bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nanowire Growth Simulation
Authors: de Souza G.B.G. , Costa B.V. 
Published: May 2020

Abstract
In this work we use a Computational Monte Carlo experiment for modeling the far from equilibrium growth of nanowires from a mixing of two particles species MBE experiment. We observed three distinct phases. There is a low temperature region where nanowires are formed. An intermediate phase, at a higher temperature where the solute floats over the solvent. Above a critical temperature a phase, similar to a paramagnetic phase in magnetic materials shows up. In this region solvent and solute are completely mixed up.
A New Universality at a First-Order Phase Transition: The Spin-flop Transition in an Anisotropic Heisenberg Antiferromagnet
Authors: Xu Jiahao , Tsai Shan-Ho , Landau David P. , Binder Kurt 
Published: May 2020

Abstract
A great triumph of statistical physics in the latter part of the 20th century was the understanding of critical behavior and universality at second-order phase transitions. In contrast, first-order transitions were believed to have no common features. However, we argue that the classic, first-order “spin-flop” transition (between the antiferromagnetic and the rotationally degenerate, canted state) in an anisotropic antiferromagnet in a magnetic field exhibits a new kind of universality. We present a finite-size scaling theory for a first-order phase transition where a continuous symmetry is broken using an approximation of Gaussian probability distributions with a phenomenological degeneracy factor “q” included, where “q” characterizes the relative degeneracy of the ordered phases. Predictions are compared with high resolution Monte Carlo simulations of the three-dimensional, XXZ Heisenberg antiferromagnet in a field to study the finite-size behavior for L×L×L simple cubic lattices. The field dependence of all moments of the order parameters exhibit universal intersections at the spin-flop transition. Our Monte Carlo data agree with theoretical predictions for asymptotic large L behavior. Our theory yields q = π, and we present numerical evidence that is compatible with this prediction. The agreement between the theory and simulation implies a heretofore unknown universality.
The impact of fluctuations on the zeros of the energy probability distribution
Authors: Rodrigues R G M , Mól L A S 
Published: May 2020

Abstract
In this communication we explore the possible effects of statistical fluctuations on the use of the Energy Probability Distribution (EPD) zeros to study phase transitions. In the EPD zeros technique one has to find the roots of a polynomial whose coefficients are given by the EPD - a histogram of energy values obtained in a Monte Carlo simulation, for example. Phase transitions are signalized by the presence of a zero that approaches the point (1,0) in the complex plane. Once the EPD estimations are usually subject to statistical fluctuations and polynomial roots are known to be sensitive to modifications in its coefficients, we have compared the roots of a given polynomial with the roots of a perturbed one, searching for possible impacts on the method. Our results show that although the overall map of zeros is modified, the location of the dominant zero, the one that indicates the presence of a phase transition, is not affected. Indeed, even for 30% perturbation only small modifications in the dominant zero location is observed.
Genetic Algorithm to search for exotically shaped fullerenes
Authors: Ferreira L. R. N. , Mendonça J. P. A. , Sato F. 
Published: May 2020

Abstract
The versatility of carbon-carbon bonds is in charge of various carbon-based structures including numerous possibilities for building fullerenes. Theoretically, it is possible to make any closed surface consisting of C atoms in a number of ways. However, the generation of possible arrangements and, furthermore, calculating the corresponding energetics is a great challenge even for a small molecule. In this context, we develop a genetic-algorithm-based code that can search for exotically shaped fullerenes. Furthermore, we discuss the construction and optimization of the algorithm assisted by some test results.
Phase Transitions in the Two-Dimensional Ising Model from the Microcanonical Perspective
Authors: Sitarachu Kedkanok , Bachmann Michael 
Published: May 2020

Abstract
The continuous ferromagnetic-paramagnetic phase transition in the two-dimensional Ising model has already been excessively studied by conventional canonical statistical analysis in the past. We use the recently developed generalized microcanonical inflection-point analysis method to investigate the least-sensitive inflection points of the microcanonical entropy and its derivatives to identify transition signals. Surprisingly, this method reveals that there are potentially two additional transitions for the Ising system besides the critical transition.
Determination of the Phase Diagram for the 3d Spin Glass ± J Model Using the Zeros of the Energy Probability Distribution
Authors: Figueiredo T. P. , Costa B.V. 
Published: May 2020

Abstract
In this work we use the zeros of the energy probability distribution to obtain the critical diagram of the three dimensional (L
3) ±J Ising model. Starting with a complete ferromagnetic system we introduce a number of p × L
3 antiferromagnetic bonds in the system. The phase diagram, temperature against antiferromagnetic bond concentration (p), is estimated for several values of p. The goal of this preliminary study is to show how it is possible to determine the phase diagram of such a complex model without the need to define any order parameter.
Strained ultra-thin films of BaO: a molecular dynamics investigation
Authors: Nascimento V. B. , Rino J.P. , Costa B.V. 
Published: May 2020

Abstract
A recent theoretical work by Bousquet and collaborators have predicted that ferroelectric ordering could be induced in the rocksalt oxides of alkaline earth metals (BaO, MgO, CaO and SrO) by strain. The expected functional properties present in these strained binary oxides, like polarization, dielectric constant and piezoelectric response, would be comparable to those of typical ferroelectric perovskites. Consequently, the strained binary oxides would be promising materials for fabrication of devices like ferroelectric memories and sensors. One possible way to explore the potential underlying these theoretical predictions is to grow thin and ultra-thin films of these binary oxides under epitaxial strain by choosing an adequate substrate. In such systems the interplay between epitaxial strain and the lack of translational symmetry (limited film thickness) may lead to the formation of interesting (anti)ferroelectric phases. Our goal in this work is to explore the potential structural and functional phase diagram of BaO ultra-thin films (thickness of only 8 BaO layers, ≈ 20 nm) obtained for different values of compressive epitaxial strain and temperature by performing a molecular dynamics investigation. A polarized phase (antipolar) is observed at a compressive strain of -9.0%, wich resists till a melting temperature around 1500 K, which is indeed high for such an ultra-thin film.
What drives public support for policies to enhance electric vehicle adoption?
Authors: Brückmann Gracia , Bernauer Thomas 
Published: May 2020

Abstract
Decarbonizing the transportation sector is crucial to limiting global warming, but faces severe political feasibility challenges due to widespread opposition by those who incur the costs. With respect to private motorized vehicles, which account for the largest share of emissions from transportation, various studies show that pull measures, such as subsidies for electric vehicles (EVs) and charging infrastructure, attract more public support than push measures, such as carbon taxes or regulation to phase out fossil fuel cars. Based on a choice experiment with a large, representative sample (N = 5325) of car holders in Switzerland, we reassess and add to these findings. We empirically focus on Switzerland because its newly registered cars have the worst emissions record in Europe. First, we reassess the presumably stronger support for pull measures by studying whether such support is (negatively) affected by revealing the cost implications in terms of means for funding these policy measures. Second, a unique feature of our study is that we examine support for policies to promote EVs both amongst non-EV and EV holders. Our hypothesis is that EV holders are likely to be more supportive of such policies, even when cost implications become apparent. Our key finding is that support for pull measures, which is high amongst non-EV holders, and even higher among technology adopters (EV holders), remains stable even when policy funding is revealed. This suggests that more ambitious pull measures in this area are politically feasible, even more so as the share of EV-adopters increases. Our research also provides a methodological template for similar research in other countries.
The effect of moisture and reinforcement on the self-sensing properties of hybrid-fiber-reinforced concrete
Authors: Maier M 
Published: May 2020

Abstract
This study presents the piezoresistive properties of a hybrid fiber-reinforced concrete (HyFRC) for structural health monitoring. This HyFRC mixture incorporates micro PVA and macro steel fibers to control the crack propagation. Investigations on self-sensing concrete (SSC) focus mainly on mixtures containing highly conductive fillers such as carbon nanotubes, graphite or nickel powder which results in a highly engineered cost-intensive mix composition for structural applications. This paper investigates the sensing ability of a cost-effective structural concrete mixture by considering the environmental conditions of engineering structures e.g. the moisture content as well as the influence of reinforcement on the electrical resistance measurement. The sensing parameters were evaluated by performing simultaneously bending tests and Electrochemical Impedance Spectroscopy (EIS) on beams with and without steel reinforcement. Furthermore, the influence of the pore solution on the EIS was investigated on specimens with varying moisture contents. The sensing behavior was compared to a control mixture without steel and PVA fibers. Results showed a significant lower initial resistivity for the HyFRC mixture compared to the ordinary concrete as well as the activation of the self-sensing properties due to the incorporated steel fibers. Finally, the influence of the pore solution on the EIS measurement is addressed and emphasizes the imperative need to consider the moisture content of self-sensing materials.
Effects of pharmacy interventions at transitions of care on patient outcomes
Authors: Fosnight Susan , King Philip , Ewald Jacqueline , Feucht John , Lamtman Angela , Kropp Denise , Dittmer Alison , Sampson Jordan , Shah Morali 
Published: May 2020

Abstract

Purpose
An interdisciplinary group developed a care transitions process with a prominent pharmacist role.

Methods
The new transitions process was initiated on a 32-bed medical/surgical unit. Demographics, reconciliation data, information on medication adherence barriers, medication recommendations, and time spent performing interventions were prospectively collected for 284 consecutive patients over 54 days after the pharmacy participation was completely implemented. Outcome data, including 30-day readmission rates and length of stay, were retrospectively collected.

Results
When comparing metrics for all intervention patients to baseline metrics from the same months of the previous year, the readmission rate was decreased from 21.0% to 15.3% and mean length of stay decreased from 5.3 days to 4.4 days. Further improvement to a 10.2% readmission rate and a 3.6-day average length of stay were observed in the subgroup of intervention patients who received all components of the pharmacy intervention. Additionally, greater improvements were observed in intervention-period patients who received the full pharmacy intervention, as compared to those receiving only parts of the pharmacy intervention, with a 10.2-percentage-point lower readmission rate (10.2% vs 20.4%, P = 0.016) and a 1.7-day shorter length of stay (3.6 days vs 5.3 days; 95% confidence interval, 0.814-2.68 days; P = 0.0003). For patients receiving any component of the pharmacy intervention, an average of 9.56 medication recommendations were made, with a mean of 0.89 change per patient deemed to be required to avoid harm and/or increased length of stay.

Conclusion
A comprehensive pharmacy intervention added to a transitions intervention resulted in an average of nearly 10 medication recommendations per patient, improved length of stay, and reduced readmission rates.
MetaFS: Performance assessment of biomarker discovery in metaproteomics
Authors: Tang Jing , Mou Minjie , Wang Yunxia , Luo Yongchao , Zhu Feng 
Published: May 2020

Abstract
Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS’s performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.
Landscape of drug-resistance mutations in kinase regulatory hotspots
Authors: Kim Pora , Li Hanyang , Wang Junmei , Zhao Zhongming 
Published: May 2020

Abstract
More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase’s secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.
rScudo: an R package for classification of molecular profiles using rank-based signatures
Authors: Ciciani Matteo , Cantore Thomas , Lauria Mario 
Published: May 2020

Abstract

Summary
The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods.

Availability and implementation
The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.
PEDL: extracting protein–protein associations using deep language models and distant supervision
Authors: Weber Leon , Thobe Kirsten , Migueles Lozano Oscar Arturo , Wolf Jana , Leser Ulf 
Published: May 2020

Abstract

Motivation
A significant portion of molecular biology investigates signalling pathways and thus depends on an up-to-date and complete resource of functional protein–protein associations (PPAs) that constitute such pathways. Despite extensive curation efforts, major pathway databases are still notoriously incomplete. Relation extraction can help to gather such pathway information from biomedical publications. Current methods for extracting PPAs typically rely exclusively on rare manually labelled data which severely limits their performance.

Results
We propose PPA Extraction with Deep Language (PEDL), a method for predicting PPAs from text that combines deep language models and distant supervision. Due to the reliance on distant supervision, PEDL has access to an order of magnitude more training data than methods solely relying on manually labelled annotations. We introduce three different datasets for PPA prediction and evaluate PEDL for the two subtasks of predicting PPAs between two proteins, as well as identifying the text spans stating the PPA. We compared PEDL with a recently published state-of-the-art model and found that on average PEDL performs better in both tasks on all three datasets. An expert evaluation demonstrates that PEDL can be used to predict PPAs that are missing from major pathway databases and that it correctly identifies the text spans supporting the PPA.

Availability and implementation
PEDL is freely available at https://github.com/leonweber/pedl. The repository also includes scripts to generate the used datasets and to reproduce the experiments from this article.

Supplementary information
Supplementary data are available at Bioinformatics online.
An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data
Authors: Chen Si-Yi , Liu Chun-Jie , Zhang Qiong , Guo An-Yuan 
Published: May 2020

Abstract

Motivation
T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge.

Results
We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology.

Availability and implementation
http://bioinfo.life.hust.edu.cn/CATT or https://github.com/GuoBioinfoLab/CATT.

Supplementary information
Supplementary data are available at Bioinformatics online.
Improved survival analysis by learning shared genomic information from pan-cancer data
Authors: Kim Sunkyu , Kim Keonwoo , Choe Junseok , Lee Inggeol , Kang Jaewoo 
Published: May 2020

AbstractMotivationRecent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.ResultsWe pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.Availability and implementationOur implementation of VAECox is available at https://github.com/dmis-lab/VAECox.Supplementary informationSupplementary data are available at Bioinformatics online.
REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets
Authors: Marchet Camille , Iqbal Zamin , Gautheret Daniel , Salson Mikaël , Chikhi Rayan 
Published: May 2020

Abstract

Motivation
In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets.

Results
We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of ∼4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances.

Availability and implementation
https://github.com/kamimrcht/REINDEER.

Supplementary information
Supplementary data are available at Bioinformatics online.