bluuebunny/crossref_metadata_2025_split huggingface dataset:
A study on the static recrystallization behavior of an ultrahigh-strength stainless steel
Authors: Wang Xiao-hui , Liu Zhen-bao , Liang Jian-xiong , Yang Zhi-yong , Sun Yong-qing , Wang Chang-jun , Qi Yue 
Published: April 2021

Abstract
The static recrystallization (SRX) behavior of an ultrahigh-strength stainless steel (UHSSS) was investigated via double-hit isothermal compression tests. The results revealed that the kinetics of static recrystallization and the corresponding microstructural evolution were not only prominently influenced by the deformation parameters and initial microstructures but also suppressed due to the precipitation of M6C carbides below 1050 °C, which sufficiently pinned the boundaries. Numerical models for predicting the recrystallized fraction and grain sizes were proposed based on the experimental results. The kinetics model could predict the process of SRX in the deformation temperature range from 1050 °C to 1150 °C well, whereas there were some deviations between the predictions and experimental results due to the interaction of the M6C carbides and SRX when the deformation temperature was below 1050 °C.
The role of copper on the restoration of graphene oxide by chemical vapor deposition
Authors: Yang Xinliang , Zhang Zijian , He Sifan , Wu Wenjie , Shu Xiangfeng , Chen Yijun , Zhang Zhaokai , Jiang Kai , Liu Jianlong , Song Yenan 
Published: April 2021

Abstract
High-quality reduced graphene oxide (rGO) sheets can be accessible through Langmuir-Blodgett self-assembly (LBSA) on copper foil and dielectric substrate under high temperature ethanol vapors via chemical vapor deposition (CVD) process. Through the LBSA forming method, a uniform and smooth graphene oxide (GO) film can be obtained on the target substrate, which is more economical and efficient compared to the traditional growth strategy. Moreover, the GO-derived graphene film repaired on copper was nearly defect-free with a negligible defect density (ID/IG ratio of <0.1) and manifested a strong 2D peak, indicating high efficiency of defects restoration. The obtained rGO sheets exhibited excellent electrical properties (1.2 kΩ/sq prepared on SiO2/Si, 0.2 kΩ/sq prepared on copper and subsequently transferred to SiO2/Si) that had surpassed other GO-derived graphene ever reported. Meanwhile, we demonstrate that Cu-vapor will degrade the restoration efficiency when introduced remotely, which is quite different from previous studies on graphene growth.
Experiment on low-frequency electromagnetic waves propagating in shock-tube-generated magnetized cylindrical enveloping plasma
Authors: GUO Shaoshuai , XIE Kai , SUN Bin , XI Ruoyao , LIU Yan 
Published: April 2021

Abstract
We propose a method of applying a static magnetic field to reduce the attenuation of the magnetic field component (S
H) of low-frequency electromagnetic (LF EM) waves in dense plasma. The principle of this method is to apply a static magnetic field to limit electron movement, thereby increasing the equivalent resistance and thus reducing the induced current and S
H. We consider the static magnetic field acting on the plasma of the entire induced current loop rather than on the local plasma, where the induced current is excited by the magnetic field component of LF EM waves. Analytical expressions of S
H suitable for magnetized cylindrical enveloping plasma are derived by adopting an equivalent circuit approach, by which S
H is calculated with respect to various plasma parameter settings. The results show that S
H can be reduced under a static magnetic field and the maximum magnetic field strength that mitigates blackout is less than 0.1 T. Experiments in which LF EM waves propagate in a shock-tube-generated magnetized cylindrical enveloping plasma are also conducted. S
H measured under the magnetic field (the magnetic field strength B
0 acting on the magnetic field probe was about 0.06 T) reduces at f = 10 MHz and f = 30 MHz when n
e ≈ 1.9 × 1013 cm−3, which is consistent with theoretical results. The verification of the theory thus suggests that applying a static magnetic field with a weak magnetic field has the potential to improve the transmission capacity of LF EM waves in dense plasma.
A physical origin of cross-polarization and higher-order modes in two-dimensional (2D) grating couplers and the related device performance limitations
Authors: Georgieva Galina , Voigt Karsten , Seiler Pascal M , Mai Christian , Petermann Klaus , Zimmermann Lars 
Published: April 2021

Abstract
We explore scattering effects as the physical origin of cross-polarization and higher-order modes in silicon photonic 2D grating couplers (GCs). A simplified analytical model is used to illustrate that in-plane scattering always takes place, independent of grating geometry and design coupling angle. Experimental investigations show furthermore that grating design parameters are especially related to the modal composition of both the target- and the cross-polarization. Scattering effects and the associated cross-polarization and higher-order modes are indicated as the main reason for the higher 2D GC insertion loss compared to standard 1D GCs. In addition, they can be responsible for a variable 2D GC spectrum shape, bandwidth and polarization dependent loss.
Recent advances in acoustic diagnostics for electrochemical power systems
Authors: Majasan Jude O , Robinson James B , Owen Rhodri E , Maier Maximilian , Radhakrishnan Anand N P , Pham Martin , Tranter Thomas G , Zhang Yeshui , Shearing Paul R , Brett Dan J L 
Published: April 2021

AbstractOver the last decade, acoustic methods, including acoustic emission (AE) and ultrasonic testing (UT), have been increasingly deployed for process diagnostics and health monitoring of electrochemical power devices, including batteries, fuel cells, and water electrolysers. These techniques are non-invasive, highly sensitive, and low-cost, providing a high level of spatial and temporal resolution and practicality. Their application in electrochemical devices is based on identifying changes in acoustic signals emitted from or propagated through materials as a result of physical, structural, and electrochemical changes within the material. These changes in acoustic signals are then correlated to critical processes and the health status of these devices. This review summarises progress in the use of acoustic methods for the process and health monitoring of major electrochemical energy conversion and storage devices. First, the fundamental principles of AE and UT are introduced, and then the application of these acoustic techniques to electrochemical power devices are discussed. Conclusions and perspectives on some of the key challenges and potential commercial and academic applications of the devices are highlighted. It is expected that, with further developments, acoustic techniques will form a key part of the suite of diagnostic techniques routinely used to monitor electrochemical devices across various processes, including fabrication, post-mortem examination and recycle decision support to aid the deployment of these devices in increasingly demanding applications.
A DFT study on the electronic structure of in-plane heterojunctions of graphene and hexagonal boron nitride nanoribbons
Authors: dos Santos Ramiro M , Giozza William F , de Sousa Junior Rafael T , da Silva Filho Demétrio A , Santos Renato B , Ribeiro Júnior Luiz A 
Published: April 2021

Abstract
The structural similarity between hexagonal boron nitride (h-BN) and graphene nanoribbons allows forming heterojunctions with small chain stress. The insulation nature of the former and the quasi-metallic property of the latter make them attractive for flat optoelectronics. Recently, shapes of graphene and h-BN domains were precisely controlled, creating sharp graphene/h-BN interfaces. Here, we investigated the electronic and structural properties of graphene (h-BN) nanoribbon domains of different sizes sandwiched between h-BN (graphene) nanoribbons forming in-plane heterojunctions. Different domain sizes for the non-passivated zigzag edge termination were studied. Results showed that the charge density is localized in the edge of the heterojunctions, regardless of the domain size. The systems with graphene domains are metallic, presenting null band gaps. The ones with the h-BN island are small-bandgap semiconductors with the highest bandgap value around 0.2 eV. The calculated bandgap has the same magnitude of the certain threshold for density functional theory. As a general trend, these materials exhibit a ferromagnetic behavior, which can be useful for magnetic applications at the nanoscale.
Microfabricated fiducial markers for digital image correlation-based micromechanical testing of LIGA Ni alloys
Authors: Liew Li-Anne , Read David T. , Martin May L. , Christenson Todd R. , Geaney John T. 
Published: April 2021

Abstract
Photolithographically defined thin film Au dots were used as micro fiducial markers for digital image correlation (DIC), to enable two-dimensional strain measurement of 200 μm-thick LIGA (Lithographie, Galvanformung, Abformung) nickel alloys. Due to the sensitivity of electrodeposited films’ microstructure and properties on the processing conditions, characterization of LIGA mechanical properties continues to be necessary for microsystems commercialization. DIC offers advantages over laser-based strain measurement techniques but creating suitable speckle patterns on specimens with dimensions under a millimeter is challenging. The material surface roughness itself is often used as the speckle pattern, or micro- or nanoparticles are applied to the surface. But for materials with highly polished surfaces, such as commercial LIGA alloys, the surface roughness is not always suitable, while application of particles still poses technical challenges in uniformity and reproducibility. We fabricated freestanding tensile specimens, with gauge sections 700 μm wide × 3 mm long × 200 μm thick, from electrodeposited Ni-10% Co using a commercial LIGA process, and conducted microtensile tests at strain rate 0.001 s−1. Designing and fabricating arrays of randomly oriented 1.5 μm-thick Au dots on the specimens provided a suitable way to obtain full-field surface strains over the entire gauge lengths and was reproducible from one specimen to another. Microfabricated fiducial markers therefore can be a useful surface-preparation approach for investigating micromechanical behavior, particularly plasticity and fracture, of LIGA films using DIC.
Documentation of Do-Not-Attempt-Cardiopulmonary-Resuscitation orders amid the COVID-19 pandemic
Authors: Connellan David , Diffley Kara , McCabe John , Cotter Aoife , McGinty Tara , Sheehan Gerard , Ryan Karen , Cullen Walter , Lambert John S , Callaly Elizabeth L , Kyne Lorraine 
Published: April 2021

Abstract

Introduction
the COVID-19 pandemic has brought the decision-making process regarding cardiopulmonary resuscitation (CPR) into focus. The aim of this study is to compare rates of Do-Not-Attempt-CPR (DNACPR) documentation in older hospitalised patients before and during the COVID-19 pandemic.

Methods
this was a retrospective repeated cross-sectional study. Data including co-morbidities and resuscitation status was collected on 300 patients with COVID-19 hospitalised from 1 March to 31 May 2020. DNACPR documentation rates in patients aged ≥65 years with a diagnosis of COVID-19 were compared to those without COVID-19 admitted during the same period and were also compared to the documentation rates pre-COVID-19 pandemic (1 March–31 May 2019).

Results
of 300 COVID-19-positive patients, 28% had a DNACPR order documented during their admission. Of 131 older (≥65 years) patients with COVID-19, 60.3% had a DNACPR order compared to 25.4% of 130 older patients without COVID-19 (P &lt; 0.0001). During a comparable time period pre-pandemic, 15.4% of 130 older patients had a DNACPR order in place (P &lt; 0.0001). Almost fifty percent of DNACPR orders were recorded within 24 h of a positive swab result for SARS-CoV-2. Of older COVID-19-positive patients, 39.2% were referred to palliative care services and 70.2% survived.

Conclusion
the COVID-19 pandemic has prompted more widespread and earlier decision-making regarding resuscitation status. Although case fatality rates were higher for older hospitalised patients with COVID-19, many older patients survived the illness. Advance care planning should be prioritised in all patients and should remain as part of good clinical practice despite the pandemic.
In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor
Authors: Zhang Yuzhao , He Xibing , Zhai Jingchen , Ji Beihong , Man Viet Hoang , Wang Junmei 
Published: April 2021

Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.
CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data
Authors: Wei Ziyang , Zhang Shuqin 
Published: April 2021

Abstract

Motivation
Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation.

Results
In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods.

Availability and implementation
The implementation of CALLR is available at https://github.com/MathSZhang/CALLR.

Supplementary information
Supplementary data are available at Bioinformatics online.
A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data
Authors: Trinh Hung-Cuong , Kwon Yung-Keun 
Published: April 2021

AbstractMotivationIt is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression.ResultsIn this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time.Availability and implementationSource code and data are freely available at https://github.com/csclab/CGA-BNI.Supplementary informationSupplementary data are available at Bioinformatics online.
Data-driven speciation tree prior for better species divergence times in calibration-poor molecular phylogenies
Authors: Tao Qiqing , Barba-Montoya Jose , Kumar Sudhir 
Published: April 2021

Abstract

Motivation
Precise time calibrations needed to estimate ages of species divergence are not always available due to fossil records' incompleteness. Consequently, clock calibrations available for Bayesian dating analyses can be few and diffused, i.e. phylogenies are calibration-poor, impeding reliable inference of the timetree of life. We examined the role of speciation birth–death (BD) tree prior on Bayesian node age estimates in calibration-poor phylogenies and tested the usefulness of an informative, data-driven tree prior to enhancing the accuracy and precision of estimated times.

Results
We present a simple method to estimate parameters of the BD tree prior from the molecular phylogeny for use in Bayesian dating analyses. The use of a data-driven birth–death (ddBD) tree prior leads to improvement in Bayesian node age estimates for calibration-poor phylogenies. We show that the ddBD tree prior, along with only a few well-constrained calibrations, can produce excellent node ages and credibility intervals, whereas the use of an uninformative, uniform (flat) tree prior may require more calibrations. Relaxed clock dating with ddBD tree prior also produced better results than a flat tree prior when using diffused node calibrations. We also suggest using ddBD tree priors to improve the detection of outliers and influential calibrations in cross-validation analyses.
These results have practical applications because the ddBD tree prior reduces the number of well-constrained calibrations necessary to obtain reliable node age estimates. This would help address key impediments in building the grand timetree of life, revealing the process of speciation and elucidating the dynamics of biological diversification.

Availability and implementation
An R module for computing the ddBD tree prior, simulated datasets and empirical datasets are available at https://github.com/cathyqqtao/ddBD-tree-prior.
Disease gene prediction with privileged information and heteroscedastic dropout
Authors: Shu Juan , Li Yu , Wang Sheng , Xi Bowei , Ma Jianzhu 
Published: April 2021

Abstract

Motivation
Recently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that similar genes are more likely to be associated with similar diseases. However, the genetic features these methods rely on are often hard to collect due to high experimental cost and various other technical limitations. Existing solutions of this problem significantly increase the risk of overfitting and decrease the generalizability of the models.

Results
In this work, we propose a graph neural network (GNN) version of the Learning under Privileged Information paradigm to predict new disease gene associations. Unlike previous gene prioritization approaches, our model does not require the genetic features to be the same at training and test stages. If a genetic feature is hard to measure and therefore missing at the test stage, our model could still efficiently incorporate its information during the training process. To implement this, we develop a Heteroscedastic Gaussian Dropout algorithm, where the dropout probability of the GNN model is determined by another GNN model with a mirrored GNN architecture. To evaluate our method, we compared our method with four state-of-the-art methods on the Online Mendelian Inheritance in Man dataset to prioritize candidate disease genes. Extensive evaluations show that our model could improve the prediction accuracy when all the features are available compared to other methods. More importantly, our model could make very accurate predictions when &gt;90% of the features are missing at the test stage.

Availability and implementation
Our method is realized with Python 3.7 and Pytorch 1.5.0 and method and data are freely available at: https://github.com/juanshu30/Disease-Gene-Prioritization-with-Privileged-Information-and-Heteroscedastic-Dropout.
Neighborhoods, Criminal Incidents, Race, and Sentencing: Exploring the Racial and Social Context of Disparities in Incarceration Sentences
Authors: Donnelly Ellen A 
Published: April 2021

AbstractAs an extra-legal factor, social context is a key contributor to racial/ethnic disparities in incarceration sentences. Neighborhoods may have important, yet underexplored influences on sentencing. This study evaluates whether the social conditions and racial characteristics of communities where defendants allegedly offend affect Black-White sentencing disparities. Three-level multilevel model results suggest larger Black populations in neighborhoods of criminal incident increase the odds of incarceration and, to a lesser extent, lengthen sentences for all defendants. Offending outside one’s residential community increases the probability and length of a prison sentence. Neighbourhood effects differ by race, however. Unlike Whites, Blacks receive more punitive sentences for committing offences in disadvantaged areas and less proportionally Black communities. Neighbourhoods thus contribute to racial differences in sentencing outcomes.