bluuebunny/crossref_metadata_2025_split huggingface dataset:
eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data
Authors: Fang Minghao , Fang Jingwen , Luo Songwen , Liu Ke , Yu Qiaoni , Yang Jiaxuan , Zhou Youyang , Li Zongkai , Sun Ruoming , Guo Chuang , Qu Kun 
Published: February 2024

Abstract
Extrachromosomal circular DNA (eccDNA) is currently attracting considerable attention from researchers due to its significant impact on tumor biogenesis. High-throughput sequencing (HTS) methods for eccDNA identification are continually evolving. However, an efficient pipeline for the integrative and comprehensive analysis of eccDNA obtained from HTS data is still lacking. Here, we introduce eccDNA-pipe, an accessible software package that offers a user-friendly pipeline for conducting eccDNA analysis starting from raw sequencing data. This dataset includes data from various sequencing techniques such as whole-genome sequencing (WGS), Circle-seq and Circulome-seq, obtained through short-read sequencing or long-read sequencing. eccDNA-pipe presents a comprehensive solution for both upstream and downstream analysis, encompassing quality control and eccDNA identification in upstream analysis and downstream tasks such as eccDNA length distribution analysis, differential analysis of genes enriched with eccDNA and visualization of eccDNA structures. Notably, eccDNA-pipe automatically generates high-quality publication-ready plots. In summary, eccDNA-pipe provides a comprehensive and user-friendly pipeline for ﻿customized analysis of eccDNA research.
Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis
Authors: Han Seokjin , Lee Ji Eun , Kang Seolhee , So Minyoung , Jin Hee , Lee Jang Ho , Baek Sunghyeob , Jun Hyungjin , Kim Tae Yong , Lee Yun-Sil 
Published: February 2024

Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms—a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial–mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
Modeling genotype–protein interaction and correlation for Alzheimer’s disease: a multi-omics imaging genetics study
Authors: Zhang Jin , Ma Zikang , Yang Yan , Guo Lei , Du Lei ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2024

Abstract
Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers’ comprehensive understanding of Alzheimer’s disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype–protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose $\textbf{M}$ulti-$\textbf{T}$ask $\textbf{G}$enotype-$\textbf{P}$rotein $\textbf{I}$nteraction and $\textbf{C}$orrelation disentangling method ($\textbf{MT-GPIC}$) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT-GPIC, which is guaranteed to converge. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative data set show that MT-GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD.
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction
Authors: Zhu Yi-Heng , Liu Zi , Liu Yan , Ji Zhiwei , Yu Dong-Jun 
Published: February 2024

AbstractEfficient and accurate recognition of protein–DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein–DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone.
Evaluating the efficacy of MEANGS for mitochondrial genome assembly of cartilaginous and ray-finned fish species
Authors: Xu Sheng-Yong , Cai Shan-Shan , Han Zhi-Qiang 
Published: February 2024

Abstract
The assembly of complete and circularized mitochondrial genomes (mitogenomes) is essential for population genetics, phylogenetics and evolution studies. Recently, Song et al. developed a seed-free tool called MEANGS for de novo mitochondrial assembly from whole genome sequencing (WGS) data in animals, achieving highly accurate and intact assemblies. However, the suitability of this tool for marine fish remains unexplored. Additionally, we have concerns regarding the overlap sequences in their original results, which may impact downstream analyses. In this Letter to the Editor, the effectiveness of MEANGS in assembling mitogenomes of cartilaginous and ray-finned fish species was assessed. Moreover, we also discussed the appropriate utilization of MEANGS in mitogenome assembly, including the implementation of the data-cut function and circular detection module. Our observations indicated that with the utilization of these modules, MEANGS efficiently assembled complete and circularized mitogenomes, even when handling large WGS datasets. Therefore, we strongly recommend users employ the data-cut function and circular detection module when using MEANGS, as the former significantly reduces runtime and the latter aids in the removal of overlapped sequences for improved circularization. Furthermore, our findings suggested that approximately 2× coverage of clean WGS data was sufficient for MEANGS to assemble mitogenomes in marine fish species. Moreover, due to its seed-free nature, MEANGS can be deemed one of the most efficient software tools for assembling mitogenomes from animal WGS data, particularly in studies with limited species or genetic background information.
Explicit solutions for the asymptotically optimal bandwidth in cross-validation
Authors: Abadir Karim M , Lubrano Michel 
Published: February 2024

Abstract
We show that least-squares cross-validation methods share a common structure that has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student-t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative, thus leading to very fast computations, their integrated squared-error dominates traditional cross-validation implementations, they alleviate the notorious sample variability of cross-validation and overcome its breakdown in the case of repeated observations. We illustrate our method with univariate and bivariate applications, of density estimation and nonparametric regressions, to a large dataset of Michigan State University academic wages and experience.
Impact of clonal haematopoiesis on atherosclerotic cardiovascular disease according to low-density lipoprotein cholesterol levels in general population
Authors: Lee Heesun , Song Han , Choi Su-Yeon , Koh Youngil , Ryu Gangpyo , Park Hyo Eun , Yoon Ji Won , Kim Min Joo , Chung Soie , Bae Jung Ho , Choi Seung Ho , Koo Bon-Kwon 
Published: February 2024

Abstract

Aims
Clonal haematopoiesis of indeterminate potential (CHIP), defined as a clonal expansion of age-related recurrent somatic mutations, has recently emerged as a novel cardiovascular risk factor. However, the precise role of CHIP in the development of atherosclerotic cardiovascular disease (ASCVD) remains unclear.

Methods and results
Among 4300 asymptomatic Korean participants aged 40–79 years, we investigated the risk of ASCVD by CHIP and the interplay between CHIP and conventional risk factors in ASCVD development. Additionally, we assessed changes in coronary arteries based on the presence of CHIP using coronary computed tomography angiography (CCTA). CHIP was present in 363 participants (8.4%), and its prevalence increased with age. Commonly mutated genes were DNMT3A, TET2, and ASXL1, in order. During the follow-up (median 4.7 years), 18 ASCVD cases (5.0%) were observed in CHIP carriers vs. 62 (1.6%) in non-carriers (P &lt; 0.001), indicating an elevated risk of ASCVD associated with CHIP [adjusted hazard ratio (HR) 2.49; 95% confidence interval (CI) 1.45–4.29; P &lt; 0.001]. Notably, with high levels of LDL cholesterol, CHIP enhanced the risk of ASCVD (adjusted HR 6.20; 95% CI 3.14–12.23; P &lt; 0.001), demonstrating synergism between CHIP and LDL cholesterol levels (S-index 4.94; 95% CI 1.08–22.53; P = 0.039). Serial CCTAs confirmed that CHIP, in conjunction with high LDL cholesterol levels, had a significant early impact on coronary arteries, revealing new measurable coronary atherosclerosis, mainly with unstable plaque, in proximal lesions.

Conclusion
The presence of CHIP was significantly associated with the risk of ASCVD, promoting the early stage of atherosclerosis through synergy with high LDL cholesterol in the general population.
Develop and Validate a Prognostic Index With Laboratory Tests to Predict Mortality in Middle-Aged and Older Adults Using Machine Learning Models: A Prospective Cohort Study
Authors: Huang Chi-Hsien , Fang Yao-Hwei , Zhang Shu , Wu I-Chien , Chuang Shu-Chun , Chang Hsing-Yi , Tsai Yi-Fen , Tseng Wei-Ting , Wu Ray-Chin , Liu Yen-Tze , Lien Li-Ming , Juan Chung-Chou , Tange Chikako , Otsuka Rei , Arai Hidenori , Hsu Chih-Cheng , Hsiung Chao Agnes 
Published: February 2024

Abstract

Background
Prognostic indices can enhance personalized predictions of health burdens. However, a simple, practical, and reproducible tool is lacking for clinical use. This study aimed to develop a machine learning-based prognostic index for predicting all-cause mortality in community-dwelling older individuals.

Methods
We utilized the Healthy Aging Longitudinal Study in Taiwan (HALST) cohort, encompassing data from 5 663 participants. Over the 5-year follow-up, 447 deaths were confirmed. A machine learning-based routine blood examination prognostic index (MARBE-PI) was developed using common laboratory tests based on machine learning techniques. Participants were grouped into multiple risk categories by stratum-specific likelihood ratio analysis based on their MARBE-PI scores. The MARBE-PI was subsequently externally validated with an independent population-based cohort from Japan.

Results
Beyond age, sex, education level, and BMI, 6 laboratory tests (low-density lipoprotein, albumin, aspartate aminotransferase, lymphocyte count, high-sensitivity C-reactive protein, and creatinine) emerged as pivotal predictors via stepwise logistic regression (LR) for 5-year mortality. The area under curves of MARBE-PI constructed by LR were 0.799 (95% confidence interval [95% CI]: 0.778–0.819) and 0.756 (95% CI: 0.694–0.814) for the internal and external validation data sets, and were 0.801 (95% CI: 0.790–0.811) and 0.809 (95% CI: 0.774–0.845) for the extended 10-year mortality in both data sets, respectively. Risk categories stratified by MARBE-PI showed a consistent dose–response association with mortality. The MARBE-PI also performed comparably with indices constructed with clinical health deficits and/or laboratory results.

Conclusions
The MARBE-PI is considered the most applicable measure for risk stratification in busy clinical settings. It holds potential to pinpoint older individuals at elevated mortality risk, thereby aiding clinical decision-making.
Daily Dynamics of Awareness of Aging and Basic Psychological Need Satisfaction and Frustration in Middle and Older Adulthood
Authors: Windsor Tim D , Wilton-Harding Bethany , Sabatini Serena 
Published: February 2024

Abstract

Objectives
This daily diary study examined associations between awareness of age-related change (AARC) and satisfaction/frustration of basic psychological needs of autonomy, competence, and relatedness in a sample of midlife and older adults. We expected that greater satisfaction and lower frustration of needs would be associated with higher AARC-gains and lower AARC-losses. We also examined whether within-person associations of need satisfaction/frustration with AARC were moderated by age.

Methods
Participants (N = 152; aged 53+) completed measures of AARC and basic psychological need satisfaction and frustration on their smartphones for 10 consecutive days. Data were analyzed using multilevel models, with time-varying basic need satisfaction/frustration variables disaggregated into between-person and within-person components.

Results
On days participants reported higher than usual overall satisfaction of needs, and lower than usual frustration of needs, AARC-gains was higher, and AARC-losses was lower. Analysis of individual needs showed that autonomy and competence were more consistently related to higher AARC-gains and lower AARC-losses than relatedness. Within-person autonomy satisfaction was more strongly (negatively) associated with AARC-losses at older ages.

Discussion
Findings suggest that daily experiences related to satisfaction and frustration of goals related to autonomy and competence in particular may be proximal antecedents of short-term variation in AARC.
Implementation of an Advance Care Planning Intervention in Nursing Homes: An International Multiple Case Study
Authors: Brazil Kevin , Walshe Catherine , Doherty Julie , Harding Andrew J E , Preston Nancy , Bavelaar Laura , Cornally Nicola , Di Giulio Paola , Gonella Silvia , Hartigan Irene , Henderson Catherine , Kaasalainen Sharon , Loucka Martin , Sussman Tamara , Vlckova Karolina , van der Steen Jenny T ,   mySupport Study Group, Achterberg Wilco , Visser Mandy , Fitzgerald Serena , Just Danielle , Wilson Christine Brown , Carter Gillian , Simionato Laura , Buckley Catherine , Foley Tony , Fox Siobhan , Timmons Suzanne , O’Caoimh Ronan , O’Connell Selena , Sweeney Catherine , Cousins Emily , De Vries Kay , Dixon Josie , Dening Karen Harrison 
Published: February 2024

Abstract

Background and Objectives
The inability of individuals in the advanced stage of dementia to communicate about preferences in care at the end-of-life poses a challenge for healthcare professionals and family carers. The proven effective Family Carer Decision Support intervention has been designed to inform family carers about end-of-life care options available to a person living with advanced dementia. The objectives of the mySupport study were to adapt the application of the intervention for use in different countries, assess impact on family satisfaction and decision-making, and identify costs and supportive conditions for the implementation of the intervention.

Research Design and Methods
A multiple-case study design was chosen where the nursing home was the case. Nursing homes were enrolled from six countries: Canada, Czech Republic, Italy, Netherlands, Republic of Ireland, and United Kingdom.

Results
Seventeen cases (nursing homes) participated, with a total of 296 interviews completed including family carers, nursing home staff, and health providers. Five themes relevant to the implementation of the intervention were identified: supportive relationships; committed staff; perceived value of the intervention; the influence of external factors on the nursing home; and resource impact of delivery.

Discussion and Implications
There is a commonality of facilitators and barriers across countries when introducing practice innovation. A key learning point was the importance of implementation being accompanied by committed and supported nursing home leadership. The nursing home context is dynamic and multiple factors influence implementation at different points of time.
An overview of the existing and future state of the art advancement of hybrid energy systems based on PV-solar and wind
Authors: Soudagar Manzoore Elahi M , Ramesh S , Khan T M Yunus , Almakayeel Naif , Ramesh R , Ghazali Nik Nazri Nik , Cuce Erdem , Shelare Sagar 
Published: February 2024

Abstract
Increasing solar and wind power use in existing power systems could create significant technical issues, especially for grids with poor connectivity or stand-alone systems needing more adequate storage capacity. This is due to the unpredictable and intermittent nature of solar and wind power. The intermittent nature of solar and wind resources can be reduced by integrating them optimally, making the entire system more reliable and cost-effective to operate. The advantages and disadvantages of hybrid wind and solar energy integration systems are discussed in this research. The impact of voltage and frequency oscillations and harmonics is amplified in weak grids, affecting both grid-connected and stand-alone systems. This may be fixed by ensuring that hybrid systems are well designed, equipped with cutting-edge quick reaction control capabilities, and optimized. This review offers an overview of existing advances in PV-solar and wind-based hybrid energy systems while exploring potential future developments. Further, this review also provides an overview of the primary studies published on optimum design considerations for compactness, topologies for power electronics, and control. As the global energy environment shifts toward sustainability and resilience, this review helps researchers, policymakers, and industry stakeholders understand, adapt, and enhance PV-solar-wind hybrid energy systems.
Steroid Drugs Inhibit Bacterial Respiratory Oxidases and Are Lethal Toward Methicillin-Resistant Staphylococcus aureus
Authors: Henry Samantha A , Webster Calum M , Shaw Lindsey N , Torres Nathanial J , Jobson Mary-Elizabeth , Totzke Brendan C , Jackson Jessica K , McGreig Jake E , Wass Mark N , Robinson Gary K , Shepherd Mark 
Published: February 2024

Abstract

Background
Cytochrome bd complexes are respiratory oxidases found exclusively in prokaryotes that are important during infection for numerous bacterial pathogens.

Methods
In silico docking was employed to screen approved drugs for their ability to bind to the quinol site of Escherichia coli cytochrome bd-I. Respiratory inhibition was assessed with oxygen electrodes using membranes isolated from E. coli and methicillin-resistant Staphylococcus aureus strains expressing single respiratory oxidases (ie, cytochromes bd, bo′, or aa3). Growth/viability assays were used to measure bacteriostatic and bactericidal effects.

Results
The steroid drugs ethinylestradiol and quinestrol inhibited E. coli bd-I activity with median inhibitory concentration (IC50) values of 47 ± 28.9 µg/mL (158 ± 97.2 µM) and 0.2 ± 0.04 µg/mL (0.5 ± 0.1 µM), respectively. Quinestrol inhibited growth of an E. coli “bd-I only” strain with an IC50 of 0.06 ± 0.02 µg/mL (0.2 ± 0.07 µM). Growth of an S. aureus “bd only” strain was inhibited by quinestrol with an IC50 of 2.2 ± 0.43 µg/mL (6.0 ± 1.2 µM). Quinestrol exhibited potent bactericidal effects against S. aureus but not E. coli.

Conclusions
Quinestrol inhibits cytochrome bd in E. coli and S. aureus membranes and inhibits the growth of both species, yet is only bactericidal toward S. aureus.
Asymptomatic Low-Density Plasmodium falciparum Infections: Parasites Under the Host's Immune Radar?
Authors: Prah Diana Ahu , Laryea-Akrong Elizabeth 
Published: February 2024

Abstract
A large body of evidence suggests that low parasite carriage in Plasmodium falciparum asymptomatic infection is required for the maintenance of malaria immunity. However, the fact that treating such infections has little to no impact on subsequent clinical malaria is rarely noted. In this paper, we review data and argue that low-density parasite carriage in asymptomatic infection may not support host immune processes and that parasites are virtually under the host's immunological radar. We also discuss factors that may be constraining parasitemia in asymptomatic infections from reaching the threshold required to cause clinical symptoms. A thorough understanding of this infectious reservoir is essential for malaria control and eradication because asymptomatic infections contribute significantly to Plasmodium transmission.