bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utilizing Fog computing and Explainable Deep Learning techniques for Gestational Diabetes prediction
Authors: Rashidy Nora El , ElSayed Nesma E. , El-Ghamry Amir , Talaat Fatma M. 
Published: February 2022

Abstract
Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22- 26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework (DRPF) consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and non- invasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and lab tests was aggregated. The results of the prediction model superior the state of the art (ACC=0.957, AUC=0.942). Regarding to explainability, we utilized Shapley additive explanation (SHAP) framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.
Effect of Processed Quinoa on Performance Traits, Small Intestine Morphology, and Blood Parameters of Ross Broiler Chickens
Authors: Zeyghami Negin , Jafari Mohammad Ali , Irani Mehrdad 
Published: February 2022

Abstract
Restrictions on access to important components of poultry rations such as corn and soybean meal are among the biggest issues in the poultry feed industry. Quinoa with a very high nutritional value can be a good alternative. The present study was aimed to investigate the effect of processed quinoa seeds on performance, small intestine morphology, and blood parameters of Ross broiler chickens. The present experiment was performed with 5 treatments, 4 repeats, and 15 chickens per pen in a completely randomized design. Hydrothermal, extrusion, and expansion methods were used to process quinoa seeds. Except for the control ration, 15% of quinoa seeds were included in the other treatments. Traits of performance, small intestine morphology, and blood parameters were recorded. Duncan test in SAS software was used to compare the means of treatments. The quinoa processing methods were caused the bodyweight increase, feed conversion ratio improvement, blood metabolites concentration decrease such as cholesterol, and characteristics improvement of small intestine morphology such as increasing the villi height (P<0.01). Quinoa processing improved its nutritional properties. In general, extrusion and expansion processing methods were more effective than the hydrothermal method in improving the quality of quinoa.
Delirium Risk Factors in Hospitalized Patient: a Comprehensive Evaluation of Underlying Diseases and Medications in Different Wards of a Large Urban Hospital Center in Iran
Authors: Arbabi Mohammad , Ziaei Elham , Amini Behnam , Ghadimi Hamidreza , Radishi Fatemeh , Shohanizad Narges , Moradi Soroush , Beikmarzehei Alireza , Hasanzadeh Alireza , Parsaei Amirhossein 
Published: February 2022

Abstract
Background: Delirium is a neurobehavioral syndrome, which is characterized by fluctuation of mental status, disorientation, confusion and inappropriate behavior and it is prevalent among hospitalized patients. Recognizing modifiable risk factors of delirium is the key point for improving our preventive strategies and restraining its devastating consequences. The present study investigated a wide range of possible predisposing factors of delirium, mainly focused on underlying diseases and mediations, of hospitalized patients in the different wards of a general hospital.Method: In a prospective, observational trial, we investigated 220 patients who had been admitted to the internal, emergency, surgery and hematology-oncology departments. We employed the Confusion Assessment Method (CAM) questionnaire, The Richmond Agitation Sedation Scale (RASS), the General Practitioner Assessment of Cognition (GPCOG), demographic questionnaire, patient interviews and medical records. Multivariate logistic regression models were used to analyze predictive value of medications and underlying diseases for daily transition to delirium.; demographics were analyzed using univariate analysis to identify those independently associated with delirium.Results: 220 patients were enrolled; the emergency department had the most incident delirium (%31.3) and the surgery section had the least (%2.4); delirium was significantly correlated with older ages and sleep disturbance. Among multiple underlying diseases and the medications evaluated in this study, we found that history of dementia, neurological diseases and malignancies increase the odds of transition to delirium and the use of anticoagulants decreases the incident delirium.Conclusion: Approximately, 1 out of 10 overall patients developed delirium; Considering underlying diseases and the medications as the predisposing factors of delirium would help to better predict those at risk.
Pregnancy and Sexual Related Problems Among Women Living on the Street at Dire Dawa City, Ethiopia 2021
Authors: Tadesse Daniel , Girma Meklit , Hailu Mickiale , Abera Legesse 
Published: February 2022

Abstract
Background: The problem of women and youths living on the street is a global phenomenon. It has created countless problems while they lived on the street. Women living on the streets are exposed to unwanted pregnancies and sexual-related problems as they are exposed to sexual exploitation, rape, and prostitution. It is a frequent observation to see women begging on the streets of major cities in Ethiopia having one or two babies by their sides. Besides they are less likely to benefit from basic reproductive health services as living in the poorest segment, delivering and caring for their children on the streets life. Little is known about the overall pregnancy and childbearing scenarios of street women and related health problems. This study will give a better in-depth understanding of the distressing and highly challenging problem among women on the street regarding pregnancy and related problems. Objective: To explore pregnancy and sexual-related problems among women living on the street of Dire Dawa administration, Eastern Ethiopia 2021.Methods: a community-based phenomenological qualitative study was conducted at Dire Dawa city. Data was collected from homeless women through focus group discussion and in-depth interviews using a semi-structured tool that has been translated into the local language, aided by a voice recorder. Then the collected data were translated and transcribed. Codes and themes were derived from the transcribed data using ATLAS to version 7. The final report was developed using a thematic narrative analysis methodResult: Women living on the street are more likely to experience physical abuse, sexual harassment, STI/HIV, unsafe sexual intercourse and unwanted pregnancy. As a result, the fate of this pregnancy is abortion which makes adolescents at high risk of getting health complications as most abortions are made in illegal and unsafe ways. Conclusion: Unwanted pregnancy, STI, HIV, rape and unsafe abortion are the major concerns as it accompanied by their homelessness situation and abusive lifestyles. The regional health bureau needs to understand the extent of the problem and plan to organize an awareness creation program on STI/HIV, risk of pregnancy and SRH services.
Application of DPSIR Model to Identify the Drivers and Impacts of Land Use and Land Cover Changes and Climate Change on Land, Water and Livelihoods in the L. Kyoga Basin: Implications for Sustainable Management
Authors: Obubu John Peter , Odong Robinson , Alamirew Tena , Fetahi Tadesse , Mengistou Seyoum 
Published: February 2022

Abstract
Background

Land use, land cover, and climate change impacts are current global challenges that are affecting many sectors, like agricultural production, socioeconomic development, water quality, and causing land fragmentation. In developing countries like Uganda, rural areas with high populations that are dependent on agriculture, are the most affected. The development of sustainable management measures requires proper identification of drivers and impacts on the environment and livelihoods of the affected communities. This study applied drivers, pressure, state, impact, and response model in the L. Kyoga basin to determine the drivers and impacts of land use, land cover, and climate change on livelihoods and the environment. The objective of this study was to determine the drivers and impacts of land use, land cover, and climate changes on the environment and livelihoods in the L. Kyoga basin, and suggest sustainable mitigation measures. Focus group discussions, key informant interviews, field observations, and literature reviews were used to collect data.
Results

Population increase and climate change were the leading drivers, while agriculture and urbanization were the primary pressures, leading to degraded land, wetlands, and forests; loss of soil fertility, hunger, poverty, poor water quality, which are getting worse. The local communities, government, and non-government institutions had responses to impacts, which included the use of agrochemicals, restoration, and conservation approaches. Most responses had promising results, although they were at a small/pilot scale level. It was also found that the application of policies and regulations to manage impacts was weak.
Conclusion

Land use, land cover changes, and climate change are taking place in the L. Kyoga basin with major impacts on land, water, and community livelihoods. With the observed increase in climate change and population growth, there is a potential of drivers and impacts getting worse. It is therefore important to expand interventions, provide relief, review policies and regulations, and enforce them. The findings are useful for decisions and policymakers to design appropriate management options.
Performance of Model-Based vs. Permutation Tests in the HEALing (Helping to End Addiction Long-TermSM) Communities Study, a Covariate Constrained Cluster Randomized Trial
Authors: Tang Xiaoyu , Heeren Timothy , Westgate Philip M , Feaster Daniel J. , Fernandez Soledad A. , Vandergrift Nathan , Cheng Debbie M. 
Published: February 2022

Abstract
Background

The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate Type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests.
Methods

The primary outcome, number of opioid overdose deaths, is count data assessed at the community-level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the Type I error rates and power for 3 tests: 1) Wald-type t-test with small-sample corrected empirical standard error estimates; 2) Wald-type z-test with model-based standard error estimates; and 3) permutation test with test statistics calculated by the difference in average residuals for the two groups.
Results

Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper Type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved Type I error rates if the constrained space was not too small. For all tests, power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA).
Conclusions

Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study.
Trial registration:
ClinicalTrials.gov http://www.clinicaltrials.gov; Identifier: NCT04111939
Machine learning-based Time Series Models for Effective CO2 Emission prediction in India
Authors: Singh Sunil Kumar , Kumari Surbhi 
Published: February 2022

Abstract
As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is very harmful for living beings. So, this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next ten years based on univariate time series data. We used three statistical models i.e., AutoRegressive Integrated Moving Average (ARIMA) model, Seasonal AutoRegressive Integrated Moving Average with eXogenous factors (SARIMAX) model, and Holt-Winter model, two machine learning models, i.e., Linear Regression and Random Forest model and one deep learning model, i.e. Long Short Term Memory (LSTM) model. The performance analysis shows that LSTM, SARIMAX, and Holt-Winter methods are accurate models and have the Least Mean Squared Error(MSE), Root Mean Square Error (RMSE), and Median Absolute Error (MedAE) for this kind of univariate data distribution.
The effectiveness of immunotherapies for patients with repeated implantation failure: a systematic review and network meta-analysis
Authors: Liu Mengqi , Yuan Yuan , Qiao Yan , Sui Xi , Tang Yuzhu , Yin Ping , Yang Dongzi 
Published: February 2022

Abstract
Background: This study compared clinical pregnancy outcomes of repeated implantation failure (RIF) patients treated with immunotherapies using a network meta-analysis.Methods: Publications are determined by searching the Pubmed, Embase and web of science databases. The search date is from the establishment of the database till August 2021. The outcomes were clinical pregnancy rate (CPR), live birth rate (LBR) and implantation rate (IR). The Cochrane bias risk assessment tool was applied to evaluate the quality of the study, and Stata 14.0 was used for the network meta-analysis.Results: A total of 16 RCTs including 2008 participants were included. The network meta-analysis results show that PBMC, PRP and SC-GCSF can significantly improve CPR compared with LMWH (PBMC: OR = 2.15; 95% CI, 1.21-3.83; PRP: OR = 2.38; 95% CI, 1.08-5.24; SC-GCSF: OR = 2.46; 95% CI, 1.05-5.72). The LBR of PRP was significantly higher than IU-GCSF (OR = 3.81; 95% CI, 1.22-11.86), LMWH (OR = 4.38; 95% CI, 1.50-12.90) and Intralipid (OR = 3.85; 95% CI, 1.03-14.29), and the LBR of PBMC was also significantly better than that of LMWH (OR = 2.35; 95% CI, 1.14-4.85). Furthermore, PRP treatment significantly improved IR compared with LMWH treatment (OR = 2.81; 95% CI, 1.07-7.4).Discussion: Based on the limited evidence from existing RCTs, PBMC and PRP seem to be the best therapeutic options for RIF patients. However, due to research quantity restrictions, more top-quality researches are required in the future to obtain additional high-level evidence.
Production of nutrient-enriched vermicompost from aquatic macrophytes supplemented with egg shell, bone meal, banana peel, and tea waste: Assessment of nutrient changes, phytotoxicity, and earthworm biodynamics
Authors: Yatoo Ali , Zaheen Zarka , Ali Md. Niamat , Baba Zahoor Ahmad , Bhat Sartaj Ahmad 
Published: February 2022

Abstract
Vermicompost is an organic fertilizer rich in nutrients, beneficial microbes, and plant growth hormones that not only enhances the growth of crops but also contributes to the improvement of the physicochemical and biological properties of the soil. However, its lower nutrient content makes it less preferable among farmers and limits its applicability. The present study, evidently the first of its kind, was investigated to enrich the nutrient content of vermicompost by supplementing the macrophyte biomass with cow manure and organic nutrient supplements (egg shell, bone meal, banana peel, and tea waste). Results showed an enhanced TKN (2.87%), TP (0.86%), TK (3.74%) and other nutrients in vermicompost amended with cow manure and nutrient supplements. Highest biomass gain (710-782 mg), growth rate (11.83-13.04 mg), and reproduction rate (3.34-3.75 cocoons per worm) was also observed in T2 and T3, indicating that amending bulking agent and nutrient supplements not only enhance the nutrient content of the final product but also improve overall earthworm activity. The stability and maturity of vermicompost, as indicated by C/N (< 20) and GI (> 80), indicates that vermicompost obtained is suitable for agricultural applications. It is concluded that amendment of cow manure and organic nutrient supplements results in producing mature and nutrient enriched vermicompost suitable for sustainable agricultural production.
Global drivers of historical true fruit fly (Diptera: Tephritidae) invasions
Authors: Trombik Jiří , Ward Samuel , Norrbom Allen L. , Liebhold Andrew M. 
Published: February 2022

Abstract
Given the high costs associated with fruit fly (Tephritidae) invasions, there is a need to better understand and predict the risks of future invasions. We assembled a global database of historical Tephritidae invasions with the objective to identify the biological and socioeconomic drivers that explain these invasions. We investigate the tendencies of certain species to invade and the characteristics of regions that make them more prone to invasions.
We used various published sources and online databases to assemble a database documenting the occurrence (presence/absence) and status (native/non-native) of individual species for each country in the world. Values of several socio-economic and environmental variables were also assembled and considered as explanatory variables. A generalized linear mixed-effects model framework was used to evaluate the utility of these variables for predicting the country-level occurrence of each species outside of its native range.
A total 44 species were identified as having been accidentally introduced in the world. Most species of invading Tephritidae have established in only five or fewer countries. The number of invasions has rapidly increased since the 1950s. Climatic similarity between native and invaded countries, gross domestic product of the invaded country and distance from the native range are significant predictors of tephritid invasions.
Our analysis revealed that both economic and biological factors explain patterns of historical Tephritid invasions. Despite the rising efforts to prevent new invasions, additional species may continue to invade, and many currently established species will likely continue to expand their ranges into new areas.
The Correlation between Childhood Hydrocephalus and Bridging Cortical Vein Cross-sectional Area: Is there an Underlying Impedance Pump Malfunction in Hydrocephalus?
Authors: Bateman Grant Alexander , Bateman Alexander Robert , Subramanian Gopinath Musuwadi 
Published: February 2022

Abstract
Dogs with a naturally occurring form of hydrocephalus have an elevated transmural venous pressure leading to cortical vein dilatation. The purpose of this study is to discover if there is vein dilatation in childhood hydrocephalus and to estimate the pressure required to maintain any enlargement found. Children with hydrocephalus between the ages of 4 and 15 years were compared with a control group. Magnetic resonance venography (MRV) and flow quantification were performed. The arterial inflow, sagittal sinus and straight sinus venous outflow were measured and the outflow percentages compared to the inflow were calculated. The cross-sectional area of the veins were measured. There were a total of 18 children with hydrocephalus, compared to 72 age and sex matched control MRV’s and 22 control flow quantification studies. In hydrocephalus, the sagittal sinus venous return was reduced by 12.9%, but the straight sinus flow was not significantly different. The superficial territory veins were 22% larger than the controls but the vein of Galen was unchanged. There is evidence of a significant increase in the superficial vein transmural pressure in childhood hydrocephalus estimated to be approximately 4 mmHg. An impedance pump model is suggested to explain these findings.