bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic Testing of Leukodystrophies Unraveling Extensive&nbsp;Heterogeneity in a Large Cohort: The Role of Five Common Diseases and Report of 38 Novel Variants
Authors: Mahdieh Nejat , Soveizi Mahdieh , Tavasoli Alireza , Rabbani Ali , Ashrafi Mahmoudreza , Kohlschütter Alfried , Rabbani Bahareh 
Published: October 2020

Abstract
Background: This study evaluates the genetic spectrum of leukodystrophies and leukoencephalopathies in Iran. Methods: 152 children, aged from 1day to 15 years, were genetically tested for leukodystrophies and leukoencephalopathies based on clinical and neuroradiological findings from 2016 to 2019. Patients with a suggestive specific leukodystrophy, e. g. metachromatic leukodystrophy, Canavan disease, Tay-Sachs disease were tested for mutations in single genes (108; 71%) while patients with less suggestive findings were evaluated by NGS. Results: 108 of 152(71%) had MRI patterns and clinical findings suggestive of a known leukodystrophy. In total, 114(75%) affected individuals had (likely) pathogenic variants which included 38 novel variants. 35 different types of leukodystrophies and genetic leukoencephalopathies were identified. The more common identified disorders included metachromatic leukodystrophy (19 of 152; 13%), Canavan disease (12; 8%), Tay-Sachs disease (11; 7%), megalencephalic leukodystrophy with subcortical cysts (7; 5%), X-linked adrenoleukodystrophy (8; 5%), Pelizaeus-Merzbacher-like disease type 1 (8; 5%), Sandhoff disease (6; 4%), Krabbe disease (5; 3%), and vanishing white matter disease (4; 3%). Whole exome sequencing (WES) revealed 26% leukodystrophies and genetic leukoencephalopathies. The total diagnosis rate was 75%. Conclusions: This unique study presents a national genetic data of leukodystrophies; it may provide clues to the genetic pool of neighboring countries. Patients with clinical and neuroradiological evidence of a genetic leukoencephalopathy should undergo a genetic analysis to reach a definitive diagnosis. This will allow a diagnosis at earlier stages of the disease, reduce the burden of uncertainty and costs, and will provide the basis for genetic counseling and family planning.
Distribution and Exposure Risk Assessment of Perfluorinated Alkyl Substances in Aquatic Products Along the Coastal Region of the South China Sea
Authors: Wang Xu-Feng , Mo Meng-Song , Chen Hai-Gang , Wang Qiang , Yang Jin-Lan , Zhao Dong-Hao 
Published: October 2020

Abstract
ObjectiveThe purpose of this investigation was to monitor the contamination characters and spatial and temporal distributions of 23 Perfluorinated alkyl substances (PFASs) in seafood along South China Coastal Regions. The exposure risks of PFOA and PFOS to human health in marine aquatic products were also evaluated.MethodsA total of 844 samples including 359 fishes, 391 shellfishes and 94 crustaceans were collected from Guangdong, Guangxi and Hainan provinces. The samples were extracted and purified using a modified QuEChERS procedure and analyzed by UPLC-MS / MS. The statistical analysis was performed in view of the detection of different PFAS components in variety of fishes, shellfishes and crustaceans, respectively.ResultsSixteen PFASs were detected in the survived samples and the total detection rates reached to 99.21 %. The highest ∑PFASs in each sample was 28.27 μg/kg, and the average and medium values of ∑PFAS were 1.83 μg/kg and 1.18 μg/kg, respectively. PFOS and PFBA have been considered as predominant PFAS components with corresponding pollution contribution rates of 29.14% and 24.71%, respectively. However, it is noted that PFNA and PFBS were primarily enriched in the oyster and mussel. During the period of this investigation (2014-2016), ∑PFAS average concentrations turned out constantly rising trend and new pollution component PFHxS detected in 2016. ∑PFAS concentrations of Guangdong, Guangxi and Hainan provinces were approximately at similar level.ConclusionThe contamination status of PFASs were getting worse in South China Coastal Regions. Safety and risk exposure assessments of PFOA and PFOS via daily diet intakes showed relatively low to local residents.
NRN1 and CAT Gene Polymorphisms, Complex Noise, and Lifestyles Interactively Affect the Risk of Noise-Induced Hearing Loss
Authors: LIU Shuang-yan , XIN Jia-Rui , LI Zheng , LEI Song , CHEN Ying-Qi , ZHAO Tian-Yu , WANG Hai-Yan , XU Liang-Wen , ZHANG Mei-Bian , YANG Lei 
Published: October 2020

Abstract
Background: Multiple genetic and environmental factors influence the severity of NIHL. However, few studies have reported interactions among such factors in modulating the risk of NIHL. This study aimed to assess for interactions among gene polymorphisms, noise metrics, and lifestyles on the risk of NIHL.Methods: A case-control study was conducted using 307 patients with NIHL and 307 matched healthy individuals from five manufacturing industries. General demographic data, lifestyle details, and noise exposure levels were recorded. The kompetitive allele-specific polymerase chain reaction (KASP) was used to analyze the genotypes of 18 single nucleotide polymorphisms (SNPs). The generalized multifactor dimensionality reduction (GMDR) method was used to examine the effects of all possible interactions. Results: The proportion of people with complex noise exposure, high CNE, high adj-CNE, smoking, propensity to watch loud videos, or sedentary lifestyle was significantly greater in the NIHL group than in the healthy group (P < 0.05). The GMDR model demonstrated a relevant interaction between NRN1 rs3805789 and CAT rs7943316. Subjects with the SNP pair of NRN1 rs3805789-CC and CAT rs7943316-AT, NRN1 rs3805789-CT and CAT rs7943316-AA, NRN1 rs3805789-CT and CAT rs7943316-TT, NRN1 rs3805789-CT/TT and CAT rs7943316-AA, or NRN1 rs3805789-CC and CAT rs7943316-AT/TT had higher risks of NIHL than those with NRN1 rs3805789-CC and CAT rs7943316-AA (P < 0.05). There was an interaction among NRN1 rs3805789, CAT rs7943316, and kurtosis. Subjects exposed to complex noise and carrying both NRN1 rs3805789-CT and CAT rs7943316-TT or NRN1 rs3805789-CT/TT and CAT rs7943316-AA had higher risks of NIHL than those exposed to steady noise and carrying both NRN1 rs3805789-CC and CAT rs7943316-AA (P < 0.05). The best six‐locus model involving NRN1 rs3805789, CAT rs7943316, smoking, video volume, physical exercise, and working pressure for the risk of NIHL was found to be the interaction (P = 0.0010). An interaction was also found among smoking, video volume, physical exercise, working pressure, and kurtosis (P = 0.0107).Conclusions: Complex noise, high CNE, high adj-CNE, smoking, high video volume, and sedentary lifestyle are environmental risk factors for NIHL. Concurrence of NRN1 rs3805789 and CAT rs7943316 constitutes a genetic risk factor for NIHL. Complex noise exposure significantly increases the risk of NIHL in subjects with a high genetic risk score. Interactions between genes and lifestyle as well as noise metrics and lifestyle affect the risk of NIHL. These results provide a theoretical basis for screening genetic and environmental risk factors to prevent NIHL.
Serum HBV RNA Correlated with Intrahepatic cccDNA More Strongly than other HBV Markers During Peg-Interferon Treatment
Authors: Wang Xiaomei , Chi Xiumei , Wu Ruihong , Xu Hongqin , Gao Xiuzhu , Yu Lei , Liu Longgen , Zhang Mingxiang , Tan Youwen , Niu Junqi , Jin Qinglong 
Published: October 2020

Abstract
Background: Serum hepatitis B virus RNA (HBV RNA) has been reported to be a surrogate marker of intrahepatic cccDNA during nucleos(t)ide analogs therapy. However, whether HBV RNA is superior to other HBV markers reflecting cccDNA profile in HBeAg-positive patients during peg-interferon (peg-IFN) treatment was still unclear.Methods: Serum HBV RNA, HBcrAg, HBV DNA, and HBsAg were longitudinally assessed among 30 HBeAg-positive patients during 48-week peg-IFN treatment. The intrahepatic cccDNA was detected at baseline and week 48, respectively. The individual correlations between HBV RNA, HBcrAg, HBV DNA, HBsAg, and cccDNA were then statistically analyzed.Results: HBV RNA levels in patients with HBeAg seroconversion decreased more rapidly compared with those without HBeAg seroconversion. Among all patients, cccDNA correlated better with HBV RNA than with HBcrAg, HBV DNA, and HBsAg at baseline. After 48 weeks of treatment, cccDNA still correlated more strongly with HBV RNA than other HBV markers. For subsequent analysis, cccDNA positively correlated with HBV RNA and HBcrAg whereas did not correlate with HBV DNA and HBsAg in patients with HBeAg seroconversion. However, cccDNA highly correlated with HBV RNA and HBV DNA, moderately correlated with HBcrAg, while no correlation was observed between cccDNA and HBsAg in patients without HBeAg seroconversion.Conclusion: Serum HBV RNA correlated more strongly than HBcrAg, HBV DNA, and HBsAg with intrahepatic cccDNA levels before and after 48-week peg-IFN treatment. The level of serum HBV RNA may be a superior surrogate marker reflecting the intrahepatic cccDNA profile in HBeAg-positive patients during peg-IFN treatment.Trial registration: ClinicalTrials, NCT03546530. Registered 1 January 2015. https://clinicaltrials.gov/ct2/results?cond=&term=NCT03546530&cntry=&state=&city=&dist=
Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic
Authors: Bakin Evgeny , Stanevich Oksana , Lioznov Dmitry , Kulikov Alexander 
Published: October 2020

Abstract
Purpose. COVID-19 pandemic has shown an urgent need for decision support systems to help doctors in a period of stress and uncertainty. However, significant differences in hospital conditions, as well as skepticism of doctors about machine learning algorithms, limit their introduction into clinical practice. Our goal was to test and apply a principle of "patient-like-mine" decision support in rapidly changing conditions of a pandemic.Methods. In the developed system we implemented a fuzzy search that allows a doctor to compare their medical case with similar cases recorded since the beginning of the pandemic in their medical center. Various distance metrics were tried for obtaining clinically relevant search results. With the use of R programming language, we designed the first version of the system in approximately a week. A set of features for the comparison of the cases was selected with the use of random forest algorithm implemented in Caret. Shiny package was chosen for the design of GUI. Results. The deployed tool allowed doctors to quickly estimate the current conditions of their patients by means of studying the most similar previous cases stored in the local health information system. Extensive testing of the system during the first wave of COVID-19 has shown that this approach helps not only to draw a conclusion about the optimal treatment tactics and to train medical staff in real-time but also to optimize patients’ individual testing plans.Conclusions. This project points to the possibility of rapid prototyping and effective usage of "patient-like-mine" search systems at the time of a pandemic caused by a poorly known pathogen.
Early glycolytic reprogramming control microglial inflammatory activation
Authors: Cheng Junjie , Zhang Rong , Ke Youliang , Xu Zhirou , Sun Renjuan , Yang Huicui , Zhen Xuechu , Zheng Long-Tai 
Published: October 2020

Abstract
Background Microglial activation-mediated neuroinflammation plays an important role in the progression of neurodegenerative diseases. Inflammatory activation of microglial cells is often accompanied by a metabolic switch from oxidative phosphorylation to aerobic glycolysis. However, the roles and molecular mechanisms of glycolysis in microglial activation and neuroinflammation are not yet fully understood.Methods The anti-inflammatory effects and its underlying mechanisms of glycolytic inhibition in vitro were examined in LPS activated BV-2 microglia or primary microglia cells by ELISA, RT-PCR, Western blot, immunoprecipitation, FACS and NF-κB luciferase reporter assays. The anti-inflammatory and neuroprotective effects of glycolytic inhibitor, 2-DG in vivo were measured in the MPTP-or LPS-induced PD models by immunofluorescence staining, behavior tests and Western blot analysis. Results We found that LPS rapidly increased glycolysis in microglial cells, and glycolysis inhibitors (2-DG and 3-BPA), siRNA Glut-1 and siRNA HK Ⅱ abolished LPS-induced microglial cell activation. Mechanistic studies demonstrated that glycolysis inhibitors significantly inhibited LPS-induced phosphorylation of mTOR, IKKβ and IκB, degradation of IκB, nuclear translocation of p65 and NF-κB transcriptional activity. In addition, 2-DG significantly inhibited LPS-induced acetylation of p65/RelA on lysine 310, which is mediated by NAD+-dependent SIRT1 and is critical for NF-kB activation. A coculture study revealed that 2-DG reduced the cytotoxicity of activated microglia toward MES23.5 dopaminergic neuron cells with no direct protective effect. An in vivo study demonstrated that 2-DG significantly ameliorated neuroinflammation and subsequent DA neuronal cell injuries in an LPS-induced Parkinson’s disease (PD) model. Furthermore, 2-DG also reduced TH-positive cell loss and microglial activation in the MPTP-induced PD model. Conclusions Collectively, our results suggest that glycolysis is actively involved in microglial activation and, hence, that inhibition of glycolysis can ameliorate microglial activation-related neuroinflammatory diseases.
Modeling and forecasting hospitalized mortality rate of COVID-19 according to delay effect of daily hospitalization
Authors: Rastaghi Sedighe , Shark Noushin Akbari , Saki Azadeh 
Published: October 2020

Abstract
Introduction: The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate. Methods: In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the "Iran-COVID-19-Data[1]" database. Five distribution delay models were compared for estimating and forecasting. Results: based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the overall hospitalized mortality rate was 12%.Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.
Experimental study on the static properties of single dimensional bidirectional bending joint
Authors: Liu Hongbo , Li Junye , Geng Dexu 
Published: October 2020

In order to increase the lateral stiffness of the joint, a new kind of single-dimensional bidirectional bending joint was developed. Based on the equilibrium analysis of force and moment of the joint and considering the influence of the joint deformation law, the static bending theoretical model of the joint was established. The experimental system was set up to carry out bending experimental. The experimental results show that the experimental data of the joint are consistent well with the theoretical data. The bending deformation has a good consistency of swinging angle from left to right. At 0.35 mPa, the bending angle of the angle reaches 57.5°, which can be used as elbow or knee joints in bionic machinery.
Identification of loose particles using deep learning convolutional network
Authors: Li Yanggang , Meng Cai , Zhang Guoqiang , Zhao Changxing 
Published: October 2020

Loose particles inside components can be threats to their reliabilities. Automatic identification of loose particles’ material has great significance for finding the source of them. Machine learning methods based on hand-craft features have been widely applied on this problem. As deep learning has made success on various domains, based on PIND (particle impact noise detection) test, a method using spectrograms and CNN (convolutional neural network) is proposed in this paper. First, signals of loose particles including different material are collected by experiments. Then signals are converted to spectrograms. Finally, spectrograms are input to CNN for training and classification. Experiments show that the method can achieve 96 % accuracy on identifying five types of loose particles and has values of practically use.
Eddy current damper capable of collecting electric energy
Authors: Yu Wenbin , Yang Guolai 
Published: October 2020

This paper presents an eddy current damper model that can store electrical energy. The damper is mainly used under strong impact load. On the basis of generating sufficient electromagnetic damping force to resist the impact load, the generated eddy current is guided out of the device through the winding and connected to the energy storage components of the external circuit. In this way, it is realized that the eddy current damper has the function of resisting the impact load. And at the same time, the eddy current damper can convert a part of the impact energy into electric energy and store it in the energy storage element. After research, the theoretical scheme is completely feasible, and the single impact energy conversion efficiency can reach 2.33 %.
Numerical simulation analysis of PELE penetrating target plates with different thicknesses
Authors: Wu Gang , Wang Xin , Ji Chong , Zhao Changxiao , Zhu Haojie 
Published: October 2020

PELE (Penetrator with Enhanced Later Effect) is a new type of ammunition, which does not need to be filled with explosives and fuses, but has the function of armor piercing projectile and grenade at the same time. The numerical simulation of a 60 mm diameter PELE penetrating target was investigated. The results show that in the process of the target plate becoming thicker, the transverse effect first increases and then weakens, and the optimal target plate thickness range is 4-6 cm; the properties of the core material have an important influence on the transverse effect of PELE; with the increase of the core radius, the radial velocity of the fragments after PELE penetrating the target first increases, then decreases and then increases, and the optimal core radius is 2-2.6 cm.
Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems' operations considering their specific purposes
Authors: Bozorg-Haddad Omid , Azad Marzie , Fallah-Mehdipour Elahe , Delpasand Mohammad , Chu Xuefeng 
Published: October 2020

Abstract
The optimal operation of reservoirs is known as a complex issue in water resources management, which requires consideration of numerous variables (such as downstream water demand and power generation). For this optimization, researchers have used evolutionary and meta-heuristic algorithms, which are generally inspired by nature. These algorithms have been developed to achieve optimal/near-optimal solutions by a smaller number of function evaluations with less calculation time. In this research, the flower pollination algorithm (FPA) was used to optimize: (1) Aidoghmoush single-reservoir system operation for agricultural water supply, (2) Bazoft single-reservoir system operation for hydropower generation, (3) multi-reservoir system operation of Karun 5, Karun 4, and Bazoft, and (4) Bazoft single-reservoir system for rule curve extraction. To demonstrate the effectiveness of the FPA, it was first applied to solve the mathematical test functions, and then used to determine optimal operations of the reservoir systems with the purposes of downstream water supply and hydropower generation. In addition, the FPA was compared with the particle swarm optimization (PSO) algorithm and the non-linear programming (NLP) method. The results for the Aidoghmoush single-reservoir system showed that the best FPA solution was similar to the NLP solution, while the best PSO solution was about 0.2% different from the NLP solution. The best values of the objective function of the PSO were approximately 3.5 times, 28%, and 43% worse than those of the FPA for the Bazoft single-reservoir system for hydropower generation, the multi-reservoir system, and the Bazoft single-reservoir system for rule curve extraction, respectively. The FPA outperformed the PSO in finding the optimal solutions. Overall, FPA is one of the new evolutionary algorithms, which is capable of determining better (closer to the ideal solution) objective functions, decreasing the calculation time, simplifying the problem, and providing better solutions for decision makers.
A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis
Authors: Heo JoonNyung , Sung MinDong , Yoon Sangchul , Jang Jinkyu , Lee Wonwoo , Han Deokjae , Kim Hyung-Jun , Kim Han-Kyeol , Han Ji Hyuk , Seog Woong , Ha Beomman , Park Yu Rang 
Published: October 2020

Background
Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened.

Objective
This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak.

Methods
The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group.

Results
The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application.

Conclusions
We developed an expert-opinion–based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.