bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perception of Effort During Activity in Patients With Chronic Hepatitis C and Nonalcoholic Fatty Liver Disease
Authors: Weinstein Ali A. , Escheik Carey , Oe Bibiana , Price Jillian K. , Gerber Lynn H. , Younossi Zobair M. 
Published: June 2015

AbstractBackgroundRatings of perceived exertion (RPE) are used to monitor and prescribe exercise intensity for a variety of patient populations. It is important to understand RPE in different patient populations to ensure appropriate prescriptions and maximize the likelihood of adherence. Chronic liver diseases (CLDs) are a constellation of diseases that are associated frequently with fatigue, metabolic abnormalities, and cardiovascular disease, all targets for prescription of exercise. However, there have been no investigations of the correlates of RPE in those with CLD.ObjectiveTo correlate RPE during a 6‐minute walk test (6MWT) with biological/physiological measures in subjects with chronic hepatitis C (CH‐C) and non‐alcoholic fatty liver disease (NAFLD).DesignObservational analytical study.SettingSpecialty clinic.ParticipantsA convenience sample of 51 subjects with NAFLD or CH‐C (age: 51.1 ± 8.8 years, 35% female) was enrolled. Subjects receiving antiviral therapies or those with recent myocardial infarction, cardiovascular, or musculoskeletal comorbidities affecting exertion were excluded.MethodsParticipants underwent fasting morning venipuncture, and resting cardiorespiratory data were collected. Then the participants completed a 6MWT. At the conclusion of the 6MWT, participants reported their RPE, and cardiorespiratory data were reassessed.Main Outcome MeasurementsRPE, 6MWT, resting/postexertion cardiorespiratory data (eg, heart rate, cardiac output), Human Activity Profile (HAP), fasting morning glucose (GLU), total cholesterol (TC), lipids, and interleukin‐8 (IL‐8) were determined.ResultsFor the entire group, RPE was significantly correlated to serum IL‐8 and GLU but not to the other factors. When we controlled for age and triglycerides, RPE remained significantly related to GLU (rs = 0.54; P = .04), maximal activity level (HAP) (rs = 0.58; P = .03), and distance walked (rs = 0.61; P = .03) in those with NAFLD. In those with CH‐C, only IL‐8 remained a strong correlate of RPE (rs = 0.54; P = .01).ConclusionsIn individuals with CH‐C, RPE was related to an inflammatory factor, whereas in individuals with NAFLD, RPE was related to a metabolic and a lifestyle factor.
Interfacial Coupling and Electronic Structure of Two-Dimensional Silicon Grown on the Ag(111) Surface at High Temperature
Authors: Feng Jiagui , Wagner Sean R. , Zhang Pengpeng 
Published: June 2015

AbstractFreestanding silicene, a monolayer of Si arranged in a honeycomb structure, has been predicted to give rise to massless Dirac fermions, akin to graphene. However, Si structures grown on a supporting substrate can show properties that strongly deviate from the freestanding case. Here, combining scanning tunneling microscopy/spectroscopy and differential conductance mapping, we show that the electrical properties of the "Equation missing" phase of few-layer Si grown on Ag(111) strongly depend on film thickness, where the electron phase coherence length decreases and the free-electron-like surface state gradually diminishes when approaching the interface. These features are presumably attributable to the inelastic inter-band electron-electron scattering originating from the overlap between the surface state, interface state and the bulk state of the substrate. We further demonstrate that the intrinsic electronic structure of the as grown "Equation missing" phase is identical to that of the "Equation missing"R30° reconstructed Ag on Si(111), both of which exhibit the parabolic energy-momentum dispersion relation with comparable electron effective masses. These findings highlight the essential role of interfacial coupling on the properties of two-dimensional Si structures grown on supporting substrates, which should be thoroughly scrutinized in pursuit of silicene.
Reconstructing propagation networks with temporal similarity
Authors: Liao Hao , Zeng An 
Published: June 2015

AbstractNode similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.
Iterative experiment design guides the characterization of a light-inducible gene expression circuit
Authors: Ruess Jakob , Parise Francesca , Milias-Argeitis Andreas , Khammash Mustafa , Lygeros John 
Published: June 2015

Significance
System identification addresses the problem of identifying unknown model parameters from measured data of a real system. In the case of biochemical reaction networks, the available measurements are typically sparse because of technical and/or economic reasons. Therefore, it is of paramount importance to maximize the information that can be gained by each experiment. Here, we apply a systematic design scheme for single-cell experiments based on information theoretic criteria. For the considered light-inducible gene expression circuit, we show that this scheme allows one to precisely identify model parameters that were practically unidentifiable from data measured in random experiments. This result provides evidence that optimal experiment design is a key requirement for the successful identification of biochemical reaction networks.
Weak form of equidistribution theorem for harmonic measures of foliations by hyperbolic surfaces
Authors: Matsumoto Shigenori 
Published: June 2015

We show that the equidistribution theorem of C. Bonatti and X. Gómez-Mont for a special kind of foliations by hyperbolic surfaces does not hold in general, and we seek a weaker form valid for general foliations by hyperbolic surfaces.
Introduction
Authors: Adams-Prassl Jeremias 
Published: June 2015

AbstractThis chapter charts the problems resulting from employment law’s near-exclusive focus on the classification of employees as party to a bilateral contract of employment, and suggests that the enquiry should be widened to encompass the concept of the employer. Subsequent sections then set out an overview of the central argument and overall structure of the work, and outline its scope and methodological approach. A final section sketches the broader implications of a reconceptualized definition of the employer for the personal scope of employment protective norms in English law: it is only by adopting a functional concept of the employer that coherent coverage can be restored, irrespective of the factual complexity of the underlying work arrangement.
Complex Employment Structures in Germany
Authors: Adams-Prassl Jeremias 
Published: June 2015

AbstractThis chapter presents a comparative exploration of the scope of employment regulation in multilateral structures under German law, particularly as regards the situation in corporate groups (the Konzern). It traces the evolution of a body of law at the intersection of company law and employment law, aimed at ensuring worker involvement throughout the enterprise as well as at the group level (codetermination, or Mitbestimmung), and explains the detailed operation of their different legal mechanisms. The comparative excursus concludes that any successful reconceptualization does not necessarily need to identify identical employers in different domains of labour law; that the unitary concept is particularly problematic insofar as it leads to rigidly formalistic outcomes; and that a reconceptualization needs to strive for a careful balance between overall coherence and flexibility, avoiding a radical departure from existing frameworks whilst at the same time remaining adaptable to future doctrinal development.
A Single Concept in Different Contexts
Authors: Adams-Prassl Jeremias 
Published: June 2015

AbstractThis chapter addresses the unitary strand to show that while some clear breaks have to be made from the existing setup, a unifying overarching concept of the employer can be maintained. Its opening sections return to the three main factors identified as driving the unitary strand in Chapter 1 (in particular privity of contract and separate legal personality), re-examining each one to demonstrate a growing incongruence between developments in the relevant areas and labour law’s rigid bilateral contractual analysis. The chapter then explores the need for an overarching concept of the employer to fulfil its key public-regulatory function of facilitating the risk/control trade-off at the heart of modern labour markets. Analysed thus, the received concept fails to achieve its basic tasks in nearly all situations beyond a unitary paradigm, whereas the reconceptualized strand ensures that the concept of the employer can grapple with complex multilateral manifestations of shared employer functions.
Gersonides
Authors: Glasner Ruth 
Published: June 2015

Abstract
Gersonides (Levi ben Gershom) was a highly original Jewish philosopher, scientist, and biblical exegete, active in Provence in the first half of the fourteenth century. The book focuses on his work as a scientist, arguing that the key to understanding his originality and his impressive achievements was his perspective of an applied mathematical scientist. It was this perspective that led him to examine Aristotelianism from points of view different from those usually adopted by contemporary scholastics. He started his way, as he himself states, as someone who is at once ‘mathematician, natural scientist and philosopher’, believing that it was in his power to solve the main problems of medieval science. He ended up concentrating on his work as a mathematical astronomer, developing techniques of observation and computation, and somewhat less optimistic about the prospect of scientific knowledge.
Reconstructing 16S rRNA genes in metagenomic data
Authors: Yuan Cheng , Lei Jikai , Cole James , Sun Yanni 
Published: June 2015

Abstract
Metagenomic data, which contains sequenced DNA reads of uncultured microbial species from environmental samples, provide a unique opportunity to thoroughly analyze microbial species that have never been identified before. Reconstructing 16S ribosomal RNA, a phylogenetic marker gene, is usually required to analyze the composition of the metagenomic data. However, massive volume of dataset, high sequence similarity between related species, skewed microbial abundance and lack of reference genes make 16S rRNA reconstruction difficult. Generic de novo assembly tools are not optimized for assembling 16S rRNA genes. In this work, we introduce a targeted rRNA assembly tool, REAGO (REconstruct 16S ribosomal RNA Genes from metagenOmic data). It addresses the above challenges by combining secondary structure-aware homology search, zproperties of rRNA genes and de novo assembly. Our experimental results show that our tool can correctly recover more rRNA genes than several popular generic metagenomic assembly tools and specially designed rRNA construction tools.
Availability and implementation: The source code of REAGO is freely available at https://github.com/chengyuan/reago.
Contact: yannisun@msu.edu
Identification of causal genes for complex traits
Authors: Hormozdiari Farhad , Kichaev Gleb , Yang Wen-Yun , Pasaniuc Bogdan , Eskin Eleazar 
Published: June 2015

Abstract
Motivation: Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider ‘causal variants’ as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations.
Results: In this work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability ρ. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2.
Availability and implementation: Software is freely available for download at genetics.cs.ucla.edu/caviar.
Contact: eeskin@cs.ucla.edu
Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
Authors: Childs Dorothee , Grimbs Sergio , Selbig Joachim 
Published: June 2015

Abstract
Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.
Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.
Contact: dorothee.childs@embl.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Robust reconstruction of gene expression profiles from reporter gene data using linear inversion
Authors: Zulkower Valentin , Page Michel , Ropers Delphine , Geiselmann Johannes , de Jong Hidde 
Published: June 2015

Abstract
Motivation: Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest.
Results: We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions.
Availability and implementation: The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https://team.inria.fr/ibis/wellinverter.
Contact: Hidde.de-Jong@inria.fr
Supplementary information: Supplementary data are available at Bioinformatics online.
cNMA: a framework of encounter complex-based normal mode analysis to model conformational changes in protein interactions
Authors: Oliwa Tomasz , Shen Yang 
Published: June 2015

Abstract
Motivation: It remains both a fundamental and practical challenge to understand and anticipate motions and conformational changes of proteins during their associations. Conventional normal mode analysis (NMA) based on anisotropic network model (ANM) addresses the challenge by generating normal modes reflecting intrinsic flexibility of proteins, which follows a conformational selection model for protein–protein interactions. But earlier studies have also found cases where conformational selection alone could not adequately explain conformational changes and other models have been proposed. Moreover, there is a pressing demand of constructing a much reduced but still relevant subset of protein conformational space to improve computational efficiency and accuracy in protein docking, especially for the difficult cases with significant conformational changes.
Method and results: With both conformational selection and induced fit models considered, we extend ANM to include concurrent but differentiated intra- and inter-molecular interactions and develop an encounter complex-based NMA (cNMA) framework. Theoretical analysis and empirical results over a large data set of significant conformational changes indicate that cNMA is capable of generating conformational vectors considerably better at approximating conformational changes with contributions from both intrinsic flexibility and inter-molecular interactions than conventional NMA only considering intrinsic flexibility does. The empirical results also indicate that a straightforward application of conventional NMA to an encounter complex often does not improve upon NMA for an individual protein under study and intra- and inter-molecular interactions need to be differentiated properly. Moreover, in addition to induced motions of a protein under study, the induced motions of its binding partner and the coupling between the two sets of protein motions present in a near-native encounter complex lead to the improved performance. A study to isolate and assess the sole contribution of intermolecular interactions toward improvements against conventional NMA further validates the additional benefit from induced-fit effects. Taken together, these results provide new insights into molecular mechanisms underlying protein interactions and new tools for dimensionality reduction for flexible protein docking.
Availability and implementation: Source codes are available upon request.
Contact: yshen@tamu.edu
In silicophenotyping via co-training for improved phenotype prediction from genotype
Authors: Roqueiro Damian , Witteveen Menno J. , Anttila Verneri , Terwindt Gisela M. , van den Maagdenberg Arn M.J.M. , Borgwardt Karsten 
Published: June 2015

AbstractMotivation: Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction.Results: Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium.Conclusions: Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction.Availability and implementation: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.htmlContact: karsten.borgwardt@bsse.ethz.ch or menno.witteveen@bsse.ethz.chSupplementary information: Supplementary data are available at Bioinformatics online.
Exploiting ontology graph for predicting sparsely annotated gene function
Authors: Wang Sheng , Cho Hyunghoon , Zhai ChengXiang , Berger Bonnie , Peng Jian 
Published: June 2015

Abstract
Motivation: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (&lt;10) annotated genes, which constitute about half of the GO terms in yeast, mouse and human, pose a unique challenge in that any prediction algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this ‘overfitting’ issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog.
Results: We propose a novel function prediction algorithm, clusDCA, which transfers information between similar functional labels to alleviate the overfitting problem for sparsely annotated functions. Our method is scalable to datasets with a large number of annotations. In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information. Furthermore, we show that our method can accurately predict genes that will be assigned a functional label that has no known annotations, based only on the ontology graph structure and genes associated with other labels, which further suggests that our method effectively utilizes the similarity between gene functions.
Availability and implementation: https://github.com/wangshenguiuc/clusDCA.
Contact: jianpeng@illinois.edu
Supplementary information: Supplementary data are available at Bioinformatics online.