bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linear relation association between TyG-BMI and BPH: a national prospective cohort study
Authors: Li Bing , Zhang Zhiqiang , Sun Chao , Sun Yaodong , Li Junping , Liu Xiaoqiang 
Published: August 2024

Abstract

Objective
This study aims to explore the relationship between the Triglyceride Glucose Body Mass Index (TyG-BMI) and the risk of developing benign prostatic hyperplasia (BPH).
Methods
Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we included 3,301 men aged 45 and above. We analyzed the association between TyG-BMI and the risk of developing BPH using multivariate logistic regression and restricted cubic spline models.
Results
During the 4-year follow-up period, 268 cases of BPH were identified. There was a positive correlation between TyG-BMI and the risk of BPH (OR = 1.22, 95% CI: 1.08–1.38, p < 0.001), with a significant dose-response relationship (P < 0.001). Stratified analyses indicated that the impact of TyG-BMI on BPH risk was consistent across various subgroups.
Conclusion
There is a linear positive correlation between TyG-BMI and the risk of developing BPH. Managing TyG-BMI levels may help reduce the risk of BPH.
Hybrid Geomagnetic Data Forecasting Model Using Wavelet Packet Decomposition and Deep Neural Networks
Authors: Zhu Libo , Li Houpu , Zhu Bo , Li Wenkui , Chen Yongbing , He Bi 
Published: August 2024

Abstract

Accurate and reliable prediction of geomagnetic data is essential for its management and application. To improve the accuracy of geomagnetic data prediction and overcome the limitations of traditional machine learning algorithms in network structure and prediction time, a hybrid prediction model combining wavelet packet decomposition (WPD) and the DLinear model is proposed. The model uses WPD to decompose the original geomagnetic data in multiple sub-layers, effectively eliminating high-frequency noise while retaining low-frequency signal input to the DLinear model to complete accurate time series prediction. To verify and compare the prediction efficiency of the proposed model with those of other models, four sets of geomagnetic data collected by the Beijing Geomagnetic Observatory in China were used to perform single- and multi-step prediction experiments. The experimental results showed the following: the proposed hybrid model had the best prediction performance in one-, three- and five-step predictions, and with the introduction of the WPD method, the prediction accuracy of the original model was effectively improved. The combination of WPD and deep learning technology in this study provides a new way to predict future geomagnetic data with high precision and reliability.
Uncertainty reduction with Hyperparameter Optimization in mineral prospectivity mapping: A Regularized Artificial Neural Network approach
Authors: Seyedhamzeh Mirakbar , Maghsoudi Abbas , Ghezelbash Reza , Hajihosseinlou Mahsa 
Published: August 2024

Abstract

Mineral prospecting mapping (MPM) is necessary for uncovering potential areas for resource exploration and development in the reconnaissance and prospecting stages. However, traditional mapping approaches often suffer from inherent uncertainties due to factors like data quality, geological complexities, and subjective interpretations. This research introduces a novel deep learning framework for MPM using an Artificial Neural Network (ANN) architecture with L1 regularization inspired by SVMs. The approach aims to reduce uncertainty in MPM By harnessing cutting-edge developments in deep learning. It utilizes an MLP architecture with L1 regularization to learn complex patterns from geoscience data and prevent overfitting. The study applies Regularized Deep Learning to create predictive models for copper mineralization prospectivity in the Sardouyeh District, Kerman, Iran. For preparation of initial outputs, we utilized multi-element geochemical patterns obtained through Principal Component Analysis (PCA), mineralization-related geological-structural layers and hydrothermal alteration evidence from the study area, which were transformed into mappable targeting criteria. Additionally, 39 known Cu-porphyry deposits/occurrences and 39 randomly chosen non-prospect locations were used as target variables for model training. The MPM models were evaluated using ROC, F1-score, confusion matrix, and precision metrics. The Regularized MLP model showed superior prediction accuracy over traditional ANN algorithms, achieving 96% accuracy, 95% recall, 97% precision, 96% F1 score, and 99% AUC for Cu-porphyry deposits. This study highlights the importance of advanced machine learning techniques, specifically Regularized Deep Learning, for optimizing hyperparameters, reducing uncertainty, increasing precision, achieving high accuracy, enhancing mineral exploration efficiency, and addressing overfitting challenges in traditional ANNs.
Assessment of Delivery Points in a Tribal District of Maharashtra” - an Observational Study
Authors: bharadwaj gauri , Sankhe Lalit , bharadwaj Shubhendra 
Published: August 2024

Abstract

Introduction - Quality of healthcare in delivery points, particularly in tribal areas, is crucial for safe childbirth and maternal health. There is a need for comprehensive evaluation of delivery points, focusing on infrastructure, supplies, adherence to protocols, and overall quality of care provided during childbirth.
Methods- A community-based study conducted in Palghar district where 15 delivery points were selected, by convenient sampling. Data was collected through observations and record reviews through LaQshya checklist. Indicators like service provision, patients’ rights, clinical services, inputs, support services, infection control services, quality management were used to score the delivery points.
Results- L3 level delivery points scored the highest in-service provision, with an average score of 19(88.18%) and highest in the area of provision patients’ rights38(95%) as compared to other levels. L3 delivery points scored was 98.2 (90.92%) in in the area of inputs, 59.2 (95.48%) in support services, 176.4 (95.8%) in clinical services, 71(95.94%) in infection control and 48.57% in quality management services.
Conclusion – L3 delivery points scored highest than all the levels in all the criteria of LaQshya checklist and L1 delivery points scored the least. Major areas of improvement were quality management, infection control and inputs.
Inhibition of STAT3/Fanconi anemia axis by Nifuroxazide is synthetic lethal with PARP inhibitor and selectively targets breast cancer stem cells
Authors: Ginestier Christophe , Rouault Célia , Bansard Lucile , Martínez-Balsalobre Elena , Bonnet Caroline , WICINSKI Julien , Debieu Sylvain , Colombeau Ludovic , Pinna Guillaume , Machu Margot , Rosnet Olivier , Chevrier Véronique , popovici Cornel , Sobol Hagay , Pasquier Eddy , Guasch Gèraldine , Rodriguez Raphaël , Pannequin Julie , Pascussi Jean-Marc , Christophe Lachaud , Charafe-Jauffret Emmanuelle , castellano Rémy , Vandamme Marie 
Published: August 2024

Abstract

The targeting of cancer stem cells (CSCs) has proven to be an effective approach for limiting tumor progression, thus necessitating the identification of new drugs with anti-CSC activity. Through a high-throughput drug repositioning screen, we identified the antibiotic Nifuroxazide (NIF) as a potent anti-CSC compound. Utilizing a click chemistry strategy, we demonstrated that NIF is a prodrug that is specifically bioactivated in breast CSCs. Mechanistically, NIF-induced CSC death is a result of a synergistic action that combines the generation of DNA interstrand crosslinks with the inhibition of the Fanconi anemia (FA) pathway activity. NIF treatment mimics FA-deficiency through the inhibition of STAT3, which we identified as a non-canonical transcription factor of FA-related genes. NIF induces a chemical HRDness in CSCs that (re)sensitizes breast cancers with innate or acquired resistance to PARP inhibitor (PARPi) in PDX models. Our results suggest that NIF may be useful in combination with PARPi for the treatment of breast tumors, regardless of their HRD status.
Robust adaptation strategy for enhancing climate resilience in irrigated agriculture
Authors: Babel Mukand , Kamalamma Ambili 
Published: August 2024

Abstract

Adaptation as a goal has become an absolute necessity in water resources projects to cope with the negative impacts of climate change. Adaptation planning needs to move from the traditional predict-then-act approach to a broader vulnerability and risk-based approach. This article presents an evaluation of certain adaptation strategies by considering the case of an irrigated agricultural system. Our evaluation includes the assessment of the response of adaptation strategies in reducing risk and their robustness in terms of the potential to maintain system performance under maximum uncertain but plausible future climate conditions. We tested two broad strategies of water management and agronomic practices for reducing climate risks in the system, of which the former, we found, is more robust and risk-tolerant. The study also investigated additional risks that may lead to maladaptation by reducing crop yield, illustrating how adaptive water management can increase the robustness and resilience of irrigated agriculture.
Comparative Analysis of ESP-Assisted Dump-flooding and Conventional Water Injection for Enhanced Oil Recovery: A Case Study of Field ABX2 in the Niger Delta
Authors: Idahor Terry , Ohenhen Ikponmwosa 
Published: August 2024

Abstract

This study evaluates the effectiveness of dump-flood and water injection techniques for enhanced oil recovery in Field ABX2 of the Niger Delta basin. Using advanced numerical reservoir modeling, the performance of ESP-powered dump-flooding was compared against conventional water injection over a 20-year period. Both methods successfully maintained reservoir pressure above the critical bubble point of 3471 psi, preventing excessive gas production. Primary recovery extracted only 15% of original oil in place (OOIP), while ESP-powered dump-flooding and water injection improved recovery to 34% and 35% of OOIP respectively after 20 years. Both secondary recovery methods exhibited excellent areal sweep efficiency and kept oil above the oil-water contact. The dump-flood approach achieved comparable results to water injection but with lower infrastructure requirements and costs. The models indicate the substantial aquifer in Field ABX2 can sustain dump-flooding for the full 20-year period. This study demonstrates ESP-assisted dump-flooding as a promising and economical enhanced oil recovery technique for Niger Delta reservoirs. The modeling approach developed here integrates reservoir characterization, well completion design, and long-term production forecasting to optimize recovery strategies.
European South and Nordic Countries: a new approach to global food parameters and public health relationship
Authors: García-Martín Rosa , de Santos Mª Mar Polo , Jarauta-Bragulat Eusebi , Colomer-Xena Yvonne , Clotet-Ballús Ramon , Pinos Helena 
Published: August 2024

Abstract

The global behaviour of food systems of two groups of European countries (three Nordic and three Mediterranean) is explored. The global food system model defined by Triptolemos Foundation is applied, using 20 variables with public and statistically reliable data series of food system parameters: availability, economy, politics, knowledge, behaviour, and culture, with attention to the study of the food/health relationship. A new index is defined: Global Food System Harmony Index (GFSHI), which allows knowing the coherence and degree of internal balance between the evaluated variables of the countries. It appears as an appropriate instrument for managing food systems. Conclusions allow us to characterize impact of food behaviour on health in each country and comparatively as a whole, both at a quantitative and qualitative level.
Safety and efficacy profile of a 90-day oral L-arginine hydrochloride intervention for patients with amyotrophic lateral sclerosis
Authors: Naito Hiroyuki , Nakamori Masahiro , Toko Megumi , Hayashi Yuki , Tazuma Taku , Watanabe Tomoaki , Ishihara Keito , Tachiyama Keisuke , Yamazaki Yu , Maruyama Hirofumi 
Published: August 2024

Abstract

Weight loss, a key indicator of malnutrition in amyotrophic lateral sclerosis (ALS) patients, negatively impacts prognosis. However, effective nutritional interventions have not been adequately established. Previous research in ALS model mice has shown that L-arginine can prolong survival, yet no human intervention studies have been conducted. This study aimed to assess the safety, tolerability, and efficacy of L-arginine hydrochloride in ALS patients. ALS patients were administered 15 g/day L-arginine hydrochloride for 90 days. Safety was primarily evaluated on days 45 and 90. Efficacy measures included changes in nutritional status, ALS Functional Rating Scale scores, and the occurrence of events such as the initiation of tracheostomy positive pressure ventilation (TPPV) and death. The study included 20 patients (40% female; mean age, 62.0 ± 6.9 years; median disease duration, 1.9 years). Six participants (30%) experienced treatment-emergent adverse events (TEAEs), including elevated creatine kinase levels, liver function test abnormalities, glucose tolerance issues, hyperammonemia, anorexia, dysgeusia, and vasculitis. No serious TEAEs were associated with L-arginine hydrochloride. Over the course of three months, the average changes in body weight and body mass index (BMI) were − 0.37 kg and − 1.1 kg/m2, respectively, which are less than the typically observed natural reduction rates. There were no events requiring TPPV initiation or deaths. This study demonstrated that the oral administration of L-arginine hydrochloride across three months was well tolerated by ALS patients, with no serious TEAEs or deaths attributed to the study drug. Trial Registration number: Japan Registry of Clinical Trials (jRCTs061230001), first registered 11/04/2023
Reduced Cross-Protective Potential of Omicron Compared to Ancestral SARS-CoV-2 Spike Vaccines Against Potentially Zoonotic Coronaviruses
Authors: Renner Tyler , Stuible Matthew , Cass Brian , Perret Sylvie , Guimond Julie , Lord-Dufour Simon , McCluskie Michael J. , Durocher Yves , Akache Bassel 
Published: August 2024

Abstract

The COVID-19 pandemic has emphasised the importance of vaccines and preparedness against viral threats crossing species barriers. In response, a worldwide vaccination campaign targeting SARS-CoV-2 was implemented, which provides some cross-protective immunological memory to other coronavirus species with zoonotic potential. Following a vaccination regimen against SARS-CoV-2 spike in a preclinical mouse model, we were able to demonstrate the induction of neutralizing antibodies towards multiple human ACE2 (hACE2)-binding sarbecovirus spikes. Importantly, compared to vaccines based on the SARS-CoV-2 Reference strain, vaccines based on Omicron spike sequences induced drastically less broadly cross-protective neutralizing antibodies against other hACE2-binding sarbecoviruses. This observation remained true whether the vaccination regimens were based on protein subunit or mRNA / LNP vaccines. Overall, while it may be necessary to update vaccine antigens to combat the evolving SARS-CoV-2 virus for enhanced protection from COVID-19, Reference-based vaccines may be a more valuable tool to protect against novel coronavirus zoonoses.
Effects of Photodynamic Therapy Using Bisdemethoxycurcumin Combined with Melatonin or Acetyl-melatonin on C. albicans
Authors: Duterte Maria Margarita D , Morales Noppawan Phumala , Pitiphat Waranuch , Puthongking Ploenthip , Damrongrungruang Teerasak 
Published: August 2024

Abstract

Objectives The current study aims to explore the efficacy of antifungal photodynamic therapy (PDT) on C. albicans biofilms by combining photosensitizers, bisdemethoxycurcumin (BDMC), and melatonin (MLT) or acetyl-melatonin (AcO-MLT). Additionally, the relationship between different types of reactive oxygen species and PDT’s antifungal efficacy was investigated.Materials and methods BDMC, MLT and AcO-MLT were applied, alone and in combination, to 48-hour C. albicans biofilm cultures (n = 6 groups). Blue and red LED light (250 mW/cm2 with 37.5 J/cm2 for single or 75 J/cm2 for dual photosensitizer groups) were used to irradiate BDMC groups and MLT/AcO-MLT groups, respectively. For combination groups, blue LEDs and subsequently red LEDs were used. Drop plate assays were performed at 0, 1 and 6 hours post-treatment. Colony forming units (CFUs) were then counted after 48 hours. Hydroxyl radicals and singlet oxygen were measured using fluorescence spectroscopy and electromagnetic spin resonance. Additionally, cell cytotoxicity was tested on human oral keratinocytes.Results Significant CFU reductions were observed with combinations 20 µM BDMC + 20 µM AcO-MLT and 60 µM BDMC + 20 µM MLT at 0 and 1 hour post-treatment, respectively. Singlet oxygen production increased with MLT/AcO-MLT addition and had moderate-substantial correlations with inhibition at all times. Hydroxyl radical production was not significantly different from the control. Additionally, BDMC exhibited subtle cytotoxicity on human oral keratinocytes.Conclusion PDT using BDMC + MLT or AcO-MLT, with blue and red LED light, effectively inhibits C. albicans biofilm through singlet oxygen generation.Clinical relevance: Melatonin acts as a photosensitizer in PDT to inhibit fungal infection.
Cloud-Enabled Blockchain and IoT-Based Assisted Living System in 6G Networks: Enhancing Quality of Life and Privacy
Authors: A Mohanaprakash T , A Deepak Kumar , P Naveen , Karuppiah Santhi 
Published: August 2024

Abstract

Owing to emerging disruptive technologies, the Internet of Things (IoT) plays an important role in innovative living domains, such as elderly and disabled healthcare service and home security and safety monitoring, and computerization control service. The IoT can improve inhabitants' quality of life and users of smart ambient assisted living (AAL) environments. The sixth (6G) networks will enable a completely linked world with terrestrial wireless communications. Blockchain-based approaches offer decentralized privacy and security and, yet they include vital delay, computational, and energy overhead that are inappropriate for most resource-reserved IoT devices. Hence, in this study, a Blockchain and IoT-based Assisted Living System (BIoT-ALS) has been proposed using 6G communication. The nodes in our proposed paradigm use smart contracts to specify norms of interaction while working together to provide storage and computing resources. Our suggested approach has encouraged confidence-free interaction and boosted user privacy by the blockchain approach. The purpose of this paper is to explain the sensor layer, a distributed signal processing system in vast, physically connected, wirelessly networked, and energy-restricted networks of sensor items. A comprehensive series of experimental tests have been conducted, showing each sensor type's accuracy and probable usage. The numerical results show that the suggested BIoT-ALS model improves the performance ratio of 99.1%, accuracy ratio of 98.8%, reliability ratio of 94.8%, an efficiency ratio of 93.6%, the throughput rate of 97.6%, and reduces the network delay of 19.2%, latency ratio 10.2%, and execution time of 20.4% compared to other popular models.
ACE2 expression in the kidneys of pregnant and postpartum rats: Physiological and pathological significance during pregnancy
Authors: Kikuta Yu , Kazama Itsuro 
Published: August 2024

Abstract

Pregnancy is associated with changes in systemic hemodynamics, such as an increase in plasma volume and decrease in blood pressure because of peripheral vasodilation. However, little is known about the mechanisms underlying the changes in renal hemodynamics, such as increases in renal blood flow and the glomerular filtration rate (GFR). Angiotensin-converting enzyme 2 (ACE2), a transmembrane glycoprotein involved in vasodilation, also acts as a receptor for the entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during coronavirus disease 2019 (COVID-19) infection. Here, we showed that in pregnant rats the renal arterioles and venules as well as the glomerular capillaries were markedly dilated, indicating renal vasodilation. Immunohistochemistry demonstrated increased ACE2 and angiotensin (1-7) (Ang (1-7)) expression within the proximal renal tubules during pregnancy. As these proteins exert vasodilatory properties, they are considered responsible for renal vasodilation and the subsequent increase in GFR. Further, the similar distribution and enhanced expression of ACE2 and transmembrane protease serine 2 (TMPRSS2) in the proximal renal tubules during pregnancy suggests their roles in the development of acute kidney injury following COVID-19 infection in pregnancy. Overall, this study highlights the physiological and pathological significance of ACE2 during pregnancy.
A draft genome assembly and resequencing analysis of Chinese cherry (Cerasus pseudocerasus) reveal structural variants associated with fruit traits
Authors: Zhuqin Liu , Xueping Wang , Xiuhua Zhao , Chao Gu 
Published: August 2024

Abstract

Chinese cherry (Cerasus pseudocerasus) is a significant fruit crop that enjoys widespread cultivation in China. Here, we report a draft autotetraploid genome assembly of the Chinese cherry cultivar ‘Huangguo’, characterized by a size of 340.99 Mb and comprised of 261,760 scaffold sequences. We further obtained resequencing data of 8 Chinese cherry varieties at an average sequencing depth of about 104× per individual. Population structure analysis revealed that the 8 varieties were partitioned into two distinct groups, and the G matrix analysis found that the closest genetic background was shared between the ‘Changbing’ and ‘Duanbing’ varieties. In addition, we have creatively established a workflow for transforming heterozygous genotypes from diploid format to tetraploid format by conducting secondary genotyping in deletion structural variations, and through this process, structural variants related to Chinese cherry fruit peel color or size were initially screened out. In summary, this study provides valuable resources for population genetic relationships and will promote functional genomics studies in Chinese cherry and other crops.
Research on Intelligent Semi-Active Control Algorithm Based on Machine Learning and Seismic Reliability of Aqueducts
Authors: Xiao Zhongyuan , Xu Jianguo , wang li , Liang Huang , Qi Wanshuai , Zhou Qi 
Published: August 2024

Abstract

Addressing the issue of poor robustness in current semi-active control algorithms and the limited generalization capability of existing deterministic analysis-based evaluation methods Building upon this, an intelligent semi-active control algorithm based on machine learning has been proposed. This has been combined with an engineering example to study semi-active control under the influence of random earthquakes. The results indicate that under the influence of random earthquakes, the seismic reliability of the machine learning-based semi-active control algorithm is significantly higher than that of the uncontrolled state for the same failure threshold