bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning
Authors: Unke Oliver T , Koner Debasish , Patra Sarbani , Käser Silvan , Meuwly Markus 
Published: February 2020

Abstract
An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective.
Applying machine learning optimization methods to the production of a quantum gas
Authors: Barker A J , Style H , Luksch K , Sunami S , Garrick D , Hill F , Foot C J , Bentine E 
Published: February 2020

Abstract
We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose–Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.
Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data
Authors: Simsek Ergun 
Published: February 2020

Abstract
Synthetically generated multi-angle reflectometry data is used to train a neural network based learning system to estimate the refractive index of atomically thin layered materials in the visible part of the electromagnetic spectrum. Unlike previously developed regression based optical characterization methods, the prediction is achieved via classification by using the probabilities of each input element belonging to a label as weighting coefficients in a simple analytical formula. Various types of activation functions and gradient descent optimizers are tested to determine the optimum combination yielding the best performance. For the verification of the proposed method’s accuracy, four different materials are studied. In all cases, the maximum error is calculated to be less than 0.3%. Considering the highly dispersive nature of the studied materials, this result is a substantial improvement in terms of accuracy and efficiency compared to traditional approaches.
American Versus European Hypertension Guidelines: The Case of White Coat Hypertension
Authors: Cuspidi Cesare , Paoletti Federico , Tadic Marijana , Sala Carla , Dell’Oro Raffaella , Grassi Guido , Mancia Giuseppe 
Published: February 2020

Abstract

BACKGROUND
The impact of the 2017 American College Cardiology/American Heart Association guidelines on reclassification of white coat hypertension (WCH) and white coat uncontrolled hypertension (WUCH) phenotypes has not been thoroughly investigated, so far. The aim of the present analysis was to compare the prevalence rates of WCH and WUCH according to either 2018 European Society Hypertension/European Society Cardiology and 2017 ACC/AHA hypertension guidelines.

METHODS
A large database of individual 24-hour ambulatory blood pressure (BP) recordings from untreated and treated hypertensive individuals with office BP ≥140 and/or 90 mm Hg was analyzed.

RESULTS
As many as 3,223 (39% men) out of 7,353 (47% men) fulfilled diagnostic criteria for WCH (n = 1,281) and WUCH (n = 1,942) according to the 2018 ESH/ESC guidelines (mean 24-hour BP &lt;130/80 mm Hg), the prevalence rate being 17.4% and 26.4%, respectively. The corresponding figures according to the 2017 ACC/AHA guidelines (mean 24-hour BP &lt;125/75 mm Hg) were 15.6 and 9.4%, respectively. Thus, a total of 1,378 patients (42.7%) either defined as WCH and WUCH by ESH/ESC guidelines, were classifiable as untreated sustained and uncontrolled sustained hypertensives by ACC/AHA guidelines.

CONCLUSIONS
The ACC/AHA reclassification of patients with office BP ≥140/90 mm Hg leads to a marked decrease in the prevalence of WCH/WUCH. This may have relevant clinical implications because the prognostic significance of these phenotypes is often ignored in clinical practice and, consequently, contributes to the high burden of cardiovascular diseases worldwide.
Presence of same-sex kin promotes explorative behavior in subadult cichlid fish
Authors: Vitt Simon , Madge Pimentel Iris , Thünken Timo 
Published: February 2020

AbstractWhile the importance of kin discrimination, that is, kin recognition and subsequent differential treatment of kin and nonkin, is well established for kin-directed cooperation or altruism, the role of kin discrimination in the context of kin competition and kin avoidance is largely unexplored. Theory predicts that individuals avoiding competition with kin should be favored by natural selection due to indirect fitness benefits. Using an experimental approach, we investigated whether the presence of same-sex kin affects avoidance and explorative behavior in subadult Pelvicachromis taeniatus, a West African cichlid fish with strong intrasexual competition in both sexes. Pelvicachromis taeniatus is capable of recognizing kin using phenotype matching and shows kin discrimination in diverse contexts. When exposed to a same-sex conspecific, both males and females tended to interact less with the related opponent. Moreover, individuals explored a novel environment faster after exposure to kin than to nonkin. This effect was more pronounced in females. Individuals avoiding the proximity of same-sex relatives may reduce kin competition over resources such as mating partners or food.
Siring success in kangaroos: size matters for those in the right place at the right time
Authors: Montana Luca , Rousseu François , Garant Dany , Festa-Bianchet Marco 
Published: February 2020

Abstract
In polygynous species, male reproductive success is predicted to be monopolized by a few dominant males. This prediction is often not supported, suggesting that ecological and alternative mating tactics influence siring success. The spatiotemporal distribution of individuals and the number of males competing for each receptive female are often overlooked because they are difficult to monitor in wild animals. We examined how spatial overlap of female–male pairs, the time spent by a male on the breeding site, number of competitors, and morphological traits influence siring probability in eastern gray kangaroos (Macropus giganteus). We compared home range overlap for 12 208 dam–male pairs and 295 known dam–sire pairs to define local competitive groups and to estimate every male’s opportunity to sire the young of each female. We compared models considering morphological traits relative to the entire population or to local competitive groups. Including local competition improved model performance because it estimated the intensity of competition and compared each male’s morphological traits to those of its competitive group. Regardless of size, males can increase their probability to sire a young by increasing their mating opportunity relative to the mother. We underline the importance of considering spatial structure to assess the intensity of competition in species where males cannot equally access all females in a population. The estimation of mating opportunity and intensity of local competition improves our understanding of how morphological traits affect siring success when each mating event involves a different set of competing males, a characteristic of most wild species.
TOD-CUP: a gene expression rank-based majority vote algorithm for tissue origin diagnosis of cancers of unknown primary
Authors: Shen Yifei , Chu Qinjie , Yin Xinxin , He Yinjun , Bai Panpan , Wang Yunfei , Fang Weijia , Timko Michael P , Fan Longjiang , Jiang Weiqin 
Published: February 2020

Abstract
Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types. Based on massive tissue-specific RNA-seq data sets (10 553) found in The Cancer Genome Atlas (TCGA), 538 feature genes (biomarkers) were selected based on their gene expression ranks and used to predict tissue types. The top scoring pairs (TSPs) classifier of the tumor type was optimized by the TCGA training samples. To test the prediction accuracy of our TOD-CUP algorithm, we analyzed (1) two microarray data sets (1029 Agilent and 2277 Affymetrix/Illumina chips) and found 91% and 94% prediction accuracy, respectively, (2) RNA-seq data from five cancer types derived from 141 public metastatic cancer tumor samples and achieved 94% accuracy and (3) a total of 25 clinical cancer samples (including 14 metastatic cancer samples) were able to classify 24/25 samples correctly (96.0% accuracy). Taken together, the TOD-CUP algorithm provides a powerful and robust means to accurately identify the tissue origin of 24 cancer types across different data platforms. To make the TOD-CUP algorithm easily accessible for clinical application, we established a Web-based server for tumor tissue origin diagnosis (http://ibi. zju.edu.cn/todcup/).
Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations
Authors: Oh Minsik , Park Sungjoon , Kim Sun , Chae Heejoon 
Published: February 2020

Abstract
Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.
Homoeologous gene expression and co-expression network analyses and evolutionary inference in allopolyploids
Authors: Hu Guanjing , Grover Corrinne E , Arick Mark A , Liu Meiling , Peterson Daniel G , Wendel Jonathan F 
Published: February 2020

AbstractPolyploidy is a widespread phenomenon throughout eukaryotes. Due to the coexistence of duplicated genomes, polyploids offer unique challenges for estimating gene expression levels, which is essential for understanding the massive and various forms of transcriptomic responses accompanying polyploidy. Although previous studies have explored the bioinformatics of polyploid transcriptomic profiling, the causes and consequences of inaccurate quantification of transcripts from duplicated gene copies have not been addressed. Using transcriptomic data from the cotton genus (Gossypium) as an example, we present an analytical workflow to evaluate a variety of bioinformatic method choices at different stages of RNA-seq analysis, from homoeolog expression quantification to downstream analysis used to infer key phenomena of polyploid expression evolution. In general, EAGLE-RC and GSNAP-PolyCat outperform other quantification pipelines tested, and their derived expression dataset best represents the expected homoeolog expression and co-expression divergence. The performance of co-expression network analysis was less affected by homoeolog quantification than by network construction methods, where weighted networks outperformed binary networks. By examining the extent and consequences of homoeolog read ambiguity, we illuminate the potential artifacts that may affect our understanding of duplicate gene expression, including an overestimation of homoeolog co-regulation and the incorrect inference of subgenome asymmetry in network topology. Taken together, our work points to a set of reasonable practices that we hope are broadly applicable to the evolutionary exploration of polyploids.
StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency
Authors: Fu Xiangzheng , Cai Lijun , Zeng Xiangxiang , Zou Quan 
Published: February 2020

Abstract

Motivation
Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements.

Results
We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM–DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy.

Availability and implementation
Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred.

Supplementary information
Supplementary data are available at Bioinformatics online.
PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data
Authors: Peabody Michael A , Lau Wing Yin Venus , Hoad Gemma R , Jia Baofeng , Maguire Finlay , Gray Kristen L , Beiko Robert G , Brinkman Fiona S L 
Published: February 2020

Abstract

Motivation
Many methods for microbial protein subcellular localization (SCL) prediction exist; however, none is readily available for analysis of metagenomic sequence data, despite growing interest from researchers studying microbial communities in humans, agri-food relevant organisms and in other environments (e.g. for identification of cell-surface biomarkers for rapid protein-based diagnostic tests). We wished to also identify new markers of water quality from freshwater samples collected from pristine versus pollution-impacted watersheds.

Results
We report PSORTm, the first bioinformatics tool designed for prediction of diverse bacterial and archaeal protein SCL from metagenomics data. PSORTm incorporates components of PSORTb, one of the most precise and widely used protein SCL predictors, with an automated classification by cell envelope. An evaluation using 5-fold cross-validation with in silico-fragmented sequences with known localization showed that PSORTm maintains PSORTb’s high precision, while sensitivity increases proportionately with metagenomic sequence fragment length. PSORTm’s read-based analysis was similar to PSORTb-based analysis of metagenome-assembled genomes (MAGs); however, the latter requires non-trivial manual classification of each MAG by cell envelope, and cannot make use of unassembled sequences. Analysis of the watershed samples revealed the importance of normalization and identified potential biomarkers of water quality. This method should be useful for examining a wide range of microbial communities, including human microbiomes, and other microbiomes of medical, environmental or industrial importance.

Availability and implementation
Documentation, source code and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open-source software under GNU General Public License Version 3).

Supplementary information
Supplementary data are available at Bioinformatics online.
Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
Authors: Cleemput Sara , Dumon Wim , Fonseca Vagner , Abdool Karim Wasim , Giovanetti Marta , Alcantara Luiz Carlos , Deforche Koen , de Oliveira Tulio 
Published: February 2020

Abstract

Summary
Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.

Availability and implementation
https://www.genomedetective.com/app/typingtool/cov

Contact
koen@emweb.be or deoliveira@ukzn.ac.za

Supplementary information
Supplementary data are available at Bioinformatics online.
CRAMER: a lightweight, highly customizable web-based genome browser supporting multiple visualization instances
Authors: Anastasiadi M , Bragin E , Biojoux P , Ahamed A , Burgin J , de Castro Cogle K , Llaneza-Lago S , Muvunyi R , Scislak M , Aktan I , Molitor C , Kurowski T , Mohareb F 
Published: February 2020

Abstract

Summary
In recent years, the ability to generate genomic data has increased dramatically along with the demand for easily personalized and customizable genome browsers for effective visualization of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provides means for creating multiple visualization instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customizable web application for interactive visualization of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualization instances in parallel while allowing users to control and customize multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customizations made by the users allowing quick navigation between instances. Currently, the browser supports visualizing a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalized tracks, providing a whole new level of customization both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualization instance.

Availability and implementation
CRAMER is implemented in JavaScript and is publicly available on GitHub on https://github.com/FadyMohareb/cramer. The application is released under an MIT licence and can be deployed on any server running Linux or Mac OS.

Contact
f.mohareb@cranfield.ac.uk

Supplementary information
Supplementary data are available at Bioinformatics online.
2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
Authors: Zhu Kaiyi , Anastassiou Dimitris 
Published: February 2020

Abstract

Summary
We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols.

Availability and implementation
The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute).

Contact
d.anastassiou@columbia.edu

Supplementary information
Supplementary data are available at Bioinformatics online.
‘Come Night-time, It’s a War Zone’: Women’s Experiences of Homelessness, Risk and Public Space
Authors: Menih Helena 
Published: February 2020

Abstract
This paper explores constructions of ‘risk’ in the lives of women experiencing homelessness in Brisbane, Australia. Drawing on ethnographic data, including in-depth interviews, informal conversations and participant observation field notes, two main themes emerged: the narratives or risk; women navigating risk on the streets by employing transiency, invisibility and squatting. The analysis indicates that women consider the ways in which risk calculations are spatially and temporally located and argues that risk is embedded in social and cultural discourses of gender. Women experiencing homelessness face particular risks due to gendered spatiality and have to manage their spatialized selves to avoid everyday risks on the streets.