bluuebunny/crossref_metadata_2025_split huggingface dataset:
MC4R-expressing glutamatergic neurons in the paraventricular hypothalamus regulate feeding and are synaptically connected to the parabrachial nucleus
Authors: Shah Bhavik P. , Vong Linh , Olson David P. , Koda Shuichi , Krashes Michael J. , Ye Chianping , Yang Zongfang , Fuller Patrick M. , Elmquist Joel K. , Lowell Bradford B. 
Published: August 2014

Significance
Both in rodents and humans, melanocortin-4 receptors (MC4Rs) suppress appetite and prevent obesity. Unfortunately, the underlying neural mechanisms by which MC4Rs regulate food intake are poorly understood. Unraveling these mechanisms may open up avenues for treating obesity. In the present study we have established that MC4Rs on neurons in the paraventricular nucleus of the hypothalamus are both necessary and sufficient for MC4R control of feeding and that these neurons are glutamatergic and not GABAergic and do not express the neuropeptides oxytocin, corticotropin-releasing hormone, prodynorphin, or vasopressin. In addition, we identify downstream projections from these glutamatergic neurons to the lateral parabrachial nucleus, which could mediate the appetite suppressing effects.
Noncontact orientation of objects in three-dimensional space using magnetic levitation
Authors: Subramaniam Anand Bala , Yang Dian , Yu Hai-Dong , Nemiroski Alex , Tricard Simon , Ellerbee Audrey K. , Soh Siowling , Whitesides George M. 
Published: August 2014

Significance
We describe several noncontact methods of orienting objects in three-dimensional (3D) space using Magnetic Levitation (MagLev), and report the discovery of a sharp geometry-dependent transition of the orientation of levitating objects. An analytical theory of the orientation of arbitrary objects in MagLev explains this transition. MagLev is capable of manipulating and orienting hard and soft objects, and objects of irregular shape. Because controlling the orientation of objects in space is a prerequisite for assembling complex structures from simpler components, this paper extends MagLev into 3D self-assembly, robotic assembly, and noncontact (stiction-free) orientation of hard and soft objects for applications in biomimetics, soft robotics, and stimulus-responsive materials, among others.
Hyperglycemia in rodent models of type 2 diabetes requires insulin-resistant alpha cells
Authors: Lee Young , Berglund Eric D. , Yu Xinxin , Wang May-Yun , Evans Matthew R. , Scherer Philipp E. , Holland William L. , Charron Maureen J. , Roth Michael G. , Unger Roger H. 
Published: August 2014

Significance
Diet-induced type 2 diabetes (T2D) is becoming a worldwide epidemic. Patients with T2D fail to respond to insulin normally and have elevated blood glucose and insulin. In autoimmune diabetes, blood glucose is elevated due to uncontrolled glucagon, a hormone normally suppressed and opposed by insulin. To study the role of glucagon in T2D, rodent models of the disease were created lacking glucagon action. These animals failed to develop hyperglycemia unless glucagon action was restored or the animals were given the high concentrations of insulin typical of animals with T2D. This indicates that the unopposed glucagon action in T2D is necessary to support elevated blood glucose of diabetes. Suppressing glucagon or its action may benefit patients with T2D.
Three-dimensional cell body shape dictates the onset of traction force generation and growth of focal adhesions
Authors: Fouchard Jonathan , Bimbard Célian , Bufi Nathalie , Durand-Smet Pauline , Proag Amsha , Richert Alain , Cardoso Olivier , Asnacios Atef 
Published: August 2014

Significance
Living cells interact with their environment through surface receptors. In particular, adhesion molecules form complexes that anchor cells to each other and to the extracellular matrix. These complexes ensure mechanical integrity of tissues and control cell function through specific biochemical signaling. This dual role is due to the ability of adhesion complexes to grow and change their composition and activity in response to mechanical forces. Here, we show how cell spreading, by modifying cell shape, controls the distribution of internal tension over adhesion complexes, inducing their growth above a well-defined spread area. Because such a threshold area was reported for many cell functions, our findings shed a new light on the possible mechanisms behind the geometric control of cell fate.
Commensal bacteria protect against food allergen sensitization
Authors: Stefka Andrew T. , Feehley Taylor , Tripathi Prabhanshu , Qiu Ju , McCoy Kathy , Mazmanian Sarkis K. , Tjota Melissa Y. , Seo Goo-Young , Cao Severine , Theriault Betty R. , Antonopoulos Dionysios A. , Zhou Liang , Chang Eugene B. , Fu Yang-Xin , Nagler Cathryn R. 
Published: August 2014

Significance
The prevalence of food allergy is rising at an alarming rate; the US Centers for Disease Control and Prevention documented an 18% increase among children in the United States between 1997 and 2007. Twenty-first century environmental interventions are implicated by this dramatic generational increase. In this report we examine how alterations in the trillions of commensal bacteria that normally populate the gastrointestinal tract influence allergic responses to food. We identify a bacterial community that protects against sensitization and describe the mechanism by which these bacteria regulate epithelial permeability to food allergens. Our data support the development of novel adjunctive probiotic therapies to potentiate the induction of tolerance to dietary allergens.
Dynamic hydration shell restores Kauzmann's 1959 explanation of how the hydrophobic factor drives protein folding
Authors: Baldwin Robert L. 
Published: August 2014

Significance
Kauzmann's proposal that the hydrophobicity of hydrocarbons in water is caused by their hydration shells is modified here by replacing his clathrate water shell with a dynamic shell formed by van der Waals attraction, as proposed by Jorgensen et al. The hydration energetics of alkanes are then shown to be proportional to n, the number of waters in the shell given by the simulations of Jorgensen et al. The Ben-Naim standard state, which is based on solute transfer between fixed positions in the gas and liquid phases, is used for the hydration energetics. Recent experimental evidence for hydration shells is reviewed.
Two jacalin-related lectins from seeds of the African breadfruit (Treculia africana L.)
Authors: Shimokawa Michiko , Nsimba-Lubaki Shadrack Makuta , Hayashi Namiko , Minami Yuji , Yagi Fumio , Hiemori Keiko , Tateno Hiroaki , Hirabayashi Jun 
Published: August 2014

Abstract
Two jacalin-related lectins (JRLs) were purified by mannose-agarose and melibiose-agarose from seeds of Treculia africana. One is galactose-recognizing JRL (gJRL), named T. africana agglutinin-G (TAA-G), and another one is mannose-recognizing JRL (mJRL), TAA-M. The yields of the two lectins from the seed flour were approximately 7.0 mg/g for gJRL and 7.2 mg/g for mJRL. The primary structure of TAA-G was determined by protein sequencing of lysyl endopeptic peptides and chymotryptic peptides. The sequence identity of TAA-G to other gJRLs was around 70%. Two-residue insertion was found around the sugar-binding sites, compared with the sequences of other gJRLs. Crystallographic studies on other gJRLs have shown that the primary sugar-binding site of gJRLs can accommodate Gal, GalNAc, and GalNAc residue of T-antigen (Galβ1-3GalNAcα-). However, hemagglutination inhibition and glycan array showed that TAA-G did not recognize GalNAc itself and T-antigen. TAA-G preferred melibiose and core 3 O-glycan.
Two-dimensional segmentation for analyzing Hi-C data
Authors: Lévy-Leduc Celine , Delattre M. , Mary-Huard T. , Robin S. 
Published: August 2014

Abstract
Motivation: The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-)interacting regions appear. The delimitation of such blocks is critical to better understand the spatial organization of the chromatin. From a computational point of view, it results in a 2D segmentation problem.
Results: We focus on the detection of cis-interacting regions, which appear to be prominent in observed data. We define a block-wise segmentation model for the detection of such regions. We prove that the maximization of the likelihood with respect to the block boundaries can be rephrased in terms of a 1D segmentation problem, for which the standard dynamic programming applies. The performance of the proposed methods is assessed by a simulation study on both synthetic and resampled data. A comparative study on public data shows good concordance with biologically confirmed regions.
Availability and implementation: The HiCseg R package is available from the Comprehensive R Archive Network and from the Web page of the corresponding author.
Contact: celine.levy-leduc@agroparistech.fr
Causal network inference using biochemical kinetics
Authors: Oates Chris J. , Dondelinger Frank , Bayani Nora , Korkola James , Gray Joe W. , Mukherjee Sach 
Published: August 2014

Abstract
Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems.
Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown.
Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates.
Contact: c.oates@warwick.ac.uk or sach@mrc-bsu.cam.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information
Authors: Kim Yongsoo , Jang Jin-Hyeok , Choi Seungjin , Hwang Daehee 
Published: August 2014

Abstract
Motivation: Time-evolving differential protein–protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time.
Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations.
Availability and implementation: Source code and sample data files are available at http://sbm.postech.ac.kr/tempi/sources.zip.
Contact: seungjin@postech.ac.kr or dhwang@dgist.ac.kr
Supplementary information: Supplementary data are available at Bioinformatics online.
Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations
Authors: Suvitaival Tommi , Rogers Simon , Kaski Samuel 
Published: August 2014

AbstractMotivation: Data analysis for metabolomics suffers from uncertainty because of the noisy measurement technology and the small sample size of experiments. Noise and the small sample size lead to a high probability of false findings. Further, individual compounds have natural variation between samples, which in many cases renders them unreliable as biomarkers. However, the levels of similar compounds are typically highly correlated, which is a phenomenon that we model in this work.Results: We propose a hierarchical Bayesian model for inferring differences between groups of samples more accurately in metabolomic studies, where the observed compounds are collinear. We discover that the method decreases the error of weak and non-existent covariate effects, and thereby reduces false-positive findings. To achieve this, the method makes use of the mass spectral peak data by clustering similar peaks into latent compounds, and by further clustering latent compounds into groups that respond in a coherent way to the experimental covariates. We demonstrate the method with three simulated studies and validate it with a metabolomic benchmark dataset.Availability and implementation: An implementation in R is available at http://research.ics.aalto.fi/mi/software/peakANOVA/.Contact: samuel.kaski@aalto.fi.
Unveiling new biological relationships using shared hits of chemical screening assay pairs
Authors: Liu Xueping , Campillos Monica 
Published: August 2014

Abstract
Motivation: Although the integration and analysis of the activity of small molecules across multiple chemical screens is a common approach to determine the specificity and toxicity of hits, the suitability of these approaches to reveal novel biological information is less explored. Here, we test the hypothesis that assays sharing selective hits are biologically related.
Results: We annotated the biological activities (i.e. biological processes or molecular activities) measured in assays and constructed chemical hit profiles with sets of compounds differing on their selectivity level for 1640 assays of ChemBank repository. We compared the similarity of chemical hit profiles of pairs of assays with their biological relationships and observed that assay pairs sharing non-promiscuous chemical hits tend to be biologically related. A detailed analysis of a network containing assay pairs with the highest hit similarity confirmed biological meaningful relationships. Furthermore, the biological roles of predicted molecular targets of the shared hits reinforced the biological associations between assay pairs.
Contact: monica.campillos@helmholtz-muenchen.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Microarray R-based analysis of complex lysate experiments with MIRACLE
Authors: List Markus , Block Ines , Pedersen Marlene Lemvig , Christiansen Helle , Schmidt Steffen , Thomassen Mads , Tan Qihua , Baumbach Jan , Mollenhauer Jan 
Published: August 2014

Abstract
Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected.
Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis.
Availability: Project URL: http://www.nanocan.org/miracle/
Contact: mlist@health.sdu.dk
Supplementary information: Supplementary data are available at Bioinformatics online.
cnvOffSeq: detecting intergenic copy number variation using off-target exome sequencing data
Authors: Bellos Evangelos , Coin Lachlan J. M. 
Published: August 2014

Abstract
Motivation: Exome sequencing technologies have transformed the field of Mendelian genetics and allowed for efficient detection of genomic variants in protein-coding regions. The target enrichment process that is intrinsic to exome sequencing is inherently imperfect, generating large amounts of unintended off-target sequence. Off-target data are characterized by very low and highly heterogeneous coverage and are usually discarded by exome analysis pipelines. We posit that off-target read depth is a rich, but overlooked, source of information that could be mined to detect intergenic copy number variation (CNV). We propose cnvOffseq, a novel normalization framework for off-target read depth that is based on local adaptive singular value decomposition (SVD). This method is designed to address the heterogeneity of the underlying data and allows for accurate and precise CNV detection and genotyping in off-target regions.
Results: cnvOffSeq was benchmarked on whole-exome sequencing samples from the 1000 Genomes Project. In a set of 104 gold standard intergenic deletions, our method achieved a sensitivity of 57.5% and a specificity of 99.2%, while maintaining a low FDR of 5%. For gold standard deletions longer than 5 kb, cnvOffSeq achieves a sensitivity of 90.4% without increasing the FDR. cnvOffSeq outperforms both whole-genome and whole-exome CNV detection methods considerably and is shown to offer a substantial improvement over naïve local SVD.
Availability and Implementation: cnvOffSeq is available at http://sourceforge.net/p/cnvoffseq/
Contact: evangelos.bellos09@imperial.ac.uk or l.coin@imb.uq.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online.
Entropy-driven partitioning of the hierarchical protein space
Authors: Rappoport Nadav , Stern Amos , Linial Nathan , Linial Michal 
Published: August 2014

Abstract
Motivation: Modern protein sequencing techniques have led to the determination of &gt;50 million protein sequences. ProtoNet is a clustering system that provides a continuous hierarchical agglomerative clustering tree for all proteins. While ProtoNet performs unsupervised classification of all included proteins, finding an optimal level of granularity for the purpose of focusing on protein functional groups remain elusive. Here, we ask whether knowledge-based annotations on protein families can support the automatic unsupervised methods for identifying high-quality protein families. We present a method that yields within the ProtoNet hierarchy an optimal partition of clusters, relative to manual annotation schemes. The method’s principle is to minimize the entropy-derived distance between annotation-based partitions and all available hierarchical partitions. We describe the best front (BF) partition of 2 478 328 proteins from UniRef50. Of 4 929 553 ProtoNet tree clusters, BF based on Pfam annotations contain 26 891 clusters. The high quality of the partition is validated by the close correspondence with the set of clusters that best describe thousands of keywords of Pfam. The BF is shown to be superior to naïve cut in the ProtoNet tree that yields a similar number of clusters. Finally, we used parameters intrinsic to the clustering process to enrich a priori the BF’s clusters. We present the entropy-based method’s benefit in overcoming the unavoidable limitations of nested clusters in ProtoNet. We suggest that this automatic information-based cluster selection can be useful for other large-scale annotation schemes, as well as for systematically testing and comparing putative families derived from alternative clustering methods.
Availability and implementation: A catalog of BF clusters for thousands of Pfam keywords is provided at http://protonet.cs.huji.ac.il/bestFront/
Contact: michall@cc.huji.ac.il
The circumgalactic medium of high-redshift galaxies
Authors: Pallottini A. , Gallerani S. , Ferrara A. 
Published: August 2014

Abstract
We study the properties of the circumgalactic medium (CGM) of high-z galaxies in the metal enrichment simulations presented in Pallottini et al. At z = 4, we find that the simulated CGM gas density profiles are self-similar, once scaled with the virial radius of the parent dark matter halo. We also find a simple analytical expression relating the neutral hydrogen equivalent width (EWH i) of CGM absorbers as a function of the line-of-sight impact parameter (b). We test our predictions against mock spectra extracted from the simulations and show that the model reproduces the EWH i(b) profile extracted from the synthetic spectra analysis. When compared with available data, our CGM model nicely predicts the observed EWH i(b) in z ≲ 2 galaxies, and supports the idea that the CGM profile does not evolve with redshift.