bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moderate‐intensity exercise performed in the evening does not impair sleep in healthy males
Authors: Miller D. J. , Sargent C. , Roach G. D. , Scanlan A. T. , Vincent G. E. , Lastella M. 
Published: May 2019

AbstractThe aim of this study was to examine the effect of single bouts of moderate‐intensity aerobic exercise and moderate‐intensity resistance exercise performed in the evening on the sleep of healthy young males. The study employed a repeated‐measures, counterbalanced, crossover design with three conditions (control, evening aerobic exercise, evening resistance exercise). Twelve male participants (mean ± SD; age: 21.9 ± 2.7 yr) attended the laboratory on three occasions separated by one day between each visit. Between 20:45 h and 21:30 h, participants completed either no exercise, 30 min of aerobic exercise at 75%HRmax, or 30 min of resistance exercise corresponding to 75% of 10‐repetition maximum. A 9‐h sleep opportunity was provided between 23:00 h and 08:00 h. Core body temperature was measured using ingestible temperature capsules and sleep was measured using polysomnography. Core body temperature was higher during the aerobic exercise and resistance exercise compared to control (p = 0.001). There was no difference in core body temperature at bedtime between the conditions. Sleep onset latency, total sleep time, slow‐wave sleep duration, REM sleep duration, wake after sleep onset and sleep efficiency were similar in each condition (p > 0.05). Single bouts of moderate‐intensity aerobic exercise or moderate‐intensity resistance exercise performed in the evening did not impact subsequent night‐time sleep. Core body temperature increased during both forms of exercise, but returned to pre‐exercise levels in the 90 min prior to bedtime. Healthy young males can engage in a single bout of moderate‐intensity aerobic exercise or moderate‐intensity resistance exercise ceasing 90 min before bed without compromising their subsequent sleep.
Dressed quantum trajectories: novel approach to the non-Markovian dynamics of open quantum systems on a wide time scale
Authors: Polyakov Evgeny A , Rubtsov Alexey N 
Published: May 2019

Abstract
A new approach to theory and simulation of the non-Markovian dynamics of open quantum systems is presented. It is based on identification of a parameter which is uniformly bounded on wide time intervals: the occupation of the virtual cloud of quanta. By ‘virtual’ we denote those bath excitations which were emitted by the open system, but eventually will be reabsorbed before any measurement of the bath state. A useful property of the virtual cloud is that the number of its quanta is expected to saturate on long times, since physically this cloud is a (retarded) polarization of the bath around the system. Therefore, the joint state of open system and virtual cloud (we call it dressed state) can be accurately represented in a truncated basis of Fock states, on a wide time scale. At the same time, there can be an arbitrarily large number of the observable quanta (which survive up to measurement), especially if the open system is under driving. However, it turns out that the statistics of the bath-measurement outcomes is classical (in a suitable measurement basis): one can employ a Monte Carlo sampling of these outcomes. Therefore, it is possible to efficiently simulate the dynamics of the observable quantum field. In this work we consider the bath measurement with respect to the coherent states, which yields the Husimi function as the positive (quasi)probability distribution of the outcomes. The joint evolution of the dressed state and the corresponding outcome is called the dressed quantum trajectory. The Monte Carlo sampling of these trajectories yields a stochastic simulation method with promising convergence properties on wide time scales.
Structural studies of metastable and equilibrium vortex lattice domains in MgB2
Authors: Louden E R , Leishman A W D , Rastovski C , Kuhn S J , DeBeer-Schmitt L , Dewhurst C D , Zhigadlo N D , Eskildsen M R 
Published: May 2019

Abstract
The vortex lattice (VL) in MgB2 is characterized by the presence of long-lived metastable states (MSs), which arise from cooling or heating across the equilibrium phase boundaries. A return to the equilibrium configuration can be achieved by inducing vortex motion. Here we report on small-angle neutron scattering studies of MgB2, focusing on the structural properties of the VL as it is gradually driven from metastable to equilibrium states (ESs) by an AC magnetic field. Measurements were performed using initial MSs obtained either by cooling or heating across the equilibrium phase transition. In all cases, the longitudinal correlation length remains constant and comparable to the sample thickness. Correspondingly, the VL may be considered as a system of straight rods, where the formation and growth of ES domains only occurs in the two-dimensional plane perpendicular to the applied field direction. Spatially resolved raster scans of the sample were performed with apertures as small as 80 μm, corresponding to only 1.2 × 106 vortices for an applied field of 0.5 T. These revealed spatial variations in the metastable and equilibrium VL populations, but individual domains were not directly resolved. A statistical analysis of the data indicates an upper limit on the average domain size of approximately 50 μm.
Thermal and structural properties of the martensitic transformations in Fe7Pd3 shape memory alloys: an ab initio-based molecular dynamics study
Authors: Holm Alexander , Mayr Stefan G 
Published: May 2019

Abstract
Ferromagnetic shape memory alloys, including the Fe7Pd3 system, constitute an upcoming class of functional materials, whose atomic-scale physical foundations are still insufficiently understood. The present work employs molecular dynamics simulations, based on ab initio derived embedded atom method potentials, to study martensitic transformations and twin variant reorientation. We address thermal and stress induced austenite-martensite transitions, twinning, as well as twin boundary mobility. While the predicted thermal properties are in accordance with experimental observations, we explore the detailed crystallography underlying transformation as well as twin boundary motion.
The symmetry energy and incompressibility constrained by the observations of glitching pulsars
Authors: Yan Yan 
Published: May 2019

Abstract
We investigate the masses of glitching pulsars in order to constrain their equation of state (EOS). The observations of glitches (sudden jumps in rotational frequency) may provide information on the interior physics of neutron stars. With the assumption that glitches are triggered by superfluid neutrons, the masses of glitching neutron stars can be estimated using observations of maximum glitches. Together with the observations of thermal emission from glitching pulsars Vela and J1709–4429, the slope of symmetry energy and incompressibility of nuclear matter at saturation density can be constrained. The slope of symmetry energy L should be larger than 67 MeV while the lower limit of incompressibility for symmetric nuclear matter K
0 is 215 MeV. We also obtain a relationship between L and K
0: 6.173 MeV + 0.283 K
0 ≤ L ≤ 7.729 MeV + 0.291 K
0. The restricted EOSs are consistent with the observations of 2-solar-mass neutron stars and gravitational waves from a binary neutron star inspiral.
Industrial symbiosis potential and urban infrastructure capacity in Mysuru, India
Authors: Chertow Marian , Gordon Matthew , Hirsch Peter , Ramaswami Anu 
Published: May 2019

Abstract
If the material intensive enterprises in an urban area of several million people shared physical resources that might otherwise be wasted, what environmental and public benefits would result? This study develops an algorithm based on lifecycle assessment tools for determining a city’s industrial symbiosis potential—that is, the sum of the wastes and byproducts from a city’s industrial enterprises that could reasonably serve as resource inputs to other local industrial processes. Rather than report, as do many previous papers, on private benefits to firms, this investigation focuses on public benefits to cities by converting the maximum quantity of resources recoverable by local enterprises into an estimate of the capacity of municipal infrastructure conserved in terms of landfill space and water demand. The results here test this novel approach for the district of Mysuru (Mysore), India. We find that the industrial symbiosis potential calculated based on analysis of the inputs and outputs of ∼1000 urban enterprises, translates into 84 000 tons of industrial waste, greater than 74 000 tons of CO2e, and 22 million liters per day of wastewater. The method introduced here demonstrates how industrial symbiosis links private production and public infrastructure to improve the resource efficiency of a city by creating an opportunity to extend the capacity of public infrastructure and generate public health co-benefits.
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
Authors: Chen Muhao , Ju Chelsea J -T , Zhou Guangyu , Chen Xuelu , Zhang Tianran , Chang Kai-Wei , Zaniolo Carlo , Wang Wei 
Published: May 2019

AbstractMotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.
scOrange—a tool for hands-on training of concepts from single-cell data analytics
Authors: Stražar Martin , Žagar Lan , Kokošar Jaka , Tanko Vesna , Erjavec Aleš , Poličar Pavlin G , Starič Anže , Demšar Janez , Shaulsky Gad , Menon Vilas , Lemire Andrew , Parikh Anup , Zupan Blaž 
Published: May 2019

Abstract

Motivation
Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve.

Results
We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training.

Availability and implementation
scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.
Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
Authors: Nair Surag , Kim Daniel S , Perricone Jacob , Kundaje Anshul 
Published: May 2019

Abstract

Motivation
Genome-wide profiles of chromatin accessibility and gene expression in diverse cellular contexts are critical to decipher the dynamics of transcriptional regulation. Recently, convolutional neural networks have been used to learn predictive cis-regulatory DNA sequence models of context-specific chromatin accessibility landscapes. However, these context-specific regulatory sequence models cannot generalize predictions across cell types.

Results
We introduce multi-modal, residual neural network architectures that integrate cis-regulatory sequence and context-specific expression of trans-regulators to predict genome-wide chromatin accessibility profiles across cellular contexts. We show that the average accessibility of a genomic region across training contexts can be a surprisingly powerful predictor. We leverage this feature and employ novel strategies for training models to enhance genome-wide prediction of shared and context-specific chromatin accessible sites across cell types. We interpret the models to reveal insights into cis- and trans-regulation of chromatin dynamics across 123 diverse cellular contexts.

Availability and implementation
The code is available at https://github.com/kundajelab/ChromDragoNN.

Supplementary information
Supplementary data are available at Bioinformatics online.
Selfish: discovery of differential chromatin interactions via a self-similarity measure
Authors: Ardakany Abbas Roayaei , Ay Ferhat , Lonardi Stefano 
Published: May 2019

AbstractMotivationHigh-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.ResultsWe present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availability and implementationhttps://github.com/ucrbioinfo/Selfish
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
Authors: Yang Hao , Chi Hao , Zeng Wen-Feng , Zhou Wen-Jing , He Si-Min 
Published: May 2019

Abstract

Motivation
De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.

Results
In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.

Availability and implementation
pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.

Supplementary information
Supplementary data are available at Bioinformatics online.
Statistical compression of protein sequences and inference of marginal probability landscapes over competing alignments using finite state models and Dirichlet priors
Authors: Sumanaweera Dinithi , Allison Lloyd , Konagurthu Arun S 
Published: May 2019

AbstractThe information criterion of minimum message length (MML) provides a powerful statistical framework for inductive reasoning from observed data. We apply MML to the problem of protein sequence comparison using finite state models with Dirichlet distributions. The resulting framework allows us to supersede the ad hoc cost functions commonly used in the field, by systematically addressing the problem of arbitrariness in alignment parameters, and the disconnect between substitution scores and gap costs. Furthermore, our framework enables the generation of marginal probability landscapes over all possible alignment hypotheses, with potential to facilitate the users to simultaneously rationalize and assess competing alignment relationships between protein sequences, beyond simply reporting a single (best) alignment. We demonstrate the performance of our program on benchmarks containing distantly related protein sequences.Availability and implementationThe open-source program supporting this work is available from: http://lcb.infotech.monash.edu.au/seqmmligner.Supplementary informationSupplementary data are available at Bioinformatics online.
PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors
Authors: Mourragui Soufiane , Loog Marco , van de Wiel Mark A , Reinders Marcel J T , Wessels Lodewyk F A 
Published: May 2019

Abstract

Motivation
Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting.

Results
We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors.

Availability and implementation
PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE).

Supplementary information
Supplementary data are available at Bioinformatics online.
Clinical Usefulness of Self-Rated Olfactory Performance—A Data Science-Based Assessment of 6000 Patients
Authors: Lötsch Jörn , Hummel Thomas 
Published: May 2019

Abstract
In clinical practice, with its time constraints, a frequent conclusion is that asking about the ability to smell may suffice to detect olfactory problems. To address this question systematically, 6049 subjects were asked about how well they can perceive odors, with 5 possible responses. Participants presented at a University Department of Otorhinolaryngology, where olfactory testing was part of the routine investigation performed in patients receiving surgery at the clinic (for various reasons). According to an odor identification test, 1227 subjects had functional anosmia and 3113 were labeled with normosmia. Measures of laboratory test performance were used to assess the success of self-estimates to capture the olfactory diagnosis. Ratings of the olfactory function as absent or impaired provided the diagnosis of anosmia at a balanced accuracy of 79%, whereas ratings of good or excellent indicated normosmia at a balanced accuracy of 64.6%. The number of incorrect judgments of anosmia increased with age, whereas false negative self-estimates of normosmia became rarer with increasing age. The subject’s sex was irrelevant in this context. Thus, when asking the question “How well can you smell odors?” and querying standardized responses, fairly accurate information can be obtained about whether or not the subject can smell. However, this has to be completed with the almost 30% (355 subjects) of anosmic patients who judged their ability to smell as at least “average.” Thus, olfactory testing using reliable and validated tests appears indispensable.