bluuebunny/crossref_metadata_2025_split huggingface dataset:
Worldwide burden of non-alcoholic fatty liver disease,1990-2021: a systematic analysis of the Global Burden of Disease Study 2021
Authors: Zhang Jie , Peng Jingyi , Liao Hongjin , Yuan Yuan , Ding Yang , Wang Yi , Zheng Yuelan , He Xingxing 
Published: November 2024

Abstract

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is the most frequent chronic liver disease globally, posing a substantial burden on global health. Therefore, it is significant to investigate its epidemiology to provide data support for liver health policy decision-making.
Methods: The age-standardized rates (ASR) and the estimated annual percentage change were utilized to quantify the epidemiological trends of NAFLD. A Gaussian process regression model and a loess smoother were used to visualize the correlation between ASR and the Socio-demographic Index (SDI). A global 32-year span analysis was conducted to analyze the incidence rates across 15 age groups and the dietary risk factors, while adjusting for SDI.
Results: In 2021, an estimated 1.27 billion people were affected by NAFLD, and it was responsible for about 0.14 million deaths and 3.67 million disability-adjusted life years. The global age-standardized incidence rate (ASIR) exhibited a steady upward trend (0.73; 95%UI, 0.69 to 0.77). Andean Latin America, Middle East and North Africa had the highest age-standardized DALY rate (ASDR) and mortality rate (ASMR) in 2021. There was a non-linear association between the SDI and ASR. Diet high in sugar sweetened beverages (rho = 0.803, p < 2.2e-16) and trans fatty acids (rho = 0.539, p < 2.2e-16) were most strongly associated with NAFLD. Before 50-year-old, the disease burden was more pronounced in males than in females.
Conclusions: The incidence of NAFLD in various regions and countries increased over the past 32 years, with a trend toward younger onset. This has imposed a significant burden on global public health. These research findings provide epidemiological evidence for policymakers to make better-informed policies and resource allocations, aiming at facilitation of more targeted management of NAFLD.
Comprehensive study on the effects of plastic mandrels on springback, crack and collapse in small radius bending of composite tubes
Authors: Zhu Yingxia , Li Hui , Lu Xinzhu , Chen Wei , Tu Wenbin 
Published: November 2024

Abstract

Traditional rigid mandrels are somewhat effective in preventing cross-sectional collapse during the bending of small-radius bimetallic composite tubes, but they are prone to inducing defects such as cracks. This study introduced five plastic mandrels of PTFE, PE, PP, POM and PVC materials, and developed the constitutive model of elastic-plastic deformable bodies that can describe their pressure-sensitive characteristics. Focusing on the bimetallic composite tube with a relative bending radius of 1.76, this research investigated the bending defect characteristics associated with plastic mandrel filling, rigid mandrel filling and non-mandrel filling, from the perspectives of springback, surface cracks, wall thinning and cross-sectional collapse. This study also evaluated the enhancement in bending quality achieved through the use of the five plastic mandrels. Results show that the springback angle of the composite tube is proportional to that of the plastic mandrel, indicating that the utilization of plastic mandrels results in an augmentation of springback angles. But the springback angle can be controlled through overbending techniques. Transverse cracks appear on the surface of the covered tube, regardless of whether a mandrel is used or the type of mandrel employed. Among these mandrels, the PTFE, PE and PP mandrels demonstrate superior performance in reducing crack distribution and wall thinning, with the PP and PE mandrels showing the best results. In contrast, mandrels with higher hardness, such as rigid, PVC and POM mandrels, are effective in preserving the tube’s cross-sectional shape and minimizing cross-sectional collapse, with the POM mandrel being the most effective. Nonetheless, these harder mandrels are less effective in reducing tube damage and wall thinning.
Mechanisms of Uniconazole-Induced Male Flower Differentiation in Female Bayberry (Morella rubra) Trees
Authors: liu juan , gong biya , zhou changfu , kong youhan , wu juanjuan , zhang ping , yang shuizhi , wang chunmei , liu changwei , Li Xianxin 
Published: November 2024

Abstract

Background
In bayberry cultivation, low yields and poor fruit quality are often linked to insufficient pollen availability and low compatibility for heterogamous pollination. To overcome these challenges and develop high-quality varieties, identifying a consistent source of high-quality, controllable pollen is essential.
Main Body:
Previous studies have shown that applying certain chemicals can induce the conversion of female flower buds into male flowers in bayberry. In this study, we applied various chemicals to the new shoots of YS bayberry plants to identify compounds that effectively promote male flower formation in female trees. We employed high-throughput techniques, including transcriptomics, to analyze the underlying mechanisms of these agents.
Conclusion
The results demonstrated that applying uniconazole to the spring shoots of female plants during the critical period of flower bud differentiation (October to November) successfully induced the formation of reproductive male flowers by mid-February of the following year. Transcriptomic analysis revealed that uniconazole reduces gibberellin translocation and levels by regulating the expression of key genes (CJ030_MR4G011967, CJ030_MR4G013737, and CJ030_MR4G006450). This reduction promotes the expression of the MADS-box gene family (CJ030_MR0G004521 and CJ030_MR4G015426), which controls the differentiation of female flower buds into male buds. The male flowers induced by uniconazole produced abundant, high-quality pollen, addressing the issue of heterogamous pollination. This study provides a novel strategy for improving bayberry quality and advancing the breeding of new varieties.
Assessing Comorbid PTSD, Depression, and Anxiety in Fibromyalgia Patients: A Retrospective Study
Authors: Rahangdale Aneesh , Ferraro Jeffrey 
Published: November 2024

Abstract

Background
Fibromyalgia frequently coexists with psychiatric disorders, creating complex challenges in managing the health and quality of life for affected individuals. Existing literature points to significant overlap between fibromyalgia and conditions like posttraumatic stress disorder (PTSD), anxiety, and depression, but no large-scale analysis within a single American healthcare system has yet been conducted.
Methods
This retrospective study analyzed 1,516 fibromyalgia patients from HCA Healthcare facilities from January 1, 2022, to December 31, 2023, including only patients aged 18 and older with at least one psychiatric comorbidity. Chi-square tests examined associations between psychiatric outcomes (PTSD, depression, anxiety) and demographic factors such as age, sex, and race. Hospital length of stay (LOS) was analyzed among comorbidity groups using the Kruskal-Wallis test, with Bonferroni correction applied for pairwise comparisons.
Results
The sample had a mean age of 52.51 years, was predominantly female (89.38%) and white (78.96%). Anxiety (60.75%) and depression (53.76%) were highly prevalent, while PTSD was less common (7.52%). Significant age differences emerged for PTSD and anxiety, with younger patients exhibiting higher rates. Sex differences were significant for anxiety only (p = .028), showing higher rates among females. Mean LOS was 4.56 days, with significant differences across comorbidity groups (p = .0009), although pairwise comparisons revealed no specific group differences.
Conclusions
Psychiatric comorbidities, especially anxiety and depression, are common in fibromyalgia patients and correlate with certain demographic factors. These findings can inform clinical practice by emphasizing the need for comprehensive, age-appropriate, and gender-sensitive approaches to holistic fibromyalgia management that address both physical symptoms and mental health concerns.
Changes in the phyllosphere and rhizosphere of a cloud forest tree fern along an elevation gradient
Authors: Vélez-Múnera Mariana , Avitia Morena , Rebollar Eria A. , Ramírez-Barahona Santiago 
Published: November 2024

Abstract

The diversity of plant-associated microbial communities is shaped by both host factors and the environment. Natural environmental gradients, specifically elevational ones, can serve as study systems to understand community and ecosystem responses to environmental changes, however the relationship between elevation and microbial diversity is not completely understood, especially in non-model systems such as wild plants. In this paper we explored the role of environmental factors in shaping the diversity and structure of the rhizosphere and phyllosphere of the cloud forest tree fern Cyathea fulva. Samples of phyllosphere, rhizosphere and soil were collected from 15 individual tree ferns across five forest plots along an elevation gradient ranging from 1978 to 2210 meters above sea level. Physicochemical soil data were collected, along with environmental data of all plots. Using 16S rRNA and ITS1 amplicon sequencing, we tested for differences in diversity and composition of bacterial and fungal communities and their potential abiotic drivers. We found that bacterial alpha diversity decreased with elevation in the phyllosphere and rhizosphere, but for fungi this pattern was only found in the rhizosphere. We also observed significant changes in community structure and composition with elevation in both the fungal and bacterial phyllosphere and rhizosphere. Our results suggest a close relationship between elevation and the overall microbial structure associated with tree ferns. We envision this information will help to further understand the dynamics between microbiota and wild plants, contributing to the conservation of necessary interactions for plants and ecosystems wellbeing.
Malware Detection via Memory Dump Images: Investigating the Role of Uneven Kernel Filters in CNNs with Visual Explainability
Authors: Alaeiyan Mohammadhadi , Lakzian Pooria 
Published: November 2024

Abstract

With the Internet becoming more accessible, protecting our digital identities has become more crucial than ever. Our devices are always online, sharing data, and exposed to numerous threats. Traditional methods of detecting malware are no longer adequate due to the rapid advancement and complexity of malware. To address these challenges, researchers have started using artificial intelligence, especially machine learning and deep learning techniques, with encouraging outcomes. One emerging approach involves converting malware binaries or memory dumps into images. However, these malware images are not recognizable to the human eye and, therefore, need to be explained in terms of how the CNNs perceive this data type. This paper applies the Grad-CAM technique to malware images collected from memory dumps to understand sufficiently how black-box CNN models make predictions with this data. Based on insights from the Grad-CAM analysis, we propose a novel CNN architecture with uneven kernel sizes that outperforms existing malware detection and classification models. We compare its performance against well-known CNN architectures, all trained and tested on malware image datasets such as Malimg, Dumpware10, MaleVis, and MalBen. Our proposed architecture achieved an impressive accuracy of 99.58\% on the Malimg dataset with fewer training parameters, surpassing previous models, which achieved a maximum accuracy of 99.26\% on the same dataset.
A Knowledge-driven Deep Learning Framework for Organoid Morphological Segmentation and Characterisation
Authors: Qin Yiming , Li Jiajia , Yin Heng , Wu Dezhi , Liu Xuejiao , Plötz Tobias , Hopp Alexander , Kurniawan Nicholas , Winkel Mathias , Harbach Philipp , Tang Chunling , Tan Feng 
Published: November 2024

Abstract

Background
Organoids have great potential to revolutionize various aspects of biomedical research and healthcare. Researchers typically use the fluorescence-based approach to analyse their dynamics, which requires specialized equipment and may interfere with their growth. Therefore, it is an open challenge to develop a general framework to analyse organoid dynamics under non-invasive and low-resource settings.
Results
In this paper, we present a knowledge-driven deep learning system named TransOrga-plus to automatically analyse organoid dynamics in a non-invasive manner. Given a bright-field microscopic image, TransOrga-plus detects organoids through a multi-modal transformer-based segmentation module. To provide customized and robust organoid analysis, a biological knowledge-driven branch is embedded into the segmentation module which integrates biological knowledge, e.g., the morphological characteristics of organoids, into the analysis process. Then, based on the detection results, a lightweight multi-object tracking module based on the decoupling of visual and identity features is introduced to track organoids over time. Finally, TransOrga-plus outputs the dynamics analysis to assist biologists for further research. To train and validate our framework, we curate a large-scale organoid dataset encompassing diverse tissue types and various microscopic imaging settings. Extensive experimental results demonstrate that our method outperforms all baselines in organoid analysis. The results show that TransOrga-plus provides comparable analytical results to biologists and significantly accelerates organoid work process.
Conclusions
In conclusion, TransOrga-plus integrates the biological expertise with cutting-edge deep learning-based model and enables the non-invasive analysis of various organoids from complex, low-resource, and time-lapse situations.
TML: A Transformer-Based Meta-Learning Framework for Cross-Project Software Defect Prediction
Authors: Bandhu Himanshu , Ali Aftab , McClean Sally , Ullah Hanif , Abu-Tair Mamun , Ziolkowski Adam , Noppen Joost 
Published: November 2024

Abstract

Identifying software defects early is crucial for enhancing software quality and reducing costs. Traditional Within-Project Defect Prediction (WPDP) methods rely on historical project-specific data, limiting their effectiveness when such data is unavailable. Cross-Project Defect Prediction (CPDP) offers a solution by leveraging defect data from different projects, but challenges arise due to the diverse nature of data distributions across projects. This paper presents a novel framework, TML (Transformer-based Meta-Learning), designed to improve CPDP performance by addressing these challenges. TML integrates transformer-based encoder networks for feature extraction, adversarial domain adaptation to align data distributions, and meta-learning to enhance generalization across projects. Additionally, it incorporates ensemble learning and Bayesian optimization to improve model robustness and predictive accuracy. The framework is evaluated on 16 datasets from four major software repositories (AEEM, NASA, Promise, JIRA). Experimental results demonstrate that TML significantly outperforms existing CPDP methods such as ENTL, EGW, and EMKCA in key performance metrics including Precision, Recall, F1-score, G-Mean, and AUC. The results consistently demonstrate the robustness of the TML framework, establishing it as a promising approach for early defect detection in diverse software development environments.
Evaluation of Wound Healing Activity of 80% Methanol Crude Extract and Solvent Fractions of Prunus Africana (Rosaceae) Stem Bark in Mice
Authors: Nibret Getinet , Birhanu Zewdu , Abdulwuhab Mohammedbrhan , Asmamaw Solomon 
Published: November 2024

Abstract

Background: Prunus africana stem bark is used for the management of wounds in different Ethiopian communities. However, there was no in vivo study that justified its claimed use for wound healing effect.
Objectives: The study aimed to evaluate the wound healing activity of 80% methanol crude extract and solvent fractions of Prunus africana (Rosaceae) stem bark in mice
Methods: The stem bark of Prunus africana was extracted with 80% methanol using maceration technique and fractionated by n-hexane, ethyl acetate, and distilled water. The crude extract and solvent fraction yields of Prunus africana were formulated as 5% and 10% weight-by-weight ointments. The acute dermal toxicity of the plant was evaluated in mice. The wound-healing effect of the extract was evaluated using excision, burn, and incision wound models. Whereas the solvent fraction ointments were investigated in excision wound models.
Results: The 10% w/w methanol extract exhibited a significant (P < 0.001) wound contraction activity starting from the 6th to 14th days of treatment as compared to the 5% w/w crude extract ointment treated group on excision wound. Aqueous and ethyl acetate fraction ointments resulted a significant (P < 0.001) wound healing activity as compared to n-hexane fraction and simple ointment base from the 4th to 18th post-wounding days.
Conclusion: In this study, 80% methanol crude extract of Prunus africana and its aqueous and ethyl acetate fraction ointments showed good wound healing activity.
Pre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model
Authors: Shetty Siddesh , Goadsby James , Magee Laura A. , Syngelaki Argyro , von Dadelszen Peter , Akolekar Ranjit , Silverio Sergio A. , Sheen Kayleigh , Wright Alan , Wright David , Nicolaides Kypros , Fox-Rushby Julia , Soley-Bori Marina 
Published: November 2024

Abstract

Objectives:
Preeclampsia (PE) occurs most commonly at term, and currently, there is no effective strategy to prevent it. The PE prevention by timed birth at term trial (PREVENT- PE) with embedded economic evaluation aims to provide cost-effectiveness evidence on whether screening for PE risk at 35+0–36+6 weeks’ gestation and offering women risk-based, planned term birth compared to usual care at term, can reduce the incidence of PE, without increasing emergency caesarean sections or neonatal morbidity (i.e., neonatal unit admission for ≥48 hours).
Study design:
This protocol outlines the methods for within-trial and intermediate-term economic evaluations.
Main outcome measures:
The economic evaluation will identify, measure and value resources and health outcomes for both mothers at risk of term PE and newborn babies, from the National Health Service (NHS) perspective. A cost-effectiveness analysis within the trial will be undertaken, and the incremental cost per PE case averted will be reported as the main outcome. Costs and health outcomes for the trial duration will be calculated using patient-level data, from case report forms and electronic medical records. A decision model will be developed to assess the cost-utility of the intervention for one year. Transition probabilities, costs, and quality-adjusted life years (QALYs) will be populated using trial data and existing literature. Uncertainty will be assessed using deterministic and probabilistic sensitivity analyses. Subgroup analyses (for example, standalone maternal/newborn perspective and by gestational age for planned birth) will be undertaken to assess heterogeneity in study results, sample size permitting.
Highly conserved sequence-specific DNA binding networks of 716 transcription factors associated with continuing divergent genomic evolution of human and chimpanzee brain development.
Authors: Glinsky Gennadi 
Published: November 2024

Abstract

Emergence during mammalian evolution of common and divergent traits of genomic regulatory networks (GRNs) encompassing ubiquitous, compositionally nearly identical yet quantitatively distinct panels of DNA sequences of transcription factor binding sites (TFBS) for 716 proteins is reported. The evolutionary-conserved foundation of these GRNs appears assembled from arrays of DNA codes for ~770 TFBS, including 65 instances of immediately adjacent TFBS for two distinct TFs. A majority of protein constituents of these GRNs (770 of 716; 98%) is defined by Gene Ontology (GO) features of sequence-specific double-stranded DNA binding (GO: 1990837). Genome-wide and individual chromosome-level analyses of 17,935 ATAC-seq-defined brain development regulatory regions (BDRRs) revealed nearly universal representations of TFBS for TF-constituents of these networks, TFBS densities of which appear consistently higher within thousands BDRRs of Modern Humans compare to Chimpanzee. Transposable elements (TE), including LTR/HERV, SINE/Alu, SVA, and LINE families, appear to harbor consensus regulatory nodes of identified herein highly conserved sequence-specific double-stranded DNA binding networks. Notably, selections of quantitative features of TFBS panels of these GRNs manifest individual chromosome-specific profiles and species-specific divergence patterns. Collectively, this contribution highlights a previously unrecognized essential function of genomic DNA sequences derived from multiple TE families in providing genome-wide regulatory seed templates of sequence-specific double-stranded DNA binding GRNs. Since DNA sequences of TFBS panels for 716 proteins are encoded by transposons that remain active in genomes of present day humans, namely SVA and LINE families, retrotransposition-mediated spread of seeds for these GRNs may contribute to continuing divergent genomic evolution of human and chimpanzee brain development.
Advanced Autonomous Detection of Ransomware Using Dynamic Crypto-Entropy Signature Analysis
Authors: Prigodichi Giovanni , Wainwright Harrison , Davis Richard , Kingsley Jasper 
Published: November 2024

Abstract

The sophistication of cyber threats requires the development of advanced detection mechanisms capable of identifying and mitigating malicious activities with minimal human intervention. The Dynamic Crypto-Entropy Signature Analysis (DCESA) framework introduces an autonomous approach to ransomware detection through the analysis of cryptographic entropy patterns inherent in malicious encryption behaviors. Through dynamically generating unique entropy signatures, DCESA effectively distinguishes between benign and malicious activities, thereby enhancing detection accuracy and reducing false positives. Empirical evaluations have demonstrated DCESA's proficiency in identifying a diverse array of ransomware strains, including previously unseen variants, with minimal impact on system performance. The integration of DCESA into cybersecurity infrastructures offers a proactive and efficient solution for mitigating the impact of ransomware attacks, thereby enhancing the overall security posture of organizations.
Hoperanchite, (NH4)2(S2O3), a new mineral from an active vent in a burning bituminous shale
Authors: Kampf Anthony R. , Adams Paul M. , Ma Chi , Lynch David K. 
Published: November 2024

Abstract
Hoperanchite (IMA2024-017), (NH4)2(S2O3), is a newly approved mineral species from an active vent in a burning bituminous shale at Hope Ranch, Santa Barbara County, California, U.S.A. Hoperanchite occurs as tabular crystals up to about 0.3 mm in diameter. Tablets are flattened on {001} and exhibit the forms {100}, {010}, {0-10}, {110}, and {-1-10}. The mineral is colorless and transparent with vitreous luster and white streak. The Mohs hardness is ~2½. The mineral has brittle tenacity, irregular fracture, and one good cleavage on {001}. The measured density is 1.68(2) g·cm−3. The mineral dissolves instantly in room temperature H2O. The mineral is optically biaxial (+), α = 1.602(2), β = 1.616(2), γ = 1.634(2) (white light); 2Vmeas = 84(2)°; orientation Y = b, X ^ a = 20° in obtuse β; nonpleochroic. Electron microprobe analysis provided the empirical formula N1.89H7.40S1.97O3. Hoperanchite is monoclinic, C2, a = 10.2313(5), b = 6.4998(3), c = 8.8098(6) Å, β = 94.611(7)°, V = 583.97(6) Å3, and Z = 4. The crystal structure (R1 = 0.0325 for 1176 I &gt; 2σI reflections) is the same as that of synthetic (NH4)2(S2O3). Hoperanchite is the first thiosulfate mineral that does not contain essential Pb.