bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Method to Achieve the Most Accurate Classification Success Using Covid-19 Images in Machine Learning Algorithms
Authors: AVUÇLU Emre 
Published: April 2022

Abstract
The COVID-19 pandemic started with the first case from China in December 2019 and continues to pose a major threat to the world's health. COVID-19 spread around the world in a short time and negatively affected people's lives in every aspect. Due to the rapid spread of COVID-19, the healthcare sectors of many countries have been caught off guard. This situation created a very heavy workload for doctors and healthcare professionals. Due to the importance of COVID-19, many studies have been conducted in the literature. These studies have been carried out and continue to be done to help experts to diagnose COVID-19 more accurately and to use appropriate treatment methods for this diagnosis. Computer aided diagnosis systems should be developed to alleviate this workload. Machine Learning Algorithms (MLAs) are of great importance in the development of computer-aided early and accurate diagnosis systems in medicine today. In this study, a method is proposed for more accurate diagnosis of COVID-19 patients using COVID-19 image data. Feature extraction was performed using the RGB values of the images. These features were used in train and test operations for MLAs. Six different MLAs were used in experimental studies (k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM), Minimum Mean Distance Classifier (MMDC)). The following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.81159, 0.94927, 0.81884. In test operations, accuracy results were obtained as follows; 0.83673, 0.93877, 0.97959, 0.85714, 0.87142, 0.85714. After the application of the proposed method, the test success rate for MLR increased to 1. The results obtained were given in the experimental studies section in detail. The results proved to be very promising. According to the results, it was seen that the proposed method could be used effectively in future studies.
Voluntary exercise attenuates mice anxiety after traumatic brain injury via inhibiting NLRP3 inflammasome activation
Authors: Hu Xiaoxuan , Ou Yuhang , Li Jiashuo , Sun Meiqi , Ge Qian , Pan Yongqi , Cai Zhenlu , Tan Ruolan , Wang Wenyu , An Jing , Lu Haixia , Wu Gang 
Published: April 2022

Abstract
Background: Psychological disorders, particularly the anxiety after traumatic brain injury (TBI) have received great attention both in clinical practice and experimental investigation. New therapeutic strategies are urgently required nowadays to improve mental condition of TBI patients. Neuroinflammation is the typical adaptive response that played important roles in psychiatric illness after brain tissue damage. Therefore, the immune-targeted therapy has aroused attention. Voluntary exercise has been considered as the most essential rehabilitative intervention following TBI. However, there were some experiments showed opposite effects, which varies with different time windows. Whether and when the voluntary exercise should be taken after TBI remain to be defined. The aim of the current study was to elucidate whether in-time voluntary exercise could improve the neurological recovery and reduce the anxiety of TBI mice by inhibiting the neuroinflammation via attenuating the activation of NLRP3 inflammasome.Methods: Kunming mice (8 weeks) were subjected to TBI and randomly divided into 4 groups One week voluntary wheel running (VWR) was applied to TBI mice soon after trauma - at 2nd day after injury. The neurofunction of TBI mice was detected by neurological severity score (NSS) and beam walking assay. Anxiety behavior was evaluated by open-field (OF) test and elevated plus maze (EPM) test. The cellular inflammatory signal, including IL-12, IFN-γ, CCL2, IL-10 and TGF-β, as well as the activation of microglia and NLRP3 inflammasome in the perilesional cortex were investigated.Results: After one week VWR, neurological function recovery was enhanced while the anxiety behavior of TBI mice was significantly eased. Accompanied with this, anti-inflammatory signaling, IL-10 in particular, greatly elevated. NLRP3 expression, caspase-1 activation and pro-inflammatory cytokines IL-1β/ IL-18 secretion were significantly inhibited. All these alterations were consistent with reduced microglia activation in the perilesional site and positively correlated with the weaken of anxiety behavior.Conclusion: In-time VWR could be a high potential therapeutic strategy for neuropsychiatric disorders following TBI by targeting neuroinflammation.
Blink Rate Variability as a Measure of Computer Vision Syndrome
Authors: Hossain Gahangir , Bello Mukhtar , Faiyazuddin Musab 
Published: April 2022

Abstract
Due to the COVID-19 pandemic and working from home with portable devices, we are spending a longtime on the device screen. Prolonged focus on devices like computer screens, tablets, e-readers, and cellphones – Video Display Terminals (VDTs) has been linked with vision problems which affect over 60 million people globally. The computer related ocular problem is collectively called Computer Vision Syndrome (CVS) [1]. CVS is multidirectional as it affects people differently and with varying associated symptoms. The goal of this research is to analyze Electroencephalograph (EEG) signals and affirm that our hypothesis that if eye Blink rate variability (BRV) is a good indicator of human attentiveness then human attention can be directly correlated to CVS. Two EEG signals were analyzed with one recorded from participant while engaged in a cognitive demanding task and compared with a control data set when participant was in a less attentive. A significant decrease in eye blink rate from 20 to 16 was observed in the signals collected from the participants engaged in cognitive task.
QTL mapping of maize kernel traits under low phosphorus stress
Authors: Jiang Tao , Zhang Chenghua , Zhang Zhi , Wen Min , Qiu Hongbo 
Published: April 2022

Abstract
Low phosphorus stress significantly impacts the development of maize kernels. In this study, the 082 maize genotype, which has high phosphorus use efficiency, and the Ye107 maize genotype, which has low phosphorus use efficiency, were utilized to construct an F2:3 population. QTL mapping was then employed to determine the genetic basis of differences in the maize kernel traits of the two parents in a low-phosphorus environment. This analysis revealed several major QTLs that control environmental impacts on kernel length, kernel width, kernel thickness, and kernel weight. These QTLs were detected in all three environments and were distributed on five genome segments of chromosomes 3, 5, 6, and 9. Most environmental insensitive QTLs had stable effects in all low-phosphorus environments, indicating that they may be broadly applicable to breeding efforts designed to generate germplasm with higher phosphorus use efficiency.
Sensitivity and Selectivity of Fluorescent Chemosensor for the Detection of Fe 3+ and its Cell Images
Authors: Marimuthu Pitchai , Ramu Andy 
Published: April 2022

Abstract
A simple probe (Z)-N'-((8-hydroxyquinolin-2-yl) methylene)nicotine hydrazide (8-HQC-NH) has been synthesized and realized as a “turn-off” fluorescent chemosensor for the detection for Fe3+ ion over the competitive metal ions in ethanol medium. The fluorescent intensity of the 8-HQC-NH probe intensely illustrated that the “turn-off” selective quenching upon the addition of Fe3+ owing to the chelation-enhanced fluorescence quenching (CHEQ) effect. The probe of 8-HQC-NH exhibited an ample linear response towards Fe3+ concentration rangingbetween 0 to 75 µM with satisfactory lower detection limits of 7x10− 8 M. Furthermore, the developed 8-HQC-NH probe offers many advantages like good biocompatibility, negligible cytotoxicity, being promising efficient fluorescent probe to recognize Fe3+ions in living cells. These features explore the way for the design of simple and efficient fluorescent method for the bio-sensing applications.
WITHDRAWN: HDAC9 promotes the susceptibility of diabetes to Contrast-induced acute kidney injury by regulating TXNIP/Trx1 pathway
Authors: Lu Heng-Cheng , Dai Wen-Ni , Liu Zhi-Wen , xiao Li , Liu Hong , sun lin , He Li-Yu 
Published: April 2022

Abstract
Background Contrast-induced acute kidney injury (CIAKI) is the third most common cause of hospital-acquired AKI. Diabetes mellitus (DM) is a major risk factor for CIAKI. Nevertheless, the mechanism of its role in diabetes susceptibility to CIAKI remains unclear. This study aimed to explore the role played by HDAC9 during the susceptibility of diabetic model to CIAKI. Methods Both in vitro and in vivo model of diabetes were induced by treating human renal tubular epithelial cells (HK-2) with high glucose (HG, 50mM) and by feeding mice with a high-fat diet (HFD) followed by intraperitoneal injection of streptozotocin (STZ), respectively. CIAKI mice models were constructed by contrast-media (iohexol), and iohexol also was treated HK-2 cells. Then, BRD-4354 (an inhibitor of HDAC9) was added into treated cells and mice. Finally, knockdown HDAC9 in HK-2 cultured with HG, and iohexol was added. The pathological changes, oxidative stress and apoptosis levels in mice kidney tissues were assessed. Meanwhile, cellular reactive oxygen species and the activity of HK-2 cells was measured. Western blot was used to determine the expression of HDAC9 and TXNIP/Trx1/P-ASK1/p38MAPK signaling pathways in cells and kidneys. Results HDAC9 was increased in both diabetic kidney tissues and HGinduced HK-2 cells. In vitro experiment indicated that HK-2 exposed to HG attenuated further damage to apoptosis and oxidative stress by iohexol via knockdown and inhibiting HDAC9. In vivo assay revealed that BRD4354 reduced diabetic mice sensitivity to CI-AKI. Mechanically, HDAC9 could activate TXNIP/Trx1/P-ASK1/p38MAPK signaling pathway involving in the susceptibility of diabetes to CIAKI. Conclusion HDAC9 promotes the sensitivity of diabetes to CIAKI; and may be involved in oxidative stress and apoptosis through regulation of the TXNIP/Trx1/P-ASK1/p38MAPK pathway.
Comparison of horizontal wind observed by wind profiler radars with ERA5 reanalysis data in Anhui, China
Authors: Deng Xueliang , He Dongyan , Zhang Guangyuan , Zhu Song , Dai Rui , Jin Xiaolong , Fu Wei , Shen Weiwei , Chen Jian , Fan Yingxiang , Wei Chunxuan , Li Xin 
Published: April 2022

Abstract
The wind profiler radar network at Anhui province in China was completely constructed for improving warning capacity of meteorological disasters in 2019. In order to verify its proper performance, vertical profiles of horizontal winds were compared with ERA5 reanalysis data during the period January to December in 2021. The wind profiler radars behave good capability in measuring the atmospheric wind with high data collection rates (> 80%). Furthermore, data availabilities of WPRs decrease with the increase of altitudes and they are mostly greater than 90% below 3km in day without precipitation, indicating that the annual mean of the effective detection height is about 2-3km with maximum value (4-6km) in summer and minimum value (around 2km) in winter. More importantly, the WPRs shows excellent agreement with the ERA5 zonal (correlation coefficients > 0.89 and standard deviations < 2.57m/s) and meridional winds (correlation coefficients > 0.76 and standard deviations < 3.26 m/s) in most stations. Also, the biases show a broadly Gaussian character in zonal winds and meridional winds and they are all close to zero. The differences of horizontal wind between wind profiler radars and ERA5 present a function of altitude. Normally, not only zonal winds but also meridional winds at low level can performance better than those at upper level. Meanwhile, due to the difference of atmospheric turbulent between day and night, statistical parameters of comparison during day time behave better than those at night. The result shows that the wind profiler radar network at Anhui is capable of providing good quality wind data in PBL for numerical model and weather processes studies in future.
Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
Authors: Segura-Bedmar Isabel , Camino-Perdones David , Guerrero-Aspizua Sara 
Published: April 2022

Abstract
Background and Objective:: Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the diagnosis even more difficult. A delayed diagnosis can negatively affect the patient’s life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments. Methods:: The paper explores several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder Representations from Transformers (BERT) to recognize rare diseases and their clinical manifestations (signs and symptoms). Results:: BioBERT, a domain-specific language representation based on BERT and trained on biomedical corpora, obtains the best results with an F1 of 85.2% for rare diseases. Since many signs are usually described by complex noun phrases that involve the use of use of overlapped, nested and discontinuous entities, the model provides lower results with an F1 of 57.2%. Conclusions:: While our results are promising, there is still much room for improvement, especially with respect to the identification of clinical manifestations (signs and symptoms).
Multi-scale Analysis of the Multi Source DEMs and Derived Topographic Parameters for Suitability of Avalanche Hazard Mapping
Authors: Dewali Sanjay Kumar , Jain Kamal , Singh Dhiraj Kumar 
Published: April 2022

Abstract
Topography is the most essential input in process of avalanche hazard modelling, simulation and mapping. Formation of the avalanches and their movement through mountainous terrain is governed by the topographic parameters and their scales. Digital Elevation Model (DEM) is the only source for deriving various topographic parameters spatially. Spatial resolution and elevation accuracy of the DEMs depend on the DEM sources. In the present work a multi-scale and multi resolution analysis is carried out using six different DEMs (SRTM -90m, ASTER- 30m, TOPO-30m, ALOS-12.5m, CartoSat 1-10m, and Airborne Survey DEM -1m) of the Manali-Dhundi area, India. The objective of present work is to understand the variation and sensitivity of various topographic parameters (point and local) at different cell sizes and neighbourhood sizes and also to identify the most suitable scales of different contributing topographic parameters for identification of the avalanche prone areas. High resolution airborne derived DEM elevation is compared with DGPS elevation value and high coefficient of correlation (R2 =0.97), and low RMSE (~0.97 m) and MAE (~0.04 m) has been observed. Further, various topographic variables (i.e. elevation, slope, aspect, curvature, terrain ruggedness) and avalanche simulation parameters (i.e. avalanche release area and debris extents in xyz direction) derived from DEMs were evaluated with the reference DEM at various spatial scales for avalanche sites of study area. Analysis results suggests that, ALOS and ASTER DEM produces fairly matching representation of actual terrain and debris extent with respect to reference DEM. However, TOPO DEM provide good accuracy for calculation of release area as compared to other DEMs. Slope and ruggedness derived from ADS DEM at 5m to 10m resolution provide fairly good representation of avalanche terrain. Extraction of topographic curvature was found most suitable at ≥ 30m resolution for avalanche terrain. Suitable scales for identification of avalanche release area and estimation of avalanche flow extents were found between 5 to 10m. In the present work validation of the estimated release area and flow extent was carried out with the limited point observations from the field. Detailed field investigation using geospatial approach of data collection will be taken in future for detailed and multiple profile-based validation of these results.
Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis
Authors: Yang Bingyi , Huang Xiaotong , Gao Huizhi , Leung Nancy , Tsang Tim , Cowling Benjamin 
Published: April 2022

Abstract
Dose fractionation of Coronavirus Disease 2019 (COVID-19) vaccine could effectively accelerate global vaccine coverage, while supporting evidence of efficacy, immunogenicity, and safety are needed, especially with emerging variants. We reviewed clinical trials reported dose-finding results to estimate the dose-response relationship of neutralizing antibodies (nAbs) of COVID-19 vaccines. We further predict the vaccine efficacy against both ancestral and variants, using previously reported correlates of protection and cross-reactivity. We also reviewed and compared T-cell responses and safety profiles between fractional and standard dose groups. We found that dose fractionation of mRNA and protein subunit vaccines could induce SARS-CoV-2 specific nAbs and T-cells that likely confer a reasonable level of protection (i.e., vaccine efficacy > 50%) against ancestral strains and variants. Safety profiles of fractional doses were non-inferior to the standard dose. Dose fractionation of mRNA and protein subunit vaccines may be safe and effective.
Serum metabolomics profiling reveals metabolic alterations prior to the diagnosis with non-small cell lung cancer among Chinese community residents: a prospective nested case-control study
Authors: Xiang Yu , Zhao Qi , Wu Yilin , Liu Xing , Zhu Junjie , Yu Yuting , Su Xuyan , Xu Kelin , Jiang Yonggen , Zhao Genming 
Published: April 2022

Abstract
Background: The present high mortality of lung cancer in China stems mainly from the lack of feasible, noninvasive early disease detection biomarkers. Serum metabolomics profiling prior to diagnosis to reveal metabolic alterations could expedite the disease detection process and suggest patients harboring disease. We identified differential expressed metabolites and their involved biological processes of non-small cell lung cancer (NSCLC) among the Chinese community residents. Methods: Using a nested case-control design, we applied ultra-high-performance liquid chromatography / mass spectrometry (LC-MS)-based serum metabolomics to reveal metabolomic alterations and indicate the NSCLC presence using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 41 patients before NSCLC diagnosis (within 3.0 y) and 38 matched cancer-free controls from the prospective Shanghai Suburban Adult Cohort recruited from April 2016 to October 2017 at Songjiang District, Shanghai, China. Results: NSCLC patients presented markedly cellular metabolism alterations in serum samples collected prior to their disease diagnoses compared with cancer-free controls. In total, we identified 18 significantly expressed metabolites whose relative abundance showed either an upward or a downward trend, with most of them where lipid and lipid-like molecules, organic acids, and nitrogen compounds. Choline metabolism in cancer exhibits a markedly metabolic disturbance in NSCLC patients, followed by sphingolipid and glycerophospholipid metabolism. Our data point to the aforementioned metabolisms and related metabolites as potential vital targets for cancer interventions and could improve the early diagnosis of NSCLC patients. Our results demonstrate the potential feasibility of LC-MS-based serum metabolomics to predict the presence of NSCLC combined with characteristics including smoking habits and the exposure history of second-hand smoking. With further validation, metabolomic techniques could be used as an effective but less-invasive tool to supplement the screening programs of NSCLC in the Chinese population. Interpretation: Chol­­­­­ine metabolism in cancer, sphingolipid, and glycerophospholipid metabolism emerge as the significant metabolic disturbance of NSCLC. Metabolites involved in these biological processes may be the distinctive features associated with NSCLC prior to diagnosis.
Effects of using first principle of instruction on content and language integrated learning class
Authors: Hao Hao , Susono Hitoshi , Geng Xuewang , Chen Li , Yamada Masanori 
Published: April 2022

Abstract
The aim of this study was to examine the effects of content and language integrated learning (CLIL) designed according to the First Principle of Instruction (FPI). Fifteen hours Japanese CLIL course was implemented. A total of 16 university students attended the course and multiple data sources, including learning tests, questionnaire feedback, and dialogue of the group discussion, were collected and examined. As a result, students' learning outcomes, including basic Japanese proficiency, intercultural communication content, and writing skills, were statistically significantly improved. Students have a high level of awareness of the elements of FPI designed in the course. In addition, all the FPI elements had a positive impact on basic Japanese proficiency except for the application element: the problem-centered, application, and integration elements positively impacted intercultural communication content and writing skills. The results showed that students had individual differences in using the worksheet to summarize their writing ideas. Students spent most of the time in the group discussions in their native language. Even when Japanese was used, Japanese words were used rather than whole sentences in most cases. The results of the quantitative and qualitative analyses showed that the use of problem-centered theory FPI had a positive impact on the design of the CLIL. However, attention needs to be paid to students' individual differences and the guidance of students in applying basic language knowledge in problem-centered learning activities. Finally, it puts forward some points that should be considered when designing CLIL in the future.
In Silico Analysis of Aberrant Gene Expression Profiles in Haematologic Malignancies and their Impact on Mismatch Repair Functions
Authors: Bhattacharya Priyanjali , Patel Trupti 
Published: April 2022

Abstract
Background - The tissue microenvironment in cancers involves complex network of biomolecular interactions. Differentially expressed genes (DEGs) in diseased conditions have crucial role in cancer diagnosis. Here, we mined expression profiles (Affymetrix microarray datasets) of six different haematologic malignancies from Gene Expression Omnibus (GEO). We filtered the common DEGs across all the six profiles and developed an interactome between these abnormally expressed genes and nine mismatch repair (MMR) genes using STRING. Results - The functional relevance was inferred for 12 DEGs which were prominently altered, and their impact direct/ indirect impact on MMR functions was prognosticated. Overall results demonstrated that these genes have a significant biological role and their functional disruption resulted in cellular incompetency and disturbances in DNA repair, which may contribute to oncologic transformation. The overexpressed gene-products - flt3, myc, rad52, and hdac9 were computationally docked to All-trans retinoic acid (ATRA), an anti-cancer drug and natural ATRA like Compound 3 from black turmeric to test their efficacy on protein modulation in cancer. Conclusion - A predictive pathway was drawn elucidating the outcomes of disturbed DEG-MMR dynamics in myeloid and lymphoid malignancies. The molecular docking revealed potency of black turmeric compound in modulating an abnormally expressed gene.