bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between periodontitis and cardiometabolic index (CMI): a study from NHANES 2009–2014
Authors: Shuning Li , Zhiyong Zhang , Wei Yang , Jilun Liu , Xuhui Fan 
Published: November 2024

AbstractCardiometabolic index (CMI) is a novel anthropometric metric that integrates lipid and adiposity characteristics. The correlation between periodontitis development and CMI is ambiguous. The objective of this study was to establish the association between CMI and periodontitis by analyzing data from the NHANES (National Health and Nutrition Examination Survey) database. A cross-sectional study was conducted on a cohort of 6188 people selected from the NHANES database, covering the period from 2009 to 2014. The study employed multivariate logistic regression to examine the independent correlation between CMI and periodontitis. Subgroup data were analyzed and interaction tests were conducted to assess the impact of variables on the correlation between CMI and periodontitis. The CMI index was significantly and positively associated with the presence of periodontitis (β = 0.03, 95%CI(0.01, 0.05), p = 0.0092). In addition, a U-shaped relationship was found between CMI index and periodontitis severity in an older American population (65 < = age < = 80, with a folding point of 1.44, p = 0.008). This study demonstrates a significant correlation between CMI and periodontitis, positioning CMI as a crucial indicator for assessing periodontal health. Future efforts should prioritize oral hygiene interventions for patients with elevated CMI levels to facilitate early intervention and enhance overall health outcomes.
Genetic diversity of Anadara tuberculosa in two localities of the Colombian Pacific Coast
Authors: Fuentes Luis , Guevara-Suarez Marcela , Zambrano María Mercedes , Jiménez Pedro , Duitama Jorge , Restrepo Silvia 
Published: November 2024

AbstractPiangua, Anadara tuberculosa, is an economically important mollusk for the human population living on the Colombian Pacific Coast. In the last years, the demand and exploitation of this mollusk have increased, putting it at risk to the point of being endangered. This research aimed to identify the genetic diversity and population structure of piangua in two localities on the Pacific Coast of Colombia. We assembled a chromosome-level genome using PacBio-Hifi and Arima sequencing. We obtained 274 scaffolds with an N50 of 45.42 Mbp, a total size of 953 Mbp, and a completeness of 91% based on BUSCO scores. The transposable elements accounted for 30.29% of the genome, and 24,317 genes were annotated. Genome-guided variant calling for 89 samples using DArT sequencing data delivered 4,825 bi-allelic SNPs, which supported genetic diversity and population structure analyses. Data showed that the piangua populations in the two localities were under expansion events more than 100k years ago. However, results also showed a reduction in genetic diversity, as evidenced by the loss of heterozygosity, which may be caused by high levels of inbreeding, probably due to a recent overexploitation. Furthermore, although we evidenced gene flow between the two localities, there is also a subtle geographical population structure between the two localities and among mangroves in one of the localities. This is the first study in Colombia that provides relevant genetic information on piangua to lay the foundations for conservation strategies.
Increased cell killing effect in neutron capture enhanced proton beam therapy
Authors: Shiba Shintaro , Shimo Takahiro , Yamanaka Masashi , Yagihashi Takayuki , Sakai Makoto , Ohno Tatsuya , Tokuuye Koichi , Omura Motoko 
Published: November 2024

AbstractThermal neutrons generated in the body during proton beam therapy (PBT) can be used to cause boron neutron capture reactions and have recently been proposed as neutron capture enhanced PBT (NCEPBT). However, the cell killing effect of NCEPBT remains underexplored. Here, we show an increase in the cell killing effect of NCEPBT. Using Monte Carlo simulations, we showed that neutrons generated by proton beam irradiation are uniformly spread on tissue culture plates. Human salivary gland tumor cell line (HSG), human osteosarcoma cell line (MG63), human tongue squamous cell carcinoma cell line (SAS), and human malignant melanoma cell line (G-361) were irradiated with X-rays, proton beams, and proton beams with 10B-enriched boronophenylalanine (boron concentration of 20 and 80 ppm). The relative biological effectiveness (RBE) values of proton beams alone, proton beams with 20 ppm boron, and proton beams with 80 ppm boron for HSG, MG63, SAS, and G-361 were 1.02, 1.07, and 1.23; 1.01, 1.08, and 1.44; 1.05, 1.09, and 1.46; and 1.04, 1.13, and 1.63, respectively. NCEPBT with high boron concentration showed high RBE and a high sensitizing effect. Our results confirm an increase in the cell killing effect of NCEPBT, should aid in its clinical use, and warrant its further investigation.
An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection
Authors: Xing Tao , Dou Yutao , Chen Xianliang , Zhou Jiansong , Xie Xiaolan , Peng Shaoliang 
Published: November 2024

AbstractMajor Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios can facilitate the timely detection of MDD. However, existing depression detection methods either consider only a single modality or do not fully account for the differences and similarities between modalities in multimodal approaches, potentially overlooking the latent information inherent in various modal data. To address these challenges, we propose EMO-GCN, a multimodal depression detection method based on an adaptive multi-graph neural network. By employing graph-based methods to model data from various modalities and extracting features from them, the potential correlations between modalities are uncovered. The model’s performance on the MODMA dataset is outstanding, achieving an accuracy (ACC) of 96.30%. Ablation studies further confirm the effectiveness of the model’s individual components.The experimental results of EMO-GCN demonstrate the application prospects of graph-based multimodal analysis in the field of mental health, offering new perspectives for future research.
Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale optimization algorithm
Authors: Zangmo Rinchen , Sudabattula Suresh Kumar , Mishra Sachin , Dharavat Nagaraju , Golla Naresh Kumar , Sharma Naveen Kumar , Jadoun Vinay Kumar 
Published: November 2024

AbstractThis research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (PLoss) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.
A cross-tissue transcriptome-wide association study reveals GRK4 as a novel susceptibility gene for COPD
Authors: Chen Guanglei , Jin Yaxian , Chu Cancan , Zheng Yuhao , Yang Changfu , Chen Yunzhi , Zhu Xing 
Published: November 2024

AbstractChronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder with environmental factors being the primary risk determinants. However, genetic factors also substantially contribute to the susceptibility and progression of COPD. Although genome-wide association studies (GWAS) have identified several loci associated with COPD susceptibility, the specific pathogenic genes underlying these loci, along with their biological functions and roles within regulatory networks, remain unclear. This lack of clarity constrains our ability to achieve a deeper understanding of the genetic basis of COPD. This study leveraged the FinnGen R11 genetic dataset, comprising 21,617 cases and 372,627 controls, along with GTEx V8 eQTLs data to conduct a cross-tissue transcriptome-wide association study (TWAS). Initially, we performed a cross-tissue TWAS analysis using the Unified Test for Molecular Signatures (UTMOST), followed by validation of the UTMOST findings in single tissues using the Functional Summary-based Imputation (FUSION) method and conditional and joint (COJO) analyses of the identified genes. Subsequently, candidate susceptibility genes were screened using Multi-marker Analysis of Genomic Annotation (MAGMA). The causal relationship between these candidate genes and COPD was further evaluated through summary data-based Mendelian randomization (SMR), colocalization analysis, and Mendelian randomization (MR). Additionally, the identified results were validated against the COPD dataset in the GWAS Catalog (GCST90399694). GeneMANIA was employed to further explore the functional significance of these susceptibility genes. In the cross-tissue TWAS analysis (UTMOST), we identified 17 susceptibility genes associated with COPD. Among these, a novel susceptibility gene, G protein-coupled receptor kinase 4 (GRK4), was validated through single-tissue TWAS (FUSION) and MAGMA analyses, with further confirmation via SMR, MR, and colocalization analyses. Moreover, GRK4 was validated in an independent dataset. This study identifies GRK4 as a potential novel susceptibility gene for COPD, which may influence disease risk by exacerbating inflammatory responses. The findings address gaps in previous single-tissue GWAS studies, revealing consistent expression and potential function of GRK4 across different tissues. However, considering the study’s limitations, further investigation and validation of GRK4’s role in COPD are warranted.
Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases
Authors: Singh Ravnoor , Kaur Satinder , Singh Gurpreet , Kaur Mehakdeep , Kaur Parminder 
Published: November 2024

AbstractMachine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. The brain is the most pivotal organ in the human body. A brain stroke is generally caused by a blockage in the brain arteries. A brain stroke is one of the primary reasons for death. Therefore, early prediction of diseases like brain stroke, heart attack can significantly help in making decisions for doctors. The research study aims to find a robust and potential technique for the early prediction of brain stroke, Alzheimer’s, heart attack, cancer, Parkinson’s and potentially reducing the incidence of severe post complications of the mentioned diseases. By considering the five datasets as input, machine learning models have been trained for the research study. Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting for brain stroke and eight individual classifiers have been used for early prediction of heart attack, cancer, Alzheimer and Parkinson’s. After analyzing the results of each classifier for each disease, the proposed method, which is a pair of random forest and decision tree using a hard voting method for early brain stroke prediction, achieves the highest accuracy of 99%, which is better than all classifiers. Along with accuracy, the proposed method attained a value of 98% in precision, an outstanding 100% in recall, and 99% in F1 score. XGBoost performed best for cancer, Parkinson’s, Alzeihmer’s and Bernoulli naive bayes performed best in case of Heart attack .Upon comparing the values of these performance metrics, they outshine all the other model’s values.
Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps
Authors: Basu Sanjay , Schillinger Dean , Patel Sadiq Y. , Rigdon Joseph 
Published: November 2024

AbstractPopulation health initiatives often rely on cold outreach to close gaps in preventive care, such as overdue screenings or immunizations. Tailoring messages to diverse patient populations remains challenging, as traditional A/B testing requires large sample sizes to test only two alternative messages. With increasing availability of large language models (LLMs), programs can utilize tiered testing among both LLM and manual human agents, presenting the dilemma of identifying which patients need different levels of human support to cost-effectively engage large populations. Using microsimulations, we compared both the statistical power and false positive rates of A/B testing and Sequential Multiple Assignment Randomized Trials (SMART) for developing personalized communications across multiple effect sizes and sample sizes. SMART showed better cost-effectiveness and net benefit across all scenarios, but superior power for detecting heterogeneous treatment effects (HTEs) only in later randomization stages, when populations were more homogeneous and subtle differences drove engagement differences.
Embedding machine-learnt sub-grid variability improves climate model precipitation patterns
Authors: Giles Daniel , Briant James , Morcrette Cyril J. , Guillas Serge 
Published: November 2024

AbstractParameterisation schemes within General Circulation Models are required to capture cloud processes and precipitation formation but exhibit long-standing known biases. Here, we develop a hybrid approach that tackles these biases by embedding a Multi-Output Gaussian Process trained to predict high resolution variability within each climate model grid box. The trained multi-output Gaussian Process model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the Gaussian Process. Ten-year predictions are generated for both control and machine learning hybrid models. The hybrid model reduces the global precipitation area-weighted root-mean squared error by up to 17% and over the tropics by up to 20%. Hybrid techniques have been known to introduce non-physical states therefore physical quantities are explored to ensure that climatic drift is not observed. Furthermore, to understand the drivers of the precipitation improvements the changes to thermodynamic profiles and the distribution of lifted index values are investigated.
Layered entrenchment maintains essentiality in the evolution of Form I Rubisco complexes
Authors: Schulz Luca , Zarzycki Jan , Steinchen Wieland , Hochberg Georg K A , Erb Tobias J 
Published: November 2024

AbstractProtein complexes composed of strictly essential subunits are abundant in nature and often arise through the gradual complexification of ancestral precursor proteins. Essentiality can arise through the accumulation of changes that are tolerated in the complex state but would be deleterious for the standalone complex components. While this theoretical framework to explain how essentiality arises has been proposed long ago, it is unclear which factors cause essentiality to persist over evolutionary timescales. In this work we show that the central enzyme of photosynthesis, ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), can easily start to depend on a newly recruited interaction partner through multiple, genetically distinct mechanisms that affect stability, solubility, and catalysis. We demonstrate that layering multiple mechanisms of essentiality can lead to its persistence, even if any given mechanism reverts. More broadly, our work highlights that new interaction partners can drastically re-shape which substitutions are tolerated in the proteins they are recruited into. This can lead to the evolution of multilayered essentiality through the exploration of areas of sequence space that are only accessible in the complex state.
Tannic acid salt-modified CoFe-layered double hydroxide boosts stable seawater oxidation at an industrial-level current density
Authors: Cai Zhengwei , Guo Yaxin , Yang Chaoxin , Li Zixiao , Sun Shengjun , Yue Meng , Wang Xiaoyan , Zhang Min , Wang Hefeng , Yao Yongchao , Zheng Dongdong , Farouk Asmaa , Ibrahim Fatma A. , Lv Yanqin , Sun Xuping , Tang Bo 
Published: November 2024

Tannic acid salt-modified CoFe layer double hydroxide on nickel foam (CoFe LDH@CoFe-TA/NF) as an efficient OER electrocatalyst, requiring an overpotential of 379 mV to achieve 1000 mA cm−2 and maintaining stability for 450 h in alkaline seawater.
The emergence of the MD − Ṁ* correlation in the magnetohydrodynamic wind scenario
Authors: Zallio Luigi , Rosotti Giovanni , Tabone Benoît , Testi Leonardo , Lodato Giuseppe , Somigliana Alice 
Published: November 2024

Context. There is still much uncertainty around the mechanism that rules the accretion of proto-planetary disks. In recent years, magnetohydrondynamic (MHD) wind-driven accretion has been proposed as a valid alternative to the more conventional viscous accretion. In particular, winds have been shown to reproduce the observed correlation between the mass of the disk MD and the mass accretion rate onto the central star Ṁ*, but this has been done only for specific conditions. It is not clear whether this implies fine tuning or if it is a general result.
Aims. We investigated under which conditions the observed correlation between the mass of the disk MD and the mass accretion rate onto the central star Ṁ*, can be obtained.
Methods. We present mainly analytical calculations, supported by Monte Carlo simulations. We also perform a comparison with the observed data to test our predictions.
Results. In the absence of a correlation between the initial mass M0 and the initial accretion timescale tacc,0 , we find that the slope of the MD − Ṁ* correlation depends on the value of the spread of the initial conditions of masses and lifetimes of disks. Then we clarify the conditions under which a disk population can be fitted with a single power law. Moreover, we derive an analytical expression for the spread of log(MD/Ṁ*) valid when the spread of tacc is taken to be constant. In the presence of a correlation between M0 and tacc,0, we derive an analytical expression for the slope of the MD−Ṁ* correlation in the initial conditions of disks and at late times. In this new scenario, we clarify under which conditions the disk population can be fitted by a single power law, and we provide empirical constraints on the parameters ruling the evolution of disks in our models.
Conclusions. We conclude that MHD winds can predict the observed values of the slope and the spread of the MD−Ṁ* correlation under a broad range of initial conditions. This is a fundamental expansion of previous works on the MHD paradigm, exploring the establishment of this fundamental correlation beyond specific initial conditions.
Non-parametric identification of single-lined binary candidates in young clusters using single-epoch spectroscopy
Authors: Rinaldi Stefano , Ramírez-Tannus María Claudia 
Published: November 2024

Aims. Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepening our understanding of these processes. We propose a method to investigate the observed radial velocity distribution of massive stars in young clusters with the goal of identifying binary systems.
Methods. We reconstruct the radial velocity distribution using a three-layer hierarchical Bayesian non-parametric approach; nonparametric methods are data-driven models able to infer arbitrary probability densities under minimal mathematical assumptions. When applying our statistical framework, it is possible to identify variable stars and binary systems because these deviate significantly from the expected intrinsic Gaussian distribution for radial velocities.
Results. We tested our method with the massive star-forming region within the giant HII region M17. We are able to confidently identify binaries and variable stars with as little as single-epoch observations. The distinction between variable and binary stars improves significantly when introducing additional epochs.
L’actionnabilité clinique des gènes
Authors: Pion Emmanuelle , Bonne Gisèle , Atalaia Antonio , Salort-Campana Emmanuelle , Gorokhova Svetlana , Attarian Shahram , Cossée Mireille , Krahn Martin 
Published: November 2024

Le séquençage à haut débit a introduit le concept de « gènes actionnables ». Ces gènes sont liés à des pathologies pour lesquelles des traitements ou des prises en charge spécifiques existent. Un diagnostic génétique précis est donc crucial pour initier des interventions précoces qui peuvent prévenir ou retarder l’évolution de maladies rares. Le séquençage à haut débit a considérablement augmenté les capacités d’analyse génétique, mais il a également entraîné une augmentation des demandes d’analyses, allongeant les délais de rendu des résultats. Une priorisation des analyses devient nécessaire, surtout lorsque des « gènes actionnables » sont suspectés. Pour les myopathies, un travail national a identifié 63 gènes actionnables, impliqués dans des pathologies pour lesquelles peuvent être initiés un traitement ciblé et/ou une prise en charge précoce, améliorant ainsi le pronostic des patients. Malgré des avancées, de nombreuses maladies rares restent sans traitements spécifiques, soulignant l’importance continue de la recherche et de l’innovation en génétique médicale.
Contrôles des paramètres des domaines nucléaires par les microtubules et la protéine MACF1
Authors: Ghasemizadeh Alireza , Gache Vincent 
Published: November 2024

Les fibres du muscle squelettique sont des syncytia formés par la fusion de dizaines ou centaines de cellules progénitrices mononucléées. Au cours de la myogenèse, les noyaux arrivant des cellules progénitrices parcourent un long chemin avant d’être positionnés à la périphérie d’une myofibre mature. Une fois à la périphérie, ces noyaux sont régulièrement espacés et chacun est transcriptionnellement responsable de sa proportion de cytoplasme environnant, connue comme « domaine nucléaire ». La perturbation de ces domaines peut être observée dans différentes myopathies, suggérant leur importance pour la fonctionnalité du muscle squelettique. Cependant, les mécanismes régulant la stabilité des domaines nucléaires sont mal connus. Nous prenons ici l’exemple de MACF1, une protéine associée aux microtubules, comme une actrice essentielle dans l’organisation des domaines nucléaires, afin de mettre en lumière le rôle potentiel des microtubules et de leur réseau protéique associé pour la stabilité de ces domaines et donc pour la fonctionnalité des myofibres.