bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantum-mechanical machinery for rational decision-making in classical guessing game
Authors: Bang Jeongho , Ryu Junghee , Pawłowski Marcin , Ham Byoung S. , Lee Jinhyoung 
Published: February 2016

AbstractIn quantum game theory, one of the most intriguing and important questions is, “Is it possible to getquantumadvantages without any modification of theclassicalgame?” The answer to this question so far has largely been negative. So far, it has usually been thought that a change of the classical game setting appears to be unavoidable for getting the quantum advantages. However, we give an affirmative answer here, focusing on the decision-making process (we call ‘reasoning’) to generate the best strategy, which may occur internally, e.g., in the player’s brain. To show this, we consider a classical guessing game. We then define a one-player reasoning problem in the context of the decision-making theory, where the machinery processes are designed to simulate classical and quantum reasoning. In such settings, we present a scenario where a rational player is able to make better use of his/her weak preferences due to quantum reasoning, without any altering or resetting of the classically defined game. We also argue in further analysis that the quantum reasoning may make the player fail, and even make the situation worse, due to any inappropriate preferences.
Zebrafish Collagen Type I: Molecular and Biochemical Characterization of the Major Structural Protein in Bone and Skin
Authors: Gistelinck C. , Gioia R. , Gagliardi A. , Tonelli F. , Marchese L. , Bianchi L. , Landi C. , Bini L. , Huysseune A. , Witten P. E. , Staes A. , Gevaert K. , De Rocker N. , Menten B. , Malfait F. , Leikin S. , Carra S. , Tenni R. , Rossi A. , De Paepe A. , Coucke P. , Willaert A. , Forlino A. 
Published: February 2016

AbstractOver the last years the zebrafish imposed itself as a powerful model to study skeletal diseases, but a limit to its use is the poor characterization of collagen type I, the most abundant protein in bone and skin. In tetrapods collagen type I is a trimer mainly composed of two α1 chains and one α2 chain, encoded by COL1A1 and COL1A2 genes, respectively. In contrast, in zebrafish three type I collagen genes exist, col1a1a, col1a1b and col1a2 coding for α1(I), α3(I) and α2(I) chains. During embryonic and larval development the three collagen type I genes showed a similar spatio-temporal expression pattern, indicating their co-regulation and interdependence at these stages. In both embryonic and adult tissues, the presence of the three α(I) chains was demonstrated, although in embryos α1(I) was present in two distinct glycosylated states, suggesting a developmental-specific collagen composition. Even though in adult bone, skin and scales equal amounts of α1(I), α3(I) and α2(I) chains are present, the presented data suggest a tissue-specific stoichiometry and/or post-translational modification status for collagen type I. In conclusion, this data will be useful to properly interpret results and insights gained from zebrafish models of skeletal diseases.
Extracellular Matrix Rigidity-dependent Sphingosine-1-phosphate Secretion Regulates Metastatic Cancer Cell Invasion and Adhesion
Authors: Ko Panseon , Kim Daehwan , You Eunae , Jung Jangho , Oh Somi , Kim Jaehyun , Lee Kwang-Ho , Rhee Sangmyung 
Published: February 2016

AbstractDynamic interaction between cancer cells and the surrounding microenvironment is critical for cancer progression via changes in cellular behavior including alteration of secreted molecules. However, the molecular mechanisms underlying the influence exerted by the cancer microenvironment on secretion of molecules during cancer progression remain largely unknown. In this study, we report that secretion of spingsine-1-phosphate (S1P) and its regulator, SphK1 expression is dependent of the substrate rigidity, which is critical for the balance between cancer cell invasion and adhesion. Conditioned media (CM) of MDA-MB-231, an aggressive breast cancer cell obtained from soft substrate (~0.5 kPa) induced chemo-attractive invasion, while CM obtained from stiff substrate (~2.5 kPa) increased cell adhesion instead. We found that the expression of SphK1 is upregulated in the stiff substrate, resulting in an increase in S1P levels in the CM. We also found that upregulation of SphK1 expression in the stiff substrate is dominant in metastatic cancer cells but not in primary cancer cells. These results suggest that alterations in the mechanical environment of the ECM surrounding the tumor cells actively regulate cellular properties such as secretion, which in turn, may contribute to cancer progression.
Superconductivity in Ta3Pd3Te14 with quasi-one-dimensional PdTe2 chains
Authors: Jiao Wen-He , He Lan-Po , Liu Yi , Xu Xiao-Feng , Li Yu-Ke , Zhang Chu-Hang , Zhou Nan , Xu Zhu-An , Li Shi-Yan , Cao Guang-Han 
Published: February 2016

AbstractWe report bulk superconductivity at 1.0 K in a low-dimensional ternary telluride Ta3Pd3Te14 containing edge-sharing PdTe2 chains along crystallographic b axis, similar to the recently discovered superconductor Ta4Pd3Te16. The electronic heat capacity data show an obvious anomaly at the transition temperature, which indicates bulk superconductivity. The specific-heat jump is ΔC/(γnTc) ≈ 1.35, suggesting a weak coupling scenario. By measuring the low-temperature thermal conductivity, we conclude that Ta3Pd3Te14 is very likely a dirty s-wave superconductor. The emergence of superconductivity in Ta3Pd3Te14 with a lower Tc, compared to that of Ta4Pd3Te16, may be attributed to the lower density of states.
Influence of light exposure at nighttime on sleep development and body growth of preterm infants
Authors: Kaneshi Yosuke , Ohta Hidenobu , Morioka Keita , Hayasaka Itaru , Uzuki Yutaka , Akimoto Takuma , Moriichi Akinori , Nakagawa Machiko , Oishi Yoshihisa , Wakamatsu Hisanori , Honma Naoki , Suma Hiroki , Sakashita Ryuichi , Tsujimura Sei-ichi , Higuchi Shigekazu , Shimokawara Miyuki , Cho Kazutoshi , Minakami Hisanori 
Published: February 2016

AbstractPrevious studies have demonstrated that a light-dark cycle has promoted better sleep development and weight gain in preterm infants than constant light or constant darkness. However, it was unknown whether brief light exposure at night for medical treatment and nursing care would compromise the benefits brought about by such a light-dark cycle. To examine such possibility, we developed a special red LED light with a wavelength of >675 nm which preterm infants cannot perceive. Preterm infants born at <36 weeks’ gestational age were randomly assigned for periodic exposure to either white or red LED light at night in a light-dark cycle after transfer from the Neonatal Intensive Care Unit to the Growing Care Unit, used for supporting infants as they mature. Activity, nighttime crying and body weight were continuously monitored from enrolment until discharge. No significant difference in rest-activity patterns, nighttime crying, or weight gain was observed between control and experimental groups. The data indicate that nursing care conducted at 3 to 4-hour intervals exposing infants to light for <15 minutes does not prevent the infants from developing circadian rest-activity patterns, or proper body growth as long as the infants are exposed to regular light-dark cycles.
Centralising rents and dispersing power while pursuing development? Exploring the strategic uses of military firms in Rwanda
Authors: Behuria Pritish 
Published: February 2016

ABSTRACT
The Rwandan Patriotic Front has achieved significant economic progress while also maintaining political stability. However, frictions among ruling elites have threatened progress. This paper explores the use of military firms in Rwanda. Such firms are used to invest in strategic industries, but the use of such firms reflects the vulnerability faced by ruling elites. Military firms serve two related purposes. First, ruling elites use such firms to centralise rents and invest in strategic sectors. Second, the proliferation of such enterprises and the separation of party- and military-owned firms contribute to dispersing power within a centralised hierarchy.
MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data
Authors: Murillo Gabriel H. , You Na , Su Xiaoquan , Cui Wei , Reilly Muredach P. , Li Mingyao , Ning Kang , Cui Xinping 
Published: February 2016

Abstract
Motivation: Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors. It also addresses the multiple testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC) techniques.
Results: A simulation study demonstrates that MultiGeMS ranks highest in precision among a selection of popular multiple sample SNV callers, while showing exceptional recall in calling common SNVs. Further, both simulation studies and real data analyses indicate that MultiGeMS is robust to low-quality data. We also demonstrate that accounting for enzymatic substitution sequencing errors not only improves SNV call precision at low mapping quality regions, but also improves recall at reference allele-dominated sites with high mapping quality.
Availability and implementation: The MultiGeMS package can be downloaded from https://github.com/cui-lab/multigems.
Contact: xinping.cui@ucr.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies
Authors: Wang Xiaowei 
Published: February 2016

Abstract
Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes targeted by miRNAs. Currently, most researchers rely on computational programs to initially identify target candidates for subsequent validation. Although considerable progress has been made in recent years for computational target prediction, there is still significant room for algorithmic improvement.
Results: Here, we present an improved target prediction algorithm, which was developed by modeling high-throughput profiling data from recent CLIPL (crosslinking and immunoprecipitation followed by RNA ligation) sequencing studies. In these CLIPL-seq studies, the RNA sequences in each miRNA-target pair were covalently linked and unambiguously determined experimentally. By analyzing the CLIPL data, many known and novel features relevant to target recognition were identified and then used to build a computational model for target prediction. Comparative analysis showed that the new algorithm had improved performance over existing algorithms when applied to independent experimental data.
Availability and implementation: All the target prediction data as well as the prediction tool can be accessed at miRDB (http://mirdb.org).
Contact: xwang@radonc.wustl.edu
Realizing privacy preserving genome-wide association studies
Authors: Simmons Sean , Berger Bonnie 
Published: February 2016

Abstract
Motivation: As genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency.
Results: Here we overcome these limitations with a substantially modified version of the neighbor distance method for performing differentially private GWAS, and thus are able to produce a more viable mechanism. Specifically, we use input perturbation and an adaptive boundary method to overcome accuracy issues. We also design and implement a convex analysis based algorithm to calculate the neighbor distance for each SNP in constant time, overcoming the major computational bottleneck in the neighbor distance method. It is our hope that methods such as ours will pave the way for more widespread use of patient data in biomedical research.
Availability and implementation: A python implementation is available at http://groups.csail.mit.edu/cb/DiffPriv/.
Contact: bab@csail.mit.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Collaborative analysis of multi-gigapixel imaging data using Cytomine
Authors: Marée Raphaël , Rollus Loïc , Stévens Benjamin , Hoyoux Renaud , Louppe Gilles , Vandaele Rémy , Begon Jean-Michel , Kainz Philipp , Geurts Pierre , Wehenkel Louis 
Published: February 2016

Abstract
Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries.
Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications.
Availability and implementation: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/. A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available.
Contact: info@cytomine.be
Supplementary information: Supplementary data are available at Bioinformatics online.
FINEMAP: efficient variable selection using summary data from genome-wide association studies
Authors: Benner Christian , Spencer Chris C.A. , Havulinna Aki S. , Salomaa Veikko , Ripatti Samuli , Pirinen Matti 
Published: February 2016

Abstract
Motivation: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.
Results: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.
Availability and implementation: FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com.
Contact: christian.benner@helsinki.fi or matti.pirinen@helsinki.fi
lodGWAS: a software package for genome-wide association analysis of biomarkers with a limit of detection
Authors: Vaez Ahmad , van der Most Peter J. , Prins Bram P. , Snieder Harold , van den Heuvel Edwin , Alizadeh Behrooz Z. , Nolte Ilja M. 
Published: February 2016

Abstract
Summary: Genome-wide association study (GWAS) of a biomarker is complicated when the assay procedure of the biomarker is restricted by a Limit of Detection (LOD). Those observations falling outside the LOD cannot be simply discarded, but should be included into the analysis by applying an appropriate statistical method. However, the problem of LOD in GWAS analysis of such biomarkers is usually overlooked. ‘lodGWAS’ is a flexible, easy-to-use R package that provides a simple and elegant way for GWAS analysis of such biomarkers while simultaneously accommodating the problem of LOD by applying a parametric survival analysis method.
Availability and implementation: http://cran.r-project.org/web/packages/lodGWAS
Contacts: a.vaez@umcg.nl or i.m.nolte@umcg.nl
Supplementary information: Supplementary data are available at Bioinformatics online.
MS-REDUCE: an ultrafast technique for reduction of big mass spectrometry data for high-throughput processing
Authors: Awan Muaaz Gul , Saeed Fahad 
Published: February 2016

Abstract
Motivation: Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks.
Results: We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100× speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server.
Availability and implementation: The developed tool and strategy has been made available to wider proteomics and parallel computing community and the code can be found at https://github.com/pcdslab/MSREDUCE
Contact: fahad.saeed@wmich.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
TFBSTools: an R/bioconductor package for transcription factor binding site analysis
Authors: Tan Ge , Lenhard Boris 
Published: February 2016

Abstract
Summary: The ability to efficiently investigate transcription factor binding sites (TFBSs) genome-wide is central to computational studies of gene regulation. TFBSTools is an R/Bioconductor package for the analysis and manipulation of TFBSs and their associated transcription factor profile matrices. TFBStools provides a toolkit for handling TFBS profile matrices, scanning sequences and alignments including whole genomes, and querying the JASPAR database. The functionality of the package can be easily extended to include advanced statistical analysis, data visualization and data integration.
Availability and implementation: The package is implemented in R and available under GPL-2 license from the Bioconductor website (http://bioconductor.org/packages/TFBSTools/).
Contact: ge.tan09@imperial.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Differential methylation analysis for BS-seq data under general experimental design
Authors: Park Yongseok , Wu Hao 
Published: February 2016

AbstractMotivation: DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods.Results: We present a novel statistical model to detect differentially methylated loci from BS-seq data under general experimental design, based on a beta-binomial regression model with ‘arcsine’ link function. Parameter estimation is based on transformed data with generalized least square approach without relying on iterative algorithm. Simulation and real data analyses demonstrate that our method is accurate, powerful, robust and computationally efficient.Availability and implementation: It is available as Bioconductor package DSS.Contact: yongpark@pitt.edu or hao.wu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Selecting a classification function for class prediction with gene expression data
Authors: Jong Victor L. , Novianti Putri W. , Roes Kit C.B. , Eijkemans Marinus J.C. 
Published: February 2016

AbstractMotivation: Class predicting with gene expression is widely used to generate diagnostic and/or prognostic models. The literature reveals that classification functions perform differently across gene expression datasets. The question, which classification function should be used for a given dataset remains to be answered. In this study, a predictive model for choosing an optimal function for class prediction on a given dataset was devised.Results: To achieve this, gene expression data were simulated for different values of gene-pairs correlations, sample size, genes' variances, deferentially expressed genes and fold changes. For each simulated dataset, ten classifiers were built and evaluated using ten classification functions. The resulting accuracies from 1152 different simulation scenarios by ten classification functions were then modeled using a linear mixed effects regression on the studied data characteristics, yielding a model that predicts the accuracy of the functions on a given data. An application of our model on eight real-life datasets showed positive correlations (0.33–0.82) between the predicted and expected accuracies.Conclusion: The here presented predictive model might serve as a guide to choose an optimal classification function among the 10 studied functions, for any given gene expression data.Availability and implementation: The R source code for the analysis and an R-package ‘SPreFuGED’ are available at Bioinformatics online.Contact: v.l.jong@umcutecht.nlSupplementary information: Supplementary data are available at Bioinformatics online.
Unipept web services for metaproteomics analysis
Authors: Mesuere Bart , Willems Toon , Van der Jeugt Felix , Devreese Bart , Vandamme Peter , Dawyndt Peter 
Published: February 2016

Abstract
Summary Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines.
Availability and implementation: The web services are freely available at http://api.unipept.ugent.be and are open sourced under the MIT license.
Contact: Unipept@ugent.be
Supplementary information: Supplementary data are available at Bioinformatics online.