bluuebunny/crossref_metadata_2025_split huggingface dataset:
Entropy Based Analysis of SARS-CoV-2 Spread in India using Informative Subtype Markers
Authors: Mathur Piyush , Goyal Pratik , Verma Garima , Yadav Pankaj 
Published: February 2021

Abstract
India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markeIndirs (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 768 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.
Targeted Sequencing and Integrative Analysis to Prioritise Candidate Genes in Neurodevelopmental Disorders
Authors: Zhang Yi , Wang Tao , Wang Yan , Xia Kun , Li Jinchen , Sun Zhongsheng 
Published: February 2021

Abstract
Neurodevelopmental disorders (NDDs) are a group of diseases characterized by highly heterogeneity and frequently co-occur symptoms. The mutational spectrum in patients with NDDs is largely incomplete. Here, we sequenced 547 genes from 1,102 patients with NDDs and validated 1,271 potential functional variants, including 108 de novo variants (DNVs) in 78 autosomal genes and seven inherited hemizygous variants in six X chromosomal genes. Notably, 36 of these 78 genes are the first to be reported in Chinese patients with NDDs. By integrating our genetic data with public data, we prioritised 212 NDD candidate genes with FDR < 0.1, including 17 novel genes. The novel candidate genes interacted or were co-expressed with known candidate genes, forming a functional network involved in known pathways. We highlighted MSL2, which carried two de novo protein-truncating variants (p.L192Vfs*3 and p.S486Ifs*11) and was most frequently connected with known candidate genes. This study provides the mutational spectrum of NDDs in China and prioritises 212 NDD candidate genes for further functional validation and genetic counseling.
Automated and Robust Organ Segmentation for 3D-based Internal dose Calculation
Authors: Nazari Mahmood , Jimenez-Franco Luis David , Schroeder Michael , Kluge Andreas , Bronzel Marcus , Kimiaei Sharok 
Published: February 2021

Abstract
Purpose: In this work we address image segmentation within dosimetry using deep learning and make three main contributions: a) to extend and op- timize the architecture of an existing Convolutional Neural Network (CNN) in order to obtain a fast, robust and accurate Computed Tomography (CT) based organ segmentation method for kidneys and livers; b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; c) to evaluate dosimetry results obtained using automated organ segmentation in comparison to manual segmentation done by two independent experts. Methods: We adapted a performant deep learning approach using CT-images to calculate organ boundaries with sufficiently high and adequate accuracy and processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the ac- tivity values from quantitatively reconstructed SPECT images for ”volumet- ric”/3D dosimetry. The retrieved activities were used to perform dosimetry calculations considering the kidneys as source organ. Results: The computational expenses of the algorithm was adequate enough to be used in clinical daily routine, required minimum pre-processing and per- formed within an acceptable accuracy of 93 . 4% for liver segmentation and of 94 . 1% for kidney segmentation. Additionally, kidney self-absorbed doses calcu- lated using automated segmentation differed 6 . 3% from dosimetries performed by two medical physicists in 8 patients. Conclusion: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radio-pharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmen- tation methodology based on CT images accelerates the organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images.Trial registration: EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13
Dissociation as a Causal Pathway from Sexual Abuse to Positive Symptoms in the Spectrum of Psychotic Disorders
Authors: Khosravi Mohsen , Bakhshani Nour-Mohammad , Kasaeiyan Rashya 
Published: February 2021

Abstract
Background: Although numerous studies have supported the role of childhood maltreatment in the etiology of psychosis, underlying mechanisms have not been well understood yet. The present study aimed to investigate the mediating role of particular forms of dissociation in the relationship between five major types of childhood abuse and psychotic symptoms among patients with schizophrenia spectrum and other psychotic disorders.Methods: In this cross-sectional correlation study, a total of 210 participants, including 140 patients with schizophrenia spectrum and other psychotic disorders (70 first-episode psychotic patients and 70 chronic psychotic patients) and 70 community controls, were selected by systematic random sampling (with the sampling interval of 3) and one-to-one matching, respectively, from among inpatients and outpatients referring to Baharan Psychiatric hospital, Zahedan, Iran, and people living in this region. In this study, the evaluation criteria included Dissociative Experiences Scale, Childhood Trauma Questionnaire-Short Form, and Positive and Negative Syndrome Scale.Results: The obtained results revealed that the mean scores of sexual abuse, emotional abuse, and physical abuse were higher in psychotic patients than community controls (without any significant difference among first-episode psychotic patients and chronic psychotic patients). Furthermore, the highest mean scores of dissociative experiences belonged to chronic psychotic patients. Regarding the three study groups, there was no significant gender-based difference between mean scores of dissociative experiences and various types of childhood maltreatment. Multiple-mediation also indicated that absorption and dissociative amnesia played a mediating role in the relationship between sexual abuse and positive symptoms. Moreover, this study implied the role of physical abuse in predicting psychotic symptoms even in the absence of sexual abuse. Conclusions: This study illustrated specific associations among childhood maltreatment, dissociative experiences, and psychotic symptoms in the clinical population. Thus, to provide appropriate interventions, patients with schizophrenia spectrum and other psychotic disorders needed to be asked about a wide range of possible adverse childhood experiences and dissociative experiences. Nevertheless, further studies using prospective or longitudinal designs need to be carried out to realize the differential contribution of various forms of childhood maltreatment and their potential interactions, more precisely.
Tele-Medicine Provides an Effective Tool for the Management of COVID-19 in Children
Authors: Poeta Marco , Pierri Luca , Bruzzese Eugenia , Nunziata Francesco , Vassallo Edoardo , Catzola Andrea , Ciccarelli Gian Paolo , Guarino Alfredo , Vecchio Andrea Lo 
Published: February 2021

Abstract
Overcrowding, the unavailability of personal protective equipment, and transmission between health care workers are the main causes of the rapid spread of coronavirus disease 2019 (COVID-19) in hospital settings. Because children have less severe symptoms than adults, we used tele-medicine (TM) as the main approach to managing subjects younger than 14 years who contacted their primary care paediatrician (PCP). A TM program for suspected paediatric COVID-19 cases was developed by the COVID-19 paediatric referral centre and PCPs in the Campania Region, Italy. A total of 269 cases of suspected COVID-19 were discussed through tele-consultation between PCPs and paediatric infectious disease specialists. The main reason for the initiation of tele-consultation by PCPs was the need for case management advice (n=206, 77%). A total of 203 children were tested for COVID-19, and 139 were positive (73 males, aged 5 years [IQR 1-10]), of whom 103 were managed at home with phone/video monitoring, 17 received a direct medical visit and discharged to home, and 19 were admitted to the COVID-19 unit. The main symptoms were fever (45%) and cough (25%). Thirty-five percent were asymptomatic and detected in family clusters. None had severe clinical outcomes.ConclusionTM is a reliable tool to limit the spread of COVID-19 and might reduce the number of unnecessary visits and hospital admissions. Through tele-consultation with an expert, PCPs can identify at-risk children who need testing and manage most cases remotely. However, standardization of the TM approach and criteria for hospital admission are needed.
Urbanization Significantly Reduces Frequency of Light Rain: an Example From Beijing City
Authors: Yang Ping , Ren Guoyu , Yan Pengcheng , Deng Jingmian 
Published: February 2021

Abstract
An hour precipitation dataset of 42 automatic weather stations is developed and applied to analyze the temporal and spatial characteristics of light precipitation in urban areas of Beijing City during 2007-2017. The stations are classified into three groups, including 18 sites in central urban area (4th RR), 10 sites in peri-urban area (4th-5th RR) and 14 sites in suburban area (5th-6th RR). Light precipitation is defined as hourly rainfall of 0.1-0.3mm. Analysis shows that light precipitation occurred in urban area in the whole day, with the peak value in 0600 LST and minimum value in 1600LST; monthly variation of light precipitation frequency (LPF) was characterized by the highest value in summer and the lowest value in winter; remarkable differences are found for the various urbanized areas, with the annual and seasonal mean LPF being generally small in central urban area and gradually increasing toward suburban area; the hourly mean LPF during morning and nighttime is higher in summer than those in other seasons in each of the urban areas; relative humidity, aerosol and wind speed might have been the major influential factors for the observed temporal and spatial pattern of light precipitation.
A New Agent-Based Model Provides Insight Into Assumptions in Modeling Forest Management Under Deep Uncertainty
Authors: Sotnik Garry , Cassell Brooke A. , Duveneck Matthew J. , Scheller Robert M. 
Published: February 2021

Abstract
ContextExploratory modeling in forestry uses a variety of approaches to study forest management questions. One key assumption that every approach makes is about the degree of deep uncertainty—the lack of knowledge required for making an informed decision—that future forest managers will face. This assumption can strongly influence simulation results and the conclusions drawn from them, but is rarely studied.ObjectivesOur objective was to measure the degree of deep uncertainty within a forest management simulation to compare alternative modeling approaches and improve understanding of when a specific approach should be applied.MethodsWe first developed a method for measuring the degree of deep uncertainty assumed by approaches to modeling forest management. Next, we developed a new extension to the LANDIS-II model, the SOSIEL Harvest Extension, which simulates alternative approaches to modeling forest management. Finally, we applied the new method and extension to comparing three alternative approaches to modeling forest management in Michigan.ResultsThe degrees of deep uncertainty varied substantially among the three modeling approaches. There is also an overall negative relationship between the degree of deep uncertainty an approach assumes a forest manager will face and the level of flexibility the approach assumes a manager will have in responding to forest change.ConclusionsQuantifying the deep uncertainty inherent in simulated forest management and comparing it across models provides an opportunity to better understand its sources and investigate differences in the assumptions made by alternative modeling approaches.
An empirical investigation of deviations from the Beer-Lambert law in near-infrared spectroscopy: A case study of lactate in aqueous solutions, serum, blood and tissue
Authors: Mamouei Mohammad , Budidha Karthik , Baishya Nystha , Qassem Meha , Kyriacou Panayiotis 
Published: February 2021

Abstract
The linear relationship between optical absorbance and the concentration of analytes -as postulated by the Beer-Lambert law- is one of the fundamental assumptions that much of the optical spectroscopy literature is explicitly or implicitly based upon. The common use of linear regression models such as principal component regression and partial least squares exemplifies how the linearity assumption is upheld in practical applications. However, the literature also establishes that deviations from the Beer-Lambert law can be expected when a) the light source is far from monochromatic, b) the concentrations of analytes are very high and c) the medium is highly scattering. The lack of a quantitative understanding of when such nonlinearities can become predominant, along with the mainstream use of nonlinear machine learning models in different fields, have given rise to the use of methods such as random forests, support vector regression, and neural networks in spectroscopic applications. This raises the question that, given the small number of samples and the high number of variables in many spectroscopic datasets, are nonlinear effects significant enough to justify the additional model complexity? In the present study, we empirically investigate this question in relation to lactate, an important biomarker. Particularly, to analyze the effects of scattering matrices, three datasets were generated by varying the concentration of lactate in phosphate buffer solution, human serum, and sheep blood. Additionally, the fourth dataset pertained to invivo, transcutaneous spectra obtained from healthy volunteers in an exercise study. Linear and nonlinear models were fitted to each dataset and measures of model performance were compared to attest the assumption of linearity. To isolate the effects of high concentrations, the phosphate buffer solution dataset was augmented with six samples with very high concentrations of lactate between (100-600 mmol/L). Subsequently, three partly overlapping datasets were extracted with lactate concentrations varying between 0-11 mmol/L, 0-20 mmol/L and 0-600 mmol/L. Similarly, the performance of linear and nonlinear models were compared in each dataset. This analysis did not provide any evidence of substantial nonlinearities due high concentrations. However, the results suggest that nonlinearities in scattering media may be substantial, justifying the use of complex, nonlinear models.
Nursing Students’ Health Literacy Level and Health Practices: a Cross Sectional Study at a University in Namibia
Authors: Munangatire Takaedza , Tomas Nestor , Mareka Violetha 
Published: February 2021

Abstract
Background: Inadequate health literacy and poor health practices among nurses could be a hindrance to empowering the population with good health practices. For the nurses to empower the population, they need to be equipped with good health literacy and good health practices. The starting point of solving this problem is through provision of deliberate health literacy and health practice education in the nursing curriculum. This study explored health literacy level and health practices of nursing students in Namibia. Specifically the study examined the health literacy level, health practices and the relationship between the two among nursing students across four levels of study. Methods: A descriptive cross-sectional study was carried out among 205 nursing students. A simple random stratified sampling method was used and data were collected using questionnaire for health literacy (16 items) and health practices (11 items. Pearson correlation, independent t-test and One-way ANOVA were used to analyse the data. Results: The overall mean general health literacy score was 13.04± 1.52. The majority (n=157;76.5%) of the students were found to have adequate health literacy scores, 21.5% with moderate health literacy scores and only 2% with inadequate health literacy scores. The overall mean health practice score was 32.4± 5.50. Most (n=106; 51.7%) of the students were found to have poor health practices, 44.4% had average health practices and 3.9% had good health practices. There was no significant relationship between health literacy levels and health practices of the students (p=0.63).Conclusions: Nursing students have good health literacy but more efforts should be applied to maintain such health literacy levels during and beyond the point of graduation. However, with poor health practices, there is need to investigate more on the contributing factors and develop strategies that can support good health practices among nursing students and maybe these can be transferred into their professional careers as nurses.
Energetic and Electronic Properties of NH3, NO2&nbsp;and&nbsp;SO2&nbsp;Interacting with GaN Nanotube: A DFT Study
Authors: Silva Andrew A. J. Anchieta da , Caetano Caio Vinícius , Guerini Silvete 
Published: February 2021

Abstract
In this work, the interaction of GaN nanotube (GaNNT) with common air pollutants of industrialized cities, such as NH3, NO2 and SO2 in different configurations was studied. For this study, the single-walled (10,0) GaNNT was used. The analysis was done via the density functional theory implemented in the SIESTA simulation software. The analysis of the results shows that the air pollutants alter the properties of nanotubes when they interact with them. The stability analysis shows that the most stable configurations are those in which adsorption occurs through a chemical process. The systems remain semiconductors, but in the case of NO2 and SO2 molecules interacting with GaNNT, there was a significant reduction in the energy gap. Our results also indicate that GaNNT is a promising material to detect and remove NH3 and NO2 molecules from the environment, however it may be not applicable to detect or remove SO2, because the latter interacts strongly with the nanotube, which prevents the GaNNT from being reused.
SMOX and SMS Responsible for Polyamine Metabolism Enhanced Adipogenesis
Authors: Yi Xia , Wu Ping , Liu Jianyun , Gong Ying , Xu Xiaoyuan , Xiong Jianjun 
Published: February 2021

Abstract
Adipose tissue regulating carbohydrate and lipid metabolism had been extensively focused. However, the regulation of amino acid metabolism during adipocyte differentiation remained detailed. Here we applied RNA-Seq technique to establish the transcriptional landscapes of amino acid metabolism during adipogenesis. We totally screened 17 differentially expressed genes (DEGs) for amino acid metabolism at 7, 14, 21, and 28 days during adipogenesis from human mesenchymal stem cells (hMSCs), especially with 13 up-regulated genes most prevalent in our adipogenic anecdote. Small molecule metabolic process was the most enriched biological process following by oxidation-reduction process. Interestingly, the enforced expression of SMOX (spermine oxidase) responsible for polyamine metabolism in arginine and proline metabolism pathway facilitated adipogenesis more than SMS (spermine synthase) using RNA interference (RNAi). The established potential regulatory network further suggested that adipocyte differentiation tightly related with the basal metabolism of amino acid metabolism with the partially confirmed SMS-SMOX-PPARG signaling pathway. It would highlight the effect of adipogenesis on amino acid metabolism in adipocyte biology and provide the potential treatment strategy for the study of fat metabolic diseases.
Electrochemical Sensitive Detection of Hydrazine Through Cobalt Doped Cerium Oxide Nanostructured Platform
Authors: Ansari Anees AHMAD , Alam Manawwer 
Published: February 2021

Abstract
Chemically synthesized cobalt-doped cerium oxide nanoparticles(CeO2:Co;NPs) were successfully prepared by complexed based co-precipitation process. The structural, morphological, chemical composition, optical properties, and electro-catalytic properties were determined by X-ray diffraction pattern(XRD), transmission electron microscopy(TEM), energy dispersive x-ray analysis (EDX), UV/Visible absorption spectroscopy, and cyclic voltammetry techniques. Owing to the wide-spread applications of CeO2 NPs in various fields of applied material sciences, transition metal ions doped CeO2:Co NPs exhibited excellent electro-catalytic properties. Outstanding physiochemical properties of CeO2 such as reversible oxidation states, high ionic mobility, large oxygen storage ability, effective large specific surface area, and the excellent current response observed in the electrocatalytic oxidation of hydrazine. The presence of transition metal ions (cobalt) improves the oxidation potential of hydrazine. Cyclic voltammetry was analog with the electrochemical impedance spectroscopy results, which revealed the enhanced with rapid sensing response against hydrazine. The electro-catalytic results of the CeO2:Co NPs electrode exhibited excellent voltammetry and impedance spectroscopy performance towards the hydrazine oxidation. The fabricated chemical sensor shows a wide linear detection range from 7.18 to 1000 µM, a low detection limit 7.2 µM, and sensitivity of 2.42 µAmLµM-1cm-2. The fabricated sensing electrode demonstrated long-term steadiness resulting it shows high sensitivity, selectivity, repeatability/reproducibility, and rapid detection of hydrazine.