bluuebunny/crossref_metadata_2025_split huggingface dataset:
The weight gain influence of pregnant women under different clinical conditions on weight at the birth of newborns.
Authors: Mariot Márcia Dornelles Machado , Kretzer Daniela Cortés , Nunes Isadora Musse , Goldani Marcelo Zubaran , Bernardi Juliana Rombaldi , Silva Clécio Homrich da 
Published: September 2020

Abstract
BACKGROUND: This study evaluate the influence of weight gain of pregnant women under different clinical conditions (Tobacco, Diabetes mellitus (DM), Hypertension, Intrauterine growth restriction (IUGR) and Control) on the birth weight of newborns. METHODS: This is an observational, longitudinal study nested in a prospective controlled cohort and conducted from 2011 to 2016 in three hospitals in the city of Porto Alegre (Brazil). Sociodemographic, prenatal (maternal gestational weight gain, among others) and perinatal (birth weight) data from 372 mother-child pairs were analyzed. Pearson's correlation was used to verify the relationship between gestational weight gain and birth weight. Multivariate regression was performed to determine the association between maternal weight gain in the presence of various health conditions and birth weight. RESULTS: There were no statistical differences in gestational weight gain in different health condition groups (p=0.092). However, women in the Hypertension group had a tendency to gain more weight (p = 0.097). Mothers in the intrauterine growth restriction group gained less weight. A positive correlation was observed between gestational weight gain and birth weight in the Tobacco (p = 0.003) and Control (p =0.001) groups, which remained positive only in the Control group after adjustment (p<0.004). CONCLUSIONS: Only weight gain in pregnant women without clinical changes during pregnancy seemed to influence the weight of the newborn. It is assumed that other factors, such as metabolic or nutritional, present among smoking, diabetes mellitus, hypertensive or with intrauterine growth restriction pregnant women have different modulations on the intrauterine environment and, therefore, exert a different influence on fetal growth.
Clinical Characteristics of COVID-19 Patients with Recurrent PCR Positivity After Hospital Discharge
Authors: Wang Kaige , Tian Panwen 
Published: September 2020

Abstract
Objective: This study aimed to investigate the clinical characteristics of COVID-19 patients with recurrent SARS-CoV-2 positivity after hospital discharge. Methods: This retrospective study included COVID-19 patients who were readmitted for recurrence of positive SARS-CoV-2 RNA. Univariate and multivariate analyses were performed to assess the risk factors associated to the duration of recurrent RNA positivity.Results: Among the 287 discharged COVID-19 patients, 33 (11.5%) patients with recurrent PCR positivity were included. Among these patients, 21 (63.7%) patients were female, their mean age was 48.7 (±19.7) years old. 22 (66.7%) patients were asymptomatic. The following clinical features were presented in other patients: cough, fatigue, sore throat, fever and expectoration. The chest CT findings revealed that 8 (24.2%) patients were characterized by deterioration compared to the previous results. The median duration of recurrent RNA positivity was 9.0 days (IQR, 6.0, 15.0). We found that increased serum SARS-CoV-2-specific IgG antibody titer, elevated serum creatinine level, and female gender were the risk factors for the prolonged duration of recurrent RNA positivity.Conclusion: SARS-CoV-2 turned positive in a minority of discharged patients with COVID-19. Most patients experienced mild clinical course. Increased IgG antibody titer, creatinine and female gender were correlated to the prolonged RNA clearance time.
Universal Differential Equations for Scientific Machine Learning
Authors: Rackauckas Christopher , Ma Yingbo , Martensen Julius , Warner Collin , Zubov Kirill , Supekar Rohit , Skinner Dominic , Ramadhan Ali , Edelman Alan 
Published: September 2020

Abstract
In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets.” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work demonstrate how a mathematical object, which we denote universal differential equations (UDEs), can be utilized as a theoretical underpinning to a diverse array of problems in scientific machine learning to yield efficient algorithms and generalized approaches. The UDE model augments scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology.
An analysis of Dyadic relationship between Islamic Banks and Takaful Operators and their impact on Economic stability
Authors: Shabbir Malik Shahzad , Malik Adnan 
Published: September 2020

Abstract
Purpose

The objective of this study is examined the dyadic relationship between Islamic banks and takaful operators and their impact on economic stability of Pakistan. It is observed that Islamic finance industry demand increase from last one decade in Pakistan. The Islamic finance industry has significant importance in existing market through multidimensional contributions and deposits.
Design/Methodology/Approach:

This study takes five Islamic banks and five takaful companies operating in Pakistan from 2009 to 2017. This study implies panel data regression through random effect model (REM), fixed effect model (FEM) and generalized methods of moments (GMM) to better understand the time invariant and intercept value among variables.
Findings:

The results reveal with these remarks that Islamic banks have a relatively more positive and significant contribution toward economic stability as compared to takaful companies. Whereas, mean deviation of takaful operators is more near to the origin as compared to Islamic banks.
Originality/Value:

It is observed from last couple decades that Islamic finance industry has increased in capital inflows as relatively compared to conventional counterpart. This study is a comparative analysis between both Islamic banks and Takaful companies’ performances in Pakistan.
Predicting the COVID-19 Patients’ Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree
Authors: Talebi Atefeh , Borumandnia Nasrin , Jafari Ramezan , Pourhoseingholi Mohamad Amin , Jafari Nematollah Jonaidi , Ashtari Sara , Roozpeykar Saeid , Bashar Farshid R. , Vahedian-Azimi Amir 
Published: September 2020

Abstract
Background: The role of chest computed tomographic (CT) to diagnosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. The aim of this study is to use non-contrast chest computed tomography (CT) scan as a helpful tool in diagnosis quantification and follow-up of patients with COVID-19. Method: This study was performed on patients with COVID-19 who underwent chest CT scan at Baqiyatallah Hospital, Tehran, Iran. The age, gender, types of lesions, other specific signs of high-resolution computed tomography (HRCT), presence of diffuse opacity, underlying diseases, number of involved lobe and total opacity score of 1078 patients were evaluated. Decision tree (DT) model was used to analyze and establish a risk assessment model of critical and non-critical situation. Results: The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to DT model, total opacity score, age, lesion types and gender were statistically significant predictors in critical patients. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8% and 97.1%, respectively.Conclusions: The presented algorithm demonstrates the factors affecting the patient's condition. Also the model can predict the critical or non-critical situation of new cases. In addition, this model has the potential characteristics for clinical applications and can also identify high-risk subpopulations that need specific prevention.
Association of Plasma Low-Density Lipoprotein Receptor-Related Protein-1 (LRP1) with Undernutrition: A Case-Control Study in Bangladeshi Adults
Authors: Hasan Md. Mehedi , Fahim Shah Mohammad , Das Subhasish , Gazi Md. Amran , Mahfuz Mustafa , Ahmed Tahmeed 
Published: September 2020

Abstract
Background: Studies revealed that LRP1 plays pivotal role in adipogenesis, whereas silencing of LRP1 expression can cause inhibition of adipogenesis in animal model and contribute to reduced body size. Therefore a reduction in the level of adipocytic LRP1 might have a role on weight loss. But there is no study that has explored the role of LRP1 on weight loss of human adults. However, it is not feasible to examine the role of adipocytic LRP1 on weight loss due to invasive collection procedure of adipose tissue. But plasma LRP1 can fill that void as it is a biomarker of adipocytic LRP1. Therefore, the aim of this study was to investigate the relationship of plasma low-density lipoprotein receptor-related protein-1 (LRP1) with undernutrition.Methods: A total of 270 Bangladeshi slum-dwelling adults (135 cases, BMI < 18.5 kg/m2 and 135 controls, BMI 18.5-24.9 kg/m2) were enrolled. Their socio-economic, demographic, anthropometric and biomedical data were collected. Plasma LRP1, C-reactive protein (CRP), alpha-1 acid glycoprotein (AGP), and ferritin levels were measured by ELISA, hemoglobin by Hemocue and zinc by atomic absorption spectrometry.Results: The median (IQR) values of plasma LRP1, CRP, AGP, Hemoglobin, zinc and ferritin were 1673.1 (1382.5- 1886.2) ng/ml, 2.1 (0.9 – 4.4) mg/L, 75.8 (62.9 – 95.9) mg/dL, 13.2 (12 – 14.3) g/dL, 0.74 (0.7 – 0.8) mg/L, 59.9 (26.4 – 123.2) ng/mL in healthy participants and 707.7 (588.6- 839.9) ng/ml, 0.8 (0.2 – 1.7) mg/L, 64.7 (52.9 – 86.9) mg/dL, 12.4 (11.4 – 13.4) g/dL, 0.71 (0.6 – 0.8) mg/mL, 43.7 (19.9 – 80.2) ng/mL in undernourished participants, respectively. A strong positive correlation (r = 0.70, p <0.05) between LRP1 and body mass index (BMI) was found. Multivariable logistic regression analysis revealed a positive association between low plasma LRP1 (Adj. OR= 0.98, CI = 0.98, 0.99 and p < 0.05) and undernutrition. Conclusions: The study found that increased level of LRP1 is associated with increased BMI, whereas lower level can contribute to low BMI.
Photonic Transformers
Authors: Douvidzon Mark , Maayani Shai , Nagar Harel , Admon Tamir , Shuvayev Vladimir , Yang Lan , Deych Lev , Roichman Yael , Carmon Tal 
Published: September 2020

Abstract
Largely-deformed1–4 microcavities5 support instabilities suppression6 chaotic ray-dynamics7, directional-emission8–10, and momentum transfer11 for nonlinear optics, but such deformations are generally possible only by fabricating discrete sets of resonators. In contrast, optical tweezers12–17 permit continuous large changes18–28, such as deformations from spherical dielectrics to triangular ones25. We report on transformable micro-photonic devices that change their functionality while operating. Assisted by computerized holographic-tweezers, we gradually deform the shape and change the functionality of a droplet whispering-gallery cavity. For example, we continuously deform hexagonal cavities to rectangular ones, and demonstrate switching to directionally emitting mode-of-operation, or splitting a resonant mode to a 10-GHz separated doublet. A continuous trend of improving spatial light modulators and tweezers suggests that our method is scalable to control the shape and functionality of many individual devices. We also demonstrate optional solidification proving the feasibility of transformers-enabled applications, including in printing 3D optical-circuits and multiwavelength optical-networks.
Novel Phytoconstituents of an Aqueous Pod Extract of Prosopis Cineraria (L.) Druce Attenuate Atherosclerotic Plaque and Hypercholesterolemia in Rabbits
Authors: Ram Heera , Jaipal Noopur , Kumar Pramod , Charan Jaykaran , Kashyap Priya , Kumar Suresh , Singh Garima , Dixit Anshuman , Hashem Abeer , Alqarawi Abdulaziz A , Abd_Allah Elsayed Fathi , Singh Bhim Pratap 
Published: September 2020

Abstract
Background and objectives: The pod of Prosopis cineraria traditionally used in several ailments and key component of traditional food recipe of the Panchkuta of western Rajasthan, India. The current study was targeted to assess ability of phytoconstituents of aqueous pod extract of Prosopis cineraria to inhibit HMG-CoA reductase activity and regress atherosclerotic plaque were investigated in in-vitro, in-vivo, and in-silico studies. Material and Methods: LCMS, GCMS, and FTIR analysis were used to characterize the phytoconstituents of the test extract. Accordingly, the in-vitro, in-vivo and in-silico assessments were performed by following the standard methods. Results: The phytochemical results shown the presence of cloprostenol, cinecromen, and dirithromycin as leading compounds. Accordingly, in-vivo assay of test extract shown HMG-CoA inhibition activity by 78.1 % (IC50 was 0.03 μg/ml). Hypercholesterolemia was induced in rabbits through oral supplementation of a high fat diet (21 % fat) with cholesterol powder supplementation. Administration of the test extract caused significant (𝑃 ≤ 0.001) improvement in the lipid profile and antioxidant levels in the test rabbits, relative to the hypercholesterolemic control rabbits. Subsequently, the reductions in the atherosclerotic plaque and improvement in lumen volume were pointedly observed in the treated groups. In-silico analyses of molecular docking and ADMET revealed significant interactions and druggability profile. Conclusion: It can be stated that the phytoconstituents of aqueous pod extract of Prosopis cineraria have the capacity to inhibit HMG-CoA reductase and regress the atherosclerotic plaque which may be beneficial to the treatment of hypercholesterolemia.
DLDTI: A learning-based framework for drug-target interaction identification using neural networks and network representation
Authors: Zhao Yihan , Zheng Kai , Guan Baoyi , Guo Mengmeng , Song Lei , Gao Jie , Qu Hua , Wang Yuhui , Shi Dazhuo , Zhang Ying 
Published: September 2020

Abstract
Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI， was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.
Evaluation of Different Functional Antioxidant Groups and Protein Responses in Asian Seabass (Lates calcarifer Bloch, 1970) as an Early Indication of Fish Health in Aquaculture Farms.
Authors: Hassan Mohamad Sofi Abu , Jaafar Siti Nur Tahirah , Yusoff Nurulnadia , Azmi Nik Nurasyikin Nik 
Published: September 2020

Abstract
The study was conducted to investigate the organ-specific antioxidants and protein damages responses in Lates calcarifer inhabiting different aquaculture farms that susceptible to different threat of pollutions. Enzymes at the front line of antioxidant system superoxidase dismutase (SOD) and catalase (CAT) evidenced to work together but respond differently in different body compartment. High SOD responses were followed with lower CAT responses observed in muscle (p < 0.05) with opposite responses exhibited by both gill and liver. The responses of SOD and CAT in muscle also showed a significant strong correlation to each other (Setiu Wetland: 0.91, Semerak: 0.79) (p < 0.01). The glutathione-dependent enzymes, glutathione-s-transferase (GST) and glutathione reductase (GR) in body compartments responded with a strong correlation to each other especially in muscle (Tumpat; muscle: 0.89, gill: 0.95 and liver: 0.54 (p < 0.01, p < 0.05), (Setiu Wetland; muscle: 0.72 (p < 0.01) and Semerak; muscle: 0.79 (p < 0.01). Opposite results were found for both protein damages biomarkers, thiols (-SH-) and carbonyl (-CH-) in comparison to biomarkers responses. In contrary to (-SH-) inconsistent results were observed for the (-CH-) with muscle found to be most oxidised. The responses of SOD, CAT, GST, GR, thiols and carbonyl were all computed into the same scale through Integrated Biomarker Score (IBR) to classify each aquaculture farms based on biomarkers responses. There was a slight variation in deviation score (A) between organs within the range of (5-7). However, Tumpat overall showed the highest IBR score and significantly higher (p < 0.05) than Setiu Wetland with a total score of combining responses score in all three organs (IBR: 84, muscle: 31, gill: 24, and liver: 29) followed by intermediate score in Semerak (IBR: 72, muscle: 23, gill: 23 and liver: 26) and Setiu Wetland (IBR: 59, muscle: 18, gill: 23 and liver: 18). These results indicated that the responses of antioxidant enzymes and protein damages in L.calcarifer from different organs are heterogeneous. Therefore, biomarkers should be selected based on their sequential groups in the antioxidant system for a better explanation of the oxidative stress evaluation in fish.
Examining the impact of Exports and Exchange rate on Economic Growth of Turkey
Authors: Muhammad Iftikhar , Shabbir Malik Shahzad 
Published: September 2020

Abstract
Purpose

This study intends to analyze the long-run and short-run relationships along with the identification of causal links between exports, economic growth, and exchange rate in Turkey.
Data/Design:

This study uses auto-regressive distributed lags (ARDL) and Granger causality over time series monthly data from the year 2010–2018. The results indicate that exports are significantly positively related to economic growth while the exchange rate is found to be negatively related to economic growth.
Findings:

Moreover, findings from the test of Granger causality indicate that a unidirectional causal association is found from exports to foreign direct investment and economic growth and from economic growth to foreign direct investment. The Granger causality results indicate that an increase in exports accelerates the economic growth of Turkey and a change in growth rate and exchange rate leads to a change in foreign direct investment.
Originality of work:

The overall findings suggest that exports should be promoted along with the liberal-investment economic policies to boost the overall economic growth in Turkey.
Health care for transgender people in Brazil: a systematic review protocol
Authors: de Lima Rafael Rodolfo Tomaz , Flor Taiana Brito Menezes , Silva Alexandre Bezerra , Noro Luiz Roberto Augusto 
Published: September 2020

Abstract
Background
At the global level, transgender people (transvestites and transsexuals) live in worse health and living conditions due to the stigma and violence they face, including within health services. In Brazil, the Unified Health System must offer comprehensive care to the population, regardless of social class and gender. The aim of this article is to establish a systematic review protocol to analyze how health care occurs for the transgender population in the Unified Health System.
Methods
This protocol was guided by PRISMA-P and the bases SCIELO, LILACS, BVS and PUBMED will be consulted. Empirical studies, with a qualitative or quantitative approach, that deal with health care for the transgender population in the Unified Health System, where the respondents are transgender people, health professionals or health managers, will be included in the systematic review. For the definition of the final sample, the articles will undergo an assessment of methodological quality and risk of bias, with the help of the critical assessment tools of the Joanna Briggs Institute, using a specific checklist for each type of study. The synthesis of the findings will be carried out through the formal narrative and the presentation of tables with summarized data, with the final writing of the review guided by PRISMA.
Discussion
Based on scientific evidence, it is intended to present an overview of health care for the transgender population in the Unified Health System. In addition, to point out possible strategies to qualify the political, organizational and attitudinal aspects related to health care for that population.
Systematic review registration:
PROSPERO CRD42020188719.