bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIH funding longevity by gender
Authors: Hechtman Lisa A. , Moore Nathan P. , Schulkey Claire E. , Miklos Andrew C. , Calcagno Anna Maria , Aragon Richard , Greenberg Judith H. 
Published: July 2018

Significance
Diversity of the biomedical workforce is essential to the scientific enterprise, yet women remain underrepresented in academic positions in biomedical sciences and compose less than one-third of National Institutes of Health (NIH) research grantees. We explored NIH grant support as a proxy for participation in academic research. We found that women had similar funding longevity as men after they received their first major NIH grants, contradicting the common assumption that across all career stages, women experience accelerated attrition compared with men. Despite longevity similarities, women composed only 31% of grantees in our analysis. This discrepancy in grantee demographics suggests that efforts may be best directed toward encouraging women to enter academia and supporting their continued grant submissions.
Microstructural engineering and strength-impact toughness prediction in ultra-low carbon bainitic steel
Authors: Sk Md. Basiruddin , Chakrabarti Debalay , Chatterjee S. 
Published: July 2018

The strength–toughness combination of an ultra-low carbon, low alloyed ‘air-cooled’ steel has been enhanced by developing and refining the granular bainite (GB) structure. Suitable alloy design and thermo-mechanical treatment involving forging, austenitisation (950–1150°C) and air-cooling resulted in the formation of GB matrix along with small islands of martensite–austenite constituents and allotriomorphic ferrite grains. Microstructural characterisation and phase transformation behaviour were studied by scanning electron microscopy and electron backscattered diffraction analysis. The effect of microstructural parameters on the strength-impact toughness combination was investigated. The steel showed an exceptionally low yield ratio, which is beneficial for seismic resistance application. GRAPHICAL ABSTRACT
Perspectives of applied collaborative sport science research within professional team sports
Authors: Malone James J. , Harper Liam D. , Jones Ben , Perry John , Barnes Chris , Towlson Chris 
Published: July 2018

AbstractThe purpose of the study was to examine the perspectives of both academics and practitioners in relation to forming applied collaborative sport science research within team sports. Ninety‐three participants who had previously engaged in collaborative research partnerships within team sports completed an online survey which focused on motivations and barriers for forming collaborations using blinded sliding scale (0–100) and rank order list. Research collaborations were mainly formed to improve the team performance (Academic: 73.6 ± 23.3; Practitioner: 84.3 ± 16.0; effect size (ES = 0.54), small). Academics ranked journal articles’ importance significantly higher than practitioners did (Academic: Mrank = 53.9; Practitioner: 36.0; z = −3.18, p = .001, p < q). However, practitioners rated one‐to‐one communication as more preferential (Academic: Mrank = 41.3; Practitioner 56.1; z = −2.62, p = .009, p < q). Some potential barriers were found in terms of staff buy in (Academic: 70.0 ± 25.5; Practitioner: 56.8 ± 27.3; ES = 0.50, small) and funding (Academic: 68.0 ± 24.9; Practitioner: 67.5 ± 28.0; ES = 0.02, trivial). Both groups revealed low motivation for invasive mechanistic research (Academic: 36.3 ± 24.2; Practitioner: 36.4 ± 27.5; ES = 0.01, trivial), with practitioners have a preference towards ‘fast’ type research. There was a general agreement between academics and practitioners for forming research collaborations. Some potential barriers still exist (e.g. staff buy in and funding), with practitioners preferring ‘fast’ informal research dissemination compared to the ‘slow’ quality control approach of academics.
Influence of upright versus time trial cycling position on determination of critical power and W′ in trained cyclists
Authors: Kordi Mehdi , Fullerton Chris , Passfield Louis , Parker Simpson Len 
Published: July 2018

AbstractBody position is known to alter power production and affect cycling performance. The aim of this study was to compare mechanical power output in two riding positions, and to calculate the effects on critical power (CP) and W′ estimates. Seven trained cyclists completed three peak power output efforts and three fixed‐duration trial (3‐, 5‐ and 12‐min) riding with their hands on the brake lever hoods (BLH), or in a time trial position (TTP). A repeated‐measures analysis of variance showed that mean power output during the 5‐min trial was significantly different between BLH and TTP positions, resulting in a significantly lower estimate of CP, but not W′, for the TTP trial. In addition, TTP decreased the performance during each trial and increased the percentage difference between BLH and TTP with greater trial duration. There were no differences in pedal cadence or heart rate during the 3‐min trial; however, TTP results for the 12‐min trial showed a significant fall in pedal cadence and a significant rise in heart rate. The findings suggest that cycling position affects power output and influences consequent CP values. Therefore, cyclists and coaches should consider the cycling position used when calculating CP.
Rwanda’s post-genocide foreign aid relations: Revisiting notions of exceptionalism
Authors: Desrosiers Marie-Eve , Swedlund Haley J 
Published: July 2018

Abstract
This article studies donor–government relations in Rwanda since the end of the 1994 genocide. The notion that Rwanda enjoyed or enjoys exceptional relations with donors because of guilt regarding their inaction during the genocide is widespread in the literature and in policy circles. To assess this myth, the article first looks at aid trends for Rwanda and comparable countries, and then takes an in-depth look at aid relations with two average-size donors: Canada and the Netherlands. It demonstrates that Rwanda is not as exceptional as claimed, but instead should be considered one amongst a group of exceptional cooperation partners. The article further highlights that donors operated informally immediately following the genocide, but soon renormalized aid relations, and that there has always been a complex set of rationales determining donor behaviour regarding Rwanda.
Uncontrolled Hypertension Is Common in Glaucoma Clinics
Authors: Varadaraj Varshini , Vardhan Ashok , Mudie Lucy I , LaBarre Sophie , Ong Yuhan , Wang Bingsong , Sherrod Cheryl , Friedman David S 
Published: July 2018

Abstract

BACKGROUND
To determine the magnitude of uncontrolled hypertension and smoking among patients visiting an eye clinic, and ascertain if referral to care providers is effective.

METHODS
Information on smoking status and blood pressure (BP) was collected among patients ≥18 years visiting an eye clinic. Those with high BP (systolic: ≥140 mm Hg and/or diastolic: ≥90 mm Hg) received a pamphlet on harms of hypertension on vision and were referred to a primary care physician. Smokers received a pamphlet on negative effects of smoking on vision and were offered referral to a tobacco quitline. Patients were followed up for referral outcome within 10 weeks from screening.

RESULTS
Screening: A total of participants screened included 140 (29.5%) with high BP and 31 (6.6%) current smokers. In the high BP group, 92 (66%) subjects were previously diagnosed with hypertension. Follow-up: Of the 140 participants with elevated BP, 84 (60%) responded to follow-up. Among these 84 participants, 57 (67.9%) had consulted primary care, of whom 5 (8.8%) reported being newly diagnosed with hypertension, and 11 (19.3%) reported a change in their antihypertensive prescription. Among the 31 smokers, 24 (77.4%) were willing for quitline referral. Sixteen (66.7%) of these patients responded to follow-up, 8 (50%) of whom reported participation in a smoking-cessation program with 1 patient (6.3%) successfully quitting smoking.

CONCLUSIONS
Nearly one-third of patients attending an eye clinic had elevated BP, and a smaller, but substantial, number of patients were current smokers. Eye clinics may serve as point for identification and referral of these patients with unmet needs.
Racial Residential Segregation and Hypertensive Disorder of Pregnancy Among Women in Chicago: Analysis of Electronic Health Record Data
Authors: Mayne Stephanie L , Yellayi Disha , Pool Lindsay R , Grobman William A , Kershaw Kiarri N 
Published: July 2018

Abstract

BACKGROUND
Racial residential segregation is associated with higher rates of chronic hypertension, as well as greater risk of preterm birth and low birthweight. However, few studies have examined associations between segregation and hypertensive disorder of pregnancy (HDP).

METHODS
Electronic health records from 4,748 singleton births among non-Hispanic black women at Prentice Women’s Hospital in Chicago, IL (2009–2013) were geocoded to the census tract level. Residential segregation was measured using the Gi* statistic, a z-score measuring the extent to which each individual’s neighborhood composition deviates from the composition of the larger surrounding area. Segregation was categorized as low (z &lt; 0), medium (z = 0–1.96) or high (z &gt; 1.96). We estimated cross-sectional associations of segregation with HDP using multilevel logistic regression models with census tract random intercepts. Models adjusted for neighborhood poverty and maternal characteristics. We also examined effect modification by neighborhood poverty.

RESULTS
Overall, 27.2% of women lived in high segregation, high-poverty neighborhoods. Racial residential segregation was not associated as a main effect with HDP in models adjusting for neighborhood poverty and maternal characteristics. However, at higher levels of neighborhood poverty (&gt;20%), women living in high- and medium-segregated neighborhoods had greater odds of HDP relative to those in low-segregation neighborhoods (P interaction: 0.002).

CONCLUSIONS
In this sample of non-Hispanic black women in Chicago, racial residential segregation was associated with greater prevalence of HDP among those living in higher poverty neighborhoods. Understanding sources of heterogeneity in the relationship between segregation and health will help refine targeted intervention efforts to reduce disparities.
The Impact on Mortality and Societal Costs From Smoking Cessation in Aesthetic Plastic Surgery in the United States
Authors: Bloom Joshua A , Rashad Ramy , Chatterjee Abhishek 
Published: July 2018

AbstractBackgroundIt is common practice to require patients to stop smoking prior to elective plastic surgery procedures. Scarce research exists describing the impact on mortality and associated societal cost savings with regard to smoking cessation in aesthetic surgery.ObjectivesOur objective is to demonstrate that smoking cessation in anticipation for aesthetic surgery significantly reduces mortality and increases societal cost savings.MethodsWe performed a systematic literature review of 5 common aesthetic procedures (reduction mammaplasty, breast augmentation, facelift, rhinoplasty, and abdominoplasty) to determine patient smoking rates and subsequent recidivism. Sensitivity analyses estimated life years saved using ranges of recidivism from our literature review and assessed total lifetime savings, including direct and productivity costs, while adjusting for inflation (3%) and interest (5%). One life saved was equated to 45 life years saved.ResultsBetween May 2008 and May 2013, 7867 patients stopped smoking prior to undergoing aesthetic plastic surgery procedures. Assuming a reported recidivism rate of 68%, smoking cessation prior to aesthetic plastic surgery is associated with 429 lives saved and a total lifetime savings of $524.4 million over the five-year period. Total lives saved ranged from 214 (84% recidivism) to 885 (34% recidivism), and total lifetime cost savings ranged from $262.2 million (84% recidivism) to $1.08 billion (34% recidivism).ConclusionsPresently, smoking cessation before aesthetic surgery significantly saves patient lives with yearly $104.9 million of societal cost savings in the United States. Future reductions in the presently high recidivism rate would lead to additional lives saved and reduced societal costs.Level of Evidence: 4
Dynamics of physiologically relevant noncanonical DNA structures: an overview from experimental and theoretical studies
Authors: Ghoshdastidar Debostuti , Bansal Manju 
Published: July 2018

AbstractDNA is a complex molecule with phenomenal inherent plasticity and the ability to form different hydrogen bonding patterns of varying stabilities. These properties enable DNA to attain a variety of structural and conformational polymorphic forms. Structurally, DNA can exist in single-stranded form or as higher-order structures, which include the canonical double helix as well as the noncanonical duplex, triplex and quadruplex species. Each of these structural forms in turn encompasses an ensemble of dynamically heterogeneous conformers depending on the sequence composition and environmental context. In vivo, the widely populated canonical B-DNA attains these noncanonical polymorphs during important cellular processes. While several investigations have focused on the structure of these noncanonical DNA, studying their dynamics has remained nontrivial. Here, we outline findings from some recent advanced experimental and molecular simulation techniques that have significantly contributed toward understanding the complex dynamics of physiologically relevant noncanonical forms of DNA.
Kinome-wide identification of phosphorylation networks in eukaryotic proteomes
Authors: Parca Luca , Ariano Bruno , Cabibbo Andrea , Paoletti Marco , Tamburrini Annalaura , Palmeri Antonio , Ausiello Gabriele , Helmer-Citterich Manuela 
Published: July 2018

Abstract

Motivation
Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind.

Results
In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome.

Availability and implementation
http://akid.bio.uniroma2.it

Supplementary information
Supplementary data are available at Bioinformatics online.
An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets
Authors: Schmidt Florian , List Markus , Cukuroglu Engin , Köhler Sebastian , Göke Jonathan , Schulz Marcel H 
Published: July 2018

Abstract

Motivation
International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.

Results
We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.

Availability and implementation
Our method is available online at https://github.com/SchulzLab/OntologyEval.

Supplementary information
Supplementary data are available at Bioinformatics online.
DeepDTA: deep drug–target binding affinity prediction
Authors: Öztürk Hakime , Özgür Arzucan , Ozkirimli Elif 
Published: July 2018

Abstract

Motivation
The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).

Results
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.

Availability and implementation
https://github.com/hkmztrk/DeepDTA

Supplementary information
Supplementary data are available at Bioinformatics online.
DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications
Authors: Sekhon Arshdeep , Singh Ritambhara , Qi Yanjun 
Published: July 2018

Abstract

Motivation
Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing differential gene regulation. Recent studies either failed to capture combinatorial effects on differential prediction or primarily only focused on cell type-specific analysis. In this paper we develop a novel attention-based deep learning architecture, DeepDiff, that provides a unified and end-to-end solution to model and to interpret how dependencies among histone modifications control the differential patterns of gene regulation. DeepDiff uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the spatial structure of input signals and to model how various histone modifications cooperate automatically. We introduce and train two levels of attention jointly with the target prediction, enabling DeepDiff to attend differentially to relevant modifications and to locate important genome positions for each modification. Additionally, DeepDiff introduces a novel deep-learning based multi-task formulation to use the cell-type-specific gene expression predictions as auxiliary tasks, encouraging richer feature embeddings in our primary task of differential expression prediction.

Results
Using data from Roadmap Epigenomics Project (REMC) for ten different pairs of cell types, we show that DeepDiff significantly outperforms the state-of-the-art baselines for differential gene expression prediction. The learned attention weights are validated by observations from previous studies about how epigenetic mechanisms connect to differential gene expression.

Availability and implementation
Codes and results are available at deepchrome.org.

Supplementary information
Supplementary data are available at Bioinformatics online.
Hierarchical HotNet: identifying hierarchies of altered subnetworks
Authors: Reyna Matthew A , Leiserson Mark D M , Raphael Benjamin J 
Published: July 2018

Abstract

Motivation
The analysis of high-dimensional ‘omics data is often informed by the use of biological interaction networks. For example, protein–protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an ‘omics dataset and are topologically close (e.g. connected) on an interaction network.

Results
We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different ‘omics datasets.

Availability and implementation
http://github.com/raphael-group/hierarchical-hotnet.

Supplementary information
Supplementary material are available at Bioinformatics online.
Automatic recognition of ligands in electron density by machine learning
Authors: Kowiel Marcin , Brzezinski Dariusz , Porebski Przemyslaw J , Shabalin Ivan G , Jaskolski Mariusz , Minor Wladek 
Published: July 2018

Abstract

Motivation
The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting.

Results
Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes.

Availability and implementation
Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob.

Supplementary information
Supplementary data are available at Bioinformatics online.