bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of Silymarin and Selenium-yeast Supplementation on Antioxidant Status, Lipid Peroxidation and Metabolic Hormones in Heat-Stressed Barbari Goats
Authors: Maurya Pankaj Kumar , Singh Rakesh Kumar , Chandra Gulab , Ranjan Koushlesh , Roy Debashis , Kumar Prabhakar , Fahim Ahmad , Sejian Veerasamy 
Published: May 2024

Abstract

The primary aim of this study was to explore the effect of silymarin (SM) and selenium-yeast (SY) on antioxidant status, lipid peroxidation and metabolic hormones in Barbari goat during heat stress. Thirty-two Barbari goats (age: 7.2±0.8 months; weight: 10.5±0.9 kg) were randomly divided into four groups as Group I (CON; n=8); Group II (SM600; n=8); Group III (SeY; n=8) and Group IV (SM600+SeY; n=8). Heat stress, oxidative stress, antioxidant status, lipid profile, lipid peroxidation, and metabolic hormones were evaluated. Maximum and minimum ambient temperature (AT), relative humidity (RH), and temperature humidity index (THI) values showed severe heat stress during the experimental period. The mean values of total antioxidant activity (TAA) were found significantly (P<0.05) higher and thiobarbituric acid reactive substance (TBARS) was found significantly (P<0.05) lower in treatment groups as compared to the control group. The mean level of SOD, CAT, and GSHPx was significantly (P<0.05) increased in SM and SY-supplemented group than the control group. Supplementation significantly (P<0.05) influenced the lipid profile in the animals. There was an increased level of HDL cholesterol and a decreased level of total cholesterol and triglycerides in the supplemented group. Plasma cortisol level was found significantly (P<0.05) lower and T3 and T4 levels were higher in the supplemented group as compared to the control group. The results of this study indicated that supplementation of SM and SY reduces oxidative and metabolic stress, and lipid peroxidation and also improves the antioxidant status in heat-stressed Barbari goats.
Soil Moisture Mapping in Indian Tropical Island Ecosystem using C-band SAR Data and Neural Network Models
Authors: Adamala Sirisha , Ayyam Velmurugan , Palanivel Swarnam , Palakuru Mahesh , Thangavel Subramani , Iyyappan Jaisankar , Kumar Nanda Bijaya 
Published: May 2024

Abstract

Soil moisture is one of the critical variables in identifying the stress or health of plants and its accurate information in agricultural land use is of greater importance under the threat of climate change. This study focuses on mapping the spatio-temporal dynamics of soil moisture in the Indian tropical Island ecosystem (Andaman and Nicobar Islands) using the fusion of radar (Sentinel-1A C-band synthetic aperture radar (SAR)) and optical/thermal (Sentinel-2A and Landsat 8) data. Field-based soil moisture measurements using the gravimetric method are monitored for different land uses of the study area. The study was carried out in the South Andaman district (E 92°’ to E 94°’ and latitude N 6°’ to N 14°’). A total of 60 surface soil samples (0–10 cm) were collected from four predominant land uses of the study area for 2020-22 years to represent real-time soil moisture status. Soil moisture index (SMI) is assessed based on thermal remote sensing data as Land Surface Temperature (LST) besides, normalized difference vegetation index (NDVI) from red and infrared bands data, and dielectric constants from soil textural analysis. Artificial Neural Network (ANN) based models were developed along with multiple linear regression (MLR) to retrieve the soil moisture accurately using satellite and field-derived input parameters such as backscatter coefficients (σ°: VV & VH), NDVI, SMI, and dielectric constants. The performance of modelled soil moisture is evaluated using different statistical index-based criteria concerning field-based soil moisture measurements (SMCv). It is found that positive correlation among (σ°: VV+VH) and (SMCv: %) for all land uses and high agreements (R2 values) between them for barren and vegetable fields. The vegetation interferes the backscatter signal and misinterprets the soil moisture estimation solely with only SAR data. However, consideration of NDVI and SMI improves the soil moisture estimation in case of vegetation abundance land uses. The comparative results showed that ANN models surpass the MLR models in soil moisture estimation with high R2 (0.67-0.99) and η (62.6-99.9) and low RMSE (0.05-2.19%) and MAE (0.03-1.74%) values without over or under estimations (Rratio ≈ 1) in South Andaman area of Andaman and Nicobar tropical ecosystem. This kind of study helps as baseline information for hydrological modelling and policy makers to plan irrigation systems, accurate management of crop quantity and quality in farms, and schedule critical irrigations accurately.
Morphology and biology of Noorda blitealis (Lepidoptera : Crambidae) immature instar for a biological control perspective
Authors: KABRE SALIFOU , EBOU DAO MADJELIA CANGRE , FOUSSENI TRAORE , WAONGO ANTOINE , GNANKINE OLIVIER 
Published: May 2024

Abstract

Noorda blitealis is a phytophagous insect that causes major losses to Moringa growers worldwide. This study was conducted to determine the bio-ecology of its larvae. A mass rearing was carried out in Central Agricultural Entomology Laboratory located in Kamboinsin, Ouagadougou from July to December 2021. Parameters such as eggs incubation period, number of larval instars and their duration, body length and color, number of Moringa folioles attacked and survival rate were collected. Results showed that Noorda blitealis (Lepidoptera: Crambidae) passed into 5 larval instars before pupating. First instar larva averaged 2.4 ± 0.8 mm in length and were light green. At the 5th instar, these larvae reach 10.8 ± 0.4 mm and their bodies take on a reddish appearance. The average egg incubation was 3.00 ± 0.35 days. Larvae and chrysalis duration phases were respectively, 10.61 ± 2.28 days and 9.78 ± 0.42 days. The larval survival rate reaches 100% for 3rd to 5th instar. A statistically significant difference was found between leaf attacked rates and larval instars (p = 0.001). Larvae in their 6th and 7th days attacked the greatest number of folioles. These data could be used to develop effective biological control methods against this insect pest.
Learning spatiotemporal dynamics with a pretrained generative model
Authors: Yang Lijun , Li Zeyu , Han Wang , Zhang Yue , Fu Qingfei , Li Jingxuan , Qin Lizi , Dong Ruoyu , Sun Hao , Deng Yue 
Published: May 2024

Abstract

Reconstructing spatiotemporal dynamics with sparse sensor measurement is an outstanding problem, commonly encountered in a wide spectrum of scientific and engineering applications. Such a problem is particularly challenging when the number and/or types of sensors (e.g., randomly placed) are extremely insufficient. Existing end-to-end learning models ordinarily suffer from the generalization issue for full-field reconstruction of spatiotemporal dynamics, especially in sparse data regimes typically seen in real-world applications. To this end, we propose a sparse-sensor-assisted score-based generative model (S3GM) to reconstruct and predict full-field spatiotemporal dynamics based on sparse measurements. Instead of learning directly the mapping between input and output pairs, an unconditioned generative model is firstly pretrained capturing the joint distribution of a vast group of pretraining data in a self-supervised manner, followed then by a sampling process conditioned on unseen sparse measurement. The efficacy of S3GM has been verified on multiple dynamical systems with various synthetic, real-world, and lab-test datasets (ranging from turbulent flow modeling to weather/climate forecasting). The results demonstrate the excellent performance of S3GM in zero-shot reconstruction and prediction of spatiotemporal dynamics even with high levels of data sparsity and noise. We find that S3GM exhibits high accuracy, generalizability, and robustness when handling different reconstruction tasks.
An interpretable latent linear model for nonlinear coupled oscillators on graphs
Authors: Goyal Agam , Wu Zhaoxing , Yim Richard P. , Chen Binhao , Xu Zihong , Lyu Hanbaek 
Published: May 2024

Abstract

A system of coupled oscillators on an arbitrary graph is locally driven by the tendency to mutual synchronization between nearby oscillators, but can and often exhibit nonlinear behavior on the whole graph. Understanding such nonlinear behavior has been a key challenge in predicting whether all oscillators in such a system will eventually synchronize. In this paper, we demonstrate that, surprisingly, such nonlinear behavior of coupled oscillators can be effectively linearized in certain latent dynamic spaces. The key insight is that there is a small number of ‘latent dynamics filters’, each with a specific association with synchronizing and non-synchronizing dynamics on subgraphs so that any observed dynamics on subgraphs can be approximated by a suitable linear combination of such elementary dynamic patterns. Taking an ensemble of subgraph-level predictions provides an interpretable predictor for whether the system on the whole graph reaches global synchronization. We propose algorithms based on supervised matrix factorization to learn such latent dynamics filters. We demonstrate that our method performs competitively in synchronization prediction tasks against baselines and black-box classification algorithms, despite its simple and interpretable architecture.
Impact of health education intervention on the patients’ adherence to malaria Artemisinin- based combination therapy in Kamuli District, Uganda
Authors: Bawate Charles , Guyah Bernard , Callender-Carter Sylvia , Ouma Collins 
Published: May 2024

Abstract

Background
Global malaria control programmes such as approaches to community engagements to deliver malaria control interventions have been successful in controlling malaria. Malaria remains a disease of public health importance especially in African region and Uganda in particular. Strategies aimed at accelerating patients’ adherence to prescribed Artemisinin-based combination therapy (ACT) treatment are needed in the fight to control and eradicate malaria. Previous studies have shown the power of health education in improving patients’ adherence to ACT. The objective of this study was to establish the impact of a community health education intervention on the patients’ adherence to malaria ACT treatment in Kamuli District, Uganda.
Methods
A pre-post-test intervention study without a control group was used to understand the impact of community health education training in improving patients’ adherence to ACT. Equal number of 1266 patients were enrolled and assigned into any of the three arms equally (422) – no follow up, follow-up on day 2 and day 4 arm at pre-test and post-test phases. Mann-Whiteny U test was used to establish the impact of the community health education on patients’ adherence to ACT. Statistical significance was established at p < 0.05.
Results
A total of 1688 patients were analyzed. At pre-test, the median age was 20 years with majority (64.3%) being females while patients’ adherence was reported to be 588/844 (69.7%). At post-test, the median age was 21 years, majority (62.6%) females, and patients’ adherence 700/844 (82.9%). A Mann-Whitney test showed a statistically significant difference in the patients’ adherence to ACT of pre-test and post-test after the intervention (U = 308904, Z = -6.409, p < 0.0001), with higher adherence at post-test (mean = 900.5) than pre-test (mean = 788.5) and small effect (0.156).
Conclusion
Community health education as an intervention was effective in improving patient’s adherence to ACT in Kamuli Division, Uganda. Although this approach could contribute to the effort of national malaria control and elimination strategy in Uganda, there is need to continuously educate communities about adherence to medicines as prescribed by health care workers since health behavior change takes time.
Exploring osteosarcoma based on the tumour microenvironment
Authors: Wu Ao , Kong Peng , Yu Peng , Yang Zhi-Kai , Li You-Tong , Xu Jia-Le , Teng Jia-Wen , Bian Si-Shan 
Published: May 2024

Abstract

Osteosarcoma is a cancerous bone tumor that develops from mesenchymal cells and is characterized by early metastasis, easy drug resistance, high disability, and mortality. Immunological characteristics of the tumor microenvironment (TME) has attracted attention for the prognosis and treatment of osteosarcoma, and there is a need to explore a signature with high sensitivity for prognosis. In the present study, a total of 84 samples of osteosarcoma were acquired from the UCSC Xena database, analyzed for immune infiltration and classified into two categories depending on their immune properties, and then screened for DEGs between the two groups and analyzed for enrichment, with the majority of DEGs enriched in the immune domain. To further analyze their immune characteristics,the immune related genes were obtained from the TIMER database, and we performed an intersection analysis to identify immune-related differentially expressed genes (IR-DEGs), which were analyzed using a univariate COX regression and LASSO analysis was used to obtain the ideal genes to constructing the risk model, and to uncover the prognostic distinctions between high-risk scoring group and low-risk scoring group, a survival analysis was conducted. The risk assessment model developed in this study revealed a notable variation in survival analysis outcomes between the high-risk and low-risk scoring groups, and the conclusions reached by the model are consistent with the findings of previous scholars and also yield meaningful results when analyzing immune checkpoints. The risk assessment model developed in this study is precise and dependable for forecasting outcomes and analyzing characteristics of osteosarcoma.
Reduction of the natural enemy population by deltamethrin and imidacloprid leads to the resurgence of brown plant hoppers (Nilaparvata lugens) in rice
Authors: H Netra , Hurali Sujay , Barkeer Srikanth , A Prabhuraj , R Badariprasad P. , Gowder S. B. 
Published: May 2024

Abstract

Brown planthopper (BPH), Nilaparvata lugens (Stal) is a monophagous and most devastating pest of rice. Application of insecticides is inevitable to control the infestation of BPH on rice crops. However, the indiscriminate use of various insecticides in rice is leading to pest resurgence. Several factors have been connected to the resurgence of brown planthoppers, especially the reduction in natural enemies and changes in plant nutrition after insecticide application. Hence, in the present study, we studied the resurgence of BPH post application of different insecticides in rice and assessed the impact of insecticides on natural enemies population during Rabi 2022-23. Among nine insecticides tested, two insecticides viz., deltamethrin 11 EC @ 15 g a.i/ha (44.70%) and imidacloprid 17.8 SL @ 25 g a.i/ha (23.01%) significantly increased the population of N. lugens over untreated check and consequently resulted in higher BPH resurgence. A greater reduction in predatory mirid bugs and spiders was observed in the deltamethrin and imidacloprid treated groups compared to the untreated check. In conclusion, we found that deltamethrin and imidacloprid induce the BPH resurgence in rice compared to other tested insecticides and the observed resurgence was partly due to the reduction of natural enemies.
Choice of Anesthetic Management Can Reduce the Use of Rescue Antiemetics After Bariatric Surgery but Effects Are Short-lived
Authors: Sprung Juraj , Warner David O. , Ghanem Omar M. , Lu Lauren Y. , Salame Marita , Schroeder Darrell R. , Weingarten Toby N. 
Published: May 2024

Abstract

Background
Postoperative nausea and vomiting (PONV) is frequent after bariatric surgery. We hypothesized that when compared with a volatile-based technique incorporating propofol infusions, the need for rescue antiemetics would be reduced by the use of two strategies: 1) the addition of dexmedetomidine infusions, and 2) the avoidance of opioid and volatile anesthetics, and that this reduction would be observed in the PACU following PACU discharge.
Methods
In this retrospective observational study we included patients undergoing bariatric surgery from 2018–2022 who received one of three anesthetic strategies: 1) volatile anesthetic and propofol infusion (VOL + PROP), 2) VOL + PROP with the addition of dexmedetomidine (DEX), or 3) opioid-sparing total intravenous anesthesia (PROP + DEX). Inverse probability of treatment weighting analysis determined the association between the need for rescue-antiemetics in the PACU following PACU discharge.
Results
332 patients received VOL + PROP, 354 VOL + PROP + DEX, and 166 PROP + DEX. PROP + DEX received fewer rescue antiemetics in PACU compared to VOL + PROP (11% vs 24%, P = 0.002), and VOL + PROP + DEX fewer compared to VOL + PROP (16% vs 24%, P = 0.023). This differential effect was limited to PACU stay; antiemetic use increased across all groups following PACU discharge until midnight (ranging from 38–46% across groups, P = 0.71) and through the first postoperative day (ranging from 47–57%, P = 0.20).
Conclusions
The benefit associated with two PONV prevention strategies, the addition of dexmedetomidine infusion or avoidance of opioid and volatile anesthetics, did not persist after PACU discharge. These findings highlight the challenge of PONV on postoperative wards, and the need for a novel multimodal team approach to mitigate this complication.
Effects of stress on burnout among infection control nurses during the COVID-19 pandemic: The mediating effects of social support and self-efficacy
Authors: Lee Su-jin , Park Ju-Young , Kim Seo-Hyeon 
Published: May 2024

Abstract

Background
This study investigated the mediating effects of self-efficacy and social support on the relationship between stress and burnout among infection control nurses (ICNs) during an emerging infectious disease pandemic.
Methods
The study participants encompassed 210 ICNs with at least six months’ experience in an infection control unit at a general hospital in South Korea during the COVID-19 pandemic. Data were analyzed using independent t-tests or one-way analysis of variance (ANOVA), while descriptive statistics were performed using SPSS/WIN 26.0 software. Hayes’s PROCESS macro 4.2 software was used to verify the significance of the indirect effects of the mediators.
Results
Stress had a significant positive effect on burnout (β = .80, p < .001), accounting for 73% of the variance. Self-efficacy (β = − .26, p < .001) and social support (β = − .11, p = .034) had a significant negative effect on burnout, accounting for 78% of the variance. Stress was lower when self-efficacy and social support were entered into the model (β = .80 → .59), indicating that self-efficacy and social support mediated the relationship between stress and burnout.
Conclusion
This study is significant in that it confirms the effects of self-efficacy and social support on the relationship between stress and burnout among ICNs. The results highlight the importance of establishing organizational support systems and developing and implementing programs for enhancing self-efficacy in order to reduce burnout among ICNs.
Solar-Assisted Photodegradation of Paracetamol Using ZnO-Coated Brass Plates in a Rotating Photoreactor
Authors: Simões Thiago B. , Silva Émerson F.M. da , Oliveira Evelle D.C. , Araújo Ivana Cecília Ferreira , Garcia Ramón R. P. , Filho José Geraldo A.P. , Almeida Luciano 
Published: May 2024

Abstract

Rotating structured photoreactor with brass sheets covered with ZnO for the degradation of paracetamol (PAR) was used. The brass plates were calcined and coated with a zinc oxide suspension. The BZnO photocatalysts were characterized using X-ray diffraction (XRD), diffuse reflectance spectroscopy (DRS), and scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). The photodegradation experiments of the PAR were performed under UVA/UVB radiation for 6 h based on a 2² factorial experimental design with the central point in triplicate, in which the influence of pH (5, 7, and 9) and the mass ratio of the catalyst per substrate area (1; 1.5 and 2 mg.cm-²) was studied. The best result was obtained at pH 5 and a ratio of 2 mg.cm-2 with a degradation of 95.0%. The influence of the type of radiation (solar, visible, and UVC) was studied, obtaining a degradation of the drug of approximately 78% with solar radiation. The photocatalyst reuse experiments showed good stability of the ZnO on the brass plates, presenting an efficiency of 88.3% after five reaction cycles. The kinetic study was conducted using the pseudo-first-order model in two stages. Toxicity tests using seeds indicated the absence of toxic compounds after photodegradation.
Integrating Healthcare Analytics to Improve Diabetes Management and Prevent Heart Attacks: A Data-Driven Approach
Authors: Bhattacharya Naboshree , Kumar Purushottam 
Published: May 2024

Abstract

Heart disease is a significant global health concern characterized by the heart's inability to adequately pump blood, leading to symptoms like weakness, difficulty breathing, and swollen feet. Detecting heart disease early is crucial, often relying on factors such as age, gender, and pulse rate analysis, as well as electrocardiogram screenings for irregular heartbeats. Risk factors include obesity, smoking, diabetes, high blood pressure, and unhealthy diets, with diabetic individuals facing elevated risks due to accelerated atherosclerosis and high blood sugar levels. Managing heart disease involves lifestyle modifications, medication adherence, and regular medical check-ups. Healthcare systems utilize data mining, machine learning, and clinical decision support systems to analyze extensive databases and predict conditions like heart disease, employing techniques such as supervised and unsupervised learning. Big data applications in healthcare, incorporating genomics data and electronic health records, provide insights into treatment effectiveness and real-time patient data analysis, facilitating personalized medicine and potentially saving lives. This research paper assesses the various components found in diabetes patients’ data to accurately forecast heart disease. It is identified by employing the Correlation-based Feature Subset Selection Technique with Best First Search, which is the most important characteristic for heart disease prediction. It has been discovered that age, gender, blood pressure diastolic, diabetes, smoking, obesity, diet, physical activity, stress, kind of chest pain, history of chest pain, troponin, ECG, and target are the most important factors for detecting heart disease. A variety of artificial intelligence methods are used and contrasted for cardiac disease, including logistic regression, K-nearest neighbor (K-NN), decision trees, random forests, and multilayer perceptrons (MLPs). Compared to using all the input features, K-NN with a subset of the features has the highest accuracy rate (80%).