bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elucidating the potential role of cerium-based nanomaterial in triggering activation of xylose-utilizing genes in Saccharomyces cerevisiae to convert lignocellulosic biomass into bioethanol
Authors: Singhvi Mamata , Kim Beom Soo 
Published: September 2024

Abstract

In this study, the primary aim was to establish an environmentally sustainable method for converting corn cob biomass waste (CCBW) into simple sugars using CeFe3O4NPs through the simultaneous pretreatment and hydrolysis (SPH) process at the fermenter level. The hydrolysis reaction yielded a maximum of 5.95 ± 0.102 g/L glucose and 4.22 ± 0.143 g/L xylose within 24 hours at 50°C in a 7.5-liter fermenter scale. The released sugars during the SPH process explain how CeFe3O4NPs work as oxidase and cellulase-hemicellulase enzymes. The study also investigates the auxiliary effect of enzymes in combination with CeFe3O4NPs on raw CCBW hydrolysis. Very small amounts of in-house generated cellulase-hemicellulase enzymes were used, resulting in higher glucose levels (20.10 ± 1.12 g/L) and xylose (14.9 ± 0.88 g/L) sugars being liberated. Saccharomyces cerevisiae converted these sugars into 15.2 ± 0.87 g/L of bioethanol with a productivity rate of 1.27 g/L/h using a hydrolysate. The presence of CeFe3O4NPs has led to the use of xylose in S. cerevisiae, a new observation that highlights the potential role of CeFe3O4NPs in enhancing the expression of genes responsible for xylose utilization. To confirm these results at a genetic level, transcriptomic analysis of yeast cells grown using control and CeFe3O4NPs- treated CCBW samples was conducted and verified by differentially expressed genes and gene ontology enrichment analysis. Up- and down-regulated genes observed in the transcriptome data were further validated using real-time PCR studies. This is the first report on such studies proving the role of CeFe3O4NPs in upregulating important xylose-utilizing genes, namely Xylulokinase (XKS1) and Xylose reductase (XR, YJR096W). This study could be a breakthrough in producing 2G biofuels by utilizing both C5 and C6 sugars at commercial scale.
Genetic Diversity and Population Structure Analyses of Some Bread Wheat (Triticum aestivum L) Genotypes
Authors: Sewore Birhanu Mecha , Abe Ayodeji , Tadesse Zerihun 
Published: September 2024

Abstract

Understanding the magnitude of the genetic diversity, population structure, and LD that exist in the crop germplasm are necessary precondition for achieving the genomic predictions of desired traits. This study investigated the genetic diversity and population structure of 185 bread wheat genotypes as a prerequisite for GWAS and genomic selection. The genotypes were evaluated under drought-stressed and well-watered conditions. Chromosome-wise genomic SNP distribution, MAF, observed heterozygosity, and the PIC were performed using GBS-based SNP markers. The relationship matrix was determined with the UPGMA module of TASSEL 5.0 to compile dendrograms. The population structure was determined with the help of the STRUCTURE 2.3.4 program. A total of 13,230 high-quality SNPs were identified and distributed across the three wheat genomes, of which 35.7% of the markers were located on the B sub-genome, 34.3% on the sub-genome A, and 30.0% on the D sub-genome. Based on the analysis of population structure, the genotypes were divided into six clusters. The mean MAF, PIC, and genetic diversity of the population were 0.24, 0.27, and 0.34, respectively. The average LD decay for the whole genome of significant marker pairs at r2 > 0.2 was 19.5 Mb for the A-genome, 23.2 Mb for the B-genome, and 24.3 Mb for the D genome. The percent membership of the genotypes to a specific cluster showed that cluster 6 had the largest membership with 22.1% of the population, whereas cluster 3 was the lowest with 12.5% of the population. The highest degree of genetic differentiation was detected in cluster 3 (Fst = 0.64), whereas the lowest was observed in cluster 2 (Fst = 0.26). The high genetic diversity identified among the clusters can be used to develop new bread wheat cultivars with desired traits. The moderate to high divergence detected among bread wheat genotypes within clusters suggested that the genotypes could be used further for GWAS.
UAV Trajectory Planning based on Improved Whale Optimization Algorithmm
Authors: huo yuhan 
Published: September 2024

Abstract

To address the shortcomings of the traditional whale optimization algorithm (WOA) in the process of the trajectory planning, which has poor global search efficiency and is easy to fall into the local optimal solution, a trajectory planning method based on the chaotic multi-strategy whale optimization algorithm(CMS-WOA) is proposed. Firstly, CMS-WOA employs Circle chaotic mapping and reverse learning for population initialization, enhancing algorithmic efficiency. Secondly, non-linear convergence factors and an adaptive weight strategy ensure a balance between global exploration and local optimization capabilities. Moreover, replacing the traditional algorithm's global search strategy with the Lévy flight strategy enhances exploration across the entire search space. Finally, when the algorithm search stagnates, the random differential mutation strategy is used to update the location information of the whale population to avoid premature convergence of the algorithm. Simulation results highlight CMS-WOA's superiority in terms of accelerated convergence, local optima evasion, and higher-quality three-dimensional trajectory planning compared to other algorithms.
A Bayesian quantile regression framework for modeling trends in air temperature: The case for Greece
Authors: Argiriou Athanassios , Mamara Anna , Tsiotas Georgios 
Published: September 2024

Abstract

This study investigates the trend dynamics of some extreme air temperature bounds estimated via quantile models. In practice, we investigate the dynamics of the 5% lower, the median and the 95% upper quantile measures of the minimum and maximum temperature in various regions of Greece. For this purpose, we propose some semi-parametric quantile trend models. Our estimation is based on a Bayesian early-rejection Markov chain Monte Carlo algorithm. The climatic data used, are the mean monthly homogenized minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. Results based on several margins of datas' distribution, reveal the strongest and the weakest inter-annual quantile trends for both temperature extremes. The results are very significant since they show the existence of time-trend heterogeneities in temperature extremes under alternative types of quantiles, time periods, geographical zones and seasons.
Burden and Determinants of Child Mortality in Somalia analysis of SDHS 2020
Authors: Dahir Hamze Gas , Ahmed Ahmed Tahir , Abdilahi Mohamed Arab 
Published: September 2024

Abstract

Child mortality remains a significant public health burden in Somalia. There are many contributing factors for child mortality which varies across contexts. Limited UNICEF studies done in Somalia addressing level of child mortality in some part of country. Thus, this study investigates country wide prevalence of child mortality and important determinants of maternal and child characteristics using data from 2020 Somali Demographic and Health Survey (SDHS). Stata version 16 with command of Prop49, 037 survivors (size) 3,161(outcome), exact (95%CI) was used to estimate the prevalence of child mortality rate using data extracted from SDHS, 2020. Binary logistic regression model is used to analyze the determinants of child mortality in Somalia after cleaning. Variables with P-values less 0.05 with 95% confidence intervals (C.I.) and Adjusted Odds Ratio (AOR) vary from 1 were reported as determinants. Study shows prevalence of child mortality in Somalia of 65 per 1000 live births. Whereas no maternal visit to a health facilities in the previous 12 months, living in rural settings, short preceding and succeeding birth intervals, and multiple births were significantly increasing the odds of child mortality in Somalia respectively: (AOR = 1.57(0.97, 2.53), (AOR = 2.034, 95% CI: 1.15–3.61), 0.49(0.26, 0.94) and (AOR = 0.44, 95% CI: 0.29–0.66), and (AOR = 4.35, 95% CI: 1.919–23.73). Prevalence of child mortality is critical burden in Somalia and this highlights poor situation of health care delivery. All identified risk factors are preventable by just improving maternal and child health access and quality of care. Improving key maternal and child health interventions including family planning service with due emphasis given on rural/nomad areas are critical measures for improving child survival in the country.
Sports Nutrition Knowledge, Source of Nutrition Information and Dietary Consumption Pattern of Ugandan Endurance Athletes: A cross-sectional study of the Sebei Sub-Region
Authors: Musau Joshua , Folasire Oluyemisi F. , Mkumbuzi Nonhlanhla S. 
Published: September 2024

Abstract

Background
Athletes’ nutrition knowledge depends on the sources of nutrition information they are exposed to and their social status. Currently, there is a dearth of literature on the nutrition knowledge of Ugandan athletes. This study assessed endurance athletes’ sports nutrition knowledge, sources of nutrition information, and their dietary consumption patterns.
Methods
A cross-sectional descriptive study including 100 purposively selected (middle and long distance) athletes from four Athletic Management camps was conducted in Kapchorwa district, Uganda. Using an interviewer-administered questionnaire, trained research assistants collected athletes’ demographic information, knowledge, practices, sources of nutrition information and consumption patterns. ANOVA, Independent t-test, Chi-square analysis, and Binary logistic analyses were done using SPSS® software version 23.0.
Results
Most athletes 74.0% (n = 74) in this study were aged between 15–17 (17.9 ± 4.0) years with at least 2 years’ experience in athletics. Majority 68.0% (n = 68) of the athletes had good sports nutrition knowledge while 32.0% (n = 32) showed poor knowledge. About 88.0% (n = 88) knew that vitamin consumption can enhance recovery after training. Athletic trainers 49.0% (n = 49) and family/parents 28.0% (n = 28) were primary sources of nutrition information, while nutritionists/dietitians 5.0% (n = 5) and the internet 4.0% (n = 4) were less consulted. Cereals were the most consumed food group 72.0% (n = 72), followed by legumes and nuts 67.0% (n = 67) while the least consumed foods were milk/egg 33.0% (n = 33), fish 25.0% (n = 25) and meat/poultry 13.0% (n = 13). Good sports nutrition knowledge was associated with adequate dietary practices (p = 0.009). Sports nutrition knowledge differed significantly with age group (F = 4.566, p = 0.013), and between female and male (F = 21.884, p < 0.000) athletes. Age group was a significant predictor (β = 1.925, Ex(β) = 6.853, C. I = 0.882–36.096, p = 0.023) of sports nutrition knowledge.
Conclusions
In this study endurance athletes had good sports nutrition knowledge, and relied primarily on their coaches/trainers for sports nutrition information. It is recommended that athletic trainers be offered supplementary training in sports nutrition to ensure proper nutrition knowledge dissemination among athletes.
Sex-specific Concordance of Striatal Transcriptional Signatures of Opioid Addiction in Human and Rodent Brains
Authors: Logan Ryan , Shelton Micah , Horan Nicole , Xue Xiangning , Maturin Lisa , Eacret Darrell , Michaud Julie , Singh Navsharan , Williams Benjamin , Gamble Mackenzie , Seggio Joseph , Kuppe-Fish Madeline , Phan BaDoi , Tseng George , Blendy Julie , Woods Leah Solberg , Palmer Abraham , George Olivier , Seney Marianne 
Published: September 2024

Abstract

Opioid use disorder (OUD) has emerged as a severe, ongoing public health emergency. Current, frontline addiction treatment strategies fail to produce lasting abstinence in most users. This underscores the lasting effects of chronic opioid exposure and emphasizes the need to understand the molecular mechanisms of drug seeking and taking, but also how those alterations persist through acute and protracted withdrawal. Here, we used RNA sequencing in post-mortem human tissue from males (n=10) and females (n=10) with OUD and age and sex-matched comparison subjects. We compared molecular alterations in the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC) between humans with OUD and rodent models across distinct stages of opioid use and withdrawal (acute and prolonged) using differential gene expression and network-based approaches. We found that the molecular signature in the NAc of females with OUD mirrored effects seen in the NAc of female mice at all stages of exposure. Conversely, males with OUD showed strong overlap in expression profile with rats in acute withdrawal. Co-expression networks involved in post-transcriptional modification of RNA and epigenetic modification of chromatin state. This study provides fundamental insight into the converging molecular pathways altered by opioids across species. Further, this work helps to disentangle which alterations observed in humans with OUD are driven by acute drug exposure and which alterations are consequences of chronic exposure.
PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning
Authors: Aggarwal Rishal , Koes David Ryan 
Published: September 2024

Abstract

Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. In this work, however, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With this algorithm, we show better prospective virtual screening performance, in terms of F1 scores, on the DUD-E dataset than random selection of ligand identified features from co-crystal structures. We also conduct experiments on the LIT-PCBA dataset and show that it provides efficient solutions for identifying active molecules. Finally, we test our method by screening the COVID moonshot dataset and show that it would be effective in identifying prospective lead molecules even in the absence of fragment screening experiments. Alongside, we provide a Google Colab notebook for ease of use of the developed method.
Host-calibrated time tree caps the age of giant viruses
Authors: Ku Chuan , Tee Hwee Sze 
Published: September 2024

Abstract

Viruses are widespread parasites with important impacts on public health, economy, and ecosystems. However, little is known about their origins, ages, and early evolutionary relationships with hosts. Here, we infer the maximum divergence times for eukaryotic giant DNA viruses (Nucleocytoviricota) with dating analyses calibrated by known host taxa. The last common ancestor of Nucleocytoviricota existed after 1,000 million years ago, suggesting a much later origin than that of the eukaryotes. The early evolution of Nucleocytoviricota either coincided with or postdated a substantial increase in the oxygen levels on the Earth’s surface during the Neoproterozoic Era. The lineage diversification of giant viruses was frequently associated with host shifts, including two major transitions from amoebozoan to animal hosts that led to the emergence of iridoviruses and African swine fever viruses within the last 400 million years. These results outline the evolutionary timescale of a major virus group and are pivotal for further understanding the virus-host interactions and their potential ecological roles in the Earth history.
Development of Secure Infrastructure for Advancing Generative AI Research in Healthcare at an Academic Medical Center
Authors: Ng Madelena Y. , Helzer Jarrod , Pfeffer Michael A. , Seto Tina , Hernandez-Boussard Tina 
Published: September 2024

Abstract

The increasing interest in leveraging generative AI models in healthcare necessitates secure infrastructure at academic medical centers. Without an all-encompassing secure system, researchers may create their own insecure microprocesses, risking the exposure of protected health information (PHI) to the public internet or its inadvertent incorporation into AI model training. To address these challenges, our institution implemented a secure pathway to the Azure OpenAI Service using our own private OpenAI instance which we fully control to facilitate high-throughput, secure LLM queries. This pathway ensures data privacy while allowing researchers to harness the capabilities of LLMs for diverse healthcare applications. Our approach supports compliant, efficient, and innovative AI research in healthcare. This paper discusses the implementation, advantages, and use cases of this secure infrastructure, underscoring the critical need for centralized, secure AI solutions in academic medical environments.
Trans Health is Public Health: The Prevalence of HIV Among Trans and Gender Expansive People in Kazakhstan
Authors: Reeder Kelsey G. , Lee Yong Gun , Sung Jimin , Vinogradov Vitaliy , Zhakupova Gulnara , Mergenova Gaukhar , Davis Alissa , Paine Emily Allen , Primbetova Sholpan , Terlikbayeva Assel , Kali Sultana , Hunt Timothy , Wu Elwin 
Published: September 2024

Abstract

Introduction: Trans and gender expansive (TGE) individuals around the world are at increased risk for contracting HIV and sexually transmitted infections (STIs), yet the combination of stigma, accessibility challenges, and a lack of trans-specific, trans-affirming interventions perpetuates rates of infection. Due to the severe paucity of data on TGE communities and HIV in Central Asia, this study describes HIV infections (both known and newly detected) and STIs among TGE in a multicity Kazakhstan study.
Methods: This study utilized behavioral and biological assay data collected in a NIDA-funded clinical trial of a behavioral HIV preventive intervention for substance using cis and trans gay and bisexual men who have sex with men (GBMSM) across three Kazakhstan cities (Almaty, Astana, and Shymkent). We specifically focus on HIV infection, as well as three other STIs (chlamydia, gonorrhea, and syphilis), among 68 TGE individuals who participated in the trial from August 2018 to March 2022.
Results: Findings reveal that while the majority (69%) of TGE participants have undergone HIV testing in their lifetime—with 32% having completed an HIV test in the prior 6 months—over a third (37%) of participants did not know their current HIV status. Fourteen (21%) of the participants were confirmed to be living with HIV, and 11 (79%) of these confirmed infections were reportedly unknown prior to testing. STI testing revealed that 47% of the TGE sample tested positive for chlamydia, gonorrhea, or syphilis, with almost 10% testing positive for more than one of these STIs.
Conclusions: Findings from this study demonstrate high rates of HIV and STIs among TGE individuals in this sample population in Kazakhstan, as well as a discrepancy between HIV status awareness and confirmed HIV diagnosis (with higher rates of confirmed HIV diagnosis). Additionally, the HIV testing rates fall short of the 90-90-90 and 95-95-95 UNAIDS targets for 2020 and 2030, respectively. These results underscore the need for additional research, interventions, and services to address HIV and other STIs and increase testing—concomitantly redressing the conditions leading to marginalization—among TGE in Kazakhstan.
Clinical Trial Number: NCT02786615
Neuropeptide Network of Polycystic Ovary Syndrome – A Review
Authors: Jayamurali Dheepthi , Ravishankar Nivetha , Manoharan Nivedita , Parasuraman Rajeshwari , Jayashankar Sri Kameshwaran , Govindarajulu Sathya Narayanan 
Published: September 2024

Background:
Polycystic Ovary Syndrome (PCOS), the ubiquitous reproductive disorder,
has been documented as highly prevalent (6-9%) in India. 10% of women globally are predicted
to have the disease. The highly mutable endocrinopathy, with differential clinical criteria for
each diagnosis of PCOS, can mask the severity of the syndrome by influencing the incidence and
occurrence of PCOS.

Area Covered:
When there is a solid theoretical hypothesis between the neuroendocrine origin
and ovarian origin of PCOS, recent evidence supports the neuroendocrine derivation of the pathology.
It is considered of neuroendocrine basis – as it controls the ovarian axis and acts as a delicate
target because it possesses receptors for various gonadal hormones, neurotransmitters & neuropeptides.
Can these neuroendocrine alterations, variations in central brain circuits, and neuropeptide
dysregulation be the tie that would link the pathophysiology of the disorder, the occurrence of all
the 1˚ and 2˚ symptoms like polycystic ovaries, hyperandrogenism, obesity, insulin resistance,
etc., in PCOS?

Conclusion:
This review anticipates providing a comprehensive overview of how neuropeptides
such as Kisspeptin, Neurokinin B, Dynorphin A, β-Endorphin, Nesfatin, Neuropeptide Y,
Phoenixin, Leptin, Ghrelin, Orexin, and Neudesin influence PCOS, the understanding of which
may help to establish potential drug candidates against precise targets in these central circuits.
Ginger-Derived Extracellular Vesicles: A Natural Solution for Alopecia
Authors: Hao Yixin , Yang Qiujun , Zhang Han , Bai Chunyu , Liu Xibin , Gao Yuhua 
Published: September 2024

Background:
Ginger (Zingiber officinale (L.) Rosc), as an edible plant-derived nanoparticle,
offers several advantages, such as a high return rate, low budget, no ethical barriers, and
good for health. Ginger-Derived Extracellular Vesicles (GDEVs) are nanoscale vesicles isolated
from ginger.

Methods:
In this study, GDEVs were used to treat the alopecia mouse model, and its main active
components and potential mechanism of action were investigated. The LC-MS/MS analysis of
GDEVs revealed the presence of 1299 chemical compounds, among which auxiliary components
were identified. Interestingly, the crux of the analysis lies in the discovery of 13 specific ingredients
that play a pivotal role in hair proliferation. The aim of this study was to investigate the protective
effect of GDEVs on hair loss. These advantages make ginger-derived nanoparticles a
promising solution to overcome technical limitations associated with mammalian nanoparticles.
This study elucidates the mechanism of action of GDEVs in the treatment of alopecia. However,
the active ingredients and mechanism of action of GDEVs in the treatment of hair loss are unknown.

Results:
GDEVs were isolated from ginger using the differential centrifugal method. Network
pharmacological analysis of the GDEVs revealed that the anti-hair loss effect of GDEVs on alopecia
was closely linked to its ability to reduce inflammation and promote the proliferation of hair
follicle stem cells. Subsequently, it was applied to the balding areas of hair-loss mice using a
brush. The results demonstrated that the application of GDEVs led to a rapid recovery of the balding
areas and promoted the growth of healthier hair.

Conclusion:
This experiment reported that GDEVs can effectively suppress the inflammatory activity
in the alopecia model mice.