bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of religiosity on youths’ attitudes towards people with disabilities in the United Arab Emirates
Authors: Khan Moien AB , Hammad Hamza , Elbarazi Iffat , Bendak Malik , Obaideen Khaled , Amanatullah Asma , Khan Bibi Sara Badshah , Ismail Leila , Kieu Alexander 
Published: March 2022

Abstract
This cross-sectional survey investigates the influence of youths’ religiosity on their attitude towards people with disabilities. The Muslim religiosity questionnaire and multidimensional attitudes scale towards persons with disabilities were used to survey 733 youths from the federal university in the United Arab Emirates. Results indicated that the youths were religious and had positive attitudes towards people with disabilities. An increase in religiosity is associated with a positive attitude towards disability, and both religiosity and total family income had a positive impact on the attitude towards people with disabilities. Reducing inequalities by including persons with disabilities is one of the objectives of the UN 2030 Agenda for Sustainable Development. Policies should aim to enhance curriculum, improvise public guidelines and partner with associated faith-based leaders to build an inclusive society for people with disabilities, thus helping to achieve sustainable development goals.
Evolutionary analysis of Japan’s nuclear wastewater discharge events considering the impact of participants’ emotions
Authors: Xin Xu , Liu Miaohui , Wang Xiaoli , Zhang Tao , Chen Kang 
Published: March 2022

Abstract
The Japanese government’s decision to discharge nuclear wastewater from the Fukushima Daiichi Nuclear Power Plant into the sea on April 13, 2021 has aroused widespread concern around the world. The Japanese people, especially the fishermen, have expressed their long and strong dissatisfaction with this decision. Motivated by this background, this paper discusses an evolutionary game considering the impact of participants’ emotions to explore the evolutionary stable strategies of both the Japanese government and Japanese fishermen. The results show that (i) The evolutionary stability strategies of the two sides of the game are different under different emotion combinations. Under the optimism of the fishermen, the fishermen’s decision making is irrational, and the equilibrium strategy of the game between the two sides is not conducive to the fishermen. (ii) The emotions of the players will not only affect their own strategic choices but also affect the strategic choice intentions of other players. (iii) Reducing the cost of nuclear wastewater treatment is currently the most feasible way to reduce the probability of the Japanese government’s discharge policy. Under the premise that the government chooses to discharge, no matter what the fishermen’s emotions are, it is the fishermen’s dominant strategy to strive for more subsidies for their own interests. The abovementioned findings can provide a decision-making reference for the Japanese government to formulate discharge strategies and for Japanese fishermen to protect their rights and interests.
A New Approach to Pollution Vulnerability Assessment in Aquifers Using K-means Analysis
Authors: Uzcategui-Salazar Marisela , Lillo Javier Ramos 
Published: March 2022

Abstract
The most used methods to evaluate the vulnerability to contamination of aquifers are based on overlay index maps, such us DRASTIC, GOD and AVI. These methods assign weighting and rating values to hydrogeological characteristics, introducing subjectivity in the evaluation. In this research, a new methodology is proposed to eliminate some of that subjectivity. The methodology evaluates the vulnerability to contamination of a detrital aquifer using K-means cluster analysis with a new set of parameters. The set is composed of some parameters extracted from these methods, as well as other new ones that have a significant influence on the movement of contaminants. Application of the Principal Components Analysis (PCA) technique before using K-means cluster allowed the selection of the relevant parameters. In order to validate the methodology, this was applied to a detrital aquifer located at central Spain (the so-called “Aluviales Jarama-Tajuña” aquifer) with a significant agricultural development. To compare the traditional methods of vulnerability assessment with the K-means cluster, nitrate concentration was used as a pollution indicator. Thus, 23 groundwater quality samples were used to correlate (Spearman´s correlation coefficient) the vulnerability values with nitrate concentration to validate the most suitable method. The results showed that GOD and AVI were not appropriate methods to evaluate the vulnerability of the aquifer, because they use few variables which do not represent relevant features for the vulnerability assessment. Alternatively, DRASTIC and K-means cluster analysis obtained higher Spearman´s correlation coefficients. The relevant features selected by PCA analysis were depth of groundwater (D), net recharge (R), and land use (L). The new proposed method grouped data in three clusters (low, moderate and high vulnerability), increasing the Spearman´s correlation from 34% of DRASTIC method to 48%. This result confirmed the advantage of applying cluster analysis in the assessment of groundwater vulnerability in detrital aquifers.
An R2R3 MYB Transcription Factor Ghmyb18 Confers Cotton Aphid Resistance Through Regulating The Synthesis of Flavonoids in Cotton Plants
Authors: Hu Zongwei , Zhang Jianmin , Zhong Xiao , Feng Wanzu , Zhang Haoran , Luo Xincheng , Wang Yuxue , Wang Yi , Liu Ting , Zhang Yi , Wang Xiangping , Yang Yazhen 
Published: March 2022

Abstract
Accumulating researches confirmed that plant MYB transcription factors are involved in the defense response to insect herbivores. However, the target genes and signaling pathways they regulate are still unknown. Here, we identified the R2R3 MYB transcription factor GhMYB18 from upland cotton (Gossypium hirsutum L.). GhMYB18 was a functional transcription activation factor. The expression level of GhMYB18 was significantly up-regulated under the conditions of aphid infestation and exogenous SA treatment. Overexpression of GhMYB18 in cotton cotyledons conferred enhanced tolerance to the aphid (Aphis gossypii Glover), while the silence of GhMYB18 expression led to increased susceptibility of cotton plants to aphid. Subsequent analysis showed that GhMYB18 activated defense-related enzymes including CAT, POD, PPO and PAL, induced the synthesis of salicylic acid and flavonoids to participate in the defense response against aphid. These results collectively prove that the cotton MYB gene GhMYB18 modulated the plant defense response to aphid attack, which may offer guide strategies for the prevention of cotton aphid.
Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning
Authors: Xiong Yongzhu , Zhu Mingyong , Li Yongyi , Huang Kekun , Chen Yankui , Liao Jingqing 
Published: March 2022

Abstract

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using Deep Learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three Machine Learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of Overall Accuracy (OA), Overall F1 score (OF) and Computational Time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers with the highest OA and the biggest OF for both validation and test, and the fastest CT for both validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. It suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.
A comparative analysis of the work environments for registered nurses, nurse aides, and caregivers using the 5 th Korean Working Conditions Survey
Authors: Kim Sung Shin , Kim Yun Jin , Park Jun Sik , Ho Seoung Hee , Kweon Hyo Sun , Bae Young Hyeon 
Published: March 2022

Abstract
Background
Since the quality of work life of healthcare workers is affected by various factors, an improvement in their work environment can reduce the burden on them, increasing their performance. This study aimed to identify the current problems in working environments for registered nurses (RNs), nurse aides (NAs), and caregivers using the 5th Korean Working Conditions Survey (KWCS), presenting measures to improve working conditions by analyzing their predictors: 1) degree of exposure to work-related risk factors (musculoskeletal and mental), 2) number of days working overtime, 3) work-family balance, 4) work situations, and 5) self-rated health.
Methods
From 50,205 participants of the 2017 KWCS, 494 RNs, 201 NAs, and 505 caregivers were included in the analysis to compare their 1) degree of exposure to work-related risk factors (musculoskeletal and mental), 2) number of days working overtime, 3) work-family balance, 4) work situations, and 5) self-rated health.
Results
There were significant differences in all the variables (exposure to musculoskeletal and mental work-related risk factors, work hours, work-family balance, work situations, self-rated health, and satisfaction with working conditions) among RNs, NAs, and caregivers (p < 0.001). The degree of work-related musculoskeletal and mental risk exposure was higher among caregivers and RNs than among NAs; the number of days of working overtime, challenges with work-family balance, and work environment satisfaction were higher among caregivers than among NAs. In addition, work situations were poorer among caregivers and NAs than among RNs. Self-rated health was the highest among caregivers, followed by RNs and NAs. The most potent predictor of self-rated health was occupation, followed by work environment satisfaction and work-family balance; the most potent predictor of work environment satisfaction was self-rated health, followed by degree of exposure to work-related musculoskeletal and mental risk factors, occupation, work-family balance, work situation, and work hours.
Conclusions
This study confirmed that a variety of factors influence work environment satisfaction. Thus, practical and realistic measures to improve work environments tailored to each healthcare occupation should be developed at the national and community levels. Further qualitative studies are needed to analyze the work environments of nurses and other care workers in depth.
Determining LEAD toxicity in pregnant women using machine learning
Authors: Chaurasia Priyanka , McClean Sally I. , Mahdi Abbas Ali , Yogarajah Pratheepan , Ansari Jamal Akhtar , Kunwar Shipra , Ahmad M. Kaleem 
Published: March 2022

Abstract
Background and Objective: LEAD, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. It can permeate through the foetus blood-brain barrier causing a negative impact on foetal growth and the developing brain. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of Blood LEAD Level is low, costly, and time-consuming.Methods: We propose a novel approach to build a low-cost point-of-care analytical device, a screening app that can predict LEAD toxicity levels in maternal blood. A computational model is built that learns from maternal data comprising of blood LEAD level and a set of sociodemographic features, and then predicts the LEAD toxicity level based on the set of input features.Results: Following feature selection methods, the 11-feature set obtained from Boruta algorithm gave the best prediction results. The k-Nearest Neighbour-based model gave 94.00% accuracy whereas the Neural Network-based model gave 92.50% accuracy when tested on 200 participants.Conclusion: The range of features identified in the built models can estimate the underlying function and can accurately model LEAD toxicity prediction and provide an understanding of toxicity level. Early identification and intervention of LEAD-exposed pregnant women will reduce LEAD poisoning in infants and thus, prevent harmful effects on health throughout childhood and adulthood. The built prediction model is beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that the app will become a part of a screening process to assist healthcare experts at the point of evaluating the LEAD toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal exposure and hence, it could also be possible to mitigate infant’s LEAD exposure by reducing transfer from the pregnant woman.
Convergence of Two Serotypes within the Epidemic ST11 KPC-Producing Klebsiella pneumoniae Creates the “Perfect Storm” in a teaching hospital
Authors: Liu Chao , Yang Ping , Zheng Jiajia , Yi Juan , Lu Ming , Shen Ning 
Published: March 2022

Abstract
Objectives.
ST11 KPC-Producing Klebsiella pneumoniae (Kp) is high prevalent in China. We investigated the transmission and evolution characteristics of the ST11 KPC-producing Kp during inter- and intro- host.
Methods.
A retrospective study was conducted in a hospital. Clinical data and antimicrobial resistance (AMR) phenotype were collected. Whole genome sequencing was performed. Transmission route was reconstructed by combined the single nucleotide polymorphism (SNPs) with clinical information.
Results.
Fifty-eight Kp strains isolated from thirty-six patients were enrolled. The information of one isolate missed. The mean age was 74.3 ± 18.0 years and 18 (50.0%) were female. Fifteen patients (41.7%%, 15/36) presented with poor prognosis. All the strains identified as ST11 and 57 strains harbored blaKPC−2. Two distinguished clades were identified based on the 1,325 hqSNPs. In clade1, Carbapenem-resistant (CR)-hvKp accounted for 48.3% (28/58), mostly presented with KL64, whereas CR-classical Klebsiella pneumoniae (cKp) commonly possessed KL47 were clustered in the Clade2. One CR-hvKp strain might origin from CR-cKp during within-host evolution. Even worse, a prolonged transmission of the CR-hvKp has spread into the healthcare institutes.
Conclusion.
Two endemic subclones of ST11 KPC-producing Kp, KL64-CR-hvKp and KL47-CR-cKp, were parallelly transmitted within the hospital and/or the healthcare institute, suggesting that ongoing genomic surveillance should be enhanced.
Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partners
Authors: Choi Juyoung 
Published: March 2022

Abstract
Biological mechanisms consist of protein interaction networks. However, most of protein interaction prediction are based on interspecies biological evidences (knowledge-based predictions) or exhibits low accuracy for weak interactions and requires high computational power (in silico docking method). In this study, I suggest a novel method to identify protein interaction using interaction energy distribution. Protein interactions with specific partners can be predicted by searching narrow funnel-like interaction energy distribution. Kinases and E3 ubiquitin ligase are important signaling components. However, as their interactions are weak and various, those interaction predictions are limited. I described that their interaction partners can be identified by analyzing interaction energy distribution with low computational cost. The accuracy of this prediction was similar or even higher than those of yeast two-hybrid screening, the most widely applied interaction screening method. Furthermore, I showed that other specific protein interactions have narrow funnel-like interaction energy distribution. In summary, this knowledge-free protein interaction prediction method would broaden our understanding of protein interaction networks.
Decoration of Ziziphus spina-christi leaves derived-activated carbon with Fe3O4 nanoparticles and SDS surfactant for removal of Cr3+ from polluted water
Authors: Koutenaei Shima Shiraghaei , Vatankhah Gholamhossein , Esmaeili Hossein 
Published: March 2022

Abstract
The purpose of this study was to remove Cr3+ ions from polluted water using AC@Fe3O4@SDS nanocomposite. The physical and surface features of the nanocomposite were investigated with EDS, DLS, XRD, BET, FTIR, SEM and TEM analyses. The BET surface area and the mean pore diameter of the AC@Fe3O4@SDS were 102.04 m2/g and 169.49 oA, respectively, indicating the high porosity of the aforementioned nanocomposite. DLS and XRD analyses showed that the mean particle size and mean crystal size of AC@Fe3O4@SDS were 78.9 nm and 28 nm, respectively. Also, the highest Cr3+ removal efficiency (99.5%) using the AC@Fe3O4@SDS nanocomposite was obtaind at pH= 7, contact time= 40 min, temperature= 55 ºC, the Cr3+ ion concentration= 10 mg/L and the nanocomposite dosage= 2 g/L, which is the highest Cr3+ removal efficiency ever achieved. Moreover, the utmost sorption capacity obtained from the Langmuir model was 120.48 mg/g, which is a significant value compared to previous studies for Cr3+ removal. Furthermore, the isotherm and kinetic studies of the sorption process indicated that the Cr3+ sorption process using the aforementioned nanocomposite followed the quasi-second order and Freundlich equations due to higher correlation coefficients. Subsequently, the thermodynamic study demonstrated that the sorption process of Cr3+ ions using AC@Fe3O4@SDS is favorable, feasible, spontaneous and endothermic.
Protective effect of a nanofilled resin based coating on wear resistance of glass ionomer cement restorative materials
Authors: Moghimi Milad , Jafarpour Dana , Ferooz Reihaneh , Rafat Bagheri 
Published: March 2022

Abstract
Background.
The effect of nanofilled resin based coating on the wear resistance of glass ionomer cements (GICs) is still controversial. This study aims to compare the wear resistance of four encapsulated GICs including two conventional and two resin-modified, and to evaluate the effect of G-Coat Plus on the wear resistance of GICs.
Methods.
A total of 80 disk-shaped specimens were prepared from two CGICs (riva self cure (SDI) and Equia Forte Fil (GC) and two RM- GICs (Ketac Nano (3M/ ESPE) and Fuji II LC (GC). The specimens of each material were divided into two groups (n = 10) based on the surface protection: no coating (NC), and coating with G-Coat Plus (GCP). All specimens were then placed in distilled water for 24 h at 37 ºC. The specimens were subjected to thermocycling for 120,000 cycles using a chewing simulator. Wear resistance was measured using a specific formula. Data was analyzed using Kruskal-Wallis test.
Results.
There was no significant difference in volume loss (mm3) between coated and uncoated groups for all materials (P > 0.05). Ketac Nano showed significantly lower volume loss (0.65 ± 0.12) compared to all other groups (P < 0.05) among uncoated specimen, and significantly lower than Fuji II LC (P = 0.035) and Equia Forte Fil (P = 0.040) among coated groups. However, no statically significant difference was observed between volume loss of coated Ketac Nano with that of riva self cure (P = 0.087).
Conclusions.
Coating with GCP did not affect the wear depth of GICs, and Ketac Nano showed significantly lower volume loss regardless of coating.
Heterozygous expression of the Alzheimer’s disease protective PLCγ2 P522R variant enhances Aβ clearance while preserving synapses.
Authors: Solomon Shiden , Sampathkumar Nirmal Kumar , Carre Ivo , Mondal Mrityunjoy , Chennell George , Vernon Anthony C. , Ruepp Marc-David , Mitchell Jacqueline 
Published: March 2022

Abstract
Background: A rare coding variant, P522R, in the phosphoplipase C gamme 2 (PLCG2) gene has been identified as protective against late onset Alzheimer’s disease (AD), but the mechanism of protection is unknown. PLCG2 is exclusively expressed in microglia within the central nervous sytem, and altered microglial function has increasingly been implicated in the progression of AD.Methods: Healthy control hiPSCs were CRISPR edited to generate cells heterozygous and homozygous for the PLCG2P522R variant. Microglia derived from these hiPSC’s were used to investigate the impact of PLCg2P522R on disease relevant processes, specifically microglial capacity to take up amyloid beta (Ab) and synapses. Targeted qPCR assessment was conducted to explore expression changes in core AD linked and microglial genes, and nitochondrial function was assessed using an Agilent Seahourse assay.Results: Heterozygous expression of the P522R variant resulted in increased microglial clearance of Ab, while preserving synapses. This was associated with the upregulation of a number of genes, including the anti-inflammatory cytokine Il-10, and the synpase-linked CX3CR1, as well as alterations in mitochondrial function, and increased cellular motility. The protective capacity of PLCg2P522R appeared crucially dependent on (gene) ‘dose’, as cells homozygous for the variant showed reduced synapse preservation, and a differential gene expression profile relative to heterozygous cells. Conclusions: These findings suggest that PLCg2P522R may result in increased surveillance by microglia, as well as priming them towards an anti-inflammatory state, with an increased capacity to respond to increasing energy demands, but highlights the delicate balance of this system, with increasing PLCg2P522R ‘dose’ resulting in reduced beneficial impacts.
High Efficient Removal of Methylene Blue Using Microcrystalline Cellulose/Polyvinyl Alcohol/Expanded Graphite 3D Porous Foam as a “Green” Adsorbent
Authors: Tan Xiaodong , Peng Qingyan , Subrova Tereza , Saskova Jana , Wiener Jakub , Venkataraman Mohanapriya , Militky jiri , Xiong Wei , Xu Jie , Mahendran Arunjunai Raj , Lammer Herfried 
Published: March 2022

Abstract
The cellulose/polyvinyl alcohol (PVA)/expanded graphite (EG) 3D porous foam, which has wide application prospects in cost-effective dye removal, was prepared by physical crosslinking and foaming technology. The prepared foam material has an obvious 3D network and porous structure, exhibiting excellent removal efficiency for methylene blue (MB) in an aqueous solution. The largest MB adsorption capacity of the foam is 110.81 mg/g. The adsorption process follows the pseudo-2nd order kinetics and the Freundlich isotherm model, indicating that the adsorption process is controlled by active surface sites and the physical adsorption process. Thermodynamic studies have shown that the adsorption process is a spontaneous and exothermic reaction. After five cycles of adsorption experiments, the composite material still exhibited a more than 70% dye removal rate. The results show that cellulose/PVA/EG 3D porous foam is effective, promising, and recyclable adsorbent, which can be used to remove MB from aqueous solutions.