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.

PubMed

Zhang, Jiang; Liu, Qi; Chen, Huafu; Yuan, Zhen; Huang, Jin; Deng, Lihua; Lu, Fengmei; Zhang, Junpeng; Wang, Yuqing; Wang, Mingwen; Chen, Liangyin

2015-01-01

Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso; Elena, Santiago F

2009-01-01

Background Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes. Results We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures. Conclusions These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote. PMID:19754933

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.

PubMed

MacGilvray, Matthew E; Shishkova, Evgenia; Chasman, Deborah; Place, Michael; Gitter, Anthony; Coon, Joshua J; Gasch, Audrey P

2018-05-01

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Identification of functional modules using network topology and high-throughput data.

PubMed

Ulitsky, Igor; Shamir, Ron

2007-01-26

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data. We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity. We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

PubMed

Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

2017-09-21

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

Large-Scale Analysis of Network Bistability for Human Cancers

PubMed Central

Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki

2010-01-01

Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

NASA Astrophysics Data System (ADS)

Bashiri, Mahdi; Farshbaf-Geranmayeh, Amir; Mogouie, Hamed

2013-11-01

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

Optical vector network analysis of ultranarrow transitions in 166Er3+ : 7LiYF4 crystal.

PubMed

Kukharchyk, N; Sholokhov, D; Morozov, O; Korableva, S L; Cole, J H; Kalachev, A A; Bushev, P A

2018-02-15

We present optical vector network analysis (OVNA) of an isotopically purified Er166 3+ :LiYF 4 7 crystal. The OVNA method is based on generation and detection of a modulated optical sideband by using a radio-frequency vector network analyzer. This technique is widely used in the field of microwave photonics for the characterization of optical responses of optical devices such as filters and high-Q resonators. However, dense solid-state atomic ensembles induce a large phase shift on one of the optical sidebands that results in the appearance of extra features on the measured transmission response. We present a simple theoretical model that accurately describes the observed spectra and helps to reconstruct the absorption profile of a solid-state atomic ensemble as well as corresponding change of the refractive index in the vicinity of atomic resonances.

Connectomic correlates of response to treatment in first-episode psychosis

PubMed Central

Crossley, Nicolas A; Marques, Tiago Reis; Taylor, Heather; Chaddock, Chris; Dell’Acqua, Flavio; Reinders, Antje A T S; Mondelli, Valeria; DiForti, Marta; Simmons, Andrew; David, Anthony S; Kapur, Shitij; Pariante, Carmine M; Murray, Robin M; Dazzan, Paola

2017-01-01

Abstract Connectomic approaches using diffusion tensor imaging have contributed to our understanding of brain changes in psychosis, and could provide further insights into the neural mechanisms underlying response to antipsychotic treatment. We here studied the brain network organization in patients at their first episode of psychosis, evaluating whether connectome-based descriptions of brain networks predict response to treatment, and whether they change after treatment. Seventy-six patients with a first episode of psychosis and 74 healthy controls were included. Thirty-three patients were classified as responders after 12 weeks of antipsychotic treatment. Baseline brain structural networks were built using whole-brain diffusion tensor imaging tractography, and analysed using graph analysis and network-based statistics to explore baseline characteristics of patients who subsequently responded to treatment. A subgroup of 43 patients was rescanned at the 12-week follow-up, to study connectomic changes over time in relation to treatment response. At baseline, those subjects who subsequently responded to treatment, compared to those that did not, showed higher global efficiency in their structural connectomes, a network configuration that theoretically facilitates the flow of information. We did not find specific connectomic changes related to treatment response after 12 weeks of treatment. Our data suggest that patients who have an efficiently-wired connectome at first onset of psychosis show a better subsequent response to antipsychotics. However, response is not accompanied by specific structural changes over time detectable with this method. PMID:28007987

Improvements to Integrated Tradespace Analysis of Communications Architectures (ITACA) Network Loading Analysis Tool

NASA Technical Reports Server (NTRS)

Lee, Nathaniel; Welch, Bryan W.

2018-01-01

NASA's SCENIC project aims to simplify and reduce the cost of space mission planning by replicating the analysis capabilities of commercially licensed software which are integrated with relevant analysis parameters specific to SCaN assets and SCaN supported user missions. SCENIC differs from current tools that perform similar analyses in that it 1) does not require any licensing fees, 2) will provide an all-in-one package for various analysis capabilities that normally requires add-ons or multiple tools to complete. As part of SCENIC's capabilities, the ITACA network loading analysis tool will be responsible for assessing the loading on a given network architecture and generating a network service schedule. ITACA will allow users to evaluate the quality of service of a given network architecture and determine whether or not the architecture will satisfy the mission's requirements. ITACA is currently under development, and the following improvements were made during the fall of 2017: optimization of runtime, augmentation of network asset pre-service configuration time, augmentation of Brent's method of root finding, augmentation of network asset FOV restrictions, augmentation of mission lifetimes, and the integration of a SCaN link budget calculation tool. The improvements resulted in (a) 25% reduction in runtime, (b) more accurate contact window predictions when compared to STK(Registered Trademark) contact window predictions, and (c) increased fidelity through the use of specific SCaN asset parameters.

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

PubMed

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

STICAP: A linear circuit analysis program with stiff systems capability. Volume 1: Theory manual. [network analysis

NASA Technical Reports Server (NTRS)

Cooke, C. H.

1975-01-01

STICAP (Stiff Circuit Analysis Program) is a FORTRAN 4 computer program written for the CDC-6400-6600 computer series and SCOPE 3.0 operating system. It provides the circuit analyst a tool for automatically computing the transient responses and frequency responses of large linear time invariant networks, both stiff and nonstiff (algorithms and numerical integration techniques are described). The circuit description and user's program input language is engineer-oriented, making simple the task of using the program. Engineering theories underlying STICAP are examined. A user's manual is included which explains user interaction with the program and gives results of typical circuit design applications. Also, the program structure from a systems programmer's viewpoint is depicted and flow charts and other software documentation are given.

Dynamical Networks Characterization of Geomagnetic Substorms and Transient Response to the Solar Wind State.

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Dods, J.; Gjerloev, J. W.

2017-12-01

Observations of how the solar wind interacts with earth's magnetosphere, and its dynamical response, are increasingly becoming a data analytics challenge. Constellations of satellites observe the solar corona, the upstream solar wind and throughout earth's magnetosphere. These data are multipoint in space and extended in time, so in principle are ideal for study using dynamical networks to characterize the full time evolving spatial pattern. We focus here on analysis of data from the full set of 100+ auroral ground based magnetometer stations that have been collated by SuperMAG. Spatio-temporal patterns of correlation between the magnetometer time series can be used to form a dynamical network [1]. The properties of the network can then be captured by (time dependent) network parameters. This offers the possibility of characterizing detailed spatio-temporal pattern by a few parameters, so that many events can then be compared [2] with each other. Whilst networks are in widespread use in the data analytics of societal and commercial data, there are additional challenges in their application to physical timeseries. Determining whether two nodes (here, ground based magnetometer stations) are connected in a network (seeing the same dynamics) requires normalization w.r.t. the detailed sensitivities and dynamical responses of specific observing stations and seasonal conductivity variations and we have developed methods to achieve this dynamical normalization. The detailed properties of the network capture time dependent spatial correlation in the magnetometer responses and we will show how this can be used to infer a transient current system response to magnetospheric activity. [l] Dods et al, J. Geophys. Res 120, doi:10.1002/2015JA02 (2015). [2] Dods et al, J. Geophys. Res. 122, doi:10.1002/2016JA02 (2017).

Integrated RNA-Seq and sRNA-Seq Analysis Identifies Chilling and Freezing Responsive Key Molecular Players and Pathways in Tea Plant (Camellia sinensis)

PubMed Central

Zheng, Chao; Zhao, Lei; Wang, Yu; Shen, Jiazhi; Zhang, Yinfei; Jia, Sisi; Li, Yusheng; Ding, Zhaotang

2015-01-01

Tea [Camellia sinensis (L) O. Kuntze, Theaceae] is one of the most popular non-alcoholic beverages worldwide. Cold stress is one of the most severe abiotic stresses that limit tea plants’ growth, survival and geographical distribution. However, the genetic regulatory network and signaling pathways involved in cold stress responses in tea plants remain unearthed. Using RNA-Seq, DGE and sRNA-Seq technologies, we performed an integrative analysis of miRNA and mRNA expression profiling and their regulatory network of tea plants under chilling (4℃) and freezing (-5℃) stress. Differentially expressed (DE) miRNA and mRNA profiles were obtained based on fold change analysis, miRNAs and target mRNAs were found to show both coherent and incoherent relationships in the regulatory network. Furthermore, we compared several key pathways (e.g., ‘Photosynthesis’), GO terms (e.g., ‘response to karrikin’) and transcriptional factors (TFs, e.g., DREB1b/CBF1) which were identified as involved in the early chilling and/or freezing response of tea plants. Intriguingly, we found that karrikins, a new group of plant growth regulators, and β-primeverosidase (BPR), a key enzyme functionally relevant with the formation of tea aroma might play an important role in both early chilling and freezing response of tea plants. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-Seq and sRNA-Seq analysis. This is the first study to simultaneously profile the expression patterns of both miRNAs and mRNAs on a genome-wide scale to elucidate the molecular mechanisms of early responses of tea plants to cold stress. In addition to gaining a deeper insight into the cold resistant characteristics of tea plants, we provide a good case study to analyse mRNA/miRNA expression and profiling of non-model plant species using next-generation sequencing technology. PMID:25901577

Integrated RNA-Seq and sRNA-Seq Analysis Identifies Chilling and Freezing Responsive Key Molecular Players and Pathways in Tea Plant (Camellia sinensis).

PubMed

Zheng, Chao; Zhao, Lei; Wang, Yu; Shen, Jiazhi; Zhang, Yinfei; Jia, Sisi; Li, Yusheng; Ding, Zhaotang

2015-01-01

Tea [Camellia sinensis (L) O. Kuntze, Theaceae] is one of the most popular non-alcoholic beverages worldwide. Cold stress is one of the most severe abiotic stresses that limit tea plants' growth, survival and geographical distribution. However, the genetic regulatory network and signaling pathways involved in cold stress responses in tea plants remain unearthed. Using RNA-Seq, DGE and sRNA-Seq technologies, we performed an integrative analysis of miRNA and mRNA expression profiling and their regulatory network of tea plants under chilling (4℃) and freezing (-5℃) stress. Differentially expressed (DE) miRNA and mRNA profiles were obtained based on fold change analysis, miRNAs and target mRNAs were found to show both coherent and incoherent relationships in the regulatory network. Furthermore, we compared several key pathways (e.g., 'Photosynthesis'), GO terms (e.g., 'response to karrikin') and transcriptional factors (TFs, e.g., DREB1b/CBF1) which were identified as involved in the early chilling and/or freezing response of tea plants. Intriguingly, we found that karrikins, a new group of plant growth regulators, and β-primeverosidase (BPR), a key enzyme functionally relevant with the formation of tea aroma might play an important role in both early chilling and freezing response of tea plants. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-Seq and sRNA-Seq analysis. This is the first study to simultaneously profile the expression patterns of both miRNAs and mRNAs on a genome-wide scale to elucidate the molecular mechanisms of early responses of tea plants to cold stress. In addition to gaining a deeper insight into the cold resistant characteristics of tea plants, we provide a good case study to analyse mRNA/miRNA expression and profiling of non-model plant species using next-generation sequencing technology.

Common Dimensional Reward Deficits Across Mood and Psychotic Disorders: A Connectome-Wide Association Study.

PubMed

Sharma, Anup; Wolf, Daniel H; Ciric, Rastko; Kable, Joseph W; Moore, Tyler M; Vandekar, Simon N; Katchmar, Natalie; Daldal, Aylin; Ruparel, Kosha; Davatzikos, Christos; Elliott, Mark A; Calkins, Monica E; Shinohara, Russell T; Bassett, Danielle S; Satterthwaite, Theodore D

2017-07-01

Anhedonia is central to multiple psychiatric disorders and causes substantial disability. A dimensional conceptualization posits that anhedonia severity is related to a transdiagnostic continuum of reward deficits in specific neural networks. Previous functional connectivity studies related to anhedonia have focused on case-control comparisons in specific disorders, using region-specific seed-based analyses. Here, the authors explore the entire functional connectome in relation to reward responsivity across a population of adults with heterogeneous psychopathology. In a sample of 225 adults from five diagnostic groups (major depressive disorder, N=32; bipolar disorder, N=50; schizophrenia, N=51; psychosis risk, N=39; and healthy control subjects, N=53), the authors conducted a connectome-wide analysis examining the relationship between a dimensional measure of reward responsivity (the reward sensitivity subscale of the Behavioral Activation Scale) and resting-state functional connectivity using multivariate distance-based matrix regression. The authors identified foci of dysconnectivity associated with reward responsivity in the nucleus accumbens, the default mode network, and the cingulo-opercular network. Follow-up analyses revealed dysconnectivity among specific large-scale functional networks and their connectivity with the nucleus accumbens. Reward deficits were associated with decreased connectivity between the nucleus accumbens and the default mode network and increased connectivity between the nucleus accumbens and the cingulo-opercular network. In addition, impaired reward responsivity was associated with default mode network hyperconnectivity and diminished connectivity between the default mode network and the cingulo-opercular network. These results emphasize the centrality of the nucleus accumbens in the pathophysiology of reward deficits and suggest that dissociable patterns of connectivity among large-scale networks are critical to the neurobiology of reward dysfunction across clinical diagnostic categories.

Social network modulation of reward-related signals

PubMed Central

Fareri, Dominic S.; Niznikiewicz, Michael A.; Lee, Victoria K.; Delgado, Mauricio R.

2012-01-01

Everyday goals and experiences are often shared with others who may hold different places within our social networks. We investigated whether the experience of sharing a reward differs with respect to social network. Twenty human participants played a card guessing game for shared monetary outcomes with three partners: a computer, a confederate (out-of-network), and a friend (in-network). Participants subjectively rated the experience of sharing a reward more positively with their friend than the other partners. Neuroimaging results support participants’ subjective reports, as ventral striatal BOLD responses were more robust when sharing monetary gains with a friend, as compared to with the confederate or computer, suggesting a higher value for sharing with an in-network partner. Interestingly, ratings of social closeness co-varied with this activity, resulting in a significant partner × closeness interaction: exploratory analysis showed that only participants reporting higher levels of closeness demonstrated partner-related differences in striatal BOLD response. These results suggest that reward valuation in social contexts is sensitive to distinctions of social network, such that sharing positive experiences with in-network others may carry higher value. PMID:22745503

Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China.

PubMed

Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn; Tey, Jeannie Su Hui

2015-01-01

We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication.

Leveraging social networking sites for disease surveillance and public sensing: the case of the 2013 avian influenza A(H7N9) outbreak in China

PubMed Central

Zhang, Emma Xuxiao; Yang, Yinping; Di Shang, Richard; Simons, Joseph John Pyne; Quek, Boon Kiat; Yin, Xiao Feng; See, Wanhan; Oh, Olivia Seen Huey; Nandar, Khine Sein Tun; Ling, Vivienne Ruo Yun; Chan, Pei Pei; Wang, Zhaoxia; Goh, Rick Siow Mong; James, Lyn

2015-01-01

We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication. PMID:26306219

The role of storm scale, position and movement in controlling urban flood response

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James

2018-01-01

The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

PubMed

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimizing a Drone Network to Deliver Automated External Defibrillators.

PubMed

Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y

2017-06-20

Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event. © 2017 American Heart Association, Inc.

A network meta-analysis on the efficacy of sixteen targeted drugs in combination with chemotherapy for treatment of advanced/metastatic colorectal cancer

PubMed Central

Ba-Sang, Dan-Zeng; Long, Zi-Wen; Teng, Hao; Zhao, Xu-Peng; Qiu, Jian; Li, Ming-Shan

2016-01-01

Objective A network meta-analysis was conducted comparing the short-term efficacies of 16 targeted drugs in combination with chemotherapy for treatment of advanced/metastatic colorectal cancer (CRC). Results Twenty-seven RCTs were ultimately incorporated into this network meta-analysis. Compared with chemotherapy alone, bevacizumab + chemotherapy, panitumumab + chemotherapy and conatumumab + chemotherapy had higher PR rate. Bevacizumab + chemotherapy, cetuximab + chemotherapy, panitumumab + chemotherapy, trebananib + chemotherapy and conatumumab + chemotherapy had higher ORR rate in comparison to chemotherapy alone. Furthermore, bevacizumab + chemotherapy had higher DCR rate than chemotherapy alone. The results of our cluster analysis showed that chemotherapy combined with bevacizumab, cetuximab, panitumumab, conatumumab, ganitumab, or brivanib + cetuximab had better efficacies for the treatment of advanced/metastatic CRC in comparison to chemotherapy alone. Materials and Methods Electronic databases were comprehensively searched for potential and related randomized controlled trials (RCTs). Direct and indirect evidence were incorporated for evaluation of stable disease (SD), progressive disease (PD), complete response (CR), partial response (PR), disease control rate (DCR) and overall response ratio (ORR) by calculating odds ratio (OR) and 95% confidence intervals (CI), and using the surface under the cumulative ranking curve (SUCRA). Conclusions These results indicated that bevacizumab + chemotherapy, panitumumab + chemotherapy, conatumumab + chemotherapy and brivanib + cetuximab + chemotherapy may have better efficacies for the treatment of advanced/metastatic CRC. PMID:27806321

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

PubMed

Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Identification of rice genes associated with cosmic-ray response via co-expression gene network analysis.

PubMed

Hwang, Sun-Goo; Kim, Dong Sub; Hwang, Jung Eun; Han, A-Reum; Jang, Cheol Seong

2014-05-15

In order to better understand the biological systems that are affected in response to cosmic ray (CR), we conducted weighted gene co-expression network analysis using the module detection method. By using the Pearson's correlation coefficient (PCC) value, we evaluated complex gene-gene functional interactions between 680 CR-responsive probes from integrated microarray data sets, which included large-scale transcriptional profiling of 1000 microarray samples. These probes were divided into 6 distinct modules that contained 20 enriched gene ontology (GO) functions, such as oxidoreductase activity, hydrolase activity, and response to stimulus and stress. In particular, modules 1 and 2 commonly showed enriched annotation categories such as oxidoreductase activity, including enriched cis-regulatory elements known as ROS-specific regulators. These results suggest that the ROS-mediated irradiation response pathway is affected by CR in modules 1 and 2. We found 243 ionizing radiation (IR)-responsive probes that exhibited similarities in expression patterns in various irradiation microarray data sets. The expression patterns of 6 randomly selected IR-responsive genes were evaluated by quantitative reverse transcription polymerase chain reaction following treatment with CR, gamma rays (GR), and ion beam (IB); similar patterns were observed among these genes under these 3 treatments. Moreover, we constructed subnetworks of IR-responsive genes and evaluated the expression levels of their neighboring genes following GR treatment; similar patterns were observed among them. These results of network-based analyses might provide a clue to understanding the complex biological system related to the CR response in plants. Copyright © 2014 Elsevier B.V. All rights reserved.

MEASUREMENT OF RURAL SULFUR DIOXIDE AND PARTICLE SULFATE: ANALYSIS OF CASTNET DATA, 1987 - 1996

EPA Science Inventory

The Clean Sir Status and Trends Network (CASTNet) was implemented by the U.S. Environmental Protection Agency (EPA) in 1991 in response to Title IX of the Clean Air Amendments of 1990, which mandated the deployment of a national ambient air monitoring network to track progress of...

An Analysis of Attitudes toward Computer Networks and Internet Addiction.

ERIC Educational Resources Information Center

Tsai, Chin-Chung; Lin, Sunny S. J.

The purpose of this study was to explore the interplay between young people's attitudes toward computer networks and Internet addiction. After analyzing questionnaire responses of an initial sample of 615 Taiwanese high school students, 78 subjects, viewed as possible Internet addicts, were selected for further explorations. It was found that…

Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

PubMed Central

Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E.; Balázsi, Gábor; Gennaro, Maria Laura

2016-01-01

Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics. PMID:27029515

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining

PubMed Central

2012-01-01

Background Fever is one of the most common adverse events of vaccines. The detailed mechanisms of fever and vaccine-associated gene interaction networks are not fully understood. In the present study, we employed a genome-wide, Centrality and Ontology-based Network Discovery using Literature data (CONDL) approach to analyse the genes and gene interaction networks associated with fever or vaccine-related fever responses. Results Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions. This resulted in a generic fever network consisting of 403 genes and 577 gene interactions. A vaccine-specific fever sub-network consisting of 29 genes and 28 gene interactions was extracted from articles that are related to both fever and vaccines. In addition, gene-vaccine interactions were identified. Vaccines (including 4 specific vaccine names) were found to directly interact with 26 genes. Gene set enrichment analysis was performed using the genes in the generated interaction networks. Moreover, the genes in these networks were prioritized using network centrality metrics. Making scientific discoveries and generating new hypotheses were possible by using network centrality and gene set enrichment analyses. For example, our study found that the genes in the generic fever network were more enriched in cell death and responses to wounding, and the vaccine sub-network had more gene enrichment in leukocyte activation and phosphorylation regulation. The most central genes in the vaccine-specific fever network are predicted to be highly relevant to vaccine-induced fever, whereas genes that are central only in the generic fever network are likely to be highly relevant to generic fever responses. Interestingly, no Toll-like receptors (TLRs) were found in the gene-vaccine interaction network. Since multiple TLRs were found in the generic fever network, it is reasonable to hypothesize that vaccine-TLR interactions may play an important role in inducing fever response, which deserves a further investigation. Conclusions This study demonstrated that ontology-based literature mining is a powerful method for analyzing gene interaction networks and generating new scientific hypotheses. PMID:23256563

Comparison of Functional Network Connectivity for Passive-Listening and Active-Response Narrative Comprehension in Adolescents

PubMed Central

Holland, Scott K.

2014-01-01

Abstract Comprehension of narrative stories plays an important role in the development of language skills. In this study, we compared brain activity elicited by a passive-listening version and an active-response (AR) version of a narrative comprehension task by using independent component (IC) analysis on functional magnetic resonance imaging data from 21 adolescents (ages 14–18 years). Furthermore, we explored differences in functional network connectivity engaged by two versions of the task and investigated the relationship between the online response time and the strength of connectivity between each pair of ICs. Despite similar brain region involvements in auditory, temporoparietal, and frontoparietal language networks for both versions, the AR version engages some additional network elements including the left dorsolateral prefrontal, anterior cingulate, and sensorimotor networks. These additional involvements are likely associated with working memory and maintenance of attention, which can be attributed to the differences in cognitive strategic aspects of the two versions. We found significant positive correlation between the online response time and the strength of connectivity between an IC in left inferior frontal region and an IC in sensorimotor region. An explanation for this finding is that longer reaction time indicates stronger connection between the frontal and sensorimotor networks caused by increased activation in adolescents who require more effort to complete the task. PMID:24689887

Theoretical Study of the Vibration Suppression on a Mistuned Bladed Disk Using a Bi-periodic Piezoelectric Network

NASA Astrophysics Data System (ADS)

Li, Lin; Deng, Pengcheng; Liu, Jiuzhou; Li, Chao

2018-03-01

The paper deals with the vibration suppression of a bladed disk with a piezoelectric network. The piezoelectric network has a different period (so called bi-period) from that of the bladed disk and there is no inductor in it. The system is simulated by an electromechanical lumped parameter model with two DOFs per sector. The research focuses on suppressing the amplitude magnification or reducing the vibration localization of the mistuned bladed disk. The dynamic equations of the system are derived. Both mechanical mistuning and electrical mistuning have been taken into account. The Modified Modal Assurance Criterion (MMAC) is used to evaluate the vibration suppression ability of the bi-periodic piezoelectric network. The Monte Carlo simulation is used to calculate the MMAC of the system with the random mistuning. As a reference, the forced responses of the bladed disk with and without the piezoelectric network are given. The results show that the piezoelectric network would effectively suppress amplitude magnification induced by mistuning. The vibration amplitude is even smaller than that of the tuned system. The robustness analysis shows that the bi-periodic piezoelectric network can provide a reliable assurance for avoiding the forced response amplification of the mistuned bladed disk. The amplified response induced by the mechanical mistuning with standard deviation 0.2 can be effectively suppressed through the bi-periodic piezoelectric network.

Local and global responses in complex gene regulation networks

NASA Astrophysics Data System (ADS)

Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

2009-04-01

An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Selective attention to temporal features on nested time scales.

PubMed

Henry, Molly J; Herrmann, Björn; Obleser, Jonas

2015-02-01

Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

[Social network analysis of interdisciplinary cooperation and networking in early prevention and intervention. A pilot study].

PubMed

Künster, A K; Knorr, C; Fegert, J M; Ziegenhain, U

2010-11-01

Child protection can only be successfully solved by interdisciplinary cooperation and networking. The individual, heterogeneous, and complex needs of families cannot be met sufficiently by one profession alone. To guarantee efficient interdisciplinary cooperation, there should not be any gaps in the network. In addition, each actor in the network should be placed at an optimal position regarding function, responsibilities, and skills. Actors that serve as allocators, such as pediatricians or youth welfare officers, should be in key player positions within the network. Furthermore, successful child protection is preventive and starts early. Social network analysis is an adequate technique to assess network structures and to plan interventions to improve networking. In addition, it is very useful to evaluate the effectiveness of interventions like round tables. We present data from our pilot project which was part of "Guter Start ins Kinderleben" ("a good start into a child's life"). Exemplary network data from one community show that networking is already quite effective with a satisfactory mean density throughout the network. There is potential for improvement in cooperation, especially at the interface between the child welfare and health systems.

Differential recruitment of theory of mind brain network across three tasks: An independent component analysis.

PubMed

Thye, Melissa D; Ammons, Carla J; Murdaugh, Donna L; Kana, Rajesh K

2018-07-16

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. In this fMRI study, three complementary, but distinct ToM tasks assessing mental state detection (e.g. RMIE: Reading the Mind in the Eyes; RMIV: Reading the Mind in the Voice) and intentional attribution (Causality task) were each analyzed using ICA in order to separately characterize the recruitment and functional connectivity of core nodes in the ToM network in response to the sub-processes of ToM. 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 with mental state detection and causal attribution, which represent possible sub-processes of the complex construct of ToM processing. Published by Elsevier B.V.

Genome-Wide Identification and Expression Analysis of the Mitogen-Activated Protein Kinase Gene Family in Cassava

PubMed Central

Yan, Yan; Wang, Lianzhe; Ding, Zehong; Tie, Weiwei; Ding, Xupo; Zeng, Changying; Wei, Yunxie; Zhao, Hongliang; Peng, Ming; Hu, Wei

2016-01-01

Mitogen-activated protein kinases (MAPKs) play central roles in plant developmental processes, hormone signaling transduction, and responses to abiotic stress. However, no data are currently available about the MAPK family in cassava, an important tropical crop. Herein, 21 MeMAPK genes were identified from cassava. Phylogenetic analysis indicated that MeMAPKs could be classified into four subfamilies. Gene structure analysis demonstrated that the number of introns in MeMAPK genes ranged from 1 to 10, suggesting large variation among cassava MAPK genes. Conserved motif analysis indicated that all MeMAPKs had typical protein kinase domains. Transcriptomic analysis suggested that MeMAPK genes showed differential expression patterns in distinct tissues and in response to drought stress between wild subspecies and cultivated varieties. Interaction networks and co-expression analyses revealed that crucial pathways controlled by MeMAPK networks may be involved in the differential response to drought stress in different accessions of cassava. Expression of nine selected MAPK genes showed that these genes could comprehensively respond to osmotic, salt, cold, oxidative stressors, and abscisic acid (ABA) signaling. These findings yield new insights into the transcriptional control of MAPK gene expression, provide an improved understanding of abiotic stress responses and signaling transduction in cassava, and lead to potential applications in the genetic improvement of cassava cultivars. PMID:27625666

Causal interactions in resting-state networks predict perceived loneliness.

PubMed

Tian, Yin; Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

Causal interactions in resting-state networks predict perceived loneliness

PubMed Central

Yang, Li; Chen, Sifan; Guo, Daqing; Ding, Zechao; Tam, Kin Yip; Yao, Dezhong

2017-01-01

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual. PMID:28545125

Fyn-Dependent Gene Networks in Acute Ethanol Sensitivity

PubMed Central

Farris, Sean P.; Miles, Michael F.

2013-01-01

Studies in humans and animal models document that acute behavioral responses to ethanol are predisposing factor for the risk of long-term drinking behavior. Prior microarray data from our laboratory document strain- and brain region-specific variation in gene expression profile responses to acute ethanol that may be underlying regulators of ethanol behavioral phenotypes. The non-receptor tyrosine kinase Fyn has previously been mechanistically implicated in the sedative-hypnotic response to acute ethanol. To further understand how Fyn may modulate ethanol behaviors, we used whole-genome expression profiling. We characterized basal and acute ethanol-evoked (3 g/kg) gene expression patterns in nucleus accumbens (NAC), prefrontal cortex (PFC), and ventral midbrain (VMB) of control and Fyn knockout mice. Bioinformatics analysis identified a set of Fyn-related gene networks differently regulated by acute ethanol across the three brain regions. In particular, our analysis suggested a coordinate basal decrease in myelin-associated gene expression within NAC and PFC as an underlying factor in sensitivity of Fyn null animals to ethanol sedation. An in silico analysis across the BXD recombinant inbred (RI) strains of mice identified a significant correlation between Fyn expression and a previously published ethanol loss-of-righting-reflex (LORR) phenotype. By combining PFC gene expression correlates to Fyn and LORR across multiple genomic datasets, we identified robust Fyn-centric gene networks related to LORR. Our results thus suggest that multiple system-wide changes exist within specific brain regions of Fyn knockout mice, and that distinct Fyn-dependent expression networks within PFC may be important determinates of the LORR due to acute ethanol. These results add to the interpretation of acute ethanol behavioral sensitivity in Fyn kinase null animals, and identify Fyn-centric gene networks influencing variance in ethanol LORR. Such networks may also inform future design of pharmacotherapies for the treatment and prevention of alcohol use disorders. PMID:24312422

Altered Connectivity of the Balance Processing Network After Tongue Stimulation in Balance-Impaired Individuals

PubMed Central

Tyler, Mitchell E.; Danilov, Yuri P.; Kaczmarek, Kurt A.; Meyerand, Mary E.

2013-01-01

Abstract Some individuals with balance impairment have hypersensitivity of the motion-sensitive visual cortices (hMT+) compared to healthy controls. Previous work showed that electrical tongue stimulation can reduce the exaggerated postural sway induced by optic flow in this subject population and decrease the hypersensitive response of hMT+. Additionally, a region within the brainstem (BS), likely containing the vestibular and trigeminal nuclei, showed increased optic flow-induced activity after tongue stimulation. The aim of this study was to understand how the modulation induced by tongue stimulation affects the balance-processing network as a whole and how modulation of BS structures can influence cortical activity. Four volumes of interest, discovered in a general linear model analysis, constitute major contributors to the balance-processing network. These regions were entered into a dynamic causal modeling analysis to map the network and measure any connection or topology changes due to the stimulation. Balance-impaired individuals had downregulated response of the primary visual cortex (V1) to visual stimuli but upregulated modulation of the connection between V1 and hMT+ by visual motion compared to healthy controls (p≤1E–5). This upregulation was decreased to near-normal levels after stimulation. Additionally, the region within the BS showed increased response to visual motion after stimulation compared to both prestimulation and controls. Stimulation to the tongue enters the central nervous system at the BS but likely propagates to the cortex through supramodal information transfer. We present a model to explain these brain responses that utilizes an anatomically present, but functionally dormant pathway of information flow within the processing network. PMID:23216162

Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

PubMed Central

Pérez-Montarelo, Dafne; Hudson, Nicholas J.; Fernández, Ana I.; Ramayo-Caldas, Yuliaxis; Dalrymple, Brian P.; Reverter, Antonio

2012-01-01

The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species. PMID:23049964

Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder.

PubMed

Worhunsky, Patrick D; Potenza, Marc N; Rogers, Robert D

2017-09-01

Continued, persistent gambling to recover accumulating losses, or 'loss-chasing', is a behavioral pattern linked particularly closely to gambling disorder (GD) but may reflect impaired decision-making processes relevant to drug addictions like cocaine-use disorder (CUD). However, little is known regarding the neurocognitive mechanisms of this complex, maladaptive behavior, particularly in individuals with addictive disorders. Seventy participants (25 GD, 18 CUD, and 27 healthy comparison (HC)) completed a loss-chase task during fMRI. Engagement of functional brain networks in response to losing outcomes and during decision-making periods preceding choices to loss-chase or to quit chasing losses were investigated using independent component analysis (ICA). An exploratory factor analysis was performed to examine patterns of coordinated engagement across identified networks. In GD relative to HC and CUD participants, choices to quit chasing were associated with greater engagement of a medial frontal executive-processing network. By comparison, CUD participants exhibited altered engagement of a striato-amygdala motivational network in response to losing outcomes as compared to HC, and during decision-making as compared to GD. Several other networks were differentially engaged during loss-chase relative to quit-chasing choices, but did not differ across participant groups. Exploratory factor analysis identified a system of coordinated activity across prefrontal executive-control networks that was greater in GD and CUD relative to HC participants and was associated with increased chasing persistence across all participants. Results provide evidence of shared and distinct neurobiological mechanisms in substance and behavioral addictions, and lend insight into potential cognitive interventions targeting loss-chasing behavior in GD. Copyright © 2017 Elsevier B.V. All rights reserved.

Stressors, Resources, and Stress Responses in Pregnant African American Women

PubMed Central

Giurgescu, Carmen; Kavanaugh, Karen; Norr, Kathleen F.; Dancy, Barbara L.; Twigg, Naomi; McFarlin, Barbara L.; Engeland, Christopher G.; Hennessy, Mary Dawn; White-Traut, Rosemary C.

2013-01-01

This research aimed to develop an initial understanding of the stressors, stress responses, and personal resources that impact African American women during pregnancy, potentially leading to preterm birth. Guided by the ecological model, a prospective, mixed-methods, complementarity design was used with 11 pregnant women and 8 of their significant others. Our integrated analysis of quantitative and qualitative data revealed 2 types of stress responses: high stress responses (7 women) and low stress responses (4 women). Patterns of stress responses were seen in psychological stress and cervical remodeling (attenuation or cervical length). All women in the high stress responses group had high depression and/or low psychological well-being and abnormal cervical remodeling at one or both data collection times. All but 1 woman had at least 3 sources of stress (racial, neighborhood, financial, or network). In contrast, 3 of the 4 women in the low stress responses group had only 2 sources of stress (racial, neighborhood, financial, or network) and 1 had none; these women also reported higher perceived support. The findings demonstrate the importance of periodically assessing stress in African American women during pregnancy, particularly related to their support network as well as the positive supports they receive. PMID:23360946

Syntactic Computations in the Language Network: Characterizing Dynamic Network Properties Using Representational Similarity Analysis

PubMed Central

Tyler, Lorraine K.; Cheung, Teresa P. L.; Devereux, Barry J.; Clarke, Alex

2013-01-01

The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (LMTG) and the anatomical connections between them. Here we use magnetoencephalography (MEG) to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g., “… landing planes …”), at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA) to characterize syntactic information represented in the LIFG and left posterior middle temporal gyrus (LpMTG) over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity. PMID:23730293

Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.

PubMed

Mach, Núria; Ramayo-Caldas, Yuliaxis; Clark, Allison; Moroldo, Marco; Robert, Céline; Barrey, Eric; López, Jesús Maria; Le Moyec, Laurence

2017-02-17

Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition. We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition. To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses.

Insula Demonstrates a Non-Linear Response to Varying Demand for Cognitive Control and Weaker Resting Connectivity With the Executive Control Network in Smokers.

PubMed

Fedota, John R; Matous, Allison L; Salmeron, Betty Jo; Gu, Hong; Ross, Thomas J; Stein, Elliot A

2016-09-01

Deficits in cognitive control processes are a primary characteristic of nicotine addiction. However, while network-based connectivity measures of dysfunction have frequently been observed, empirical evidence of task-based dysfunction in these processes has been inconsistent. Here, in a sample of smokers (n=35) and non-smokers (n=21), a previously validated parametric flanker task is employed to characterize addiction-related alterations in responses to varying (ie, high, intermediate, and low) demands for cognitive control. This approach yields a demand-response curve that aims to characterize potential non-linear responses to increased demand for control, including insensitivities or lags in fully activating the cognitive control network. We further used task-based differences in activation between groups as seeds for resting-state analysis of network dysfunction in an effort to more closely link prior inconsistencies in task-related activation with evidence of impaired network connectivity in smokers. For both smokers and non-smokers, neuroimaging results showed similar increases in activation in brain areas associated with cognitive control. However, reduced activation in right insula was seen only in smokers and only when processing intermediate demand for cognitive control. Further, in smokers, this task-modulated right insula showed weaker functional connectivity with the superior frontal gyrus, a component of the task-positive executive control network. These results demonstrate that the neural instantiation of salience attribution in smokers is both more effortful to fully activate and has more difficulty communicating with the exogenous, task-positive, executive control network. Together, these findings further articulate the cognitive control dysfunction associated with smoking and illustrate a specific brain circuit potentially responsible.

Metagenomic Insights of Microbial Feedbacks to Elevated CO2 (Invited)

NASA Astrophysics Data System (ADS)

Zhou, J.; Tu, Q.; Wu, L.; He, Z.; Deng, Y.; Van Nostrand, J. D.

2013-12-01

Understanding the responses of biological communities to elevated CO2 (eCO2) is a central issue in ecology and global change biology, but its impacts on the diversity, composition, structure, function, interactions and dynamics of soil microbial communities remain elusive. In this study, we first examined microbial responses to eCO2 among six FACE sites/ecosystems using a comprehensive functional gene microarray (GeoChip), and then focused on details of metagenome sequencing analysis in one particular site. GeoChip is a comprehensive functional gene array for examining the relationships between microbial community structure and ecosystem functioning and is a very powerful technology for biogeochemical, ecological and environmental studies. The current version of GeoChip (GeoChip 5.0) contains approximately 162,000 probes from 378,000 genes involved in C, N, S and P cycling, organic contaminant degradation, metal resistance, antibiotic resistance, stress responses, metal homeostasis, virulence, pigment production, bacterial phage-mediated lysis, soil beneficial microorganisms, and specific probes for viruses, protists, and fungi. Our experimental results revealed that both ecosystem and CO2 significantly (p < 0.05) affected the functional composition, structure and metabolic potential of soil microbial communities with the ecosystem having much greater influence (~47%) than CO2 (~1.3%) or CO2 and ecosystem (~4.1%). On one hand, microbial responses to eCO2 shared some common patterns among different ecosystems, such as increased abundances for key functional genes involved in nitrogen fixation, carbon fixation and degradation, and denitrification. On the other hand, more ecosystem-specific microbial responses were identified in each individual ecosystem. Such changes in the soil microbial community structure were closely correlated with geographic distance, soil NO3-N, NH4-N and C/N ratio. Further metagenome sequencing analysis of soil microbial communities in one particular site showed eCO2 altered the overall structure of soil microbial communities with ambient CO2 samples retaining a higher functional gene diversity than eCO2 samples. Also the taxonomic diversity of functional genes decreased at eCO2. Random matrix theory (RMT)-based network analysis showed that the identified networks under ambient and elevated CO2 were substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher order organization (meta-modules), topological roles of individual nodes, and network hubs, indicating that elevated CO2 dramatically altered the network interactions among different phylogenetic and functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen content, indicating the potential importance of network interactions in ecosystem functioning. Taken together, this study indicates that eCO2 may decrease the overall functional and taxonomic diversity of soil microbial communities, but such effects appeared to be ecosystem-specific, which makes it more challenging for predicting global or regional terrestrial ecosystems responses to eCO2.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

PubMed

Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E; Conolly, Rory B

2010-02-01

The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell biology has advanced rapidly with increasing availability of new data and powerful simulation techniques, a quantitative orientation is still lacking in life sciences education to make efficient use of these new tools to implement the new toxicity testing paradigm. A revamped undergraduate curriculum in the biological sciences including compulsory courses in mathematics and analysis of dynamical systems is required to address this gap. In parallel, dissemination of computational systems biology techniques and other analytical tools among practicing toxicologists and risk assessment professionals will help accelerate implementation of the new toxicity testing vision.

Coexpression network analysis of the genes regulated by two types of resistance responses to powdery mildew in wheat

PubMed Central

Zhang, Juncheng; Zheng, Hongyuan; Li, Yiwen; Li, Hongjie; Liu, Xin; Qin, Huanju; Dong, Lingli; Wang, Daowen

2016-01-01

Powdery mildew disease caused by Blumeria graminis f. sp. tritici (Bgt) inflicts severe economic losses in wheat crops. A systematic understanding of the molecular mechanisms involved in wheat resistance to Bgt is essential for effectively controlling the disease. Here, using the diploid wheat Triticum urartu as a host, the genes regulated by immune (IM) and hypersensitive reaction (HR) resistance responses to Bgt were investigated through transcriptome sequencing. Four gene coexpression networks (GCNs) were developed using transcriptomic data generated for 20 T. urartu accessions showing IM, HR or susceptible responses. The powdery mildew resistance regulated (PMRR) genes whose expression was significantly correlated with Bgt resistance were identified, and they tended to be hubs and enriched in six major modules. A wide occurrence of negative regulation of PMRR genes was observed. Three new candidate immune receptor genes (TRIUR3_13045, TRIUR3_01037 and TRIUR3_06195) positively associated with Bgt resistance were discovered. Finally, the involvement of TRIUR3_01037 in Bgt resistance was tentatively verified through cosegregation analysis in a F2 population and functional expression assay in Bgt susceptible leaf cells. This research provides insights into the global network properties of PMRR genes. Potential molecular differences between IM and HR resistance responses to Bgt are discussed. PMID:27033636

The influence of management and environment on local health department organizational structure and adaptation: a longitudinal network analysis.

PubMed

Keeling, Jonathan W; Pryde, Julie A; Merrill, Jacqueline A

2013-01-01

The nation's 2862 local health departments (LHDs) are the primary means for assuring public health services for all populations. The objective of this study is to assess the effect of organizational network analysis on management decisions in LHDs and to demonstrate the technique's ability to detect organizational adaptation over time. We conducted a longitudinal network analysis in a full-service LHD with 113 employees serving about 187,000 persons. Network survey data were collected from employees at 3 times: months 0, 8, and 34. At time 1 the initial analysis was presented to LHD managers as an intervention with information on evidence-based management strategies to address the findings. At times 2 and 3 interviews documented managers' decision making and events in the task environment. Response rates for the 3 network analyses were 90%, 97%, and 83%. Postintervention (time 2) results showed beneficial changes in network measures of communication and integration. Screening and case identification increased for chlamydia and for gonorrhea. Outbreak mitigation was accelerated by cross-divisional teaming. Network measurements at time 3 showed LHD adaptation to H1N1 and budget constraints with increased centralization. Task redundancy increased dramatically after National Incident Management System training. Organizational network analysis supports LHD management with empirical evidence that can be translated into strategic decisions about communication, allocation of resources, and addressing knowledge gaps. Specific population health outcomes were traced directly to management decisions based on network evidence. The technique can help managers improve how LHDs function as organizations and contribute to our understanding of public health systems.

Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.

PubMed

Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing; Wu, Hulin

2014-01-01

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

Emerging trends in the functional genomics of the abiotic stress response in crop plants.

PubMed

Vij, Shubha; Tyagi, Akhilesh K

2007-05-01

Plants are exposed to different abiotic stresses, such as water deficit, high temperature, salinity, cold, heavy metals and mechanical wounding, under field conditions. It is estimated that such stress conditions can potentially reduce the yield of crop plants by more than 50%. Investigations of the physiological, biochemical and molecular aspects of stress tolerance have been conducted to unravel the intrinsic mechanisms developed during evolution to mitigate against stress by plants. Before the advent of the genomics era, researchers primarily used a gene-by-gene approach to decipher the function of the genes involved in the abiotic stress response. However, abiotic stress tolerance is a complex trait and, although large numbers of genes have been identified to be involved in the abiotic stress response, there remain large gaps in our understanding of the trait. The availability of the genome sequences of certain important plant species has enabled the use of strategies, such as genome-wide expression profiling, to identify the genes associated with the stress response, followed by the verification of gene function by the analysis of mutants and transgenics. Certain components of both abscisic acid-dependent and -independent cascades involved in the stress response have already been identified. Information originating from the genome-wide analysis of abiotic stress tolerance will help to provide an insight into the stress-responsive network(s), and may allow the modification of this network to reduce the loss caused by stress and to increase agricultural productivity.

ERLN Technical Support for Labs

EPA Pesticide Factsheets

The Environmental Response Laboratory Network provides policies and guidance on lab and data requirements, Standardized Analytical Methods, and technical support for water and radiological sampling and analysis

Neural control and transient analysis of the LCL-type resonant converter

NASA Astrophysics Data System (ADS)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines.

PubMed

Jensch, Antje; Thomaseth, Caterina; Radde, Nicole E

2017-01-25

Positive and negative feedback loops are ubiquitous motifs in biochemical signaling pathways. The mitogen-activated protein kinase (MAPK) pathway module is part of many distinct signaling networks and comprises several of these motifs, whose functioning depends on the cell line at hand and on the particular context. The maintainance of specificity of the response of the MAPK module to distinct stimuli has become a key paradigm especially in PC-12 cells, where the same module leads to different cell fates, depending on the stimulating growth factor. This cell fate is regulated by differences in the ERK (MAPK) activation profile, which shows a transient response upon stimulation with EGF, while the response is sustained in case of NGF. This behavior was explained by different effective network topologies. It is widely believed that this sustained response requires a bistable system. In this study we present a sampling-based Bayesian model analysis on a dataset, in which PC-12 cells have been stimulated with different growth factors. This is combined with novel analysis methods to investigate the role of feedback interconnections to shape ERK response. Results strongly suggest that, besides bistability, an additional effect called quasi-bistability can contribute to explain the observed responses of the system to different stimuli. Quasi-bistability is the ability of a monostable system to maintain two distinct states over a long time period upon a transient signal, which is also related to positive feedback, but cannot be detected by standard steady state analysis methods. Although applied on a specific example, our framework is generic enough to be also relevant for other regulatory network modeling studies that comprise positive feedback to explain cellular decision making processes. Overall, this study advices to focus not only on steady states, but also to take transient behavior into account in the analysis.

Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network.

PubMed

Elton, Amanda; Tripathi, Shanti P; Mletzko, Tanja; Young, Jonathan; Cisler, Josh M; James, G Andrew; Kilts, Clinton D

2014-04-01

Childhood adversity represents a major risk factor for drug addiction and other mental disorders. However, the specific mechanisms by which childhood adversity impacts human brain organization to confer greater vulnerability for negative outcomes in adulthood is largely unknown. As an impaired process in drug addiction, inhibitory control of behavior was investigated as a target of childhood maltreatment (abuse and neglect). Forty adults without Axis-I psychiatric disorders (21 females) completed a Childhood Trauma Questionnaire (CTQ) and underwent functional MRI (fMRI) while performing a stop-signal task. A group independent component analysis identified a putative brain inhibitory control network. Graph theoretical analyses and structural equation modeling investigated the impact of childhood maltreatment on the functional organization of this neural processing network. Graph theory outcomes revealed sex differences in the relationship between network functional connectivity and inhibitory control which were dependent on the severity of childhood maltreatment exposure. A network effective connectivity analysis indicated that a maltreatment dose-related negative modulation of dorsal anterior cingulate (dACC) activity by the left inferior frontal cortex (IFC) predicted better response inhibition and lesser attention deficit hyperactivity disorder (ADHD) symptoms in females, but poorer response inhibition and greater ADHD symptoms in males. Less inhibition of the right IFC by dACC in males with higher CTQ scores improved inhibitory control ability. The childhood maltreatment-related reorganization of a brain inhibitory control network provides sex-dependent mechanisms by which childhood adversity may confer greater risk for drug use and related disorders and by which adaptive brain responses protect individuals from this risk factor. Copyright © 2013 Wiley Periodicals, Inc.

IL-32 is a molecular marker of a host defense network in human tuberculosis.

PubMed

Montoya, Dennis; Inkeles, Megan S; Liu, Phillip T; Realegeno, Susan; Teles, Rosane M B; Vaidya, Poorva; Munoz, Marcos A; Schenk, Mirjam; Swindell, William R; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R; Modlin, Robert L

2014-08-20

Tuberculosis is a leading cause of infectious disease-related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ- and IL-15-induced "defense response" genes. IL-32 induced the vitamin D-dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15-induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15-induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. Copyright © 2014, American Association for the Advancement of Science.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

PubMed

Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

CoryneRegNet: an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks.

PubMed

Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

2006-02-14

The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation.

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

PubMed

Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2016-03-01

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

PubMed Central

Jarnuczak, Andrew F.; Eyers, Claire E.; Schwartz, Jean‐Marc; Grant, Christopher M.

2015-01-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC‐based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. PMID:25689132

Analog-to-digital clinical data collection on networked workstations with graphic user interface.

PubMed

Lunt, D

1991-02-01

An innovative respiratory examination system has been developed that combines physiological response measurement, real-time graphic displays, user-driven operating sequences, and networked file archiving and review into a scientific research and clinical diagnosis tool. This newly constructed computer network is being used to enhance the research center's ability to perform patient pulmonary function examinations. Respiratory data are simultaneously acquired and graphically presented during patient breathing maneuvers and rapidly transformed into graphic and numeric reports, suitable for statistical analysis or database access. The environment consists of the hardware (Macintosh computer, MacADIOS converters, analog amplifiers), the software (HyperCard v2.0, HyperTalk, XCMDs), and the network (AppleTalk, fileservers, printers) as building blocks for data acquisition, analysis, editing, and storage. System operation modules include: Calibration, Examination, Reports, On-line Help Library, Graphic/Data Editing, and Network Storage.

Comparative transcriptome and gene co-expression network analysis reveal genes and signaling pathways adaptively responsive to varied adverse stresses in the insect fungal pathogen, Beauveria bassiana.

PubMed

He, Zhangjiang; Zhao, Xin; Lu, Zhuoyue; Wang, Huifang; Liu, Pengfei; Zeng, Fanqin; Zhang, Yongjun

2018-01-01

Sensing, responding, and adapting to the surrounding environment are crucial for all living organisms to survive, proliferate, and differentiate in their biological niches. Beauveria bassiana is an economically important insect-pathogenic fungus which is widely used as a biocontrol agent to control a variety of insect pests. The fungal pathogen unavoidably encounters a variety of adverse environmental stresses and defense response from the host insects during application of the fungal agents. However, few are known about the transcription response of the fungus to respond or adapt varied adverse stresses. Here, we comparatively analyzed the transcriptome of B. bassiana in globe genome under the varied stationary-phase stresses including osmotic agent (0.8 M NaCl), high temperature (32 °C), cell wall-perturbing agent (Congo red), and oxidative agents (H 2 O 2 or menadione). Total of 12,412 reads were obtained, and mapped to the 6767 genes of the B. bassiana. All of these stresses caused transcription responses involved in basal metabolism, cell wall construction, stress response or cell rescue/detoxification, signaling transduction and gene transcription regulation, and likely other cellular processes. An array of genes displayed similar transcription patterns in response to at least two of the five stresses, suggesting a shared transcription response to varied adverse stresses. Gene co-expression network analysis revealed that mTOR signaling pathway, but not HOG1 MAP kinase pathway, played a central role in regulation the varied adverse stress responses, which was verified by RNAi-mediated knockdown of TOR1. Our findings provided an insight of transcription response and gene co-expression network of B. bassiana in adaptation to varied environments. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

NASA Astrophysics Data System (ADS)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Identifying a system of predominant negative symptoms: Network analysis of three randomized clinical trials.

PubMed

Levine, Stephen Z; Leucht, Stefan

2016-12-01

Reasons for the recent mixed success of research into negative symptoms may be informed by conceptualizing negative symptoms as a system that is identifiable from network analysis. We aimed to identify: (I) negative symptom systems; (I) central negative symptoms within each system; and (III) differences between the systems, based on network analysis of negative symptoms for baseline, endpoint and change. Patients with chronic schizophrenia and predominant negative symptoms participated in three clinical trials that compared placebo and amisulpride to 60days (n=487). Networks analyses were computed from the Scale for the Assessment of Negative Symptoms (SANS) scores for baseline and endpoint for severity, and estimated change based on mixed models. Central symptoms to each network were identified. The networks were contrasted for connectivity with permutation tests. Network analysis showed that the baseline and endpoint symptom severity systems formed symptom groups of Affect, Poor responsiveness, Lack of interest, and Apathy-inattentiveness. The baseline and endpoint networks did not significantly differ in terms of connectivity, but both significantly (P<0.05) differed to the change network. In the change network the apathy-inattentiveness symptom group split into three other groups. The most central symptoms were Decreased Spontaneous Movements at baseline and endpoint, and Poverty of Speech for estimated change. Results provide preliminary evidence for: (I) a replicable negative symptom severity system; and (II) symptoms with high centrality (e.g., Decreased Spontaneous Movement), that may be future treatment targets following replication to ensure the curent results generalize to other samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Immunotherapy in advanced melanoma: a network meta-analysis.

PubMed

Pyo, Jung-Soo; Kang, Guhyun

2017-05-01

The aim of this study was to compare the effects of various immunotherapeutic agents and chemotherapy for unresected or metastatic melanomas. We performed a network meta-analysis using a Bayesian statistical model to compare objective response rate (ORR) of various immunotherapies from 12 randomized controlled studies. The estimated ORRs of immunotherapy and chemotherapy were 0.224 and 0.108, respectively. The ORRs of immunotherapy in untreated and pretreated patients were 0.279 and 0.176, respectively. In network meta-analysis, the odds ratios for ORR of nivolumab (1 mg/kg)/ipilmumab (3 mg/kg), pembrolizumab 10 mg/kg and nivolumab 3 mg/kg were 8.54, 5.39 and 4.35, respectively, compared with chemotherapy alone. Our data showed that various immunotherapies had higher ORRs rather than chemotherapy alone.

Tunable thermo-responsive hydrogels: synthesis, structural analysis and drug release studies.

PubMed

Cirillo, Giuseppe; Spataro, Tania; Curcio, Manuela; Spizzirri, U Gianfranco; Nicoletta, Fiore Pasquale; Picci, Nevio; Iemma, Francesca

2015-03-01

Thermo-responsive hydrogel films, synthesized by UV-initiated radical polymerization, are proposed as delivery devices for non-steroidal anti-inflammatory drugs (Diclofenac sodium and Naproxen). N-isopropylacrylamide and N,N'-ethylenebisacrylamide were chosen as thermo-sensitive monomer and crosslinker, respectively. Infrared spectroscopy was used to assess the incorporation of monomers into the network, and the network density of hydrogel films was found to strictly depend on both feed composition and film thickness. Calorimetric analyses showed negative thermo-responsive behaviour with shrinking/swelling transition values in the range 32.8-36.1°C. Equilibrium swelling studies around the LCST allowed the correlation between the structural changes and the temperature variations. The mesh size, indeed, rapidly changed from a collapsed to a swollen state, with beneficial effects in applications such as size-selective permeation or controlled drug delivery, while the crosslinking degree, the film thickness, and the loading method deeply influenced the drug release profiles at 25 and 40°C. The analysis of both 3D-network structure, release kinetics and diffusional constraints at different temperatures was evaluated by mathematical modelling. Copyright © 2014 Elsevier B.V. All rights reserved.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

Combined analysis of mRNA and miRNA identifies dehydration and salinity responsive key molecular players in citrus roots.

PubMed

Xie, Rangjin; Zhang, Jin; Ma, Yanyan; Pan, Xiaoting; Dong, Cuicui; Pang, Shaoping; He, Shaolan; Deng, Lie; Yi, Shilai; Zheng, Yongqiang; Lv, Qiang

2017-02-06

Citrus is one of the most economically important fruit crops around world. Drought and salinity stresses adversely affected its productivity and fruit quality. However, the genetic regulatory networks and signaling pathways involved in drought and salinity remain to be elucidated. With RNA-seq and sRNA-seq, an integrative analysis of miRNA and mRNA expression profiling and their regulatory networks were conducted using citrus roots subjected to dehydration and salt treatment. Differentially expressed (DE) mRNA and miRNA profiles were obtained according to fold change analysis and the relationships between miRNAs and target mRNAs were found to be coherent and incoherent in the regulatory networks. GO enrichment analysis revealed that some crucial biological processes related to signal transduction (e.g. 'MAPK cascade'), hormone-mediated signaling pathways (e.g. abscisic acid- activated signaling pathway'), reactive oxygen species (ROS) metabolic process (e.g. 'hydrogen peroxide catabolic process') and transcription factors (e.g., 'MYB, ZFP and bZIP') were involved in dehydration and/or salt treatment. The molecular players in response to dehydration and salt treatment were partially overlapping. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-seq and sRNA-seq analysis. This study provides new insights into the molecular mechanisms how citrus roots respond to dehydration and salt treatment.

Combined analysis of mRNA and miRNA identifies dehydration and salinity responsive key molecular players in citrus roots

PubMed Central

Xie, Rangjin; Zhang, Jin; Ma, Yanyan; Pan, Xiaoting; Dong, Cuicui; Pang, Shaoping; He, Shaolan; Deng, Lie; Yi, Shilai; Zheng, Yongqiang; Lv, Qiang

2017-01-01

Citrus is one of the most economically important fruit crops around world. Drought and salinity stresses adversely affected its productivity and fruit quality. However, the genetic regulatory networks and signaling pathways involved in drought and salinity remain to be elucidated. With RNA-seq and sRNA-seq, an integrative analysis of miRNA and mRNA expression profiling and their regulatory networks were conducted using citrus roots subjected to dehydration and salt treatment. Differentially expressed (DE) mRNA and miRNA profiles were obtained according to fold change analysis and the relationships between miRNAs and target mRNAs were found to be coherent and incoherent in the regulatory networks. GO enrichment analysis revealed that some crucial biological processes related to signal transduction (e.g. ‘MAPK cascade’), hormone-mediated signaling pathways (e.g. abscisic acid- activated signaling pathway’), reactive oxygen species (ROS) metabolic process (e.g. ‘hydrogen peroxide catabolic process’) and transcription factors (e.g., ‘MYB, ZFP and bZIP’) were involved in dehydration and/or salt treatment. The molecular players in response to dehydration and salt treatment were partially overlapping. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis further confirmed the results from RNA-seq and sRNA-seq analysis. This study provides new insights into the molecular mechanisms how citrus roots respond to dehydration and salt treatment. PMID:28165059

A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies.

PubMed

Biffi, E; Menegon, A; Regalia, G; Maida, S; Ferrigno, G; Pedrocchi, A

2011-08-15

Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks. Copyright © 2011 Elsevier B.V. All rights reserved.

Autonomous smart sensor network for full-scale structural health monitoring

NASA Astrophysics Data System (ADS)

Rice, Jennifer A.; Mechitov, Kirill A.; Spencer, B. F., Jr.; Agha, Gul A.

2010-04-01

The demands of aging infrastructure require effective methods for structural monitoring and maintenance. Wireless smart sensor networks offer the ability to enhance structural health monitoring (SHM) practices through the utilization of onboard computation to achieve distributed data management. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While smart sensor technology is not new, the number of full-scale SHM applications has been limited. This slow progress is due, in part, to the complex network management issues that arise when moving from a laboratory setting to a full-scale monitoring implementation. This paper presents flexible network management software that enables continuous and autonomous operation of wireless smart sensor networks for full-scale SHM applications. The software components combine sleep/wake cycling for enhanced power management with threshold detection for triggering network wide tasks, such as synchronized sensing or decentralized modal analysis, during periods of critical structural response.

Distributions of Irritative Zones Are Related to Individual Alterations of Resting-State Networks in Focal Epilepsy

PubMed Central

Song, Yinchen; Sanganahalli, Basavaraju G.; Hyder, Fahmeed; Lin, Wei-Chiang; Riera, Jorge J.

2015-01-01

Alterations in the connectivity patterns of the fMRI-based resting-state networks (RSNs) have been reported in several types of epilepsies. Evidence pointed out these alterations might be associated with the genesis and propagation of interictal epileptiform discharges (IEDs). IEDs also evoke blood-oxygen-level dependent (BOLD) responses, which have been used to delineate irritative zones during preoperative work-up. Therefore, one may expect a relationship between the topology of the IED-evoked BOLD response network and the altered spatial patterns of the RSNs. In this study, we used EEG recordings and fMRI data obtained simultaneously from a chronic model of focal epilepsy in Wistar rats to verify our hypothesis. We found that IED-evoked BOLD response networks comprise both cortical and subcortical structures with a rat-dependent topology. In all rats, IEDs evoke both activation and deactivation types of BOLD responses. Using a Granger causality method, we found that in many cases areas with BOLD deactivation have directed influences on areas with activation (p<0.05). We were able to predict topological properties (i.e., focal/diffused, unilateral/bilateral) of the IED-evoked BOLD response network by performing hierarchical clustering analysis on major spatial features of the RSNs. All these results suggest that IEDs and disruptions in the RSNs found previously in humans may be different manifestations of the same transient events, probably reflecting altered consciousness. In our opinion, the shutdown of specific nodes of the default mode network may cause uncontrollable excitability in other functionally connected brain areas. We conclude that IED-evoked BOLD responses (i.e., activation and deactivation) and alterations of RSNs are intrinsically related, and speculate that an understanding of their interplay is necessary to discriminate focal epileptogenesis and network propagation phenomena across different brain modules via hub-based connectivity. PMID:26226628

Systematic review with network meta-analysis: the efficacy of anti-tumour necrosis factor-alpha agents for the treatment of ulcerative colitis.

PubMed

Stidham, R W; Lee, T C H; Higgins, P D R; Deshpande, A R; Sussman, D A; Singal, A G; Elmunzer, B J; Saini, S D; Vijan, S; Waljee, A K

2014-04-01

Antibodies against tumour necrosis factor-alpha (anti-TNF) are effective therapies in the treatment of ulcerative colitis (UC), but their comparative efficacy is unknown. To perform a network meta-analysis comparing the efficacy of anti-TNF agents in UC. After screening 506 studies, reviewers extracted information on seven studies. Traditional meta-analysis (TMA) was used to compare each anti-TNF agent to placebo. Bayesian network meta-analysis (NMA) was performed to compare the effects of anti-TNF agents to placebo. In addition, sample sizes for comparative efficacy trials were calculated. Compared to placebo, TMA revealed that anti-TNF agents result in a higher likelihood of induction of remission and response (RR: 2.45, 95% CI: 1.72-3.47 and RR: 1.65, 95% CI: 1.37-1.99 respectively) as well as maintenance of remission and response (RR: 2.00, 95% CI: 1.52-2.62 and RR: 1.76, 95% CI: 1.46-2.14 respectively). Individually, infliximab, adalimumab and goliumumab resulted in a higher likelihood of induction and maintenance for both remission and response. NMA found nonsignificant trends in comparisons of the individual agents. The required sample sizes for direct head-to-head trials between infliximab and adalimumab for induction and maintenance are 174 and 204 subjects respectively. This study demonstrates that, compared to placebo, infliximab, adalimumab and golimumab are all effective for the induction and maintenance of remission in ulcerative colitis. However, network meta-analysis demonstrates that no single agent is clinically superior to the others and therefore, other factors such as cost, safety, route of administration and patient preference should dictate our choice of anti-TNF agents. A randomised comparative efficacy trial between infliximab and adalimumab in UC is of practical size and should be performed. © 2014 John Wiley & Sons Ltd.

Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

DTIC Science & Technology

2008-03-01

of biochemical networks. Trends Biochemical Sci 31: 284–291. 56. Blinov ML, Faeder JR, Goldstein B , Hlavacek WS (2006) A network model of early events...activation is dependent on the nature of connectivity of the two receptors to B -Raf and C-Raf, which form a partially incoherent bifan. The incoherent bifan...Wooster, R., Stratton, M. R., and Futreal, P. A. (2002) Mutations of the BRaf gene in human cancer. Nature 417, 949–954 11. Goydos, J. S., Mann, B

Acupuncture mobilizes the brain's default mode and its anti-correlated network in healthy subjects.

PubMed

Hui, Kathleen K S; Marina, Ovidiu; Claunch, Joshua D; Nixon, Erika E; Fang, Jiliang; Liu, Jing; Li, Ming; Napadow, Vitaly; Vangel, Mark; Makris, Nikos; Chan, Suk-Tak; Kwong, Kenneth K; Rosen, Bruce R

2009-09-01

Previous work has shown that acupuncture stimulation evokes deactivation of a limbic-paralimbic-neocortical network (LPNN) as well as activation of somatosensory brain regions. This study explores the activity and functional connectivity of these regions during acupuncture vs. tactile stimulation and vs. acupuncture associated with inadvertent sharp pain. Acupuncture during 201 scans and tactile stimulation during 74 scans for comparison at acupoints LI4, ST36 and LV3 was monitored with fMRI and psychophysical response in 48 healthy subjects. Clusters of deactivated regions in the medial prefrontal, medial parietal and medial temporal lobes as well as activated regions in the sensorimotor and a few paralimbic structures can be identified during acupuncture by general linear model analysis and seed-based cross correlation analysis. Importantly, these clusters showed virtual identity with the default mode network and the anti-correlated task-positive network in response to stimulation. In addition, the amygdala and hypothalamus, structures not routinely reported in the default mode literature, were frequently involved in acupuncture. When acupuncture induced sharp pain, the deactivation was attenuated or became activated instead. Tactile stimulation induced greater activation of the somatosensory regions but less extensive deactivation of the LPNN. These results indicate that the deactivation of the LPNN during acupuncture cannot be completely explained by the demand of attention that is commonly proposed in the default mode literature. Our results suggest that acupuncture mobilizes the anti-correlated functional networks of the brain to mediate its actions, and that the effect is dependent on the psychophysical response.

Differential reward network functional connectivity in cannabis dependent and non-dependent users☆

PubMed Central

Filbey, Francesca M.; Dunlop, Joseph

2015-01-01

Background Emergent studies show that similar to other substances of abuse, cue-reactivity to cannabis is also associated with neural response in the brain’s reward pathway (Filbey et al., 2009). However, the inter-relatedness of brain regions during cue-reactivity in cannabis users remains unknown. Methods In this study, we conducted a series of investigations to determine functional connectivity during cue-reactivity in 71 cannabis users. First, we used psychophysiological interaction (PPI) analysis to examine coherent neural response to cannabis cues. Second, we evaluated whether these patterns of network functional connectivity differentiated dependent and non-dependent users. Finally, as an exploratory analysis, we determined the directionality of these connections via Granger connectivity analyses. Results PPI analyses showed reward network functional connectivity with the nucleus accumbens (NAc) seed region during cue exposure. Between-group contrasts found differential effects of dependence status. Dependent users (N = 31) had greater functional connectivity with amygdala and anterior cingulate gyrus (ACG) seeds while the non-dependent users (N = 24) had greater functional connectivity with the NAc, orbitofrontal cortex (OFC) and hippocampus seeds. Granger analyses showed that hippocampal and ACG activation preceded neural response in reward areas. Conclusions Both PPI and Granger analyses demonstrated strong functional coherence in reward regions during exposure to cannabis cues in current cannabis users. Functional connectivity (but not regional activation) in the reward network differentiated dependent from non-dependent cannabis users. Our findings suggest that repeated cannabis exposure causes observable changes in functional connectivity in the reward network and should be considered in intervention strategies. PMID:24838032

Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data.

PubMed

Li, Cheng-Wei; Chen, Bor-Sen

2016-01-01

Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.

Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition

PubMed Central

Summerfield, Taryn L.; Yu, Lianbo; Gulati, Parul; Zhang, Jie; Huang, Kun; Romero, Roberto; Kniss, Douglas A.

2011-01-01

A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals. PMID:21655103

Invasive species information networks: Collaboration at multiple scales for prevention, early detection, and rapid response to invasive alien species

USGS Publications Warehouse

Simpson, Annie; Jarnevich, Catherine S.; Madsen, John; Westbrooks, Randy G.; Fournier, Christine; Mehrhoff, Les; Browne, Michael; Graham, Jim; Sellers, Elizabeth A.

2009-01-01

Accurate analysis of present distributions and effective modeling of future distributions of invasive alien species (IAS) are both highly dependent on the availability and accessibility of occurrence data and natural history information about the species. Invasive alien species monitoring and detection networks (such as the Invasive Plant Atlas of New England and the Invasive Plant Atlas of the MidSouth) generate occurrence data at local and regional levels within the United States, which are shared through the US National Institute of Invasive Species Science. The Inter-American Biodiversity Information Network's Invasives Information Network (I3N), facilitates cooperation on sharing invasive species occurrence data throughout the Western Hemisphere. The I3N and other national and regional networks expose their data globally via the Global Invasive Species Information Network (GISIN). International and interdisciplinary cooperation on data sharing strengthens cooperation on strategies and responses to invasions. However, limitations to effective collaboration among invasive species networks leading to successful early detection and rapid response to invasive species include: lack of interoperability; data accessibility; funding; and technical expertise. This paper proposes various solutions to these obstacles at different geographic levels and briefly describes success stories from the invasive species information networks mentioned above. Using biological informatics to facilitate global information sharing is especially critical in invasive species science, as research has shown that one of the best indicators of the invasiveness of a species is whether it has been invasive elsewhere. Data must also be shared across disciplines because natural history information (e.g. diet, predators, habitat requirements, etc.) about a species in its native range is vital for effective prevention, detection, and rapid response to an invasion. Finally, it has been our experience that sharing information, including invasive species dispersal mechanisms and rates, impacts, and prevention and control strategies, enables resource managers and decision-makers to mount a more effective response to biological invasions.

Network pharmacology of JAK inhibitors

PubMed Central

Moodley, Devapregasan; Yoshida, Hideyuki; Mostafavi, Sara; Asinovski, Natasha; Ortiz-Lopez, Adriana; Symanowicz, Peter; Telliez, Jean-Baptiste; Hegen, Martin; Clark, James D.; Mathis, Diane; Benoist, Christophe

2016-01-01

Small-molecule inhibitors of the Janus kinase family (JAKis) are clinically efficacious in multiple autoimmune diseases, albeit with increased risk of certain infections. Their precise mechanism of action is unclear, with JAKs being signaling hubs for several cytokines. We assessed the in vivo impact of pan- and isoform-specific JAKi in mice by immunologic and genomic profiling. Effects were broad across the immunogenomic network, with overlap between inhibitors. Natural killer (NK) cell and macrophage homeostasis were most immediately perturbed, with network-level analysis revealing a rewiring of coregulated modules of NK cell transcripts. The repression of IFN signature genes after repeated JAKi treatment continued even after drug clearance, with persistent changes in chromatin accessibility and phospho-STAT responsiveness to IFN. Thus, clinical use and future development of JAKi might need to balance effects on immunological networks, rather than expect that JAKis affect a particular cytokine response and be cued to long-lasting epigenomic modifications rather than by short-term pharmacokinetics. PMID:27516546

Modeling the low-velocity impact characteristics of woven glass epoxy composite laminates using artificial neural networks

NASA Astrophysics Data System (ADS)

Mathivanan, N. Rajesh; Mouli, Chandra

2012-12-01

In this work, a new methodology based on artificial neural networks (ANN) has been developed to study the low-velocity impact characteristics of woven glass epoxy laminates of EP3 grade. To train and test the networks, multiple impact cases have been generated using statistical analysis of variance (ANOVA). Experimental tests were performed using an instrumented falling-weight impact-testing machine. Different impact velocities and impact energies on different thicknesses of laminates were considered as the input parameters of the ANN model. This model is a feed-forward back-propagation neural network. Using the input/output data of the experiments, the model was trained and tested. Further, the effects of the low-velocity impact response of the laminates at different energy levels were investigated by studying the cause-effect relationship among the influential factors using response surface methodology. The most significant parameter is determined from the other input variables through ANOVA.

Distributed task coding throughout the multiple demand network of the human frontal-insular cortex.

PubMed

Stiers, Peter; Mennes, Maarten; Sunaert, Stefan

2010-08-01

The large variety of tasks that humans can perform is governed by a small number of key frontal-insular regions that are commonly active during task performance. Little is known about how this network distinguishes different tasks. We report on fMRI data in twelve participants while they performed four cognitive tasks. Of 20 commonly active frontal-insular regions in each hemisphere, five showed a BOLD response increase with increased task demands, regardless of the task. Although active in all tasks, each task invoked a unique response pattern across the voxels in each area that proved reliable in split-half multi-voxel correlation analysis. Consequently, voxels differed in their preference for one or more of the tasks. Voxel-based functional connectivity analyses revealed that same preference voxels distributed across all areas of the network constituted functional sub-networks that characterized the task being executed. Copyright 2010 Elsevier Inc. All rights reserved.

Navigating an ocean of information: how Community Care of North Carolina uses data to improve care and control costs.

PubMed

Barrington, Randy

2014-01-01

Community Care of North Carolina's 14 networks use data analysis to provide relevant solutions that are responsive to unique regional environments. This article describes some of the ways that these networks use data to improve patient self-management, to meet providers' needs, to improve quality of care, and to control costs.

A microRNA-mRNA expression network during oral siphon regeneration in Ciona.

PubMed

Spina, Elijah J; Guzman, Elmer; Zhou, Hongjun; Kosik, Kenneth S; Smith, William C

2017-05-15

Here we present a parallel study of mRNA and microRNA expression during oral siphon (OS) regeneration in Ciona robusta , and the derived network of their interactions. In the process of identifying 248 mRNAs and 15 microRNAs as differentially expressed, we also identified 57 novel microRNAs, several of which are among the most highly differentially expressed. Analysis of functional categories identified enriched transcripts related to stress responses and apoptosis at the wound healing stage, signaling pathways including Wnt and TGFβ during early regrowth, and negative regulation of extracellular proteases in late stage regeneration. Consistent with the expression results, we found that inhibition of TGFβ signaling blocked OS regeneration. A correlation network was subsequently inferred for all predicted microRNA-mRNA target pairs expressed during regeneration. Network-based clustering associated transcripts into 22 non-overlapping groups, the functional analysis of which showed enrichment of stress response, signaling pathway and extracellular protease categories that could be related to specific microRNAs. Predicted targets of the miR-9 cluster suggest a role in regulating differentiation and the proliferative state of neural progenitors through regulation of the cytoskeleton and cell cycle. © 2017. Published by The Company of Biologists Ltd.

A microRNA-mRNA expression network during oral siphon regeneration in Ciona

PubMed Central

Spina, Elijah J.; Guzman, Elmer; Zhou, Hongjun; Kosik, Kenneth S.

2017-01-01

Here we present a parallel study of mRNA and microRNA expression during oral siphon (OS) regeneration in Ciona robusta, and the derived network of their interactions. In the process of identifying 248 mRNAs and 15 microRNAs as differentially expressed, we also identified 57 novel microRNAs, several of which are among the most highly differentially expressed. Analysis of functional categories identified enriched transcripts related to stress responses and apoptosis at the wound healing stage, signaling pathways including Wnt and TGFβ during early regrowth, and negative regulation of extracellular proteases in late stage regeneration. Consistent with the expression results, we found that inhibition of TGFβ signaling blocked OS regeneration. A correlation network was subsequently inferred for all predicted microRNA-mRNA target pairs expressed during regeneration. Network-based clustering associated transcripts into 22 non-overlapping groups, the functional analysis of which showed enrichment of stress response, signaling pathway and extracellular protease categories that could be related to specific microRNAs. Predicted targets of the miR-9 cluster suggest a role in regulating differentiation and the proliferative state of neural progenitors through regulation of the cytoskeleton and cell cycle. PMID:28432214

Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

PubMed

Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

2016-08-26

Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and further contribute to tyrosine kinase inhibitor (e.g., gefitinib) resistance. In summary, we demonstrated that network properties such as network entropy and unbalanced motifs associated with tumor initiation, progression, and anticancer drug responses, suggesting new potential network-based prognostic and predictive measure in cancer.

Mapping one strong 'Ohana: using network analysis and GIS to enhance the effectiveness of a statewide coalition to prevent child abuse and neglect.

PubMed

Cardazone, Gina; U Sy, Angela; Chik, Ivan; Corlew, Laura Kate

2014-06-01

Network analysis and GIS enable the presentation of meaningful data about organizational relationships and community characteristics, respectively. Together, these tools can provide a concrete representation of the ecological context in which coalitions operate, and may help coalitions identify opportunities for growth and enhanced effectiveness. This study uses network analysis and GIS mapping as part of an evaluation of the One Strong 'Ohana (OSO) campaign. The OSO campaign was launched in 2012 via a partnership between the Hawai'i Children's Trust Fund (HCTF) and the Joyful Heart Foundation. The OSO campaign uses a collaborative approach aimed at increasing public awareness of child maltreatment and protective factors that can prevent maltreatment, as well as enhancing the effectiveness of the HCTF Coalition. This study focuses on three elements of the OSO campaign evaluation: (1) Network analysis exploring the relationships between 24 active Coalition member organizations, (2) GIS mapping of responses to a randomized statewide phone survey (n = 1,450) assessing awareness of factors contributing to child maltreatment, and (3) Combined GIS maps and network data, illustrating opportunities for geographically-targeted coalition building and public awareness activities.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

NASA Astrophysics Data System (ADS)

Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

2017-02-01

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

PubMed

Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K; Chowdhury, Rajdeep; Jalan, Sarika

2017-02-03

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Identification of key candidate genes and pathways in hepatitis B virus-associated acute liver failure by bioinformatical analysis

PubMed Central

Lin, Huapeng; Zhang, Qian; Li, Xiaocheng; Wu, Yushen; Liu, Ye; Hu, Yingchun

2018-01-01

Abstract Hepatitis B virus-associated acute liver failure (HBV-ALF) is a rare but life-threatening syndrome that carried a high morbidity and mortality. Our study aimed to explore the possible molecular mechanisms of HBV-ALF by means of bioinformatics analysis. In this study, genes expression microarray datasets of HBV-ALF from Gene Expression Omnibus were collected, and then we identified differentially expressed genes (DEGs) by the limma package in R. After functional enrichment analysis, we constructed the protein–protein interaction (PPI) network by the Search Tool for the Retrieval of Interacting Genes online database and weighted genes coexpression network by the WGCNA package in R. Subsequently, we picked out the hub genes among the DEGs. A total of 423 DEGs with 198 upregulated genes and 225 downregulated genes were identified between HBV-ALF and normal samples. The upregulated genes were mainly enriched in immune response, and the downregulated genes were mainly enriched in complement and coagulation cascades. Orosomucoid 1 (ORM1), orosomucoid 2 (ORM2), plasminogen (PLG), and aldehyde oxidase 1 (AOX1) were picked out as the hub genes that with a high degree in both PPI network and weighted genes coexpression network. The weighted genes coexpression network analysis found out 3 of the 5 modules that upregulated genes enriched in were closely related to immune system. The downregulated genes enriched in only one module, and the genes in this module majorly enriched in the complement and coagulation cascades pathway. In conclusion, 4 genes (ORM1, ORM2, PLG, and AOX1) with immune response and the complement and coagulation cascades pathway may take part in the pathogenesis of HBV-ALF, and these candidate genes and pathways could be therapeutic targets for HBV-ALF. PMID:29384847

Antiviral Potential of ERK/MAPK and PI3K/AKT/mTOR Signaling Modulation for Middle East Respiratory Syndrome Coronavirus Infection as Identified by Temporal Kinome Analysis

PubMed Central

Ork, Britini; Hart, Brit J.; Holbrook, Michael R.; Frieman, Matthew B.; Traynor, Dawn; Johnson, Reed F.; Dyall, Julie; Olinger, Gene G.; Hensley, Lisa E.

2014-01-01

Middle East respiratory syndrome coronavirus (MERS-CoV) is a lineage C betacoronavirus, and infections with this virus can result in acute respiratory syndrome with renal failure. Globally, MERS-CoV has been responsible for 877 laboratory-confirmed infections, including 317 deaths, since September 2012. As there is a paucity of information regarding the molecular pathogenesis associated with this virus or the identities of novel antiviral drug targets, we performed temporal kinome analysis on human hepatocytes infected with the Erasmus isolate of MERS-CoV with peptide kinome arrays. bioinformatics analysis of our kinome data, including pathway overrepresentation analysis (ORA) and functional network analysis, suggested that extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) and phosphoinositol 3-kinase (PI3K)/serine-threonine kinase (AKT)/mammalian target of rapamycin (mTOR) signaling responses were specifically modulated in response to MERS-CoV infection in vitro throughout the course of infection. The overrepresentation of specific intermediates within these pathways determined by pathway and functional network analysis of our kinome data correlated with similar patterns of phosphorylation determined through Western blot array analysis. In addition, analysis of the effects of specific kinase inhibitors on MERS-CoV infection in tissue culture models confirmed these cellular response observations. Further, we have demonstrated that a subset of licensed kinase inhibitors targeting the ERK/MAPK and PI3K/AKT/mTOR pathways significantly inhibited MERS-CoV replication in vitro whether they were added before or after viral infection. Taken together, our data suggest that ERK/MAPK and PI3K/AKT/mTOR signaling responses play important roles in MERS-CoV infection and may represent novel drug targets for therapeutic intervention strategies. PMID:25487801

Advanced Fault Diagnosis Methods in Molecular Networks

PubMed Central

Habibi, Iman; Emamian, Effat S.; Abdi, Ali

2014-01-01

Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

Responses to auxin signals: an operating principle for dynamical sensitivity yet high resilience

PubMed Central

Bravi, B.; Martin, O. C.

2018-01-01

Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses. PMID:29410878

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, Mark; Wootton, J. Timothy; Doak, Daniel F.; Emmerson, Mark; Estes, James A.; Tinker, M. Timothy

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (∼25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.

SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

PubMed

Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

2015-07-29

Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th . SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

Molecular networks discriminating mouse bladder responses to intravesical bacillus Calmette-Guerin (BCG), LPS, and TNF-α

PubMed Central

Saban, Marcia R; O'Donnell, Michael A; Hurst, Robert E; Wu, Xue-Ru; Simpson, Cindy; Dozmorov, Igor; Davis, Carole; Saban, Ricardo

2008-01-01

Background Despite being a mainstay for treating superficial bladder carcinoma and a promising agent for interstitial cystitis, the precise mechanism of Bacillus Calmette-Guerin (BCG) remains poorly understood. It is particularly unclear whether BCG is capable of altering gene expression in the bladder target organ beyond its well-recognized pro-inflammatory effects and how this relates to its therapeutic efficacy. The objective of this study was to determine differentially expressed genes in the mouse bladder following chronic intravesical BCG therapy and to compare the results to non-specific pro inflammatory stimuli (LPS and TNF-α). For this purpose, C57BL/6 female mice received four weekly instillations of BCG, LPS, or TNF-α. Seven days after the last instillation, the urothelium along with the submucosa was removed from detrusor muscle and the RNA was extracted from both layers for cDNA array experiments. Microarray results were normalized by a robust regression analysis and only genes with an expression above a conditional threshold of 0.001 (3SD above background) were selected for analysis. Next, genes presenting a 3-fold ratio in regard to the control group were entered in Ingenuity Pathway Analysis (IPA) for a comparative analysis in order to determine genes specifically regulated by BCG, TNF-α, and LPS. In addition, the transcriptome was precipitated with an antibody against RNA polymerase II and real-time polymerase chain reaction assay (Q-PCR) was used to confirm some of the BCG-specific transcripts. Results Molecular networks of treatment-specific genes generated several hypotheses regarding the mode of action of BCG. BCG-specific genes involved small GTPases and BCG-specific networks overlapped with the following canonical signaling pathways: axonal guidance, B cell receptor, aryl hydrocarbon receptor, IL-6, PPAR, Wnt/β-catenin, and cAMP. In addition, a specific detrusor network expressed a high degree of overlap with the development of the lymphatic system. Interestingly, TNF-α-specific networks overlapped with the following canonical signaling pathways: PPAR, death receptor, and apoptosis. Finally, LPS-specific networks overlapped with the LPS/IL-1 mediated inhibition of RXR. Because NF-kappaB occupied a central position in several networks, we further determined whether this transcription factor was part of the responses to BCG. Electrophoretic mobility shift assays confirmed the participation of NF-kappaB in the mouse bladder responses to BCG. In addition, BCG treatment of a human urothelial cancer cell line (J82) also increased the binding activity of NF-kappaB, as determined by precipitation of the chromatin by a NF-kappaB-p65 antibody and Q-PCR of genes bearing a NF-kappaB consensus sequence. Next, we tested the hypothesis of whether small GTPases such as LRG-47 are involved in the uptake of BCG by the bladder urothelium. Conclusion As expected, BCG treatment induces the transcription of genes belonging to common pro-inflammatory networks. However, BCG also induces unique genes belonging to molecular networks involved in axonal guidance and lymphatic system development within the bladder target organ. In addition, NF-kappaB seems to play a predominant role in the bladder responses to BCG therapy. Finally, in intact urothelium, BCG-GFP internalizes in LRG-47-positive vesicles. These results provide a molecular framework for the further study of the involvement of immune and nervous systems in the bladder responses to BCG therapy. PMID:18267009

Network meta-analysis of first- and second-generation protease inhibitors for chronic hepatitis C genotype 1: efficacy based on RVR and SVR 24.

PubMed

Borba, Helena H; Wiens, Astrid; Steimbach, Laiza M; Perlin, Cassio M; Tonin, Fernanda S; Pedroso, Maria L A; Fernandez-Llimos, Fernando; Pontarolo, Roberto

2017-01-01

This study aimed to compare the efficacy among direct-acting antiviral agents (first and second-generation direct-acting antiviral agents (DAAs)) with placebo and with standard dual therapy (pegylated interferon + ribavirin (Peg-IFN + RBV)) in terms of rapid virologic response (RVR) and sustained virologic response (SVR) in chronic hepatitis C genotype 1 treatment. We performed a systematic review of randomized controlled trials (RCTs) in MEDLINE, International Pharmaceutical Abstracts, Cochrane Library, SCIELO, and Scopus and conducted a network meta-analysis to compare the efficacy of boceprevir (BOC), daclatasvir (DCV), grazoprevir, simeprevir (SMV) and telaprevir (TVR), in treatment-naive and treatment-experienced patients. Sixteen studies encompassing 7171 patients were analysed. Associations between DAAs therapies (IFN-free regimens) could not be addressed since no common comparator was found in the RCTs among these associations and the other agents included in the present analysis. All agents were more efficacious than placebo or Peg-IFN + RBV in terms of RVR, while only BOC and SMV showed statistically significant superiority for the SVR outcome when compared to placebo or standard dual therapy. No significant differences between the DAAs were observed. The analysis prioritized treatment with DCV for both efficacy outcomes. Node-splitting analysis showed that our networks are robust (p > 0.05). The superiority of DAAs over placebo or standard dual therapy with Peg-IFN + RBV was confirmed, indicating the greater efficacy of DCV. This study is the first network meta-analysis that included RVR as an outcome in the evaluation of these agents via indirect comparison. Further investigation should be carried out addressing safety and tolerability outcomes.

Bulgarian Seismological and GPS/GNSS networks-current status and practical implementation

NASA Astrophysics Data System (ADS)

Solakov, Dimcho; Simeonova, Stela; Georgiev, Ivan; Dimitrova, Lilia; Slavcheva, Krasimira; Raykova, Plamena

2016-04-01

The scientific information is the latest and one of the best bedrock on which effective policy to combat and cope with natural disasters have to be built. Understanding, monitoring and information for future natural disasters are the way to assist the government and society. Different types of networks provide reliable information on various natural disasters. For example, one of the main priorities of the networks are directed to study seismicity of the Earth, its physical phenomena and fields - with an emphasis on tectonic movements and related risk processes, global changes, rotation and position of the Earth in space. Therefore seismological network using advanced electronic systems and digital seismographs transmission of signals from seismic stations to the centres and the registration, processing and archiving of information is carried out by a specialized computer system. Thus improve the monitoring and analysis of seismicity in the whole plan. Another type networks as permanent GPS/GNSS networks are associated with processing and data analysis, as well as monitoring of recent movements of the earth crust. In this study we focus on Seismological and GPS/GNSS networks on the territory in Bulgaria. At present NIGGG-BAS runs both Bulgarian seismological and GPS/GNSS networks. The Bulgarian seismological network - NOTSSI (National Operative Telemetric System for Seismological Information) was founded at the end of 1980. The network comprises today 15 permanent seismic stations spanning the entire territory of the country and two local net works that are deployed around the town of Provadia and Kozloduy Nuclear Power Plant in Bulgaria. Since 2005-2006, real-time data exchange between Bulgaria and Greece, Romania, Serbia, Macedonia, Slovakia, Slovenia, Austria and other regional and national seismological data centers was implemented. NIGGG, respectively NOTSSI, is responsible for rapid earthquake determination, public information trough media, and information of responsible governmental authorities if necessary urgent activities to be undertaken. The available infrastructure - permanent GNSS stations, spread all over the country allow performing permanent monitoring of the Earth's crust movements on the basis of the obtained velocities of the permanent stations and the time series with their coordinates. Additional information for the current movements is obtained by the processing and analysis of the regular GNSS measurements of geodynamic network. In the GNSS Analysis Center are acquired, processed and analyzed data from more than 70 permanent stations on Bulgarian territory. In the analysis are included also data from permanent stations on the Balkan Peninsula and from the European Permanent Network. Along with the seismological and geological information, the quantitative assessment of the movements of the Earth's crust is of the substantial importance for monitoring of the active tectonic structures and is the base for the seismic hazard assessment.

Questionable assumptions hampered interpretation of a network meta-analysis of primary care depression treatments.

PubMed

Linde, Klaus; Rücker, Gerta; Schneider, Antonius; Kriston, Levente

2016-03-01

We aimed to evaluate the underlying assumptions of a network meta-analysis investigating which depression treatment works best in primary care and to highlight challenges and pitfalls of interpretation under consideration of these assumptions. We reviewed 100 randomized trials investigating pharmacologic and psychological treatments for primary care patients with depression. Network meta-analysis was carried out within a frequentist framework using response to treatment as outcome measure. Transitivity was assessed by epidemiologic judgment based on theoretical and empirical investigation of the distribution of trial characteristics across comparisons. Homogeneity and consistency were investigated by decomposing the Q statistic. There were important clinical and statistically significant differences between "pure" drug trials comparing pharmacologic substances with each other or placebo (63 trials) and trials including a psychological treatment arm (37 trials). Overall network meta-analysis produced results well comparable with separate meta-analyses of drug trials and psychological trials. Although the homogeneity and consistency assumptions were mostly met, we considered the transitivity assumption unjustifiable. An exchange of experience between reviewers and, if possible, some guidance on how reviewers addressing important clinical questions can proceed in situations where important assumptions for valid network meta-analysis are not met would be desirable. Copyright © 2016 Elsevier Inc. All rights reserved.

A review and guidance for pattern selection in spatiotemporal system

NASA Astrophysics Data System (ADS)

Wang, Chunni; Ma, Jun

2018-03-01

Pattern estimation and selection in media can give important clues to understand the collective response to external stimulus by detecting the observable variables. Both reaction-diffusion systems (RDs) and neuronal networks can be treated as multi-agent systems from molecular level, intrinsic cooperation, competition. An external stimulus or attack can cause collapse of spatial order and distribution, while appropriate noise can enhance the consensus in the spatiotemporal systems. Pattern formation and synchronization stability can bridge isolated oscillators and the network by coupling these nodes with appropriate connection types. As a result, the dynamical behaviors can be detected and discussed by developing different spatial patterns and realizing network synchronization. Indeed, the collective response of network and multi-agent system depends on the local kinetics of nodes and cells. It is better to know the standard bifurcation analysis and stability control schemes before dealing with network problems. In this review, dynamics discussion and synchronization control on low-dimensional systems, pattern formation and synchronization stability on network, wave stability in RDs and neuronal network are summarized. Finally, possible guidance is presented when some physical effects such as polarization field and electromagnetic induction are considered.

Synchronization of networked chaotic oscillators under external periodic driving.

PubMed

Yang, Wenchao; Lin, Weijie; Wang, Xingang; Huang, Liang

2015-03-01

The dynamical responses of a complex system to external perturbations are of both fundamental interest and practical significance. Here, by the model of networked chaotic oscillators, we investigate how the synchronization behavior of a complex network is influenced by an externally added periodic driving. Interestingly, it is found that by a slight change of the properties of the external driving, e.g., the frequency or phase lag between its intrinsic oscillation and external driving, the network synchronizability could be significantly modified. We demonstrate this phenomenon by different network models and, based on the method of master stability function, give an analysis on the underlying mechanisms. Our studies highlight the importance of external perturbations on the collective behaviors of complex networks, and also provide an alternate approach for controlling network synchronization.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx; Campos-Cantón, I.

We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enablemore » future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.« less

Quantifying the spatio-temporal pattern of the ground impact of space weather events using dynamical networks formed from the SuperMAG database of ground based magnetometer stations.

NASA Astrophysics Data System (ADS)

Dods, Joe; Chapman, Sandra; Gjerloev, Jesper

2016-04-01

Quantitative understanding of the full spatial-temporal pattern of space weather is important in order to estimate the ground impact. Geomagnetic indices such as AE track the peak of a geomagnetic storm or substorm, but cannot capture the full spatial-temporal pattern. Observations by the ~100 ground based magnetometers in the northern hemisphere have the potential to capture the detailed evolution of a given space weather event. We present the first analysis of the full available set of ground based magnetometer observations of substorms using dynamical networks. SuperMAG offers a database containing ground station magnetometer data at a cadence of 1min from 100s stations situated across the globe. We use this data to form dynamic networks which capture spatial dynamics on timescales from the fast reconfiguration seen in the aurora, to that of the substorm cycle. Windowed linear cross-correlation between pairs of magnetometer time series along with a threshold is used to determine which stations are correlated and hence connected in the network. Variations in ground conductivity and differences in the response functions of magnetometers at individual stations are overcome by normalizing to long term averages of the cross-correlation. These results are tested against surrogate data in which phases have been randomised. The network is then a collection of connected points (ground stations); the structure of the network and its variation as a function of time quantify the detailed dynamical processes of the substorm. The network properties can be captured quantitatively in time dependent dimensionless network parameters and we will discuss their behaviour for examples of 'typical' substorms and storms. The network parameters provide a detailed benchmark to compare data with models of substorm dynamics, and can provide new insights on the similarities and differences between substorms and how they correlate with external driving and the internal state of the magnetosphere. We can also investigate the solar wind control of the magnetospheric-ionospheric convection system using dynamical networks. The dynamical networks are first interpolated onto a regular grid. Statistically averaged network responses are then formed for a variety of solar wind conditions, including investigating the network response to southward turnings. [1] Dods, J., S. C. Chapman, and J. W. Gjerloev (2015), Network analysis of geomagnetic substorms using the SuperMAG database of ground-based magnetometer stations, J. Geophys. Res. Space Physics, 120, 7774-7784, doi:10.1002/2015JA021456

ERLN Biological Focus Area

EPA Pesticide Factsheets

The Environmental Response Laboratory Network supports the goal to increase national capacity for biological analysis of environmental samples. This includes methods development and verification, technology transfer, and collaboration with USDA, FERN, CDC.

A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatment-seeking veterans.

PubMed

Ross, Jana; Murphy, Dominic; Armour, Cherie

2018-05-28

Network analysis is a relatively new methodology for studying psychological disorders. It focuses on the associations between individual symptoms which are hypothesized to mutually interact with each other. The current study represents the first network analysis conducted with treatment-seeking military veterans in UK. The study aimed to examine the network structure of posttraumatic stress disorder (PTSD) symptoms and four domains of functional impairment by identifying the most central (i.e., important) symptoms of PTSD and by identifying those symptoms of PTSD that are related to functional impairment. Participants were 331 military veterans with probable PTSD. In the first step, a network of PTSD symptoms based on the PTSD Checklist for DSM-5 was estimated. In the second step, functional impairment items were added to the network. The most central symptoms of PTSD were recurrent thoughts, nightmares, negative emotional state, detachment and exaggerated startle response. Functional impairment was related to a number of different PTSD symptoms. Impairments in close relationships were associated primarily with the negative alterations in cognitions and mood symptoms and impairments in home management were associated primarily with the reexperiencing symptoms. The results are discussed in relation to previous PTSD network studies and include implications for clinical practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.

PubMed

Li, Yongsheng; Sahni, Nidhi; Yi, Song

2016-11-29

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

Anticipating flash-floods: Multi-scale aspects of the social response

NASA Astrophysics Data System (ADS)

Lutoff, Céline; Creutin, Jean-Dominique; Ruin, Isabelle; Borga, Marco

2016-10-01

This paper aims at exploring the anticipation phase before a flash flood, corresponding to the time between the first climatic signs and the peak-flow. We focus the analysis on people's behaviors observing how they use this period to organize themselves for facing the event. The analysis is made through the definition of three specific scales: the timeliness scale, an analytical scale of anticipatory actions and the scale of human response network. Using a cross-scale and cross level analysis enables to define different phases in the anticipation period where different kind of environmental precursors are mobilized by the actors in order to make sense of the situation and adapt. Three main points deserve attention at the end: firstly, the concepts of timeliness, anticipatory actions and crisis network scales enable to understand differently what happens both physically and socially during an extreme event; secondly, analyzing the precursors shows that each level of crisis network uses different kinds of signs for estimating the situation, organizing and reacting; thirdly, there is a potential for improvement in observation on both social and physical processes at different scales, for verifying the theory of the anticipatory phases.

Stochastic analysis of epidemics on adaptive time varying networks

NASA Astrophysics Data System (ADS)

Kotnis, Bhushan; Kuri, Joy

2013-06-01

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Reliability of a Parallel Pipe Network

NASA Technical Reports Server (NTRS)

Herrera, Edgar; Chamis, Christopher (Technical Monitor)

2001-01-01

The goal of this NASA-funded research is to advance research and education objectives in theoretical and computational probabilistic structural analysis, reliability, and life prediction methods for improved aerospace and aircraft propulsion system components. Reliability methods are used to quantify response uncertainties due to inherent uncertainties in design variables. In this report, several reliability methods are applied to a parallel pipe network. The observed responses are the head delivered by a main pump and the head values of two parallel lines at certain flow rates. The probability that the flow rates in the lines will be less than their specified minimums will be discussed.

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

NASA Astrophysics Data System (ADS)

Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

2009-10-01

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

Biocomputional construction of a gene network under acid stress in Synechocystis sp. PCC 6803.

PubMed

Li, Yi; Rao, Nini; Yang, Feng; Zhang, Ying; Yang, Yang; Liu, Han-ming; Guo, Fengbiao; Huang, Jian

2014-01-01

Acid stress is one of the most serious threats that cyanobacteria have to face, and it has an impact at all levels from genome to phenotype. However, very little is known about the detailed response mechanism to acid stress in this species. We present here a general analysis of the gene regulatory network of Synechocystis sp. PCC 6803 in response to acid stress using comparative genome analysis and biocomputational prediction. In this study, we collected 85 genes and used them as an initial template to predict new genes through co-regulation, protein-protein interactions and the phylogenetic profile, and 179 new genes were obtained to form a complete template. In addition, we found that 11 enriched pathways such as glycolysis are closely related to the acid stress response. Finally, we constructed a regulatory network for the intricate relationship of these genes and summarize the key steps in response to acid stress. This is the first time a bioinformatic approach has been taken systematically to gene interactions in cyanobacteria and the elaboration of their cell metabolism and regulatory pathways under acid stress, which is more efficient than a traditional experimental study. The results also provide theoretical support for similar research into environmental stresses in cyanobacteria and possible industrial applications. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

PubMed Central

Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

2006-01-01

Background The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. Description CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. Conclusion CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation. PMID:16478536

Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer.

PubMed

Quigley, David A; Kandyba, Eve; Huang, Phillips; Halliwill, Kyle D; Sjölund, Jonas; Pelorosso, Facundo; Wong, Christine E; Hirst, Gillian L; Wu, Di; Delrosario, Reyno; Kumar, Atul; Balmain, Allan

2016-07-26

Inherited germline polymorphisms can cause gene expression levels in normal tissues to differ substantially between individuals. We present an analysis of the genetic architecture of normal adult skin from 470 genetically unique mice, demonstrating the effect of germline variants, skin tissue location, and perturbation by exogenous inflammation or tumorigenesis on gene signaling pathways. Gene networks related to specific cell types and signaling pathways, including sonic hedgehog (Shh), Wnt, Lgr family stem cell markers, and keratins, differed at these tissue sites, suggesting mechanisms for the differential susceptibility of dorsal and tail skin to development of skin diseases and tumorigenesis. The Pten tumor suppressor gene network is rewired in premalignant tumors compared to normal tissue, but this response to perturbation is lost during malignant progression. We present a software package for expression quantitative trait loci (eQTL) network analysis and demonstrate how network analysis of whole tissues provides insights into interactions between cell compartments and signaling molecules. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Introduction to stream network habitat analysis

USGS Publications Warehouse

Bartholow, John M.; Waddle, Terry J.

1986-01-01

Increasing demands on stream resources by a variety of users have resulted in an increased emphasis on studies that evaluate the cumulative effects of basinwide water management programs. Network habitat analysis refers to the evaluation of an entire river basin (or network) by predicting its habitat response to alternative management regimes. The analysis principally focuses on the biological and hydrological components of the riv er basin, which include both micro- and macrohabitat. (The terms micro- and macrohabitat are further defined and discussed later in this document.) Both conceptual and analytic models are frequently used for simplifying and integrating the various components of the basin. The model predictions can be used in developing management recommendations to preserve, restore, or enhance instream fish habitat. A network habitat analysis should begin with a clear and concise statement of the study objectives and a thorough understanding of the institutional setting in which the study results will be applied. This includes the legal, social, and political considerations inherent in any water management setting. The institutional environment may dictate the focus and level of detail required of the study to a far greater extent than the technical considerations. After the study objectives, including species on interest, and institutional setting are collectively defined, the technical aspects should be scoped to determine the spatial and temporal requirements of the analysis. A macro level approach should be taken first to identify critical biological elements and requirements. Next, habitat availability is quantified much as in a "standard" river segment analysis, with the likely incorporation of some macrohabitat components, such as stream temperature. Individual river segments may be aggregated to represent the networkwide habitat response of alternative water management schemes. Things learned about problems caused or opportunities generated may be fed back to the design of new alternatives, which themselves may be similarly tested. One may get as sophisticated an analysis as the decisionmaking process demands. Figure 1 shows a decision point that asks whether the results from the micro- or macrohabitat models display cumulative or synergistic effects. If they do, then network habitat analysis is the appropriate tool. We are left, however, in a difficult bind. We may not know a priori whether the effects are cumulative or synergistic unless some network-type questions are investigated as part of the scoping process. The next several sections raise issues designed to alert the modeler to relevant questions necessary to address this paradox.

Tracking and imaging humans on heterogeneous infrared sensor arrays for law enforcement applications

NASA Astrophysics Data System (ADS)

Feller, Steven D.; Zheng, Y.; Cull, Evan; Brady, David J.

2002-08-01

We present a plan for the integration of geometric constraints in the source, sensor and analysis levels of sensor networks. The goal of geometric analysis is to reduce the dimensionality and complexity of distributed sensor data analysis so as to achieve real-time recognition and response to significant events. Application scenarios include biometric tracking of individuals, counting and analysis of individuals in groups of humans and distributed sentient environments. We are particularly interested in using this approach to provide networks of low cost point detectors, such as infrared motion detectors, with complex imaging capabilities. By extending the capabilities of simple sensors, we expect to reduce the cost of perimeter and site security applications.

Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis.

PubMed

Ye, Zheng; Doñamayor, Nuria; Münte, Thomas F

2014-02-01

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. congruent endings. Copyright © 2012 Wiley Periodicals, Inc.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

PubMed

Jarnuczak, Andrew F; Eyers, Claire E; Schwartz, Jean-Marc; Grant, Christopher M; Hubbard, Simon J

2015-09-01

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs. © 2015 The Authors. PROTEOMICS published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

DiffNet: automatic differential functional summarization of dE-MAP networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2014-10-01

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility. Copyright © 2014 Elsevier Inc. All rights reserved.

The regulatory network analysis of long noncoding RNAs in human colorectal cancer.

PubMed

Zhang, Yuwei; Tao, Yang; Li, Yang; Zhao, Jinshun; Zhang, Lina; Zhang, Xiaohong; Dong, Changzheng; Xie, Yangyang; Dai, Xiaoyu; Zhang, Xinjun; Liao, Qi

2018-05-01

Colorectal cancer (CRC) is among one of the most prevalent and lethiferous diseases worldwide. Long noncoding RNAs (lncRNAs) are commonly accepted to function as a key regulatory factor in human cancer, but the potential regulatory mechanisms of CRC-associated lncRNA are largely obscure. Here, we integrated several expression profiles to obtain 55 differentially expressed (DE) lncRNAs. We first detected lncRNA interactions with transcription factors, microRNAs, mRNAs, and RNA-binding proteins to construct a regulatory network and then create functional enrichment analyses for them using bioinformatics approaches. We found the upregulated genes in the regulatory network are enriched in cell cycle and DNA damage response, while the downregulated genes are enriched in cell differentiation, cellular response, and cell signaling. We then employed module-based methods to mine several intriguing modules from the overall network, which helps to classify the functions of genes more specifically. Next, we confirmed the validity of our network by comparisons with a randomized network using computational method. Finally, we attempted to annotate lncRNA functions based on the regulatory network, which indicated its potential application. Our study of the lncRNA regulatory network provided significant clues to unveil lncRNAs potential regulatory mechanisms in CRC and laid a foundation for further experimental investigation.

Boundary Spanners, Network Capital and the Rise of Edu-Businesses: The Case of News Corporation and Its Emerging Education Agenda

ERIC Educational Resources Information Center

Hogan, Anna

2015-01-01

This paper provides a critical analysis of News Corporation and argues that through the acquisition of high profile policy actor, Joel Klein, News Corporation has been able to assemble significant "network capital" to position itself as an entity apparently responsible for the public good and with a role to play in public policymaking.…

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

PubMed Central

Baumbach, Jan

2007-01-01

Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at . PMID:17986320

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions.

PubMed

Jackson, Rebecca L; Hoffman, Paul; Pobric, Gorana; Lambon Ralph, Matthew A

2016-02-03

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. Copyright © 2016 Jackson et al.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

PubMed Central

Jackson, Rebecca L.; Hoffman, Paul; Pobric, Gorana

2016-01-01

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing. SIGNIFICANCE STATEMENT Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex. PMID:26843633

Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.

PubMed

Wang, Qian; Lou, Zhifeng; Zhai, Liansuo; Zhao, Haibin

2017-06-01

To identify significant biomarkers for detection of pneumococcal meningitis based on ego network. Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis. By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively. The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

Gene Networks and Functional Features of Gravitropic response in Rice Shoot Bases

NASA Astrophysics Data System (ADS)

Hu, Liwei; Zang, Aiping; Ai, Qianru; Chen, Haiying; Li, Lin; Li, Rui; Su, Feng; Chen, Xijiang; Rong, Hui; Dou, Xianying; Reinhold-Hurek, Barbara; Li, Qi; Cai, Weiming

To delineate key genes and the corresponding physiological functions as well as the coordina-tion of genes involved in the gravitropism of rice shoot bases, we used whole-genome microarray analysis of upper and lower parts of rice shoot bases at 0.5 h and 6 h after gravistimulation. And bio-information analysis was applied including GO-analysis, expression tendency and net-work analysis. In the lower shoot bases, auxin-mediated signaling pathway and glutathione transferase activity with the biggest enrichment were activated at 0.5 h, while cytokinin stimu-lus and photosynthesis were activated at 6 h. Meanwhile, several processes were suppressed in the lower shoot bases, including: xyloglucan:xyloglucosyl transferase activity, glucan metabolic processes, and ATPase activity at 0.5 h; and tRNA isopentenyltransferase activity, and chiti-nase activity, etc. at 6 h. Gene expression profile responding to gravistimulation suggested that the asymmetrically activation of several phytohormone signaling pathways including auxin, gib-berellin and cytokinin brassinolide ethylene and cytokinin-related genes were involved in the differentially growth between the upper and lower parts of rice shoot bases, and so do cell wall-related genes. Topological analysis of the coexpression networks revealed the core statue of AY177699.1(apetala3-like protein) and AK105103.1 at 0.5 h; AK062612.1 (ethylene response factor) and AK099932.1 (lectin-like receptor kinase 72) at 6 h. All the core factors have the function "response to endogenous stimulus". Additionally, AK108057.1(similar to germin-like protein precursor) was discovered as the most important core gene in the upper shoot bases in 6h after gravistimualtion while AK067424.1(cellulose synthase-like protein), AK120101.1 (Zinc finger, B-box domain containing protein) and CR278698 (ATPase associated with various cel-lular activities cellulose synthase-like protein) contribute equally to gravitropic response in the lower shoot bases.

Genome-Wide Analysis of the Complex Transcriptional Networks of Rice Developing Seeds

PubMed Central

Xue, Liang-Jiao; Zhang, Jing-Jing; Xue, Hong-Wei

2012-01-01

Background The development of rice (Oryza sativa) seed is closely associated with assimilates storage and plant yield, and is fine controlled by complex regulatory networks. Exhaustive transcriptome analysis of developing rice embryo and endosperm will help to characterize the genes possibly involved in the regulation of seed development and provide clues of yield and quality improvement. Principal Findings Our analysis showed that genes involved in metabolism regulation, hormone response and cellular organization processes are predominantly expressed during rice development. Interestingly, 191 transcription factor (TF)-encoding genes are predominantly expressed in seed and 59 TFs are regulated during seed development, some of which are homologs of seed-specific TFs or regulators of Arabidopsis seed development. Gene co-expression network analysis showed these TFs associated with multiple cellular and metabolism pathways, indicating a complex regulation of rice seed development. Further, by employing a cold-resistant cultivar Hanfeng (HF), genome-wide analyses of seed transcriptome at normal and low temperature reveal that rice seed is sensitive to low temperature at early stage and many genes associated with seed development are down-regulated by low temperature, indicating that the delayed development of rice seed by low temperature is mainly caused by the inhibition of the development-related genes. The transcriptional response of seed and seedling to low temperature is different, and the differential expressions of genes in signaling and metabolism pathways may contribute to the chilling tolerance of HF during seed development. Conclusions These results provide informative clues and will significantly improve the understanding of rice seed development regulation and the mechanism of cold response in rice seed. PMID:22363552

Gabapentin enacarbil, pregabalin and rotigotine are equally effective in restless legs syndrome: a comparative meta-analysis.

PubMed

Iftikhar, I H; Alghothani, L; Trotti, L M

2017-12-01

To synthesize evidence from available randomized controlled trials (RCT) to compare the efficacies of dopaminergic drugs (pramipexole, ropinirole and rotigotine) and α-2-δ ligands (gabapentin enacarbil and pregabalin) for the treatment of restless legs syndrome (RLS). We searched PubMed for all eligible RCTs. Network meta-analysis using frequentist methodology with random effect models was performed for mean changes in scores on the International RLS Study Group Rating Scale (IRLS) and for responder rates on Clinical Global Impressions-Improvement (CGI-I); analyzed as odds ratio (OR). Network meta-analysis of mean changes in IRLS data from 35 studies with 7333 participants showed that all treatments, in specific gabapentin enacarbil, followed by pregabalin and rotigotine were superior to placebo [mean reduction in IRLS scores: -5.31 (-6.74 to -3.87), -5.20 (-6.91 to -3.49), 5.17 (3.73-6.61), respectively] but there were no significant differences between active treatments. Network meta-analysis of 5137 participants from 24 studies showed that gabapentin enacarbil and rotigotine were associated with the highest CGI-I response rates [ORs: 5.68; (95% CI, 4.14-7.21); and 4.68 (2.87-6.49), compared to placebo, respectively]. No significant inter-treatment differences exist, except for that between gabapentin enacarbil and ropinirole. Based on IRLS scores and CGI-I response rates, while gabapentin enacarbil, pregabalin and rotigotine stand out as the most efficacious of all examined drugs, it is noteworthy that no significant inter-treatment differences exist, except for that between gabapentin enacarbil and ropiniriole (for CGI-I response rates). © 2017 EAN.

Responses to a self-presented suicide attempt in social media: a social network analysis.

PubMed

Fu, King-Wa; Cheng, Qijin; Wong, Paul W C; Yip, Paul S F

2013-01-01

The self-presentation of suicidal acts in social media has become a public health concern. This article centers on a Chinese microblogger who posted a wrist-cutting picture that was widely circulated in Chinese social media in 2011. This exploratory study examines written reactions of a group of Chinese microbloggers exposed to the post containing a self-harming message and photo. In addition, we investigate the pattern of information diffusion via a social network. We systematically collected and analyzed 5,971 generated microblogs and the network of information diffusion. We found that a significant portion of written responses (36.6%) could help vulnerable netizens by providing peer-support and calls for help. These responses were reposted and diffused via an online social network with markedly more clusters of users--and at a faster pace-- than a set of randomly generated networks. We conclude that social media can be a double-edged sword: While it may contagiously affect others by spreading suicidal thoughts and acts, it may also play a positive role by assisting people at risk for suicide, providing rescue or support. More research is needed to learn how suicidally vulnerable people interact with online suicide information, and how we can effectively intervene.

System Analysis for the Huntsville Operation Support Center, Distributed Computer System

NASA Technical Reports Server (NTRS)

Ingels, F. M.; Massey, D.

1985-01-01

HOSC as a distributed computing system, is responsible for data acquisition and analysis during Space Shuttle operations. HOSC also provides computing services for Marshall Space Flight Center's nonmission activities. As mission and nonmission activities change, so do the support functions of HOSC change, demonstrating the need for some method of simulating activity at HOSC in various configurations. The simulation developed in this work primarily models the HYPERchannel network. The model simulates the activity of a steady state network, reporting statistics such as, transmitted bits, collision statistics, frame sequences transmitted, and average message delay. These statistics are used to evaluate such performance indicators as throughout, utilization, and delay. Thus the overall performance of the network is evaluated, as well as predicting possible overload conditions.

System identification of a tied arch bridge using reference-based wireless sensor networks

NASA Astrophysics Data System (ADS)

Hietbrink, Colby; Whelan, Matthew J.

2012-04-01

Vibration-based methods of structural health monitoring are generally founded on the principle that localized damage to a structure would exhibit changes within the global dynamic response. Upon this basis, accelerometers provide a unique health monitoring strategy in that a distributed network of sensors provides the technical feasibility to isolate the onset of damage without requiring that any sensor be located exactly on or in close proximity to the damage. While in theory this may be sufficient, practical experience has shown significant improvement in the application of damage diagnostic routines when mode shapes characterized by strongly localized behavior of specific elements are captured by the instrumentation array. In traditional applications, this presents a challenge since the cost and complexity of cable-based systems often effectively limits the number of instrumented locations thereby constraining the modal parameter extraction to only global modal responses. The advent of the low-cost RF chip transceiver with wireless networking capabilities has afforded a means by which a substantial number of output locations can be measured through referencebased testing using large-scale wireless sensor networks. In the current study, this approach was applied to the Prairie du Chien Bridge over the Mississippi River to extract operational mode shapes with high spatial reconstruction, including strongly localized modes. The tied arch bridge was instrumented at over 230 locations with single-axis accelerometers conditioned and acquired over a high-rate lossless wireless sensor network with simultaneous sampling capabilities. Acquisition of the dynamic response of the web plates of the arch rib was specifically targeted within the instrumentation array for diagnostic purposes. Reference-based operational modal analysis of the full structure through data-driven stochastic subspace identification is presented alongside finite element analysis results for confirmation of modal parameter plausibility. Particular emphasis is placed on the identification and reconstruction of modal response with large contribution from the arch rib web plates.

Species co-occurrence analysis predicts management outcomes for multiple threats.

PubMed

Tulloch, Ayesha I T; Chadès, Iadine; Lindenmayer, David B

2018-03-01

Mitigating the impacts of global anthropogenic change on species is conservation's greatest challenge. Forecasting the effects of actions to mitigate threats is hampered by incomplete information on species' responses. We develop an approach to predict community restructuring under threat management, which combines models of responses to threats with network analyses of species co-occurrence. We discover that contributions by species to network co-occurrence predict their recovery under reduction of multiple threats. Highly connected species are likely to benefit more from threat management than poorly connected species. Importantly, we show that information from a few species on co-occurrence and expected responses to alternative threat management actions can be used to train a response model for an entire community. We use a unique management dataset for a threatened bird community to validate our predictions and, in doing so, demonstrate positive feedbacks in occurrence and co-occurrence resulting from shared threat management responses during ecosystem recovery.

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing

PubMed Central

de Anda-Jáuregui, Guillermo; Guo, Kai; McGregor, Brett A.; Hur, Junguk

2018-01-01

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space. PMID:29545755

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing.

PubMed

de Anda-Jáuregui, Guillermo; Guo, Kai; McGregor, Brett A; Hur, Junguk

2018-01-01

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

Systematic review, network meta-analysis and economic evaluation of biological therapy for the management of active psoriatic arthritis.

PubMed

Cawson, Matthew Richard; Mitchell, Stephen Andrew; Knight, Chris; Wildey, Henry; Spurden, Dean; Bird, Alex; Orme, Michelle Elaine

2014-01-20

An updated economic evaluation was conducted to compare the cost-effectiveness of the four tumour necrosis factor (TNF)-α inhibitors adalimumab, etanercept, golimumab and infliximab in active, progressive psoriatic arthritis (PsA) where response to standard treatment has been inadequate. A systematic review was conducted to identify relevant, recently published studies and the new trial data were synthesised, via a Bayesian network meta-analysis (NMA), to estimate the relative efficacy of the TNF-α inhibitors in terms of Psoriatic Arthritis Response Criteria (PsARC) response, Health Assessment Questionnaire (HAQ) scores and Psoriasis Area and Severity Index (PASI). A previously developed economic model was updated with the new meta-analysis results and current cost data. The model was adapted to delineate patients by PASI 50%, 75% and 90% response rates to differentiate between psoriasis outcomes. All four licensed TNF-α inhibitors were significantly more effective than placebo in achieving PsARC response in patients with active PsA. Adalimumab, etanercept and infliximab were significantly more effective than placebo in improving HAQ scores in patients who had achieved a PsARC response and in improving HAQ scores in PsARC non-responders. In an analysis using 1,000 model simulations, on average etanercept was the most cost-effective treatment and, at the National Institute for Health and Care Excellence willingness-to-pay threshold of between £20,000 to £30,000, etanercept is the preferred option. The economic analysis agrees with the conclusions from the previous models, in that biologics are shown to be cost-effective for treating patients with active PsA compared with the conventional management strategy. In particular, etanercept is cost-effective compared with the other biologic treatments.

Robust Selectivity for Faces in the Human Amygdala in the Absence of Expressions

PubMed Central

Mende-Siedlecki, Peter; Verosky, Sara C.; Turk-Browne, Nicholas B.; Todorov, Alexander

2014-01-01

There is a well-established posterior network of cortical regions that plays a central role in face processing and that has been investigated extensively. In contrast, although responsive to faces, the amygdala is not considered a core face-selective region, and its face selectivity has never been a topic of systematic research in human neuroimaging studies. Here, we conducted a large-scale group analysis of fMRI data from 215 participants. We replicated the posterior network observed in prior studies but found equally robust and reliable responses to faces in the amygdala. These responses were detectable in most individual participants, but they were also highly sensitive to the initial statistical threshold and habituated more rapidly than the responses in posterior face-selective regions. A multivariate analysis showed that the pattern of responses to faces across voxels in the amygdala had high reliability over time. Finally, functional connectivity analyses showed stronger coupling between the amygdala and posterior face-selective regions during the perception of faces than during the perception of control visual categories. These findings suggest that the amygdala should be considered a core face-selective region. PMID:23984945

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

PubMed

Gao, Xue-Xin; Gao, Lei; Wang, Jiu-Qiang; Qu, Su-Su; Qu, Yue; Sun, Hong-Lei; Liu, Si-Dang; Shang, Ying-Li

2016-07-12

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Policy Making to Build Relationships: A Grounded Theory Analysis of Interviews and Documents Relating to H1N1, Ebola, and the U.S. Public Health Preparedness Network.

PubMed

Myers, Nathan

2016-01-01

In the last five years, the American public health emergency preparedness and response system has been tested by two significant threats, H1N1 and Ebola. While neither proved as dangerous as initially feared, these viruses highlighted on-going issues with collaborations in the field of public health and health care. Strengths were identified within the network, but also challenges that must be resolved before the U.S. faces a major pandemic. Employing interview data from public health emergency response practitioners and documentary evidence from the H1N1 and Ebola responses, this qualitative analysis uses the grounded theory approach to identify key areas for collaborative improvement. The grounded theory developed calls for a stronger policy framework at the federal level to facilitate more collaboration between U.S. agencies and facilitate more collaboration at the state and local level.

Convergent genetic and expression data implicate immunity in Alzheimer's disease

PubMed Central

Jones, Lesley; Lambert, Jean-Charles; Wang, Li-San; Choi, Seung-Hoan; Harold, Denise; Vedernikov, Alexey; Escott-Price, Valentina; Stone, Timothy; Richards, Alexander; Bellenguez, Céline; Ibrahim-Verbaas, Carla A; Naj, Adam C; Sims, Rebecca; Gerrish, Amy; Jun, Gyungah; DeStefano, Anita L; Bis, Joshua C; Beecham, Gary W; Grenier-Boley, Benjamin; Russo, Giancarlo; Thornton-Wells, Tricia A; Jones, Nicola; Smith, Albert V; Chouraki, Vincent; Thomas, Charlene; Ikram, M Arfan; Zelenika, Diana; Vardarajan, Badri N; Kamatani, Yoichiro; Lin, Chiao-Feng; Schmidt, Helena; Kunkle, Brian; Dunstan, Melanie L; Ruiz, Agustin; Bihoreau, Marie-Thérèse; Reitz, Christiane; Pasquier, Florence; Hollingworth, Paul; Hanon, Olivier; Fitzpatrick, Annette L; Buxbaum, Joseph D; Campion, Dominique; Crane, Paul K; Becker, Tim; Gudnason, Vilmundur; Cruchaga, Carlos; Craig, David; Amin, Najaf; Berr, Claudine; Lopez, Oscar L; De Jager, Philip L; Deramecourt, Vincent; Johnston, Janet A; Evans, Denis; Lovestone, Simon; Letteneur, Luc; Kornhuber, Johanes; Tárraga, Lluís; Rubinsztein, David C; Eiriksdottir, Gudny; Sleegers, Kristel; Goate, Alison M; Fiévet, Nathalie; Huentelman, Matthew J; Gill, Michael; Emilsson, Valur; Brown, Kristelle; Kamboh, M Ilyas; Keller, Lina; Barberger-Gateau, Pascale; McGuinness, Bernadette; Larson, Eric B; Myers, Amanda J; Dufouil, Carole; Todd, Stephen; Wallon, David; Love, Seth; Kehoe, Pat; Rogaeva, Ekaterina; Gallacher, John; George-Hyslop, Peter St; Clarimon, Jordi; Lleὀ, Alberti; Bayer, Anthony; Tsuang, Debby W; Yu, Lei; Tsolaki, Magda; Bossù, Paola; Spalletta, Gianfranco; Proitsi, Petra; Collinge, John; Sorbi, Sandro; Garcia, Florentino Sanchez; Fox, Nick; Hardy, John; Naranjo, Maria Candida Deniz; Razquin, Cristina; Bosco, Paola; Clarke, Robert; Brayne, Carol; Galimberti, Daniela; Mancuso, Michelangelo; Moebus, Susanne; Mecocci, Patrizia; del Zompo, Maria; Maier, Wolfgang; Hampel, Harald; Pilotto, Alberto; Bullido, Maria; Panza, Francesco; Caffarra, Paolo; Nacmias, Benedetta; Gilbert, John R; Mayhaus, Manuel; Jessen, Frank; Dichgans, Martin; Lannfelt, Lars; Hakonarson, Hakon; Pichler, Sabrina; Carrasquillo, Minerva M; Ingelsson, Martin; Beekly, Duane; Alavarez, Victoria; Zou, Fanggeng; Valladares, Otto; Younkin, Steven G; Coto, Eliecer; Hamilton-Nelson, Kara L; Mateo, Ignacio; Owen, Michael J; Faber, Kelley M; Jonsson, Palmi V; Combarros, Onofre; O'Donovan, Michael C; Cantwell, Laura B; Soininen, Hilkka; Blacker, Deborah; Mead, Simon; Mosley, Thomas H; Bennett, David A; Harris, Tamara B; Fratiglioni, Laura; Holmes, Clive; de Bruijn, Renee FAG; Passmore, Peter; Montine, Thomas J; Bettens, Karolien; Rotter, Jerome I; Brice, Alexis; Morgan, Kevin; Foroud, Tatiana M; Kukull, Walter A; Hannequin, Didier; Powell, John F; Nalls, Michael A; Ritchie, Karen; Lunetta, Kathryn L; Kauwe, John SK; Boerwinkle, Eric; Riemenschneider, Matthias; Boada, Mercè; Hiltunen, Mikko; Martin, Eden R; Pastor, Pau; Schmidt, Reinhold; Rujescu, Dan; Dartigues, Jean-François; Mayeux, Richard; Tzourio, Christophe; Hofman, Albert; Nöthen, Markus M; Graff, Caroline; Psaty, Bruce M; Haines, Jonathan L; Lathrop, Mark; Pericak-Vance, Margaret A; Launer, Lenore J; Farrer, Lindsay A; van Duijn, Cornelia M; Van Broekhoven, Christine; Ramirez, Alfredo; Schellenberg, Gerard D; Seshadri, Sudha; Amouyel, Philippe; Holmans, Peter A

2015-01-01

Background Late–onset Alzheimer's disease (AD) is heritable with 20 genes showing genome wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease we extended these genetic data in a pathway analysis. Methods The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (p = 3.27×10-12 after multiple testing correction for pathways), regulation of endocytosis (p = 1.31×10-11), cholesterol transport (p = 2.96 × 10-9) and proteasome-ubiquitin activity (p = 1.34×10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected p 0.002 – 0.05). Conclusions The immune response, regulation of endocytosis, cholesterol transport and protein ubiquitination represent prime targets for AD therapeutics. PMID:25533204

Convergent genetic and expression data implicate immunity in Alzheimer's disease.

PubMed

2015-06-01

Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis. The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 × 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 × 10(-11)), cholesterol transport (P = 2.96 × 10(-9)), and proteasome-ubiquitin activity (P = 1.34 × 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05). The immune response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. Copyright © 2015. Published by Elsevier Inc.

State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study

ERIC Educational Resources Information Center

Janoos, Firdaus; Brown, Gregory; Morocz, Istvan A.; Wells, William M., III

2013-01-01

The neural correlates of "working memory" (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task…

Analyzing Feedback Control Systems

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.

1987-01-01

Interactive controls analysis (INCA) program developed to provide user-friendly environment for design and analysis of linear control systems, primarily feedback control. Designed for use with both small- and large-order systems. Using interactive-graphics capability, INCA user quickly plots root locus, frequency response, or time response of either continuous-time system or sampled-data system. Configuration and parameters easily changed, allowing user to design compensation networks and perform sensitivity analyses in very convenient manner. Written in Pascal and FORTRAN.

Perfume Fragrance Discrimination Using Resistance And Capacitance Responses Of Polymer Sensors

NASA Astrophysics Data System (ADS)

Lima, John Paul Hempel; Vandendriessche, Thomas; Fonseca, Fernando J.; Lammertyn, Jeroen; Nicolai, Bart M.; de Andrade, Adnei Melges

2009-05-01

This work shows a comparison between electrical resistance and capacitance responses of ethanol and five different fragrances using an electronic nose based on conducting polymers. Gas chromatography—mass spectrometry (GC-MS) measurements were performed to evaluate the main differences between the analytes. It is shown that although the fragrances are quite similar in their compositions the sensors are able to discriminate them through PCA (Principal Component Analysis) and ANNs (Artificial Neural Network) analysis.

Social networks help to infer causality in the tumor microenvironment.

PubMed

Crespo, Isaac; Doucey, Marie-Agnès; Xenarios, Ioannis

2016-03-15

Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems--such as the tumor microenvironment--where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Suppression of epidemic spreading in complex networks by local information based behavioral responses.

PubMed

Zhang, Hai-Feng; Xie, Jia-Rong; Tang, Ming; Lai, Ying-Cheng

2014-12-01

The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.

Suppression of epidemic spreading in complex networks by local information based behavioral responses

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Xie, Jia-Rong; Tang, Ming; Lai, Ying-Cheng

2014-12-01

The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.

The salience network causally influences default mode network activity during moral reasoning

PubMed Central

Wilson, Stephen M.; D’Esposito, Mark; Kayser, Andrew S.; Grossman, Scott N.; Poorzand, Pardis; Seeley, William W.; Miller, Bruce L.; Rankin, Katherine P.

2013-01-01

Large-scale brain networks are integral to the coordination of human behaviour, and their anatomy provides insights into the clinical presentation and progression of neurodegenerative illnesses such as Alzheimer’s disease, which targets the default mode network, and behavioural variant frontotemporal dementia, which targets a more anterior salience network. Although the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, patients with Alzheimer’s disease give normal responses to these dilemmas whereas patients with behavioural variant frontotemporal dementia give abnormal responses to these dilemmas. We hypothesized that this apparent discrepancy between activation- and patient-based studies of moral reasoning might reflect a modulatory role for the salience network in regulating default mode network activation. Using functional magnetic resonance imaging to characterize network activity of patients with behavioural variant frontotemporal dementia and healthy control subjects, we present four converging lines of evidence supporting a causal influence from the salience network to the default mode network during moral reasoning. First, as previously reported, the default mode network is recruited when healthy subjects deliberate about ‘personal’ moral dilemmas, but patients with behavioural variant frontotemporal dementia producing atrophy in the salience network give abnormally utilitarian responses to these dilemmas. Second, patients with behavioural variant frontotemporal dementia have reduced recruitment of the default mode network compared with healthy control subjects when deliberating about these dilemmas. Third, a Granger causality analysis of functional neuroimaging data from healthy control subjects demonstrates directed functional connectivity from nodes of the salience network to nodes of the default mode network during moral reasoning. Fourth, this Granger causal influence is diminished in patients with behavioural variant frontotemporal dementia. These findings are consistent with a broader model in which the salience network modulates the activity of other large-scale networks, and suggest a revision to a previously proposed ‘dual-process’ account of moral reasoning. These findings also characterize network interactions underlying abnormal moral reasoning in frontotemporal dementia, which may serve as a model for the aberrant judgement and interpersonal behaviour observed in this disease and in other disorders of social function. More broadly, these findings link recent work on the dynamic interrelationships between large-scale brain networks to observable impairments in dementia syndromes, which may shed light on how diseases that target one network also alter the function of interrelated networks. PMID:23576128

Development and psychometric testing of the clinical networks engagement tool

PubMed Central

Hecker, Kent G.; Rabatach, Leora; Noseworthy, Tom W.; White, Deborah E.

2017-01-01

Background Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks. Methods The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores. Results Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: global engagement, inform (provided with information), involve (worked together to address concerns), and empower (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that inform, involve, and empower were all significant predictors of global engagement, with involve as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores. Conclusions This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement. PMID:28350834

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Exploring pathway interactions in insulin resistant mouse liver

PubMed Central

2011-01-01

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. PMID:21843341

PHILIS (PORTABLE HIGH-THROUGHPUT INTEGRATED LABORATORY IDENTIFICATION SYSTEM)

EPA Pesticide Factsheets

These mobile laboratory assets, for the on-site analysis of chemical warfare agent (CWA) and toxic industrial compound (TIC) contaminated environmental samples, are part of the evolving Environmental Response Laboratory Network (ERLN).

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Modeling learning in brain stem and cerebellar sites responsible for VOR plasticity

NASA Technical Reports Server (NTRS)

Quinn, K. J.; Didier, A. J.; Baker, J. F.; Peterson, B. W.

1998-01-01

A simple model of vestibuloocular reflex (VOR) function was used to analyze several hypotheses currently held concerning the characteristics of VOR plasticity. The network included a direct vestibular pathway and an indirect path via the cerebellum. An optimization analysis of this model suggests that regulation of brain stem sites is critical for the proper modification of VOR gain. A more physiologically plausible learning rule was also applied to this network. Analysis of these simulation results suggests that the preferred error correction signal controlling gain modification of the VOR is the direct output of the accessory optic system (AOS) to the vestibular nuclei vs. a signal relayed through the cerebellum via floccular Purkinje cells. The potential anatomical and physiological basis for this conclusion is discussed, in relation to our current understanding of the latency of the adapted VOR response.

Using the OASES-A to illustrate how network analysis can be applied to understand the experience of stuttering.

PubMed

Siew, Cynthia S Q; Pelczarski, Kristin M; Yaruss, J Scott; Vitevitch, Michael S

Network science uses mathematical and computational techniques to examine how individual entities in a system, represented by nodes, interact, as represented by connections between nodes. This approach has been used by Cramer et al. (2010) to make "symptom networks" to examine various psychological disorders. In the present analysis we examined a network created from the items in the Overall Assessment of the Speaker's Experience of Stuttering-Adult (OASES-A), a commonly used measure for evaluating adverse impact in the lives of people who stutter. The items of the OASES-A were represented as nodes in the network. Connections between nodes were placed if responses to those two items in the OASES-A had a correlation coefficient greater than ±0.5. Several network analyses revealed which nodes were "important" in the network. Several centrally located nodes and "key players" in the network were identified. A community detection analysis found groupings of nodes that differed slightly from the subheadings of the OASES-A. Centrally located nodes and "key players" in the network may help clinicians prioritize treatment. The different community structure found for people who stutter suggests that the way people who stutter view stuttering may differ from the way that scientists and clinicians view stuttering. Finally, the present analyses illustrate how the network approach might be applied to other speech, language, and hearing disorders to better understand how those disorders are experienced and to provide insights for their treatment. Copyright © 2016 Elsevier Inc. All rights reserved.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.

PubMed

Park, Rachel; O'Brien, Thomas F; Huang, Susan S; Baker, Meghan A; Yokoe, Deborah S; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-11-01

While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

Text mining and network analysis to find functional associations of genes in high altitude diseases.

PubMed

Bhasuran, Balu; Subramanian, Devika; Natarajan, Jeyakumar

2018-05-02

Travel to elevations above 2500 m is associated with the risk of developing one or more forms of acute altitude illness such as acute mountain sickness (AMS), high altitude cerebral edema (HACE) or high altitude pulmonary edema (HAPE). Our work aims to identify the functional association of genes involved in high altitude diseases. In this work we identified the gene networks responsible for high altitude diseases by using the principle of gene co-occurrence statistics from literature and network analysis. First, we mined the literature data from PubMed on high-altitude diseases, and extracted the co-occurring gene pairs. Next, based on their co-occurrence frequency, gene pairs were ranked. Finally, a gene association network was created using statistical measures to explore potential relationships. Network analysis results revealed that EPO, ACE, IL6 and TNF are the top five genes that were found to co-occur with 20 or more genes, while the association between EPAS1 and EGLN1 genes is strongly substantiated. The network constructed from this study proposes a large number of genes that work in-toto in high altitude conditions. Overall, the result provides a good reference for further study of the genetic relationships in high altitude diseases. Copyright © 2018 Elsevier Ltd. All rights reserved.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

PubMed Central

Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

2017-01-01

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. PMID:28287094

Identification of Neurodegenerative Factors Using Translatome-Regulatory Network Analysis

PubMed Central

Brichta, Lars; Shin, William; Jackson-Lewis, Vernice; Blesa, Javier; Yap, Ee-Lynn; Walker, Zachary; Zhang, Jack; Roussarie, Jean-Pierre; Alvarez, Mariano J.; Califano, Andrea; Przedborski, Serge; Greengard, Paul

2016-01-01

For degenerative disorders of the central nervous system, the major obstacle to therapeutic advancement has been the challenge of identifying the key molecular mechanisms underlying neuronal loss. We developed a combinatorial approach including translational profiling and brain regulatory network analysis to search for key determinants of neuronal survival or death. Following the generation of transgenic mice for cell type-specific profiling of midbrain dopaminergic neurons, we established and compared translatome libraries reflecting the molecular signature of these cells at baseline or under degenerative stress. Analysis of these libraries by interrogating a context-specific brain regulatory network led to the identification of a repertoire of intrinsic upstream regulators that drive the dopaminergic stress response. The altered activity of these regulators was not associated with changes in their expression levels. This strategy can be generalized for the elucidation of novel molecular determinants involved in the degeneration of other classes of neurons. PMID:26214373

Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia.

PubMed

Oh, Jooyoung; Chun, Ji-Won; Kim, Eunseong; Park, Hae-Jeong; Lee, Boreom; Kim, Jae-Jin

2017-01-01

Patients with schizophrenia exhibit several cognitive deficits, including memory impairment. Problems with recognition memory can hinder socially adaptive behavior. Previous investigations have suggested that altered activation of the frontotemporal area plays an important role in recognition memory impairment. However, the cerebral networks related to these deficits are not known. The aim of this study was to elucidate the brain networks required for recognizing socially relevant information in patients with schizophrenia performing an old-new recognition task. Sixteen patients with schizophrenia and 16 controls participated in this study. First, the subjects performed the theme-identification task during functional magnetic resonance imaging. In this task, pictures depicting social situations were presented with three words, and the subjects were asked to select the best theme word for each picture. The subjects then performed an old-new recognition task in which they were asked to discriminate whether the presented words were old or new. Task performance and neural responses in the old-new recognition task were compared between the subject groups. An independent component analysis of the functional connectivity was performed. The patients with schizophrenia exhibited decreased discriminability and increased activation of the right superior temporal gyrus compared with the controls during correct responses. Furthermore, aberrant network activities were found in the frontopolar and language comprehension networks in the patients. The functional connectivity analysis showed aberrant connectivity in the frontopolar and language comprehension networks in the patients with schizophrenia, and these aberrations possibly contribute to their low recognition performance and social dysfunction. These results suggest that the frontopolar and language comprehension networks are potential therapeutic targets in patients with schizophrenia.

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, M.; Wootton, J.T.; Doak, D.F.; Emmerson, M.; Estes, J.A.; Tinker, M.T.

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (??25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities. ?? 2011 by the Ecological Society of America.

”We own the illness”: a qualitative study of networks in two communities with mixed ethnicity in Northern Norway

PubMed Central

Langås-Larsen, Anette; Salamonsen, Anita; Kristoffersen, Agnete Egilsdatter; Hamran, Torunn; Evjen, Bjørg; Stub, Trine

2018-01-01

ABSTRACT Background: When people in Northern Norway get ill, they often use traditional medicine. The global aim of this study was to examine the extended family networks’ function and responsibility in cases of illness in the family, in two Northern Norwegian communities with a population of mixed ethnicity. Methods: Semi-structured individual interviews with 13 participants and 4 focus group interviews with total 11 participants were conducted. The text data was transcribed verbatim and analysed based on the criteria for content analysis. Results: The participants grew up in areas where it was common to seek help from traditional healers. They were organized in networks and shared responsibility for the patient and they provided practical help and support for the family. According to the networks, health-care personnel should make room for the entire network to visit the patient in severe and life-threatening situations. Conclusion: Traditional networks are an extra resource for people in these communities. The networks seem to be essential in handling and disseminating hope and manageability on an individual as well as a collective level. Health personnel working in communities with mixed ethnicity should have thorough knowledge of the mixed culture, including the importance of traditional network to the patients. PMID:29466927

Monitoring Instrument Performance in Regional Broadband Seismic Network Using Ambient Seismic Noise

NASA Astrophysics Data System (ADS)

Ye, F.; Lyu, S.; Lin, J.

2017-12-01

In the past ten years, the number of seismic stations has increased significantly, and regional seismic networks with advanced technology have been gradually developed all over the world. The resulting broadband data help to improve the seismological research. It is important to monitor the performance of broadband instruments in a new network in a long period of time to ensure the accuracy of seismic records. Here, we propose a method that uses ambient noise data in the period range 5-25 s to monitor instrument performance and check data quality in situ. The method is based on an analysis of amplitude and phase index parameters calculated from pairwise cross-correlations of three stations, which provides multiple references for reliable error estimates. Index parameters calculated daily during a two-year observation period are evaluated to identify stations with instrument response errors in near real time. During data processing, initial instrument responses are used in place of available instrument responses to simulate instrument response errors, which are then used to verify our results. We also examine feasibility of the tailing noise using data from stations selected from USArray in different locations and analyze the possible instrumental errors resulting in time-shifts used to verify the method. Additionally, we show an application that effects of instrument response errors that experience pole-zeros variations on monitoring temporal variations in crustal properties appear statistically significant velocity perturbation larger than the standard deviation. The results indicate that monitoring seismic instrument performance helps eliminate data pollution before analysis begins.

Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections

PubMed Central

Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael

2016-01-01

Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573

OSI Network-layer Abstraction: Analysis of Simulation Dynamics and Performance Indicators

NASA Astrophysics Data System (ADS)

Lawniczak, Anna T.; Gerisch, Alf; Di Stefano, Bruno

2005-06-01

The Open Systems Interconnection (OSI) reference model provides a conceptual framework for communication among computers in a data communication network. The Network Layer of this model is responsible for the routing and forwarding of packets of data. We investigate the OSI Network Layer and develop an abstraction suitable for the study of various network performance indicators, e.g. throughput, average packet delay, average packet speed, average packet path-length, etc. We investigate how the network dynamics and the network performance indicators are affected by various routing algorithms and by the addition of randomly generated links into a regular network connection topology of fixed size. We observe that the network dynamics is not simply the sum of effects resulting from adding individual links to the connection topology but rather is governed nonlinearly by the complex interactions caused by the existence of all randomly added and already existing links in the network. Data for our study was gathered using Netzwerk-1, a C++ simulation tool that we developed for our abstraction.

In silico analysis of high affinity potassium transporter (HKT) isoforms in different plants

PubMed Central

2014-01-01

Background High affinity potassium transporters (HKTs) are located in the plasma membrane of the vessels and have significant influence on salt tolerance in some plants. They exclude Na+ from the parenchyma cells to reduce Na+ concentration. Despite many studies, the underlying regulatory mechanisms and the exact functions of HKTs within different genomic backgrounds are relatively unknown. In this study, various bioinformatics techniques, including promoter analysis, identification of HKT-surrounding genes, and construction of gene networks, were applied to investigate the HKT regulatory mechanism. Results Promoter analysis showed that rice HKTs carry ABA response elements. Additionally, jasmonic acid response elements were detected on promoter region of TmHKT1;5. In silico synteny highlighted several unknown and new loci near rice, Arabidopsis thaliana and Physcomitrella patent HKTs, which may play a significant role in salt stress tolerance in concert with HKTs. Gene network prediction unravelled that crosstalk between jasmonate and ethylene reduces AtHKT1;1 expression. Furthermore, antiporter and transferase proteins were found in AtHKT1;1 gene network. Interestingly, regulatory elements on the promoter region of HKT in wild genotype (TmHKT1;5) were more frequent and variable than the ones in cultivated wheat (TaHKT1;5) which provides the possibility of rapid response and better understanding of environmental conditions for wild genotype. Conclusion Detecting ABA and jasmonic acid response elements on promoter regions of HKTs provide valuable clues on underlying regulatory mechanisms of HKTs. In silico synteny and pathway discovery indicated several candidates which act in concert with HKTs in stress condition. We highlighted different arrangement of regulatory elements on promoter region of wild wheat (TmHKT1;5) compared to bread wheat (TaHKT1;5) in this study. PMID:25279141

Selenium supplementation prevents metabolic and transcriptomic responses to cadmium in mouse lung.

PubMed

Hu, Xin; Chandler, Joshua D; Fernandes, Jolyn; Orr, Michael L; Hao, Li; Uppal, Karan; Neujahr, David C; Jones, Dean P; Go, Young-Mi

2018-04-12

The protective effect of selenium (Se) on cadmium (Cd) toxicity is well documented, but underlying mechanisms are unclear. Male mice fed standard diet were given Cd (CdCl 2 , 18 μmol/L) in drinking water with or without Se (Na 2 SeO 4, 20 μmol/L) for 16 weeks. Lungs were analyzed for Cd concentration, transcriptomics and metabolomics. Data were analyzed with biostatistics, bioinformatics, pathway enrichment analysis, and combined transcriptome-metabolome-wide association study. Mice treated with Cd had higher lung Cd content (1.7 ± 0.4 pmol/mg protein) than control mice (0.8 ± 0.3 pmol/mg protein) or mice treated with Cd and Se (0.4 ± 0.1 pmol/mg protein). Gene set enrichment analysis of transcriptomics data showed that Se prevented Cd effects on inflammatory and myogenesis genes and diminished Cd effects on several other pathways. Similarly, Se prevented Cd-disrupted metabolic pathways in amino acid metabolism and urea cycle. Integrated transcriptome and metabolome network analysis showed that Cd treatment had a network structure with fewer gene-metabolite clusters compared to control. Centrality measurements showed that Se counteracted changes in a group of Cd-responsive genes including Zdhhc11, (protein-cysteine S-palmitoyltransferase), Ighg1 (immunoglobulin heavy constant gamma-1) and associated changes in metabolite concentrations. Co-administration of Se with Cd prevented Cd increase in lung and prevented Cd-associated pathway and network responses of the transcriptome and metabolome. Se protection against Cd toxicity in lung involves complex systems responses. Environmental Cd stimulates proinflammatory and profibrotic signaling. The present results indicate that dietary or supplemental Se could be useful to mitigate Cd toxicity. Published by Elsevier B.V.

Artificial neural network implementation of a near-ideal error prediction controller

NASA Technical Reports Server (NTRS)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

The power of charisma—perceived charisma inhibits the frontal executive network of believers in intercessory prayer

PubMed Central

Stødkilde-Jørgensen, Hans; Geertz, Armin W.; Lund, Torben E.; Roepstorff, Andreas

2011-01-01

This study used functional magnetic resonance imaging to investigate how assumptions about speakers’ abilities changed the evoked BOLD response in secular and Christian participants who received intercessory prayer. We find that recipients’ assumptions about senders’ charismatic abilities have important effects on their executive network. Most notably, the Christian participants deactivated the frontal network consisting of the medial and the dorsolateral prefrontal cortex bilaterally in response to speakers who they believed had healing abilities. An independent analysis across subjects revealed that this deactivation predicted the Christian participants’ subsequent ratings of the speakers’ charisma and experience of God’s presence during prayer. These observations point to an important mechanism of authority that may facilitate charismatic influence, a mechanism which is likely to be present in other interpersonal interactions as well. PMID:20228138

Global Landscape of a Co-Expressed Gene Network in Barley and its Application to Gene Discovery in Triticeae Crops

PubMed Central

Mochida, Keiichi; Uehara-Yamaguchi, Yukiko; Yoshida, Takuhiro; Sakurai, Tetsuya; Shinozaki, Kazuo

2011-01-01

Accumulated transcriptome data can be used to investigate regulatory networks of genes involved in various biological systems. Co-expression analysis data sets generated from comprehensively collected transcriptome data sets now represent efficient resources that are capable of facilitating the discovery of genes with closely correlated expression patterns. In order to construct a co-expression network for barley, we analyzed 45 publicly available experimental series, which are composed of 1,347 sets of GeneChip data for barley. On the basis of a gene-to-gene weighted correlation coefficient, we constructed a global barley co-expression network and classified it into clusters of subnetwork modules. The resulting clusters are candidates for functional regulatory modules in the barley transcriptome. To annotate each of the modules, we performed comparative annotation using genes in Arabidopsis and Brachypodium distachyon. On the basis of a comparative analysis between barley and two model species, we investigated functional properties from the representative distributions of the gene ontology (GO) terms. Modules putatively involved in drought stress response and cellulose biogenesis have been identified. These modules are discussed to demonstrate the effectiveness of the co-expression analysis. Furthermore, we applied the data set of co-expressed genes coupled with comparative analysis in attempts to discover potentially Triticeae-specific network modules. These results demonstrate that analysis of the co-expression network of the barley transcriptome together with comparative analysis should promote the process of gene discovery in barley. Furthermore, the insights obtained should be transferable to investigations of Triticeae plants. The associated data set generated in this analysis is publicly accessible at http://coexpression.psc.riken.jp/barley/. PMID:21441235

A network meta-analysis of randomized controlled trials for comparing the effectiveness and safety profile of treatments with marketing authorization for relapsing multiple sclerosis.

PubMed

Hadjigeorgiou, G M; Doxani, C; Miligkos, M; Ziakas, P; Bakalos, G; Papadimitriou, D; Mprotsis, T; Grigoriadis, N; Zintzaras, E

2013-12-01

The relative effectiveness and safety profile of the treatments with marketing authorization for relapsing multiple sclerosis (MS) are not well known because randomized controlled trials with head-to-head comparisons between these treatments do not exist. Thus, a network of multiple-treatments meta-analysis was performed using four clinical outcomes: 'patients free of relapse', 'patients without disease progression', 'patients without MRI progression' and 'patients with adverse events'. Randomized controlled trials (RCTs) on MS were systematically searched in PubMed and Cochrane Central Register of Controlled Trial. The network analysis performed pairwise comparisons between the marketed treatments (Betaferon 250mcg, Avonex 30mcg, Rebif 44mcg, Rebif 22mcg, Aubagio 7 mg, Aubagio 14 mg, Copaxone 20 mg, Tysabri 300 mg, Gilenya 0·5 mg and Novantrone 12 mg/m(2)) using direct and indirect analyses. The analysis included 48 articles, involving 20 455 patients with MS. The direct analysis showed better response for more than one outcome for Gilenya compared with Avonex ('patients free of relapse' and 'patients without MRI progression') and for Betaferon compared with Avonex ('patients without disease progression' and 'patients without MRI progression'). The indirect analysis indicated that Tysabri may have better relative effectiveness compared with the other treatments for two outcomes: 'patients free of relapse' and 'patients without MRI progression'. Regarding 'patients with adverse events', no data were available for all comparisons to make fair inferences. This was an attempt, for the first time, to compare the efficacy and safety profile of existing approved treatments for relapsing MS. Although some treatments have shown better response, the results of the network analysis should be interpreted with caution because of the lack of RCTs with head-to-head comparisons between treatments. © 2013 John Wiley & Sons Ltd.

Dynamic networks of PTSD symptoms during conflict.

PubMed

Greene, Talya; Gelkopf, Marc; Epskamp, Sacha; Fried, Eiko

2018-02-28

Conceptualizing posttraumatic stress disorder (PTSD) symptoms as a dynamic system of causal elements could provide valuable insights into the way that PTSD develops and is maintained in traumatized individuals. We present the first study to apply a multilevel network model to produce an exploratory empirical conceptualization of dynamic networks of PTSD symptoms, using data collected during a period of conflict. Intensive longitudinal assessment data were collected during the Israel-Gaza War in July-August 2014. The final sample (n = 96) comprised a general population sample of Israeli adult civilians exposed to rocket fire. Participants completed twice-daily reports of PTSD symptoms via smartphone for 30 days. We used a multilevel vector auto-regression model to produce contemporaneous and temporal networks, and a partial correlation network model to obtain a between-subjects network. Multilevel network analysis found strong positive contemporaneous associations between hypervigilance and startle response, avoidance of thoughts and avoidance of reminders, and between flashbacks and emotional reactivity. The temporal network indicated the central role of startle response as a predictor of future PTSD symptomatology, together with restricted affect, blame, negative emotions, and avoidance of thoughts. There were some notable differences between the temporal and contemporaneous networks, including the presence of a number of negative associations, particularly from blame. The between-person network indicated flashbacks and emotional reactivity to be the most central symptoms. This study suggests various symptoms that could potentially be driving the development of PTSD. We discuss clinical implications such as identifying particular symptoms as targets for interventions.

Decreased functional connectivity to posterior cingulate cortex in major depressive disorder.

PubMed

Yang, Rui; Gao, Chengge; Wu, Xiaoping; Yang, Junle; Li, Shengbin; Cheng, Hu

2016-09-30

The default mode network (DMN) and its interaction with other key networks such as the salience network and executive network are keys to understand psychiatric and neurological disorders including major depressive disorder (MDD). In this study, we combined independent component analysis and seed based connectivity analysis to study the posterior default mode network between 20 patients with MDD and 25 normal controls, as well as pre-treatment and post-treatment conditions of the patients. Both correlated and anti-correlated networks centered at the posterior cingulate cortex (PCC) were examined (PCC+ and PCC-). Our results showed aberrant functional connectivity of the PCC+ and PCC- networks between patients and normal controls. Specifically, normal controls exhibited significantly higher connectivity between the PCC and frontal/temporal regions for the PCC+ network and stronger connectivity strength between the PCC and the insula/middle frontal cortex for the PCC- network. The overall connectivity strength of the PCC+ and PCC- networks was also significantly lower in MDD. Because the PCC is a hub in the DMN that interacts with other networks, our result suggested a stronger interaction between the DMN and the salience network but a weak interaction between the DMN and the executive network in MDD. The treatment using sertraline did increase the functional connectivity strength, especially in the PCC+ network. Despite a large inter-subject variability in the overall connectivity strengths and change of the PCC network in response to the treatment, a high correlation between change of connectivity strength and the Hamilton depression score was observed for both the PCC+ and PCC- network. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Data Mining of Network Logs

NASA Technical Reports Server (NTRS)

Collazo, Carlimar

2011-01-01

The statement of purpose is to analyze network monitoring logs to support the computer incident response team. Specifically, gain a clear understanding of the Uniform Resource Locator (URL) and its structure, and provide a way to breakdown a URL based on protocol, host name domain name, path, and other attributes. Finally, provide a method to perform data reduction by identifying the different types of advertisements shown on a webpage for incident data analysis. The procedures used for analysis and data reduction will be a computer program which would analyze the URL and identify and advertisement links from the actual content links.

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

PubMed

Bruggeman, F J; Snoep, J L; Westerhoff, H V

2008-11-01

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows.

PubMed

Tapio, Ilma; Fischer, Daniel; Blasco, Lucia; Tapio, Miika; Wallace, R John; Bayat, Ali R; Ventto, Laura; Kahala, Minna; Negussie, Enyew; Shingfield, Kevin J; Vilkki, Johanna

2017-01-01

The ruminal microbiome, comprising large numbers of bacteria, ciliate protozoa, archaea and fungi, responds to diet and dietary additives in a complex way. The aim of this study was to investigate the benefits of increasing the depth of the community analysis in describing and explaining responses to dietary changes. Quantitative PCR, ssu rRNA amplicon based taxa composition, diversity and co-occurrence network analyses were applied to ruminal digesta samples obtained from four multiparous Nordic Red dairy cows fitted with rumen cannulae. The cows received diets with forage:concentrate ratio either 35:65 (diet H) or 65:35 (L), supplemented or not with sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplied in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and four 35-day periods. Digesta samples were collected on days 22 and 24 and combined. QPCR provided a broad picture in which a large fall in the abundance of fungi was seen with SO in the H but not the L diet. Amplicon sequencing showed higher community diversity indices in L as compared to H diets and revealed diet specific taxa abundance changes, highlighting large differences in protozoal and fungal composition. Methanobrevibacter ruminantium and Mbb. gottschalkii dominated archaeal communities, and their abundance correlated negatively with each other. Co-occurrence network analysis provided evidence that no microbial domain played a more central role in network formation, that some minor-abundance taxa were at nodes of highest centrality, and that microbial interactions were diet specific. Networks added new dimensions to our understanding of the diet effect on rumen microbial community interactions.

Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows

PubMed Central

Fischer, Daniel; Blasco, Lucia; Tapio, Miika; Wallace, R. John; Bayat, Ali R.; Ventto, Laura; Kahala, Minna; Negussie, Enyew; Vilkki, Johanna

2017-01-01

The ruminal microbiome, comprising large numbers of bacteria, ciliate protozoa, archaea and fungi, responds to diet and dietary additives in a complex way. The aim of this study was to investigate the benefits of increasing the depth of the community analysis in describing and explaining responses to dietary changes. Quantitative PCR, ssu rRNA amplicon based taxa composition, diversity and co-occurrence network analyses were applied to ruminal digesta samples obtained from four multiparous Nordic Red dairy cows fitted with rumen cannulae. The cows received diets with forage:concentrate ratio either 35:65 (diet H) or 65:35 (L), supplemented or not with sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplied in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and four 35-day periods. Digesta samples were collected on days 22 and 24 and combined. QPCR provided a broad picture in which a large fall in the abundance of fungi was seen with SO in the H but not the L diet. Amplicon sequencing showed higher community diversity indices in L as compared to H diets and revealed diet specific taxa abundance changes, highlighting large differences in protozoal and fungal composition. Methanobrevibacter ruminantium and Mbb. gottschalkii dominated archaeal communities, and their abundance correlated negatively with each other. Co-occurrence network analysis provided evidence that no microbial domain played a more central role in network formation, that some minor-abundance taxa were at nodes of highest centrality, and that microbial interactions were diet specific. Networks added new dimensions to our understanding of the diet effect on rumen microbial community interactions. PMID:28704445

Insights into the etiology-associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas.

PubMed

Seshachalam, Veerabrahma Pratap; Sekar, Karthik; Hui, Kam M

2018-04-19

Hepatitis B virus, hepatitis C virus, alcoholic consumption and non-alcoholic fatty liver are the major known risk factors for Hepatocellular carcinoma (HCC). There have been very few studies comparing the underlying biological mechanisms associated with the different etiologies of HCC. In this study, we hypothesized the existence of different regulatory networks associated with different liver disease etiologies involved in hepatocarcinogenesis. Using upstream regulatory analysis tool in ingenuity pathway analysis software, URs were predicted using differential expressed genes for HCC to facilitate the interrogation of global gene regulation. Analysis of regulatory networks for HBV HCC revealed E2F1 as activated UR, regulating genes involved in cell cycle and DNA replication and HNF4A and HNF1A as inhibited UR. In HCV HCC, IFNG, involved in cellular movement and signaling was activated while IL1RN, MAPK1 involved in IL-22 signaling and immune response was inhibited. In Alcoholic-consumption HCC, ERBB2 involved in inflammatory response and cellular movement was activated, whereas HNF4A, NUPR1 were inhibited. For HCC derived from Non-alcoholic fatty liver disease, miR-1249-5p was activated and NUPR1 involved in cell cycle and apoptosis was inhibited. The prognostic value of representative genes identified in the regulatory networks for HBV HCC can be further validated by an independent HBV HCC dataset established in our laboratory with survival data. Our study identified functionally distinct candidate URs for HCC developed from different etiologic risk factors. Further functional validation studies of these regulatory networks could facilitate the management of HCC towards personalized medicine. This article is protected by copyright. All rights reserved.

Using deep neural networks to augment NIF post-shot analysis

NASA Astrophysics Data System (ADS)

Humbird, Kelli; Peterson, Luc; McClarren, Ryan; Field, John; Gaffney, Jim; Kruse, Michael; Nora, Ryan; Spears, Brian

2017-10-01

Post-shot analysis of National Ignition Facility (NIF) experiments is the process of determining which simulation inputs yield results consistent with experimental observations. This analysis is typically accomplished by running suites of manually adjusted simulations, or Monte Carlo sampling surrogate models that approximate the response surfaces of the physics code. These approaches are expensive and often find simulations that match only a small subset of observables simultaneously. We demonstrate an alternative method for performing post-shot analysis using inverse models, which map directly from experimental observables to simulation inputs with quantified uncertainties. The models are created using a novel machine learning algorithm which automates the construction and initialization of deep neural networks to optimize predictive accuracy. We show how these neural networks, trained on large databases of post-shot simulations, can rigorously quantify the agreement between simulation and experiment. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

PubMed

Popov, Tzvetan; Westner, Britta U; Silton, Rebecca L; Sass, Sarah M; Spielberg, Jeffrey M; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A

2018-05-02

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation. SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample ( n = 96) and replication sample ( n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes. Copyright © 2018 the authors 0270-6474/18/384348-09$15.00/0.

Visualizando el desarrollo de la nanomedicina en México.

PubMed

Robles-Belmont, Eduardo; Gortari-Rabiela, Rebeca de; Galarza-Barrios, Pilar; Siqueiros-García, Jesús Mario; Ruiz-León, Alejandro Arnulfo

2017-01-01

In this article we present a set of different visualizations of Mexico's nanomedicine scientific production data. Visualizations were developed using different methodologies for data analysis and visualization such as social network analysis, geography of science maps, and complex network communities analysis. Results are a multi-dimensional overview of the evolution of nanomedicine in Mexico. Moreover, visualizations allowed to identify trends and patterns of collaboration at the national and international level. Trends are also found in the knowledge structure of themes and disciplines. Finally, we identified the scientific communities in Mexico that are responsible for the new knowledge production in this emergent field of science. Copyright: © 2017 SecretarÍa de Salud

Collective Dynamics for Heterogeneous Networks of Theta Neurons

NASA Astrophysics Data System (ADS)

Luke, Tanushree

Collective behavior in neural networks has often been used as an indicator of communication between different brain areas. These collective synchronization and desynchronization patterns are also considered an important feature in understanding normal and abnormal brain function. To understand the emergence of these collective patterns, I create an analytic model that identifies all such macroscopic steady-states attainable by a network of Type-I neurons. This network, whose basic unit is the model "theta'' neuron, contains a mixture of excitable and spiking neurons coupled via a smooth pulse-like synapse. Applying the Ott-Antonsen reduction method in the thermodynamic limit, I obtain a low-dimensional evolution equation that describes the asymptotic dynamics of the macroscopic mean field of the network. This model can be used as the basis in understanding more complicated neuronal networks when additional dynamical features are included. From this reduced dynamical equation for the mean field, I show that the network exhibits three collective attracting steady-states. The first two are equilibrium states that both reflect partial synchronization in the network, whereas the third is a limit cycle in which the degree of network synchronization oscillates in time. In addition to a comprehensive identification of all possible attracting macro-states, this analytic model permits a complete bifurcation analysis of the collective behavior of the network with respect to three key network features: the degree of excitability of the neurons, the heterogeneity of the population, and the overall coupling strength. The network typically tends towards the two macroscopic equilibrium states when the neuron's intrinsic dynamics and the network interactions reinforce each other. In contrast, the limit cycle state, bifurcations, and multistability tend to occur when there is competition between these network features. I also outline here an extension of the above model where the neurons' excitability now varies in time sinuosoidally, thus simulating a parabolic bursting network. This time-varying excitability can lead to the emergence of macroscopic chaos and multistability in the collective behavior of the network. Finally, I expand the single population model described above to examine a two-population neuronal network where each population has its own unique mixture of excitable and spiking neurons, as well as its own coupling strength (either excitatory or inhibitory in nature). Specifically, I consider the situation where the first population is only allowed to influence the second population without any feedback, thus effectively creating a feed-forward "driver-response" system. In this special arrangement, the driver's asymptotic macroscopic dynamics are fully explored in the comprehensive analysis of the single population. Then, in the presence of an influence from the driver, the modified dynamics of the second population, which now acts as a response population, can also be fully analyzed. As in the time-varying model, these modifications give rise to richer dynamics to the response population than those found from the single population formalism, including multi-periodicity and chaos.

Neural Network and Response Surface Methodology for Rocket Engine Component Optimization

NASA Technical Reports Server (NTRS)

Vaidyanathan, Rajkumar; Papita, Nilay; Shyy, Wei; Tucker, P. Kevin; Griffin, Lisa W.; Haftka, Raphael; Fitz-Coy, Norman; McConnaughey, Helen (Technical Monitor)

2000-01-01

The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment

PubMed Central

Uddin, Raihan; Singh, Shiva M.

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning. PMID:29066959

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

PubMed

Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.

Mitochondrial network complexity emerges from fission/fusion dynamics.

PubMed

Zamponi, Nahuel; Zamponi, Emiliano; Cannas, Sergio A; Billoni, Orlando V; Helguera, Pablo R; Chialvo, Dante R

2018-01-10

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality.

PubMed

Prell, Christina; Sun, Laixiang; Feng, Kuishuang; Myroniuk, Tyler W

2015-01-01

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries' mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country's boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries' level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990-2010) to estimate the relationships between countries' level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country's level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure.

A Combined Computational and Genetic Approach Uncovers Network Interactions of the Cyanobacterial Circadian Clock.

PubMed

Boyd, Joseph S; Cheng, Ryan R; Paddock, Mark L; Sancar, Cigdem; Morcos, Faruck; Golden, Susan S

2016-09-15

Two-component systems (TCS) that employ histidine kinases (HK) and response regulators (RR) are critical mediators of cellular signaling in bacteria. In the model cyanobacterium Synechococcus elongatus PCC 7942, TCSs control global rhythms of transcription that reflect an integration of time information from the circadian clock with a variety of cellular and environmental inputs. The HK CikA and the SasA/RpaA TCS transduce time information from the circadian oscillator to modulate downstream cellular processes. Despite immense progress in understanding of the circadian clock itself, many of the connections between the clock and other cellular signaling systems have remained enigmatic. To narrow the search for additional TCS components that connect to the clock, we utilized direct-coupling analysis (DCA), a statistical analysis of covariant residues among related amino acid sequences, to infer coevolution of new and known clock TCS components. DCA revealed a high degree of interaction specificity between SasA and CikA with RpaA, as expected, but also with the phosphate-responsive response regulator SphR. Coevolutionary analysis also predicted strong specificity between RpaA and a previously undescribed kinase, HK0480 (herein CikB). A knockout of the gene for CikB (cikB) in a sasA cikA null background eliminated the RpaA phosphorylation and RpaA-controlled transcription that is otherwise present in that background and suppressed cell elongation, supporting the notion that CikB is an interactor with RpaA and the clock network. This study demonstrates the power of DCA to identify subnetworks and key interactions in signaling pathways and of combinatorial mutagenesis to explore the phenotypic consequences. Such a combined strategy is broadly applicable to other prokaryotic systems. Signaling networks are complex and extensive, comprising multiple integrated pathways that respond to cellular and environmental cues. A TCS interaction model, based on DCA, independently confirmed known interactions and revealed a core set of subnetworks within the larger HK-RR set. We validated high-scoring candidate proteins via combinatorial genetics, demonstrating that DCA can be utilized to reduce the search space of complex protein networks and to infer undiscovered specific interactions for signaling proteins in vivo Significantly, new interactions that link circadian response to cell division and fitness in a light/dark cycle were uncovered. The combined analysis also uncovered a more basic core clock, illustrating the synergy and applicability of a combined computational and genetic approach for investigating prokaryotic signaling networks. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Brain networks associated with cognitive and hedonic responses to a meal.

PubMed

Pribic, T; Kilpatrick, L; Ciccantelli, B; Malagelada, C; Accarino, A; Rovira, A; Pareto, D; Mayer, E; Azpiroz, F

2017-06-01

We recently reported interrelated digestive, cognitive, and hedonic responses to a meal. The aim of this study was to identify brain networks related to the hedonic response to eating. Thirty-eight healthy subjects (20-38 age range) were evaluated after a 5-hour fast and after ingestion of a test meal (juice and warm ham and cheese sandwich, 300 mL, 425 kcal). Perceptual and affective responses (satiety, abdominal fullness, digestive well-being, and positive mood), and resting scans of the brain using functional MRI (3T Trio, Siemens, Germany) were evaluated immediately before and after the test meal. A high-order group independent component analysis was performed to investigate ingestion-related changes in the intrinsic connectivity of brain networks, with a focus on thalamic and insular networks. Ingestion induced satiation (3.3±0.4 score increase; P<.001) and abdominal fullness (2.4±0.3 score increase; P<.001). These sensations included an affective dimension involving digestive well-being (2.8±0.3 score increase; P<.001) and positive mood (1.8±0.2 score increase; P<.001). In general, thalamo-cortical connectivity increased with meal ingestion while insular-cortical connectivity mainly decreased. Furthermore, larger meal-induced changes (increase/decrease) in specific thalamic connections were associated with smaller changes in satiety/fullness. In contrast, a larger meal-induced decrease in insular-anterior cingulate cortex connectivity was associated with increased satiety, fullness, and digestive well-being. Perceptual and emotional responses to food intake are related to brain connectivity in defined functional networks. Brain imaging may provide objective biomarkers of subjective effects of meal ingestion. © 2017 John Wiley & Sons Ltd.

Graph theoretical analysis of functional network for comprehension of sign language.

PubMed

Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

2017-09-15

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

Pinning synchronization of memristor-based neural networks with time-varying delays.

PubMed

Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

2017-09-01

In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sensory-Motor Networks Involved in Speech Production and Motor Control: An fMRI Study

PubMed Central

Behroozmand, Roozbeh; Shebek, Rachel; Hansen, Daniel R.; Oya, Hiroyuki; Robin, Donald A.; Howard, Matthew A.; Greenlee, Jeremy D.W.

2015-01-01

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking. PMID:25623499

Contextualization of drug-mediator relations using evidence networks.

PubMed

Tran, Hai Joey; Speyer, Gil; Kiefer, Jeff; Kim, Seungchan

2017-05-31

Genomic analysis of drug response can provide unique insights into therapies that can be used to match the "right drug to the right patient." However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results. In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to "explain" the relation between a drug and a mediator.  RESULTS: We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator. We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.

Experimental Analysis and Computational Modeling of Network States and Drug Responses in the PI3K/Akt/mTOR Network

DTIC Science & Technology

2010-09-01

Fixation was in 2% formaldehyde, followed by permeabilization in 100% methanol at -20 C. Blocking and incubation with primary/secondary antibodies ...was performed in Odyssey blocking buffer (LICOR). Primary antibodies were obtained from Cell Signaling, BD Biosciences, and Santa Cruz...Biotechnology, and Alexa fluor 488-, 555-, or 647-conjugated secondary antibodies were obtained from Invitrogen. Data were collected using an Applied Precision

Topological Patterns for Scalable Representation and Analysis of Dataflow Graphs

DTIC Science & Technology

2011-11-01

dimensional mesh structure. Such a structure is of particular use to model DSP architectures in which data flows across a network of processing elements...ACSSC.1998.751616 3. Andrews, J.G., Ghosh, A., Muhamed, R.: Fundamentals of WiMAX: understanding broad- band wireless networking . Prentice Hall (2007... SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 23 19a. NAME OF RESPONSIBLE PERSON a. REPORT

Positive Feedback Amplifies the Response of Mitochondrial Membrane Potential to Glucose Concentration in Clonal Pancreatic Beta Cells

PubMed Central

GERENCSER, Akos A.; MOOKERJEE, Shona A.; JASTROCH, Martin; BRAND, Martin D.

2016-01-01

Analysis of the cellular mechanisms of metabolic disorders, including type 2 diabetes mellitus, is complicated by the large number of reactions and interactions in metabolic networks. Metabolic control analysis with appropriate modularization is a powerful method for simplifying and analyzing these networks. To analyze control of cellular energy metabolism in adherent cell cultures of the INS-1 832/13 pancreatic β-cell model we adapted our microscopy assay of absolute mitochondrial membrane potential (ΔψM) to a fluorescence microplate reader format, and applied it in conjunction with cell respirometry. In these cells the sensitive response of ΔψM to extracellular glucose concentration drives glucose-stimulated insulin secretion. Using metabolic control analysis we identified the control properties that generate this sensitive response. Force-flux relationships between ΔψM and respiration were used to calculate kinetic responses to ΔψM of processes both upstream (glucose oxidation) and downstream (proton leak and ATP turnover) of ΔψM. The analysis revealed that glucose-evoked ΔψM hyperpolarization is amplified by increased glucose oxidation activity caused by factors downstream of ΔψM. At high glucose, the hyperpolarized ΔψM is stabilized almost completely by the action of glucose oxidation, whereas proton leak also contribute to the homeostatic control of ΔψM at low glucose. These findings suggest a strong positive feedback loop in the regulation of β-cell energetics, and a possible regulatory role of proton leak in the fasting state. Analysis of islet bioenergetics from published cases of type 2 diabetes suggests that disruption of this feedback can explain the damaged bioenergetic response of β-cells to glucose. PMID:27771512

Neurostimulation options for failed back surgery syndrome: The need for rational and objective measurements. Proposal of an international clinical network using an integrated database and health economic analysis: the PROBACK network.

PubMed

Rigoard, P; Slavin, K

2015-03-01

In the context of failed back surgery syndrome (FBSS) treatment, the current practice in neurostimulation varies from center-to-center and most clinical decisions are based on an individual diagnosis. Neurostimulation evaluation tools and pain relief assessment are of major concern, as they now constitute one of the main biases of clinical trials. Moreover, the proliferation of technological devices, in a fertile and unsatisfied market, fosters and only furthers the confusion. There are three options available to apply scientific debates to our daily neurostimulation practice: intentional ignorance, standardized evidence-based practice or alternative data mining approach. In view of the impossibility of conducting multiple randomized clinical trials comparing various devices, one by one, the proposed concept would be to redefine the indications and the respective roles of the various spinal cord and peripheral nerve stimulation devices with large-scale computational modeling/data mining approach, by conducting a multicenter prospective database registry, supported by a clinician's global network called "PROBACK". We chose to specifically analyze 6 parameters: device coverage performance/coverage selectivity/persistence of the long-term electrical response (technical criteria) and comparative mapping of patient pain relief/persistence of the long-term clinical response/safety and complications occurrence (clinical criteria). Two types of analysis will be performed: immediate analysis (including cost analysis) and computational analysis, i.e. demonstration of the robustness of certain correlations of variables, in order to extract response predictors. By creating an international prospective database, the purpose of the PROBACK project was to set up a process of extraction and comparative analysis of data derived from the selection, implantation and follow-up of FBSS patients candidates for implanted neurostimulation. This evaluation strategy should help to change the opinions of each implanter and each health system towards a more rational decision-making approach subtended by mathematical reality. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Switching "on" and "off" the adhesion in stimuli-responsive elastomers.

PubMed

Kaiser, S; Radl, S V; Manhart, J; Ayalur-Karunakaran, S; Griesser, T; Moser, A; Ganser, C; Teichert, C; Kern, W; Schlögl, S

2018-03-28

The present work aims at the preparation of dry adhesives with switchable bonding properties by using the reversible nature of the [4πs+4πs] cycloaddition of anthracenes. Photo-responsive hydrogenated carboxylated nitrile butadiene rubber with photo-responsive pendant anthracene groups is prepared by one-pot synthesis. The formation of 3D networks relies on the photodimerization of the anthracene moieties upon UV exposure (λ > 300 nm). Controlled cleavage of the crosslink sites is achieved by either deep UV exposure (λ = 254 nm) or thermal dissociation at 70 °C. The kinetics of the optical and thermal cleavage routes are compared in thin films using UV-vis spectroscopy and their influence on the reversibility of the network is detailed. Going from thin films to free standing samples the modulation of the network structure and thermo-mechanical properties over repeated crosslinking and cleavage cycles are characterized by low-field NMR spectroscopy and dynamic mechanical analysis. The applicability of the stimuli-responsive networks as adhesives with reversible bonding properties is demonstrated. The results evidence that the reversibility of the crosslinking reaction enables a controlled switching "on" and "off" of adhesion properties. The recovery of the adhesion force amounts to 75 and 80% for photo- and thermal dissociation, respectively. Spatial control of adhesion properties is evidenced by adhesion force mapping experiments of photo-patterned films.

Genome-wide association study and biological pathway analysis of the Eimeria maxima response in broilers.

PubMed

Hamzić, Edin; Buitenhuis, Bart; Hérault, Frédéric; Hawken, Rachel; Abrahamsen, Mitchel S; Servin, Bertrand; Elsen, Jean-Michel; Pinard-van der Laan, Marie-Hélène; Bed'Hom, Bertrand

2015-11-25

Coccidiosis is the most common and costly disease in the poultry industry and is caused by protozoans of the Eimeria genus. The current control of coccidiosis, based on the use of anticoccidial drugs and vaccination, faces serious obstacles such as drug resistance and the high costs for the development of efficient vaccines, respectively. Therefore, the current control programs must be expanded with complementary approaches such as the use of genetics to improve the host response to Eimeria infections. Recently, we have performed a large-scale challenge study on Cobb500 broilers using E. maxima for which we investigated variability among animals in response to the challenge. As a follow-up to this challenge study, we performed a genome-wide association study (GWAS) to identify genomic regions underlying variability of the measured traits in the response to Eimeria maxima in broilers. Furthermore, we conducted a post-GWAS functional analysis to increase our biological understanding of the underlying response to Eimeria maxima challenge. In total, we identified 22 single nucleotide polymorphisms (SNPs) with q value <0.1 distributed across five chromosomes. The highly significant SNPs were associated with body weight gain (three SNPs on GGA5, one SNP on GGA1 and one SNP on GGA3), plasma coloration measured as optical density at wavelengths in the range 465-510 nm (10 SNPs and all on GGA10) and the percentage of β2-globulin in blood plasma (15 SNPs on GGA1 and one SNP on GGA2). Biological pathways related to metabolic processes, cell proliferation, and primary innate immune processes were among the most frequent significantly enriched biological pathways. Furthermore, the network-based analysis produced two networks of high confidence, with one centered on large tumor suppressor kinase 1 (LATS1) and 2 (LATS2) and the second involving the myosin heavy chain 6 (MYH6). We identified several strong candidate genes and genomic regions associated with traits measured in response to Eimeria maxima in broilers. Furthermore, the post-GWAS functional analysis indicates that biological pathways and networks involved in tissue proliferation and repair along with the primary innate immune response may play the most important role during the early stage of Eimeria maxima infection in broilers.

Network analysis applications in hydrology

NASA Astrophysics Data System (ADS)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

Mechanisms of CCl4-induced liver fibrosis with combined transcriptomic and proteomic analysis.

PubMed

Dong, Shu; Chen, Qi-Long; Song, Ya-Nan; Sun, Yang; Wei, Bin; Li, Xiao-Yan; Hu, Yi-Yang; Liu, Ping; Su, Shi-Bing

2016-01-01

The classic toxicity of carbon tetrachloride (CCl4) is to induce liver lesion and liver fibrosis. Liver fibrosis is a consequence of chronic liver lesion, which can progress into liver cirrhosis even hepatocarcinoma. However, the toxicological mechanisms of CCl4-induced liver fibrosis remain not fully understood. We combined transcriptomic and proteomic analysis and biological network technology, predicted toxicological targets and regulatory networks of CCl4 in liver fibrosis. Wistar rats were treated with CCl4 for 9 weeks. Histopathological changes, hydroxyproline (Hyp) contents, serum ALT and AST in the CCl4-treated group were significantly higher than that of CCl4-untreated group. CCl4-treated and -untreated liver tissues were examined by microarray and iTRAQ. The results showed that 3535 genes (fold change ≥ 1.5, P < 0.05) and 1412 proteins (fold change ≥ 1.2, P < 0.05) were differentially expressed. Moreover, the integrative analysis of transcriptomics and proteomics data showed 523 overlapped proteins, enriched in 182 GO terms including oxidation reduction, response to oxidative stress, inflammatory response, extracellular matrix organization, etc. Furthermore, KEGG pathway analysis showed that 36 pathways including retinol metabolism, PPAR signaling pathway, glycolysis/gluconeogenesis, arachidonic acid metabolism, metabolism of xenobiotics by cytochrome P450 and drug metabolism. Network of protein-protein interaction (PPI) and key function with their related targets were performed and the degree of network was calculated with Cytoscape. The expression of key targets such as CYP4A3, ALDH2 and ALDH7A1 decreased after CCl4 treatment. Therefore, the toxicological mechanisms of CCl4-induced liver fibrosis may be related with multi biological process, pathway and targets which may provide potential protection reaction mechanism for CCl4 detoxication in the liver.

Social Network Analysis of Crowds

DTIC Science & Technology

2009-08-06

crowd responses to non-lethal weapons d tan sys ems – Prior, existing social relationships – Real time social interactions – Formal/informal...Crowd Behavior Testbed Layout Video Cameras on Trusses Importance of Social Factors • Response to non-lethal weapons fire depends on social ... relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time Social Interactions – Formal/Informal Hierarchies • Therefore

An Approach and Instrumentation for Management System Analysis

DTIC Science & Technology

1974-10-01

Benefit Analysis Systems Analysis Manpower Planning Resource Planning Information Theory 20. ABSTRACT (Conlliwa on ravaraa alda It nacaaaary...participants the data necessary to trace both formal and informal information flows and make cost- benefit judgments about specific communications. The...network within a manage- ment structure and to provide a basis tor preliminary cost- benefit evaluations. This objective was in response to Phase I of the

Informal Training in Staff Networks to Support Dissemination of Health Promotion Programs

PubMed Central

Ramanadhan, Shoba; Wiecha, Jean L.; Gortmaker, Steven L.; Emmons, Karen M.; Viswanath, Kasisomayajula

2011-01-01

Purpose To study informal skill transfer via staff networks as a complement to formal training among afterschool childcare providers implementing a health promotion program. Design Cross-sectional, sociometric network analysis. Setting Boston Young Men’s Christian Association (YMCA) afterschool programs implementing the iPLAY program. Participants All 91 staff members at 20 sites were eligible; 80 completed the survey (88% response rate). Measures At the network level, network density measured system-level connectedness. At the staff level, the independent variable was out degree, the number of individuals to whom respondents noted a program-related connection. The dependent variable was skill gains, the number of key implementation skills gained from the network. Analysis We mapped the staff program-related social network. We utilized multiple linear regression to estimate the relationship between out degree and skill gains, and we adjusted for clustering of staff in sites. Results Most staff (77%) reported gaining at least one skill from the network, but only 2% of potential network connections were established. The regression model showed that out degree (i.e., number of program-related contacts) was significantly associated with skill gains (β = .48, p < .01) independent of other variables. Conclusion Informal skill transfer in staff networks may be a useful complement to formal training for implementation of health promotion programs, but informal skill transfer was likely underutilized in this network. Future research employing longitudinal and/or multisite data should examine these findings in greater detail. PMID:20809826

Computational Modeling of Allosteric Regulation in the Hsp90 Chaperones: A Statistical Ensemble Analysis of Protein Structure Networks and Allosteric Communications

PubMed Central

Blacklock, Kristin; Verkhivker, Gennady M.

2014-01-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks. PMID:24922508

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications.

PubMed

Blacklock, Kristin; Verkhivker, Gennady M

2014-06-01

A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple communication routes. This may be a universal requirement encoded in protein structures to balance the inherent tension between resilience and efficiency of the residue interaction networks.

The simple neuroendocrine-immune regulatory network in oyster Crassostrea gigas mediates complex functions.

PubMed

Liu, Zhaoqun; Wang, Lingling; Zhou, Zhi; Sun, Ying; Wang, Mengqiang; Wang, Hao; Hou, Zhanhui; Gao, Dahai; Gao, Qiang; Song, Linsheng

2016-05-19

The neuroendocrine-immune (NEI) regulatory network is a complex system, which plays an indispensable role in the immunity of the host. In the present study, the bioinformatical analysis of the transcriptomic data from oyster Crassostrea gigas and further biological validation revealed that oyster TNF (CgTNF-1 CGI_10018786) could activate the transcription factors NF-κB and HSF (heat shock transcription factor) through MAPK signaling pathway, and then regulate apoptosis, redox reaction, neuro-regulation and protein folding in oyster haemocytes. The activated immune cells then released neurotransmitters including acetylcholine, norepinephrine and [Met(5)]-enkephalin to regulate the immune response by arising the expression of three TNF (CGI_10005109, CGI_10005110 and CGI_10006440) and translocating two NF-κB (Cgp65, CGI_10018142 and CgRel, CGI_10021567) between the cytoplasm and nuclei of haemocytes. Neurotransmitters exhibited the immunomodulation effects by influencing apoptosis and phagocytosis of oyster haemocytes. Acetylcholine and norepinephrine could down-regulate the immune response, while [Met(5)]-enkephalin up-regulate the immune response. These results suggested that the simple neuroendocrine-immune regulatory network in oyster might be activated by oyster TNF and then regulate the immune response by virtue of neurotransmitters, cytokines and transcription factors.

The simple neuroendocrine-immune regulatory network in oyster Crassostrea gigas mediates complex functions

NASA Astrophysics Data System (ADS)

Liu, Zhaoqun; Wang, Lingling; Zhou, Zhi; Sun, Ying; Wang, Mengqiang; Wang, Hao; Hou, Zhanhui; Gao, Dahai; Gao, Qiang; Song, Linsheng

2016-05-01

The neuroendocrine-immune (NEI) regulatory network is a complex system, which plays an indispensable role in the immunity of the host. In the present study, the bioinformatical analysis of the transcriptomic data from oyster Crassostrea gigas and further biological validation revealed that oyster TNF (CgTNF-1 CGI_10018786) could activate the transcription factors NF-κB and HSF (heat shock transcription factor) through MAPK signaling pathway, and then regulate apoptosis, redox reaction, neuro-regulation and protein folding in oyster haemocytes. The activated immune cells then released neurotransmitters including acetylcholine, norepinephrine and [Met5]-enkephalin to regulate the immune response by arising the expression of three TNF (CGI_10005109, CGI_10005110 and CGI_10006440) and translocating two NF-κB (Cgp65, CGI_10018142 and CgRel, CGI_10021567) between the cytoplasm and nuclei of haemocytes. Neurotransmitters exhibited the immunomodulation effects by influencing apoptosis and phagocytosis of oyster haemocytes. Acetylcholine and norepinephrine could down-regulate the immune response, while [Met5]-enkephalin up-regulate the immune response. These results suggested that the simple neuroendocrine-immune regulatory network in oyster might be activated by oyster TNF and then regulate the immune response by virtue of neurotransmitters, cytokines and transcription factors.

Efficient Analysis of Complex Structures

NASA Technical Reports Server (NTRS)

Kapania, Rakesh K.

2000-01-01

Last various accomplishments achieved during this project are : (1) A Survey of Neural Network (NN) applications using MATLAB NN Toolbox on structural engineering especially on equivalent continuum models (Appendix A). (2) Application of NN and GAs to simulate and synthesize substructures: 1-D and 2-D beam problems (Appendix B). (3) Development of an equivalent plate-model analysis method (EPA) for static and vibration analysis of general trapezoidal built-up wing structures composed of skins, spars and ribs. Calculation of all sorts of test cases and comparison with measurements or FEA results. (Appendix C). (4) Basic work on using second order sensitivities on simulating wing modal response, discussion of sensitivity evaluation approaches, and some results (Appendix D). (5) Establishing a general methodology of simulating the modal responses by direct application of NN and by sensitivity techniques, in a design space composed of a number of design points. Comparison is made through examples using these two methods (Appendix E). (6) Establishing a general methodology of efficient analysis of complex wing structures by indirect application of NN: the NN-aided Equivalent Plate Analysis. Training of the Neural Networks for this purpose in several cases of design spaces, which can be applicable for actual design of complex wings (Appendix F).

A topology visualization early warning distribution algorithm for large-scale network security incidents.

PubMed

He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe

2013-01-01

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

How to Identify Success Among Networks That Promote Active Living.

PubMed

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

Integrative proteome analysis of Brachypodium distachyon roots and leaves reveals a synergetic responsive network under H2O2 stress.

PubMed

Bian, Yan-Wei; Lv, Dong-Wen; Cheng, Zhi-Wei; Gu, Ai-Qin; Cao, Hui; Yan, Yue-Ming

2015-10-14

The plant oxidative stress response is vital for defense against various abiotic and biotic stresses. In this study, ultrastructural changes and the proteomic response to H2O2 stress in roots and leaves of the model plant Brachypodium distachyon were studied. Transmission electron microscopy (TEM) showed that the ultrastructural damage in roots was more serious than in leaves. Particularly, the ultrastructures of organelles and the nucleus in root tip cells were damaged, leading to the inhibition of normal biological activities of roots, which then spread throughout the plant. Based on two-dimensional electrophoresis (2-DE) and MALDI-TOF/TOF-MS, 84 and 53 differentially accumulated protein (DAP) spots representing 75 and 45 unique proteins responsive to H2O2 stress in roots and leaves, respectively, were identified. These protein species were mainly involved in signal transduction, energy metabolism, redox homeostasis/stress defense, protein folding/degradation, and cell wall/cell structure. Interestingly, two 14-3-3 proteins (GF14-B and GF14-D) were identified as DAPs in both roots and leaves. Protein-protein interaction (PPI) analysis revealed a synergetic H2O2-responsive network. Copyright © 2015 Elsevier B.V. All rights reserved.

A Systems Biology Study in Tomato Fruit Reveals Correlations between the Ascorbate Pool and Genes Involved in Ribosome Biogenesis, Translation, and the Heat-Shock Response

PubMed Central

Stevens, Rebecca G.; Baldet, Pierre; Bouchet, Jean-Paul; Causse, Mathilde; Deborde, Catherine; Deschodt, Claire; Faurobert, Mireille; Garchery, Cécile; Garcia, Virginie; Gautier, Hélène; Gouble, Barbara; Maucourt, Mickaël; Moing, Annick; Page, David; Petit, Johann; Poëssel, Jean-Luc; Truffault, Vincent; Rothan, Christophe

2018-01-01

Changing the balance between ascorbate, monodehydroascorbate, and dehydroascorbate in plant cells by manipulating the activity of enzymes involved in ascorbate synthesis or recycling of oxidized and reduced forms leads to multiple phenotypes. A systems biology approach including network analysis of the transcriptome, proteome and metabolites of RNAi lines for ascorbate oxidase, monodehydroascorbate reductase and galactonolactone dehydrogenase has been carried out in orange fruit pericarp of tomato (Solanum lycopersicum). The transcriptome of the RNAi ascorbate oxidase lines is inversed compared to the monodehydroascorbate reductase and galactonolactone dehydrogenase lines. Differentially expressed genes are involved in ribosome biogenesis and translation. This transcriptome inversion is also seen in response to different stresses in Arabidopsis. The transcriptome response is not well correlated with the proteome which, with the metabolites, are correlated to the activity of the ascorbate redox enzymes—ascorbate oxidase and monodehydroascorbate reductase. Differentially accumulated proteins include metacaspase, protein disulphide isomerase, chaperone DnaK and carbonic anhydrase and the metabolites chlorogenic acid, dehydroascorbate and alanine. The hub genes identified from the network analysis are involved in signaling, the heat-shock response and ribosome biogenesis. The results from this study therefore reveal one or several putative signals from the ascorbate pool which modify the transcriptional response and elements downstream. PMID:29491875

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2017-03-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77+/-0.08 and 0.75+/-0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74+/-0.07 and 0.74+/-0.08 with a large network. The AUCs were 0.73+/-0.08 and 0.62+/-0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77+/-0.08 and 0.73+/-0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72+/-0.06 and 0.64+/-0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

Deciphering the hormonal signalling network behind the systemic resistance induced by Trichoderma harzianum in tomato

PubMed Central

Martínez-Medina, Ainhoa; Fernández, Iván; Sánchez-Guzmán, María J.; Jung, Sabine C.; Pascual, Jose A.; Pozo, María J.

2013-01-01

Root colonization by selected Trichoderma isolates can activate in the plant a systemic defense response that is effective against a broad-spectrum of plant pathogens. Diverse plant hormones play pivotal roles in the regulation of the defense signaling network that leads to the induction of systemic resistance triggered by beneficial organisms [induced systemic resistance (ISR)]. Among them, jasmonic acid (JA) and ethylene (ET) signaling pathways are generally essential for ISR. However, Trichoderma ISR (TISR) is believed to involve a wider variety of signaling routes, interconnected in a complex network of cross-communicating hormone pathways. Using tomato as a model, an integrative analysis of the main mechanisms involved in the systemic resistance induced by Trichoderma harzianum against the necrotrophic leaf pathogen Botrytis cinerea was performed. Root colonization by T. harzianum rendered the leaves more resistant to B. cinerea independently of major effects on plant nutrition. The analysis of disease development in shoots of tomato mutant lines impaired in the synthesis of the key defense-related hormones JA, ET, salicylic acid (SA), and abscisic acid (ABA), and the peptide prosystemin (PS) evidenced the requirement of intact JA, SA, and ABA signaling pathways for a functional TISR. Expression analysis of several hormone-related marker genes point to the role of priming for enhanced JA-dependent defense responses upon pathogen infection. Together, our results indicate that although TISR induced in tomato against necrotrophs is mainly based on boosted JA-dependent responses, the pathways regulated by the plant hormones SA- and ABA are also required for successful TISR development. PMID:23805146

Network evaluation: principles, structures and outcomes of the German working group of Health Promoting Universities.

PubMed

Stock, Christiane; Milz, Simone; Meier, Sabine

2010-03-01

With more than 60 participating universities, the German working group of Health Promoting Universities (German HPU network) is the largest and most active network of universities as healthy settings. This study aims at evaluating processes and effects of the German HPU network and at supporting the future development of the network. The evaluation was based on the multi faceted network assessment instrument developed by Broesskamp-Stone (7). We used a document analysis, two expert interviews and a survey among members (n = 33) to collect relevant data for the assessment. The analysis showed that the visions of the network can be regarded as fulfilled in most aspects. The members of the network received network support through trustful and mutual relationships. The network ranked high on general network principles like implementation of mutual relationships, sharing of information, risks and resources, equal access to resources, responsibility and consensus orientation. However, a high degree of centralization was found as a negative indicator. Other critical aspects of the network's structures and processes have been the regional predominance of universities from the northern and middle part of Germany, the low representation of students in the network, and the low proportion of members that could successfully implement health promotion into the guiding principles of their university. Overall, the evaluation has shown that the network has worked effectively, has developed meaningful processes and structures and has formulated practical guidelines. Since its 12 years of existence the German HPU network has been able to adapt and to adequately respond to changing contextual conditions regarding health promotion at universities in Germany. The network should develop strategies to counteract the critical aspects and detected imbalances in order to further increase its impact on universities as healthy settings.

Systematic identification of light-regulated cold-responsive proteome in a model cyanobacterium.

PubMed

Chen, Weiyang; Fang, Longfa; Huang, Xiahe; Ge, Haitao; Wang, Jinlong; Wang, Xiaorong; Zhang, Yuanya; Sui, Na; Xu, Wu; Wang, Yingchun

2018-05-15

Differential expression of cold-responsive proteins is necessary for cyanobacteria to acclimate to cold stress frequently occurring in their natural habitats. Accumulating evidence indicates that cold-induced expression of certain proteins is dependent on light illumination, but a systematic identification of light-dependent and/or light-independent cold-responsive proteins in cyanobacteria is still lacking. Herein, we comprehensively identified cold-responsive proteins in a model cyanobacterium Synechocystis sp. PCC 6803 (Hereafter Synechocystis) that was cold-stressed in light or in dark. In total, 72 proteins were identified as cold-responsive, including 19 and 17 proteins whose cold-responsiveness are light-dependent and light-independent, respectively. Bioinformatic analysis revealed that outer membrane proteins, proteins involved in translation, and proteins involved in divergent types of stress responses were highly enriched in the cold-responsive proteins. Moreover, a protein network responsible for nitrogen assimilation and amino acid biosynthesis, transcription, and translation were upregulated in response to the cold stress. The network contains both light-dependent and light-independent cold-responsive proteins, probably for fine tuning its activity to endow Synechocystis the flexibility necessary for cold adaptation in their natural habitats, where days and nights are alternating. Together, our results should serve as an important resource for future study toward understanding the mechanism of cold acclimation in cyanobacteria. Photosynthetic cyanobacteria need to acclimate to frequently occurring abiotic stresses such as cold in their natural habitats, and the acclimation process has to be coordinated with photosynthesis, the light-dependent process that provides carbon and energy for propagation of cyanobacteria. It is conceivable that cold-induced differential protein expression can also be regulated by light. Hence it is important to systematically identify cold responsive proteins that are regulated or not regulated by light to better understand the mechanism of cold acclimation in cyanobacteria. In this manuscript, we identified a network involved in protein synthesis that were upregulated by cold. The network contains both light-dependent and light-independent cold-inducible proteins, presumably for fine tuning the activity of the network in natural habitats of cyanobacteria where days and nights are alternating. This finding underscores the significance of proteome reprograming toward enhancing protein synthesis in cold adaptation. Copyright © 2018 Elsevier B.V. All rights reserved.

Master Regulators of Oncogenic KRAS Response in Pancreatic Cancer: An Integrative Network Biology Analysis

PubMed Central

2017-01-01

Background KRAS is the most frequently mutated gene in pancreatic ductal adenocarcinoma (PDAC), but the mechanisms underlying the transcriptional response to oncogenic KRAS are still not fully understood. We aimed to uncover transcription factors that regulate the transcriptional response of oncogenic KRAS in pancreatic cancer and to understand their clinical relevance. Methods and Findings We applied a well-established network biology approach (master regulator analysis) to combine a transcriptional signature for oncogenic KRAS derived from a murine isogenic cell line with a coexpression network derived by integrating 560 human pancreatic cancer cases across seven studies. The datasets included the ICGC cohort (n = 242), the TCGA cohort (n = 178), and five smaller studies (n = 17, 25, 26, 36, and 36). 55 transcription factors were coexpressed with a significant number of genes in the transcriptional signature (gene set enrichment analysis [GSEA] p < 0.01). Community detection in the coexpression network identified 27 of the 55 transcription factors contributing to three major biological processes: Notch pathway, down-regulated Hedgehog/Wnt pathway, and cell cycle. The activities of these processes define three distinct subtypes of PDAC, which demonstrate differences in survival and mutational load as well as stromal and immune cell composition. The Hedgehog subgroup showed worst survival (hazard ratio 1.73, 95% CI 1.1 to 2.72, coxPH test p = 0.018) and the Notch subgroup the best (hazard ratio 0.62, 95% CI 0.42 to 0.93, coxPH test p = 0.019). The cell cycle subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admixture (ANOVA p < 2.2e–16). This study is limited by the information provided in published datasets, not all of which provide mutational profiles, survival data, or the specifics of treatment history. Conclusions Our results characterize the regulatory mechanisms underlying the transcriptional response to oncogenic KRAS and provide a framework to develop strategies for specific subtypes of this disease using current therapeutics and by identifying targets for new groups. PMID:28141826

Response of asymmetric carbon nanotube network devices to sub-terahertz and terahertz radiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gayduchenko, I., E-mail: igorandg@gmail.com, E-mail: gefedorov@mail.ru; National Research Centre “Kurchatov Institute,” Moscow 123128; Kardakova, A.

2015-11-21

Demand for efficient terahertz radiation detectors resulted in intensive study of the asymmetric carbon nanostructures as a possible solution for that problem. It was maintained that photothermoelectric effect under certain conditions results in strong response of such devices to terahertz radiation even at room temperature. In this work, we investigate different mechanisms underlying the response of asymmetric carbon nanotube (CNT) based devices to sub-terahertz and terahertz radiation. Our structures are formed with CNT networks instead of individual CNTs so that effects probed are more generic and not caused by peculiarities of an individual nanoscale object. We conclude that the DCmore » voltage response observed in our structures is not only thermal in origin. So called diode-type response caused by asymmetry of the device IV characteristic turns out to be dominant at room temperature. Quantitative analysis provides further routes for the optimization of the device configuration, which may result in appearance of novel terahertz radiation detectors.« less

Curve Number and Peakflow Responses Following the Cerro Grande Fire on a Small Watershed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Springer, E. P.; Hawkins, Richard H.

The Curve Number (CN) method is routinely used to estimate runoff and peakflows following forest fires, but there has been essentially no literature on the estimated value and temporal variation of CNs following wildland fires. In May 2000, the Cerro Grande Fire burned the headwaters of the major watersheds that cross Los Alamos National Laboratory, and a stream gauging network presented an opportunity to assess CNs following the fire. Analysis of rainfall-runoff events indicated that the pre-fire watershed response was complacent or limited watershed area contributed to runoff. The post-fire response indicated that the complacent behavior continued so the watershedmore » response was not dramatically changed. Peakflows did increase by 2 orders of magnitude following the fire, and this was hypothesized to be a function of increase in runoff volume and changes in watershed network allowing more efficient delivery of runoff. More observations and analyses following fires are needed to support definition of CNs for post-fire response and mitigation efforts.« less

Alterations of Brain Functional Architecture Associated with Psychopathic Traits in Male Adolescents with Conduct Disorder.

PubMed

Pu, Weidan; Luo, Qiang; Jiang, Yali; Gao, Yidian; Ming, Qingsen; Yao, Shuqiao

2017-09-12

Psychopathic traits of conduct disorder (CD) have a core callous-unemotional (CU) component and an impulsive-antisocial component. Previous task-driven fMRI studies have suggested that psychopathic traits are associated with dysfunction of several brain areas involved in different cognitive functions (e.g., empathy, reward, and response inhibition etc.), but the relationship between psychopathic traits and intrinsic brain functional architecture has not yet been explored in CD. Using a holistic brain-wide functional connectivity analysis, this study delineated the alterations in brain functional networks in patients with conduct disorder. Compared with matched healthy controls, we found decreased anti-synchronization between the fronto-parietal network (FPN) and default mode network (DMN), and increased intra-network synchronization within the frontothalamic-basal ganglia, right frontoparietal, and temporal/limbic/visual networks in CD patients. Correlation analysis showed that the weakened FPN-DMN interaction was associated with CU traits, while the heightened intra-network functional connectivity was related to impulsivity traits in CD patients. Our findings suggest that decoupling of cognitive control (FPN) with social understanding of others (DMN) is associated with the CU traits, and hyper-functions of the reward and motor inhibition systems elevate impulsiveness in CD.

Network inoculation: Heteroclinics and phase transitions in an epidemic model

NASA Astrophysics Data System (ADS)

Yang, Hui; Rogers, Tim; Gross, Thilo

2016-08-01

In epidemiological modelling, dynamics on networks, and, in particular, adaptive and heterogeneous networks have recently received much interest. Here, we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model, qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description, one of these corresponds to a local bifurcation, whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region, exposure of the system to a pathogen will lead to an outbreak that collapses but leaves the network in a configuration where the disease cannot reinvade, despite every agent returning to the susceptible class. We argue that this behaviour and the associated phase transitions can be expected to occur in a wide class of models of sufficient complexity.

Investigation of global and local network properties of music perception with culturally different styles of music.

PubMed

Li, Yan; Rui, Xue; Li, Shuyu; Pu, Fang

2014-11-01

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved. Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties. Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception. Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception. Copyright © 2014 Elsevier Ltd. All rights reserved.

State Support: A Prerequisite for Global Health Network Effectiveness

PubMed Central

Marten, Robert; Smith, Richard D.

2018-01-01

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks’ success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks’ effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. PMID:29524958

Investigating brain functional evolution and plasticity using microelectrode array technology.

PubMed

Napoli, Alessandro; Obeid, Iyad

2015-10-01

The aim of this work was to investigate long and short-term plasticity responsible for memory formation in dissociated neuronal networks. In order to address this issue, a set of experiments was designed and implemented in which the microelectrode array electrode grid was divided into four quadrants, two of which were chronically stimulated, every two days for one hour with a stimulation paradigm that varied over time. Overall network and quadrant responses were then analyzed to quantify what level of plasticity took place in the network and how this was due to the stimulation interruption. The results demonstrate that there were no spatial differences in the stimulus-evoked activity within quadrants. Furthermore, the implemented stimulation protocol induced depression effects in the neuronal networks as demonstrated by the consistently lower network activity following stimulation sessions. Finally, the analysis demonstrated that the inhibitory effects of the stimulation decreased over time, thus suggesting a habituation phenomenon. These findings are sufficient to conclude that electrical stimulation is an important tool to interact with dissociated neuronal cultures, but localized stimuli are not enough to drive spatial synaptic potentiation or depression. On the contrary, the ability to modulate synaptic temporal plasticity was a feasible task to achieve by chronic network stimulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Macromolecular networks and intelligence in microorganisms

PubMed Central

Westerhoff, Hans V.; Brooks, Aaron N.; Simeonidis, Evangelos; García-Contreras, Rodolfo; He, Fei; Boogerd, Fred C.; Jackson, Victoria J.; Goncharuk, Valeri; Kolodkin, Alexey

2014-01-01

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence.” Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence,” all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence.” We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo. PMID:25101076

Information exchange networks for chronic illness care in primary care practices: an observational study

PubMed Central

2010-01-01

Background Information exchange networks for chronic illness care may influence the uptake of innovations in patient care. Valid and feasible methods are needed to document and analyse information exchange networks in healthcare settings. This observational study aimed to examine the usefulness of methods to study information exchange networks in primary care practices, related to chronic heart failure, diabetes and chronic obstructive pulmonary disease. Methods The study was linked to a quality improvement project in the Netherlands. All health professionals in the practices were asked to complete a short questionnaire that documented their information exchange relations. Feasibility was determined in terms of response rates and reliability in terms of reciprocity of reports of receiving and providing information. For each practice, a number of network characteristics were derived for each of the chronic conditions. Results Ten of the 21 practices in the quality improvement project agreed to participate in this network study. The response rates were high in all but one of the participating practices. For the analysis, we used data from 67 health professionals from eight practices. The agreement between receiving and providing information was, on average, 65.6%. The values for density, centralization, hierarchy, and overlap of the information exchange networks showed substantial variation between the practices as well as between the chronic conditions. The most central individual in the information exchange network could be a nurse or a physician. Conclusions Further research is needed to refine the measure of information networks and to test the impact of network characteristics on the uptake of innovations. PMID:20205758

IL-32 is a molecular marker of a host defense network in human tuberculosis

PubMed Central

Montoya, Dennis; Inkeles, Megan S.; Liu, Phillip T.; Realegeno, Susan; Teles, Rosane M. B.; Vaidya, Poorva; Munoz, Marcos A.; Schenk, Mirjam; Swindell, William R.; Chun, Rene; Zavala, Kathryn; Hewison, Martin; Adams, John S.; Horvath, Steve; Pellegrini, Matteo; Bloom, Barry R.; Modlin, Robert L.

2014-01-01

Tuberculosis is a leading cause of infectious disease–related death worldwide; however, only 10% of people infected with Mycobacterium tuberculosis develop disease. Factors that contribute to protection could prove to be promising targets for M. tuberculosis therapies. Analysis of peripheral blood gene expression profiles of active tuberculosis patients has identified correlates of risk for disease or pathogenesis. We sought to identify potential human candidate markers of host defense by studying gene expression profiles of macrophages, cells that, upon infection by M. tuberculosis, can mount an antimicrobial response. Weighted gene coexpression network analysis revealed an association between the cytokine interleukin-32 (IL-32) and the vitamin D antimicrobial pathway in a network of interferon-γ– and IL-15–induced “defense response” genes. IL-32 induced the vitamin D–dependent antimicrobial peptides cathelicidin and DEFB4 and to generate antimicrobial activity in vitro, dependent on the presence of adequate 25-hydroxyvitamin D. In addition, the IL-15–induced defense response macrophage gene network was integrated with ranked pairwise comparisons of gene expression from five different clinical data sets of latent compared with active tuberculosis or healthy controls and a coexpression network derived from gene expression in patients with tuberculosis undergoing chemotherapy. Together, these analyses identified eight common genes, including IL-32, as molecular markers of latent tuberculosis and the IL-15–induced gene network. As maintaining M. tuberculosis in a latent state and preventing transition to active disease may represent a form of host resistance, these results identify IL-32 as one functional marker and potential correlate of protection against active tuberculosis. PMID:25143364

Construction and comparison of gene co-expression networks shows complex plant immune responses

PubMed Central

López, Camilo; López-Kleine, Liliana

2014-01-01

Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses. PMID:25320678

Optimal Signal Processing in Small Stochastic Biochemical Networks

PubMed Central

Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

2007-01-01

We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

Simulation and Analysis of the AFLC Bulk Data Network Using Abstract Data Types.

DTIC Science & Technology

1981-12-01

performs. Simulation is more expensive than queueing, but it is often ə the only way to study complex funtional relationships in a large system. Unlike... relationship between through- put, response and cost is shown in Figure 2. At a given cost level, additional throughput can be obtained at the expense...improved by adding resources, but this increases the total cost of the system. Network models are used to study the relationship between cost

Representation of visual symbols in the visual word processing network.

PubMed

Muayqil, Taim; Davies-Thompson, Jodie; Barton, Jason J S

2015-03-01

Previous studies have shown that word processing involves a predominantly left-sided occipitotemporal network. Words are a form of symbolic representation, in that they are arbitrary perceptual stimuli that represent other objects, actions or concepts. Lesions of parts of the visual word processing network can cause alexia, which can be associated with difficulty processing other types of symbols such as musical notation or road signs. We investigated whether components of the visual word processing network were also activated by other types of symbols. In 16 music-literate subjects, we defined the visual word network using fMRI and examined responses to four symbolic categories: visual words, musical notation, instructive symbols (e.g. traffic signs), and flags and logos. For each category we compared responses not only to scrambled stimuli, but also to similar stimuli that lacked symbolic meaning. The left visual word form area and a homologous right fusiform region responded similarly to all four categories, but equally to both symbolic and non-symbolic equivalents. Greater response to symbolic than non-symbolic stimuli occurred only in the left inferior frontal and middle temporal gyri, but only for words, and in the case of the left inferior frontal gyri, also for musical notation. A whole-brain analysis comparing symbolic versus non-symbolic stimuli revealed a distributed network of inferior temporooccipital and parietal regions that differed for different symbols. The fusiform gyri are involved in processing the form of many symbolic stimuli, but not specifically for stimuli with symbolic content. Selectivity for stimuli with symbolic content only emerges in the visual word network at the level of the middle temporal and inferior frontal gyri, but is specific for words and musical notation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Single-cell resolution of intracellular T cell Ca2+ dynamics in response to frequency-based H2O2 stimulation.

PubMed

Kniss-James, Ariel S; Rivet, Catherine A; Chingozha, Loice; Lu, Hang; Kemp, Melissa L

2017-03-01

Adaptive immune cells, such as T cells, integrate information from their extracellular environment through complex signaling networks with exquisite sensitivity in order to direct decisions on proliferation, apoptosis, and cytokine production. These signaling networks are reliant on the interplay between finely tuned secondary messengers, such as Ca 2+ and H 2 O 2 . Frequency response analysis, originally developed in control engineering, is a tool used for discerning complex networks. This analytical technique has been shown to be useful for understanding biological systems and facilitates identification of the dominant behaviour of the system. We probed intracellular Ca 2+ dynamics in the frequency domain to investigate the complex relationship between two second messenger signaling molecules, H 2 O 2 and Ca 2+ , during T cell activation with single cell resolution. Single-cell analysis provides a unique platform for interrogating and monitoring cellular processes of interest. We utilized a previously developed microfluidic device to monitor individual T cells through time while applying a dynamic input to reveal a natural frequency of the system at approximately 2.78 mHz stimulation. Although our network was much larger with more unknown connections than previous applications, we are able to derive features from our data, observe forced oscillations associated with specific amplitudes and frequencies of stimuli, and arrive at conclusions about potential transfer function fits as well as the underlying population dynamics.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan

PubMed Central

Park, Rachel; O'Brien, Thomas F.; Huang, Susan S.; Baker, Meghan A.; Yokoe, Deborah S.; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-01-01

Objectives While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Methods Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Results Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Conclusion Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures. PMID:27530311

Distributed architecture and distributed processing mode in urban sewage treatment

NASA Astrophysics Data System (ADS)

Zhou, Ruipeng; Yang, Yuanming

2017-05-01

Decentralized rural sewage treatment facility over the broad area, a larger operation and management difficult, based on the analysis of rural sewage treatment model based on the response to these challenges, we describe the principle, structure and function in networking technology and network communications technology as the core of distributed remote monitoring system, through the application of case analysis to explore remote monitoring system features in a decentralized rural sewage treatment facilities in the daily operation and management. Practice shows that the remote monitoring system to provide technical support for the long-term operation and effective supervision of the facilities, and reduced operating, maintenance and supervision costs for development.

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

Large networks of interacting dynamical systems are well-known for the complex behaviours they are able to display, even when each node features a quite simple dynamics. Despite examples of such networks being widespread both in nature and in technological applications, the interplay between the local and the macroscopic behaviour, through the interconnection topology, is still not completely understood. Moreover, traditional analytical methods for dynamical response analysis fail because of the intrinsically large dimension of the phase space of the network which makes the general problem intractable. Therefore, in this paper we develop an approach aiming to condense all the information in a compact description based on partial differential equations. By focusing on propagative phenomena, rigorous conditions under which the original network dynamical properties can be successfully analysed within the proposed framework are derived as well. A network of Fitzhugh-Nagumo systems is finally used to illustrate the effectiveness of the proposed method.

Few-mode fiber, splice and SDM component characterization by spatially-diverse optical vector network analysis.

PubMed

Rommel, Simon; Mendinueta, José Manuel Delgado; Klaus, Werner; Sakaguchi, Jun; Olmos, Juan José Vegas; Awaji, Yoshinari; Monroy, Idelfonso Tafur; Wada, Naoya

2017-09-18

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 108-channel photonic lantern spatial multiplexer, coupled to a 36-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.

Robustness and Recovery of Lifeline Infrastructure and Ecosystem Networks

NASA Astrophysics Data System (ADS)

Bhatia, U.; Ganguly, A. R.

2015-12-01

Disruptive events, both natural and man-made, can have widespread impacts on both natural systems and lifeline infrastructure networks leading to the loss of biodiversity and essential functionality, respectively. Projected sea-level rise and climate change can further increase the frequency and severity of large-scale floods on urban-coastal megacities. Nevertheless, Failure in infrastructure systems can trigger cascading impacts on dependent ecosystems, and vice-versa. An important consideration in the behavior of the isolated networks and inter-connected networks following disruptive events is their resilience, or the ability of the network to "bounce back" to a pre-disaster state. Conventional risk analysis and subsequent risk management frameworks have focused on identifying the components' vulnerability and strengthening of the isolated components to withstand these disruptions. But high interconnectedness of these systems, and evolving nature of hazards, particularly in the context of climate extremes, make the component level analysis unrealistic. In this study, we discuss the complex network-based resilience framework to understand fragility and recovery strategies for infrastructure systems impacted by climate-related hazards. We extend the proposed framework to assess the response of ecological networks to multiple species loss and design the restoration management framework to identify the most efficient restoration sequence of species, which can potentially lead to disproportionate gains in biodiversity.

Dynamic behavior of acrylic acid clusters as quasi-mobile nodes in a model of hydrogel network

NASA Astrophysics Data System (ADS)

Zidek, Jan; Milchev, Andrey; Vilgis, Thomas A.

2012-12-01

Using a molecular dynamics simulation, we study the thermo-mechanical behavior of a model hydrogel subject to deformation and change in temperature. The model is found to describe qualitatively poly-lactide-glycolide hydrogels in which acrylic acid (AA)-groups are believed to play the role of quasi-mobile nodes in the formation of a network. From our extensive analysis of the structure, formation, and disintegration of the AA-groups, we are able to elucidate the relationship between structure and viscous-elastic behavior of the model hydrogel. Thus, in qualitative agreement with observations, we find a softening of the mechanical response at large deformations, which is enhanced by growing temperature. Several observables as the non-affinity parameter A and the network rearrangement parameter V indicate the existence of a (temperature-dependent) threshold degree of deformation beyond which the quasi-elastic response of the model system turns over into plastic (ductile) one. The critical stretching when the affinity of the deformation is lost can be clearly located in terms of A and V as well as by analysis of the energy density of the system. The observed stress-strain relationship matches that of known experimental systems.

Strong-motion observations of the M 7.8 Gorkha, Nepal, earthquake sequence and development of the N-shake strong-motion network

USGS Publications Warehouse

Dixit, Amod; Ringler, Adam; Sumy, Danielle F.; Cochran, Elizabeth S.; Hough, Susan E.; Martin, Stacey; Gibbons, Steven; Luetgert, James H.; Galetzka, John; Shrestha, Surya; Rajaure, Sudhir; McNamara, Daniel E.

2015-01-01

We present and describe strong-motion data observations from the 2015 M 7.8 Gorkha, Nepal, earthquake sequence collected using existing and new Quake-Catcher Network (QCN) and U.S. Geological Survey NetQuakes sensors located in the Kathmandu Valley. A comparison of QCN data with waveforms recorded by a conventional strong-motion (NetQuakes) instrument validates the QCN data. We present preliminary analysis of spectral accelerations, and peak ground acceleration and velocity for earthquakes up to M 7.3 from the QCN stations, as well as preliminary analysis of the mainshock recording from the NetQuakes station. We show that mainshock peak accelerations were lower than expected and conclude the Kathmandu Valley experienced a pervasively nonlinear response during the mainshock. Phase picks from the QCN and NetQuakes data are also used to improve aftershock locations. This study confirms the utility of QCN instruments to contribute to ground-motion investigations and aftershock response in regions where conventional instrumentation and open-access seismic data are limited. Initial pilot installations of QCN instruments in 2014 are now being expanded to create the Nepal–Shaking Hazard Assessment for Kathmandu and its Environment (N-SHAKE) network.

Using social network analysis to examine collaborative relationships among PhD and DNP students and faculty in a research-intensive university school of nursing.

PubMed

Merrill, Jacqueline A; Yoon, Sunmoo; Larson, Elaine; Honig, Judy; Reame, Nancy

2013-01-01

The nursing profession has seen a dramatic rise in the number of schools offering both DNP and PhD nursing programs. Information is limited on the impact of this parallel approach in doctoral education on the quality and scope of scholarly interactions or institutional culture.The authors studied collaboration characteristics across the DNP and PhD programs of a research-intensive university school of nursing, before and after programmatic enhancements. An IRB-approved online survey was delivered to faculty and students of both programs at baseline and one year after curricular changes. Response rates were 70% and 74%, respectively. The responses were analyzed by using social network analysis and descriptive statistics to characterize the number and strength of connections between and within student groups, and between students and faculty. At baseline, the flow of communication was centralized primarily through faculty. At Time 2, density of links between students increased and network centralization decreased, suggesting more distributed communication. This nonlinear quantitative approach may be a useful addition to the evaluation strategies for doctoral education initiatives. Copyright © 2013 Elsevier Inc. All rights reserved.

Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins

PubMed Central

Varrella, Stefano; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G.; Costantini, Maria

2016-01-01

Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure. PMID:26914213

The analysis of social network data: an exciting frontier for statisticians.

PubMed

O'Malley, A James

2013-02-20

The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person- to-person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF. Copyright © 2012 John Wiley & Sons, Ltd.

A multilevel path analysis of contact frequency between social network members

NASA Astrophysics Data System (ADS)

van den Berg, Pauline; Arentze, Theo; Timmermans, Harry

2012-04-01

Recently, there has been an increasing interest in the role of social networks in spatial-choice and travel behavior. It has been acknowledged that social activities and the travel for these activities can emerge from individuals' social networks and that social activities are responsible for an important portion of travel demand. The influence of information and communication technologies (ICT's) is also important in this respect. The purpose of the paper is to examine the effects of characteristics of egos and ego-alter relationships on the frequency of social interaction by different communication modes, using multilevel path analysis. The analyses are based on social network data collected in 2008 in the Eindhoven region in the Netherlands among 116 respondents. The results indicate a complementary relationship between contact frequencies by different modes. The contact frequencies of the different modes, especially face-to-face and telephone, can also be largely explained by the ego's personal characteristics and the type of relationship and the distance between ego and alter.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

Different adaptation strategies of two citrus scion/rootstock combinations in response to drought stress

PubMed Central

Coelho Filho, Mauricio Antônio; Morillon, Raphaël; Bonatto, Diego; da Silva Gesteira, Abelmon

2017-01-01

Scion/rootstock interaction is important for plant development and for breeding programs. In this context, polyploid rootstocks presented several advantages, mainly in relation to biotic and abiotic stresses. Here we analyzed the response to drought of two different scion/rootstock combinations presenting different polyploidy: the diploid (2x) and autotetraploid (4x) Rangpur lime (Citrus limonia, Osbeck) rootstocks grafted with 2x Valencia Delta sweet orange (Citrus sinensis) scions, named V/2xRL and V/4xRL, respectively. Based on previous gene expression data, we developed an interactomic approach to identify proteins involved in V/2xRL and V/4xRL response to drought. A main interactomic network containing 3,830 nodes and 97,652 edges was built from V/2xRL and V/4xRL data. Exclusive proteins of the V/2xRL and V/4xRL networks (2,056 and 1,001, respectively), as well as common to both networks (773) were identified. Functional clusters were obtained and two models of drought stress response for the V/2xRL and V/4xRL genotypes were designed. Even if the V/2xRL plant implement some tolerance mechanisms, the global plant response to drought was rapid and quickly exhaustive resulting in a general tendency to dehydration avoidance, which presented some advantage in short and strong drought stress conditions, but which, in long terms, does not allow the plant survival. At the contrary, the V/4xRL plants presented a response which strong impacts on development but that present some advantages in case of prolonged drought. Finally, some specific proteins, which presented high centrality on interactomic analysis were identified as good candidates for subsequent functional analysis of citrus genes related to drought response, as well as be good markers of one or another physiological mechanism implemented by the plants. PMID:28545114

Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity.

PubMed

Adhikari, Bhim M; Sathian, K; Epstein, Charles M; Lamichhane, Bidhan; Dhamala, Mukesh

2014-05-01

Oscillatory interactions within functionally specialized but distributed brain regions are believed to be central to perceptual and cognitive functions. Here, using human scalp electroencephalography (EEG) recordings combined with source reconstruction techniques, we study how oscillatory activity functionally organizes different neocortical regions during a tactile discrimination task near the limit of spatial acuity. While undergoing EEG recordings, blindfolded participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. The average brain response differed significantly for trials with correct and incorrect perceptual responses in the timeframe approximately between 130 and 175ms. During trials with correct responses, source-level peak activity appeared in the left primary somatosensory cortex (SI) at around 45ms, in the right lateral occipital complex (LOC) at 130ms, in the right posterior intraparietal sulcus (pIPS) at 160ms, and finally in the left dorsolateral prefrontal cortex (dlPFC) at 175ms. Spectral interdependency analysis of activity in these nodes showed two distinct distributed networks, a dominantly feedforward network in the beta band (12-30Hz) that included all four nodes and a recurrent network in the gamma band (30-100Hz) that linked SI, pIPS and dlPFC. Measures of network activity in both bands were correlated with the accuracy of task performance. These findings suggest that beta and gamma band oscillatory networks coordinate activity between neocortical regions mediating sensory and cognitive processing to arrive at tactile perceptual decisions. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Dynamic functional connectivity and individual differences in emotions during social stress.

PubMed

Tobia, Michael J; Hayashi, Koby; Ballard, Grey; Gotlib, Ian H; Waugh, Christian E

2017-12-01

Exposure to acute stress induces multiple emotional responses, each with their own unique temporal dynamics. Dynamic functional connectivity (dFC) measures the temporal variability of network synchrony and captures individual differences in network neurodynamics. This study investigated the relationship between dFC and individual differences in emotions induced by an acute psychosocial stressor. Sixteen healthy adult women underwent fMRI scanning during a social evaluative threat (SET) task, and retrospectively completed questionnaires that assessed individual differences in subjectively experienced positive and negative emotions about stress and stress relief during the task. Group dFC was decomposed with parallel factor analysis (PARAFAC) into 10 components, each with a temporal signature, spatial network of functionally connected regions, and vector of participant loadings that captures individual differences in dFC. Participant loadings of two networks were positively correlated with stress-related emotions, indicating the existence of networks for positive and negative emotions. The emotion-related networks involved the ventromedial prefrontal cortex, cingulate cortex, anterior insula, and amygdala, among other distributed brain regions, and time signatures for these emotion-related networks were uncorrelated. These findings demonstrate that individual differences in stress-induced positive and negative emotions are each uniquely associated with large-scale brain networks, and suggest that dFC is a mechanism that generates individual differences in the emotional components of the stress response. Hum Brain Mapp 38:6185-6205, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics.

PubMed

Tan, Shangjin; Zhou, Jin; Zhu, Xiaoshan; Yu, Shichen; Zhan, Wugen; Wang, Bo; Cai, Zhonghua

2015-02-01

Algal blooms are a worldwide phenomenon and the biological interactions that underlie their regulation are only just beginning to be understood. It is established that algal microorganisms associate with many other ubiquitous, oceanic organisms, but the interactions that lead to the dynamics of bloom formation are currently unknown. To address this gap, we used network approaches to investigate the association patterns among microeukaryotes and bacterioplankton in response to a natural Scrippsiella trochoidea bloom. This is the first study to apply network approaches to bloom dynamics. To this end, terminal restriction fragment (T-RF) length polymorphism analysis showed dramatic changes in community compositions of microeukaryotes and bacterioplankton over the blooming period. A variance ratio test revealed significant positive overall associations both within and between microeukaryotic and bacterioplankton communities. An association network generated from significant correlations between T-RFs revealed that S. trochoidea had few connections to other microeukaryotes and bacterioplankton and was placed on the edge. This lack of connectivity allowed for the S. trochoidea sub-network to break off from the overall network. These results allowed us to propose a conceptual model for explaining how changes in microbial associations regulate the dynamics of an algal bloom. In addition, key T-RFs were screened by principal components analysis, correlation coefficients, and network analysis. Dominant T-RFs were then identified through 18S and 16S rRNA gene clone libraries. Results showed that microeukaryotes clustered predominantly with Dinophyceae and Perkinsea while the majority of bacterioplankton identified were Alphaproteobacteria, Gammaproteobacteria, and Bacteroidetes. The ecologi-cal roles of both were discussed in the context of these findings. © 2014 Phycological Society of America.

Meta-type analysis of dopaminergic effects on gene expression in the neuroendocrine brain of female goldfish.

PubMed

Popesku, Jason T; Martyniuk, Christopher J; Trudeau, Vance L

2012-01-01

Dopamine (DA) is a major neurotransmitter important for neuroendocrine control and recent studies have described genomic signaling pathways activated and inhibited by DA agonists and antagonists in the goldfish brain. Here we perform a meta-type analysis using microarray datasets from experiments conducted with female goldfish to characterize the gene expression responses that underlie dopaminergic signaling. Sexually mature, pre-spawning [gonadosomatic index (GSI) = 4.5 ± 1.3%] or sexually regressing (GSI = 3 ± 0.4%) female goldfish (15-40 g) injected intraperitoneally with either SKF 38393, LY 171555, SCH 23390, sulpiride, or a combination of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and α-methyl-p-tyrosine. Microarray meta-type analysis identified 268 genes in the telencephalon and hypothalamus as having reciprocal (i.e., opposite between agonism and antagonism/depletion) fold change responses, suggesting that these transcripts are likely targets for DA-mediated regulation. Noteworthy genes included ependymin, vimentin, and aromatase, genes that support the significance of DA in neuronal plasticity and tissue remodeling. Sub-network enrichment analysis (SNEA) was used to identify common gene regulators and binding proteins associated with the differentially expressed genes mediated by DA. SNEA analysis identified gene expression targets that were related to three major categories that included cell signaling (STAT3, SP1, SMAD, Jun/Fos), immune response (IL-6, IL-1β, TNFs, cytokine, NF-κB), and cell proliferation and growth (IGF1, TGFβ1). These gene networks are also known to be associated with neurodegenerative disorders such as Parkinsons' disease, well-known to be associated with loss of dopaminergic neurons. This study identifies genes and networks that underlie DA signaling in the vertebrate CNS and provides targets that may be key neuroendocrine regulators. The results provide a foundation for future work on dopaminergic regulation of gene expression in fish model systems.

Meta-Type Analysis of Dopaminergic Effects on Gene Expression in the Neuroendocrine Brain of Female Goldfish

PubMed Central

Popesku, Jason T.; Martyniuk, Christopher J.; Trudeau, Vance L.

2012-01-01

Dopamine (DA) is a major neurotransmitter important for neuroendocrine control and recent studies have described genomic signaling pathways activated and inhibited by DA agonists and antagonists in the goldfish brain. Here we perform a meta-type analysis using microarray datasets from experiments conducted with female goldfish to characterize the gene expression responses that underlie dopaminergic signaling. Sexually mature, pre-spawning [gonadosomatic index (GSI) = 4.5 ± 1.3%] or sexually regressing (GSI = 3 ± 0.4%) female goldfish (15–40 g) injected intraperitoneally with either SKF 38393, LY 171555, SCH 23390, sulpiride, or a combination of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and α-methyl-p-tyrosine. Microarray meta-type analysis identified 268 genes in the telencephalon and hypothalamus as having reciprocal (i.e., opposite between agonism and antagonism/depletion) fold change responses, suggesting that these transcripts are likely targets for DA-mediated regulation. Noteworthy genes included ependymin, vimentin, and aromatase, genes that support the significance of DA in neuronal plasticity and tissue remodeling. Sub-network enrichment analysis (SNEA) was used to identify common gene regulators and binding proteins associated with the differentially expressed genes mediated by DA. SNEA analysis identified gene expression targets that were related to three major categories that included cell signaling (STAT3, SP1, SMAD, Jun/Fos), immune response (IL-6, IL-1β, TNFs, cytokine, NF-κB), and cell proliferation and growth (IGF1, TGFβ1). These gene networks are also known to be associated with neurodegenerative disorders such as Parkinsons’ disease, well-known to be associated with loss of dopaminergic neurons. This study identifies genes and networks that underlie DA signaling in the vertebrate CNS and provides targets that may be key neuroendocrine regulators. The results provide a foundation for future work on dopaminergic regulation of gene expression in fish model systems. PMID:23130016

Participatory Design Methods for C2 Systems (Proceedings/Presentation)

DTIC Science & Technology

2006-01-01

Cognitive Task Analysis (CTA) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON OF ABSTRACT OF PAGES Janet E. Miller...systems to support cognitive work such as is accomplished in a network-centric -environment. Cognitive task analysis (CTA) methods are used to...of cognitive task analysis methodologies exist (Schraagen et al., 2000). However, many of these methods are skeptically viewed by a domain’s

Social Network Analysis of Crowds

DTIC Science & Technology

2009-10-29

Response to non-lethal weapons fire depends on social relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time...weapons and systems – Prior, existing social relationships – Real time social interactions – Formal/informal hierarchies 13-Feb-15 24UNCLASSIFIED

DIGE-based protein expression analysis of B[a]P-exposed hepatoma cells reveals a complex stress response including alterations in oxidative stress, cell cycle control, and cytoskeleton motility at toxic and subacute concentrations.

PubMed

Dautel, Franziska; Kalkhof, Stefan; Trump, Saskia; Michaelson, Jacob; Beyer, Andreas; Lehmann, Irina; von Bergen, Martin

2011-02-04

Although the effects of high concentrations of the carcinogen benzo[a]pyrene (B[a]P) have been studied extensively, little is known about its effects at subacute toxic concentrations, which are typical for environmental pollutants. We exposed murine Hepa1c1c7 cells to a toxic concentration (5 μM) and a subacute concentration (50 nM) of B[a]P over a period of 2-24 h to differentiate between acute and pseudochronic effects and conducted a time-course analysis of B[a]P-influenced protein expression by DIGE. In total, a set of 120 spots were found to be significantly altered due to B[a]P exposure of which 112 were subsequently identified by mass spectrometry. Clustering and principal component analysis were conducted to identify sets of proteins responding in a concerted manner to the exposure. Our results indicate an immediate response to the contaminant at the protein level and demonstrate that B[a]P exposure alters the cellular response by disturbing proteins involved in oxidative stress, cell cycle regulation, apoptosis, and cytoskeleton organization. Furthermore, network analysis of protein-protein interactions revealed a complex network of interacting, B[a]P-regulated proteins mostly belonging to the cytoskeleton organization and several signal transduction pathways.

Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity.

PubMed

Zhang, J D; Berntenis, N; Roth, A; Ebeling, M

2014-06-01

Gene signatures of drug-induced toxicity are of broad interest, but they are often identified from small-scale, single-time point experiments, and are therefore of limited applicability. To address this issue, we performed multivariate analysis of gene expression, cell-based assays, and histopathological data in the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database. Data mining highlights four genes-EGR1, ATF3, GDF15 and FGF21-that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death. Modelling and simulation reveals that these early stress-response genes form a functional network with evolutionarily conserved structure and intrinsic dynamics. This is underlined by the fact that early induction of this network in vivo predicts drug-induced liver and kidney pathology with high accuracy. Our findings demonstrate the value of early gene-expression signatures in predicting and understanding compound-induced toxicity. The identified network can empower first-line tests that reduce animal use and costs of safety evaluation.

Dengue-2 structural proteins associate with human proteins to produce a coagulation and innate immune response biased interactome.

PubMed

Folly, Brenda B; Weffort-Santos, Almeriane M; Fathman, C G; Soares, Luis R B

2011-01-31

Dengue virus infection is a public health threat to hundreds of millions of individuals in the tropical regions of the globe. Although Dengue infection usually manifests itself in its mildest, though often debilitating clinical form, dengue fever, life-threatening complications commonly arise in the form of hemorrhagic shock and encephalitis. The etiological basis for the virus-induced pathology in general, and the different clinical manifestations in particular, are not well understood. We reasoned that a detailed knowledge of the global biological processes affected by virus entry into a cell might help shed new light on this long-standing problem. A bacterial two-hybrid screen using DENV2 structural proteins as bait was performed, and the results were used to feed a manually curated, global dengue-human protein interaction network. Gene ontology and pathway enrichment, along with network topology and microarray meta-analysis, were used to generate hypothesis regarding dengue disease biology. Combining bioinformatic tools with two-hybrid technology, we screened human cDNA libraries to catalogue proteins physically interacting with the DENV2 virus structural proteins, Env, cap and PrM. We identified 31 interacting human proteins representing distinct biological processes that are closely related to the major clinical diagnostic feature of dengue infection: haemostatic imbalance. In addition, we found dengue-binding human proteins involved with additional key aspects, previously described as fundamental for virus entry into cells and the innate immune response to infection. Construction of a DENV2-human global protein interaction network revealed interesting biological properties suggested by simple network topology analysis. Our experimental strategy revealed that dengue structural proteins interact with human protein targets involved in the maintenance of blood coagulation and innate anti-viral response processes, and predicts that the interaction of dengue proteins with a proposed human protein interaction network produces a modified biological outcome that may be behind the hallmark pathologies of dengue infection.

Sensory-motor networks involved in speech production and motor control: an fMRI study.

PubMed

Behroozmand, Roozbeh; Shebek, Rachel; Hansen, Daniel R; Oya, Hiroyuki; Robin, Donald A; Howard, Matthew A; Greenlee, Jeremy D W

2015-04-01

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch-shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking. Copyright © 2015 Elsevier Inc. All rights reserved.

Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data

PubMed Central

2011-01-01

Background Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context. Results We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'. Conclusions Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations. PMID:21693013

Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data.

PubMed

Szczurek, Ewa; Markowetz, Florian; Gat-Viks, Irit; Biecek, Przemysław; Tiuryn, Jerzy; Vingron, Martin

2011-06-21

Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context. We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'. Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.

The Use of RNA Sequencing and Correlation Network Analysis to Study Potential Regulators of Crabapple Leaf Color Transformation.

PubMed

Yang, Tuo; Li, Keting; Hao, Suxiao; Zhang, Jie; Song, Tingting; Tian, Ji; Yao, Yuncong

2018-05-01

Anthocyanins are plant pigments that contribute to the color of leaves, flowers and fruits, and that are beneficial to human health in the form of dietary antioxidants. The study of a transformable crabapple cultivar, 'India magic', which has red buds and green mature leaves, using mRNA profiling of four leaf developmental stages, allowed us to characterize molecular mechanisms regulating red color formation in early leaf development and the subsequent rapid down-regulation of anthocyanin biosynthesis. This analysis of differential gene expression during leaf development revealed that ethylene signaling-responsive genes are up-regulated during leaf pigmentation. Genes in the ethylene response factor (ERF), SPL, NAC, WRKY and MADS-box transcription factor (TF) families were identified in two weighted gene co-expression network analysis (WGCNA) modules as having a close relationship to anthocyanin accumulation. Analyses of network hub genes indicated that SPL TFs are located in central positions within anthocyanin-related modules. Furthermore, cis-motif and yeast one-hybrid assays suggested that several anthocyanin biosynthetic or regulatory genes are potential targets of SPL8 and SPL13B. Transient silencing of these two genes confirmed that they play a role in co-ordinating anthocyanin biosynthesis and crabapple leaf development. We present a high-resolution method for identifying regulatory modules associated with leaf pigmentation, which provides a platform for functional genomic studies of anthocyanin biosynthesis.

Prediction of road accidents: A Bayesian hierarchical approach.

PubMed

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.

Developing laboratory networks: a practical guide and application.

PubMed

Kirk, Carol J; Shult, Peter A

2010-01-01

The role of the public health laboratory (PHL) in support of public health response has expanded beyond testing to include a number of other core functions, such as emergency response, training and outreach, communications, laboratory-based surveillance, and laboratory data management. These functions can only be accomplished by a network that includes public health and other agency laboratories and clinical laboratories. It is a primary responsibility of the PHL to develop and maintain such a network. In this article, we present practical recommendations-based on 17 years of network development experience-for the development of statewide laboratory networks. These recommendations, and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of laboratory networks will enhance each state's public health system and is critical to the development of a robust national Laboratory Response Network.

Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position, Economic Wealth, Pollution and Mortality

PubMed Central

Prell, Christina; Sun, Laixiang; Feng, Kuishuang; Myroniuk, Tyler W.

2015-01-01

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries’ mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country’s boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries’ level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990–2010) to estimate the relationships between countries’ level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country’s level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure. PMID:26642202

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow

USGS Publications Warehouse

Burn, Donald H.; Hannaford, Jamie; Hodgkins, Glenn A.; Whitfield, Paul H.; Thorne, Robin; Marsh, Terry

2012-01-01

Reference hydrologic networks (RHNs) can play an important role in monitoring for changes in the hydrological regime related to climate variation and change. Currently, the literature concerning hydrological response to climate variations is complex and confounded by the combinations of many methods of analysis, wide variations in hydrology, and the inclusion of data series that include changes in land use, storage regulation and water use in addition to those of climate. Three case studies that illustrate a variety of approaches to the analysis of data from RHNs are presented and used, together with a summary of studies from the literature, to develop approaches for the investigation of changes in the hydrological regime at a continental or global scale, particularly for international comparison. We present recommendations for an analysis framework and the next steps to advance such an initiative. There is a particular focus on the desirability of establishing standardized procedures and methodologies for both the creation of new national RHNs and the systematic analysis of data derived from a collection of RHNs.

Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

PubMed

Valt, Christian; Klein, Christoph; Boehm, Stephan G

2015-08-01

Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

Generalized synchronization between chimera states

NASA Astrophysics Data System (ADS)

Andrzejak, Ralph G.; Ruzzene, Giulia; Malvestio, Irene

2017-05-01

Networks of coupled oscillators in chimera states are characterized by an intriguing interplay of synchronous and asynchronous motion. While chimera states were initially discovered in mathematical model systems, there is growing experimental and conceptual evidence that they manifest themselves also in natural and man-made networks. In real-world systems, however, synchronization and desynchronization are not only important within individual networks but also across different interacting networks. It is therefore essential to investigate if chimera states can be synchronized across networks. To address this open problem, we use the classical setting of ring networks of non-locally coupled identical phase oscillators. We apply diffusive drive-response couplings between pairs of such networks that individually show chimera states when there is no coupling between them. The drive and response networks are either identical or they differ by a variable mismatch in their phase lag parameters. In both cases, already for weak couplings, the coherent domain of the response network aligns its position to the one of the driver networks. For identical networks, a sufficiently strong coupling leads to identical synchronization between the drive and response. For non-identical networks, we use the auxiliary system approach to demonstrate that generalized synchronization is established instead. In this case, the response network continues to show a chimera dynamics which however remains distinct from the one of the driver. Hence, segregated synchronized and desynchronized domains in individual networks congregate in generalized synchronization across networks.

Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

DOE PAGES

Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

2015-05-28

Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combinedmore » omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.« less

Hyperconnective and hypoconnective cortical and subcortical functional networks in multiple system atrophy.

PubMed

Rosskopf, Johannes; Gorges, Martin; Müller, Hans-Peter; Pinkhardt, Elmar H; Ludolph, Albert C; Kassubek, Jan

2018-04-01

In multiple system atrophy (MSA), the organization of the functional brain connectivity within cortical and subcortical networks and its clinical correlates remains to be investigated. Whole-brain based 'resting-state' fMRI data were obtained from 22 MSA patients (11 MSA-C, 11 MSA-P) and 22 matched healthy controls, together with standardized clinical assessment and video-oculographic recordings (EyeLink ® ). MSA patients vs. controls showed significantly higher ponto-cerebellar functional connectivity and lower default mode network connectivity (p < .05, corrected). No differences were observed in the motor network and in the control network. The higher the ponto-cerebellar network functional connectivity was, the more pronounced was smooth pursuit impairment. This functional connectivity analysis supports a network-dependent combination of hyper- and hypoconnectivity states in MSA, in agreement with adaptive compensatory responses (hyperconnectivity) and a function disconnection syndrome (hypoconnectivity) that may occur in a consecutive sequence. Copyright © 2018 Elsevier Ltd. All rights reserved.

Consciousness, cognition and brain networks: New perspectives.

PubMed

Aldana, E M; Valverde, J L; Fábregas, N

2016-10-01

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

A Channel Network Evolution Model with Subsurface Saturation Mechanism and Analysis of the Chaotic Behavior of the Model

DTIC Science & Technology

1990-09-01

between basin shapes and hydrologic responses is fundamental for the purpose of hydrologic predictions , especially in ungaged basins. Another goal is...47] studied this model and showed analitically how very small differences in the c field generated completely different leaf vein network structures... predictability impossible. Complexity is by no means a requirement in order for a system to exhibit SIC. A system as simple as the logistic equation x,,,,=ax,,(l

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harmsen, S.C.

The telemetered southern Great Basin seismic network (SGBSN) is operated for the Department of Energy`s Yucca Mountain Project (YMP). The US Geological Survey, Branch of Earthquake and Landslide Hazards, maintained this network until September 30, 1992, at which time all operational and analysis responsibilities were transferred to the University of Nevada at Reno Seismological Laboratory (UNRSL). This report contains preliminary earthquake and chemical explosion hypocenter listings and preliminary earthquake focal mechanism solutions for USGS/SGBSN data for the period January 1, 1992 through September 30, 1992, 15:00 UTC.

A Real-World Network Modeling Project

DTIC Science & Technology

2014-02-12

about the project, which accounts for a third of their class grade. As can be expected, giving substantial weight to the project increases active student...analysis, humanitarian aid warehouses, Israeli traffic analysis, London Olympic Games transport, medical evacuation, Monterey fire department...responsiveness, Monterey Peninsula evacuation, natural gas pipeline transport, rail transport of new cars, ski lifts for Keystone Colorado, small boat attack

A Scalable Approach for Discovering Conserved Active Subnetworks across Species

PubMed Central

Verfaillie, Catherine M.; Hu, Wei-Shou; Myers, Chad L.

2010-01-01

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network - cross(X)-species - Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks. PMID:21170309

State Support: A Prerequisite for Global Health Network Effectiveness Comment on "Four Challenges that Global Health Networks Face".

PubMed

Marten, Robert; Smith, Richard D

2017-07-24

Shiffman recently summarized lessons for network effectiveness from an impressive collection of case-studies. However, in common with most global health governance analysis in recent years, Shiffman underplays the important role of states in these global networks. As the body which decides and signs international agreements, often provides the resourcing, and is responsible for implementing initiatives all contributing to the prioritization of certain issues over others, state recognition and support is a prerequisite to enabling and determining global health networks' success. The role of states deserves greater attention, analysis and consideration. We reflect upon the underappreciated role of the state within the current discourse on global health. We present the tobacco case study to illustrate the decisive role of states in determining progress for global health networks, and highlight how states use a legitimacy loop to gain legitimacy from and provide legitimacy to global health networks. Moving forward in assessing global health networks' effectiveness, further investigating state support as a determinant of success will be critical. Understanding how global health networks and states interact and evolve to shape and support their respective interests should be a focus for future research. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gene regulation is governed by a core network in hepatocellular carcinoma.

PubMed

Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further experimental validation is worthwhile.

Frequency-specific alterations in functional connectivity in treatment-resistant and -sensitive major depressive disorder.

PubMed

He, Zongling; Cui, Qian; Zheng, Junjie; Duan, Xujun; Pang, Yajing; Gao, Qing; Han, Shaoqiang; Long, Zhiliang; Wang, Yifeng; Li, Jiao; Wang, Xiao; Zhao, Jingping; Chen, Huafu

2016-11-01

Major depressive disorder (MDD) may involve alterations in brain functional connectivity in multiple neural circuits and present large-scale network dysfunction. Patients with treatment-resistant depression (TRD) and treatment-sensitive depression (TSD) show different responses to antidepressants and aberrant brain functions. This study aims to investigate functional connectivity patterns of TRD and TSD at the whole brain resting state. Seventeen patients with TRD, 17 patients with TSD, and 17 healthy controls matched with age, gender, and years of education were recruited in this study. The brain was divided using an automated anatomical labeling atlas into 90 regions of interest, which were used to construct the entire brain functional networks. An analysis method called network-based statistic was used to explore the dysconnected subnetworks of TRD and TSD at different frequency bands. At resting state, TSD and TRD present characteristic patterns of network dysfunction at special frequency bands. The dysconnected subnetwork of TSD mainly lies in the fronto-parietal top-down control network. Moreover, the abnormal neural circuits of TRD are extensive and complex. These circuits not only depend on the abnormal affective network but also involve other networks, including salience network, auditory network, visual network, and language processing cortex. Our findings reflect that the pathological mechanism of TSD may refer to impairment in cognitive control, whereas TRD mainly triggers the dysfunction of emotion processing and affective cognition. This study reveals that differences in brain functional connectivity at resting state reflect distinct pathophysiological mechanisms in TSD and TRD. These findings may be helpful in differentiating two types of MDD and predicting treatment responses. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuronal correlates of a virtual-reality-based passive sensory P300 network.

PubMed

Chen, Chun-Chuan; Syue, Kai-Syun; Li, Kai-Chiun; Yeh, Shih-Ching

2014-01-01

P300, a positive event-related potential (ERP) evoked at around 300 ms after stimulus, can be elicited using an active or passive oddball paradigm. Active P300 requires a person's intentional response, whereas passive P300 does not require an intentional response. Passive P300 has been used in incommunicative patients for consciousness detection and brain computer interface. Active and passive P300 differ in amplitude, but not in latency or scalp distribution. However, no study has addressed the mechanism underlying the production of passive P300. In particular, it remains unclear whether the passive P300 shares an identical active P300 generating network architecture when no response is required. This study aims to explore the hierarchical network of passive sensory P300 production using dynamic causal modelling (DCM) for ERP and a novel virtual reality (VR)-based passive oddball paradigm. Moreover, we investigated the causal relationship of this passive P300 network and the changes in connection strength to address the possible functional roles. A classical ERP analysis was performed to verify that the proposed VR-based game can reliably elicit passive P300. The DCM results suggested that the passive and active P300 share the same parietal-frontal neural network for attentional control and, underlying the passive network, the feed-forward modulation is stronger than the feed-back one. The functional role of this forward modulation may indicate the delivery of sensory information, automatic detection of differences, and stimulus-driven attentional processes involved in performing this passive task. To our best knowledge, this is the first study to address the passive P300 network. The results of this study may provide a reference for future clinical studies on addressing the network alternations under pathological states of incommunicative patients. However, caution is required when comparing patients' analytic results with this study. For example, the task presented here is not applicable to incommunicative patients.

Neuronal Correlates of a Virtual-Reality-Based Passive Sensory P300 Network

PubMed Central

Chen, Chun-Chuan; Syue, Kai-Syun; Li, Kai-Chiun; Yeh, Shih-Ching

2014-01-01

P300, a positive event-related potential (ERP) evoked at around 300 ms after stimulus, can be elicited using an active or passive oddball paradigm. Active P300 requires a person’s intentional response, whereas passive P300 does not require an intentional response. Passive P300 has been used in incommunicative patients for consciousness detection and brain computer interface. Active and passive P300 differ in amplitude, but not in latency or scalp distribution. However, no study has addressed the mechanism underlying the production of passive P300. In particular, it remains unclear whether the passive P300 shares an identical active P300 generating network architecture when no response is required. This study aims to explore the hierarchical network of passive sensory P300 production using dynamic causal modelling (DCM) for ERP and a novel virtual reality (VR)-based passive oddball paradigm. Moreover, we investigated the causal relationship of this passive P300 network and the changes in connection strength to address the possible functional roles. A classical ERP analysis was performed to verify that the proposed VR-based game can reliably elicit passive P300. The DCM results suggested that the passive and active P300 share the same parietal-frontal neural network for attentional control and, underlying the passive network, the feed-forward modulation is stronger than the feed-back one. The functional role of this forward modulation may indicate the delivery of sensory information, automatic detection of differences, and stimulus-driven attentional processes involved in performing this passive task. To our best knowledge, this is the first study to address the passive P300 network. The results of this study may provide a reference for future clinical studies on addressing the network alternations under pathological states of incommunicative patients. However, caution is required when comparing patients’ analytic results with this study. For example, the task presented here is not applicable to incommunicative patients. PMID:25401520

A multivariate analysis of age-related differences in functional networks supporting conflict resolution.

PubMed

Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars

2014-02-01

Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.

Neural correlates of depressive realism – An fMRI study on causal attribution in depression

PubMed Central

Seidel, Eva-Maria; Satterthwaite, Theodore D.; Eickhoff, Simon B.; Schneider, Frank; Gur, Ruben C.; Wolf, Daniel H.; Habel, Ute; Derntl, Birgit

2013-01-01

Background Biased causal attribution is a critical factor in the cognitive model of depression. Whereas depressed patients interpret events negatively, healthy people show a self-serving bias (internal attribution of positive events and external attribution of negative events). Methods Using fMRI, depressed patients (n=15) and healthy controls (n=15) were confronted with positive and negative social events and made causal attributions (internal vs. external). Functional data were analyzed using a mixed effects model. Results Behaviourally, controls showed a self-serving bias, whereas patients demonstrated a balanced attributional pattern. Analysis of functional data revealed a significant group difference in a fronto-temporal network. Higher activation of this network was associated with non self-serving attributions in controls but self-serving attributions in patients. Applying a psycho-physiological interaction analysis, we observed reduced coupling between a dorsomedial PFC seed region and limbic areas during self-serving attributions in patients compared to controls. Limitations Results of the PPI analysis are preliminary given the liberal statistical threshold. Conclusions The association of the behaviourally less frequent attributional pattern with activation in a fronto-temporal network suggests that non self-serving responses may produce a self-related response conflict in controls, while self-serving responses produce this conflict in patients. Moreover, attribution-modulated coupling between the dorsomedial PFC and limbic regions was weaker in patients than controls. This preliminary finding suggests that depression may be associated with disturbances in fronto-limbic coupling during attributional decisions. Our results implicate that treatment of major depression may benefit from approaches that facilitate reinterpretation of emotional events in a more positive, more self-serving way. PMID:22377511

Efficient Power Network Analysis with Modeling of Inductive Effects

NASA Astrophysics Data System (ADS)

Zeng, Shan; Yu, Wenjian; Hong, Xianlong; Cheng, Chung-Kuan

In this paper, an efficient method is proposed to accurately analyze large-scale power/ground (P/G) networks, where inductive parasitics are modeled with the partial reluctance. The method is based on frequency-domain circuit analysis and the technique of vector fitting [14], and obtains the time-domain voltage response at given P/G nodes. The frequency-domain circuit equation including partial reluctances is derived, and then solved with the GMRES algorithm with rescaling, preconditioning and recycling techniques. With the merit of sparsified reluctance matrix and iterative solving techniques for the frequency-domain circuit equations, the proposed method is able to handle large-scale P/G networks with complete inductive modeling. Numerical results show that the proposed method is orders of magnitude faster than HSPICE, several times faster than INDUCTWISE [4], and capable of handling the inductive P/G structures with more than 100, 000 wire segments.

Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

PubMed

Ornostay, Anna; Cowie, Andrew M; Hindle, Matthew; Baker, Christopher J O; Martyniuk, Christopher J

2013-12-01

The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species. © 2013.

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

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 apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. 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.

Neural Network Analysis of Musculoskeletal Responses to Electrical AC-Stimulus

DTIC Science & Technology

2001-10-25

FAM, SOM I. INTRODUCTION Various kinds of electrical measurements have been used to investigate the human body, including the mechanical or chemical...measurements were obtained from five points; namely, the patients’ wrists, ankles and back. The electrodes of an IF Electrotherapy unit [5] were attached...musculoskeletal responses to electrical AC-stimulus M. Hannula1, E. Alasaarela2 , J. Laitinen1 1Institute of Technology , Oulu Polytechnic, Oulu, Finland

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

Brain Circuitry Supporting Multi-Organ Autonomic Outflow in Response to Nausea.

PubMed

Sclocco, Roberta; Kim, Jieun; Garcia, Ronald G; Sheehan, James D; Beissner, Florian; Bianchi, Anna M; Cerutti, Sergio; Kuo, Braden; Barbieri, Riccardo; Napadow, Vitaly

2016-02-01

While autonomic outflow is an important co-factor of nausea physiology, central control of this outflow is poorly understood. We evaluated sympathetic (skin conductance level) and cardiovagal (high-frequency heart rate variability) modulation, collected synchronously with functional MRI (fMRI) data during nauseogenic visual stimulation aimed to induce vection in susceptible individuals. Autonomic data guided analysis of neuroimaging data, using a stimulus-based (analysis windows set by visual stimulation protocol) and percept-based (windows set by subjects' ratings) approach. Increased sympathetic and decreased parasympathetic modulation was associated with robust and anti-correlated brain activity in response to nausea. Specifically, greater autonomic response was associated with reduced fMRI signal in brain regions such as the insula, suggesting an inhibitory relationship with premotor brainstem nuclei. Interestingly, some sympathetic/parasympathetic specificity was noted. Activity in default mode network and visual motion areas was anti-correlated with parasympathetic outflow at peak nausea. In contrast, lateral prefrontal cortical activity was anti-correlated with sympathetic outflow during recovery, soon after cessation of nauseogenic stimulation. These results suggest divergent central autonomic control for sympathetic and parasympathetic response to nausea. Autonomic outflow and the central autonomic network underlying ANS response to nausea may be an important determinant of overall nausea intensity and, ultimately, a potential therapeutic target. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance

PubMed Central

Montastier, Emilie; Villa-Vialaneix, Nathalie; Caspar-Bauguil, Sylvie; Hlavaty, Petr; Tvrzicka, Eva; Gonzalez, Ignacio; Saris, Wim H. M.; Langin, Dominique; Kunesova, Marie; Viguerie, Nathalie

2015-01-01

Nutrigenomics investigates relationships between nutrients and all genome-encoded molecular entities. This holistic approach requires systems biology to scrutinize the effects of diet on tissue biology. To decipher the adipose tissue (AT) response to diet induced weight changes we focused on key molecular (lipids and transcripts) AT species during a longitudinal dietary intervention. To obtain a systems model, a network approach was used to combine all sets of variables (bio-clinical, fatty acids and mRNA levels) and get an overview of their interactions. AT fatty acids and mRNA levels were quantified in 135 obese women at baseline, after an 8-week low calorie diet (LCD) and after 6 months of ad libitum weight maintenance diet (WMD). After LCD, individuals were stratified a posteriori according to weight change during WMD. A 3 steps approach was used to infer a global model involving the 3 sets of variables. It consisted in inferring intra-omic networks with sparse partial correlations and inter-omic networks with regularized canonical correlation analysis and finally combining the obtained omic-specific network in a single global model. The resulting networks were analyzed using node clustering, systematic important node extraction and cluster comparisons. Overall, AT showed both constant and phase-specific biological signatures in response to dietary intervention. AT from women regaining weight displayed growth factors, angiogenesis and proliferation signaling signatures, suggesting unfavorable tissue hyperplasia. By contrast, after LCD a strong positive relationship between AT myristoleic acid (a fatty acid with low AT level) content and de novo lipogenesis mRNAs was found. This relationship was also observed, after WMD, in the group of women that continued to lose weight. This original system biology approach provides novel insight in the AT response to weight control by highlighting the central role of myristoleic acid that may account for the beneficial effects of weight loss. PMID:25590576

Robust Resilience of the Frontotemporal Syntax System to Aging

PubMed Central

Samu, Dávid; Davis, Simon W.; Geerligs, Linda; Mustafa, Abdur; Tyler, Lorraine K.

2016-01-01

Brain function is thought to become less specialized with age. However, this view is largely based on findings of increased activation during tasks that fail to separate task-related processes (e.g., attention, decision making) from the cognitive process under examination. Here we take a systems-level approach to separate processes specific to language comprehension from those related to general task demands and to examine age differences in functional connectivity both within and between those systems. A large population-based sample (N = 111; 22–87 years) from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) was scanned using functional MRI during two versions of an experiment: a natural listening version in which participants simply listened to spoken sentences and an explicit task version in which they rated the acceptability of the same sentences. Independent components analysis across the combined data from both versions showed that although task-free language comprehension activates only the auditory and frontotemporal (FTN) syntax networks, performing a simple task with the same sentences recruits several additional networks. Remarkably, functionality of the critical FTN is maintained across age groups, showing no difference in within-network connectivity or responsivity to syntactic processing demands despite gray matter loss and reduced connectivity to task-related networks. We found no evidence for reduced specialization or compensation with age. Overt task performance was maintained across the lifespan and performance in older, but not younger, adults related to crystallized knowledge, suggesting that decreased between-network connectivity may be compensated for by older adults' richer knowledge base. SIGNIFICANCE STATEMENT Understanding spoken language requires the rapid integration of information at many different levels of analysis. Given the complexity and speed of this process, it is remarkably well preserved with age. Although previous work claims that this preserved functionality is due to compensatory activation of regions outside the frontotemporal language network, we use a novel systems-level approach to show that these “compensatory” activations simply reflect age differences in response to experimental task demands. Natural, task-free language comprehension solely recruits auditory and frontotemporal networks, the latter of which is similarly responsive to language-processing demands across the lifespan. These findings challenge the conventional approach to neurocognitive aging by showing that the neural underpinnings of a given cognitive function depend on how you test it. PMID:27170120

Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties.

PubMed

Ponsuksili, Siriluck; Du, Yang; Hadlich, Frieder; Siengdee, Puntita; Murani, Eduard; Schwerin, Manfred; Wimmers, Klaus

2013-08-05

Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes. We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits. Porcine muscle co-expression transcript networks that correlated to post mortem traits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

Nonequilibrium landscape theory of neural networks.

PubMed

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-11-05

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

Nonequilibrium landscape theory of neural networks

PubMed Central

Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

2013-01-01

The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

[ANOREXIA AND BULIMIA: IMPACT ON NETWORK SOCIETY].

PubMed

Alex Sánchez, María Dolores

2015-01-01

The Information and Communication Technologies (ICT) have an increasing influence on the way we relate and in shaping personal identity. The phenomenon of online social networking emerges strongly and contributes to the development of new spaces breaking with the official discourse that marks the scientific evidence on health. This paper analyzes the impact of ICT in relation to the identity of the digital natives and eating disorders (ED). Particular attention to how the network society determines the response of young people in situations of social tension is dedicated. To do this, provides a perspective on the concept of interaction from the analysis of the discourse on anorexia and bulimia in the network, and how to care nurses should consider these factors to improve efficiency and quality in clinical care and patient care.

Specific CA3 neurons decode neural information of dentate granule cells evoked by paired-pulse stimulation in co-cultured networks.

PubMed

Poli, Daniele; DeMarse, Thomas B; Wheeler, Bruce C; Brewer, Gregory J

2017-07-01

CA3 and dentate gyrus (DG) neurons are cultured in two-chamber devices on multi-electrode arrays (MEAs) and connected via micro-tunnels. In order to evoke time-locked activity, paired-pulse stimulation is applied to 22 different sites and repeated 25 times in each well in 5 MEA co-cultures and results compared to CA3-CA3 and DG-DG networks homologous controls. In these hippocampal sub-regions, we focus on the mechanisms underpinning a network's ability to decode the identity of site specific stimulation from analysis of evoked network responses using a support vector machine classifier. Our results indicate that a pool of CA3 neurons is able to reliably decode the identity of DG stimulation site information.

Overview of artificial neural networks.

PubMed

Zou, Jinming; Han, Yi; So, Sung-Sau

2008-01-01

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties. The ANN is one of many versatile tools to meet the demand in drug discovery modeling. Compared to a traditional regression approach, the ANN is capable of modeling complex nonlinear relationships. The ANN also has excellent fault tolerance and is fast and highly scalable with parallel processing. This chapter introduces the background of ANN development and outlines the basic concepts crucially important for understanding more sophisticated ANN. Several commonly used learning methods and network setups are discussed briefly at the end of the chapter.

Inter-organizational network in Indonesia during disasters: Examples and research agenda on disaster management

NASA Astrophysics Data System (ADS)

Bisri, M. B. F.

2017-02-01

Indonesia is facing various type of disaster risks, each with its own nature (sudden or slow onset, purely natural or man-made) and coverage of affected areas. Whereas science, technology and engineering intervention requires different modalities for each hazard, little has been known on whether the institutional setup and organizations involvement requires a different or similar types of intervention. Under a decentralized disaster management system, potential involvement of international organizations in response and growing diversified organizations involved in responding to disaster, it is important to understand the nature of inter-organizational network during various type of disasters in Indonesia. This paper is mixture of in-depth literature review and multiple case studies on utilization of social network analysis (SNA) in modelling inter-organizational network during various disasters in Indonesia.

Autaptic effects on synchrony of neurons coupled by electrical synapses

NASA Astrophysics Data System (ADS)

Kim, Youngtae

2017-07-01

In this paper, we numerically study the effects of a special synapse known as autapse on synchronization of population of Morris-Lecar (ML) neurons coupled by electrical synapses. Several configurations of the ML neuronal populations such as a pair or a ring or a globally coupled network with and without autapses are examined. While most of the papers on the autaptic effects on synchronization have used networks of neurons of same spiking rate, we use the network of neurons of different spiking rates. We find that the optimal autaptic coupling strength and the autaptic time delay enhance synchronization in our neural networks. We use the phase response curve analysis to explain the enhanced synchronization by autapses. Our findings reveal the important relationship between the intraneuronal feedback loop and the interneuronal coupling.

Personal Network Correlates of Alcohol, Cigarette, and Marijuana Use Among Homeless Youth

PubMed Central

Wenzel, Suzanne L.; Tucker, Joan S.; Golinelli, Daniela; Green, Harold D.; Zhou, Annie

2013-01-01

Background Youth who are homeless and on their own are among the most marginalized individuals in the United States and face multiple risks, including use of substances. This study investigates how the use of alcohol, cigarettes, and marijuana among homeless youth may be influenced by characteristics of their social networks. Methods Homeless youth aged 13–24 were randomly sampled from 41 service and street sites in Los Angeles County (N = 419). Predictors of substance use were examined using linear regression analysis (for average number of drinks and average number of cigarettes per day) and negative binomal regression analysis (for frequency of past month marijuana use). Results Youth with more substance users in their networks reported greater alcohol, cigarette, and marijuana consumption regardless of whether these network members provided tangible or emotional support. Marijuana use was more frequent for youth who met more network members through homeless settings, but less frequent among those who met more network members through treatment or AA/NA. Greater alcohol use occurred among youth who met more network members through substance use-related activities. Youth having more adults in positions of responsibility in their networks consumed less alcohol, and those with more school attendees in their networks consumed less alcohol and cigarettes. Conclusions Findings highlight the importance of social context in understanding substance use among homeless youth. Results also support the relevance of network-based interventions to change social context for substance using youth, in terms of both enhancing pro-social influences and reducing exposure to substance use. PMID:20656423

Use of social network analysis in maternity care to identify the profession most suited for case manager role.

PubMed

Groenen, Carola J M; van Duijnhoven, Noortje T L; Faber, Marjan J; Koetsenruijter, Jan; Kremer, Jan A M; Vandenbussche, Frank P H A

2017-02-01

To improve Dutch maternity care, professionals start working in interdisciplinary patient-centred networks, which includes the patients as a member. The introduction of the case manager is expected to work positively on both the individual and the network level. However, case management is new in Dutch maternity care. The present study aims to define the profession that would be most suitable to fulfil the role of case manager. The maternal care network in the Nijmegen region was determined by using Social Network Analysis (SNA). SNA is a quantitative methodology that measures and analyses patient-related connections between different professionals working in a network. To identify the case manager we focused on the position, reach, and connections in the network of the maternal care professionals. Maternity healthcare professionals in a single region of the Netherlands with an average of 4,500 births/year. The participants were 214 individual healthcare workers from eight different professions. The total network showed 3948 connections between 214 maternity healthcare professionals with a density of 0.08. Each profession had some central individuals in the network. The 52 community-based midwives were responsible for 51% of all measured connections. The youth health doctors and nurses were mostly situated on the periphery and less connected. The betweenness centrality had the highest score in obstetricians and community-based midwives. Only the community-based midwives had connections with all other groups of professions. Almost all professionals in the network could reach other professionals in two steps. Copyright © 2016 Elsevier Ltd. All rights reserved.

Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms

NASA Astrophysics Data System (ADS)

Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

2017-03-01

Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

Personal network correlates of alcohol, cigarette, and marijuana use among homeless youth.

PubMed

Wenzel, Suzanne L; Tucker, Joan S; Golinelli, Daniela; Green, Harold D; Zhou, Annie

2010-11-01

Youth who are homeless and on their own are among the most marginalized individuals in the United States and face multiple risks, including use of substances. This study investigates how the use of alcohol, cigarettes, and marijuana among homeless youth may be influenced by characteristics of their social networks. Homeless youth aged 13-24 were randomly sampled from 41 service and street sites in Los Angeles County (N=419). Predictors of substance use were examined using linear regression analysis (for average number of drinks and average number of cigarettes per day) and negative binomial regression analysis (for frequency of past month marijuana use). Youth with more substance users in their networks reported greater alcohol, cigarette, and marijuana consumption regardless of whether these network members provided tangible or emotional support. Marijuana use was more frequent for youth who met more network members through homeless settings, but less frequent among those who met more network members through treatment or AA/NA. Greater alcohol use occurred among youth who met more network members through substance use-related activities. Youth having more adults in positions of responsibility in their networks consumed less alcohol, and those with more school attendees in their networks consumed less alcohol and cigarettes. Findings highlight the importance of social context in understanding substance use among homeless youth. Results also support the relevance of network-based interventions to change social context for substance-using youth, in terms of both enhancing pro-social influences and reducing exposure to substance use. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Transcriptome analysis reveals regulatory networks underlying differential susceptibility to Botrytis cinerea in response to nitrogen availability in Solanum lycopersicum

PubMed Central

Vega, Andrea; Canessa, Paulo; Hoppe, Gustavo; Retamal, Ignacio; Moyano, Tomas C.; Canales, Javier; Gutiérrez, Rodrigo A.; Rubilar, Joselyn

2015-01-01

Nitrogen (N) is one of the main limiting nutrients for plant growth and crop yield. It is well documented that changes in nitrate availability, the main N source found in agricultural soils, influences a myriad of developmental programs and processes including the plant defense response. Indeed, many agronomical reports indicate that the plant N nutritional status influences their ability to respond effectively when challenged by different pathogens. However, the molecular mechanisms involved in N-modulation of plant susceptibility to pathogens are poorly characterized. In this work, we show that Solanum lycopersicum defense response to the necrotrophic fungus Botrytis cinerea is affected by plant N availability, with higher susceptibility in nitrate-limiting conditions. Global gene expression responses of tomato against B. cinerea under contrasting nitrate conditions reveals that plant primary metabolism is affected by the fungal infection regardless of N regimes. This result suggests that differential susceptibility to pathogen attack under contrasting N conditions is not only explained by a metabolic alteration. We used a systems biology approach to identify the transcriptional regulatory network implicated in plant response to the fungus infection under contrasting nitrate conditions. Interestingly, hub genes in this network are known key transcription factors involved in ethylene and jasmonic acid signaling. This result positions these hormones as key integrators of nitrate and defense against B. cinerea in tomato plants. Our results provide insights into potential crosstalk mechanisms between necrotrophic defense response and N status in plants. PMID:26583019

Alerting, orienting or executive attention networks: differential patters of pupil dilations

PubMed Central

Geva, Ronny; Zivan, Michal; Warsha, Aviv; Olchik, Dov

2013-01-01

Attention capacities, alerting responses, orienting to sensory stimulation, and executive monitoring of performance are considered independent yet interrelated systems. These operations play integral roles in regulating the behavior of diverse species along the evolutionary ladder. Each of the primary attention constructs—alerting, orienting, and executive monitoring—involves salient autonomic correlates as evidenced by changes in reactive pupil dilation (PD), heart rate, and skin conductance. Recent technological advances that use remote high-resolution recording may allow the discernment of temporo-spatial attributes of autonomic responses that characterize the alerting, orienting, and executive monitoring networks during free viewing, irrespective of voluntary performance. This may deepen the understanding of the roles of autonomic regulation in these mental operations and may deepen our understanding of behavioral changes in verbal as well as in non-verbal species. The aim of this study was to explore differences between psychosensory PD responses in alerting, orienting, and executive conflict monitoring tasks to generate estimates of concurrent locus coeruleus (LC) noradrenergic input trajectories in healthy human adults using the attention networks test (ANT). The analysis revealed a construct-specific pattern of pupil responses: alerting is characterized by an early component (Pa), its acceleration enables covert orienting, and executive control is evidenced by a prominent late component (Pe). PD characteristics seem to be task-sensitive, allowing exploration of mental operations irrespective of conscious voluntary responses. These data may facilitate development of studies designed to assess mental operations in diverse species using autonomic responses. PMID:24133422

Improving subjective pattern recognition in chemical senses through reduction of nonlinear effects in evaluation of sparse data

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Rasouli, Firooz; Wrenn, Susan E.; Subbiah, M.

2002-11-01

Artificial neural network models are typically useful in pattern recognition and extraction of important features in large data sets. These models are implemented in a wide variety of contexts and with diverse type of input-output data. The underlying mathematics of supervised training of neural networks is ultimately tied to the ability to approximate the nonlinearities that are inherent in network"s generalization ability. The quality and availability of sufficient data points for training and validation play a key role in the generalization ability of the network. A potential domain of applications of neural networks is in analysis of subjective data, such as in consumer science, affective neuroscience and perception of chemical senses. In applications of ANN to subjective data, it is common to rely on knowledge of the science and context for data acquisition, for instance as a priori probabilities in the Bayesian framework. In this paper, we discuss the circumstances that create challenges for success of neural network models for subjective data analysis, such as sparseness of data and cost of acquisition of additional samples. In particular, in the case of affect and perception of chemical senses, we suggest that inherent ambiguity of subjective responses could be offset by a combination of human-machine expert. We propose a method of pre- and post-processing for blind analysis of data that that relies on heuristics from human performance in interpretation of data. In particular, we offer an information-theoretic smoothing (ITS) algorithm that optimizes that geometric visualization of multi-dimensional data and improves human interpretation of the input-output view of neural network implementations. The pre- and post-processing algorithms and ITS are unsupervised. Finally, we discuss the details of an example of blind data analysis from actual taste-smell subjective data, and demonstrate the usefulness of PCA in reduction of dimensionality, as well as ITS.

Power structure among the actors of financial support to the poor to access health services: Social network analysis approach.

PubMed

Etemadi, Manal; Gorji, Hasan Abolghasem; Kangarani, Hannaneh Mohammadi; Ashtarian, Kioomars

2017-12-01

The extent of universal health coverage in terms of financial protection is worrisome in Iran. There are challenges in health policies to guarantee financial accessibility to health services, especially for poor people. Various institutions offer support to ensure that the poor have financial access to health services. The aim of this study is to investigate the relationship network among the institutions active in this field. This study is a policy document analysis. It evaluates the country's legal documents in the field of financial support to the poor for healthcare after the Islamic Revolution in Iran. The researchers looked for the documents on the related websites and referred to the related organizations. The social network analysis approach was chosen for the analysis of the documents. Block-modelling and multi-dimensional scaling (MDS) was used to determine the network structures. The UCINET software was employed to analyse the data. Most the main actors of this network are chosen from the government budget. There is no legal communication and cooperation among some of the actors because of their improper position in the network. Seven blocks have been clustered by CONCOR in terms of the actor's degree of similarity. The social distance among the actors of the seven blocks is very short. Power distribution in the field of financial support to the poor has a fragmented structure; however, it is mainly run by a dominant block consisting of The Supreme Council of Welfare and Social Security, Health Insurance Organization, and the Ministry of Health and Medical Education. The financial support for the poor network involves multiple actors. This variety has created a series of confusions in terms of the type, level, and scope of responsibilities among the actors. The weak presence legislative and regulatory institutions and also non-governmental institutions are the main weak points of this network. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Preliminary Examination of the Relationship Between Social Networking Interactions, Internet Use, and Thwarted Belongingness.

PubMed

Moberg, Fallon B; Anestis, Michael D

2015-01-01

Joiner's (2005) interpersonal-psychological theory of suicide hypothesizes that suicidal desire develops in response to the joint presence of thwarted belongingness and perceived burdensomeness. To consider the potential influence of online interactions and behaviors on these outcomes. To address this, we administered an online protocol assessing suicidal desire and online interactions in a sample of 305 undergraduates (83.6% female). We hypothesized negative interactions on social networking sites and a preference for online social interactions would be associated with thwarted belongingness. We also conducted an exploratory analysis examining the associations between Internet usage and perceived burdensomeness. Higher levels of negative interactions on social networking sites, but no other variables, significantly predicted thwarted belongingness. Our exploratory analysis showed that none of our predictors were associated with perceived burdensomeness after accounting for demographics, depression, and thwarted belongingness. Our findings indicate that a general tendency to have negative interactions on social networking sites could possibly impact suicidal desire and that these effects are significant above and beyond depression symptoms. Furthermore, no other aspect of problematic Internet use significantly predicted our outcomes in multivariate analyses, indicating that social networking in particular may have a robust effect on thwarted belongingness.

Griffiths effects of the susceptible-infected-susceptible epidemic model on random power-law networks.

PubMed

Cota, Wesley; Ferreira, Silvio C; Ódor, Géza

2016-03-01

We provide numerical evidence for slow dynamics of the susceptible-infected-susceptible model evolving on finite-size random networks with power-law degree distributions. Extensive simulations were done by averaging the activity density over many realizations of networks. We investigated the effects of outliers in both highly fluctuating (natural cutoff) and nonfluctuating (hard cutoff) most connected vertices. Logarithmic and power-law decays in time were found for natural and hard cutoffs, respectively. This happens in extended regions of the control parameter space λ(1)<λ<λ(2), suggesting Griffiths effects, induced by the topological inhomogeneities. Optimal fluctuation theory considering sample-to-sample fluctuations of the pseudothresholds is presented to explain the observed slow dynamics. A quasistationary analysis shows that response functions remain bounded at λ(2). We argue these to be signals of a smeared transition. However, in the thermodynamic limit the Griffiths effects loose their relevancy and have a conventional critical point at λ(c)=0. Since many real networks are composed by heterogeneous and weakly connected modules, the slow dynamics found in our analysis of independent and finite networks can play an important role for the deeper understanding of such systems.

Network Analysis Implicates Alpha-Synuclein (Snca) in the Regulation of Ovariectomy-Induced Bone Loss

PubMed Central

Calabrese, Gina; Mesner, Larry D.; Foley, Patricia L.; Rosen, Clifford J.; Farber, Charles R.

2016-01-01

The postmenopausal period in women is associated with decreased circulating estrogen levels, which accelerate bone loss and increase the risk of fracture. Here, we gained novel insight into the molecular mechanisms mediating bone loss in ovariectomized (OVX) mice, a model of human menopause, using co-expression network analysis. Specifically, we generated a co-expression network consisting of 53 gene modules using expression profiles from intact and OVX mice from a panel of inbred strains. The expression of four modules was altered by OVX, including module 23 whose expression was decreased by OVX across all strains. Module 23 was enriched for genes involved in the response to oxidative stress, a process known to be involved in OVX-induced bone loss. Additionally, module 23 homologs were co-expressed in human bone marrow. Alpha synuclein (Snca) was one of the most highly connected “hub” genes in module 23. We characterized mice deficient in Snca and observed a 40% reduction in OVX-induced bone loss. Furthermore, protection was associated with the altered expression of specific network modules, including module 23. In summary, the results of this study suggest that Snca regulates bone network homeostasis and ovariectomy-induced bone loss. PMID:27378017

Dynamic Channel Network Extraction from Satellite Imagery of the Jamuna River

NASA Astrophysics Data System (ADS)

Addink, E. A.; Marra, W. A.; Kleinhans, M. G.

2010-12-01

Evolution of the largest rivers on Earth is poorly understood while their response to global change is dramatic, such as severe drought and flooding problems. Rivers with high annual dynamics, like the Jamuna, allow us to study their response to changing conditions. Most remote-sensing work so far focused only on pixel-based analysis of channels and change detection or manual digitisation of channels, which is far from urgently needed quantifiers of pattern and pattern change. Using a series of Landsat TM images taken at irregular intervals showing inter- and intra-annual variation, we demonstrate that braided rivers can be represented as nearly chain-like directional networks. These can be studied with novel methods gleaned from neurology. These networks provide an integral spatial description of the network and should not be confused with hierarchical hydrological stream network descriptions developed in the ’60s to describe drainage basins. The images were first classified into water, bare sediment and vegetation. The contiguous water body of the river was then selected and translated into a network description with bifurcations and confluences at the nodes, and interconnecting channels. Along the entire river the well-known braiding indices were derived from the network. The channel width is a crucial attribute of the channel network as this allows the calculation of bifurcation asymmetry. The width was also used with channel length as weights to all the elements in the network in the calculation of more advanced measures for the nature and evolution of the channel network. The key step here is to describe river network evolution by identifying the same node in multiple subsequent images as well as new and abandoned nodes, in order to distinguish migration of bifurcations from avulsion processes. Once identified through time, the changes in node position and the changes in the connected channels can be quantified. These changes can potentially be linked to channel migration and vegetation cover along the channels. A network evolves in time by adding or removing channels and their bifurcation- and confluence couples. Using the network topology, we quantified network properties such as `centrality’, which provides a measure for the overall importance of individual channels in a network. This is a novel and robust indicator to assess the effect of a change or engineering measure in a channel on the entire network. The physical basis for downstream propagation of information through a fluvial network is the flood conveyance and sediment transport, and for upstream propagation it is the backwater effect. Using the dynamic network description we can start quantifying the effects of local changes in the network on the entire upstream and downstream network. We conclude that the developed workflow allows the use of novel and useful measures borrowed from other sciences in river network analysis, and provides, e.g., the assessment of the importance of individual branches in a large complicated network.

The Neural Basis of Recollection Rejection: Increases in Hippocampal-Prefrontal Connectivity in the Absence of a Shared Recall-to-Reject and Target Recollection Network.

PubMed

Bowman, Caitlin R; Dennis, Nancy A

2016-08-01

Recollection rejection or "recall-to-reject" is a mechanism that has been posited to help maintain accurate memory by preventing the occurrence of false memories. Recollection rejection occurs when the presentation of a new item during recognition triggers recall of an associated target, a mismatch in features between the new and old items is registered, and the lure is correctly rejected. Critically, this characterization of recollection rejection involves a recall signal that is conceptually similar to recollection as elicited by a target. However, previous neuroimaging studies have not evaluated the extent to which recollection rejection and target recollection rely on a common neural signal but have instead focused on recollection rejection as a postretrieval monitoring process. This study utilized a false memory paradigm in conjunction with an adapted remember-know-new response paradigm that separated "new" responses based on recollection rejection from those that were based on a lack of familiarity with the item. This procedure allowed for parallel recollection rejection and target recollection contrasts to be computed. Results revealed that, contrary to predictions from theoretical and behavioral literature, there was virtually no evidence of a common retrieval mechanism supporting recollection rejection and target recollection. Instead of the typical target recollection network, recollection rejection recruited a network of lateral prefrontal and bilateral parietal regions that is consistent with the retrieval monitoring network identified in previous neuroimaging studies of recollection rejection. However, a functional connectivity analysis revealed a component of the frontoparietal rejection network that showed increased coupling with the right hippocampus during recollection rejection responses. As such, we demonstrate a possible link between PFC monitoring network and basic retrieval mechanisms within the hippocampus that was not revealed with univariate analyses alone.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Network Motif Basis of Threshold Responses

EPA Science Inventory

There has been a long-running debate over the existence of thresholds for adverse effects. The difficulty stems from two fundamental challenges: (i) statistical analysis by itself cannot prove the existence of a threshold, i.e., a dose below which there is no effect; and (ii) the...

Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

PubMed Central

Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

2014-01-01

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

Analysis of Handoff Mechanisms in Mobile IP

NASA Astrophysics Data System (ADS)

Jayaraj, Maria Nadine Simonel; Issac, Biju; Haldar, Manas Kumar

2011-06-01

One of the most important challenges in mobile Internet Protocol (IP) is to provide service for a mobile node to maintain its connectivity to network when it moves from one domain to another. IP is responsible for routing packets across network. The first major version of IP is the Internet Protocol version 4 (IPv4). It is one of the dominant protocols relevant to wireless network. Later a newer version of IP called the IPv6 was proposed. Mobile IPv6 is mainly introduced for the purpose of mobility. Mobility management enables network to locate roaming nodes in order to deliver packets and maintain connections with them when moving into new domains. Handoff occurs when a mobile node moves from one network to another. It is a key factor of mobility because a mobile node can trigger several handoffs during a session. This paper briefly explains on mobile IP and its handoff issues, along with the drawbacks of mobile IP.

SNM-DAT: Simulation of a heterogeneous network for nuclear border security

NASA Astrophysics Data System (ADS)

Nemzek, R.; Kenyon, G.; Koehler, A.; Lee, D. M.; Priedhorsky, W.; Raby, E. Y.

2007-08-01

We approach the problem of detecting Special Nuclear Material (SNM) smuggling across open borders by modeling a heterogeneous sensor network using an agent-based simulation. Our simulation SNM Data Analysis Tool (SNM-DAT) combines fixed seismic, metal, and radiation detectors with a mobile gamma spectrometer. Decision making within the simulation determines threat levels by combined signatures. The spectrometer is a limited-availability asset, and is only deployed for substantial threats. "Crossers" can be benign or carrying shielded SNM. Signatures and sensors are physics based, allowing us to model realistic sensor networks. The heterogeneous network provides great gains in detection efficiency compared to a radiation-only system. We can improve the simulation through better sensor and terrain models, additional signatures, and crossers that mimic actual trans-border traffic. We expect further gains in our ability to design sensor networks as we learn the emergent properties of heterogeneous detection, and potential adversary responses.

Assembly of porous hierarchical copolymers/resin proppants: New approaches to smart proppant immobilization via molecular anchors.

PubMed

Alexander, Shirin; Dunnill, Charles W; Barron, Andrew R

2016-03-15

The assembly of temperature/pH sensitive complex microparticle structures through chemisorption and physisorption provides a responsive system that offers application as routes to immobilization of proppants in-situ. Thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) along with energy dispersive X-ray analysis (EDX) have been used to characterize a series of bi-functionalized monolayers and/or multilayers grown on alumina microparticles and investigate the reactive nature of both temperature sensitive cross-linker (epoxy resin) with the layers and pH-responsive bridging layer (polyetheramine). The bifunctional acids, behaving as molecular anchors, allow for a controlled reaction with a cross-linker (resin or polymer) with the formation of networks, which is either irreversible or reversible based on the nature of the cross-linker. The networks results in formation of porous hierarchical particles that offer a potential route to the creation of immobile proppant pack. Copyright © 2015 Elsevier Inc. All rights reserved.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

PubMed Central

2013-01-01

Background Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking. Results In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p < 0.01) in 10 ecotypes, including 498 transcription factors and 315 transposable elements. The majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. Conclusions A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems. PMID:24148294

Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources

PubMed Central

Griffiths, Emily C.; Pedersen, Amy B.; Fenton, Andy; Petchey, Owen L.

2014-01-01

Simultaneous infection by multiple parasite species (viruses, bacteria, helminths, protozoa or fungi) is commonplace. Most reports show co-infected humans to have worse health than those with single infections. However, we have little understanding of how co-infecting parasites interact within human hosts. We used data from over 300 published studies to construct a network that offers the first broad indications of how groups of co-infecting parasites tend to interact. The network had three levels comprising parasites, the resources they consume and the immune responses they elicit, connected by potential, observed and experimentally proved links. Pairs of parasite species had most potential to interact indirectly through shared resources, rather than through immune responses or other parasites. In addition, the network comprised 10 tightly knit groups, eight of which were associated with particular body parts, and seven of which were dominated by parasite–resource links. Reported co-infection in humans is therefore structured by physical location within the body, with bottom-up, resource-mediated processes most often influencing how, where and which co-infecting parasites interact. The many indirect interactions show how treating an infection could affect other infections in co-infected patients, but the compartmentalized structure of the network will limit how far these indirect effects are likely to spread. PMID:24619434

Analysis of a summary network of co-infection in humans reveals that parasites interact most via shared resources.

PubMed

Griffiths, Emily C; Pedersen, Amy B; Fenton, Andy; Petchey, Owen L

2014-05-07

Simultaneous infection by multiple parasite species (viruses, bacteria, helminths, protozoa or fungi) is commonplace. Most reports show co-infected humans to have worse health than those with single infections. However, we have little understanding of how co-infecting parasites interact within human hosts. We used data from over 300 published studies to construct a network that offers the first broad indications of how groups of co-infecting parasites tend to interact. The network had three levels comprising parasites, the resources they consume and the immune responses they elicit, connected by potential, observed and experimentally proved links. Pairs of parasite species had most potential to interact indirectly through shared resources, rather than through immune responses or other parasites. In addition, the network comprised 10 tightly knit groups, eight of which were associated with particular body parts, and seven of which were dominated by parasite-resource links. Reported co-infection in humans is therefore structured by physical location within the body, with bottom-up, resource-mediated processes most often influencing how, where and which co-infecting parasites interact. The many indirect interactions show how treating an infection could affect other infections in co-infected patients, but the compartmentalized structure of the network will limit how far these indirect effects are likely to spread.

Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

PubMed Central

Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

2012-01-01

Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

How to Identify Success Among Networks That Promote Active Living

PubMed Central

Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-01-01

Objectives. We evaluated organization- and network-level factors that influence organizations’ perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. Methods. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. Results. A total of 53 of 59 “whole networks” met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%–100%). Occupying a leadership position (P < .01), the amount of time with the network (P < .05), and support from community leaders (P < .05) emerged as correlates of perceived success. Conclusions. Organizations’ perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities. PMID:26378863

Increased Sleep Depth in Developing Neural Networks: New Insights from Sleep Restriction in Children

PubMed Central

Kurth, Salome; Dean, Douglas C.; Achermann, Peter; O’Muircheartaigh, Jonathan; Huber, Reto; Deoni, Sean C. L.; LeBourgeois, Monique K.

2016-01-01

Brain networks respond to sleep deprivation or restriction with increased sleep depth, which is quantified as slow-wave activity (SWA) in the sleep electroencephalogram (EEG). When adults are sleep deprived, this homeostatic response is most pronounced over prefrontal brain regions. However, it is unknown how children’s developing brain networks respond to acute sleep restriction, and whether this response is linked to myelination, an ongoing process in childhood that is critical for brain development and cortical integration. We implemented a bedtime delay protocol in 5- to 12-year-old children to obtain partial sleep restriction (1-night; 50% of their habitual sleep). High-density sleep EEG was assessed during habitual and restricted sleep and brain myelin content was obtained using mcDESPOT magnetic resonance imaging. The effect of sleep restriction was analyzed using statistical non-parametric mapping with supra-threshold cluster analysis. We observed a localized homeostatic SWA response following sleep restriction in a specific parieto-occipital region. The restricted/habitual SWA ratio was negatively associated with myelin water fraction in the optic radiation, a developing fiber bundle. This relationship occurred bilaterally over parieto-temporal areas and was adjacent to, but did not overlap with the parieto-occipital region showing the most pronounced homeostatic SWA response. These results provide evidence for increased sleep need in posterior neural networks in children. Sleep need in parieto-temporal areas is related to myelin content, yet it remains speculative whether age-related myelin growth drives the fading of the posterior homeostatic SWA response during the transition to adulthood. Whether chronic insufficient sleep in the sensitive period of early life alters the anatomical generators of deep sleep slow-waves is an important unanswered question. PMID:27708567

Exploring Androgen-Regulated Pathways in Teleost Fish Using Transcriptomics and Proteomics

PubMed Central

Martyniuk, Christopher J.; Denslow, Nancy D.

2012-01-01

In the environment, there are aquatic pollutants that disrupt androgen signaling in fish. Laboratory and field-based experiments have utilized omics technologies to characterize the molecular mechanisms underlying androgen-receptor agonism/antagonism. Transcriptomics and proteomics studies with 17β-trenbolone, a growth-promoting pharmaceutical found in water systems surrounding cattle feed lots, and androgens such as 17α-methyltestosterone and 17α-methyldihydrotestosterone, have been conducted in ovary and liver of fish that include the fathead minnow (FHM) (Pimephales promelas), common carp (Cyprinus carpio), Qurt medaka (Oryzias latipes), and zebrafish (Danio rerio). In this mini-review, we survey recent omics studies in fish and reveal that, despite the diversity of species and tissues examined, there are common cellular responses that are observed with waterborne androgenic treatments. Recurring themes in gene ontology include apoptosis, transport and oxidation of lipids, synthesis and transport of hormones, immune response, protein metabolism, and cell proliferation. However, we also discuss other mechanisms other than androgen receptor (AR) activation, such as responses to toxicant stress, estrogen receptor agonism, aromatization of androgens into estrogens, and inhibitory feedback mechanisms by high levels of androgens that may also explain molecular responses in fish. To further explore androgen-responsive protein networks, a sub-network enrichment analysis was performed on protein data collected from the livers of female FHMs exposed to 17β-trenbolone. We construct a putative AR-regulated protein/cell process network in the liver that includes B-lymphocyte differentiation, xenobiotic clearance, low-density lipoprotein oxidation, proliferation of smooth muscle cells, and permeability of blood vessels. We demonstrate that construction of protein networks can offer insight into cell processes that are potentially regulated by androgens. PMID:22596056

Interactions between auditory and visual semantic stimulus classes: evidence for common processing networks for speech and body actions.

PubMed

Meyer, Georg F; Greenlee, Mark; Wuerger, Sophie

2011-09-01

Incongruencies between auditory and visual signals negatively affect human performance and cause selective activation in neuroimaging studies; therefore, they are increasingly used to probe audiovisual integration mechanisms. An open question is whether the increased BOLD response reflects computational demands in integrating mismatching low-level signals or reflects simultaneous unimodal conceptual representations of the competing signals. To address this question, we explore the effect of semantic congruency within and across three signal categories (speech, body actions, and unfamiliar patterns) for signals with matched low-level statistics. In a localizer experiment, unimodal (auditory and visual) and bimodal stimuli were used to identify ROIs. All three semantic categories cause overlapping activation patterns. We find no evidence for areas that show greater BOLD response to bimodal stimuli than predicted by the sum of the two unimodal responses. Conjunction analysis of the unimodal responses in each category identifies a network including posterior temporal, inferior frontal, and premotor areas. Semantic congruency effects are measured in the main experiment. We find that incongruent combinations of two meaningful stimuli (speech and body actions) but not combinations of meaningful with meaningless stimuli lead to increased BOLD response in the posterior STS (pSTS) bilaterally, the left SMA, the inferior frontal gyrus, the inferior parietal lobule, and the anterior insula. These interactions are not seen in premotor areas. Our findings are consistent with the hypothesis that pSTS and frontal areas form a recognition network that combines sensory categorical representations (in pSTS) with action hypothesis generation in inferior frontal gyrus/premotor areas. We argue that the same neural networks process speech and body actions.

Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

PubMed

Zhang, Junfeng; Zhao, Xudong; Huang, Jun

2016-11-01

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

An incoherent regulatory network architecture that orchestrates B cell diversification in response to antigen signaling

PubMed Central

Sciammas, Roger; Li, Ying; Warmflash, Aryeh; Song, Yiqiang; Dinner, Aaron R; Singh, Harinder

2011-01-01

The B-lymphocyte lineage is a leading system for analyzing gene regulatory networks (GRNs) that orchestrate distinct cell fate transitions. Upon antigen recognition, B cells can diversify their immunoglobulin (Ig) repertoire via somatic hypermutation (SHM) and/or class switch DNA recombination (CSR) before differentiating into antibody-secreting plasma cells. We construct a mathematical model for a GRN underlying this developmental dynamic. The intensity of signaling through the Ig receptor is shown to control the bimodal expression of a pivotal transcription factor, IRF-4, which dictates B cell fate outcomes. Computational modeling coupled with experimental analysis supports a model of ‘kinetic control', in which B cell developmental trajectories pass through an obligate transient state of variable duration that promotes diversification of the antibody repertoire by SHM/CSR in direct response to antigens. More generally, this network motif could be used to translate a morphogen gradient into developmental inductive events of varying time, thereby enabling the specification of distinct cell fates. PMID:21613984

Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

PubMed

Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

2017-10-01

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

Positive Feedback Amplifies the Response of Mitochondrial Membrane Potential to Glucose Concentration in Clonal Pancreatic Beta Cells.

PubMed

Gerencser, Akos A; Mookerjee, Shona A; Jastroch, Martin; Brand, Martin D

2017-05-01

Analysis of the cellular mechanisms of metabolic disorders, including type 2 diabetes mellitus, is complicated by the large number of reactions and interactions in metabolic networks. Metabolic control analysis with appropriate modularization is a powerful method for simplifying and analyzing these networks. To analyze control of cellular energy metabolism in adherent cell cultures of the INS-1 832/13 pancreatic β-cell model we adapted our microscopy assay of absolute mitochondrial membrane potential (ΔψM) to a fluorescence microplate reader format, and applied it in conjunction with cell respirometry. In these cells the sensitive response of ΔψM to extracellular glucose concentration drives glucose-stimulated insulin secretion. Using metabolic control analysis we identified the control properties that generate this sensitive response. Force-flux relationships between ΔψM and respiration were used to calculate kinetic responses to ΔψM of processes both upstream (glucose oxidation) and downstream (proton leak and ATP turnover) of ΔψM. The analysis revealed that glucose-evoked ΔψM hyperpolarization is amplified by increased glucose oxidation activity caused by factors downstream of ΔψM. At high glucose, the hyperpolarized ΔψM is stabilized almost completely by the action of glucose oxidation, whereas proton leak also contributes to the homeostatic control of ΔψM at low glucose. These findings suggest a strong positive feedback loop in the regulation of β-cell energetics, and a possible regulatory role of proton leak in the fasting state. Analysis of islet bioenergetics from published cases of type 2 diabetes suggests that disruption of this feedback can explain the damaged bioenergetic response of β-cells to glucose. This article is part of a Special Issue entitled: Oxidative Stress and Mitochondrial Quality in Diabetes/Obesity and Critical Illness Spectrum of Diseases - edited by P. Hemachandra Reddy. Copyright © 2016 Elsevier B.V. All rights reserved.

The new challenges of multiplex networks: Measures and models

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

2017-02-01

What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than only by the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.

Genome-wide analyses of the bZIP family reveal their involvement in the development, ripening and abiotic stress response in banana

PubMed Central

Hu, Wei; Wang, Lianzhe; Tie, Weiwei; Yan, Yan; Ding, Zehong; Liu, Juhua; Li, Meiying; Peng, Ming; Xu, Biyu; Jin, Zhiqiang

2016-01-01

The leucine zipper (bZIP) transcription factors play important roles in multiple biological processes. However, less information is available regarding the bZIP family in the important fruit crop banana. In this study, 121 bZIP transcription factor genes were identified in the banana genome. Phylogenetic analysis showed that MabZIPs were classified into 11 subfamilies. The majority of MabZIP genes in the same subfamily shared similar gene structures and conserved motifs. The comprehensive transcriptome analysis of two banana genotypes revealed the differential expression patterns of MabZIP genes in different organs, in various stages of fruit development and ripening, and in responses to abiotic stresses, including drought, cold, and salt. Interaction networks and co-expression assays showed that group A MabZIP-mediated networks participated in various stress signaling, which was strongly activated in Musa ABB Pisang Awak. This study provided new insights into the complicated transcriptional control of MabZIP genes and provided robust tissue-specific, development-dependent, and abiotic stress-responsive candidate MabZIP genes for potential applications in the genetic improvement of banana cultivars. PMID:27445085

Information-dependent enrichment analysis reveals time-dependent transcriptional regulation of the estrogen pathway of toxicity.

PubMed

Pendse, Salil N; Maertens, Alexandra; Rosenberg, Michael; Roy, Dipanwita; Fasani, Rick A; Vantangoli, Marguerite M; Madnick, Samantha J; Boekelheide, Kim; Fornace, Albert J; Odwin, Shelly-Ann; Yager, James D; Hartung, Thomas; Andersen, Melvin E; McMullen, Patrick D

2017-04-01

The twenty-first century vision for toxicology involves a transition away from high-dose animal studies to in vitro and computational models (NRC in Toxicity testing in the 21st century: a vision and a strategy, The National Academies Press, Washington, DC, 2007). This transition requires mapping pathways of toxicity by understanding how in vitro systems respond to chemical perturbation. Uncovering transcription factors/signaling networks responsible for gene expression patterns is essential for defining pathways of toxicity, and ultimately, for determining the chemical modes of action through which a toxicant acts. Traditionally, transcription factor identification is achieved via chromatin immunoprecipitation studies and summarized by calculating which transcription factors are statistically associated with up- and downregulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be as useful for determining transcription factor associations. To move away from an arbitrary statistical or fold-change-based cutoff, we developed, in the context of the Mapping the Human Toxome project, an enrichment paradigm called information-dependent enrichment analysis (IDEA) to guide identification of the transcription factor network. We used a test case of activation in MCF-7 cells by 17β estradiol (E2). Using this new approach, we established a time course for transcriptional and functional responses to E2. ERα and ERβ were associated with short-term transcriptional changes in response to E2. Sustained exposure led to recruitment of additional transcription factors and alteration of cell cycle machinery. TFAP2C and SOX2 were the transcription factors most highly correlated with dose. E2F7, E2F1, and Foxm1, which are involved in cell proliferation, were enriched only at 24 h. IDEA should be useful for identifying candidate pathways of toxicity. IDEA outperforms gene set enrichment analysis (GSEA) and provides similar results to weighted gene correlation network analysis, a platform that helps to identify genes not annotated to pathways.

Enhancing Electrophoretic Display Lifetime: Thiol-Polybutadiene Evaporation Barrier Property Response to Network Microstructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Caitlyn Christian

An evaporation barrier is required to enhance the lifetime of electrophoretic deposition (EPD) displays. As EPD functions on the basis of reversible deposition and resuspension of colloids suspended in a solvent, evaporation of the solvent ultimately leads to device failure. Incorporation of a thiol-polybutadiene elastomer into EPD displays enabled display lifetime surpassing six months in counting and catalyzed rigid display transition into a flexible package. Final flexible display transition to mass production compels an electronic-ink approach to encapsulate display suspension within an elastomer shell. Final thiol-polybutadiene photosensitive resin network microstructure was idealized to be dense, homogeneous, and expose an elasticmore » response to deformation. Research at hand details an approach to understanding microstructural change within display elastomers. Polybutadiene-based resin properties are modified via polymer chain structure, with and without added aromatic urethane methacrylate difunctionality, and in measuring network response to variation in thiol and initiator concentration. Dynamic mechanical analysis results signify that cross-linked segments within a difunctionalized polybutadiene network were on average eight times more elastically active than that of linked segments within a non-functionalized polybutadiene network. Difunctionalized polybutadiene samples also showed a 2.5 times greater maximum elastic modulus than non-functionalized samples. Hybrid polymer composed of both polybutadiene chains encompassed TE-2000 stiffness and B-1000 elasticity for use in encapsulating display suspension. Later experiments measured kinetic and rheological response due to alteration in dithiol cross-linker chain length via real time Fourier transform infrared spectroscopy and real-time dynamic rheology. Distinct differences were discovered between dithiol resin systems, as maximum thiol conversion achieved in short and long chain length dithiols was 86% and 11%, respectively. Oscillatory real-time rheological experiments confirmed a more uniform network to better dissipate applied shear in short chain length dithiol systems, as long chain length dithiols relayed a steep internal stress build-up due to less cross-links and chain entanglements. Thorough understanding of network formation aids the production of a stronger and impermeable elastomeric barrier for preservation of EPD displays.« less

Synchronization stability of memristor-based complex-valued neural networks with time delays.

PubMed

Liu, Dan; Zhu, Song; Ye, Er

2017-12-01

This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

The auxin response factor gene family in banana: genome-wide identification and expression analyses during development, ripening, and abiotic stress

PubMed Central

Hu, Wei; Zuo, Jiao; Hou, Xiaowan; Yan, Yan; Wei, Yunxie; Liu, Juhua; Li, Meiying; Xu, Biyu; Jin, Zhiqiang

2015-01-01

Auxin signaling regulates various auxin-responsive genes via two types of transcriptional regulators, Auxin Response Factors (ARF) and Aux/IAA. ARF transcription factors act as critical components of auxin signaling that play important roles in modulating various biological processes. However, limited information about this gene family in fruit crops is currently available. Herein, 47 ARF genes were identified in banana based on its genome sequence. Phylogenetic analysis of the ARFs from banana, rice, and Arabidopsis suggested that the ARFs could be divided into four subgroups, among which most ARFs from the banana showed a closer relationship with those from rice than those from Arabidopsis. Conserved motif analysis showed that all identified MaARFs had typical DNA-binding and ARF domains, but 12 members lacked the dimerization domain. Gene structure analysis showed that the number of exons in MaARF genes ranged from 5 to 21, suggesting large variation amongst banana ARF genes. The comprehensive expression profiles of MaARF genes yielded useful information about their involvement in diverse tissues, different stages of fruit development and ripening, and responses to abiotic stresses in different varieties. Interaction networks and co-expression assays indicated the strong transcriptional response of banana ARFs and ARF-mediated networks in early fruit development for different varieties. Our systematic analysis of MaARFs revealed robust tissue-specific, development-dependent, and abiotic stress-responsive candidate MaARF genes for further functional assays in planta. These findings could lead to potential applications in the genetic improvement of banana cultivars, and yield new insights into the complexity of the control of MaARF gene expression at the transcriptional level. Finally, they support the hypothesis that ARFs are a crucial component of the auxin signaling pathway, which regulates a wide range of physiological processes. PMID:26442055

Integration of new biological and physical retrospective dosimetry methods into EU emergency response plans - joint RENEB and EURADOS inter-laboratory comparisons.

PubMed

Ainsbury, Elizabeth; Badie, Christophe; Barnard, Stephen; Manning, Grainne; Moquet, Jayne; Abend, Michael; Antunes, Ana Catarina; Barrios, Lleonard; Bassinet, Celine; Beinke, Christina; Bortolin, Emanuela; Bossin, Lily; Bricknell, Clare; Brzoska, Kamil; Buraczewska, Iwona; Castaño, Carlos Huertas; Čemusová, Zina; Christiansson, Maria; Cordero, Santiago Mateos; Cosler, Guillaume; Monaca, Sara Della; Desangles, François; Discher, Michael; Dominguez, Inmaculada; Doucha-Senf, Sven; Eakins, Jon; Fattibene, Paola; Filippi, Silvia; Frenzel, Monika; Georgieva, Dimka; Gregoire, Eric; Guogyte, Kamile; Hadjidekova, Valeria; Hadjiiska, Ljubomira; Hristova, Rositsa; Karakosta, Maria; Kis, Enikő; Kriehuber, Ralf; Lee, Jungil; Lloyd, David; Lumniczky, Katalin; Lyng, Fiona; Macaeva, Ellina; Majewski, Matthaeus; Vanda Martins, S; McKeever, Stephen W S; Meade, Aidan; Medipally, Dinesh; Meschini, Roberta; M'kacher, Radhia; Gil, Octávia Monteiro; Montero, Alegria; Moreno, Mercedes; Noditi, Mihaela; Oestreicher, Ursula; Oskamp, Dominik; Palitti, Fabrizio; Palma, Valentina; Pantelias, Gabriel; Pateux, Jerome; Patrono, Clarice; Pepe, Gaetano; Port, Matthias; Prieto, María Jesús; Quattrini, Maria Cristina; Quintens, Roel; Ricoul, Michelle; Roy, Laurence; Sabatier, Laure; Sebastià, Natividad; Sholom, Sergey; Sommer, Sylwester; Staynova, Albena; Strunz, Sonja; Terzoudi, Georgia; Testa, Antonella; Trompier, Francois; Valente, Marco; Hoey, Olivier Van; Veronese, Ivan; Wojcik, Andrzej; Woda, Clemens

2017-01-01

RENEB, 'Realising the European Network of Biodosimetry and Physical Retrospective Dosimetry,' is a network for research and emergency response mutual assistance in biodosimetry within the EU. Within this extremely active network, a number of new dosimetry methods have recently been proposed or developed. There is a requirement to test and/or validate these candidate techniques and inter-comparison exercises are a well-established method for such validation. The authors present details of inter-comparisons of four such new methods: dicentric chromosome analysis including telomere and centromere staining; the gene expression assay carried out in whole blood; Raman spectroscopy on blood lymphocytes, and detection of radiation-induced thermoluminescent signals in glass screens taken from mobile phones. In general the results show good agreement between the laboratories and methods within the expected levels of uncertainty, and thus demonstrate that there is a lot of potential for each of the candidate techniques. Further work is required before the new methods can be included within the suite of reliable dosimetry methods for use by RENEB partners and others in routine and emergency response scenarios.

Predicting Essential Components of Signal Transduction Networks: A Dynamic Model of Guard Cell Abscisic Acid Signaling

PubMed Central

Li, Song; Assmann, Sarah M; Albert, Réka

2006-01-01

Plants both lose water and take in carbon dioxide through microscopic stomatal pores, each of which is regulated by a surrounding pair of guard cells. During drought, the plant hormone abscisic acid (ABA) inhibits stomatal opening and promotes stomatal closure, thereby promoting water conservation. Dozens of cellular components have been identified to function in ABA regulation of guard cell volume and thus of stomatal aperture, but a dynamic description is still not available for this complex process. Here we synthesize experimental results into a consistent guard cell signal transduction network for ABA-induced stomatal closure, and develop a dynamic model of this process. Our model captures the regulation of more than 40 identified network components, and accords well with previous experimental results at both the pathway and whole-cell physiological level. By simulating gene disruptions and pharmacological interventions we find that the network is robust against a significant fraction of possible perturbations. Our analysis reveals the novel predictions that the disruption of membrane depolarizability, anion efflux, actin cytoskeleton reorganization, cytosolic pH increase, the phosphatidic acid pathway, or K+ efflux through slowly activating K+ channels at the plasma membrane lead to the strongest reduction in ABA responsiveness. Initial experimental analysis assessing ABA-induced stomatal closure in the presence of cytosolic pH clamp imposed by the weak acid butyrate is consistent with model prediction. Simulations of stomatal response as derived from our model provide an efficient tool for the identification of candidate manipulations that have the best chance of conferring increased drought stress tolerance and for the prioritization of future wet bench analyses. Our method can be readily applied to other biological signaling networks to identify key regulatory components in systems where quantitative information is limited. PMID:16968132

Continuous-time model of structural balance

PubMed Central

Marvel, Seth A.; Kleinberg, Jon; Kleinberg, Robert D.; Strogatz, Steven H.

2011-01-01

It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX/dt = X2, where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network. Previous simulations suggested that only two types of behavior were possible for this system: Either all relationships become friendly or two hostile factions emerge. Here we prove that for generic initial conditions, these are indeed the only possible outcomes. Our analysis yields a closed-form expression for faction membership as a function of the initial conditions and implies that the initial amount of friendliness in large social networks (started from random initial conditions) determines whether they will end up in intractable conflict or global harmony. PMID:21199953

Continuous-time model of structural balance.

PubMed

Marvel, Seth A; Kleinberg, Jon; Kleinberg, Robert D; Strogatz, Steven H

2011-02-01

It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX/dt = X(2), where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network. Previous simulations suggested that only two types of behavior were possible for this system: Either all relationships become friendly or two hostile factions emerge. Here we prove that for generic initial conditions, these are indeed the only possible outcomes. Our analysis yields a closed-form expression for faction membership as a function of the initial conditions and implies that the initial amount of friendliness in large social networks (started from random initial conditions) determines whether they will end up in intractable conflict or global harmony.

Distinct Tensile Response of Model Semi-flexible Elastomer Networks

NASA Astrophysics Data System (ADS)

Aguilera-Mercado, Bernardo M.; Cohen, Claude; Escobedo, Fernando A.

2011-03-01

Through coarse-grained molecular modeling, we study how the elastic response strongly depends upon nanostructural heterogeneities in model networks made of semi-flexible chains exhibiting both regular and realistic connectivity. Idealized regular polymer networks have been shown to display a peculiar elastic response similar to that of super-tough natural materials (e.g., organic adhesives inside abalone shells). We investigate the impact of chain stiffness, and the effect of including tri-block copolymer chains, on the network's topology and elastic response. We find in some systems a dual tensile response: a liquid-like behavior at small deformations, and a distinct saw-tooth shaped stress-strain curve at moderate to large deformations. Additionally, stiffer regular networks exhibit a marked hysteresis over loading-unloading cycles that can be deleted by heating-cooling cycles or by performing deformations along different axes. Furthermore, small variations of chain stiffness may entirely change the nature of the network's tensile response from an entropic to an enthalpic elastic regime, and micro-phase separation of different blocks within elastomer networks may significantly enhance their mechanical strength. This work was supported by the American Chemical Society.

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era.

PubMed

Best, Michele; Sakande, Jean

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state's public health system and is critical to the development of a robust national laboratory response network to meet global health security threats.

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era

PubMed Central

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state’s public health system and is critical to the development of a robust national laboratory response network to meet global health security threats. PMID:28879137

Health policy and systems research collaboration pathways: lessons from a network science analysis.

PubMed

English, Krista M; Pourbohloul, Babak

2017-08-28

The 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs). We used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR. This method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field. Network analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990-2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.

Genetic analysis of salinity responses in Medicago genotypes

USDA-ARS?s Scientific Manuscript database

Reduced availability of clean water in arid and semi-arid regions will require the use of low-quality/alternative waters for irrigation. The main consideration for using low-quality/alternative waters is often their salt concentration. Plants respond to salinity stress through a complex network of p...

Application of circular consensus sequencing and network analysis to characterize the bovine IgG repertoire

USDA-ARS?s Scientific Manuscript database

Background: Vertebrate immune systems generate diverse repertoires of antibodies capable of mediating response to a variety of antigens. Next generation sequencing methods provide unique approaches to a number of immuno-based research areas including antibody discovery and engineering, disease surve...

Proteomic analysis of propiconazole responses in mouse liver: comparison of genomic and proteomic profiles

EPA Science Inventory

We have performed for the first time a comprehensive profiling of changes in protein expression of soluble proteins in livers from mice treated with the mouse liver tumorigen, propiconazole, to uncover the pathways and networks altered by this fungicide. Utilizing twodimensional...