bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing High-Resolution Precipitation Extremes in Central Asia: Evaluation and Future Projections
Authors: Gummadi Sridhar , Samineni Srinivasan , Lopez-Lavalle Luis Augusto Becerra 
Published: June 2024

Abstract

The impact of anthropogenic climate change on ecosystem sustainability in Central Asia's semi-arid and arid regions relies significantly on changes in extreme precipitation events. Accurate forecasting of these events is crucial for tailored adaptation strategies. This study examines projected changes in mean and extreme precipitation indices in Central Asia from 1985 to 2100. Utilizing ERA5, CPC, and high-resolution NEX-GDDP data from CMIP6 models, four SSP scenarios were assessed over three-time frames. The CMIP6 Multi-Model Ensemble (MME) shows coherence in simulating mean annual precipitation, albeit with weaker performance in mountainous regions. It consistently underestimates PD10MM and SDII while overestimating CDD in high-altitude areas with more precipitation. Projections indicate a potential up to 50% increase in mean annual precipitation across most of Central Asia, notably amplifying from the mid-future onward. Precipitation extremes like SDII, RX1DAY, and days with over 10 mm of precipitation are increasing spatiotemporally. Conversely, CDD may decrease in eastern Central Asia but increase in the west by the century's end. These shifts signify a rising wetness trend in Central Asia under warming conditions, resulting in more frequent heavy precipitation events and fewer dry spells, especially in high-emission scenarios.
Validation of Pre - Treatments for Cost Effective Production of Bioethanol From Floral Wastes
Authors: Rachel Shalini , Raj M. Shailaja , Shajan Maria 
Published: June 2024

Abstract

The present study has focused on validating pre- treatment methods for cost effective production of bioethanol from discarded and otherwise waste flowers which are renewable, abundantly available and eco-friendly. Floral waste was collected from various dumpsites and banquet halls and subjected to physical, chemical and biological pre- treatments. Biological pre- treatment by enzymatic hydrolysis using crude cellulase enzyme (5%) yielded 39.4 ± 0.03 g/L of alcohol which is 24.20% and 31.60% more than the alcohol obtained by physical pre- treatment (thermal hydrolysis) and chemical pre- treatment (1% KOH), therefore simultaneous saccharification and fermentation was optimised. A maximum of 396 ± 6.48 g/L bioethanol was obtained after 96 hrs of fermentation with the isolated yeast, Pichia kudriavzevii CY 902 at pH 5.5 and 37°C. The minimum ethanol selling price (MESP) of bioethanol produced in our study was enumerated to be 30.43 Rs/ L which is 68.31% lesser than the market price of ethanol in India today, making our methodology for production of bioethanol from mixed floral wastes very competitive and cost effective to the existing methodologies.
Maternal loss-of-function of Nlrp2 results in failure of epigenetic reprogramming in mouse oocytes
Authors: Anvar Zahra , Jochum Michael D. , Chakchouk Imen , Sharif Momal , Demond Hannah , To Alvin K. , Kraushaar Daniel C. , Wan Ying-Wooi , Andrews Simon , Kelsey Gavin , Veyver Ignatia B. 
Published: June 2024

Abstract

Background: NLRP2 belongs to the subcortical maternal complex (SCMC) of mammalian oocytes and preimplantation embryos. This multiprotein complex, encoded by maternal-effect genes, plays a pivotal role in the zygote-to-embryo transition, early embryogenesis, and epigenetic (re)programming. The maternal inactivation of genes encoding SCMC proteins has been linked to infertility and subfertility in mice and humans. However, the underlying molecular mechanisms for the diverse functions of the SCMC, particularly how this cytoplasmic structure influences DNA methylation, which is a nuclear process, are not fully understood.
Results: We undertook joint transcriptome and DNA methylome profiling of pre-ovulatory germinal-vesicle oocytes from Nlrp2-null, heterozygous (Het), and wild-type (WT) female mice. We identified numerous differentially expressed genes (DEGs) in Het and Nlrp2-null when compared to WT oocytes. The genes for several crucial factors involved in oocyte transcriptome modulation and epigenetic reprogramming, such as DNMT1, UHRF1, KDM1B and ZFP57 were overexpressed in Het and Nlrp2-null oocytes. Absence or reduction of Nlrp2, did not alter the distinctive global DNA methylation landscape of oocytes, including the bimodal pattern of the oocyte methylome. Additionally, although the methylation profile of germline differentially methylated regions (gDMRs) of imprinted genes was preserved in oocytes of Het and Nlrp2-null mice, we found altered methylation in oocytes of both genotypes at a small percentage of the oocyte-characteristic hyper- and hypomethylated domains. Through a tiling approach, we identified specific DNA methylation differences between the genotypes, with approximately 1.3% of examined tiles exhibiting differential methylation in Het and Nlrp2-null compared to WT oocytes.
Conclusions: Surprisingly, considering the well-known correlation between transcription and DNA methylation in developing oocytes, we observed no correlation between gene expression differences and gene-body DNA methylation differences in Nlrp2-null versus WT oocytes or Het versus WT oocytes. We therefore conclude that post-transcriptional changes in the stability of transcripts rather than altered transcription is primarily responsible for transcriptome differences in Nlrp2-null and Het oocytes.
Global and regional burden of bacterial antimicrobial resistance in endocarditis in 2019: a systematic analysis
Authors: He Xinhui , Zhao Hongmei , Han Jinghong , Chen Yuhan , Liang Huning , Han Kejun 
Published: June 2024

Abstract

Background The burden of endocarditis has sharply increased and antimicrobial resistance (AMR) poses an urgent threat to human health worldwide. There are currently no detailed data about the burden of bacterial AMR in endocarditis. This study aimed to comprehensively investigate the AMR burden of endocarditis to date. Methods We obtained data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). We estimated deaths, disability-adjusted life-years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) attributable to and associated with bacterial AMR in endocarditis for 7 GBD super-regions, 21 regions, 16 bacterial pathogens, 14 antibiotic classes, and 68 pathogen-antibiotic combinations in 2019. Five broad components were the backbone of our approach and the estimates were based on two counterfactual scenarios: drug-susceptible infection and no infection. Results Globally, there were 16.37 thousand deaths (95% uncertainty interval [UI]: 11.54–23.05) attributed to and 65.59 thousand deaths (95% UI: 47.15–91.12) associated with bacterial AMR in endocarditis in 2019. The all-age death rates were higher in central Europe and high-income regions, and lower in sub-Saharan Africa. Escherichia coli, Staphylococcus aureus, Acinetobacter baumannii, and Klebsiella pneumoniae played the dominant roles in AMR, and high resistance to various multiple antibiotics, including fluoroquinolones, carbapenems, third-generation cephalosporins, and methicillin. There were 2 pathogen-antibiotic combinations that caused more than 1000 resistance-attributable deaths: methicillin-resistant Staphylococcus aureus and carbapenem-resistant Acinetobacter baumannii. Conclusions AMR in endocarditis is a non-ignorable health problem, and it is an urgent priority to identify tailored strategies that can effectively reduce the burden of bacterial AMR in endocarditis based on region-specific pathogen-antibiotic condition and resources.
Sampling the Diversity of Butterflies and Habitat Preferences in the Zunheboto District of Nagaland, India.
Authors: MISHRA JYOTI RANJAN , ACHUMI TOBOLI K , DEB MITRAJIT 
Published: June 2024

Abstract

Recently, butterflies have been frequently utilized as bioindicators for assessing environmental changes. Over the last few decades, urbanization and modernization in northeast India have taken place at an overwhelming and very fast pace. In this study, butterfly diversity and habitat preferences were evaluated in the Zunheboto district of Nagaland, India. The aim of this study was to evaluate the changes in species composition across different habitats over time. This study recorded 43 species across six families and employed direct observation methods across jhum fields of different ages (15 years and 5 years) and village areas to derive species richness and diversity indices. Our results indicate the significant presence of 24 species in the Nymphalidae family, suggesting high adaptability to various habitats. The more recent jhum fields showed an increase in biodiversity, resulting in vegetation destruction and thus more light penetration; hence, it becomes easier for generalist species to colonize. The study also underlines the ecological significance of village areas as biodiversity hotspots supporting unique species not found in agricultural fields. These findings highlight the need for habitat preservation and emphasize the potential resilience of butterfly populations to environmental changes under sustainable management. This paper sheds light on the changes in butterfly diversity with respect to habitat changes and underlines the necessity of conservation strategies to incorporate habitat protection and sustainable land use practices.
WEAP: An automatic and accelerated pipeline for analysing multi-sample whole exome sequencing data
Authors: Sarma Ranjan Jyoti , Nachimuthu  
Published: June 2024

Abstract

Background
Whole Exome Sequencing (WES) is commonly used for SNP discovery in the coding regions of the human genome and has a wide range of clinical applications. Being an intensive time-consuming task, automation is key to uncomplicating and performing straightforward data analysis.
Method
The WEAP workflow starts with the alignment of FASTQ files to a reference genome, variant calling, and annotation without user intervention. WEAP utilizes the GATK workflow incorporating popular NGS analysis tools such as bwa-mem2, samtools, GATK, bcftools, and anoovar coupled with GNU parallel.
Results
WEAP successfully identified and annotated germline and somatic variants. The major steps aligning to the reference genome, converting files, and removing duplicates in germline variant discovery were made several folds (1.5 to 3.6 folds) faster in parallel mode than in serial mode. In tumor analysis, creating a PoN from 40 samples was about 3 times faster in parallel mode. Tumor-only analysis was 1.4 to 7.7 times faster in different steps. When comparing tumor samples with matched normal tissues, the time taken was significantly reduced, making the process 1.8 to 3.6 times faster.
Conclusions
WEAP accepts Quality Control (QC) checked and trimmed FASTQ reads, and provides annotated variants that enable non-bioinformaticians to perform flawless variant calling from WES data. WEAP uses GNU parallel for multiple sample processing one at a time leveraging native parallel processing of the implemented tools and software to perform the analysis faster. A comparison between the parallel mode and serial mode of WEAP revealed that WEAP can be one of the best alternative tools for end-to-end analysis of WES data integrating gold standard GATK best practices workflow.
Assessment of seasonal fluctuation in heavy metal contamination in sediments and surface water of Narmada River, India
Authors: Rahi Dal Chand , Chandak Rajeev , Vishwakarma Amit 
Published: June 2024

ABSTRACT

This study aimed to assess the seasonal fluctuation of heavy metal contamination in the sediments and surface water of the Narmada River. In this context, samples were gathered from six stations along the river in 2021–2022 and their concentrations were determined by utilizing Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The results in sediments, the average concentrations of As, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were 0.05, 1.03, 2.47, 1.64, 750.17, 17.75, 0.54, 0.13, and 1.12 mg/kg and in water, the average concentrations of Co, Fe, Ni, and Zn were 0.03 μg/l, 0.01 mg/l, 0.08 μg/l, and 0.39 μg/l. Seasonal fluctuation in sediments revealed that concentration of metals As, Cu, Fe, Ni, and Zn peaked during the rainy climate, while Co and Cr peaked during the post-monsoon and Mn and Pb peaked during the summer climate. Seasonal fluctuation in water, Co, Ni, and Zn exhibits their highest concentrations during the post-monsoon. The finding of pollution indices revealed that the contamination level was low to moderate. Cluster analysis revealed anthropogenic and agricultural runoff water as a contributor to contamination. The findings of this research enhance comprehension of heavy metal contamination in sediment and water of the Narmada River.
Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India
Authors: Rajput Jitendra , Kushwaha Nand Lal , Srivastava Aman , Pande Chaitanya B. , Suna Triptimayee , Sena D. R. , Singh D. K. , Mishra A. K. , Sahoo P. K. , Elbeltagi Ahmed 
Published: June 2024

ABSTRACT

Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash–Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model's robust performance underscores its potential application in water resource management.
Rapid Metabolism of Monthly Extended-Release Buprenorphine
Formulation: A Case Report
Authors: Tolba Hossameldin , Foad Wael , El Hayek Samer 
Published: June 2024

Background:
Medication-assisted treatment constitutes the gold standard management
for patients with opioid use disorder. Many factors can alter the response to medications, including
genetic variations. In this case report, we discuss the presentation of a patient maintained on subcutaneous
extended-release buprenorphine formulation who repeatedly presented objective and subjective
signs of opioid withdrawal when he was switched from weekly to monthly formulation. We
particularly highlight the role of metabolic pharmacogenes in this presentation.

Case Presentation:
Mr. Y, a 31-year-old single man, presented to our rehabilitation center seeking
assistance for his polysubstance dependence, mainly opioid use disorder. As part of his multidisciplinary
treatment plan, he was started on weekly extended-release buprenorphine. After maintenance
for several months, he was transitioned to the monthly equivalent formulation. Since the transition,
he began to develop severe withdrawal symptoms 1-2 weeks before his next due injection.
This was paralleled by a noted decrease in buprenorphine level on his urine drug screening test. As
soon as the patient was placed back on the weekly formulation, his symptoms resolved.

Conclusion:
This is a novel case highlighting the potential role of pharmacogenomics in clinical
presentation and response to medications. Switching patients from weekly to monthly extendedrelease
buprenorphine formulation might make rapid or ultra-rapid metabolizers at risk of earlyonset
withdrawal symptoms. =In such a case, earlier provision of the injection or switching to another
formulation or medication can be considered.
Nicotianamine as a Potential Active Component of Lotus Seed Extract for Inhibiting Angiotensin-converting Enzyme
Authors: Nishibori Naoyoshi , Morita Kyoji , Sagara Takefumi 
Published: June 2024

Aims:
Hypertension is one of the chronic diseases causing a serious health problem in many countries worldwide. Therefore, the prevention and amelioration of hypertension with medicinal food is considered to be important for maintaining good health and well-being. The main objective of this study is to verify that the extract of lotus seed has a significant inhibitory effect on the angiotensin I-converting enzyme (ACE).

Background:
Lotus (Nelumbo nucifera) is well known to have a variety of biological and med-ical activities and is popularly used as a traditional medicinal food in several East Asian coun-tries. In a previous study, we reported that the aqueous extract of lotus root used as a popular foodstuff caused an inhibitory effect on the angiotensin I-converting enzyme (ACE), suggesting its possible effectiveness for improving high blood pressure.

Objective:
The purpose of the present study was to reveal the effect of lotus seed extract on ACE activity and to identify a potentially active component of the extract originating from lotus seed.

Methods:
The ACE inhibitory activity was determined using Hippuryl-L-histidyl-L-leucin (HHL) as substrate, and the amount of hippuric acid formed enzymatically was determined using an HPLC system.

Results:
The lotus seed extract was shown to strongly inhibit the ACE activity, thus ameliorating the hypertensive state. Furthermore, the fractionation of the lotus seed extract provided evidence for suggesting nicotianamine as a putative ACE-inhibiting component contained in the extract.

Conclusion:
These findings were considered to propose the possibility that the lotus seed and the lotus root might benefit patients with hypertension.
Addressing Hospital Overwhelm During the COVID-19 Pandemic by Using a Primary Health Care–Based Integrated Health System: Modeling Study
Authors: Huang Jiaoling , Qian Ying , Yan Yuge , Liang Hong , Zhao Laijun 
Published: June 2024

Abstract

Background
After strict COVID-19–related restrictions were lifted, health systems globally were overwhelmed. Much has been discussed about how health systems could better prepare for future pandemics; however, primary health care (PHC) has been largely ignored.

Objective
We aimed to investigate what combined policies PHC could apply to strengthen the health care system via a bottom-up approach, so as to better respond to a public health emergency.

Methods
We developed a system dynamics model to replicate Shanghai’s response when COVID-19–related restrictions were lifted. We then simulated an alternative PHC-based integrated health system and tested the following three interventions: first contact in PHC with telemedicine services, recommendation to secondary care, and return to PHC for recovery.

Results
The simulation results showed that each selected intervention could alleviate hospital overwhelm. Increasing the rate of first contact in PHC with telemedicine increased hospital bed availability by 6% to 12% and reduced the cumulative number of deaths by 35%. More precise recommendations had a limited impact on hospital overwhelm (<1%), but the simulation results showed that underrecommendation (rate: 80%) would result in a 19% increase in cumulative deaths. Increasing the rate of return to PHC from 5% to 20% improved hospital bed availability by 6% to 16% and reduced the cumulative number of deaths by 46%. Moreover, combining all 3 interventions had a multiplier effect; bed availability increased by 683%, and the cumulative number of deaths dropped by 75%.

Conclusions
Rather than focusing on the allocation of medical resources in secondary care, we determined that an optimal PHC-based integrated strategy would be to have a 60% rate of first contact in PHC, a 110% recommendation rate, and a 20% rate of return to PHC. This could increase health system resilience during public health emergencies.
Evaluating and Enhancing Large Language Models’ Performance in Domain-Specific Medicine: Development and Usability Study With DocOA
Authors: Chen Xi , Wang Li , You MingKe , Liu WeiZhi , Fu Yu , Xu Jie , Zhang Shaoting , Chen Gang , Li Kang , Li Jian 
Published: June 2024

Background
The efficacy of large language models (LLMs) in domain-specific medicine, particularly for managing complex diseases such as osteoarthritis (OA), remains largely unexplored.

Objective
This study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using OA management as a case study.

Methods
A domain-specific benchmark framework was developed to evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through retrieval-augmented generation, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations.

Results
Results showed that general LLMs such as GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements.

Conclusions
This study introduces a novel benchmark framework that assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.
Synthesis of Chitosan Silica Membrane from Petung Bamboo (<i>Dendrocalamus asper</i>) Leaves and Its Application as Pb(II) Metallic Adsorbent
Authors: Hasri Hasri , Pratiwi Diana Eka , Safitri Isriyanti , Negara Satria Putra Jaya 
Published: June 2024

Membrane synthesis through a phase inversion method using chitosan and sodium silicate solutions has been conducted. This research aims to characterize the silica chitosan membrane (SCM) of petung bamboo leaves and determine the synthesized product's adsorption capacity for Pb(II) ions. The XRF characterization showed the silica content of petung bamboo leaves with a percentage of 78.03%. SEM analysis before adsorption is around 13.0 μm, and the pore diameter after adsorption is around 9.7 μm. The results of adsorption analysis of Pb(II) metal using AAS showed that the SCM variation A at an initial concentration of 10.0000 ppm Pb(II) metal adsorbed was 9.8101 ppm, and at an initial concentration of 25.0000 ppm Pb(II) metal was 22.3421 ppm. The variation B at an initial concentration of 10.0000 ppm Pb(II) metal adsorbed was 9.8870 ppm and at an initial concentration of 25.0000 ppm Pb(II) metal adsorbed was 23.5806 ppm. The variation C at an initial concentration of 10.0000 ppm Pb(II) metal adsorbed was 9.9639 ppm, and at an initial concentration of 25.0000 ppm Pb(II) metal was 24.1855 ppm. The results of this research conclude that the highest SCM adsorption power is variation C (2%:22.95%) with a percentage of 99.63%.