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https://www.readbyqxmd.com/read/28548460/internally-generated-hippocampal-sequences-as-a-vantage-point-to-probe-future-oriented-cognition
#1
REVIEW
Giovanni Pezzulo, Caleb Kemere, Matthijs A A van der Meer
Information processing in the rodent hippocampus is fundamentally shaped by internally generated sequences (IGSs), expressed during two different network states: theta sequences, which repeat and reset at the ∼8 Hz theta rhythm associated with active behavior, and punctate sharp wave-ripple (SWR) sequences associated with wakeful rest or slow-wave sleep. A potpourri of diverse functional roles has been proposed for these IGSs, resulting in a fragmented conceptual landscape. Here, we advance a unitary view of IGSs, proposing that they reflect an inferential process that samples a policy from the animal's generative model, supported by hippocampus-specific priors...
May 2017: Annals of the New York Academy of Sciences
https://www.readbyqxmd.com/read/28546527/effect-of-necrosis-on-the-mirna-mrna-regulatory-network-in-crt-mg-human-astroglioma-cells
#2
So-Hee Ahn, Jung-Hyuck Ahn, Dong-Ryeol Ryu, Jisoo Lee, Min-Sun Cho, Youn-Hee Choi
Purpose: Glioblastoma multiforme (GBM) is the most common adult primary intracranial tumor. The remarkable features of GBM include central necrosis. MicroRNAs (miRNAs) have been considered as diagnostic/prognostic biomarkers for many cancers, including glioblastoma. However, the effect of necrosis on the miRNA expression profile and predicted miRNA-mRNA regulatory information remain unclear. The purpose of this study is to examine the effect of necrotic cells on the modulation of miRNA and mRNA expression profiles and miRNA-mRNA network in CRT-MG cells...
May 22, 2017: Cancer Research and Treatment: Official Journal of Korean Cancer Association
https://www.readbyqxmd.com/read/28545448/limitations-of-a-metabolic-network-based-reverse-ecology-method-for-inferring-host-pathogen-interactions
#3
Kazuhiro Takemoto, Kazuki Aie
BACKGROUND: Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. RESULTS: We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545146/transcriptomics-technologies
#4
Rohan Lowe, Neil Shirley, Mark Bleackley, Stephen Dolan, Thomas Shafee
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline...
May 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28545060/signet-a-signaling-network-data-simulator-to-enable-signaling-network-inference
#5
Elizabeth A Coker, Costas Mitsopoulos, Paul Workman, Bissan Al-Lazikani
Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks...
2017: PloS One
https://www.readbyqxmd.com/read/28544882/inference-and-evolutionary-analysis-of-genome-scale-regulatory-networks-in-large-phylogenies
#6
Christopher Koch, Jay Konieczka, Toni Delorey, Ana Lyons, Amanda Socha, Kathleen Davis, Sara A Knaack, Dawn Thompson, Erin K O'Shea, Aviv Regev, Sushmita Roy
Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information...
May 24, 2017: Cell Systems
https://www.readbyqxmd.com/read/28542497/the-structural-basis-of-a-high-affinity-atp-binding-%C3%AE%C2%B5-subunit-from-a-bacterial-atp-synthase
#7
Alexander Krah, Yasuyuki Kato-Yamada, Shoji Takada
The ε subunit from bacterial ATP synthases functions as an ATP sensor, preventing ATPase activity when the ATP concentration in bacterial cells crosses a certain threshold. The R103A/R115A double mutant of the ε subunit from thermophilic Bacillus PS3 has been shown to bind ATP two orders of magnitude stronger than the wild type protein. We use molecular dynamics simulations and free energy calculations to derive the structural basis of the high affinity ATP binding to the R103A/R115A double mutant. Our results suggest that the double mutant is stabilized by an enhanced hydrogen-bond network and fewer repulsive contacts in the ligand binding site...
2017: PloS One
https://www.readbyqxmd.com/read/28541380/a-fully-bayesian-latent-variable-model-for-integrative-clustering-analysis-of-multi-type-omics-data
#8
Qianxing Mo, Ronglai Shen, Cui Guo, Marina Vannucci, Keith S Chan, Susan G Hilsenbeck
Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data...
May 24, 2017: Biostatistics
https://www.readbyqxmd.com/read/28541220/mgt-sm-a-method-for-constructing-cellular-signal-transduction-networks
#9
Min Li, Ruiqing Zheng, Yaohang Li, Fang-Xiang Wu, Jianxin Wang
A cellular signal transduction network is an important means to describe biological responses to environmental stimuli and exchange of biological signals. Constructing the cellular signal transduction network provides an important basis for the study of the biological activities, the mechanism of the diseases, drug targets and so on. The statistical approaches to network inference are popular in literature. Granger test has been used as an effective method for causality inference. Compared with bivariate granger tests, multivariate granger tests reduce the indirect causality and were used widely for the construction of cellular signal transduction networks...
May 19, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541219/inferring-social-influence-of-anti-tobacco-mass-media-campaign
#10
Qianyi Zhan, Jiawei Zhang, Philip Yu, Sherry Emery, Junyuan Xie
Anti-tobacco mass media campaigns are designed to influence tobacco users. It has been proved that campaigns will produce users' changes in awareness, knowledge, and attitudes, and also produce meaningful behavior change of audience. Antismoking television advertising is the most important part in the campaign. Meanwhile nowadays successful online social networks are creating new media environment, however little is known about the relation between social conversations and anti-tobacco campaigns. This paper aims to infer social influence of these campaigns, and the problem is formally referred to as the Social Influence inference of anti-Tobacco mass mEdia campaigns (SITE) problem...
May 23, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/28539122/an-inference-method-from-multi-layered-structure-of-biomedical-data
#11
Myungjun Kim, Yonghyun Nam, Hyunjung Shin
BACKGROUND: Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. METHODS: To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required...
May 18, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28534782/reinforce-an-ensemble-approach-for-inferring-ppi-network-from-ap-ms-data
#12
Bo Tian, Qiong Duan, Can Zhao, Ben Teng, Zengyou He
Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets...
May 17, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28528256/evaluation-of-artificial-time-series-microarray-data-for-dynamic-gene-regulatory-network-inference
#13
P Xenitidis, I Seimenis, S Kakolyris, A Adamopoulos
High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process...
May 17, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28527316/effects-of-contextual-relevance-on-pragmatic-inference-during-conversation-an-fmri-study
#14
Wangshu Feng, Yue Wu, Catherine Jan, Hongbo Yu, Xiaoming Jiang, Xiaolin Zhou
Contextual relevance, which is vital for understanding conversational implicatures (CI), engages both the frontal-temporal language and theory-of-mind networks. Here we investigate how contextual relevance affects CI processing and regulates the connectivity between CI-processing-related brain regions. Participants listened to dialogues in which the level of contextual relevance to dialogue-final utterance (reply) was manipulated. This utterance was either direct, indirect but relevant, irrelevant with contextual hint, or irrelevant with no contextual hint...
May 17, 2017: Brain and Language
https://www.readbyqxmd.com/read/28526529/adding-biological-meaning-to-human-protein-protein-interactions-identified-by-yeast-two-hybrid-screenings-a-guide-through-bioinformatics-tools
#15
Juliana Felgueiras, Joana Vieira Silva, Margarida Fardilha
"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases...
May 16, 2017: Journal of Proteomics
https://www.readbyqxmd.com/read/28526212/3d-deeply-supervised-network-for-automated-segmentation-of-volumetric-medical-images
#16
Qi Dou, Lequan Yu, Hao Chen, Yueming Jin, Xin Yang, Jing Qin, Pheng-Ann Heng
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN...
May 8, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28523139/an-overview-of-the-use-of-artificial-neural-networks-in-lung-cancer-research
#17
EDITORIAL
Luca Bertolaccini, Piergiorgio Solli, Alessandro Pardolesi, Antonello Pasini
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification...
April 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/28522969/equilibrium-propagation-bridging-the-gap-between-energy-based-models-and-backpropagation
#18
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28520713/combining-inferred-regulatory-and-reconstructed-metabolic-networks-enhances-phenotype-prediction-in-yeast
#19
Zhuo Wang, Samuel A Danziger, Benjamin D Heavner, Shuyi Ma, Jennifer J Smith, Song Li, Thurston Herricks, Evangelos Simeonidis, Nitin S Baliga, John D Aitchison, Nathan D Price
Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models...
May 17, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28520480/review-of-recent-methodological-developments-in-group-randomized-trials-part-2-analysis
#20
Elizabeth L Turner, Melanie Prague, John A Gallis, Fan Li, David M Murray
In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e...
May 18, 2017: American Journal of Public Health
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