bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of secondary metabolites, nutraceutical potential and amino acid profile of fresh dates (Phoenix dactylifera) alcoholic beverage
Authors: Choudhary Aditya , Kumar Aditya , Kandpal Rohan , Gupta Arun Kumar , Jha Avinash Kumar , Naik Bindu , Kumar Vijay , Rustagi Sarvesh , Chutia Hemanta , Khan Javed Masood 
Published: July 2024

AbstractThe present study comprehensively investigated the fermentation process of fresh dates pulp to alcoholic beverage, analyzing various aspects including physicochemical properties, nutraceutical potential, amino acid composition, color properties, sensory evaluation, and microbial load. Over the fermentation period, the alcohol content of the beverage reached 8.79%, suggesting it is an off-dry wine. Total Soluble Solids (TSS) decreased from an initial 24.17°Brix to 5°Brix. The pH level dropped from 7 to 3.9, and the titratable acidity increased from 0.15 to 0.22%. The nutraceutical potential of the alcoholic beverage displayed dynamic transformations, with total phenolic content (TPC) decreasing from 23 to 9 mg GAE mL−1, while total flavonoid content (TFC) increased significantly (p < 0.05). Amino acid analysis revealed distinct concentrations, with alcoholic beverage exhibiting higher levels of essential amino acids due to fermentation. Color analysis showed a shift towards reddish hues, and a progression towards a more yellowish color during fermentation. Sensory evaluation indicated a non-significant difference (p > 0.05) between the alcoholic beverage and juice in terms of color, but the alcoholic beverage scored higher in appearance, flavor, texture, taste, mouthfeel, and overall acceptability.Microbial load increased from an initial 5–6 log CFU mL−1 to 8–9 log CFU mL−1 after 5 days, indicating the occurrence of alcoholic fermentation. Kinetics study was also performed for different quality parameters. This is the first study provides a comprehensive understanding of the multifaceted changes occurring during the fermentation process, offering valuable insights for winemakers and researchers.
Graphical Abstract
Possible detection of atmospheric bioaerosol via LiDAR: a wavelength-based simulation study
Authors: Shin Juseon , Noh Youngmin 
Published: July 2024

AbstractThis study explores potential of LiDAR technology to rapidly detect aerosolized biological terror agents in the atmosphere. It assesses the application by simulating extinction coefficients and the Ångström exponent at various wavelengths (266, 1064, 1571, and 2000 nm), focusing on differentiating bioaerosols from typical atmospheric particles. The simulation analysis evaluates changes in aerosol distributions and related extinction coefficient and Ångström exponent shifts under clean, normal, and bad atmospheric conditions. The findings indicate that the 1064 nm wavelength effectively detects bioaerosol presence, with a combination of 1064 nm and 1571 nm providing optimal Ångström exponent use for particle size differentiation. This dual-wavelength approach is highlighted as a practical method for bioaerosol detection, showcasing a significant sensitivity to variations in particle quantity and size, which are critical in biological threat scenarios. In conclusion, the study offers guidance for selecting LiDAR wavelengths for biological agent detection systems. While providing a theoretical framework for practical applications, it also underlines the need for further experimental work to confirm findings and fine-tune technology for real-world monitoring and threat management. This research contributes to the development of effective monitoring strategies against the backdrop of biological terror threats.
Graphical Abstract
Dynamic and Thermodynamic Control of the Response of Winter Climate and Extreme Weather to Projected Arctic Sea‐Ice Loss
Authors: Ye Kunhui , Woollings Tim , Sparrow Sarah N. 
Published: July 2024

AbstractA novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project.
Evaluation of an In‐Canopy Wind and Wind Adjustment Factor Model for Wildfire Spread Applications Across Scales
Authors: Hung Wei‐Ting , Campbell Patrick C. , Moon Zachary , Saylor Rick , Kochendorfer John , Lee Temple R. , Massman William 
Published: July 2024

AbstractThe representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination of the air‐surface exchange processes of heat, momentum, and trace gases. Because of the relationship between wind speed and fire behaviors, the influence of the canopy on near‐surface wind speed is critical for prognostic fire spread models used in regional NWP models. In practice, the wind speed at the midflame point of fires (midflame wind speed) is used to determine the rate of fire spread. However, the wind speeds from most in situ measurements and NWP models are taken at some reference height above the canopy and fire flames. Hence, this study develops a modular and computationally‐efficient one‐dimensional model set composed of a canopy wind model and a wind adjustment factor (WAF) model for NWP applications across scales. The model set uses prescribed foliage shape functions to represent the vertical vegetation profile and its impacts on the three‐dimensional structure of horizontal wind speeds. Results from the canopy wind model well agree with ground‐based observations with average mean absolute bias, root mean square error and determination coefficients around 0.18 m s−1, 0.40 m s−1and 0.90, respectively. The WAF model provides midflame wind speeds by estimating the WAF based on canopy, fire and flame characteristics. Various user‐definable options provide flexibility to adapt to variations in canopy characteristics and additional complexities associated with wildfires. The model set is expected to improve NWP models by providing an improved representation of the sub‐grid wind flows at any spatial scale.
Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation
Authors: Zheng Shufen , Tsao Philip S. , Pan Cuiping 
Published: July 2024

AbstractAbdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.
Photothermal CO2 conversion to ethanol through photothermal heterojunction-nanosheet arrays
Authors: Li Xiaodong , Li Li , Chu Xingyuan , Liu Xiaohui , Chen Guangbo , Guo Quanquan , Zhang Zhen , Wang Mingchao , Wang Shuming , Tahn Alexander , Sun Yongfu , Feng Xinliang 
Published: July 2024

AbstractPhotothermal CO2 conversion to ethanol offers a sustainable solution for achieving net-zero carbon management. However, serious carrier recombination and high C-C coupling energy barrier cause poor performance in ethanol generation. Here, we report a Cu/Cu2Se-Cu2O heterojunction-nanosheet array, showcasing a good ethanol yield under visible–near-infrared light without external heating. The Z-scheme Cu2Se-Cu2O heterostructure provides spatially separated sites for CO2 reduction and water oxidation with boosted carrier transport efficiency. The microreactors induced by Cu2Se nanosheets improve the local concentration of intermediates (CH3* and CO*), thereby promoting C-C coupling process. Photothermal effect of Cu2Se nanosheets elevates system’s temperature to around 200 °C. Through synergizing electron and heat flows, we achieve an ethanol generation rate of 149.45 µmol g−1 h−1, with an electron selectivity of 48.75% and an apparent quantum yield of 0.286%. Our work can serve as inspiration for developing photothermal catalysts for CO2 conversion into multi-carbon chemicals using solar energy.
Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4
Authors: Zhou Juexiao , He Xiaonan , Sun Liyuan , Xu Jiannan , Chen Xiuying , Chu Yuetan , Zhou Longxi , Liao Xingyu , Zhang Bin , Afvari Shawn , Gao Xin 
Published: July 2024

AbstractLarge language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.
Equity in action: The Diagnostic Working Group of The Undiagnosed Diseases Network International
Authors: Palmer Elizabeth Emma , Cederroth Helene , Cederroth Mikk , Delgado-Vega Angelica Maria , Roberts Natalie , Taylan Fulya , Nordgren Ann , Botto Lorenzo D. 
Published: July 2024

AbstractRare diseases are recognized as a global public health priority. A timely and accurate diagnosis is a critical enabler for precise and personalized health care. However, barriers to rare disease diagnoses are especially steep for those from historically underserved communities, including low- and middle-income countries. The Undiagnosed Diseases Network International (UDNI) was launched in 2015 to help fill the knowledge gaps that impede diagnosis for rare diseases, and to foster the translation of research into medical practice, aided by active patient involvement. To better pursue these goals, in 2021 the UDNI established the Diagnostic Working Group of the UDNI (UDNI DWG) as a community of practice that would (a) accelerate diagnoses for more families; (b) support and share knowledge and skills by developing Undiagnosed Diseases Programs, particularly those in lower resource areas; and (c) promote discovery and expand global medical knowledge. This Perspectives article documents the initial establishment and iterative co-design of the UDNI DWG.
Transcriptome analysis reveals genes associated with late blight resistance in potato
Authors: Bhatia Nisha , Tiwari Jagesh Kumar , Kumari Chandresh , Zinta Rasna , Sharma Sanjeev , Buckseth Tanuja , Thakur Ajay K. , Singh Rajesh K. , Kumar Vinod 
Published: July 2024

AbstractLate blight is a serious disease of potato worldwide. Our study aimed to unveil genes involved in late blight resistance in potato by RNA-seq analysis after artificial inoculation under controlled conditions. In this study, two potato somatic hybrids (P7 and Crd6) and three varieties such as Kufri Girdhari, Kufri Jyoti and Kufri Bahar (control) were used. Transcriptiome analysis revealed statistically significant (p < 0.05) differentially expressed genes (DEGs), which were analysed into up-regulated and down-regulated genes. Further, DEGs were functionally characterized by the Gene Ontology annotations and the Kyoto Encyclopedia of Genes and Genomes pathways. Overall, some of the up-regulated genes in resistant genotypes were disease resistance proteins such as CC-NBS-LRR resistance protein, ankyrin repeat family protein, cytochrome P450, leucine-rich repeat family protein/protein kinase family, and MYB transcription factor. Sequence diversity analysis based on 38 peptide sequences representing 18 genes showed distinct variation and the presence of three motifs in 15 amino acid sequences. Selected genes were also validated by real-time quantitative polymerase chain reaction analysis. Interestingly, gene expression markers were developed for late blight resistant genotypes. Our study elucidates genes involved in imparting late blight resistance in potato, which will be beneficial for its management strategies in the future.
Development and validation of risk prediction model for post-donation renal function in living kidney donors
Authors: Lim Seong Jun , Kwon Jieun , Ko Youngmin , Kwon Hye Eun , Lee Jae Jun , Kim Jin-Myung , Jung Joo Hee , Kwon Hyunwook , Kim Young Hoon , Park Jae Berm , Lee Kyo Won , Shin Sung 
Published: July 2024

AbstractThis study aimed to create and validate a predictive model for renal function following live kidney donation, using pre-donation factors. Accurately predicting remaining renal function post live kidney donation is currently insufficient, necessitating an effective assessment tool. A multicenter retrospective study of 2318 live kidney donors from two independent centers (May 2007–December 2019) was conducted. The primary endpoint was the reduction in eGFR to below 60 mL/min/m2 6 months post-donation. The primary endpoint was achieved in 14.4% of the training cohort and 25.8% of the validation cohort. Sex, age, BMI, hypertension, preoperative eGFR, and remnant kidney proportion (RKP) measured by computerized tomography (CT) volumetry were found significant in the univariable analysis. These variables informed a scoring system based on multivariable analysis: sex (male: 1, female: 0), age at operation (< 30: 0, 30–39: 1, 40–59: 2, ≥ 60: 3), preoperative eGFR (≥ 100: 0, 90–99: 2, 80–89: 4, < 80: 5), and RKP (≥ 52%: 0, < 52%: 1). The total score ranged from 0 to 10. The model showed good discrimination for the primary endpoint in both cohorts. The prediction model provides a useful tool for estimating post-donation renal dysfunction risk, factoring in the side of the donated kidney. It offers potential enhancement to pre-donation evaluations.
Biocontrol potential of endophytic fungi against phytopathogenic nematodes on potato (Solanum tuberosum L.)
Authors: Ghareeb Rehab Y. , Jaremko Mariusz , Abdelsalam Nader R. , Abdelhamid Mohamed M. A. , EL-Argawy Eman , Ghozlan Mahmoud H. 
Published: July 2024

AbstractRoot-knot nematodes (RKNs) are a vital pest that causes significant yield losses and economic damage to potato plants. The use of chemical pesticides to control these nematodes has led to environmental concerns and the development of resistance in the nematode populations. Endophytic fungi offer an eco-friendly alternative to control these pests and produce secondary metabolites that have nematicidal activity against RKNs. The objective of this study is to assess the efficacy of Aspergillus flavus (ON146363), an entophyte fungus isolated from Trigonella foenum-graecum seeds, against Meloidogyne incognita in filtered culture broth using GC–MS analysis. Among them, various nematicidal secondary metabolites were produced: Gadoleic acid, Oleic acid di-ethanolamide, Oleic acid, and Palmitic acid. In addition, biochemical compounds such as Gallic acid, Catechin, Protocatechuic acid, Esculatin, Vanillic acid, Pyrocatechol, Coumarine, Cinnamic acid, 4, 3-indol butyl acetic acid and Naphthyl acetic acid by HPLC. The fungus was identified through morphological and molecular analysis, including ITS 1–4 regions of ribosomal DNA. In vitro experiments showed that culture filtrate of A. flavus had a variable effect on reducing the number of egg hatchings and larval mortality, with higher concentrations showing greater efficacy than Abamectin. The fungus inhibited the development and multiplication of M. incognita in potato plants, reducing the number of galls and eggs by 90% and 89%, respectively. A. flavus increased the activity of defense-related enzymes Chitinas, Catalyse, and Peroxidase after 15, 45, and 60 days. Leaching of the concentrated culture significantly reduced the second juveniles’ stage to 97% /250 g soil and decreased the penetration of nematodes into the roots. A. flavus cultural filtrates via soil spraying improved seedling growth and reduced nematode propagation, resulting in systemic resistance to nematode infection. Therefore, A. flavus can be an effective biological control agent for root-knot nematodes in potato plants. This approach provides a sustainable solution for farmers and minimizes the environmental impact.
On leveraging self-supervised learning for accurate HCV genotyping
Authors: Fahmy Ahmed M. , Hammad Muhammed S. , Mabrouk Mai S. , Al-atabany Walid I. 
Published: July 2024

AbstractHepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.
A retrospective analysis of the clinical profile and factors associated with mortality and poor hospital outcomes in adult Guillain–Barre syndrome patients
Authors: Tewedaj Zinabu Derso , Huluka Dawit Kebede , Kebede Yabets Tesfaye , Abebe Abel Tezera , Hussen Meksud Shemsu , Mohammed Bekri Delil , Juhar Leja Hamza 
Published: July 2024

AbstractGuillain–Barré syndrome (GBS) is an acute autoimmune polyneuropathy with substantial geographic variations in demography, antecedent events, clinical manifestations, electrophysiological sub-types, diagnostic findings, treatment modalities, and prognostic indicators. However, there is limited contemporary data on GBS patient profiles and prognostic factors from low-resource settings like Ethiopia. The objective of this study is to investigate the clinical profile, factors associated with mortality, and hospital outcomes among GBS patients admitted to Tikur Anbessa Specialized Hospital (TASH) in Addis Ababa, Ethiopia. A retrospective cross-sectional study was conducted among 60 GBS patients admitted to TASH from January 2018 to December 2022. Data on demographics, clinical features, treatments, complications, and outcomes were extracted from medical records. Bivariate and multivariate logistic regression analyses identified factors associated with mortality and poor hospital outcomes. The cohort had a mean age of 28.5 years, with 76.7% aged 14–34 years. Males comprised 61.7% of cases. Ascending paralysis (76.7%) was the predominant presentation. Absent or reduced reflexes were seen in 91.7% of patients. The most common antecedent event was gastroenteritis (26.7%), followed by upper respiratory tract infection (URTI) (15%) and vaccination (11.7%). The mean interval from symptom onset to hospital presentation was 8.77 days, and the peak symptom severity was 4.47 days. The axonal variant (75.5%) was the most common subtype, followed by the demyelinating variant (24.5%). Intravenous immunoglobulin was administered to 41.7% of patients. Respiratory failure requiring invasive mechanical ventilator (MV) support occurred in 26.7% of cases. The mortality rate was 10%, with mechanical ventilation being the only factor significantly associated with mortality (95% CI 2.067–184.858; P < 0.010). At discharge, 55% had a good outcome, and 45% had a poor outcome, according to the Hughes Functional Disability Scale (HFDS). Mechanical ventilation (AOR 0.024, 95% CI 0.001–0.607) and a GBS disability score > 3 (AOR 0.106, 95% CI 0.024–0.467) were factors significantly associated with poor hospital outcomes. GBS in this cohort primarily affected individuals of young age, commonly preceded by gastroenteritis and characterized by a high frequency of the axonal variant. Mechanical ventilation was found to be significantly linked to mortality. Alongside mechanical ventilation requirements, severe disability upon presentation emerged as a crucial determinant of poor outcomes upon discharge, underscoring the importance of early identification of high-risk patients and prompt interventions.