bluuebunny/crossref_metadata_2025_split huggingface dataset:
HLA-EpiCheck: novel approach for HLA B-cell epitope prediction using 3D-surface patch descriptors derived from molecular dynamic simulations
Authors: Amaya-Ramirez Diego , Devriese Magali , Lhotte Romain , Usureau Cédric , Smaïl-Tabbone Malika , Taupin Jean-Luc , Devignes Marie-Dominique 
Published: December 2024

Abstract

Motivation
The human leukocyte antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLAs present on the graft by donor-specific antibodies raised by the recipient. It is therefore of key importance to identify all potentially immunogenic B-cell epitopes on HLAs in order to refine organ allocation. Such HLAs epitopes are currently characterized by the presence of polymorphic residues called “eplets”. However, many polymorphic positions in HLAs sequences are not yet experimentally confirmed as eplets associated with a HLA epitope. Moreover, structural studies of these epitopes only consider 3D static structures.

Results
We present here a machine-learning approach for predicting HLA epitopes, based on 3D-surface patches and molecular dynamics simulations. A collection of 3D-surface patches labeled as Epitope (2117) or Nonepitope (4769) according to Human Leukocyte Antigen Eplet Registry information was derived from 207 HLAs (61 solved and 146 predicted structures). Descriptors derived from static and dynamic patch properties were computed and three tree-based models were trained on a reduced non-redundant dataset. HLA-Epicheck is the prediction system formed by the three models. It leverages dynamic descriptors of 3D-surface patches for more than half of its prediction performance. Epitope predictions on unconfirmed eplets (absent from the initial dataset) are compared with experimental results and notable consistency is found.

Availability and implementation
Structural data and MD trajectories are deposited as open data under doi: 10.57745/GXZHH8. In-house scripts and machine-learning models for HLA-EpiCheck are available from https://gitlab.inria.fr/capsid.public_codes/hla-epicheck.
CCfrag: scanning folding potential of coiled-coil fragments with AlphaFold
Authors: Martinez-Goikoetxea Mikel 
Published: December 2024

Abstract

Motivation
Coiled coils are a widespread structural motif consisting of multiple α-helices that wind around a central axis to bury their hydrophobic core. While AlphaFold has emerged as an effective coiled-coil modeling tool, capable of accurately predicting changes in periodicity and core geometry along coiled-coil stalks, it is not without limitations, such as the generation of spuriously bent models and the inability to effectively model globally non-canonical-coiled coils. To overcome these limitations, we investigated whether dividing full-length sequences into fragments would result in better models.

Results
We developed CCfrag to leverage AlphaFold for the piece-wise modeling of coiled coils. The user can create a specification, defined by window size, length of overlap, and oligomerization state, and the program produces the files necessary to run AlphaFold predictions. The structural models and their scores are then integrated into a rich per-residue representation defined by sequence- or structure-based features. Our results suggest that removing coiled-coil sequences from their native context can improve prediction confidence and results in better models. In this article, we present various use cases of CCfrag and propose that fragment-based prediction is useful for understanding the properties of long, fibrous coiled coils by revealing local features not seen in full-length models.

Availability and implementation
The program is implemented as a Python module. The code and its documentation are available at https://github.com/Mikel-MG/CCfrag.
MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions
Authors: Rustamov Khondamir R , Baev Artyom Y 
Published: December 2024

Abstract

Motivation
Understanding the conformational landscape of protein–ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer’s ability to predict conformational landscapes, particularly for proteins with multiple conformational states.

Results
We verified this approach by predicting the conformational landscapes of chemokine receptor 4 (CXCR4) and glucagon receptor (GCGR) in the presence of their agonists and antagonists. In our experiments, AF-Multimer predicted the structures of CXCR4 and GCGR predominantly in active state in the presence of agonists and in inactive state in the presence of antagonists. Moreover, we tested our approach with proteins known to switch between monomeric and dimeric states, such as lymphotactin, SH3, and thermonuclease. AFcluster-Multimer accurately predicted conformational states during oligomerization, which AFcluster with AlphaFold2 alone fails to achieve. In conclusion, MSA clustering enhances AF-Multimer’s ability to predict protein conformational landscapes and mechanistic effects of ligand binding, offering a robust tool for understanding protein–ligand interactions.

Availability and implementation
Code for running AFcluster-Multimer is available at https://github.com/KhondamirRustamov/AF-Multimer-cluster
Pod5Viewer: a GUI for inspecting raw nanopore sequencing data
Authors: Dietrich Vincent , Alagna Nicolò , Helm Mark , Gerber Susanne , Butto Tamer 
Published: December 2024

Abstract

Motivation
Oxford Nanopore Technologies recently adopted the POD5 file format for storing raw nanopore sequencing data. The information stored in these files provides detailed insights into the sequencing features and enhances the understanding of raw nanopore data. However, the process of visualizing the data can be cumbersome, especially for users without programming skills. To address this issue, we developed the pod5Viewer, a GUI application for inspecting POD5 files.

Results
The pod5Viewer offers straightforward access to raw sequencing data and associated metadata in POD5 files. It includes functionalities for viewing, plotting, and exporting individual reads. Designed with user-friendliness in mind, the pod5Viewer is easy to install and use, making it suitable for users with all technical backgrounds.

Availability and implementation
The pod5Viewer is available as open source from the pod5Viewer Github repository (https://github.com/dietvin/pod5Viewer)
TargetSA: adaptive simulated annealing for target-specific drug design
Authors: Xue Zhe , Sun Chenwei , Zheng Wenhao , Lv Jiancheng , Liu Xianggen 
Published: December 2024

Abstract

Motivation
The burgeoning field of target-specific drug design has attracted considerable attention, focusing on identifying compounds with high binding affinity toward specific target pockets. Nevertheless, existing target-specific deep generative models encounter notable challenges. Some models heavily rely on elaborate datasets and complicated training methodologies, while others neglect the multi-constraint optimization problem inherent in drug design, resulting in generated molecules with irrational structures or chemical properties.

Results
To address these issues, we propose a novel framework (TargetSA) that leverages adaptive simulated annealing (SA) for target-specific molecular generation and multi-constraint optimization. The SA process explores the discrete structural space of molecules, progressively converging toward the optimal solution that fulfills the predefined objective. To propose novel compounds, we first predict promising editing positions based on historical experience, and then iteratively edit molecular graphs through four operations (insertion, replacement, deletion, and cyclization). Together, these operations collectively constitute a complete operation set, facilitating a thorough exploration of the drug-like space. Furthermore, we introduce a reversible sampling strategy to re-accept currently suboptimal solutions, greatly enhancing the generation quality. Empirical evaluations demonstrate that TargetSA achieves state-of-the-art performance in generating high-affinity molecules (average vina dock −9.09) while maintaining desirable chemical properties.

Availability and implementation
https://github.com/XueZhe-Zachary/TargetSA
The athletes heart—from acute stimulus to chronic adaptation
Authors: Maxwell Joseph D , Oxborough David 
Published: December 2024

Abstract

Introduction
The complex phenomenon of the athlete’s heart (AH) describes the chronic physiological structural and functional adaptation secondary to repeated exposure of an acute exercise stimulus.

Sources of Data
This narrative review is based on published evidence.

Areas of agreement
Highly trained athletic individuals frequently display cardiac parameters which are suggestive of an AH and can exceed the traditional ‘normal’ limits.

Area of controversy
The physiological processes underpinning the extent of cardiac adaption and how this is closely linked to exercise type, but also sex, ethnicity, and body size.

Growing points
Since its seminal description by Morganroth and colleagues in 1975, our understanding of the AH has evolved in tandem with improvements in cardiac imaging techniques alongside the exploration of more diverse athletic populations. This narrative review aims to provide a balanced discussion of the multi-factorial nature of structure and function of the AH with specific reference to the unique physiological exercise stimuli.

Areas timely for developing research
Despite great interest in cardiac adaptations across a broad spectrum of athletic populations, future research designs should consider the use of new and novel imaging techniques to enhance our understanding of the acute cardiovascular responses which ultimately mediates such adaptations, especially in athletic populations underrepresented in the literature.
Prevalence of hypothyroidism in older adults and its association with cognition: a cross-sectional study from a South Indian ageing urban cohort
Authors: Jessy Angeline S , G. Sandhya , S. Monisha , S. Sundarakumar Jonas , Stezin Albert , Issac Thomas Gregor 
Published: December 2024

Abstract
The study conducted on a South Indian urban cohort aimed to emphasize the prevalence and patterns of hypothyroidism and its association with cognition among individuals aged 45 years and above. A cross-sectional design was adopted, utilizing data from the Tata Longitudinal Study of Aging cohort, comprising 1201 non-demented participants in Bangalore, South India. The study contains detailed clinical assessments, including medical history, physical examination and cognitive tests such as the COGNITO battery, Hindi Mental State Examination and Addenbrooke’s Cognition Examination III. Biochemical tests were utilized to quantify plasma levels of thyroid-stimulating hormone, triiodothyronine and thyroxine. Participants were categorized based on medication history and thyroid hormone levels. The study findings showed a 17.69% prevalence of hypothyroidism, with 6.22% being classified as overt hypothyroidism and 93.78% as subclinical hypothyroidism. The prevalence was significantly higher in females compared with males (P = 0.043). Individuals with hypothyroidism are more frequently diagnosed with mild cognitive impairment than people with euthyroid (P = 0.008). Furthermore, on the classification based on thyroid medication history, the ineffective treatment group performed poorer in Addenbrooke’s Cognition Examination III fluency (P = 0.006), auditory attention (P = 0.001) and form matching (P = 0.024) tasks compared with the adequately treated group. The partially treated group performed poorer in visual attention (P = 0.005) and vocabulary (P = 0.043) compared with the effectively treated group. The study identified a notable prevalence of hypothyroidism in the cohort, with females exhibiting a higher prevalence. Our study suggests that the timely management of thyroid disorders with medications is crucial not only to prevent hormonal imbalances but also to improve cognitive functioning.
Evaluating clinical meaningfulness of anti-β-amyloid therapies amidst amyloid-related imaging abnormalities concern in Alzheimer’s disease
Authors: Aljuhani Manal , Ashraf Azhaar , Edison Paul 
Published: December 2024

Abstract
Alzheimer’s disease is the most prevalent form of dementia in the elderly, which is clinically characterized by a gradual and progressive deterioration of cognitive functions. The central and early role of β-amyloid in the pathogenesis of Alzheimer’s disease is supported by a plethora of studies including genetic analyses, biomarker research and genome-wide association studies in both familial (early-onset) and sporadic (late-onset) forms of Alzheimer’s. Monoclonal antibodies directed against β-amyloid demonstrate slowing of the clinical deterioration of patients with early Alzheimer’s disease. Aducanumab, lecanemab and donanemab clinical trials showed slowing of Alzheimer’s disease progression on composite scores by 25–40% based on the measure used. Anti-β-amyloid antibodies can cause side effects of bleeding and swelling in the brain, called amyloid-related imaging abnormalities. Amyloid-related imaging abnormalities typically occur early in treatment and are often asymptomatic, and though in rare cases, they can lead to serious or life-threatening events. The aim of this review is to evaluate the clinical meaningfulness of anti-β-amyloid therapies amidst amyloid-related imaging abnormalities concern in Alzheimer’s disease.
Rewiring the disordered connectome with circuit-based paired stimulation after stroke—a randomized, double-blind and controlled Phase II trial
Authors: Xing Xiang-Xin , Wu Jia-Jia , Qu Jiao , Ma Jie , Xu Rong , Zhu Yu , Zheng Mou-Xiong , Hua Xu-Yun , Xu Jian-Guang 
Published: December 2024

Abstract
The cortico-cortical paired associative stimulation, a combined stimulation based on two brain regions, may be an effective strategy for stroke rehabilitation. Our aim was to confirm that the cortico-cortical paired associative stimulation strengthens the connection between brain regions in the motor circuit and promotes improvements in motor function. This was a randomized double-blind, controlled Phase II trial. 44 Stroke patients were treated in a rehabilitation hospital from October 2020 to January 2021 and were randomly assigned to the sham stimulation group and the cortico-cortical paired associative stimulation group. Patients in both groups received 12 days of rehabilitation therapy. Cortico-cortical paired associative stimulation group received one treatment of cortico-cortical paired associative stimulation invention. Both groups received behavioural assessments such as the Fugl–Meyer upper-extremity scale and resting-state functional MRI scans prior to the intervention and on Day 14. 40 patients completed the intervention session. The results of Fugl–Meyer upper-extremity scale showed a more significant improvement in motor function in the cortico-cortical paired associative stimulation group (6.33 ± 1.29) than in the sham stimulation group (3.16 ± 1.38) (P &lt; 0.001). The functional connectivity showed that cortico-cortical paired associative stimulation strengthens connections between brain regions. Correlation analysis confirmed that the enhancement of functional connectivity was positively correlated with the recovery of Fugl–Meyer upper-extremity scale (r2 = 0.146, P = 0.034; r2 = 0.211, P = 0.0093). The results of functional connectivity suggest that cortico-cortical paired associative stimulation strengthens connections between brain regions. It is expected that this study will provide a positive viewpoint for the neurorehabilitation of stroke patients based on the circuit-level plasticity. (Chinese Clinical Trial Registry: ChiCTR2000036685).
Harnessing the power of machine learning into tissue engineering: current progress and future prospects
Authors: Wu Yiyang , Ding Xiaotong , Wang Yiwei , Ouyang Defang 
Published: December 2024

Abstract
Tissue engineering is a discipline based on cell biology and materials science with the primary goal of rebuilding and regenerating lost and damaged tissues and organs. Tissue engineering has developed rapidly in recent years, while scaffolds, growth factors, and stem cells have been successfully used for the reconstruction of various tissues and organs. However, time-consuming production, high cost, and unpredictable tissue growth still need to be addressed. Machine learning is an emerging interdisciplinary discipline that combines computer science and powerful data sets, with great potential to accelerate scientific discovery and enhance clinical practice. The convergence of machine learning and tissue engineering, while in its infancy, promises transformative progress. This paper will review the latest progress in the application of machine learning to tissue engineering, summarize the latest applications in biomaterials design, scaffold fabrication, tissue regeneration, and organ transplantation, and discuss the challenges and future prospects of interdisciplinary collaboration, with a view to providing scientific references for researchers to make greater progress in tissue engineering and machine learning.
Impact of Clinician Feedback Reports on Antibiotic Use in Children Hospitalized With Community-acquired Pneumonia
Authors: Chiotos Kathleen , Dutcher Lauren , Grundmeier Robert W , Meyahnwi Didien , Lautenbach Ebbing , Neuhauser Melinda M , Hicks Lauri A , Hamilton Keith W , Li Yun , Szymczak Julia E , Muller Brandi M , Congdon Morgan , Kane Emily , Hart Jessica , Utidjian Levon , Cressman Leigh , Jaskowiak-Barr Anne , Gerber Jeffrey S 
Published: December 2024

Abstract

Background
Feedback reports summarizing clinician performance are effective tools for improving antibiotic use in the ambulatory setting, but the effectiveness of feedback reports in the hospital setting is unknown.

Methods
Quasi-experimental study conducted between December 2021 and November 2023 within a pediatric health system measuring the impact of clinician feedback reports delivered by email and reviewed in a monthly meeting on appropriate antibiotic use in children hospitalized with community-acquired pneumonia (CAP). We used an interrupted time series analysis (ITSA) to estimate the immediate change and change over time in the proportion of CAP encounters adherent to validated metrics of antibiotic choice and duration, then used Poisson regression to estimate intervention effect as a risk ratio (RR).

Results
Preintervention, 213 of 413 (52%) encounters received the appropriate antibiotic choice and duration, which increased to 308 of 387 (80%) postintervention. The ITSA demonstrated an immediate 18% increase in the proportion of CAP encounters receiving both the appropriate antibiotic choice and duration (95% confidence interval [CI]: 3–33%), with no further change over time (−0.3% per month, 95% CI: −2%–2%). In the Poisson model adjusted for age, sex, race, season, site, and intensive care unit admission, the intervention was associated with a 32% increase in the rate of appropriate antibiotic choice and duration (RR 1.32, 95% CI: 1.12–1.56, P &lt; .01). No difference in length of stay or revisits were detected postintervention.

Conclusions
The intervention was associated with an increase in clinician adherence to antibiotic choice and duration recommendations for children hospitalized with CAP.
Multiethnic prevalence of the APOL1 G1 and G2 variants among the Israeli dialysis population
Authors: Ben-Ruby Dror , Atias-Varon Danit , Kagan Maayan , Chowers Guy , Shlomovitz Omer , Slabodnik-Kaner Keren , Mano Neta , Avayou Shany , Atsmony Yariv , Levin Dana , Dotan Edo , Calderon-Margalit Ronit , Shnaider Alla , Haviv Yosef S , Birk Ohad S , Hadar Noam , Anikster Yair , Berar Yanay Noa , Chernin Gil , Kruzel-Davila Etty , Beckerman Pazit , Rozen-Zvi Benaya , Doctor Gabriel T , Stanescu Horia C , Shemer Revital , Pras Elon , Reznik-Wolf Haike , Nahum Ayelet Hashahar , Dominissini Dan , Skorecki Karl , Vivante Asaf 
Published: December 2024

ABSTRACT

Background and hypothesis
The two apolipoprotein L1 (APOL1) variants, G1 and G2, are common in populations of sub-Saharan African ancestry. Individuals with two of these alleles (G1 or G2) have an increased risk for a spectrum of non-diabetic chronic kidney diseases. However, these variants are typically not observed outside of populations that self-identify as current continental Africans or having clear recent African ancestry such as, most notably, African Americans, and other large population groups in the Americas and several European countries. We hypothesized that the diverse ethnic groups within the Israeli population may exhibit varying levels of recent African ancestry. Therefore, it is plausible that APOL1 risk alleles might be present even in individuals who do not self-identify as being of sub-Saharan African descent.

Methods
We non-selectively screened people with kidney failure across Israel for APOL1 risk variants using restriction fragment length polymorphism.

Results
We recruited 1744 individuals from 38 dialysis units in Israel. We identified eight patients of Moroccan Jewish, Bedouin, or Muslim Arab ancestry, who carry at least one G1 or G2 allele. None of the eight patients carried the protective APOL1 p.N264K variant. Furthermore, despite all Bedouin individuals being G2 heterozygous, the G2 minor allele frequency was significantly enriched in kidney failure cases compared to ethnically matched controls (P = .006).

Conclusions
These findings show that APOL1 G1 and G2 allelic variants are present in populations previously not appreciated to possess recent sub-Saharan ancestry and suggest that a single G2 risk variant may confer increased risk for chronic kidney disease in certain population contexts.
FLDS: differentially private federated learning with double shufflers
Authors: Qi Qingqiang , Yang Xingye , Hu Chengyu , Tang Peng , Su Zhiyuan , Guo Shanqing 
Published: December 2024

Abstract
Federated learning (FL) often uses local differential privacy (LDP) to prevent leaking data privacy through gradients. However, due to the high dimension of gradients, LDP will encounter the problem of privacy budget explosion in the application, resulting in low accuracy of the training model. To overcome this shortcoming, we propose a differential privacy FL protocol incorporating a control matrix and double shuffles. The control matrix, generated by the analyzer, is responsible for governing the selection and upload of clients’ gradients. Double shufflers shuffle the control matrix and clients’ gradients, respectively, so that the control matrix is invisible to the server and the gradient is anonymous to the server. In addition, the existing differential private FL often uses the same clipping scale for gradients clipping to facilitate determining the noise scale. However, this will bring too many clipping errors for the large gradients and too many noise errors for the small ones. To solve these problems, we propose an adaptive clipping scheme. Experiments on the real-world datasets show that our proposed methods achieve higher testing accuracy.