bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interfacial spin-orbit torques and magnetic anisotropy in WSe2/permalloy bilayers
Authors: Hidding Jan , Tirion Sytze H , Momand Jamo , Kaverzin Alexey , Mostovoy Maxim , Van Wees Bart J , Kooi Bart J , Guimarães Marcos H D 
Published: September 2021

Abstract
Transition metal dichalcogenides (TMDs) are promising materials for efficient generation of current-induced spin-orbit torques (SOTs) on an adjacent ferromagnetic layer. Numerous effects, both interfacial and bulk, have been put forward to explain the different torques previously observed. Thus far, however, there is no clear consensus on the microscopic origin underlying the SOTs observed in these TMD/ferromagnet bilayers. To shine light on the microscopic mechanisms at play, here we perform thickness dependent SOT measurements on the semiconducting WSe2/permalloy bilayer with various WSe2 layer thickness, down to the monolayer limit. We observe a large out-of-plane field-like torque with spin-torque conductivities up to

1
×

10
4

ℏ

/

2
e

Ω
m

−
1

. For some devices, we also observe a smaller in-plane antidamping-like torque, with spin-torque conductivities up to

4
×

10
3

ℏ

/

2
e

Ω
m

−
1

, comparable to other TMD-based systems. Both torques show no clear dependence on the WSe2 thickness, as expected for a Rashba system. Unexpectedly, we observe a strong in-plane magnetic anisotropy—up to about

6.6
×

10
4

erg cm−3—induced in permalloy by the underlying hexagonal WSe2 crystal. Using scanning transmission electron microscopy, we confirm that the easy axis of the magnetic anisotropy is aligned to the armchair direction of the WSe2. Our results indicate a strong interplay between the ferromagnet and TMD, and unveil the nature of the SOTs in TMD-based devices. These findings open new avenues for possible methods for optimizing the torques and the interaction with interfaced magnets, important for future non-volatile magnetic devices for data processing and storage.
Molecular Advances in Larval Fruit Moth Identification to Facilitate Fruit Export From Western United States Under Systems Approaches
Authors: Yokomi Raymond , Delgado Jennifer K , Unruh Thomas R , Barcenas Nina M , Garczynski Stephen F , Walse Spencer , Pérez de León Adalberto A , Cooper William Rodney 
Published: September 2021

AbstractMolecular advances facilitate fruit export by improving rapid pest diagnosis by polymerase chain reaction (PCR) and advanced sequencing technology. Improved pest detection provides timely certification of the quarantine pest-free status in the commodity being exported, avoiding unnecessary commodity treatment. The U.S.–Japan Systems Approach to export fresh cherries from the Western United States that targets the codling moth, Cydia pomonella (Linnaeus) (Lepidoptera: Tortricidae), is used as an example. Suspect codling moth larvae interdicted at cherry packing houses are distinguished by PCR from other internal fruit moth larvae such as the oriental fruit moth, Grapholita molesta (Busck) (Lepidoptera: Tortricidae); lesser appleworm, G. prunivora (Walsh) (Lepidoptera: Tortricidae); cherry fruitworm, G. packardi (Zeller) (Lepidoptera: Tortricidae); and filbertworm, Cydia latiferreana (Walsingham) (Lepidoptera: Tortricidae). Identification is confirmed by sequencing the amplicon of a 301 bp region of the COI gene produced by PCR of the DNA from a suspect moth and comparing this sequence of COI gene sequences of other internal fruit feeders of pome fruit. This sequence comparison results in unambiguous pest identification. These findings are discussed in the context of systems approach research to meet evolving needs of phytosanitary requirements for global export of fruits.
Reference Samples to Compare Next-Generation Sequencing Test Performance for Oncology Therapeutics and Diagnostics
Authors: Pfeifer John D , Loberg Robert , Lofton-Day Catherine , Zehnbauer Barbara A 
Published: September 2021

Abstract

Objectives
Diversity of laboratory-developed tests (LDTs) using next-generation sequencing (NGS) raises concerns about their accuracy for selection of targeted therapies. A working group developed a pilot study of traceable reference samples to measure NGS LDT performance among a cohort of clinical laboratories.

Methods
Human cell lines were engineered via CRISPR/Cas9 and prepared as formalin-fixed, paraffin-embedded cell pellets (“wet” samples) to assess the entire NGS test cycle. In silico mutagenized NGS sequence files (“dry” samples) were used to assess the bioinformatics component of the NGS test cycle. Single and multinucleotide variants (n = 36) of KRAS and NRAS were tested at 5% or 15% variant allele fraction to determine eligibility for therapy with the EGFR inhibitor panitumumab in the setting of metastatic colorectal cancer.

Results
Twenty-one (21/21) laboratories tested wet samples; 19 of 21 analyzed dry samples. Of the laboratories that tested both the wet and dry samples, 7 (37%) of 19 laboratories correctly reported all variants, 3 (16%) of 19 had fewer than five errors, and 9 (47%) of 19 had five or more errors. Most errors were false negatives.

Conclusions
Genetically engineered cell lines and mutagenized sequence files are complementary reference samples for evaluating NGS test performance among clinical laboratories using LDTs. Variable accuracy in detection of genetic variants among some LDTs may identify different patient populations for targeted therapy.
New Empirical Tests for Classic Litigation Selection Models: Evidence from a Low Settlement Environment
Authors: Chang Yun-chien , Hubbard William 
Published: September 2021

AbstractLaw and economics theorists have long advanced theories of litigation and settlement, including the canonical Landes–Posner–Gould (LPG) and Priest and Klein (PK) models. Famously, PK predict that, as settlement rates rise, plaintiff win rates approach 50%. So far, though, empirical testing of these models has been hampered by two major limitations: first, these models make clear predictions about the effect of case stakes on settlement rates and plaintiff win rates, but lack of reliable data on stakes means these predictions have gone untested. Second, most of the studies have used data from the United States, a high-settlement, high-litigation-costs setting, and the generalizability of these models to other institutional settings has been less explored. We use a novel data set of Taiwanese court data to test previously untested predictions of the LPG and PK models and explore the extent to which these models apply to a low-settlement, low-litigation-cost setting. We find strong support for the predictions of the LPG model that we test but at best weak support for the 50% hypothesis of the PK model, consistent with recent research suggesting that the hypothesis will have limited applicability in a low-settlement, low-litigation-cost environment. These results suggest directions for research and provide guidance to policy on the role that stakes play in the filing and settlement of claims.
The Border and the Way We Live Now
Authors: Moya* Paula M L 
Published: September 2021

Abstract
In this essay-review, Paula Moya discusses three recent scholarly books by T. Jackie Cuevas, Marissa López, and Roberto Hernández that react to the negative racialization of Mexican origin people in the US by analyzing a variety of strategies employed by Chicanx writers and artists in literary and cultural artifacts produced from the 19th century to the present. Cuevas argues that Chicanx scholars need to better acknowledge the wide variety of types of Chicanx people living in the US, including those who are gender variant. López charts how some writers rebuff limiting ethnic stereotypes by engaging in a politics of performative, aggressive abjection as a way of refusing to perform institutionally recognized latinidad. And in his focus on two transnational sites along the US-Mexico border, Hernández seeks to analyze the historical causes and underlying logic of our modern/colonial world system by articulating the not always evident relationship of local eruptions of violence to the global flows of racial capitalism. Together, they unite in fighting the forces of dishistoricization by recovering the history of Chicanx people in the US and imagining for the community a decolonial future that can elude the violent constraints of racial subordination.
Liquid-Immersion Reprocessing Effects on Filtration Efficiency of ‘Single-Use’ Commercial Medical Face Masks
Authors: Everts Richard J , Al Ghusaini Shadha , Telfar-Barnard Lucy , Barclay Ella , Tan Shaun , Jekel Sonja , Jennings Lance , Choi Dong Hoon , Hilson Dougal , Gibson Barbara 
Published: September 2021

Abstract

Purpose
Medical masks have inferior filtration efficiency and fit to filtering facepiece respirators (FFRs) but are widely used in healthcare and the community. These masks are intended for disposal after use but in the event of mask shortage re-use after reprocessing may be an option. We investigated eight reprocessing methods that each involved washing or soaking in liquid, are likely to eliminate respiratory viruses, and are safe and available in most community and healthcare settings.

Methods
Three brands of EN 14683 standards-compliant commercial medical mask were each reprocessed 10 times by one of eight methods. We measured filtration efficiency for poly-dispersed sodium chloride particles and pressure differential.

Results
Compared with new medical masks, reprocessed masks had significantly reduced filtration efficiency. The reduction was mild-moderate (6.5–25.8%) after warm water wash, hot water soak or boiling water soak; and moderate-large (24.1–51.5%) after detergent, soap or laundry machine wash, or bleach soak. There were mixed and minor changes in pressure differential. Most reprocessed standards-compliant masks had better filtration efficiency than new non-standard commercial masks and then cotton and cotton-polyester mix fabric samples, even triple-layered fabrics.

Conclusions
High-quality commercial medical masks reprocessed 10 times by water immersion methods had better filtration efficiency than new non-standard masks and washable fabrics. These findings have particular relevance for community and low-resource healthcare settings.
A general optimization protocol for molecular property prediction using a deep learning network
Authors: Chen Jen-Hao , Tseng Yufeng Jane 
Published: September 2021

Abstract
The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded in improving the model performance, better improvement may be achieved when specific combinations of these methods and practices are applied. In this work, three high-performance optimization methods in the literature that have been shown to dramatically improve model performance from other fields are used and discussed, eventually resulting in a general procedure for generating optimized CNN models on different properties of molecules. The three techniques are the dynamic batch size strategy for different enumeration ratios of the SMILES representation of compounds, Bayesian optimization for selecting the hyperparameters of a model and feature learning using chemical features obtained by a feedforward neural network, which are concatenated with the learned molecular feature vector. A total of seven different molecular properties (water solubility, lipophilicity, hydration energy, electronic properties, blood–brain barrier permeability and inhibition) are used. We demonstrate how each of the three techniques can affect the model and how the best model can generally benefit from using Bayesian optimization combined with dynamic batch size tuning.
Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins
Authors: Kamiński Kamil , Ludwiczak Jan , Jasiński Maciej , Bukala Adriana , Madaj Rafal , Szczepaniak Krzysztof , Dunin-Horkawicz Stanisław 
Published: September 2021

Abstract
The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.
Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity
Authors: Zhou Wenyang , Xu Chang , Wang Pingping , Anashkina Anastasia A , Jiang Qinghua 
Published: September 2021

Abstract
Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.
Consensus-based clustering of single cells by reconstructing cell-to-cell dissimilarity
Authors: Wang Chunxiang , Mu Zengchao , Mou Chaozhou , Zheng Hongyu , Liu Juntao 
Published: September 2021

Abstract
The development of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) technology has led to great opportunities for the identification of heterogeneous cell types in complex tissues. Clustering algorithms are of great importance to effectively identify different cell types. In addition, the definition of the distance between each two cells is a critical step for most clustering algorithms. In this study, we found that different distance measures have considerably different effects on clustering algorithms. Moreover, there is no specific distance measure that is applicable to all datasets. In this study, we introduce a new single-cell clustering method called SD-h, which generates an applicable distance measure for different kinds of datasets by optimally synthesizing commonly used distance measures. Then, hierarchical clustering is performed based on the new distance measure for more accurate cell-type clustering. SD-h was tested on nine frequently used scRNA-seq datasets and it showed great superiority over almost all the compared leading single-cell clustering algorithms.
A protocol for dynamic model calibration
Authors: Villaverde Alejandro F , Pathirana Dilan , Fröhlich Fabian , Hasenauer Jan , Banga Julio R 
Published: September 2021

AbstractOrdinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning
Authors: Li Hui-Sheng , Ou-Yang Le , Zhu Yuan , Yan Hong , Zhang Xiao-Fei 
Published: September 2021

Abstract
The identification of differentially expressed genes between different cell groups is a crucial step in analyzing single-cell RNA-sequencing (scRNA-seq) data. Even though various differential expression analysis methods for scRNA-seq data have been proposed based on different model assumptions and strategies recently, the differentially expressed genes identified by them are quite different from each other, and the performances of them depend on the underlying data structures. In this paper, we propose a new ensemble learning-based differential expression analysis method, scDEA, to produce a more stable and accurate result. scDEA integrates the P-values obtained from 12 individual differential expression analysis methods for each gene using a P-value combination method. Comprehensive experiments show that scDEA outperforms the state-of-the-art individual methods with different experimental settings and evaluation metrics. We expect that scDEA will serve a wide range of users, including biologists, bioinformaticians and data scientists, who need to detect differentially expressed genes in scRNA-seq data.
A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets
Authors: Russo Giulia , Di Salvatore Valentina , Sgroi Giuseppe , Parasiliti Palumbo Giuseppe Alessandro , Reche Pedro A , Pappalardo Francesco 
Published: September 2021

Abstract
The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.
VOLTA: adVanced mOLecular neTwork Analysis
Authors: Pavel Alisa , Federico Antonio , del Giudice Giusy , Serra Angela , Greco Dario 
Published: September 2021

Abstract

Motivation
Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines.

Results
We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a ‘plug and play’ system is provided.

Availability and implementation
The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719.

Supplementary information
Supplementary data are available at Bioinformatics online.
LipidQuant 1.0: automated data processing in lipid class separation–mass spectrometry quantitative workflows
Authors: Wolrab Denise , Cífková Eva , Čáň Pavel , Lísa Miroslav , Peterka Ondřej , Chocholoušková Michaela , Jirásko Robert , Holčapek Michal 
Published: September 2021

Abstract

Summary
We present the LipidQuant 1.0 tool for automated data processing workflows in lipidomic quantitation based on lipid class separation coupled with high-resolution mass spectrometry. Lipid class separation workflows, such as hydrophilic interaction liquid chromatography or supercritical fluid chromatography, should be preferred in lipidomic quantitation due to the coionization of lipid class internal standards with analytes from the same class. The individual steps in the LipidQuant workflow are explained, including lipid identification, quantitation, isotopic correction and reporting results. We show the application of LipidQuant data processing to a small cohort of human serum samples.

Availability and implementation
The LipidQuant 1.0 is freely available at Zenodo https://doi.org/10.5281/zenodo.5151201 and https://holcapek.upce.cz/lipidquant.php.

Supplementary information
Supplementary data are available at Bioinformatics online.
Genetic Contribution to Concern for Nature and Proenvironmental Behavior
Authors: Chang Chia-chen , Nghiem Thi Phuong Le , Fan Qiao , Tan Claudia L Y , Oh Rachel Rui Ying , Lin Brenda B , Shanahan Danielle F , Fuller Richard A , Gaston Kevin J , Carrasco L Roman 
Published: September 2021

Abstract
Earth is undergoing a devastating extinction crisis caused by human impacts on nature, but only a fraction of society is strongly concerned and acting on the crisis. Understanding what determines people's concern for nature, environmental movement activism, and personal conservation behavior is fundamental if sustainability is to be achieved. Despite its potential importance, the study of the genetic contribution to concern for nature and proenvironmental behaviors has been neglected. Using a twin data set (N = 2312), we show moderate heritability (30%–40%) for concern for nature, environmental movement activism, and personal conservation behavior and high genetic correlations between them (.6–.7), suggesting a partially shared genetic basis. Our results shed light on the individual variation in sustainable behaviors, highlighting the importance of understanding both the environmental and genetic components in the pursuit of sustainability.