bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in the Messenger RNA Expression of Toll-Like Receptors 2 and 4 in Healthy Infants According to Age
Authors: Galliano Ilaria , Garro Maria , Savino Andrea , Manzoni Paolo , Daprà Valentina , Montanari Paola , Bergallo Massimiliano , Savino Francesco 
Published: April 2018

Background Toll-like receptors (TLRs) are potentially useful indicators of several pediatric disease states. Here, we explore the mechanisms by which inflammation is regulated by interactions between microbiota and the host. Little data are available regarding the expression of TLRs in postnatal healthy infants. TLR 2 and TLR4 are extracellular TLRs that act as innate immune receptors by recognizing a wide range of endogenous ligands and microorganisms.
Methods The aim of this study was to use real-time polymerase chain reaction to investigate the expression of the messenger RNAs (mRNAs) of TLR2 and TLR4 in blood samples obtained from healthy full-term infants and toddlers.
Results We analyzed the mRNA expression levels of TLRs in 88 healthy term children separated according to age. The median expression level of TLR2 was 1.49 ± 1.10 arbitrary units (AU) (n = 25) in infants younger than 3 months, 0.67 ± 0.72 AU (n = 25) in infants aged between 3 and 12 months, and 0.03 ± 0.02 AU (n= 38) in infants older than 12 months. The median expression level of TLR4 was 1.25 ± 0.79 AU (n = 25) in infants younger than 3 months, 0.75 ± 0.54 AU (n = 25) in infants aged 3 to 12 months, and 0.44 ± 0.28 AU (n = 38) in infants older than 12 months. There was difference in the mRNA expression level of TLR2 and TLR4 between infants aged 0 to 3 and 3 to 12 months and those aged more than 1 year (p < 0.0001 and p < 0.0001, respectively)
Conclusion We found that the expression levels of TLR2 and TLR4 were associated with age. In particular, we observed that their expression increased during the suckling period and then clearly decreased once the infants reached 1 year of age (p < 0.001). These findings could be related to microbial colonization and the immune system.
The Use of Dynamic Assist Orthosis for Muscle Reeducation following Brachial Plexus Injury and Reconstruction
Authors: Chinchalkar Shrikant , Larocerie-Salgado Juliana , Cepek Jeremy , Grenier Marie-Lyne 
Published: April 2018

AbstractTherapeutic management of brachial plexus injuries remains complex. The impact of brachial plexus injuries on everyday human functioning should not be underestimated. Early active-assisted range of motion following such injuries may prevent myostatic contractures, minimize muscle atrophy, facilitate muscle fiber recruitment, and enable a faster return to baseline strength levels. The dynamic assist elbow flexion orthosis proposed is designed to provide patients with a graded system for muscle reeducation and function. No clinical data are currently available on the use of this orthosis design; however, this article presents a treatment option based on sound clinical reasoning to facilitate rehabilitation following this devastating injury.
Compliance on Mandatory Data Reporting in Registered Obstetrics Trials
Authors: Evans M. , Athale Abha , Jawhar Omar , Wright Micah , Bryant Kristine , Vassar Matt , Checketts Jake 
Published: April 2018

Background The Food and Drug Administration's (FDA's) Amendments Act established a legal mandate requiring registration of certain drug, device, and biologics trials in ClinicalTrials.gov prior to patient enrollment. One provision of the act requires investigators to report trial results in ClinicalTrials.gov within 1 year of completion. Preliminary evidence suggests that overall compliance rates are inadequate, and rates specific to obstetrics have not been investigated.
Objective The purpose of this study was to examine the rate of compliance for mandatory reporting of results from obstetrics trials to ClinicalTrials.gov and to determine whether compliance rates were associated with funding type.
Study Design We performed a registry-based study of clinical trials pertaining to obstetrics. ClinicalTrials.gov was cross-referenced with Drugs@FDA to determine which trials required mandatory reporting. We used obstetrics trials registered on ClinicalTrials.gov with at least 1 US site. Phase 0, Phase 1, and trials not reporting a phase were excluded. Furthermore, only trials of interventions approved by the FDA were retained.
Results Our search returned 973 trials, of which 325 (33.4%) were screened for eligibility. Of the 325 completed trials, 74 (22.8% or 7.6% of the total) met all inclusion criteria and were evaluated for compliance. Thirty-seven of these trials (50%) did not list results, whereas the remaining 37 trials (50%) contained results on ClinicalTrials.gov. Trials funded by the National Institutes of Health (87.5%; ⅞) and industry (80%; 12/15) had higher rates of compliance than trials funded by other (43.9%; 18/41) or unspecified (0%; 0/10) sources.
Conclusion Half of all applicable obstetrics trials did not report results. Furthermore, rates of compliance appeared to vary by funding source, with trials funded by the National Institutes of Health or industry appearing to have a higher rate of compliance to mandatory data reporting. Greater awareness of federal regulations is needed, and changes should be implemented to increase reporting.
A gene–phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach
Authors: Xing Wenhui , Qi Junsheng , Yuan Xiaohui , Li Lin , Zhang Xiaoyu , Fu Yuhua , Xiong Shengwu , Hu Lun , Peng Jing 
Published: April 2018

Abstract

Motivation
The fundamental challenge of modern genetic analysis is to establish gene-phenotype correlations that are often found in the large-scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study- or author-specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants.

Results
We have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature. Combined with abbreviation revision and sentence template extraction, we improved the unsupervised word-embedding-to-sentence-embedding cascaded approach as representation learning to recognize the various broad phenotypic information in literature. In addition, the dictionary- and rule-based method was applied for gene recognition. Finally, we integrated one of famous information extraction system OLLIE to identify gene-phenotype relations. To demonstrate the applicability of the pipeline, we established two types of comparison experiment using model organism Arabidopsis thaliana. In the comparison of state-of-the-art baselines, our approach obtained the best performance (F1-Measure of 66.83%). We also applied the pipeline to 481 full-articles from TAIR gene-phenotype manual relationship dataset to prove the validity. The results showed that our proposed pipeline can cover 70.94% of the original dataset and add 373 new relations to expand it.

Availability and implementation
The source code is available at http://www.wutbiolab.cn: 82/Gene-Phenotype-Relation-Extraction-Pipeline.zip.

Supplementary information
Supplementary data are available at Bioinformatics online.
A powerful approach reveals numerous expression quantitative trait haplotypes in multiple tissues
Authors: Ying Dingge , Li Mulin Jun , Sham Pak Chung , Li Miaoxin 
Published: April 2018

Abstract

Motivation
Recently many studies showed single nucleotide polymorphisms (SNPs) affect gene expression and contribute to development of complex traits/diseases in a tissue context-dependent manner. However, little is known about haplotype’s influence on gene expression and complex traits, which reflects the interaction effect between SNPs.

Results
In the present study, we firstly proposed a regulatory region guided eQTL haplotype association analysis approach, and then systematically investigate the expression quantitative trait loci (eQTL) haplotypes in 20 different tissues by the approach. The approach has a powerful design of reducing computational burden by the utilization of regulatory predictions for candidate SNP selection and multiple testing corrections on non-independent haplotypes. The application results in multiple tissues showed that haplotype-based eQTLs not only increased the number of eQTL genes in a tissue specific manner, but were also enriched in loci that associated with complex traits in a tissue-matched manner. In addition, we found that tag SNPs of eQTL haplotypes from whole blood were selectively enriched in certain combination of regulatory elements (e.g. promoters and enhancers) according to predicted chromatin states. In summary, this eQTL haplotype detection approach, together with the application results, shed insights into synergistic effect of sequence variants on gene expression and their susceptibility to complex diseases.

Availability and implementation
The executable application ‘eHaplo’ is implemented in Java and is publicly available at http://grass.cgs.hku.hk/limx/ehaplo/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition
Authors: Wu Han-Ming , Tien Yin-Jing , Ho Meng-Ru , Hwu Hai-Gwo , Lin Wen-chang , Tao Mi-Hua , Chen Chun-houh 
Published: April 2018

AbstractMotivationHeatmap is a popular visualization technique in biology and related fields. In this study, we extend heatmaps within the framework of matrix visualization (MV) by incorporating a covariate adjustment process through the estimation of conditional correlations. MV can explore the embedded information structure of high-dimensional large-scale datasets effectively without dimension reduction. The benefit of the proposed covariate-adjusted heatmap is in the exploration of conditional association structures among the subjects or variables that cannot be done with conventional MV.ResultsFor adjustment of a discrete covariate, the conditional correlation is estimated by the within and between analysis. This procedure decomposes a correlation matrix into the within- and between-component matrices. The contribution of the covariate effects can then be assessed through the relative structure of the between-component to the original correlation matrix while the within-component acts as a residual. When a covariate is of continuous nature, the conditional correlation is equivalent to the partial correlation under the assumption of a joint normal distribution. A test is then employed to identify the variable pairs which possess the most significant differences at varying levels of correlation before and after a covariate adjustment. In addition, a z-score significance map is constructed to visualize these results. A simulation and three biological datasets are employed to illustrate the power and versatility of our proposed method.Availability and implementationGAP is available to readers and is free to non-commercial applications. The installation instructions, the user’s manual, and the detailed tutorials can be found at http://gap.stat.sinica.edu.tw/Software/GAP.Supplementary informationSupplementary Data are available at Bioinformatics online.
lncFunTK: a toolkit for functional annotation of long noncoding RNAs
Authors: Zhou Jiajian , Huang Yile , Ding Yingzhe , Yuan Jie , Wang Huating , Sun Hao 
Published: April 2018

Abstract

Motivation
Thousands of long noncoding RNAs (lncRNAs) were newly identified from high throughput RNA-seq data. Functional annotation and prioritization of these lncRNAs for further experimental validation as well as the functional investigation is the bottleneck step for many noncoding RNA studies.

Results
Here we describe lncFunTK that can run either as standard application or webserver for this purpose. It integrates high throughput sequencing data (i.e. ChIP-seq, CLIP-seq and RNA-seq) to construct the regulatory network associated with lncRNAs. Through the network, it calculates the Functional Information Score (FIS) of each individual lncRNA for prioritizing and inferring its functions through Gene Ontology (GO) terms of neighboring genes. In addition, it also provides utility scripts to support the input data preprocessing and the parameter optimizing. We further demonstrate that lncFunTK can be widely used in various biological systems for lncRNA prioritization and functional annotation.

Availability and implementation
The lncFunTK standalone version is an open source package and freely available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk under the MIT license. A webserver implementation is also available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk/runlncfuntk.html.

Supplementary information
Supplementary data are available at Bioinformatics online.
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
Authors: Jones David T , Kandathil Shaun M 
Published: April 2018

Abstract

Motivation
In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue–residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation.

Results
Comparisons against CCMpred and MetaPSICOV2 show that using pairwise covariance data calculated from raw alignments as input allows us to match or exceed the performance of both of these methods. Almost all of the achieved precision is obtained when considering relatively local windows (around 15 residues) around any member of a given residue pairing; larger window sizes have comparable performance. Assessment on a set of shallow sequence alignments (fewer than 160 effective sequences) indicates that the new method is substantially more precise than CCMpred and MetaPSICOV2 in this regime, suggesting that improved precision is attainable on smaller sequence families. Overall, the performance of DeepCov is competitive with the state of the art, and our results demonstrate that global models, which employ features from all parts of the input alignment when predicting individual contacts, are not strictly needed in order to attain precise contact predictions.

Availability and implementation
DeepCov is freely available at https://github.com/psipred/DeepCov.

Supplementary information
Supplementary data are available at Bioinformatics online.
TMCrys: predict propensity of success for transmembrane protein crystallization
Authors: Varga Julia K , Tusnády Gábor E 
Published: April 2018

Abstract

Motivation
Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine––the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service.

Results
Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs.

Availability and implementation
https://github.com/brgenzim/tmcrys

Supplementary information
Supplementary data are available at Bioinformatics online.
Quantification of biases in predictions of protein stability changes upon mutations
Authors: Pucci Fabrizio , Bernaerts Katrien V , Kwasigroch Jean Marc , Rooman Marianne 
Published: April 2018

Abstract

Motivation
Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations.

Results
We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (ΔΔG0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured ΔΔG0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used ΔΔG0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed.

Supplementary information
Supplementary data are available at Bioinformatics online.

Note
The article 10.1093/bioinformatics/bty340/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.
Trends in user ratings and reviews of a popular yet inaccurate blood pressure-measuring smartphone app
Authors: Plante Timothy B , O’Kelly Anna C , Macfarlane Zane T , Urrea Bruno , Appel Lawrence J , Miller III Edgar R , Blumenthal Roger S , Martin Seth S 
Published: April 2018

Abstract

Objective
To understand whether user reviews of Instant Blood Pressure (IBP), an inaccurate, unregulated BP-measuring app reflected IBP’s inaccuracy, to understand drivers for high and low ratings, and to understand if disclaimers prevented medical use.

Materials and Methods
All iTunes app reviews for IBP v1.2.3 were downloaded and assessed for themes by two reviewers. Summary statistics for themes were tabulated with their associated star ratings.

Results
Common themes included perceived accuracy (42% of all reviews, star rating mean 4.8, median 5), inaccuracy (10%, 2.0, 1), and convenience (34%, 4.7, 5). Nine percent documented IBP use in medical conditions (4.6, 5), and 2% mentioned IBP’s disclaimer (2.7, 3).

Discussion
User reviews and ratings of a popular, inaccurate BP-measuring app were positive and uncommonly commented on its inaccuracy. Disclaimers attempting to prevent medical use of the app were ineffective. These findings support the need for more rigorous regulatory review of apps prior to their release.
Predictors of viewing progress notes among users of VA’s electronic health portal who receive mental health care
Authors: Dobscha Steven K , Denneson Lauren M , Pisciotta Maura K , Bourne Donald S , Chen Jason I , Philipps-Moses David , Woods Susan S 
Published: April 2018

Abstract

Objective
Consistent with the OpenNotes movement, the Veterans Health Administration (VHA) offers patients online access to their clinical notes through the Blue Button feature in its electronic patient health portal, My HealtheVet. We identified demographic, diagnostic, and knowledge-related predictors of viewing clinical notes among veterans receiving VHA mental health care who recently used My HealtheVet.

Materials and Methods
Three hundred and thirty-eight patients receiving mental health care from 1 VHA medical center who had logged into My HealtheVet in the prior 6 months completed self-report questionnaires assessing their viewing of clinical notes. Additional data were extracted from VHA’s Patient Care Database. Multivariable logistic regression was used to examine predictors of viewing notes.

Results
Fifty percent of respondents reported having read their notes. In the final multivariable model, post-traumatic stress disorder (PTSD) diagnosis [odds ratio (OR) = 2.30 (1.31–4.07)], speaking with their mental health clinician about their ability to view notes [OR = 3.84 (1.69–8.72)], and being very or extremely confident in understanding the purpose and uses of Blue Button [OR = 9.80 (2.23–43.07) and OR = 13.36 (2.74–65.20), respectively] were associated with viewing notes.

Discussion
Patient recall of mental health clinicians speaking to them about their ability to view notes, and confidence in understanding the use and purposes of Blue Button, were stronger predictors of viewing notes than demographic variables. PTSD diagnosis was the only clinical characteristic associated with viewing notes.

Conclusion
The findings support the value of mental health clinicians openly discussing the availability of notes with patients if they wish to help them take advantage of their potential benefits.
Single-atom catalysis: a new field that learns from tradition
Authors: Ball Philip 
Published: April 2018

Abstract
Much of industrial chemical processing (in the petrochemicals industry, for example), and a great deal of laboratory chemical synthesis, involves catalysts that both lower the energy barrier to reaction and may help steer a reaction along a particular path. Traditionally, catalysts have come in two classes: heterogeneous, typically meaning that the catalyst is an extended solid; and homogeneous, where the catalyst is a small molecule that shares a solvent with the reactants. In heterogeneous catalysis, the reaction generally takes place on a surface, involving molecules attached there by covalent bonds. Homogeneous catalysts are often organometallic compounds, in which a metal atom or small cluster of atoms supplies the active site for reaction.
In recent years, these distinctions have become somewhat blurred thanks to the advent of single-atom catalysis, where the catalytic site consists of a single atom (as in many homogeneous catalysts) attached to or embedded in a surface. The emergence of this field might be regarded as the logical conclusion of the use of ‘supported metal clusters’—small metal particles of nanometer scale and below, containing perhaps hundreds, tens or just a few atoms. It has became clear that such clusters can sometimes provide greater product selectivity and activity than macro-sized particles or powders of the same metal, partly because the active sites might be atoms at particular locations (such as edges and corners) in the nanoscale particles. By reducing their scale down to the level of single atoms, one can optimize these properties. At the same time, the potential uniformity of the atoms’ environments makes such catalysts more amenable to rational design and modeling to understand mechanism.
This field represents an appealing blend of fundamental chemistry and physics—from the quantum-mechanical level upwards—and applied research aimed at producing many of the products vital to society, such as fuels and materials. Researchers in China have been strongly active in this field in recent years (see, for example, refs [1–5]). Jean Marie Basset of the King Abdullah University of Science and Technology in Thuwal, Saudi Arabia, is one of the leading practitioners in the area, and National Science Review spoke to him about the development and prospects of the field.