bluuebunny/crossref_metadata_2025_split huggingface dataset:
A composition transferable and time-scale consistent coarse-grained model for cis-polyisoprene and vinyl-polybutadiene oligomeric blends
Authors: Ohkuma Takahiro , Kremer Kurt 
Published: May 2020

Abstract
We study a coarse-grained model for a binary blend system composed of cis-polyisoprene and vinyl-polybutadiene. Since the slow relaxation dynamics of polymers may require very long simulation times, coarse-grained descriptions are regularly used in order to reduce computational cost while keeping the essential physics. Relaxation dynamics of a coarse-grained model is sometimes accelerated by the smooth coarse-grained potentials. However, the magnitude of the acceleration may be different in different components in a multi-component system. In order to simulate a time-scale consistent dynamics, the acceleration effects should be the same across the different components. Here, we investigate a time-scale consistent coarse-grained model for a binary polymer blend. For the coarse-grained equation of motion, we adopt the Langevin equation and adjust the friction coefficients by focusing on the relaxation times of the first normal mode of the polymers. A united-atom model is used as a reference system of the coarse-graining. Since it is found that the solubility parameter of the atomistic model is much larger than the experimental result, our simulation model is not applicable for the quantitative predictions, but we utilize it as a example system to study a time scale mismatch of a coarse-grained model. We find that the coarse-grained potentials and the friction coefficients derived for one blend composition captures different compositions of the blend. Furthermore, it is found that the magnitude of the acceleration effects of the blend rarely depends on the composition ratio. This implies that our coarse-grained model can be used for inhomogeneous systems.
What Adaptations and Adaptation Studies Tell Us (and Sometimes Don't Tell Us) about Politics, Culture, and Social Change
Authors: Grossman Julie 
Published: May 2020

Abstract
This essay explores the implications of a Supreme Court dissent written by US Chief Justice John Roberts in the style of film noir. The article analyses Roberts’s adaptation, discussing more broadly the use of classic literary types such as the hardboiled detective and the western cowboy in social and political discourse. The article argues for the significance of adaptation studies in our polarized contemporary society because its valuing of change counters formulaic appropriations of the past and fixed ideas and models of experience. The essay suggests not only that political leaders mired in the past cannot adapt to changing society and social roles, but also that adaptation studies hones readers’ discernment, especially called for when public figures use fictional patterns to address real-world circumstances.
Construct Validity of the Stroop Color-Word Test: Influence of Speed of Visual Search, Verbal Fluency, Working Memory, Cognitive Flexibility, and Conflict Monitoring
Authors: Periáñez José A , Lubrini Genny , García-Gutiérrez Ana , Ríos-Lago Marcos 
Published: May 2020

Abstract

Objective
85 years after the description of the Stroop interference effect, there is still a lack of consensus regarding the cognitive constructs underlying scores from standardized versions of the test. The present work aimed to clarify the cognitive mechanisms underlying direct (word-reading, color-naming, and color-word) and derived scores (interference, difference, ratio, and relative scores) from Golden’s standardized version of the test.

Method
After a comprehensive review of the literature, five cognitive processes were selected for analysis: speed of visual search, phonemic verbal fluency, working memory, cognitive flexibility, and conflict monitoring. These constructs were operationalized by scoring five cognitive tasks (WAIS-IV Digit Symbol, phonemic verbal fluency [letter A], WAIS-IV Digit Span, TMT B-A, and reaction times to the incongruent condition of a computerized Stroop task, respectively). About 83 healthy individuals (mean age = 25.2 years) participated in the study. Correlation and regression analyses were used to clarify the contribution of the five cognitive processes on the prediction of Stroop scores.

Results
Data analyses revealed that Stroop word-reading reflected speed of visual search. Stroop color-naming reflected working memory and speed of visual search. Stroop color-word reflected working memory, conflict monitoring, and speed of visual search. Whereas the interference score was predicted by both conflict monitoring and working memory, the ratio score (color-word divided by color-naming) was predicted by conflict monitoring alone.

Conclusion
The present results will help neuropsychologists to interpret altered patient scores in terms of a failure of the cognitive mechanisms detailed here, benefitting from the solid background of preceding experimental work.
Temporally variable predation risk and fear retention in Trinidadian guppies
Authors: Crane Adam L , Feyten Laurence E A , Ramnarine Indar W , Brown Grant E 
Published: May 2020

AbstractPredation fear is a unifying theme across vertebrate taxa. Here, we explored how the frequency and duration of predation risk affects postrisk fear behavior in Trinidadian guppies. We first exposed individuals to visual cues of potential predators for 3 days, either frequently (6×/day) or infrequently (1×/day). Each exposure lasted for either a relatively brief (5 min) or long (30 min) duration, whereas a control group consisted of no risk exposures. One day later, we quantified guppy behavior. All background risk treatments induced a fear response toward a novel odor (i.e., neophobia), and individuals previously exposed to frequent bouts of brief risk showed elevated baseline fear. Although neophobic responses were initially similar across risk treatments (1 day later), retention of this response differed. After 8 days, only individuals previously exposed to brief bouts of risk (both frequent and infrequent) maintained neophobic responses, whereas their initially higher level of baseline fear remained elevated but was no longer significantly different from the control. These results increase our understanding of temporal factors that affect the intensity and retention of fear that persists after risk exposure, which may have applications across vertebrates in relation to problems with fearful phenotypes.
MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs
Authors: Wang Wei , Dai QiuYing , Li Fang , Xiong Yi , Wei Dong-Qing 
Published: May 2020

Abstract
The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques are subject to further improvement. In this study, we propose a deep learning model, called Multi-Label Classifications with Deep Forest, termed MLCDForest, to address multi-label classification on tissue prediction for a given lncRNA, which can be regarded as an implementation of the deep forest model in multi-label classification. The MLCDForest is a sequential multi-label-grained scanning method, which distinguishes from the standard deep forest model. It is proposed to train in sequential of multi-labels with label correlation considered. A systematic comparison using the lncRNA-disease association datasets demonstrates that our method consistently shows superior performance over the state-of-the-art methods in disease prediction. Considering label correlation in the sequential multi-label-grained scanning, our model provides a powerful tool to make multi-label classification and tissue prediction based on given lncRNAs.
MetaBCC-LR: metagenomics binning by coverage and composition for long reads
Authors: Wickramarachchi Anuradha , Mallawaarachchi Vijini , Rajan Vaibhav , Lin Yu 
Published: May 2020

Abstract

Motivation
Metagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyse metagenomic data, binning is considered a crucial step to characterize the different species of micro-organisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this article, we present MetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition.

Results
We evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving ∼13% improvement in F1-score and ∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long-read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read-based metagenomics analyses to support a wide range of applications.

Availability and implementation
The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR.

Supplementary information
Supplementary data are available at Bioinformatics online.
MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model
Authors: Venkatesh Gopalakrishnan , Grover Aayush , Srinivasaraghavan G , Rao Shrisha 
Published: May 2020

Abstract

Motivation
Accurate prediction of binding between a major histocompatibility complex (MHC) allele and a peptide plays a major role in the synthesis of personalized cancer vaccines. The immune system struggles to distinguish between a cancerous and a healthy cell. In a patient suffering from cancer who has a particular MHC allele, only those peptides that bind with the MHC allele with high affinity, help the immune system recognize the cancerous cells.

Results
MHCAttnNet is a deep neural model that uses an attention mechanism to capture the relevant subsequences of the amino acid sequences of peptides and MHC alleles. It then uses this to accurately predict the MHC-peptide binding. MHCAttnNet achieves an AUC-PRC score of 94.18% with 161 class I MHC alleles, which outperforms the state-of-the-art models for this task. MHCAttnNet also achieves a better F1-score in comparison to the state-of-the-art models while covering a larger number of class II MHC alleles. The attention mechanism used by MHCAttnNet provides a heatmap over the amino acids thus indicating the important subsequences present in the amino acid sequence. This approach also allows us to focus on a much smaller number of relevant trigrams corresponding to the amino acid sequence of an MHC allele, from 9251 possible trigrams to about 258. This significantly reduces the number of amino acid subsequences that need to be clinically tested.

Availability and implementation
The data and source code are available at https://github.com/gopuvenkat/MHCAttnNet.
EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening
Authors: Pires Douglas E V , Veloso Wandré N P , Myung YooChan , Rodrigues Carlos H M , Silk Michael , Rezende Pâmela M , Silva Francislon , Xavier Joicymara S , Velloso João P L , da Silveira Carlos H , Ascher David B 
Published: May 2020

AbstractSummaryEasyVS is a web-based platform built to simplify molecule library selection and virtual screening. With an intuitive interface, the tool allows users to go from selecting a protein target with a known structure and tailoring a purchasable molecule library to performing and visualizing docking in a few clicks. Our system also allows users to filter screening libraries based on molecule properties, cluster molecules by similarity and personalize docking parameters.Availability and implementationEasyVS is freely available as an easy-to-use web interface at http://biosig.unimelb.edu.au/easyvs.Contactdouglas.pires@unimelb.edu.au or david.ascher@unimelb.edu.auSupplementary informationSupplementary data are available at Bioinformatics online.
ipcoal: an interactive Python package for simulating and analyzing genealogies and sequences on a species tree or network
Authors: McKenzie Patrick F , Eaton Deren A R 
Published: May 2020

Abstract

Summary
ipcoal is a free and open source Python package for simulating and analyzing genealogies and sequences. It automates the task of describing complex demographic models (e.g. with divergence times, effective population sizes, migration events) to the msprime coalescent simulator by parsing a user-supplied species tree or network. Genealogies, sequences and metadata are returned in tabular format allowing for easy downstream analyses. ipcoal includes phylogenetic inference tools to automate gene tree inference from simulated sequence data, and visualization tools for analyzing results and verifying model accuracy. The ipcoal package is a powerful tool for posterior predictive data analysis, for methods validation and for teaching coalescent methods in an interactive and visual environment.

Availability and implementation
Source code is available from the GitHub repository (https://github.com/pmckenz1/ipcoal/) and is distributed for packaged installation with conda. Complete documentation and interactive notebooks prepared for teaching purposes, including an empirical example, are available at https://ipcoal.readthedocs.io/.

Contact
p.mckenzie@columbia.edu
Acid Suppression Medications During Hospitalization as a Risk Factor for Recurrence of Clostridioides difficile Infection: Systematic Review and Meta-analysis
Authors: Mehta Palna , Nahass Ronald G , Brunetti Luigi 
Published: May 2020

Abstract

Background
Studies have had conflicting results regarding the influence of acid-suppression medications (ASMs) during hospitalization on the recurrence of Clostridioides difficile infection (CDI).

Methods
A systematic review and meta-analysis investigating the association between recurrent CDI and ASM use in inpatients was performed. Relevant literature was identified using Medline, Google Scholar, and Web of Science. All human studies were considered regardless of publication date. Case-control and cohort studies and clinical trials were included if they contained the necessary information to calculate appropriate statistics related to the objective of this study. Review articles, meta-analyses, and commentaries were excluded; however, their references were searched to identify any studies missed. The random-effects model was selected since significant heterogeneity in study design was identified. To evaluate the sensitivity of the analysis various subgroup analyses were performed.

Results
Our search identified 9 studies involving 5668 patients of whom 1003 (17.7%) developed recurrent CDI. Patients on ASM were 64% more likely to develop recurrent CDI than patients not on ASM (OR, 1.64; 95% CI, 1.13–2.38; P = .009; I2 = 79.54%). Proton pump inhibitor (PPI) use was associated with an 84% increased risk of recurrent CDI versus no ASM (OR, 1.84; 95% CI, 1.18–2.85; P = .007; I2 = 83.4%).

Conclusions
ASM use during hospitalization was associated with a 64% increase in recurrent CDI. The association was greater with PPI use. Due to significant heterogeneity in the analyses, additional studies are essential to further elucidate iatrogenic effects of ASM. Unnecessary PPI use should be discontinued.
Lessons From COVID-19 in Children: Key Hypotheses to Guide Preventative and Therapeutic Strategies
Authors: Singh Tulika , Heston Sarah M , Langel Stephanie N , Blasi Maria , Hurst Jillian H , Fouda Genevieve G , Kelly Matthew S , Permar Sallie R 
Published: May 2020

Abstract
The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), reveals a peculiar trend of milder disease and lower case fatality in children compared with adults. Consistent epidemiologic evidence of reduced severity of infection in children across different populations and countries suggests there are underlying biological differences between children and adults that mediate differential disease pathogenesis. This presents a unique opportunity to learn about disease-modifying host factors from pediatric populations. Our review summarizes the current knowledge of pediatric clinical disease, role in transmission, risks for severe disease, protective immunity, as well as novel therapies and vaccine trials for children. We then define key hypotheses and areas for future research that can use the pediatric model of disease, transmission, and immunity to develop preventive and therapeutic strategies for people of all age groups.
Reliability of DQcube Based on g-Extra Conditional Fault
Authors: Zhang Hong , Meng Jixiang 
Published: May 2020

Abstract
Diagnosability and connectivity are important metrics for the reliability and fault diagnosis capability of interconnection networks, respectively. The g-extra connectivity of a graph G, denoted by $\kappa _g(G)$, is the minimum number of vertices whose deletion will disconnect the network and every remaining component has more than $g$ vertices. The g-extra conditional diagnosability of graph G, denoted by $t_g(G)$, is the maximum number of faulty vertices that the graph G can guarantee to identify under the condition that every fault-free component contains at least g+1 vertices. In this paper, we first determine that g-extra connectivity of DQcube is $\kappa _g(G)=(g+1)(n+1)-\frac{g(g+3)}{2}$ for $0\leq g\leq n-3$ and then show that the g-extra conditional diagnosability of DQcube under the PMC model $(n\geq 4, 1\leq g\leq n-3)$ and the MM$^\ast$ model $(n\geq 7, 1\leq g\leq \frac{n-3}{4})$ is $t_g(G)=(g+1)(n+1)-\frac{g(g+3)}{2}+g$, respectively.
Domestic Outsourcing and Workers’ Bargaining Power in a Simple Production System
Authors: Barba Aldo 
Published: May 2020

AbstractOutsourcing is normally conceived as the result of a cost-minimizing choice of a new technique that also implies a redefinition of the boundaries between firms and sectors. In this paper, we will argue instead that many outsourcing activities do not necessarily imply technical change and that the phenomenon can be explained by placing it in connection with the radical modification of the way in which wages are set for workers in a wide range of poorly regulated firms and industries. More than as an aspect of the spread of technical progress, outsourcing will be analyzed as an important mechanism through which workers are divided and their bargaining power is weakened, thus changing the outcome of the distributive conflict between profit and wages.
Multimodality imaging in takotsubo syndrome: a joint consensus document of the European Association of Cardiovascular Imaging (EACVI) and the Japanese Society of Echocardiography (JSE)
Authors: Citro Rodolfo , Okura Hiroyuki , Ghadri Jelena R , Izumi Chisato , Meimoun Patrick , Izumo Masaki , Dawson Dana , Kaji Shuichiro , Eitel Ingo , Kagiyama Nobuyuki , Kobayashi Yukari , Templin Christian , Delgado Victoria , Nakatani Satoshi , Popescu Bogdan A ,   EACVI Scientific Documents Committee, Bertrand Philippe , Donal Erwan , Dweck Marc , Galderisi Maurizio , Haugaa Kristina H , Sade Leyla Elif , Stankovic Ivan , Cosyns Bernard , Edvardsen Thor 
Published: May 2020

Abstract
Takotsubo syndrome (TTS) is a complex and still poorly recognized heart disease with a wide spectrum of possible clinical presentations. Despite its reversibility, it is associated with serious adverse in-hospital events and high complication rates during follow-up. Multimodality imaging is helpful for establishing the diagnosis, guiding therapy, and stratifying prognosis of TTS patients in both the acute and post-acute phase. Echocardiography plays a key role, particularly in the acute care setting, allowing for the assessment of left ventricular (LV) systolic and diastolic function and the identification of the typical apical-midventricular ballooning pattern, as well as the circumferential pattern of wall motion abnormalities. It is also useful in the early detection of complications (i.e. LV outflow tract obstruction, mitral regurgitation, right ventricular involvement, LV thrombi, and pericardial effusion) and monitoring of systolic function recovery. Left ventriculography allows the evaluation of LV function and morphology, identifying the typical TTS patterns when echocardiography is not available or wall motion abnormalities cannot be properly assessed with ultrasound. Cardiac magnetic resonance provides a more comprehensive depiction of cardiac morphology and function and tissue characterization and offers additional value to other imaging modalities for differential diagnosis (myocardial infarction and myocarditis). Coronary computed tomography angiography has a substantial role in the diagnostic workup of patients with acute chest pain and a doubtful TTS diagnosis to rule out other medical conditions. It can be considered as a non-invasive appropriate alternative to coronary angiography in several clinical scenarios. Although the role of nuclear imaging in TTS has not yet been well established, the combination of perfusion and metabolic imaging may provide useful information on myocardial function in both the acute and post-acute phase.
Friendly fire: the trade impact of the Russia sanctions and counter-sanctions
Authors: Crozet Matthieu , Hinz Julian 
Published: May 2020

SUMMARY
Economic sanctions are a frequently used instrument of foreign policy. In a diplomatic conflict, they aim to elicit a change in the policies of a foreign government by damaging their economy. Sanctions, however, are also likely to affect the sanctioning country. This paper evaluates these costs, in terms of export losses, for the diplomatic crisis between the Russian Federation and 37 countries over the conflict in Ukraine that started in 2014. We first gauge the impact of the diplomatic conflict using a traditional trade framework and quantify the trade losses in a general equilibrium counterfactual analysis. Losses for the Russian Federation amount to US$53 billion or 7.4% of predicted total exports from 2014 until the end of 2015. Western sanctioning countries, however, have also been impacted with an estimated loss of US$42 billion, 0.3% of their total exports. Interestingly, we find that the bulk of the impact stems from products that are not directly targeted by Russian retaliation, an effect that we coin friendly fire – an unintended, largely self-inflicted cost for Western sanctioning countries. We investigate the underlying mechanism at the product- and firm-level data. Results indicate that the drop of Western exports has not been driven by a change in Russian consumers’ preferences, but mainly by an increase in country risk affecting international transactions with Russia.