bluuebunny/crossref_metadata_2025_split huggingface dataset:
MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.
Authors: Banerjee Polash 
Published: August 2021

Abstract
Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.
Development and Preliminary Evaluation of an Education Program for Primary Care Teams on Discussing Firearms Storage Safety with Veterans
Authors: Dobscha Steven K. , Clark Khaya D. , Karras Elizabeth , Simonetti Joseph A. , Newell Summer , Kenyon Emily A. , Boster Jennie , Gerrity Martha , Elliott Victoria 
Published: August 2021

Abstract
Background
Reducing access to lethal means is one of the few empirically supported approaches for lowering suicide rates, and safe firearms storage practices have been associated with reduced risk of death by suicide. Although there is substantial opportunity for primary care to assist in addressing lethal means with veterans, approaches to intervention and educating staff are not well documented. We sought to 1) describe development of an education program for primary care teams to help them discuss firearms storage safety (FSS) with veterans during primary care visits; and 2) conduct a preliminary evaluation of the pilot education program.
Methods
We used an iterative process involving veterans and primary care staff stakeholders to develop program content, format, and supplemental materials. A grounded theory approach was used to analyze data from focus groups and individual interviews. Following piloting of the program with 71 staff members in two primary care clinics, we analyzed pre- and post-training participant surveys of program satisfaction and attitudes and behaviors related to firearms safety.
Results
During the development phase, 68 veterans and 107 staff members participated in four veteran focus groups and four primary care focus groups, respectively, and/or individual interviews. The program that was developed, “’Just in Case’: Discussing means safety with veterans at elevated risk for suicide,” addresses knowledge and skills learning objectives, and includes video demonstrations and skills practice. Survey data obtained just prior to the two pilot training sessions showed low self-reported rates of discussing firearms safety with veterans who may be at elevated risk for suicide. Immediate post-training data showed generally high satisfaction with the program and significant improvements in participant self-reported ratings of the importance of, and comfort with FSS.
Conclusions
The program shows promise as a means for educating primary care staff to deliver messaging about firearms safety to veterans. Additional research is needed to refine and evaluate impacts of this or similar training programs on clinician and veteran behaviors over time.
QTL Identification, Fine Mapping, and Marker Development for Breeding Peanut (Arachis hypogaea L.) Resistant to Bacterial Wilt
Authors: Qi Feiyan , Sun Ziqi , Liu Hua , Zheng Zheng , Qin Li , Shi Lei , Chen Qingzheng , Liu Haidong , Lin Xiufang , Miao Lijuan , Tian Mengdi , Wang Xiao , Huang Bingyan , Dong Wenzhao , Zhang Xinyou 
Published: August 2021

Abstract
Bacterial wilt, caused by Ralstonia solanacearum, is a major disease detrimental to peanut production in China. Breeding disease-resistant peanut varieties is the most economical and effective way to prevent the disease and yield loss. Fine mapping the QTLs for bacterial wilt resistance is critical for the marker-assisted breeding of disease-resistant varieties. A recombinant inbred population comprising 512 lines was used to construct a high-density genetic linkage map and to identify QTLs for bacterial wilt resistance following restriction-site-associated DNA sequencing. The genetic map, which included 5,120 SNP markers, covered a length of 3,184 cM with an average marker distance of 0.6 cM. Four QTLs for bacterial wilt resistance were mapped on four chromosomes. One major QTL, qBWA12, was stably detected in all four development stages investigated over the three trail years. Additionally, qBWA12 spanned a 2.6 cM region, corresponding to approximately 0.4 Mb and was fine mapped to a 216.7 kb region by applying KASP markers that were polymorphic between the two parents based on whole-genome resequencing data. In a large collection of breeding and germplasm lines, it was proved that KASP marker A12.4097252 can be applied for the marker-assisted breeding to develop peanut varieties resistant to bacterial wilt. Of the 19 candidate genes in the region covered by qBWA12, nine NBS-LRR genes should be further investigated regarding their potential contribution to the resistance of peanut against bacterial wilt.
Cordycepin Prevents and Ameliorates Experimental Autoimmune Encephalomyelitis by Inhibiting Leukocyte Infiltration and Reducing Neuroinflammation
Authors: Song Ying-Chyi , Liu Chuan-Teng , Lee Hui-Ju , Yen Hung-Rong 
Published: August 2021

Abstract
BackgroundMultiple sclerosis (MS) is a neuroinflammatory autoimmune disease characterized by multifocal perivascular infiltration of immune cells in the central nervous system (CNS). Current treatment for MS is unsatisfactory, and we aimed to search for immunomodulatory agents from bioactive constituents of natural origin. Cordycepin (3'-deoxyadenosine), an adenosine analogue initially extracted from the fungus Cordyceps militarisa, is one of the candidates that has multiple actions. However, its effect on MS is unknown.MethodsWe first investigated the in vitro effects of cordycepin on mouse and human dendritic cells, microglial cells, macrophages, and T cell subsets. Cordycepin was administered to the experimental autoimmune encephalomyelitis (EAE) model at various time points to evaluate the effects in preventive and therapeutic model. Chemokine array and next-generation sequencing (NGS) were applied to identify the key molecules and genes involved in the mechanism. ResultsCordycepin attenuated the activation of LPS-induced mouse bone marrow-derived dendritic cells (BMDCs) and human monocyte-derived dendritic cells (MoDCs) through the inhibition of the AKT, ERK, NF-κB, and ROS pathways and impaired the migration of BMDCs through the downregulation of adhesion molecules and chemokine receptors in vitro. In EAE model, preventive treatment with cordycepin decreased the expression of trafficking factors in the CNS, inhibited the secretion of inflammatory cytokines (IFN-g , IL-6, TNF-a, and IL-17) induced by specific peptides, and attenuated clinical symptoms. A chemokine array indicated that cordycepin treatment reversed the high levels of CCL6, PARRES2, IL-16, CXCL10, and CCL12 in the brain and spinal cord of EAE mice, consistent with the RNA-seq data. Moreover, cordycepin suppressed the release of neuroinflammatory cytokines by activated microglial cells, macrophages, Th17 cells, Tc1 cells, and Th1 cells in vitro. Furthermore, cordycepin treatment exerted therapeutic effects on attenuating the clinical disease severity in the early disease onset stage and late disease progression stage. ConclusionsOur study suggests that cordycepin treatment may not only prevent the occurrence of MS by inhibiting DC activation and migration but also potentially ameliorates the progression of MS by reducing neuroinflammation, which may provide insights into the development of new approaches for the treatment of MS.
Genome-Wide Identification, Evolutionary And Expression Analyses of LEA Gene Family In Peanut (Arachis Hypogaea L.)
Authors: Huang RuoLan , Xiao Dong , Wang Xin , Shen Yi , Zhan Jie , Wang AiQing , He LongFei 
Published: August 2021

Abstract
Background: Late embryogenesis abundant (LEA) proteins are a group of highly hydrophilic glycine-rich proteins, which accumulate in the late stage of seed maturation and are associated with many abiotic stresses. However, few peanut LEA genes had been reported, and the research on the number, location, structure, molecular phylogeny and expression of AhLEAs was very limited. Results: In this study, 126 LEA genes were identified in the peanut genome through genome-wide analysis and were further divided into eight groups. Sequence analysis showed that most of the AhLEAs (85.7 %) had no or only one intron. LEA genes were randomly distributed on 20 chromosomes. Compared with tandem duplication, segmental duplication played a more critical role in AhLEAs amplication, and 93 segmental duplication AhLEAs and 5 pairs of tandem duplication genes were identified. Synteny analysis showed that some AhLEAs genes come from a common ancestor, and genome rearrangement and translocation occurred among these genomes. Almost all promoters of LEAs contain ABRE, MYB recognition sites, MYC recognition sites, and ERE cis-acting elements, suggesting that the LEA genes were involved in stress response. Gene expression analyses revealed that most of the LEAs were expressed in the late stages of peanut embryonic development. LEA3 (AH16G06810.1, AH06G03960.1), and Dehydrin (AH07G18700.1, AH17G19710.1) were highly expressed in roots, stems, leaves and flowers. Moreover, 100 AhLEAs were involved in response to drought, low-temperature, or Al stresses. Some LEAs that were regulated by different abiotic stresses were also regulated by hormones including ABA, brassinolide, ethylene and salicylic acid. Interestingly, AhLEAs that were up-regulated by ethylene and salicylic acid showed obvious subfamily preferences.Conclusions: AhLEAs are involved in abiotic stress response, and segmental duplication plays an important role in the evolution and amplification of AhLEAs. The genome-wide identification, classification, evolutionary and expression analyses of the AhLEA gene family provide a foundation for further exploring the LEA genes’ function in response to abiotic stress in peanuts.
Observation of light induced dynamical band structure via multi-band high harmonic spectroscopy
Authors: Uzan-Narovlansky Ayelet , Jimenez-Galan Alvaro , Orenstein Gal , Silva Rui , Arusi-Parpar Talya , Shames Sergei , Bruner Barry , Yan Binghai , Smirnova Olga , Ivanov Misha (Mikhail) , Dudovich Nirit 
Published: August 2021

Abstract
Intense light-matter interactions have revolutionized our ability to probe and manipulate quantum systems at sub-femtosecond time scales, opening routes to all-optical control of electronic currents in solids at petahertz rates, the time-scale of a single optical cycle. Such control typically requires electric field amplitudes ~V/A, when the voltage drop across a lattice site becomes comparable to the characteristic band gap energies. In this regime, intense light-matter interaction induces significant modifications of electronic and optical properties and is certain to dramatically modify the band structure of the light-dressed crystal. Yet, identifying and characterizing such modifications remains an outstanding problem. As the oscillating electric field changes from V/A to zero within a quarter-cycle of the driving field, does the band-structure follow, and how can it be defined? Here we address this fundamental question, proposing all-optical spectroscopy of strongly driven crystals, and probe laser-induced closing of the band-gap between adjacent conduction bands. Our work reveals the link between extreme nonlinear light matter interactions in strongly driven crystals to the sub-cycle modifications in their effective band structure.
First-Line Pembrolizumab Plus Chemotherapy for Extensive-Stage Small-Cell Lung Cancer: A United States-Based Cost-Effectiveness Analysis
Authors: Zhu Youwen , Hu Huabin , Ding Dong , Li Shuosha , Liao Mengting , Shi Yin , Huang Jin 
Published: August 2021

Abstract
Background: The clinical trial of Keynote-604 showed that pembrolizumab plus chemotherapy could generate clinical benefits for Extensive-Stage Small-Cell Lung Cancer (ES-SCLC). We aim to assess the efficacy and cost of pembrolizumab combined with chemotherapy in the first-line treatment setting of ES-SCLC from the United States (US) payers’ perspective.Methods: A synthetical Markov model was used to evaluate cost and effectiveness of pembrolizumab plus platinum-etoposide (EP) versus EP in first-line therapy for ES-SCLC from the data of Keynote-604. Lifetime costs life-years (LYs), quality adjusted LYs (QALYs), and incremental cost-effectiveness ratios (ICERs) were estimated. One-way and probabilistic sensitivity analyses were performed. Furthermore, we performed subgroup analysis.Results: Pembrolizumab plus EP resulted in additional 0.18 QALYs (0.32 LYs) and corresponding incremental costs $113,625, resulting an ICER of $647,509 per QALY versus EP. the price of pembrolizumab had a significant impact on ICER. Probabilistic sensitivity analysis indicated that pembrolizumab combined chemotherapy may become a cost-effective option with a probability of 0%. Besides, subgroup analysis suggested that all subgroups were not cost-effective.Conclusion: From the perspective of the US payer, pembrolizumab plus EP is not a cost-effective option for first-line treatment patients with ES-SCLC at a WTP threshold of $150,000 per QALY.
HAO1-Mediated Oxalate Metabolism Promotes Lung Pre-Metastatic Niche Formation by Inducing Neutrophil Extracellular Traps
Authors: Zeng Zhicheng , Xu Shaowan , Wang Feifei , Peng Xin , Zhang Wanning , Zhan Yizhi , Ding Yanqing , Liu Ziguang , Liang Li 
Published: August 2021

Abstract
Background

Metabolic reprogramming has been shown to be involved in cancer-induced PMN formation, but the underlying mechanisms have been insufficiently explored.
Methods

HAO1 expression in lung tissues and alveolar epithelial cells were deteted by qPCR and Western blotting. The effect of HAO1 on the lung metastasis of cancer was investigated by orthotropic metastasis assay. Lungs and cells oxalate levels were determined using an oxalate assay kit. The effect of oxalate on neutrophil extracellular trap formation was investigated by immunofluorescence. The effect of oxalate on proliferation of breast cancer cells was revealed by immunofluorescence by colony formation assay.
Results

HAO1 was up-regulated in the alveolar epithelial cells of mice bearing metastatic breast cancer cells at the pre-metastatic stage. Upregulation of HAO1 led to oxalate accumulation in lung tissues and alveolar epithelial cells. Pharmacologic inhibition of HAO1 could effectively suppress the lung oxalate accumulation induced by primary cancer. Lung oxalate accumulation induced NET formation by activating NADPH oxidase. Lung oxalate accumulation promoted the proliferation of metastatic cancer cells by activating the MAPK signaling pathway. Breast cancer cells induced HAO1 expression and oxalate accumulation in alveolar epithelial cells by activating TLR3-IRF3 signaling.
Conclusion

These findings underscore the role of HAO1-mediated oxalate metabolism in cancer-induced lung PMN formation and metastasis. HAO1 could be an appealing therapeutic target for preventing lung metastasis of cancer.
An Intracellular Human Single Chain Antibody to Matrix Protein (M1) of H5N1 Virus
Authors: Sun He , Wu Guangmou , Zhang Jiyuan , Wang Yu , Qiu Yue , Man Hongyang , Zhang Guoli , Li Zehong , Yue Yuhuan , Tian Yuan 
Published: August 2021

Abstract
Background: Influenza virus matrix protein M1 is encoded by viral RNA fragment 7 and is the most abundant protein in virus particles. M1 is expressed in the late stages of viral replication and exerts functionality by inhibiting viral transcription. The M1 protein sequence is an attractive target for antibody drugs.Methods: The M1 protein sequence was amplified by RT-PCR using cDNA from the H5N1 virus as a template; the M1 protein was then expressed and purified. A human strain, high affinity, and single chain antibody (HuScFv) against M1 protein was obtained by phage antibody library screening using M1 as an antigen. A recombinant TAT-HuScFv protein was expressed by fusion with the TAT protein transduction domain (PTD) gene of HIV to prepare a human intracellular antibody against avian influenza virus. The differences between HuScFv and TAT-HUScFv were verified by various experiments and the amino acid binding site of the M1 protein was determined.Results: The M1 protein of H5N1, HuScFv, and TAT-HuScFv, were successfully purified and expressed by and in E. coli. Further analysis demonstrated that TAT-HuScFv inhibited the hemagglutination activity of the 300TCID50 H1N1 virus, thus providing preliminary validation of the universality of the antibody. After two rounds of M1 protein decomposition, the TAT-HuScFv antigen binding site was identified as Alanine (A) at position 239. Collectively, our data describe a recombinant antibody with high binding activity against the conserved sequences of avian influenza viruses. This intracellular recombinant antibody blocked the M1 protein that infected intracellular viruses, thus inhibiting the replication and reproduction of H5N1 viruses.Conclusion: Recombinant HuScFv was successfully identified using the Tomlinson (I+J) phage antibody library and successfully linked to the TAT protein transductive domain of the HIV virus. Compared with the HuScFv, the addition of the TAT peptide improved its ability to penetrate the cell membrane. A definite amino acid binding site was identified after the decomposition of M1 protein, thus providing a target and reference for the development of antibody drugs and the study of new drugs.
Can Passive Measurement of Physiological Distress Help Better Predict Suicidal Thinking?
Authors: Kleiman Evan , Bentley Kate , Maimone Joseph , Lee Sarah , Kilbury Erin , Fortgang Rebecca , Zuromski Kelly , Huffman Jeff , Nock Matthew 
Published: August 2021

Abstract
There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants (n=25, 56% female, M age= 33.48 years) completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect).These findings suggest that physiological data, under certain contexts, may be useful in better predicting -- and ultimately, preventing -- acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking
Comparison of Domain Specific Connectivity Metrics for Estimation Brain Network Indices in Boys With ADHD-C
Authors: AYDIN SERAP , CETIN Fatih Hilmi , UYTUN Merve , BABADAG Zehra , GUVEN Ahmet Sami , ISIK Yasemen 
Published: August 2021

Abstract
The study includes Graph Theoretic advanced EEG analysis in order to investigate the impact of pharmacological treatment with osmotic release oral system-methylphenidate for a month in 18 boys (aged between 7-12 years) with Attention-Deficit-Hyperactivity Disorder, combined type. In analysis, neurofunctional dependency levels across the cortex are estimated by using six different connectivity estimation approaches based on three separate domains such as time (Pearson Correlation, Spearman Correlation), frequency (Directed Transfer Function, Partial Directed Coherence) and phase (Phase Locking Value, Phase Lag Index). All methods are examined for eyes-opened resting-state EEG segments of 2 sec over a single trial of 1 min in both pre- and post-treatment recordings. Then, five quantitative brain network indices of transitivity, clustering coefficients, assortativity, global efficiency and modularity are computed from connectivity estimations. Performance of connectivity estimators are compared to each other with respect to two-class classifications (pre-treatment features vs post-treatment features) by using Support Vector Machines driven by brain network indices. The highest classification accuracy of 80.74% is obtained with Pearson Correlation. Statistical one-way Anova test, pair-wise multiple comparison test and step-wise logistic regression modelling are all used to observe the most sensitive network index estimated from Pearson Correlations. In addition to quantitative results, statistical box-plots of the estimated network indices are shown graphically. When modularity index is excluded from the features, classification accuracy is increased to 83.79%. Overall results reveal that brain segregation and resilience are increased by the treatment. In particular, the most meaningful brain network measures can be estimated from time domain statistical correlations between resting-state eyes open EEG segments in order to understand the alterations in neuronal transmission mechanism across the cortex in response to a specific treatment in pediatric psychiatry.