bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020
Authors: Talmoudi Khouloud , Safer Mouna , Letaief Hejer , Hchaichi Aicha , Harizi Chahida , Dhaouadi Sonia , Derouiche Sondes , Bouaziz Ilhem , Gharbi Donia , Najar Nourhene , Osman Molka , Cherif Ines , Mlallekh Rym , Ben-Ayed Oumaima , Ayedi Yosr , Bouabid Leila , Bougatef Souha , Ben-Alaya Nissaf Bouafif ép , Chahed Mohamed Kouni 
Published: August 2020

Abstract
Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73-3.69] to 1.77 [95% CI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84-0.94]) by national lockdown measure.Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.
Lipidomic profiling reveals distinct differences in plasma lipid composition in overweight or obese adolescent students
Authors: Yang Longyan , Yin Ruili , Wang Xiaojing , Yu Ke 
Published: August 2020

Abstract
Introduction: The relationship between dyslipidemia and obesity has been widely reported, but the global lipid profiles associated with the development of obesity still need to be clarified. An investigation into the association between the lipidomic plasma profile and adolescent obesity may provide new insights into the development of obesity. Methods: Mass spectrometry coupled with liquid chromatography was applied to detect the global lipidome in the fasting plasma from 90 Chinese adolescents, including 30 obese adolescents, 30 overweight adolescents, and 30 adolescents with a normal body mass index (BMI). All participants underwent anthropometric measurements by using InBody. Clinical biochemical indicators were measured by Cobas Elecsys.Results: Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features among obese students, exhibiting characteristics close to overweight students, but differing significantly from normal students. Compared with normal and overweight students, levels of triglyceride (TG), 18-hydroxycortisol, isohumulinone A, and 11-dihydro-12-norneoquassin were up-regulated in the obese group, while phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysoPC (LPC), lysoPE (LPE), and phosphatidylinositol (PI) were significantly down-regulated in the obese group. Then, we conducted Venn diagrams and selected 8 significant metabolites from the 3 paired comparisons. Most of the selected features were significantly correlated with the anthropometric measurements.Conclusions: This study demonstrated evidence for a relationship between the eight significant metabolites with obese adolescents. These lipid features may provide a basis for evaluating risk and monitoring the development of obesity.
Seasonal Variation in Gut Microbiota Composition: Comparative Analysis of Siberian Musk Deer (Moschus moschiferus) and Forest Musk Deer (Moschus berezovskii)
Authors: Su Rina , Erdenedalai Myagmarsuren , Dalai Menggen , Batkhuu L , Chi Chimedragchaa , Hasi Surong 
Published: August 2020

Abstract
Background: Musk deer is famous for its secretion of musk, but due to various factors that the musk deer population has dropped sharply and it is difficult to expand herd sizes.In light of the rapid development of animal husbandry and scientific technology, as well as the urgent need for wildlife conservation, research on the gut microbiota of musk deer is warranted. The gut microbiota is a complex ecosystem that is regulated by many factors, such as dietary, environmental, host factors, and seasonal changes. Results: We studied the gut microbiota in Siberian and Forest musk deer using high-throughput sequencing of the V3–V4 region of the 16S rRNA gene. Fecal samples were collected during two seasons to determine associations between community diversity, composition, and seasonal factors. The results showed that the Firmicutes and Bacteroidetes were the predominant phyla in Siberian and Forest musk deer , their relative abundance was significantly different with species and seasonal factors. The Siberian musk deer population sampled in this study had a wider feeding range and no dietary limitations. Affected by the type of food sources and the feed intake , the microbial α-diversity is higher in summer than winter. Conclusions: The gut bacterial composition of these two musk deer is significantly different, and the abundance between core bacteria is affected by seasonal factors. This study is the first to analyze the composition of the gut microflora in Siberian musk deer, and these results provide a foundation for improving feeding for musk deer populations and further studies investigating the environmental impact of musk deer.
Hill-based dissimilarity indices and null models for analysis of microbial community assembly
Authors: Modin Oskar , Liébana Raquel , Saheb-Alam Soroush , Wilén Britt-Marie , Suarez Carolina , Hermansson Malte , Persson Frank 
Published: August 2020

Abstract
Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.
Composing High-level Stream Processing Pipelines
Authors: Mahapatra Tanmaya 
Published: August 2020

Abstract
The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.
Gastrointestinal perforation in anti-NXP2 antibody-associated juvenile dermatomyositis: Case reports and a review of the literature
Authors: Xu Yingjie , Ma Xiaolin , Zhou Zhixuan , Li Jianguo , Huo jun , Zhu Jia , Kang Min , Lai Jianming , Li Xiaohui 
Published: August 2020

Abstract
Background: To summarize the characteristics of gastrointestinal (GI) perforation in anti-nuclear matrix protein 2 (NXP2) antibody-associated juvenile dermatomyositis (JDM). Methods: Five patients with GI perforation from a JDM cohort of 120 cases are described. Relevant literature was reviewed.Results: Five patients, including four females and one male, were included in the study. The age of onset of these patients ranged from 3.3 to 9.5 years with the median age of 5.0 years. When these patients were complicated by GI perforation, childhood myositis assessment score (CMAS) ranged from 1 to 5 with the median score of 2. Myositis-specific antibody (MSA) spectrum analysis indicated that the five patients were anti-NXP2 antibody positive. The initial symptoms of GI perforation were progressive abdominal pain and intermittent fever. Two patients also presented with ureteral calculus with hydronephrosis and ureteral stricture. Surgery was performed in four patients. One patient failed to undergo a repair as the perforation was high in position. For the other three patients, perforation repair was successful, of which two patients failed due to recurrent perforation. At 24 months postoperative follow-up, one patient was in complete remission on prednisone (Pred) and methotrexate(MTX) treatment, and her ureteral stricture had disappeared. The other four patients died. Adding these cases with 16 other patients described in the literature, the symptom at onset was progressive abdominal pain, which often occurred within 10 months after JDM was diagnosed. Perforation most commonly occurred in the duodenum, although it also occurred at multiple sites or was recurrent. The mortality rate of GI perforation in JDM was 38% (8/21). Conclusions: All the five perforation cases in our study subjected to MSA analysis were anti-NXP2 antibody positive. The symptom at onset was abdominal pain. The most common site of perforation was the duodenum in the retroperitoneum, and the lack of acute abdominal manifestations prevented early diagnosis. GI perforation may be a fatal complication in JDM, and early diagnosis is very important. More research is needed to determine the pathogenesis and predictive factors of GI perforation in JDM.
Preoperative predictors of enucleation time during en bloc ‘no-touch’ holmium laser enucleation of the prostate
Authors: Lin Chun-Hsuan , Li Ching-Chia , Wu Wen-Jeng , Wen Sheng-Chen 
Published: August 2020

Abstract
Background: To evaluate preoperative predictors of enucleation time during en bloc ‘no-touch’ holmium laser enucleation of the prostate (HoLEP)Methods: We enrolled 135 patients with symptomatic benign prostatic hyperplasia (BPH) treated with en bloc ‘no-touch’ HoLEP from July 2017 to March 2019 by a single surgeon. Preoperative, perioperative, and postoperative clinical variables were examined. Stepwise linear regression was performed to determine clinical variables associated with enucleation times. Result: The average (range) enucleation time was 46.1 (12–220) minutes, and the overall operation time was 71 (18–250) minutes. History of antiplatelet agents, history of urinary tract infection (UTI), and increasing specimen weight were each significantly associated with increasing enucleation time. No category IV complications were recorded, and all complications were evenly distributed among the groups according to the HoLEP specimen weight.Conclusion: En bloc ‘no-touch’ HoLEP was found to be an efficient and reproducible surgical method for treating BPH. Prostatic gland size was significantly associated with increased enucleation times. Similarly, history of UTI and antiplatelet agents were correlated with increased operative time.
Study of 10kW molten carbonate fuel cell power generation system and its performance test
Authors: Lu Chengzhuang , Zhang Ruiyun , Li Hao , Cheng Jian , Xu Shisen , Yang Guanjun , Huang Hua 
Published: August 2020

Abstract
High-temperature fuel cells are a power technology that can improve the efficiency of electricity generation and achieve near-zero emissions of carbon dioxide. The present work explores the performance of the 10kW high-temperature molten carbonate fuel cell (MCFC). The key materials of the molten carbonate fuel cell single cell were characterized and analyzed by X-ray diffraction(XRD) and scanning electron microscope (SEM). The results show that the pore size of key electrode material was 6.5 μm and the matrix material is α-LiAlO 2 .The open circuit voltage of the single cell is 1.23 V in experiment. The current density is greater than 100 mA / cm^2 when the operating voltage is 0.7 V. The 10 kW fuel cell stack was constitutive of 80 pieces single fuel cells with area of 2000 cm 2 . The open circuit voltage of the stack reaches above 85 V. The fuel cell stack power and current density can reach 11.7 kW and 104.5 mA/cm^2 when the operating voltage is 56 V. The influence and long-term stable operation of the stack were also analyzed and discussed. The successful operation of 10kW high temperature fuel cell promotes the scale of domestic fuel cell and provides the research basis of fuel cell capacity enhancement and distributed generation in the next step.
Association between circulating microRNAs 486, 146b and 15b and serum betatrophin levels in obese; type 2 diabetic and non-diabetic children
Authors: Mohany khalid Mohammad , Alrugaie Osamah , Alwutayd Osama , Al-Nafeesah Abdullah , Saleem Tahia H. 
Published: August 2020

Abstract
Background: This study tested the association between serum levels of microRNA-486, -146b and -15b and betatrophin in normal and obese children with/without type 2 diabetes mellitus (T2DM).Methods: the study included 120 children; divided into three groups: G1 (50 healthy), G2 (35 obese) and G3 (35 obese with T2DM). The levels of microRNA-486, 146b and 15b and serum betatrophin were measured by their corresponding methods.Results: serum microRNA-486, -146b, -15b and betatrophin levels were significantly high in G3 followed by G2 then G1 (p=0.002, >0.001, >0.001, and >0.001, respectively). Especially in G3, these levels correlated positively with the BMI percentile (r=0.44, 0.58, 0.38, and 0.46, p=0.007, >0.001, 0.021, and 0.005, respectively), serum glucose (r=0.56, 0.49, 0.82, 0.60, and 0.42, p>0.001, 0.003, >0.001, and >0.001, respectively) and HbA1c% (r=0.56, 0.39, 0.66, and 0.42, p>0.001, 0.019, >0.001, and 0.032, respectively) while, showed negative correlations with correlated with serum insulin levels (r=-0.37, -0.42, -0.58, and -0.41, p=0.021, 0.012, >0.001 and 0.013, respectively) and with serum C-peptide levels (r=-0.76, -0.50, -0.35 and -0.42, p>0.001, 0.002, 0.036 and 0.011, respectively). Serum betatrophin levels correlated positively with microRNA-486, -146b and -15b levels in G2 (r=0.35, 0.80, and 0.67, p= 0.036, >0.001, and,>0.001, respectively), and in G3 (r=0.57, 0.36, and 0.38, p >0.001, 0.029 and, 0.023, respectively).Conclusions: Circulating microRNA-486, 146b and 15b increase significantly in obese children with T2DM and these levels correlate positively with serum betatrophin levels. Further studies are required to test the role of targeting of these microRNAs and betatrophin in the timely management of obesity and/or T2DM in children.
Epidemiology and Multidrug Resistance of Strongyle Nematodes in Ordos Merino Sheep
Authors: Eye Ledege , Wuen Jiya , He Xiuling , Buyin Buhe , Hai Ying , HASI Surong 
Published: August 2020

Abstract
Background: Regular whole-flock treatments and long-term use of similar anthelmintics have led to the development of anthelmintic resistance and widespread epidemics of nematodiasis in sheep. This study was performed to understand the prevalence of gastrointestinal nematodes in Ordos Merino sheep and evaluate the efficacy of currently used anthelmintics. Results: Between March 2017 and April 2019, a total of 4014 fresh fecal samples were collected from sheep, and fecal nematode eggs were qualitatively and quantitatively analyzed to understand the prevalence of sheep nematodiasis. The anthelmintic efficacy of currently used drugs was evaluated in naturally infected sheep. Severe infection by gastrointestinal nematodes was observed in the Ordos Merino sheep. The infection rates of the nematodes in 3 consecutive years were 84.3%, 36.9%, and 42.3%. Haemonchus contortus and Nematodirus sp. were the predominant nematode species, with infection rates of 84.3% and 65.6%, respectively, in 2017. Moreover, these species had acquired high resistance to ivermectin, doramectin, albendazole, and levamisole; the fecal egg count reduction percentages were 6.9%, 1.7%, 3.5%, and 79.0%, respectively, after a single administration. Nitroxynil and closantel showed strong anthelmintic efficacy against the predominant species Haemonchus contortus and other nematodes, but they had almost no effect on Nematodirus sp. Conclusions: The prevalence of gastrointestinal nematodes in Ordos Merino sheep and their resistance to commonly used anthelmintics were comprehensively evaluated, and the drugs with high efficacy against the predominant species were identified. The findings of this study will provide a good foundation for the appropriate use of anthelmintics.
Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study
Authors: Serviá Luis , Montserrat Neus , Badia Mariona , Llompart-Pou Juan Antonio , Barea-Mendoza Jesús , Chico-Fernández Mario , Sánchez-Casado Marcelino , Jimenez-Moragas José Manuel , Mayor-García Dolores María , Cabello Javier Trujillano 
Published: August 2020

Abstract
Background: Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.Methods: We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC.Results: In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC.Conclusion: Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.
Analysis of HSD11B2 as a prognostic marker in Melanoma via TCGA data mining
Authors: Fu Hongyi , Tang Yaqin , Ding Ying 
Published: August 2020

Abstract
Background: Hydroxysteroid 11-Beta Dehydrogenase 2 (HSD11B2) expression has been reported to be present in melanoma. We investigated the association of HSD11B2 with melanoma using publicly available data from The Cancer Genome Atlas (TCGA). Methods: The relationship between clinical pathologic features and HSD11B2 were analysed via Wilcoxon signed-rank test and logistic regression. Clinicopathologic characteristics associated with overall survival in melanoma patients were calculated using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis (GSEA) and gene co-association of HSD11B2 were performed using TCGA data set. Results: Reduced HSD11B2 expression was significantly lower in melanoma patients compared to normal patients (p value = 3.004e-122) and also associated with lower survivability. low HSD11B2 expression in melanoma was also significantly associated with cancer stages T (p value = 0.002) and N (p value < 0.001) and age (p value = 0.003). Genes TFCP2L1, PRR15L, ATP6V1B1, C9orf152, AC009948.1, AL391244.1, WDCP, HNRNPCP2 and GTF2E1 were all shown to be co-associated with changes in HSD1B2 expression. Multiple signalling pathway including cytosolic DNA sensing pathway, JAK STAT signalling pathway, NOD like receptor signalling pathway, T cell receptor signalling pathway and Toll like receptor signalling pathway were differentially enriched in low HSD11B2 expression phenotype. Conclusion: Our study revealed that HSD11B2 expression is closely associated with melanoma development and age, as well as multiple cancer related genes and pathways, thus highlighting HSD11B2 as a potential therapeutic marker of melanoma.