bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamical repertoire of brain networks in mindfulness cognitive behavioural therapy during rumination: A randomized controlled trial
Authors: Vohryzek Jakub , van der Velden Anne Maj , Kuyken Willem , Montero-Marin Jesus , Collin Guusje , Kringelbach Morten L , Ruhe Eric
Published: February 2025

BACKGROUND: Depression is a prevalent and debilitating affective disorder characterised by the dominance and persistence of depressive rumination. Mindfulness-based cognitive therapy (MBCT) is an effective treatment for recurrent depression developed specifically to target rumination and recurrence risk by training metacognitive awareness and adaptive attention, emotion and self-regulation skills. Yet, the underlying mechanisms by which mindfulness training impacts maladaptive depressive rumination is not well understood, and a deeper understanding of its effects on the complex brain dynamics during depressive rumination is needed. METHOD: In a randomised controlled functional magnetic resonance imaging (fMRI) study (N = 80), we used LEiDA (Leading Eigenvector Dynamics Analysis) to determine the key substates during resting state fMRI of an experimentally induced rumination state before and after treatment with MBCT (N = 27) for recurrent depression in addition to treatment as usual (TAU) or TAU alone (N = 21). We determined the probability of occurrence (fractional occupancy) and the duration (lifetime) of underlying substates (phase-locking patterns) before and after treatment for both groups. RESULTS: We found that MBCT training compared with TAU altered the fractional occupancy of a 'Salience-somatomotor' substate during the depressive rumination induction. These dynamic network changes in turn were associated with reduced depressive symptoms after treatment and at three months follow up. CONCLUSION: In a depressive ruminative state, changes in the dynamics of the somatosensory-salience network following mindfulness training was associated with improved clinical outcomes which provides insight into candidate brain mechanisms or markers of treatment response.
Development and Evaluation of Heart Failure Patients Oral Comfort Assessment Questionnaire
Authors: Lu Mei , Yang Runyu , Li Xiaona , Wang Xinkun , Tao Ming
Published: February 2025

Background

Our objective was to develop a reliable and valid questionnaire to assess the oral comfort status of inpatients with heart failure, providing medical staff with a scientifically grounded tool for evaluating their oral comfort.

Methods

A comprehensive pool of questionnaire items was developed through a literature review, qualitative interviews, and expert consultation. Six experts participated in two rounds of consultation to refine the items, followed by a pre-investigation involving 20 patients. Finally, 192 patients treated at Zun Yi Medical University between June and September 2020 were selected for project analysis and to assess the reliability and validity of the questionnaire.

Results

Through exploratory factor analysis, four common factors were identified, accounting for a cumulative variance contribution rate of 59.611%. The finalized questionnaire comprised 24 items, demonstrating robust reliability and validity. The Cronbach’s α coefficient was 0.810, indicating high internal consistency, while the Spearman-Brown coefficient was 0.732. The Pearson correlation coefficient was 0.233 (P < 0.05) when compared with the Beck Oral Scoring Method (BOAS table). The content validity index (CVI) for individual items ranged from 0.833 to 1. The overall content validity index (S-CVI/UA) for the questionnaire was 0.875, with an average content validity index (S-CVI/Ave) of 0.979.

Conclusion

The oral comfort assessment questionnaire developed in this study for inpatients with heart failure exhibits excellent reliability and validity, establishing it as an effective and valuable tool for assessing the oral comfort status of this patient population.
Personal Diet Inference Through Mixture of Concentration Constraints and Intake Preferences
Authors: Turkia Jari , Schwab Ursula , Hautamäki Ville
Published: February 2025

Maintaining proper nutrition is crucial for preserving health and preventing diseases. However, what constitutes proper nutrition may vary among individuals; recent evidence indicates that the effects of diet and also nutrients can differ considerably, driven by unique personal characteristics. This variability is reflected in blood markers, such as cholesterol and insulin concentrations, which respond differently to nutrient intake. This study captures this variation through a hierarchical, multivariate model that relates nutrient intake to various concentrations and applies it to personal diet inference. Our main contribution is a method for personal diet inference using a conditional two-component Bayesian mixture model that leverages the Nordic Nutrition Recommendations 2023 as a prior for healthy intake and infers personalized recommendations as posterior distributions. The first component identifies dietary options predicted to produce healthy levels across all considered blood markers, while the second component determines the diet closest to predefined personal preferences within these valid options. This preference component minimizes required dietary adjustments to promote patient adherence while providing well-defined values for nutrients less critical to blood marker regulation. We evaluated our method using nutritional data from two different studies: one in prediabetic patients and the other in patients with kidney dysfunction. Through numerical simulations, we demonstrated that personalized diets derived from our model could restore normal plasma concentrations. As the results align with the current nutritional literature, the use of Bayesian inference could open new opportunities to advance research using observational nutrition data. We aim for future clinical studies to validate these computational insights for statistically justified and more precise personal nutritional counseling.
Liver transplant rejection features memory cell priming, spatially differentiated networks and novel drug targets
Authors: Ningappa Mylarappa , Ashokkumar Chethan , Murali Divya , Rajasundaram Dhivyaa , Higgs Brandon W , McDonald William , Schmitt Lori , Zhang Liang , Chattopadhyay Ansuman , Kane Matt , Murphy Patrick , Soltys Kyle , Mazariegos George , Gupta Shakti , Kar Upendra , Billiar Timothy , Zeevi Adriana , Xu Qingyong , Reyes-Múgica Miguel , Subramaniam Shankar , Sindhi Rakesh
Published: February 2025

Acute T-cell-mediated transplant rejection, the earliest contributor to immunological graft failure responds variably to T-cell suppression. Early rejection can co-exist with donor-specific antibodies which also shorten graft survival, without affecting graft histology visibly. Here, during early liver transplant rejection, blood leukocytes manifest primed T-cells with heat shock protein (HSP) signaling and transcriptional programs for memory cell expansion. Corresponding biopsies demonstrate upregulated immune synapse and proteasomal genes in T-cell-infiltrated portal and central regions of the liver lobule. With donor-specific antibodies, the intervening intralobular region demonstrates a germinal-center-like allograft response with upregulated CD40, chemokine, T-follicular and complement signaling, which intensifies along the direction of sinusoidal blood flow from portal to central regions. Proteasomal and HSP90 inhibitors suppress donor-specific alloresponses of T- and B-cells in blood samples from patients with early rejection. The molecular injury spectrum of early rejection is complex, spatially differentiated and reveals novel early immunosuppressive strategies to extend graft survival.
Haplotype-based Parallel PBWT for Biobank Scale Data
Authors: Tang Kecong , Sanaullah Ahsan , Zhi Degui , Zhang Shaojie
Published: February 2025

Durbin’s positional Burrows-Wheeler transform (PBWT) enables algorithms with the optimal time complexity ofO(MN) for reporting all vs all haplotype matches in a population panel withMhaplotypes andNvariant sites. However, even this efficiency may still be too slow when the number of haplotypes reaches millions. To further reduce the run time, in this paper, a parallel version of the PBWT algorithms is introduced for all versus all haplotype matching, which is called HP-PBWT (haplotype-based parallel PBWT). HP-PBWT parallelly executes the PBWT by splitting a haplotype panel into blocks of haplotypes. HP-PBWT algorithms achieve parallelization for PBWT construction, reporting all versus all L-long matches, and reporting all versus all set-maximal matches while maintaining memory efficiency. HP-PBWT has antime complexity in PBWT construction, and antime complexity for reporting all versus all L-long matches and reporting all versus all set-maximal matches, whereTis the number of threads andc*is the maximum number of matches (of length L or maximum divergence value for L-long matches and set-maximal matches, re-spectively) per haplotype per site. HP-PBWT achieves 4-fold speed-up in UK Biobank genotyping array data with 30 threads in the IO-included benchmarks. When applying HP-PBWT to a dataset of 8 million randomized haplotypes (random binary strings of equal length) in the IO-excluded benchmarks, it can achieve a 22-fold speed-up with 60 cores on the Amazon EC2 server. With further hardware optimization, HP-PBWT is expected to handle billions of haplotypes efficiently.
Regulatory T and activated T cells in the chronic unpredictable mild stress mice: sex differences and effects of treatments
Authors: Niu Mengqi , Zhang Yingqian , Chen Tangcong , Luo Yueyang , Li Mengdie , Maes Michael
Published: February 2025

Major Depressive Disorder (MDD) is strongly associated with immune dysregulation, which is characterized by excessive T-cell activation and a reduction in regulatory T cells (Tregs), leading to a disruption of immune homeostasis. Pharmacological treatments such as fluoxetine, simvastatin, curcumin, and S-adenosylmethionine (SAMe) have shown promise in alleviating depressive symptoms; however, their precise effects on immune responses remain inadequately understood. This study investigates the immune status of regulatory and activated T cells in MDD using the chronic unpredictable mild stress (CUMS) mouse model. Specifically, it examines how fluoxetine, simvastatin, curcumin, and SAMe modulate immune responses and mitigate depressive-like behaviors. Additionally, the study explores the impact of sex differences on immune responses. The study consisted of six groups, with 12 mice per group, ensuring equal male and female representation. These included a control group, a CUMS group, and groups treated with fluoxetine, simvastatin, curcumin, and SAMe. Behavioral changes were evaluated using sucrose preference and forced swimming tests. Immune cell markers from the spleen were analyzed by flow cytometry. The results showed that CUMS mice experienced a breakdown in immune tolerance due to Treg depletion and an increase in the CD154/CD152 ratio. Treatments with fluoxetine, simvastatin, curcumin, and SAMe improved depressive-like behaviors, with SAMe potentially exerting its antidepressant effects through the normalization of the CD154/CD152 ratio. Furthermore, sex differences played a critical role in immune responses: female mice exhibited lower levels of immune regulation and T-cell activation compared to males, along with a higher CD154/CD152 ratio, indicative of immune imbalance.
SpecImmune accurately genotypes diverse immune-related gene families using long-read data
Authors: Wang Shuai , Wang Xuedong , Wang Mengyao , Zhou Qian , Li Shuai Cheng
Published: February 2025

Polymorphic immune-related genes (HLA, KIR, IG, TCR, and CYP) exhibit significant complexity due to their extensive heterozygosity and inter-loci homology, necessitating specific methods for accurate characterization. We present SpecImmune, the first comprehensive tool leveraging long-read sequencing data to resolve the full spectrum of these immune-related genes. The method adopts an iterative graph-based algorithm for haplotype reconstruction. We validated SpecImmune across 1,019 samples from the 1kGP ONT cohort, 42 PacBio CLR and 9 PacBio HiFi samples from the HGSVC project, and 47 PacBio HiFi and 37 ONT samples from the HPRC project. SpecImmune achieved an accuracy of 98% in HLA typing, which represents a 12% improvement over both SpecHLA and HLA*LA. SpecImmune is the initial method to type multiple CYP loci, as well as the foremost approach to allow precise KIR and germline IG/TCR typing using long reads. Comprehensive genotyping of these loci by SpecImmune unveils a new observation of substantial linkage disequilibrium among HLA, KIR, and CYP loci. The proteins derived from these loci exhibit strong binding affinities, which suggest the origin of the marked linkage disequilibrium. Further, SpecImmune unveils a novel finding of elevated IG/TCR heterozygosity in African populations. Additionally, SpecImmune facilitates the detection ofde novomutations and enables allele-specific drug recommendations.
Chemotype diversity of foliar volatile emissions in Cork oak suggests independent geographic variations in quality and quantity
Authors: Staudt Michael , Rivet Coralie , Erdogan Meltem
Published: February 2025

Knowledge of the intraspecific variability of volatiles produced by plants is central for estimating their fluxes from ecosystems and for understanding their evolution in an ecological and phylogenetic context. Past studies suggested that leaf volatile emissions from Cork oak (Quercus suberL.) exhibit a particular high degree of qualitative and quantitative polymorphism. However, the extent of inherent emission variability across its range is not known.

We investigated leaf emissions and ecophysiological variables of 241 Cork oak seedlings from ten provenances. To minimize environmental influences, emissions were determined at 30 °C and saturating light on seed-grown saplings of similar age grown under the same conditions. All individuals, except for three apparent non-emitters, released the same five monoterpenes at a rate of 2559 ± 120 ng m-2s-1. Northern provenances tended to have higher mean emission rates and lower photosynthetic rates than southern populations, resulting in significant differences in their apparent carbon losses by volatile emissions. Independently, the emission composition varied discontinuously among individuals according to three distinct chemotypes, indicating inherent differences in the activity of two types of monoterpene synthases: one producing α-, β-pinene and sabinene, and the other limonene (plus myrcene as a by-product). Chemotype frequencies differed among provenances, particularly between South-Eastern Mediterranean and South-Western Atlantic provenances. Regarding ecophysiological leaf traits, we found no significant difference between chemotypes.

The study confirms that Cork oak is a strong monoterpene emitter, showing independent intraspecific variability in emission quality and quantity, with non-emitters being rare. Comparison of the emission variability with those reported for other oak species suggests that an ancestral Pinene/sabine chemotype has diversified within the oak subgenusCerrisduring its radiation. This diversification is less pronounced in Cork oak than in other sympatric oaks, possibly due to differential fragmentation and expansion of their ranges in the past.
Harnessing subtractive genomics for drug target identification inStreptococcus agalactiaeserotype v (atcc baa-611 / 2603 v/r) strain: Anin-silicoapproach
Authors: Chowdhury Ashiqur Rahman Khan , Tithi Farjana Yasmin , Bhuiyan Nusrat Zahan , Ishita Afsana Ferdousi , Sohel Md Mahmodul Hasan
Published: February 2025

Developing a therapeutic target for bacterial disease is challenging.In silicosubtractive genomics methodology offer a promising alternative to traditional drug discovery methods.Streptococcus agalactiaeinfections depend on two crucial criteria: drug-resistance and the existence of virulence factors. It is essential to underline thatS. agalactiaestrains have emerged to be resistant to several drugs. Hence, there is a need for research on novel drugs and techniques that are potent, economical, productive, and dependable to combatS. agalactiaeinfections. In this study advanced computational techniques were exploited to examine potential druggable targets exclusive to this pathogen. Our study uncovered 200 non-homologous proteins inS. agalactiaeserotype V (Strain ATCC BAA-611 / 2603 V/R) and identified 68 essential proteins indispensable for the bacterium’s survival. Therefore, these 68 proteins are potential targets for drug development. Subcellular localization analysis unveiled that the pathogen’s cytoplasmic membrane contained essential proteins among these vital non-homologous proteins. On the other hand, based on virulent protein predictions, six proteins were seen to be virulent. Among these, we prioritized two proteins (Sensor protein LytS and Galactosyl transferase CpsE which are exclusively found inS. agalactiae) as potential druggable targets and selected them for further structural investigation. The proteins chosen could serve as a foundation for the identification of a promising therapeutic compound that has the potential to neutralize these enzymatic proteins, thereby contributing to the reduction of risks linked to the drug-resistantS. agalactiae.
Conditioned versus innate effort-based tasks reveal divergence in antidepressant effect on motivational state in male mice
Authors: Xeni Foteini , Marangoni Caterina , Lin Lynn , Robinson Emma S J , Jackson Megan G
Published: February 2025

Antidepressant-induced apathy syndrome is reported in a high number of patients. It is characterised by loss of motivation for daily activities and emotional blunting. It has a negative impact on quality of life and treatment outcome, yet the changes in underlying neurobiology driving this syndrome remain unclear. To begin to address this, a comprehensive understanding of how different classes of antidepressant treatment impact on behaviours relevant to apathy is critical. Rodent motivation for reward is commonly assessed using effort-based operant conditioning paradigms such as the Effort for Reward task. However, motivation to perform spontaneous/innate behaviours may provide additional insight into changes in behaviour reflective of daily activities. We tested the acute and chronic effects of antidepressants on the Effort for Reward task, and the spontaneous/innate Effort Based Forage task. Acute treatment revealed important divergence in drug effect between tasks, where selective serotonin reuptake inhibitor (SSRI)/serotonin and noradrenaline reuptake inhibitor (SNRI) treatment impaired foraging behaviour in the Effort Based Forage task, but enhanced high effort, high value reward responding in the Effort for Reward task. Treatment with a noradrenaline reuptake inhibitor (NRI) or multimodal agent impaired foraging behaviour but did not affect high reward responding in the Effort for Reward task. Conversely, chronic treatment with an SSRI but not SNRI enhanced motivated foraging behaviour but led to a general impairment in Effort for Reward task output. Together, these data demonstrate that SSRI treatment induces opposing effects on conditioned versus innate motivation which may have significant translational relevance when interpreting drug effect.
Encapsidic production and isolation of degradation-prone polypeptides
Authors: Gawin Agnieszka , Pankowski Jędrzej , Zarechyntsava Maria , Kwasna Dominika , Kloska Damian , Koziej Lukasz , Glatt Sebastian , Kachamakova-Trojanowska Neli , Azuma Yusuke
Published: February 2025

Degradation during production and delivery is a significant bottleneck in developing biomolecular therapies. Here, we show that encapsulation in protein cages formed by engineered variants of a cage-forming lumazine synthase establishes an effective route for microbial production and isolation of otherwise difficult-to-express, degradation-prone polypeptides. In this system, genetic fusion to a cage component protomer ensures efficient guest packaging while being produced in host bacterial cells. Meanwhile, the controlled opening outside the cellular context allows facile isolation of cargo via sequence-specific protease cleavage. Furthermore, modular patchwork assembly avoids guest overloading, preventing unwanted incomplete cage assembly and the formation of insoluble aggregates. The general applicability of our “encapsidic” production approach was demonstrated by the efficient production of six intrinsically disordered polypeptides that have proven therapeutic potentials.
Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study
Authors: Chen Xueying , Wang Hao , Broce Iris , Dale Anders , Yu Bing , Zhou Laura Y , Li Xihao , Argos Maria , Daviglus Martha L , Cai Jianwen , Franceschini Nora , Sofer Tamar
Published: February 2025

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. Studies of genetic association in these admixed populations often use methods such as admixture mapping, which relies on inferred counts of “local” ancestry, i.e., of the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate the “old” local ancestry inference performed using the state-of-the-art inference method, RFMix, alongside “new” inferences performed using Fast Local Ancestry Estimation (FLARE), which also used an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old RFMix and new FLARE inferences were highly similar for both global and local ancestries, with FLARE-inferred datasets yielding admixture mapping results consistent with those computed from RFMix. However, in some genomic regions the old and new local ancestries have relatively lower correlations (Pearson R < 0.9). Most of these genomic regions (86.42%) were mapped to either ENCODE blacklist regions, or to gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97) between old and new local ancestries.
scCompare: a web app for single-cell RNA sequencing dataset comparisons across multiple auto-immune diseases
Authors: Yadav Shweta , Sahasrabudhe Pranav , Pingili Mulini , Chang Dan , Wang Jing
Published: February 2025

Motivation

Single-cell RNA sequencing (scRNA-seq) datasets have been widely used to identify the cell types and marker genes with pivotal roles in driving pathogenesis and progression of auto-immune diseases. Comparative analysis of different cell types or diseases across multiple scRNA-seq datasets can reveal the homogeneous and heterogeneous pathogenesis, and a comprehensive web-based comparison tool that could streamline this process is not yet available.

Results

We introduce scCompare, a web-based platform for scRNA-seq comparisons in autoimmune diseases. scCompare includes 2,125 differential gene lists from 100 scRNA-seq datasets in 22 auto-immune diseases and a query system supporting 170 standardized keywords from four attributes (disease, cell type, tissue, and treatment). scCompare also provides three modules enabling several comparative analysis and visualization options. geneQuery supports comparisons of queried genes across differential gene lists identified from multiple scRNA-seq datasets. DEGEnricher performs cell-type-specific enrichment analysis across studies based on a user-input gene list. DEGCompare allows interactive comparisons of multiple differential gene lists of many studies and performs pathway enrichment analyses. Using two case studies as examples, we demonstrated that scCompare represents a unique platform for biologists to identify, compare and validate the pathogenesis at the single-cell levels among auto-immune diseases.

Availability and implementation

scCompare is freely available athttps://sccompare.shinyapps.io/main/. The source code is available athttps://github.com/abbviegrc/scCompare.
NMR Spectroscopy for the Validation of AlphaFold2 Structures
Authors: Williams Jake , Gagnon Isabelle A. , Sachleben Joseph R.
Published: February 2025

The introduction of AlphaFold has fundamentally changed our ability to predict the structure of proteins from their primary sequence of amino acids. As machine learning (ML) and artificial intelligence (AI) based protein prediction continues to advance, we examine the potential of hybrid techniques that combine experiment and computation that may yield more accurate structures than AI alone with significantly reduced experimental burden. We have developed heuristics comparing N-edited NOESY spectra and AlphaFold predicted structures that seek to determine whether the predicted structure reasonably describes the structure of the protein which generated the NOESY. We present a large collection of data connecting entries across the BMRB, PDB and AlphaFold Database that includes experimentally derived structures and corresponding spectra, establishing it as a means to develop and test hybrid methods utilizing AlphaFold and NMR spectra to perform structure determination. These data test the new heuristics’ ability to identify inaccurate AlphaFold structures. A support vector machine was developed to test the consistency of NMR data with predicted structure and we show its application to the structure of an unsolved engineered protein, LoTOP.
A Multi-Model Ensemble Reveals Soil Carbon Gains from Regenerative Practices in the U.S. Midwest Cropland
Authors: Basso Bruno , Tadiello Tommaso , Millar Neville , Robertson G. Philip , Paustian Keith , Maureira Fidel S. , Albarenque Susana , Baer Brian , Price Lydia , Sharma Prateek , Villalobos Chris , Fowler Ames , Delandmeter Mathieu , Acutis Marco , Archontoulis Sotirios , Covey Kristofer R. , Doro Luca , Dumont Benjamin , Grace Peter R. , Hoogenboom Gerrit , Jones James W. , Perego Alessia , Ruane Alexander , Stöckle Claudio O. , Zhang Yao
Published: February 2025

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 ± 0.12 Mg ha-1yr-1aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.
SPTnet: a deep learning framework for end-to-end single-particle tracking and motion dynamics analysis
Authors: Bi Cheng , Scrudders Kevin L. , Zheng Yue , Mahmoodi Maryam , Low-Nam Shalini T. , Huang Fang
Published: February 2025

Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and motion parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a Transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy videos with a precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.
Persistent glycolysis defines the foreign body response to polymeric implants
Authors: Rempe Christian , Callaghan Neal , Fong-Hollohan Lauren , Nersesian Sarah , Froom Zachary , Medd Kyle , Ahmed Ibrahim , Karakach Tobias , Boudreau Jeanette E. , Bezuhly Michael , Huyer Locke Davenport
Published: February 2025

Non-degradable polymeric implantable medical devices are a mainstay of modern healthcare but can frequently lead to severe complications. These complications are largely attributable to the foreign body response (FBR), which is characterized by excessive inflammation and fibrosis in response to implanted materials. The pathologic mechanisms underpinning the FBR remain elusive; however, metabolism is increasingly regarded as a critical regulator of innate immune function. We conducted comprehensive metabolic profiling of implant-associated macrophages and multinucleated giant cells in response to the subcutaneous implantation of clinically relevant implantable materials in a mouse model of implant fibrosis. Leveraging novel metabolic characterization methods for analysis of both metabolic dependence and enzyme expression in heterogeneous peri-implant tissues, we demonstrate that peri-implant macrophages are glycolytic at least up to six weeks post-implantation. Glycolytically dependent peri-implant macrophages’ expression of glucose transporter 1 (GLUT1) increased temporally and with proximity to the implant-tissue interface. Paired rate-limiting metabolic enzyme expression analysis showed notable increases in biosynthetic pathways (G6PD and ACC1), matched with increased mitochondrial staining intensity in GLUT1Hicells at chronic timepoints, which were not notable at early timepoints. Notably, we identified a glycolytic dependence of multinucleated macrophages associated with polymeric materials: these cells expressed higher levels of GLUT1 than mononuclear macrophages of comparable metabolic phenotype. Our findings highlight GLUT1-dependent glycolysis as the definitive metabolic system used by peri-implant macrophages and multinucleated cells in the FBR, highlighting this pathway as a potential target for the development of novel therapeutic approaches.
Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs
Authors: Liu Jian , Neupane Pawan , Cheng Jianlin
Published: February 2025

Motivation

Estimation of protein complex structure accuracy is an essential step in protein complex structure prediction and is also important for users to select good structural models for various applications, such as protein function analysis and drug design. Despite the success of structure prediction methods such as AlphaFold2 and AlphaFold3, predicting the quality of predicted complex structures (structural models) and selecting top ones from large model pools remains challenging.

Results

We present GATE, a novel method that uses graph transformers on pairwise model similarity graphs to predict the quality (accuracy) of complex structural models. By integrating single-model and multi-model quality features, GATE captures both the characteristics of individual models and the geometric similarity between them to make robust predictions. On the dataset of the 15th Critical Assessment of Protein Structure Prediction (CASP15), GATE achieved the highest Pearson’s correlation (0.748) and the lowest ranking loss (0.1191) compared to existing methods. In the blind CASP16 experiment, GATE was ranked 4th according to the overall sum of z-scores of multiple metrics based on both TM-score and Oligo-GDTTS scores. In terms of per-target average metrics based on TM-score, GATE achieved a Pearson’s correlation of 0.7076 (1st place among all methods), a Spearman’s correlation of 0.4514 (3rd place), a ranking loss of 0.1221 (3rd place), and an Area Under the Curve (AUC) score of 0.6680 (3rd place), highlighting its strong, balanced ability of estimating complex model accuracy and selecting good models.

Availability

The source code of GATE is freely available athttps://github.com/BioinfoMachineLearning/GATE.
CellFateExplorer: An integrated platform for exploring cell fate
Authors: Huang Zhaoyang , Ma Haonan , Peng Yuchuan , Zhao Chenguang , Yu Liang
Published: February 2025

Trajectory inference algorithms based on single-cell omics data are powerful tools for predicting and dissecting cell differentiation. However, most existing tools are tailored to specific datasets and lack generalizability across diverse data types. To ad-dress this limitation, we developed CellFateExplorer, systematically evaluates the performance of x trajectory inference methods across y datasets. Through an interactive web interface, CellFateExplorer provides guidance on method selection and down-stream analysis for specific datasets. In summary, CellFateExplorer is an integrated platform for exploring cell fate.
Patients’ perception of anaesthesia and participation in their own perioperative safety- An observational study
Authors: Agarwal Jyotsna , Cherian Meena Nathan , Hussain Sana Yasmin , Panjiar Pratibha , Kumar Santosh , Cheng Davy
Published: February 2025

Patients for Patients Safety (PFPS) is a World Health Organization (WHO) program which advocates meaningful engagement of patients and families to improve patient safety. Perioperative patient safety encompasses the surgical risks, as well as risks due to anesthesia. This study was conceptualized to assess patient perception of their involvement in enhancing their own perioperative safety and also assess the current state of patient knowledge regarding anesthesia.

This observational cross-sectional study was conducted in a tertiary care teaching hospital of India. One hundred sixty-three consenting adults, aged 19 years to 75 years, scheduled for elective surgery, between May and July 2022, having American Society of Anesthesiologists grade 1 to 3 participated in the study. Patients having psychiatric disorder, cognitive impairment, speech and hearing problems were excluded.

A pre-validated questionnaire of 21 questions was administered in pre-operative area. Data collection was done in online MS excel spreadsheet format.

In our study it was encouraging to note that the general awareness on requirement of anesthesia for surgery was widely acknowledged. Nonetheless, the information about anesthesia as a separate specialty, and anesthesiologists as specialized doctors, was lacking (43/163, 28.8%). The inadequate awareness about anesthesia specialty as found in our study, might not be a true reflection of a wider population, which calls attention to invest in anesthesia awareness programs. Our study revealed a very promising initial step in patient empowerment, as majority of patients (100/163; 61.3%) were amenable to participate in their own perioperative safety. Regardless of education status, the population in general needs to be familiarized with the shared patient responsibility in enhancing patient safety, just like other public health programs. Our study aligns with the recently proposed concrete interventions for improving quality of perioperative care at a global stage which contributes to Sustainable Development Goals targets towards achieving Universal Health Coverage.
Impact of bariatric surgery on monthly earnings and employment: a national linked data study in England, 2014-2022
Authors: Bermingham Charlotte R. , Ayoubkhani Daniel , Zaccardi Francesco , Coulman Karen , Valabhji Jonathan , Khunti Kamlesh , Pournaras Dimitri J. , Santos Rita , Islam Nazrul , Razieh Cameron , Dolby Ted , Nafilyan Vahé
Published: February 2025

Objective

Evaluate the impact of bariatric surgery on monthly earnings and employee status among working-age adults, and examine variations across sociodemographic characteristics.

Design

Retrospective longitudinal cohort study using national, linked administrative datasets.

Setting

Hospital inpatient services in England between 1 April 2014 and 31 December 2022.

Participants

40,662 individuals who had a bariatric surgery procedure and obesity diagnosis during the study period, with no bariatric surgery history in the previous 5 years, and were 25 to 64 years old at the date of surgery. We also included 49,921 individuals sampled from the general population who had not had bariatric surgery matched by age and sex to those in the cohort who had bariatric surgery.

Main outcome measures

Monthly employee pay – for all months and only months where the individual was in paid employment – expressed in 2023 prices; paid employee status.

Results

Among people living with obesity who had bariatric surgery, there was a sustained increase in monthly employee pay from six months after surgery with a mean increase of £84 per month 5 years after surgery compared with the six months before surgery. Among those in paid employment, there was a sustained increase in the probability of being a paid employee from 4 months after bariatric surgery, with a mean increase of 4.3 percentage points 5 years after surgery. The increases in pay and probability of employment were greater for males. The increase in employee pay was not sustained over the 5-year follow up time for the youngest age groups.

Conclusions

Bariatric surgery is associated with an increased probability of being employed, resulting in increased earnings. These findings suggest that living with obesity negatively impacts labour market outcomes and that obesity management interventions are likely to generate economic benefits both to individuals and on a macroeconomic level by increasing the likelihood of employment of people living with obesity.
Senescence-related cytokine levels are associated with HIV-1 serostatus and persistence
Authors: Li Yijia , Soto-Ramirez Zoamy N. , Roscher Jenny , Medvec Tom , Alaoui-El-Azher Mounia , Piazza Paolo , Chen Yue , Sluis-Cremer Nicolas , Rinaldo Charles , Macatangay Bernard JC
Published: February 2025

Background

HIV-1 is associated with accelerated aging. The senescence-associated secretory phenotype (SASP) includes biological and cytokine profiles that induce cellular senescence and inflammaging. In this study, we leveraged the Multicenter AIDS Cohort Study (MACS) to evaluate the role of SASP in aging, HIV-1 reservoir, and inflammation in people with HIV-1 (PWH) on long-term suppressive antiretroviral therapy (ART).

Methods

In this retrospective study we included plasma and serum samples from 27 virally- suppressed PWH and 10 people without HIV-1 (PWoH) collected in 2019 and 2023. SASP markers were quantified in the 2019 and 2023 samples. Plasma residual viremia, intact and defective proviral DNA were quantified in the 2019 samples. Correlations between SASP markers and HIV-1 reservoir were performed using the Spearman test, and the sparse partial least squares discrimination analysis was used to identify variables that distinguish HIV-1 serostatus.

Results

All study participants were male with a median age of 59 years. SASP markers did not show significant changes longitudinally in either group. We identified a set of markers that had moderate performance in distinguishing PWH and PWoH, including CMV serum antibody titer, matrix metalloproteinase 9 (MMP-9), Growth/differentiation factor-15, Stanniocalcin-1 and SerpinE1. Among all the SASP markers, MMP-9 was significantly associated with intact HIV-1 proviral levels (rho=0.60, P=0.002).

Conclusion

In this cohort study, we revealed the relationship between SASP markers and HIV-1 persistence. Future interventions targeting the senescence pathways may impact HIV-1 persistence.
Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents
Authors: Mermer Omer , Zhang Eddie , Demir Ibrahim
Published: February 2025

Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear and ensemble machine learning (ML) models and applies explainable AI (XAI) techniques to understand the contribution of input features. Data from AgInjuryNews (2015–2024) was preprocessed to extract relevant attributes such as location, time, age, and safety measures. The dataset comprised 2,421 incidents categorized as fatal or non-fatal. Various ML models, including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were trained and evaluated using standard performance metrics. Ensemble models demonstrated superior accuracy and recall compared to linear models, with XGBoost achieving a recall of 100% for fatal injuries. However, all models faced challenges in predicting non-fatal injuries due to class imbalance. SHAP analysis provided insights into feature importance, with age, gender, location, and time emerging as the most influential predictors across models. This research highlights the effectiveness of ensemble ML models in injury prediction while emphasizing the need for balanced datasets and XAI techniques for actionable insights. The findings have practical implications for enhancing agricultural safety and guiding policy interventions.

Highlights

This study analyzed 2,421 agricultural injury incidents from AgInjuryNews (2015– 2024) and utilized machine learning models to predict injury severity, focusing on both fatal and non-fatal outcomes.

Ensemble models, such as XGBoost and Random Forest, outperformed linear models in accuracy and recall, especially in predicting fatal injuries, although challenges in non-fatal predictions due to class imbalance were observed.

Key predictors identified through SHAP analysis included age, gender, location, and time, providing interpretable insights into the factors influencing injury severity.

The integration of explainable AI (XAI) enhanced the transparency of machine learning predictions, enabling stakeholders to prioritize targeted safety interventions effectively.

This research highlights the potential of combining ensemble ML models with XAI techniques to improve agricultural safety practices and provides a foundation for addressing data challenges in future studies.

Graphical Abstract
Comment on “Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition” [J. Chem. Phys. 160, 074105 (2024)]
Authors: Ramos Alexandre F.
Published: February 2025

The authors of the commented article claim that the exact time-dependent solutions of the stochastic model for a self-repressing gene previously obtained by us are both “incomplete and incorrect” because their calculations issued complex numbers as decaying rates of the system to steady state. We show that the imaginary decaying rates result from a methodological artifact. We use a linear algebraic approach to show that the decaying rates are those reported in [‘Exact time-dependent solutions for a self-regulating gene.’ Phys. Rev. E 83: 062902 (2011)]. Thus, our solution is both complete and correct. Additionally, the authors claim that they have discovered a new class of operator having complex eigenvalues and non-orthogonal eigenfunctions. We show that the operator can actually be written in the usual self-adjoint form, and, hence, it has real eigenvalues and orthogonal eigenfunctions.
Chemotherapeutic 6-thio-2’-deoxyguanosine selectively targets and inhibits telomerase by inducing a non-productive telomere-bound telomerase complex
Authors: Sanford Samantha L , Badstübner Mareike , Gerber Michael , Mannherz William , Lampl Noah , Dannenberg Rachel , Hinchie Angela , Schaich Matthew A , Myong Sua , Hedglin Mark , Agarwal Suneet , Alder Jonathan K , Stone Michael D. , Opresko Patricia L
Published: February 2025

Most cancers upregulate the telomere lengthening enzyme telomerase to achieve unlimited cell division. How chemotherapeutic nucleoside 6-thio-2’-deoxyguanosine (6-thio-dG) targets telomerase to inhibit telomere maintenance in cancer cells and tumors was unclear. Here, we demonstrate that telomerase insertion of 6-thio-dGTP prevents synthesis of additional telomeric repeats but does not disrupt telomerase binding to telomeres. Specifically, 6-thio-dG inhibits telomere extension after telomerase translocates along its product DNA to reposition the template, inducing a non-productive complex rather than enzyme dissociation. Furthermore, we provide direct evidence that 6-thio-dG treatment inhibits telomere synthesis by telomerase in cancer cells. In agreement, telomerase-expressing cancer cells harboring critically short telomeres are more sensitive to 6-thio-dG and show a greater induction of telomere losses compared to cancer cells with long telomere reserves. Our studies reveal that telomere length and telomerase status determine 6-thio-dG sensitivity and uncover the molecular mechanism by which 6-thio-dG selectively inhibits telomerase synthesis of telomeric DNA.
Sex Differences in Cancer Functional Genomics: Gene Dependency and Drug Sensitivity
Authors: Zeltser Nicole , Zhu Chenghao , Oh Jieun , Li Constance H. , Boutros Paul C.
Published: February 2025

Patient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences across the central dogma have been identified that may drive these phenotypic differences. Despite a growing catalog of specific genomic, transcriptomic and proteomic sex differences in a range of cancer types, their functional consequences remain unclear. To assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data expand our understanding of the propagation of sex-differing molecular features to functional outcomes, and their role in influencing patient phenotypes. They highlight the importance of considering sex-specific effects in mechanistic and functional studies.