bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on single-pulse UIS capability of SiC MOSFET with different P-base morphology
Authors: Wang Antao , Bai Yun , Li Chengzhan , Wu Zhikang , Tang Yidan , Tian Xiaoli , Yang Chengyue , Hao Jilong , Liu Xinyu 
Published: December 2022

Abstract
At present, the single-pulse unclamped inductive switching (UIS) characteristics of SiC MOSFET have been fully studied. The current mainstream research suggests that the failure mechanism of MOSFET under a single UIS shock is divided into parasitic BJT conduction and metallization failure. However, there are still some shortcomings in the current research. First, it is difficult to distinguish these two failure mechanisms. Second, it is difficult to propose feasible optimization schemes. Therefore, it is still necessary to further explore the mechanism of single-pulse UIS. In this study, Sentaurus TCAD software was used to simulate different P-base doped MOSFETs, and the effect of doping concentration and morphology of the P-base region on the single-pulse UIS failure mechanism was revealed. By designing the P-base region, the BJT conduction failure and metallization failure can be distinguished. In addition, the study also proved the relationship between breakdown voltage and the maximum temperature (T
max) in the UIS process, and on this basis, an optimization scheme for the single UIS characteristics of MOSFET was proposed.
Conditions for the open pores formation in medical textile materials for the treatment of wounds using iron oxide nanopowders
Authors: Riabchykov Mykola , Tkachuk Oksana , Nazarchuk Liudmyla , Alexandrov Alexandr 
Published: December 2022

Abstract
One of the tasks of dressings in the process of wound treatment is to remove exudate. In the optimal case, this intensity is determined individually for each wound. Active medical textiles take advantage of the capillary effect that occurs in thin channels. For these purposes, medical materials are created from porous structures. To create back pressure in foamed medical materials, the pores must be in the form of channels, which is not realized with conventional technologies. The purpose of this study is to analyze the performance of magnetic nanopowders on the pore structure in the production of porous materials. Research methods include methods of chemical synthesis, methods of microscopic analysis, methods of modeling, statistical analysis. The main result is the study of the formation of necessary forms in foamed materials during the use of magnetic nanopowders as additives. This effect makes it possible to increase the efficiency of foam structures when used as medical materials.
Flame-retardant polymethacrylimide foams modified by grafting with amino-terminated phosphorous polyborosiloxane
Authors: Zhu ZhengLiang , Chen WeiWang , Dong YuTong , Zhou XiaoMeng 
Published: December 2022

AbstractTo improve the flame retardancy of Polymethacrylimide (PMI) foam, in this study, a series of flame-retardant PMI foams were prepared by grafting with flame retardant amino-terminated phosphorous polyborosiloxane (N-PBSi), using tert-butanol (TBA) as the foaming agent. The structure, mechanical properties, thermal behavior, and flame retardancy of the resultant PMI foams were characterized. These as-prepared foams tended to be more compact in structure as N-PBSi content increased. The tensile, compressive, and bending strength of these PMI foams therefore was greatly enhanced, which were about 2 times, 9 times, and 3 times that of pure PMI foam, respectively. Besides, the introduction of N-PBSi also attributed to PMI foams better performance in char forming, especially in the air, which is beneficial for obtaining better retardancy against flame. Their advantages in flame retardancy and smoke inhabitation were confirmed by limiting oxygen index (LOI) and cone calorimeter tests (CCT). The total heat release (THR) and the peak of smoke generation rate (pSPR) of PMI/N-PBSi-20 were reduced by 23.1% and 69.9%, respectively. The N-PBSi incorporated were thought to not only generate phosphorus-containing fragments to capture free radicals in gas phase, but also rearrange in the solid phase to form a denser carbon layer to provide a better barrier between external heat and internal flammable pyrolysis gases. Given these, it can be envisaged that the flame-retardant PMI foams modified by N-PBSi may be more attractive in a wider range of applications.
Material transformers: deep learning language models for generative materials design
Authors: Fu Nihang , Wei Lai , Song Yuqi , Li Qinyang , Xin Rui , Omee Sadman Sadeed , Dong Rongzhi , Siriwardane Edirisuriya M Dilanga , Hu Jianjun 
Published: December 2022

Abstract
Pre-trained transformer language models (LMs) on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to learn the composition patterns for the generative design of material compositions. Here we train a series of seven modern transformer models (GPT, GPT-2, GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) for materials design using the expanded formulas of the ICSD, OQMD, and Materials Projects databases. Six different datasets with/out non-charge-neutral or EB samples are used to benchmark the generative design performances and uncover the biases of modern transformer models for the generative design of materials compositions. Our experiments show that the materials transformers based on causal LMs can generate chemically valid material compositions with as high as 97.61% to be charge neutral and 91.22% to be electronegativity balanced, which has more than six times higher enrichment compared to the baseline pseudo-random sampling algorithm. Our LMs also demonstrate high generation novelty and their potential in new materials discovery is proved by their capability to recover the leave-out materials. We also find that the properties of the generated compositions can be tailored by training the models with selected training sets such as high-bandgap samples. Our experiments also show that different models each have their own preference in terms of the properties of the generated samples and their running time complexity varies a lot. We have applied our materials transformers to discover a set of new materials as validated using density functional theory calculations. All our trained materials transformer models and code can be accessed freely at http://www.github.com/usccolumbia/MTransformer.
Concentrated electrolytes for rechargeable lithium metal batteries
Authors: Tian Chunxi , Qin Kun , Suo Liumin 
Published: December 2022

Abstract
Traditional lithium-ion batteries with graphite anodes have gradually been limited by the glass ceiling of energy density. As a result, lithium metal batteries (LMBs), regarded as the ideal alternative, have attracted considerable attention. However, lithium is highly reactive and susceptible to most electrolytes, resulting in poor cycle performance. In addition, lithium grows Li dendrites during charging, adversely affecting the safety of LMBs. Therefore, LMBs are more sensitive to the chemical composition of electrolytes and their relative ratios (concentrations). Recently, concentrated electrolytes have been widely demonstrated to be friendly to lithium metal anodes (LMAs). This review focuses on the progress of concentrated electrolytes in LMBs, including the solvation structure varying with concentration, unique functions in stabilizing the LMA, and their interfacial chemistry with LMA.
SWR1 chromatin remodeling complex prevents mitotic slippage during spindle position checkpoint arrest
Authors: Caydasi Ayse Koca , Khmelinskii Anton , Darieva Zoulfia , Kurtulmus Bahtiyar , Knop Michael , Pereira Gislene 
Published: December 2022

Caydasi et al. identified the SWR1 chromatin-remodeling complex (SWR1-C) among novel factors that are essential for the integrity of the spindle position checkpoint (SPOC). They show that cells lacking SWR1-C activate the SPOC upon spindle misorientation but undergo mitotic slippage due to the inability of holding a prolonged SPOC arrest.
Respiratory tract infection: an unfamiliar risk factor in high-altitude pulmonary edema
Authors: Choudhary Raushni , Kumari Swati , Ali Manzoor , Thinlas Tashi , Rabyang Stanzen , Mishra Aastha 
Published: December 2022

Abstract
The dramatic changes in physiology at high altitude (HA) as a result of the characteristic hypobaric hypoxia condition can modify innate and adaptive defense mechanisms of the body. As a consequence, few sojourners visiting HA with mild or asymptomatic infection may have an enhanced susceptibility to high-altitude pulmonary edema (HAPE), an acute but severe altitude sickness. It develops upon rapid ascent to altitudes above 2500 m, in otherwise healthy individuals. Though HAPE has been studied extensively, an elaborate exploration of the HA disease burden and the potential risk factors associated with its manifestation are poorly described. The present review discusses respiratory tract infection (RTI) as an unfamiliar but important risk factor in enhancing HAPE susceptibility in sojourners for two primary reasons. First, the symptoms of RTI s resemble those of HAPE. Secondly, the imbalanced pathways contributing to vascular dysfunction in HAPE also participate in the pathogenesis of the infectious processes. These pathways have a crucial role in shaping host response against viral and bacterial infections and may further worsen the clinical outcomes at HA. Respiratory tract pathogenic agents, if screened in HAPE patients, can help in ascertaining their role in disease risk and also point toward their association with the disease severity. The microbial screenings and identifications of pathogens with diseases are the foundation for describing potential molecular mechanisms underlying host response to the microbial challenge. The prior knowledge of such infections may predict the manifestation of disease etiology and provide better therapeutic options.
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants
Authors: Ge Fang , Li Chen , Iqbal Shahid , Muhammad Arif , Li Fuyi , Thafar Maha A , Yan Zihao , Worachartcheewan Apilak , Xu Xiaofeng , Song Jiangning , Yu Dong-Jun 
Published: December 2022

Abstract
Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a ‘one-stop’ framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants’ pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.
dSCOPE: a software to detect sequences critical for liquid–liquid phase separation
Authors: Yu Kai , Liu Zekun , Cheng Haoyang , Li Shihua , Zhang Qingfeng , Liu Jia , Ju Huai-Qiang , Zuo Zhixiang , Zhao Qi , Kang Shiyang , Liu Ze-Xian 
Published: December 2022

Abstract
Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid–liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings
Authors: Chen Yuanyuan , Zhang Hao , Sun Xiao 
Published: December 2022

Abstract
The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM’s exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.
Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers
Authors: Du Wenjie , Yang Xiaoting , Wu Di , Ma FenFen , Zhang Baicheng , Bao Chaochao , Huo Yaoyuan , Jiang Jun , Chen Xin , Wang Yang 
Published: December 2022

Abstract
The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model’s success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.
Chemotherapy induced oxidative stress in the ovary: drug-dependent mechanisms and potential interventions
Authors: Trujillo Madison , Odle Angela K , Aykin-Burns Nukhet , Allen Antiño R 
Published: December 2022

AbstractCancer incidence and relative survival are expected to increase over the next few decades. With the majority of patients receiving combinatorial chemotherapy, an increasing proportion of patients experience long-term side effects from treatment—including reproductive disorders and infertility. A limited number of studies have examined mechanisms of single-agent chemotherapy-induced gonadotoxicity, with chemotherapy-induced oxidative stress being implicated in the loss of reproductive functions. Current methods of female fertility preservation are costly, invasive, only moderately successful, and seldom presented to cancer patients. The potential of antioxidants to alleviate chemotherapy has been overlooked at a time when it is becoming increasingly important to develop strategies to protect reproductive functions during chemotherapy. This review will summarize the importance of reactive oxygen species homeostasis in reproduction, chemotherapy-induced mitochondrial dysfunction in oocytes, chemotherapy-induced oxidative stress, and several promising natural adjuvants.
Methods of assessing value for money of UK-based early childhood public health interventions: a systematic literature review
Authors: Murphy Peter , Hinde Sebastian , Fulbright Helen , Padgett Louise , Richardson Gerry 
Published: December 2022

AbstractIntroductionEconomic evaluation has an important role to play in the demonstration of value for money of early childhood public health interventions; however, concerns have been raised regarding their consistent application and relevance to commissioners. This systematic review of the literature therefore aims to collate the breadth of the existing economic evaluation evidence of these interventions and to identify the approaches adopted in the assessment of value.Source of dataRecently published literature in Medline, EMBASE, EconLit, Health Management Information Consortium, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, Health Technology Assessment, NHS EED and Web of Science.Areas of agreementThe importance of the early childhood period on future health and well-being as well as the potential to impact health inequalities making for a strong narrative case for expenditure in early childhood public health.Areas of controversyThe most appropriate approaches to evaluating value for money of such preventative interventions relevant for UK decision-makers given the evident challenges.Growing pointsThe presented review considered inconsistencies across methodological approaches used to demonstrate value for money. The results showed a mixed picture in terms of demonstrating value for money.Areas timely for developing researchFuture resource allocations decisions regarding early childhood public health interventions may benefit from consistency in the evaluative frameworks and health outcomes captured, as well as consistency in approaches to incorporating non-health costs and outcomes, incorporating equity concerns and the use of appropriate time horizons.
The Epidemiology of Long Coronavirus Disease in US Adults
Authors: Robertson McKaylee M , Qasmieh Saba A , Kulkarni Sarah G , Teasdale Chloe A , Jones Heidi E , McNairy Margaret , Borrell Luisa N , Nash Denis 
Published: December 2022

Abstract

Background
We estimated the prevalence of long COVID and impact on daily living among a representative sample of adults in the United States.

Methods
We conducted a population-representative survey, 30 June–2 July 2022, of a random sample of 3042 US adults aged 18 years or older and weighted to the 2020 US population. Using questions developed by the UK's Office of National Statistics, we estimated the prevalence of long COVID, by sociodemographics, adjusting for gender and age.

Results
An estimated 7.3% (95% confidence interval: 6.1–8.5%) of all respondents reported long COVID, corresponding to approximately 18 828 696 adults. One-quarter (25.3% [18.2–32.4%]) of respondents with long COVID reported their day-to-day activities were impacted “a lot” and 28.9% had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection more than 12 months ago. The prevalence of long COVID was higher among respondents who were female (adjusted prevalence ratio [aPR]: 1.84 [1.40–2.42]), had comorbidities (aPR: 1.55 [1.19–2.00]), or were not (vs were) boosted (aPR: 1.67 [1.19–2.34]) or not vaccinated (vs boosted) (aPR: 1.41 [1.05–1.91]).

Conclusions
We observed a high burden of long COVID, substantial variability in prevalence of SARS-CoV-2, and risk factors unique from SARS-CoV-2 risk, suggesting areas for future research. Population-based surveys are an important surveillance tool and supplement to ongoing efforts to monitor long COVID.
Malware Family Prediction with an Awareness of Label Uncertainty
Authors: Paik Joon-Young , Jin Rize 
Published: December 2022

Abstract
Malware family prediction has been mainly formulated as a multiclass classification to predict one malware family. This approach suffers from label uncertainty, which can mislead malware analysts. To render malware prediction less susceptible to uncertainty, malware family prediction, which entails predicting one or more families, is performed in this study. In this regard, an encoder–decoder malware family prediction model, EnDePMal, with label uncertainty awareness, is proposed. EnDePMal aims to predict all malware families related to samples and preserve their priorities. It comprises a residual neural network-based encoder and a long short-term memory-based decoder with an attention mechanism. The model uses a sequence of malware family names, but not a family name, as a label. Once a visualized malware image is input into EnDePMal, its encoder extracts the important features from the image. Subsequently, its decoder generates family names, where the attention mechanism allows it to focus on relevant features by attending to the encoder’s output. Experimental results show that EnDePMal can predict 77.64% of malware family sequences that preserve their priorities. Moreover, it achieves an accuracy of 93.49% and an F1-score of 0.9282 for malware families with the highest priority, rendering it comparable to the typical multiclass classification model.