bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prokaryotic ATP-binding cassette type F proteins in overcoming ribosomal stalling: mechanisms, evolution, and perspective for applications in bio-manufacturing
Authors: Takada Hiraku , Sugimoto Ryota , Oshima Taku 
Published: December 2024

Abstract
ATP-binding cassette type F (ABCF) proteins are key components of prokaryotic translation systems, resolving ribosomal stalling. These adenosine triphosphatases (ATPases) contain 2 ATPase domains and an interdomain linker, the length and composition of which are key determinants of their function. Antibiotic resistance ABCF proteins counteract ribosome-targeting antibiotics by binding to the E site of the 70S ribosome, promoting drug dissociation. In contrast, housekeeping ABCF proteins, such as YfmR and YkpA in Bacillus subtilis, resolve intrinsic translation challenges without conferring antibiotic resistance. YfmR addresses stalling at proline-rich motifs, while YkpA resolves stalling caused by charged motifs. This review draws on the work of Chadani, Boël, Fega, and our own studies to compare the structural and functional diversity of ABCF proteins across bacterial species. It highlights the key roles of antibiotic resistance/P site tRNA interaction motif domains in defining their specific functions and explores future research directions to further our understanding of ABCF proteins in translation control.
CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data
Authors: Zhang Yi , Wang Yin , Liu Xinyuan , Feng Xi 
Published: December 2024

Abstract
A key challenge in analyzing single-cell RNA sequencing data is the large number of false zeros, known as “dropout zeros”, which are caused by technical limitations such as shallow sequencing depth or inefficient mRNA capture. To address this challenge, we propose a novel imputation model called CPARI, which combines cell partitioning with our designed absolute and relative imputation methods. Initially, CPARI employs a new approach to select highly variable genes and constructs an average consensus matrix using C-mean fuzzy clustering-based blockchain technology to obtain results at different resolutions. Hierarchical clustering is then applied to further refine these blocks, resulting in well-defined cellular partitions. Subsequently, CPARI identifies dropout events and determines the imputation positions of these identified zeros. An autoencoder is trained within each cellular block to learn gene features and reconstruct data. Our uniquely defined absolute imputation technique is first applied to the identified positions, followed by our relative imputation technique to address remaining dropout zeros, ensuring that both global consistency and local variation are maintained. Through comprehensive analyses conducted on simulated and real scRNA-seq datasets, including quantitative assessment, differential expression analysis, cell clustering, cell trajectory inference, robustness evaluation, and large-scale data imputation, CPARI demonstrates superior performance compared to 12 other art-of-state imputation models. Additionally, ablation experiments further confirm the significance and necessity of both the cell partitioning and relative imputation components of CPARI. Notably, CPARI as a new denoising approach could distinguish between real biological zeros and dropout zeros and minimize false positives, and maximize the accuracy of imputation.
Distinct transcriptional changes distinguish efficient and poor remyelination in multiple sclerosis
Authors: Chen J Q Alida , McNamara Niamh B , Engelenburg Hendrik J , Jongejan Aldo , Wever Dennis D , Hopman Kaya , van Rixel Esmée , Nijhuis Paul J H , de Winter Fred , Moerland Perry D , Smolders Joost , Verhaagen Joost , Hamann Jörg , Huitinga Inge 
Published: December 2024

Abstract
Multiple sclerosis (MS) is a highly heterogeneous disease with varying remyelination potential across individuals and between lesions. However, the molecular mechanisms underlying the potential to remyelinate remain poorly understood. In this study, we aimed to take advantage of the intrinsic heterogeneity in remyelinating capacity between MS donors and lesions to uncover known and novel pro-remyelinating molecules for MS therapies.
To elucidate distinct molecular signatures underlying the potential to remyelinate, we stratified MS donors from the Netherlands Brain Bank cohort (n=239) based on proportions of remyelinated lesions (RLs) into efficiently remyelinating donors (ERDs; n=21) and poorly remyelinating donors (PRDs; n=19). We performed bulk RNA sequencing of RLs, active lesions with ramified and amoeboid microglia/macrophage morphology (ALs non-foamy), active lesions with foamy microglia/macrophage morphology (ALs foamy), and normal-appearing white matter (NAWM) from ERDs and PRDs.
We found that ALs non-foamy were positively correlated with remyelination, whereas ALs foamy were not, indicating a role for microglia/macrophage state in influencing remyelination potential. Bioinformatics analyses were performed to identify key pathways and molecules implicated in the remyelination process. We found distinct differences between the donors with differing remyelination potential in comparable MS lesion types. RLs and ALs non-foamy of ERDs versus PRDs showed upregulation of epithelial–mesenchymal transition pathway, while in ALs foamy of PRDs, inflammation and damage-associated pathways (i.e. MTORC1 signaling, TNF signaling and oxidative phosphorylation) were upregulated compared to ALs foamy of ERDs, suggesting that these latter pathways may counteract remyelination. We found genes significantly upregulated in RLs and/or ALs non-foamy of ERDs that have previously been associated with remyelination, including CXCL12, EGF, HGF, IGF2, IL10, PDGFB, PPARG, and TREM2, illustrating the strength of our donor and lesion stratification. TGFB1, TGFB2, EGF, and IGF1 were determined as key upstream regulators of genes upregulated in RLs and ALs non-foamy of ERDs. We further identified potential novel pro-remyelinating molecules, such as BTC, GDF10, GDF15, CCN1, CCN4, FGF5, FGF10, and INHBB.
Our study identified both known and novel genes associated with efficient remyelination that may facilitate the development of therapeutic strategies to promote tissue repair and clinical recovery in MS.
Predicting incident dementia in community-dwelling older adults using primary and secondary care data from electronic health records
Authors: Georgiev Konstantin , Wang Yiqing , Conkie Andrew , Sinclair Annie , Christodoulou Vyron , Seyedzadeh Saleh , Price Malcolm , Wales Ann , Mills Nicholas L , Shenkin Susan D , McPeake Joanne , Fleuriot Jacques D , Anand Atul 
Published: December 2024

Abstract
Predicting risk of future dementia is essential for primary prevention strategies, particularly in the era of novel immunotherapies. However, few studies have developed population-level prediction models using existing routine healthcare data. In this longitudinal retrospective cohort study, we predicted incident dementia using primary and secondary care health records at 5, 10 and 13 years in 144 113 Scottish older adults who were dementia-free prior to 1st April 2009. Gradient-boosting (XGBoost) prediction models were trained on two feature subsets: data-driven (using all 171 extracted variables) and clinically supervised (22 curated variables). We used a random-stratified internal validation set to rank top predictors in each model, assessing performance stratified by age and socioeconomic deprivation. Predictions were stratified into 10 equally sized risk deciles and ranked by response rate. Over 13 years of follow-up, 11 143 (8%) patients developed dementia. The data-driven models achieved marginally better precision-recall area-under-the-curve scores of 0.18, 0.26 and 0.30 compared to clinically supervised models with scores of 0.17, 0.27 and 0.29 for incident dementia at 5, 10 and 13 years, respectively. The clinically supervised model achieved comparable specificity 0.88 [95% confidence interval (CI) 0.87–0.88] and sensitivity (0.55, 95% CI 0.53–0.57) to the data-driven model for prediction at 13 years. The most important model features were age, deprivation and frailty, measured by a modified electronic frailty index excluding known cognitive deficits. Model precision was consistent across socioeconomic deprivation quintiles but lower in younger-onset (&lt;70 years) dementia cases. At 13 years, dementia was diagnosed in 32% of the population classified as highest risk with 40% of individuals in this group below the age of 80. Personalized estimates of future dementia risk from routinely collected healthcare data could influence risk factor modification and help to target brain imaging and novel immunotherapies in selected individuals with pre-symptomatic disease.
Neural encoding of semantic structures during sentence production
Authors: Giglio Laura , Hagoort Peter , Ostarek Markus 
Published: December 2024

Abstract
The neural representations for compositional processing have so far been mostly studied during sentence comprehension. In an fMRI study of sentence production, we investigated the brain representations for compositional processing during speaking. We used a rapid serial visual presentation sentence recall paradigm to elicit sentence production from the conceptual memory of an event. With voxel-wise encoding models, we probed the specificity of the compositional structure built during the production of each sentence, comparing an unstructured model of word meaning without relational information with a model that encodes abstract thematic relations and a model encoding event-specific relational structure. Whole-brain analyses revealed that sentence meaning at different levels of specificity was encoded in a large left frontal-parietal-temporal network. A comparison with semantic structures composed during the comprehension of the same sentences showed similarly distributed brain activity patterns. An ROI analysis over left fronto-temporal language parcels showed that event-specific relational structure above word-specific information was encoded in the left inferior frontal gyrus. Overall, we found evidence for the encoding of sentence meaning during sentence production in a distributed brain network and for the encoding of event-specific semantic structures in the left inferior frontal gyrus.
Dissociating goal from outcome during action observation
Authors: Liu Shuchen , Wurm Moritz F , Caramazza Alfonso 
Published: December 2024

Abstract
Understanding the goal of an observed action requires computing representations that are invariant to specific instantiations of the action. For example, we can accurately infer the goal of an action even when the agent’s desired outcome is not achieved. Observing actions consistently recruits a set of frontoparietal and posterior temporal regions, often labeled the “action observation network.” While progress has been made in charting which regions of the action observation network are involved in understanding goals of observed actions, it is not clear where goals are represented independently of outcomes. We used functional magnetic resonance-based multivariate pattern analysis to identify such regions. Human participants (20 females, 12 males) watched videos of successful and failed attempts of actions with different goals involving two different object types. We found that the bilateral anterior inferior parietal lobe and the right ventral premotor cortex distinguished between object-specific action goals regardless of outcomes. The left anterior inferior parietal lobe encodes action goals regardless of both outcomes and object types. Our results provide insights into the neural basis of representing action goals and the different roles of frontoparietal and posterior temporal regions in action understanding.
The relationship between brain structure and function during novel grammar learning across development
Authors: Koning Merel E E , Wyman Nina K , Menks Willeke M , Ekerdt Clara , Fernández Guillén , Kidd Evan , Lemhöfer Kristin , McQueen James M , Janzen Gabriele 
Published: December 2024

Abstract
In this study, we explored the relationship between developmental differences in gray matter structure and grammar learning ability in 159 Dutch-speaking individuals (8 to 25 yr). The data were collected as part of a recent large-scale functional MRI study (Menks WM, Ekerdt C, Lemhöfer K, Kidd E, Fernández G, McQueen JM, Janzen G. Developmental changes in brain activation during novel grammar learning in 8–25-year-olds. Dev Cogn Neurosci. 2024;66:101347. https://doi.org/10.1016/j.dcn.2024.101347) in which participants implicitly learned Icelandic morphosyntactic rules and performed a grammaticality judgment task in the scanner. Behaviorally, Menks et al. (2024) showed that grammaticality judgment task performance increased steadily from 8 to 15.4 yr, after which age had no further effect. We show in the current study that this age-related grammaticality judgment task performance was negatively related to cortical gray matter volume and cortical thickness in many clusters throughout the brain. Hippocampal volume was positively related to age-related grammaticality judgment task performance and L2 (English) vocabulary knowledge. Furthermore, we found that grammaticality judgment task performance, L2 grammar proficiency, and L2 vocabulary knowledge were positively related to gray matter maturation within parietal regions, overlapping with the functional MRI clusters that were reported previously in Menks et al. (2024) and which showed increased brain activation in relation to grammar learning. We propose that this overlap in functional and structural results indicates that brain maturation in parietal regions plays an important role in second language learning.
Non-traditional risk factors for atrial fibrillation: epidemiology, mechanisms, and strategies
Authors: Lu Yingli , Sun Ying , Cai Lingli , Yu Bowei , Wang Yuying , Tan Xiao , Wan Heng , Xu Dachun , Zhang Junfeng , Qi Lu , Sanders Prashanthan , Wang Ningjian 
Published: December 2024

Abstract
Atrial fibrillation (AF) has become the pre-dominant arrhythmia worldwide and is associated with high morbidity and mortality. Its pathogenesis is intricately linked to the deleterious impact of cardiovascular risk factors, emphasizing the pivotal imperative for early detection and mitigation strategies targeting these factors for the prevention of primary AF. While traditional risk factors are well recognized, an increasing number of novel risk factors have been identified in recent decades. This review explores the emerging non-traditional risk factors for the primary prevention of AF, including unhealthy lifestyle factors in current society (sleep, night shift work, and diet), biomarkers (gut microbiota, hyperuricaemia, and homocysteine), adverse conditions or diseases (depression, epilepsy, clonal haematopoiesis of indeterminate potential, infections, and asthma), and environmental factors (acoustic pollution and other environmental factors). Unlike traditional risk factors, individuals have limited control over many of these non-traditional risk factors, posing challenges to conventional prevention strategies. The purpose of this review is to outline the current evidence on the associations of non-traditional risk factors with new-onset AF and the potential mechanisms related to these risk factors. Furthermore, this review aims to explore potential interventions targeting these risk factors at both the individual and societal levels to mitigate the growing burden of AF, suggesting guideline updates for primary AF prevention.
Factors influencing forearm swelling after transradial artery intervention and establishment of a predictive model: a prospective study
Authors: Duan Juntao , Zhang Sai , Liu Congying , Tong Sumei 
Published: December 2024

Abstract

Aims
Forearm swelling is a common complication following transradial artery interventions. Identifying the factors that influence this swelling and developing predictive models for it are crucial yet underexplored issues. This study was performed to investigate the risk factors for forearm swelling after transradial artery puncture and construct a predictive model.

Methods and results
In total, 209 inpatients who underwent coronary angiography and/or interventional therapy at a tertiary hospital in Beijing between June and November 2023 were consecutively enrolled in this prospective study. The patients were divided into two groups: those who developed forearm swelling after puncture (swelling group) and those who did not (non-swelling group). The groups were compared, and logistic regression was used to identify risk factors for forearm swelling. The predictive accuracy of the model was evaluated using receiver operating characteristic analysis and the area under the curve. Among the 209 patients [132 (63.2%) men, 77 (36.8%) women], 27 developed forearm swelling. Independent predictors of swelling were the operation time, radial artery access, irregular tourniquet compression, number of tourniquet compressions, inappropriate wrist movement, and timing of the first tourniquet deflation. The predictive model achieved an area under the curve of 0.971 (95% confidence interval, 0.949–0.992).

Conclusion
This single-centre diagnostic study demonstrated that the predictive model has excellent accuracy. Its application could help prevent unnecessary forearm swelling following radial artery interventions.
A genome-scale metabolic reconstruction provides insight into the metabolism of the thermophilic bacterium Rhodothermus marinus
Authors: Kristjansdottir Thordis , Hreggvidsson Gudmundur O , Gudmundsdottir Elisabet Eik , Bjornsdottir Snaedis H , Fridjonsson Olafur H , Stefansson Sigmar Karl , Nordberg Karlsson Eva , Vanhalst Justine , Reynisson Birkir , Gudmundsson Steinn 
Published: December 2024

Abstract
The thermophilic bacterium Rhodothermus marinus has mainly been studied for its thermostable enzymes. More recently, the potential of using the species as a cell factory and in biorefinery platforms has been explored, due to the elevated growth temperature, native production of compounds such as carotenoids and exopolysaccharides, the ability to grow on a wide range of carbon sources including polysaccharides, and available genetic tools. A comprehensive understanding of the metabolism of cell factories is important. Here, we report a genome-scale metabolic model of R. marinus DSM 4252T. Moreover, the genome of the genetically amenable R. marinus ISCaR-493 was sequenced and the analysis of the core genome indicated that the model could be used for both strains. Bioreactor growth data were obtained, used for constraining the model and the predicted and experimental growth rates were compared. The model correctly predicted the growth rates of both strains. During the reconstruction process, different aspects of the R. marinus metabolism were reviewed and subsequently, both cell densities and carotenoid production were investigated for strain ISCaR-493 under different growth conditions. Additionally, the dxs gene, which was not found in the R. marinus genomes, from Thermus thermophilus was cloned on a shuttle vector into strain ISCaR-493 resulting in a higher yield of carotenoids.
Optimal experimental design for full waveform inversion using a wavenumber sampling criterion—Part 2: 3-D extension and practical application
Authors: Abdellaziz Amine , Brossier Romain , Métivier Ludovic , Oudet Édouard 
Published: December 2024

SUMMARY
In a companion paper, we have proposed an optimal experimental design (OED) strategy for seismic imaging. Using the theory of diffraction tomography, for a specified target, given an acquisition geometry and a prior information on the subsurface velocity, we characterize the wavenumber space of the image that would result at the image point from such an acquisition. Our strategy relies on optimizing the sources and receivers positions so as to guarantee a regular sampling of the wavenumber space within the pre-computed envelope. The regularity of a given sampling is assessed through the use of Voronoi tessellations, associated with the wavenumber points cloud. Computing a centroidal Voronoi tessellation, which can be formulated as an optimization problem, guarantees a regular tessellation, which in turns provides a regularly spaced wavenumber points cloud. We have introduced this method in a 2-D context, in relatively simple settings. In this second paper, we present the extension of this method to a more realistic framework and investigate its application to synthetic case studies. This extension consists in considering now a fully 3-D geometry, as well as specific deployment constraints with polygonal shaped deployment zones and non-accessible (exclusion) zones for the sources and receivers. Such type of constraints are commonly met in survey design and it is important to be able to take them into account in an OED algorithm. We investigate on simple velocity models the feasibility and limitations of our method for target-oriented seismic imaging using full waveform inversion.
Pentoxifylline adjunct to risperidone for negative symptoms of stable schizophrenia: a randomized, double-blind, placebo-controlled trial
Authors: Shamabadi Ahmad , Rafiei-Tabatabaei Elham-Sadat , Kazemzadeh Kimia , Farahmand Kimia , Fallahpour Bita , Khodaei Ardakani Mohammad-Reza , Akhondzadeh Shahin 
Published: December 2024

Abstract

Background
Negative symptoms of schizophrenia represent an unmet therapeutic need for many patients in whom pentoxifylline may be effective in terms of its dopaminergic, anti-inflammatory, and cerebral blood flow–increasing properties. This study aimed to evaluate pentoxifylline as a therapeutic agent for improving negative symptoms of schizophrenia.

Methods
Chronic schizophrenia outpatients experiencing significant negative symptoms were randomly allocated to receive pentoxifylline 400 mg or matched placebo every 12 hours for 8 weeks. All patients were clinically stable as they had received risperidone for at least 2 months, which was continued. The participants were assessed using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale, Extrapyramidal Symptom Rating Scale, and side effect checklist.

Results
The patients’ baseline characteristics were comparable between the groups. There was a significant time–treatment interaction effect on PANSS negative subscale scores (ηP2=0.075), with the pentoxifylline group showing significantly greater reductions until weeks 4 (Cohen d = 0.512) and 8 (Cohen d = 0.622). Also, this group showed a significantly better response by week 8. Other PANSS scores, Hamilton Depression Rating Scale scores, Extrapyramidal Symptom Rating Scale scores, and side effect frequencies were comparable between the groups. Pentoxifylline showed a nonsignificant higher remission of 37.1% compared with 14.7% in the placebo group.

Conclusions
Pentoxifylline was safely and tolerably beneficial for the primary negative symptoms of chronic schizophrenia.