bluuebunny/crossref_metadata_2025_split huggingface dataset:
Liquid Biopsy in Cholestatic Liver Disease Using Hepatocyte-Specific Methylation Markers
Authors: Punia Sohan , Juran Brian D. , Ali Ahmad H. , Schlicht Erik M. , Moore Raymond M. , Sun Zhifu , Lazaridis Konstantinos N. 
Published: February 2021

Abstract
Background: Quantification of circulating organ-specific cell-free DNA (cfDNA) provides a sensitive measure of ongoing cell death that could benefit evaluation of the cholestatic liver diseases primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC), which lack reliable non-invasive biomarkers. Our goal in this pilot study was to determine whether liver-specific cfDNA levels are increased in PBC and PSC patients relative to controls and in advanced versus early disease, to evaluate their potential as novel disease biomarkers.Methods: Peripheral blood derived bisulfite-treated DNA was PCR amplified from patients with PBC (n=48), PSC (n=48) and controls (n=96) to evaluate methylation status at 16 CpG sites reported to be specifically unmethylated in liver tissue near the genes IGF2R, ITIH4 and VTN. Amplicons were used to prepare paired end libraries which were sequenced on a MiSeq sequencer. Trimmed reads were aligned and used to determine unmethylation ratios and to calculate concentration of liver-specific cfDNA. Comparisons between groups were performed using the two-tailed Mann-Whitney Test and relationships between variables were evaluated using Pearson’s Correlation. Results: Levels of liver-specific cfDNA, as measured at the 3 genetic loci, were increased in PBC and PSC patients relative to controls and in late-stage relative to early-stage patients. As well, cfDNA levels were correlated with levels of alkaline phosphatase, a commonly used biochemical test to evaluate disease severity in liver disease, in patients, but not in controls. Conclusions: cfDNA offers promise as a non-invasive liquid-biopsy to evaluate liver-specific cell-death in patients with cholestatic liver diseases.
Predicting Stroke Risk in Patients with Atrial Fibrillation Using Machine Learning
Authors: Jung Seonwoo , Lee Eunjoo , Lee Minji , Bae Sejin , Kim Yeon-Yong , Lee Doheon , Song Min-Keun , Yoo Sunyong 
Published: February 2021

Abstract
Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first attack and re-attack of cerebrovascular diseases by determining the medication. Although several statistical methods have been developed to assess the stroke risk in AF patients, considerable improvement is needed in predictive performance. We propose a machine learning-based approach based on the massive and complex Korean National Health Insurance (KNHIS) data. We extracted 72-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with stroke occurrence. Then, we constructed the stroke risk prediction model based on a deep neural network. The extracted features were used as input, and the occurrence of stroke after the diagnosis of AF was the output used to train the model. When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that stroke risk was predicted with high accuracy, sensitivity, and specificity. As part of preventive medicine, this study could help AF patients prepare for stroke prevention based on predicted stoke associated feature and risk scores.
NAP-CNB: Bioinformatic Pipeline to Predict MHC-I-Restricted T Cell Epitopes in Mice
Authors: Sánchez-García Rubén , Macías José R , Sanz-Pamplona Rebeca , Méndez-Pérez Almudena , Alemany Ramon , Veiga Esteban , Sorzano Carlos Óscar Sánchez , Muñoz-Barrutia Arrate , Wert-Carvajal Carlos 
Published: February 2021

Abstract
Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenicity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system’s predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.
Assessing the Role of Initial Conditions in the Local Structural Identifiability of Large Dynamic Models
Authors: Joubert Dominique , Stigter J.D. , Molenaar Jaap 
Published: February 2021

Abstract
Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input-output data. The many papers that have been written on this topic collectively stress theimportance of this a priori analysis in the model development process. The story however, often ends with a structurallyunidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model’s unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model’s identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the “unidentifiablemodel” problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied toget the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.
Spatial distribution of potentially toxic element and the potential ecological risk of wolfberry-soil system in genuine producing area of Ningxia，China
Authors: Zhou Tongning , Qin Jiaqi , Wang Yan , Zhang Yahong , Ma Caihong , Yang Jianjun , Zhu Meilin 
Published: February 2021

Abstract
The potentially toxic element (PTE) content and physical and chemical properties of 37 wolfberry and root soil samples obtained from Zhongning county and Shapotou district (Ningxia Hui Autonomous Region) were analyzed, and a potential ecological risk assessment was performed. In addition, multiple linear regression and geostatistical methods were used to predict and analyze the PTE concentrations and potential ecological risks of both soil and wolfberries. The PTE content in the soil was within the standard limits, and only one wolfberry sample contained Cd exceeding the standard limit (by 2.7%). The other potentially toxic elements (PTEs) did not exceed the standard limits. In the regression fitting, the models obtained for Cu, Zn, Pb, and Ni contents in the fruits showed a good correlation (p < 0.05), the PTE content of the soil showed an overall trend of high concentrations in the central part of the studied region. The ecological risk of Hg in the soil is rated as high, whereas the risks of the remaining PTEs were mostly medium or low. Further, the predicted PTE content of the wolfberries was generally high in the western region and low in the eastern region, although there were slight variations for different PTEs in some areas.
Predictor Analysis of Middle and Low Achieving Students in Cell Biology Online Course in Health Nutrition Undergraduate Program
Authors: Susilowati Rina , Fachiroh Jajah , Purnomosari Dewajani , Paramita Dewi Kartikawati , Sumiwi Yustina Andwi Ari , Septyaningtrias Dian Eurike 
Published: February 2021

Abstract
Background

Lack of direct contact with the students in online courses can result in lower lecturer’s awareness of their engagement and progress. Examination scores generally have the highest proportion in determining students’ grades. Predicting examination scores from the earliest point of the course may be useful in designing timely and appropriate interventions.
Aim

To analyze the predictors of midterm and final examination scores in an online cell biology course of health nutrition in the undergraduate program at the Faculty of Medicine, Public Health and Nursing Universitas Gadjah Mada, Yogyakarta Indonesia.
Methods

The learning materials including texts, PowerPoints, videos and miniquizzes were uploaded in a course management system before conducting online meetings. In total, 10 different topics were delivered. Pretest and posttest, both not used for grading, were given at the first and last online meeting. Components of the final score were classroom assessments, group assignments, midterm and final examinations. Students (n = 154) were divided into three groups based on their final examination scores, i.e., low, middle and high score. Each component of the final score was reported as mean ± standard deviation and the mean difference between groups was analyzed. Linear regression analysis was performed to reveal the main predictors of the midterm and final exam scores. Two-step cluster analysis determined by the earliest-obtained scores was performed to identify low achieving students.
Results

Students with higher final examination scores had significantly higher scores for pretest, posttest, pre and post-midterm class assessments, midterm exam and group assignments (p < 0.05). Premidterm class assessment was the main predictor of the midterm exam score. Midterm exam score was the strongest predictor of final exam score and clustering using midterm-premidterm scores identified 75% of low achieving students. To assist students with low and middle achievement, several modifications were considered such as providing longer and multiple access to the learning and formative test materials, facilitating a communication platform with fellow students and sending personal encouraging messages.
Conclusion

Scores obtained in various activities during the course potentially predict student grades. The activities should be optimized to improve students’ achievement especially for the less privileged students.
Forecast and Uncertainty Analysis of Extreme Precipitation in China from Ensemble of Multiple Climate Models
Authors: Deng Peng , Zhu Jianting 
Published: February 2021

Abstract
Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.
Human Performance in Three-hands Tasks
Authors: Noccaro A. , Eden J. , Pino G. , Formica D. , Burdet E. 
Published: February 2021

Abstract
The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of morethan two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination witha partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: i) how does simultaneous control of three hands compare to a cooperating dyad; ii) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of tasks core, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.
Promoter Methylation Changes in the Placental&nbsp;DIO3 and CRH Genes Are Involved in the Second Trimester Maternal Depression Induced Preterm Birth and Small for Gestational Age Birth
Authors: Yang Jianhui , Zhang Yumin , Deng Jiahui , Lin Xuemei , Xie Lili , Deng Xiaochun , Liu Honglin , Chen Peishan , Huang Yuejun 
Published: February 2021

Abstract
Background

Recent studies suggest that the incidence of preterm birth and SGA birth related to maternal depression, but the mechanism is unclear. The aim of this study was to explore the placental epigenetic changes involved in maternal depression induced preterm birth and small for gestational age birth.
Methods

Three hundred forty-five pregnant women were enrolled in this cohort study. Maternal depression in the second and third trimesters was assessed using a self-rating depression scale (SDS). We selected placental samples from pregnant women with depression and an equivalent number for samples from pregnant women without depression. Methylation of the promoter regions of the placental DIO3 and CRH genes was determined using next generation sequencing based on bisulfite sequencing PCR (NGS-BSP).
Results

There were 97 (28.1%) and 95 (27.5%) pregnant women who had depression in the second trimester and third trimester, respectively. The risk factors of preterm birth were older maternal age (RR = 1.43, 95%CI = 1.01–2.03), uterine infection (RR = 129.31, 95%CI = 2.16-7725.55), and maternal depression in the second trimester (RR = 79.97, 95%CI = 3.57-1792.56). The risk factors of SGA birth were low maternal BMI (RR = 0.71, 95%CI = 0.54ཞ0.92), hypertensive disorder complicating pregnancy (HDCP, RR = 4.7, 95%CI = 1.18ཞ18.72), and maternal depression in the second trimester (RR = 3.71, 95%CI = 1.31ཞ12.16). Pregnant women with depression had higher placental methylation of CRH and DIO3 genes, and there was a correlation between placental methylation of CRH and DIO3 genes.
Conclusion

Our study suggested that the changes in the promoter region of the placental DIO3 and CRH genes were involved in maternal depression in the second trimester induced preterm birth and small for gestational age birth.
Identification of a 9-gene signature related to stemness for prognosis in hepatocellular carcinoma basing on weighted gene coexpression network analysis
Authors: Yao Xinghong , Lu Chengquan , Pang Qidi , Zeng Ye 
Published: February 2021

Abstract
Background

Cancer stem cells (CSCs) are heterogeneous with self-renewal and differentiation ability. The mRNA expression-based stemness index (mRNAsi) described the similarity between tumor cells and CSCs, which is positively associated with the poor prognosis of cancer patients. However, the key prognostic genes related to mRNAsi in hepatocellular carcinoma (HCC) remains unclear.
Methods

In this study, RNA sequencing (RNA-seq) data of 374 liver HCC (LIHC) and 50 normal liver tissue samples were downloaded from The Cancer Genome Atlas (TCGA) database, with the corresponding clinical phenotypes. Differential expression of genes between tumor and normal tissues were analyzed. Modules correlated to mRNAsi and hub genes were identified using the weighted gene coexpression network analysis (WGCNA) package. Then, the prognostic genes associated with mRNAsi were identified. The expressions of the prognostic genes in HCC tissues were achieved by the human protein atlas (HPA) database. Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the prognostic genes were performed. Finally, the correlations between hub genes and clinical characteristics, immune subtypes, and tumor microenvironment were analyzed.
Results

A total of 1146 downregulated genes and 5593 upregulated genes were identified in LIHC, compared with normal tissues. A 9-gene signature related to mRNAsi and HCC prognosis including PSMG3, SNRPD1, DTYMK, PIGU, NME1, TXNL4A, IPO4, PES1, and REXO4 was obtained and their expressions were confirmed by the HPA database. High expression of this signature indicates poor prognosis of HCC. Among them, DTYMK and NME1 enriched in pyrimidine metabolism, SNRPD1 and TXNL4A enriched in spliceosome and PIGU enriched in glycosyl phosphatidylinositol (GPI)-anchor biosynthesis pathways. PIGU was an independent prognostic factor of HCC, which was significantly associated with clinical characteristics. High expression of the 9-gene signature was negatively correlated with the stromal cell infiltration, and positively correlated with specific immune subtypes-related to angiogenesis, M1/M2 macrophage polarization, and M2 response.
Conclusion

A 9-gene signature related to mRNAsi and poor prognosis in HCC were identified, which can be used as biomarkers for the diagnosis of HCC and functional mechanism exploration of CSCs in HCC.
The Psychometric Properties of the Bangla Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): A Large-scale Validation Study
Authors: Islam Md. Saiful , Ferdous Most. Zannatul , Sujan Md. Safaet Hossain , Tasnim Rafia , Masud Jakir Hossain Bhuiyan , Kundu Sourav , Mosa Abu Syed Md. , Choudhuri M. Shahabuddin K. , Kira Ibrahim A. , Gozal David 
Published: February 2021

Abstract
Background: The Posttraumatic Stress Disorder Checklist (PCL-5) is the most widely used screening tool in assessing posttraumatic stress disorder symptoms, based on the Diagnostic and Statistical Manual of Mental disorders (DSM-5) criteria. This study aimed to evaluate the psychometric properties of the newly translated Bangla PCL-5. Methods: A cross-sectional survey was carried out among 10,605 individuals (61.0% male; mean age: 23.6±5.5 [13-71 years]) during May and June 2020, several months after the onset of the COVID-19 outbreak in Bangladesh. The survey included Bangla PCL-5 and PHQ-9 depression scale. We used confirmatory factor analysis to test the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor Hybrid model. Results: Bangla PCL-5 displayed adequate internal consistency (Cronbach’s alpha = 0.90). The Bangla PCL-5 score was significantly correlated with scores of the PHQ-9 depression scale. Confirmatory factor analyses indicated the models had a good fit to the data, including the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor Hybrid model.Conclusions: The Bangla PCL-5 appears to be a valid and reliable psychometric screening tool that may be employed in the prospective evaluation of posttraumatic stress disorder in Bangladesh.
Nonclassical Atomic System Dynamics Time-dependently Interacts With Finite Entangled Pair Coherent Parametric Converter Cavity Fields
Authors: mohamed A.B. , Khalil E. M. , Abd-Rabbou M. Y. 
Published: February 2021

Abstract
We consider a time-dependent model that describes a qubit time-dependently interacts with a cavity containing finite entangled pair coherent parametric converter fields. The dynamics of some quantum phenomena, as: phase space information, quantum entanglement and squeezing, are explored by atomic Husimi function, atomic Wehrl entropy, variance, and entropy squeezing.

The influences of the unitary qubit-cavity interaction, the difference between the two-mode photon numbers, the initial atomic coherence, and the time-dependent qubit location are investigated. It is found that the regularity, the amplitudes and the frequency of the quantum phenomena can be controlled by the physical parameters. For the initial atomic pure state, the qubit-cavity entanglement, the qubit phase space information, and atomic squeezing can be generated strongly compared to those of the initial atomic mixed state.

The time-dependent location parameters enhance the generated quantum phenomena, and its effect can be enhanced by the parameters of the two-mode photon numbers and the initial atomic coherence.