bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcriptome Assembly at Single-Cell Resolution with Beaver
Authors: Shi Qian , Zhang Qimin , Shao Mingfu
Published: November 2024

Emerging single-cell RNA sequencing techniques (scRNA-seq) has enabled the study of cellular transcriptome heterogeneity, yet accurate reconstruction of full-length transcripts at single-cell resolution remains challenging due to high dropout rates and sparse coverage. While meta-assembly approaches offer promising solutions by integrating information across multiple cells, current methods struggle to balance consensus assembly with cell-specific transcriptional signatures. Here, we present Beaver, a cell-specific transcript assembler designed for short-read scRNA-seq data. Beaver implements a transcript fragment graph to organize individual assemblies and designs an efficient dynamic programming algorithm that searches for candidate full-length transcripts from the graph. Beaver in-corporates two random forest models trained on 51 meticulously engineered features that accurately estimate the likelihood of each candidate transcript being expressed in individual cells. Our experiments, performed using both real and simulated Smart-seq3 scRNA-seq data, firmly show that Beaver substantially outperforms existing meta-assemblers and single-sample assemblers. At the same level of sensitivity, Beaver achieved 32.0%-64.6%, 13.5%-36.6%, and 9.8%-36.3% higher precision in average compared to meta-assemblers Aletsch, TransMeta, and PsiCLASS, respectively, with similar improvements over single-sample assemblers Scallop2 (10.1%-43.6%) and StringTie2 (24.3%-67.0%). Beaver is freely available athttps://github.com/Shao-Group/beaver. Scripts that reproduce the experimental results of this manuscript are available athttps://github.com/Shao-Group/beaver-test.
Single Cell Transcriptomics Reveal Genomic Indicators of Nonalcoholic Fatty Liver Disease
Authors: Thakur Madhavendra
Published: November 2024

Nonalcoholic fatty liver disease (NAFLD) affects roughly 25% of people globally. This paper seeks to use single cell RNA sequencing (scRNA-seq) to identify genomic markers for the identification of NAFLD and the evaluation of therapeutic methodsin silico. Publicly available scRNA-seq datasets of livers of both control and NAFLD mice were analyzed in R using the Seurat package for scRNA-seq analysis. Datasets were clustered as needed using the Seurat standard preprocessing workflow, and the zonation of hepatocyte clusters was identified based on established marker genes. When examining hepatocyte reprogramming during NAFLD, trends were seen in the zonation of hepatocytes expressing fructose metabolism pathways such as Aldob, Slc2a5 and Khk. In one dataset, healthy livers showed elevated expression of fructose metabolism pathways in central hepatocytes, while NAFLD livers exhibited this expression predominantly in portal hepatocytes. Conversely, in a second dataset, this zonation pattern was reversed, with central hepatocytes in NAFLD livers showing higher fructose metabolism expression compared to portal regions. This consistent observation of a zonation switch across datasets strengthens our understanding of hepatocyte reprogramming during NAFLD and fibrosis. Further, the direction of this switching may provide insights into fibrosis etiology, potentially reflecting differences in diet and metabolic stressors that contribute to fibrosis progression. It could also prove valuable inin silicoevaluations of therapies for NAFLD by identifying liver fibrosis.
Influence of metal ions on the isothermal self-assembly of DNA nanostructures
Authors: Rodriguez Arlin , Madhanagopal Bharath Raj , Sarkar Kahini , Nowzari Zohreh , Mathivanan Johnsi , Talbot Hannah , Morya Vinod , Halvorsen Ken , Vangaveti Sweta , Berglund J. Andrew , Chandrasekaran Arun Richard
Published: November 2024

DNA nanostructures are typically assembled by thermal annealing in buffers containing magnesium. We demonstrate the assembly of DNA nanostructures at constant temperatures ranging from 4 °C to 50 °C in solutions containing different metal ions. The choice of metal ions and the assembly temperature influence the isothermal assembly of several DNA motifs and designed three-dimensional DNA crystals. Molecular dynamics simulations show more fluctuations of the DNA structure in select monovalent ions (Na+and K+) compared to divalent ions (Mg2+and Ca2+). A key highlight is the successful assembly of DNA motifs in nickel-containing buffer at temperatures below 40 °C, otherwise unachievable at higher temperatures, or using an annealing protocol. DNA nanostructures isothermally assembled in different ions do not affect the viability of fibroblasts, myoblasts, and myotubes and or the immune response in myoblasts. The use of ions other than the typically-used magnesium holds key potential in biological and materials science applications that require minimal amounts of magnesium.
Efficient Finite-Difference Estimation of Second-Order Parametric Sensitivities for Stochastic Discrete Biochemical Systems
Authors: Jabeen Fauzia , Ilie Silvana
Published: November 2024

Biochemical reaction systems in a cell exhibit a stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have low population numbers. As a result, stochastic models are vital to accurately describe the system dynamics. Sensitivity analysis is an important method for studying the influence of the variations in various parameters on the output of a biochemical model. We propose a finite-difference strategy for approximating second-order parametric sensitivities for stochastic discrete models of biochemically reacting systems. This strategy utilizes adaptive tau-leaping schemes and coupling of the perturbed and nominal processes for an efficient sensitivity estimation. The advantages of the new technique are demonstrated through its application to several biochemical system models with practical significance.
Association of rareAPOEmissense variants with Alzheimer's disease in the Japanese population
Authors: Miyashita Akinori , Obinata Ai , Hara Norikazu , Mitsumori Risa , Kaneda Daita , Hashizume Yoshio , Sano Terunori , Takao Masaki , Gabdulkhaev Ramil , Tada Mari , Kakita Akiyoshi , Arakawa Akira , Morishima Maho , Murayama Shigeo , Saito Yuko , Hatsuta Hiroyuki , Matsubara Tomoyasu , Akagi Akio , Riku Yuichi , Miyahara Hiroaki , Sone Jun , Yoshida Mari , Yamaguchi Haruyasu , Tsukie Tamao , Hasegawa Mai , Kasuga Kensaku , Kikuchi Masataka , Kuwano Ryozo , Iwatsubo Takeshi , Niida Shumpei , Ozaki Kouichi , Ikeuchi Takeshi
Published: November 2024

Background:APOEis a major susceptibility gene for Alzheimer's disease (AD). Recent studies in Europe and the US have identified rare missense variants (RMVs) inAPOEwhich are significantly associated with AD. However, little is known regardingAPOERMVs in East Asians, including the Japanese, and their association with AD and lipid metabolism.Objective:To identifyAPOERMVs in the Japanese population and investigate their association with AD and lipid metabolism, including low-density lipoprotein cholesterol levels.Subjects:APOERMVs were explored in the NIG (2,589 subjects) and ToMMo (3,307 subjects) cohorts. A case-control study was performed involving 6,471 AD and 20,270 control subjects.Methods:Sanger sequencing or whole-exome sequencing was performed on NIG subjects. We used genotype data from ToMMo.APOERMV frequencies in the Japanese were compared with various ethnic populations. Associations amongAPOERMV genotypes, AD, and lipoproteins were examined.Results:Fourteen RMVs were identified (minor allele frequency 0.02-0.73%), 10 of which were specifically found in East Asians. Five RMVs previously reported overseas were not detected in Japanese individuals. Two RMVs (rs140808909 and rs190853081) exhibiting complete linkage disequilibrium were associated with AD protective effects:PBonferroni= 3.63E-02, OR (95% CI) = 0.70 (0.54-0.92). No significant differences in cholesterol levels were observed between RMV carriers and non-carriers.Conclusions:APOERMVs were identified in the Japanese population, with two showing potential protective effects against AD. Further studies involving larger cohorts are required to confirm our findings and investigate the role ofAPOERMVs in AD and lipid metabolism.
“FGFR4 p.Gly388Arg Mutations in PBMCs of LAM Patients: A Potential Systemic Driver of Neoplastic-like Behaviour”
Authors: Koc-Gunel Sinem , Ryan Amy L. , Winter Melanie , Wagner T.O.F.
Published: November 2024

Lymphangioleiomyomatosis (LAM) is a rare, progressive lung disease with neoplastic traits, primarily driven by mutations in the TSC2 gene, which lead to hyperactivation of the mTOR pathway and uncontrolled cell growth. However, additional mutations may contribute to disease progression. In this study, we investigate the potential role of co-mutations, focusing on the FGFR4 p.Gly388Arg polymorphism, which has previously been associated with aggressive cancer progression, as a possible co-driver in LAM. Peripheral blood mononuclear cells (PBMCs) were isolated from seven sporadic LAM patients and analyzed using Next-Generation Sequencing (NGS) to identify tumorigenic mutations. The FGFR4 p.Gly388Arg variant was identified in four patients, with allelic frequencies ranging from 49% to 99%. The highest frequency was observed in a patient with severe bullous lung disease, who ultimately required lung transplantation. Our analysis revealed a strong positive correlation (r = 0.85, p = 0.15) between FGFR4 allelic frequency and lung function decline (FEV1%), as well as a moderate positive correlation (r = 0.55, p = 0.20) between FGFR4 mutation status (presence vs. absence) and FEV1% decline. Although these correlations did not reach statistical significance, the trends suggest that FGFR4 mutations may contribute to disease progression in LAM. These findings indicate that FGFR4 mutations could play a role in the systemic nature of LAM, potentially exacerbating disease severity. Further research is needed to evaluate FGFR4 as a biomarker and therapeutic target in conjunction with mTOR inhibitors for the treatment of LAM.
Predictors of functional outcome following acute ischemic stroke secondary to M1 MCA/ICA occlusion in the extended time window
Authors: Constantakis John , Steiner Quinn , Reher Thomas , Choi Timothy , Holangel Fauzia , Zhao Qianqian , Bennett Nicole , Nair Veena A. , Adelman Eric , Prabhakaran Vivek , Aagard-Kienitz Beverly , Famakin Bolanle
Published: November 2024

Introduction

A validated clinical decision tool predictive of favorable functional outcomes following endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) remains elusive. We performed a retrospective case series of patients at our regional Comprehensive Stroke Center, over a 4-year period, who have undergone EVT to elucidate patient characteristics and factors associated with a favorable functional outcome after EVT.

Methods

We reviewed all cases of EVT at our institution from 2/2018 - 2/2022 in the extended time window from 6 – 24 hours. Demographic, clinical, imaging, and procedure co-variates were included. A favorable clinical outcome was defined as a modified Rankin scale or 0-2. We included patients with M1 or ICA occlusion treated with EVT within 6-24 hours after symptom onset. We used a univariate and multivariate logistic regression analysis to identify patient factors associated with a favorable clinical outcome at 90 days.

Results

Our analysis demonstrates that higher recanalization score based on the mTICI scale (2B-3) was a strong indicator of favorable outcome per both our multivariate and univariate analysis (OR 4.11; CI 1.10 – 15.31; p 0.035). Our data also showed signal that the younger age (p0.013), lower baseline NIHSS (p0.043), shorter hospital length of stay (LOS) (p0.030), and absence of pre-existing hypertension (p0.026) may also be a predictor of favorable outcome per our univariate analysis.

Conclusion

Patients without pre-existing hypertension had more favorable outcomes following EVT in the expanded time window. This is consistent with other multicenter data in the expanded time window that demonstrates greater odds of a poor outcome with elevated pre-, peri-, and post-endovascular treatment blood pressure. Our data also demonstrates mTICI score is a strong predictor of favorable outcome even when controlled for other variables. Other factors that may indicate a favorable outcome include younger age, lower baseline NIHSS, and shorter hospital LOS.
Stemness prediction models reveal glioma aggressiveness and therapeutic targets in gliomas
Authors: Simões Renan L.S. , Marção Maycon , Santos Emerson de Souza , Uyemura Sergio Akira , Malta Tathiane M.
Published: November 2024

Gliomas are complex and heterogeneous primary brain tumors with a high degree of therapeutic resistance, particularly glioblastomas, which carry a poor prognosis. Cellular plasticity and stem cell-like features, or “stemness,” are increasingly recognized as key contributors to tumor progression and treatment resistance. In this study, we introduce two machine-learning-based prediction models designed to assess stemness in glioma samples using bulk gene expression data. One model captures fetal astrocyte characteristics (ASTsi), while the other identifies glioma stem cell traits (GSCsi). ASTsi was notably correlated with poor prognosis in IDHmut gliomas, whereas GSCsi was more indicative of stemness in IDHwt subtypes. Longitudinal data analysis showed that IDHwt and grade IV gliomas exhibit shifts in stemness indices upon recurrence, suggesting a phenotypic change linked to therapy resistance. Additionally, single-cell transcriptomic analysis confirmed that GSCsi can detect stem-like cell subsets in IDHwt gliomas. This approach enhances our understanding of glioma heterogeneity and reveals potential therapeutic targets.
Reducing CETP activity prevents memory decline in an Alzheimer’s disease mouse model
Authors: Phénix Jasmine , Sarty Isabel , Katz Megan S. , Nie Hannah , Kerksiek Anja , Kiss Robert S. , Lütjohann Dieter , Pastor William A. , Poirier Judes , Munter Lisa Marie ,   PREVENT-AD Research Group
Published: November 2024

Epidemiological studies have shown that lower activity of the cholesteryl ester transfer protein (CETP) correlates with reduced Alzheimer’s disease (AD) risk. While small molecule CETP inhibitors like evacetrapib have previously been assessed for cardiovascular diseases, their involvement in AD has not been investigated. Here, we establish CETP as a novel pharmacological target for AD treatment. Using CETP transgenic mice crossed to a mouse model of amyloidosis and administering evacetrapib, we provide evidence that CETP inhibition maintained memory independent of classic AD markers, increased hippocampal cholesterol, altered plasma lipoproteins, and changed transcription of genes linked to brain barriers. Using proteomic data of cerebrospinal fluid from cognitively unimpaired individuals at risk for AD (the PREVENT-AD cohort), we confirm that our mouse model reflects physiological changes in pre-symptomatic human subjects. We propose the repurposing of CETP inhibitors as an effective therapeutic strategy to delay or prevent cognitive impairment in AD.
Microbiology and Anthropology: a multidisciplinary approach for estimating time since death during Winter in rural New South Wales, Australia
Authors: Green Hayley , Espedido Bjorn , Ligot Rogine , Jensen Slade O. , Morton Charles Oliver
Published: November 2024

Accurate determination of the post-mortem interval (PMI) is critical in forensic casework. Most studies conclude that PMI determinations are dependent on local climatic and geographical factors. Despite this, there is little data in an Australian environment outside of entomological studies. In the absence of insect data on or around the remains, alternative methods are required.

Anthropological methods of observing and scoring the extent of decomposition at the time of discovery provide only broad estimates of PMI. Microbial succession is responsible for these observable soft tissue changes, particularly in early and late decomposition. The aim of this study was to combine anthropological and microbiological methods to provide data for determining PMI in a temperate Australian climate.

Microbial DNA was isolated from skin and cavity swabs and used to perform 16S rRNA metagenomic analysis of pooled DNA samples to allow the choice of target taxa for qPCR. qPCR indicated significant changes in microbial communities with a dominant population ofGammaproteobacteriaat early time points giving way toFirmicutesandBacteroidetesnear the end of the experiment. Expressing qPCR data as a ratio ofLactobacillales/Enterobacteriaceaeprovided data that could be useful in determining early and late decomposition. The genusPsychrobacterwas identified as a good indicator of late decomposition in winter conditions. qPCR analysis, with further refinements, could be part of an accurate quantitative method of determining PMI.
Individualized cortical thickness asymmetry in Autism Spectrum Disorders and Schizophrenia
Authors: Echave Marta Martin , Schnack Hugo G. , Díaz-Caneja Covadonga M. , Pina-Camacho Laura , Janssen Niels , Gordaliza Pedro M. , Kho Kuan H. , Buimer Elizabeth E.L. , van Haren Neeltje E.M. , Kahn René S. , Hulshoff Pol Hilleke E. , Parellada Mara , Arango Celso , Janssen Joost
Published: November 2024

Introduction

Cortical thickness asymmetry has been proposed as a latent biomarker for Autism Spectrum Disorders (ASD) and schizophrenia (SZ). However, the degree of abnormal asymmetry at the individual level in ASD and SZ remains unclear. To investigate this, we applied normative modeling.

Methods

Normative means for the whole brain and regional (160 cortical parcels) cortical thickness asymmetry index (AI) were established using a training set of healthy subjects (n=4,904, 45.15% male, age range: 6-95 years), controlling for age, sex, image quality and scanner. We calculated z-scores to quantify individual deviations from the normative mean in a test set consisting of healthy controls (HCtest, n=526, 40% male), participants with ASD (n=135, 83% male) and SZ (n=287, 81% male). Regional deviance was assessed by counting the number of individuals with significant deviations below (infra-normal, z-score ≤ -1.96) or above (supra-normal, z-score ≥ 1.96) normative means in each parcel. We also evaluated individual deviance by counting the number of regions with significant deviations for each participant. A data-driven multivariate approach was employed to determine whether joint regional deviance was associated with diagnosis.

Results

There were no differences for deviance of whole brain AI between any of the groups. Distributions of individual deviances overlapped across all 160 regions, with only one superior temporal region in which SZ individuals showed a higher proportion of supra-normal AI values compared to HCtest(HCtest= 1.14%, SZ = 5.92%,χ2 = 15.45, PFDR< 0.05, ω = 0.14). The SZ group also had a higher average number of regions with significant deviations than HCtest(infra-normal: z = -4.21, p < 0.01; supra-normal: z = -4.33, p < 0.01). Multivariate analysis showed no association between inter-regional heterogeneity of AI and diagnosis. Results were consistent when using a higher resolution parcellation, alternative asymmetry calculations, analysis restricted to males, and after controlling for handedness and IQ.

Conclusions

Our findings indicate that whole brain, regional and inter-regional variability in cortical thickness AI among those with ASD is entirely accounted for by normative variation. This study challenges the utility of cortical thickness asymmetry as a biomarker for ASD.
Thoracic Computed Tomographic Abnormalities in Patients with Spinal Tuberculosis: A Prospective Evaluation
Authors: Sahu Anil Kumar , Kumar Neeraj , Garg Ravindra Kumar , Malhotra Hardeep Singh , Malhotra Kiran Preet , Rizvi Imran , Jain Amita , Parihar Anit , Verma Rajesh , Sharma Praveen Kumar , Uniyal Ravi , Pandey Shweta
Published: November 2024

Background

Spinal tuberculosis is the most common osteoarticular tuberculosis. Many published isolated reports indicated that many patients with spinal tuberculosis have lung abnormalities. In this study, we aimed to prospectively evaluate the prevalence and spectrum of lung abnormalities.

Methods

We enrolled consecutive newly diagnosed patients with spinal tuberculosis. They were subjected to spinal magnetic resonance imaging and computed tomography (CT) of the chest. They were followed for 6 months. The outcome was assessed by using the modified Rankin scale.

Results

Out of 66 patients, 22 (33.3%) patients had CT thorax abnormalities. The most frequent finding was lung fibrosis (13 patients). Six patients had consolidation. Miliary tuberculosis was demonstrated in one patient. Cavitation, atelectasis, emphysema, and traction bronchiectasis were noted in one patient each. Nine patients had pleural effusion. Pleural thickening and empyema were noted in two patients each. Empyema was noted in 2 patients. Hydropneumothorax was seen in one patient. One patient had endobronchial tuberculosis. Hilar and Mediastinal lymphadenopathy was noted in 6 patients. At inclusion, 59 (89.4%) patients had modified Rankin scale scores≥3. After 6 months, 58 (87.9%) patients showed improvement and achieved a score <3. In 4 patients, spinal surgery was needed. The remaining three patient’s disability statuses remained unaltered. CT chest abnormalities were not associated with improvement in disability.

Conclusion

One-third of patients with spinal tuberculosis had tuberculous CT thorax abnormalities. The most frequent CT thorax finding was lung fibrosis. CT abnormalities did not affect the outcome.
Persistent Health and Cognitive Impairments Up to Four Years Post-COVID-19 in Young Students: The Impact of Virus Variants and Vaccination Timing
Authors: Latifi Ashkan , Flegr Jaroslav
Published: November 2024

The long-term consequences of COVID-19 infection are becoming increasingly evident in recent studies. This repeated cross-sectional study aimed to explore the long-term health and cognitive effects of COVID-19, focusing on how virus variants, vaccination, illness severity, and time since infection impact post-COVID-19 outcomes. We examined three cohorts of university students (N=584) and used non-parametric methods to assess correlations of various health and cognitive variables with SARS-CoV-2 infection, COVID-19 severity, vaccination status, time since infection, time since vaccination, and virus variants. Our results show that some health and cognitive impairments persist, with some even progressively worsening especially fatigue in women and memory in men, up to four years post-infection, with the Wuhan variant having the most significant long-term impact and the Omicron variant the least. Interestingly, the severity of the acute illness was not correlated with the variant of SARS-CoV-2. The analysis also revealed that individuals who contracted COVID-19 after vaccination had better health and cognitive outcomes compared to those infected before vaccination. The study also discusses some limitations inherent in cross-sectional studies, particularly those arising from the stronger tendency of individuals with poorer health, compared to healthier individuals, to avoid infection and prioritize vaccination. To mitigate potential bias, the study proposes focusing on factors such as illness severity and time since infection or vaccination when analyzing persistent symptoms.
Evaluation of Radiology Residents’ Reporting Skills Using Large Language Models: An Observational Study
Authors: Atsukawa Natsuko , Tatekawa Hiroyuki , Oura Tatsushi , Matsushita Shu , Horiuchi Daisuke , Takita Hirotaka , Mitsuyama Yasuhito , Omori Ayako , Shimono Taro , Miki Yukio , Ueda Daiju
Published: November 2024

Background

Large language models (LLMs) have the potential to objectively evaluate radiology resident reports; however, research on their use for feedback in radiology training and assessment of resident skill development remains limited.

Purpose

This study aimed to assess the effectiveness of LLMs in revising radiology reports by comparing them with reports verified by board-certified radiologists and to analyze the progression of resident’s reporting skills over time.

Materials and methods

To identify the LLM that best aligned with human radiologists, 100 reports were randomly selected from a total of 7376 reports authored by nine first-year radiology residents. The reports were evaluated based on six criteria: (1) Addition of missing positive findings, (2) Deletion of findings, (3) Addition of negative findings, (4) Correction of the expression of findings, (5) Correction of the diagnosis, and (6) Proposal of additional examinations or treatments. Reports were segmented into four time-based terms, and 900 reports (450 CT and 450 MRI) were randomly chosen from the initial and final terms of the residents’ first year. The revised rates for each criterion were compared between the first and last terms using the Wilcoxon Signed-Rank test.

Results

Among the LLMs tested, GPT-4o demonstrated the highest level of agreement with board-certified radiologists. Significant improvements were noted in Criteria 1–3 when comparing reports from the first and last terms (all P < 0.023) using GPT-4o. In contrast, no significant changes were observed for Criteria 4–6. Despite this, all criteria except for Criterion 6 showed progressive enhancement over time.

Conclusion

LLMs can effectively provide feedback on commonly corrected areas in radiology reports, enabling residents to objectively identify and improve their weaknesses and monitor their progress. Additionally, LLMs may help reduce the workload of radiologists’ mentors.
Application of Bayesian spatial modelling to uncover geographical disparities and improve antimicrobial resistant surveillance
Authors: Wozniak Teresa Maria , Young Alys , Conlan David , Shausan Aminath , Dyda Amalie , Sartorius Benn , Cespedes Marcela
Published: November 2024

Introduction

Disease surveillance is an essential element of an effective response to antimicrobial resistance (AMR). Associations between AMR cases and area-level drivers such as remoteness and socio-economic disadvantage have been observed, but spatial associations when modelling routinely collected surveillance data that are often imperfect or missing have not been previously possible.

Aim

We aimed to use spatial modelling to adjust for area-level variables and to enhance AMR surveillance for missing or sparse data, in an effort to provide clinicians and policy makers with more actionable epidemiological information.

Methods

We used monthly antimicrobial susceptibility data for methicillin-resistantStaphylococcus aureus(MRSA) from a surveillance system in Australia. MRSA was assessed for the effects of age, sex, socio-economic and access to healthcare services indices by fitting Bayesian spatial models.

Results

We analysed data for 77, 760 MRSA isolates between 2016 and 2022. We observed significant spatial heterogeneity in MRSA and found significant associations with age, sex and remoteness, but not socio-economic status. MRSA infections were highest in adult females aged 16-60 living in very remote regions and lowest in senior males aged 60+ years living in inner regional areas..

Conclusion

Current disease surveillance approaches for antimicrobial resistant infections have limited spatial comparability, are not timely, and at risk of sampling bias. Bayesian spatial models borrow information from neighbouring regions to adjust for unbalanced geographical information and can fill information gaps of current MRSA surveillance. Assessment of disease spatial variation is especially critical in settings which have diverse geography, dispersed populations or in regions with limited microbiological capacity.
A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data
Authors: Hossain Md Sakhawat , Goyal Ravi , Martin Natasha K , DeGruttola Victor , Chowdhury Mohammad Mihrab , McMahan Christopher , Rennert Lior
Published: November 2024

Background

Our research focuses on local level estimation of the effective reproductive number, which describes the transmissibility of an infectious disease and represents the average number of individuals one infectious person infects at a given time. The ability to accurately estimate the infectious disease reproductive number in geographically granular regions is critical for disaster planning and resource allocation. However, not all regions have sufficient infectious disease outcome data for estimation.

Methods

We propose a two-step approach that incorporates existingRtestimation procedures (EpiEstim, EpiFilter, and EpiNow2) using data from geographic regions with sufficient data (step 1) into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predictRtin regions with sparse or missing data (step 2). Our flexible framework effectively allows for implementing any existing estimation procedure forRtin regions with coarse or entirely missing data. We perform external validation to evaluate predictive performance.

Results

We applied our method to estimateRtusing data from South Carolina (SC) counties and ZIP codes during the first COVID-19 wave (‘Wave 1’, June 16, 2020 – August 31, 2020) and the second wave (‘Wave 2’, December 16, 2020 – March 02, 2021). Among the three methods used in the first step, EpiNow2 yielded the highest accuracy ofRtprediction in the regions with entirely missing data. Median county-level percentage agreement (PA) was 90.9% (IQR: 89.9-92.0%) and 92.5% (IQR: 91.6-93.4%) for Wave 1 and 2, respectively. Median zip code-level PA was 95.2% (Interquartile Range, IQR: 94.4-95.7%) and 96.5% (IQR: 95.8-97.1%) for Wave 1 and 2, respectively. Using EpiEstim and EpiFilter yielded median PA ranging from 81.9%-90.0% and 87.2%-92.1% (respectively) across both waves and geographic granularities.

Conclusion

These findings demonstrate that the proposed methodology is a useful tool for small-area estimation ofRt, as our flexible framework yields high prediction accuracy for regions with entirely missing data regardless of the (step 1) estimation procedure used.
Evaluating Synthetic Diffusion MRI Maps created with Diffusion Denoising Probabilistic Models
Authors: Chattopadhyay Tamoghna , Jagad Chirag , Kush Rudransh , Desai Vraj Dharmesh , Thomopoulos Sophia I. , Villalón-Reina Julio E. , Thompson Paul M.
Published: November 2024

Generative AI models, such as Stable Diffusion, DALL-E, and MidJourney, have recently gained widespread attention as they can generate high-quality synthetic images by learning the distribution of complex, high-dimensional image data. These models are now being adapted for medical and neuroimaging data, where AI-based tasks such as diagnostic classification and predictive modeling typically use deep learning methods, such as convolutional neural networks (CNNs) and vision transformers (ViTs), with interpretability enhancements. In our study, we trained latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) specifically to generate synthetic diffusion tensor imaging (DTI) maps. We developed models that generate synthetic DTI maps of mean diffusivity by training on real 3D DTI scans, and evaluating realism and diversity of the synthetic data using maximum mean discrepancy (MMD) and multi-scale structural similarity index (MS-SSIM). We also assess the performance of a 3D CNN-based sex classifier, by training on combinations of real and synthetic DTIs, to check if performance improved when adding the synthetic scans during training, as a form of data augmentation. Our approach efficiently produces realistic and diverse synthetic data, potentially helping to create interpretable AI-driven maps for neuroscience research and clinical diagnostics.
Wild gut microbiome suppresses the opportunistic pathogenAeromonasin medaka under domesticated rearing conditions
Authors: Kawano Keisuke , Kawabe Kai , Sano Yuki , Hori Tomoyuki , Kihara Minoru , Kikuchi Yoshitomo , Itoh Hideomi
Published: November 2024

Background

The gut microbiome plays a crucial role in the metabolic health and pathogen resistance of various host animals. It is also well established that external environmental factors can influence the gut microbiome, leading to differences in its composition. However, the functional implications of these differences remain poorly understood. This study examined the gut microbiome of medaka (Oryzias latipesspecies complex) by comparing domesticated and wild populations, with the aim of gaining insights into the functional significance of their specific characteristics, particularly those of the wild-type microbiome.

Results

For the comparative analysis of the gut microbiome, 48 domesticated and 122 wild medaka were collected from multiple laboratories, pet stores and streams across Japan. The results showed that wild medaka exhibited significantly higher gut microbiome diversity, with a broader range of bacterial members. In contrast, the gut microbiome of domesticated medaka harbored lower microbial diversity and was consistently dominated byAeromonas, a typical opportunistic pathogen in fish. Additionally, 88.6% ofAeromonasisolates from domesticated medaka exhibited haemolytic activity. Moreover, a domesticated rearing experiment with wild populations showed no proliferation or dominance ofAeromonasin their gut, as observed in domesticated medaka. A further rearing experiment revealed that pre-exposing antibiotic-treated medaka to sediments from their natural habitats preventedAeromonascolonisation, even when reared under domesticated conditions.

Conclusions

These findings suggest that the habitat-derived wild gut microbiome can inhibitAeromonasproliferation in domesticated fish, highlighting its potential to mitigate opportunistic diseases in aquaculture.
Proteostasis dysregulation in p.A53T-α-Synuclein iPSC-derived astrocytes exacerbates neurodegeneration in a Parkinson’s disease model with Lewy-like pathology
Authors: Paschou Christina , Apokotou Olympia , Kollias Anastasios , Charmpi Konstantina , Dede Sofia , Samiotaki Martina , Palese Francesca , Dimoula Konstantina , Emmanouilidou Evangelia , Taoufik Era , Zurzolo Chiara , Matsas Rebecca , Papastefanaki Florentia
Published: November 2024

Alpha-Synuclein (αSyn) plays a central role in Parkinson’s disease (PD) and the p.A53T mutation causes an early-onset familial form of PD with severe manifestations. The pathological effects of the p.A53T-αSyn mutation have been extensively investigated in neurons, yet the consequences on astrocytes and astrocytic contribution to PD pathology are understudied. Here, we differentiated induced pluripotent stem cells from PD patients carrying the p.A53T-αSyn mutation to astrocytes, which uncovered cell-intrinsic phenotypes, including calcium dyshomeostasis and accumulation of protein aggregates. Proteomic profiling and functional analyses revealed perturbed protein catabolic processes, involving the proteasome and autophagy, associated with lysosomal malfunction. Dopamine neurons co-cultured with p.A53T-αSyn astrocytes displayed exacerbated neurodegeneration with hallmark Lewy-like pathologies, reversed by control astrocytes at least due to their ability to resolve neuronal αSyn aggregates by endocytic clearance. Our findings underscore a critical impact of p.A53T-αSyn on astrocytic protein quality control mechanisms, positioning astrocytes as important contributors to PD neuropathology.

Highlights

iPSC-derived astrocytes from PD patients with the p.A53T-αSyn mutation display cell-autonomous pathological phenotypes and toxic αSyn accumulation

Proteome and functional analyses reveal failure of major proteostasis mechanisms in p.A53T-αSyn astrocytes, largely related to lysosomal malfunction

Inherent Lewy-like pathological features are identified in p.A53T-αSyn astrocyte-neuron co-cultures

p.A53T-αSyn astrocytes induce PD-relevant neuropathology to healthy neurons

Control but not p.A53T-αSyn astrocytes alleviate neuropathology in co-cultured neurons
Deciphering gene regulatory programs underlying functionally divergent naïve T cell subsets
Authors: Zhu Hongya , Jiang Ya , McNairn Adrian J. , Fogarty Elizabeth A. , Tabilas Cybelle , Patel Ravi K. , Chobirko Jason D. , Munn Paul R. , Smith Norah L. , Grenier Jennifer K. , Rudd Brian D. , Grimson Andrew
Published: November 2024

Naïve CD8+ T cells are a heterogeneous population, with different subsets possessing distinct functions and kinetics upon activation. However, the gene regulatory circuits differentiating these naïve subsets are not well studied. In this work, we analyzed a large collection of public and newly generated RNA-seq and ATAC-seq profiles of different subsets of naïve CD8+ T cells, revealing significant differences in the gene regulatory landscapes between subsets. We leveraged these data by employing a network inference algorithm, Inferelator, to identify the transcriptional regulatory circuits active in each subset. The predicted transcriptional network of the naïve CD8+ T cell pool was validated by multiple orthogonal approaches, including CUT&Tag and Micro-C. Interestingly, our network analysis revealed a novel role forEomesin promoting effector cell differentiation in specific cell subsets. Moreover, we uncovered multiple novel regulators across a variety of subsets and discovered several modules of genes that were co-regulated by shared sets of transcription factors in distinct subsets. Collectively, our data defines the gene regulatory programs differentiating naïve CD8+ T cells and facilitates the identification of novel transcription factors that may alter the propensity of naïve CD8+ T cells to become effector or memory cells after infection.
Human cerebellum and ventral tegmental area interact during extinction of learned fear
Authors: Nio Enzo , Pereira Patrick Pais , Diekmann Nicolas , Petrenko Mykola , Doubliez Alice , Ernst Thomas M. , Batsikadze Giorgi , Maderwald Stefan , Deuschl Cornelius , Üngör Metin , Cheng Sen , Merz Christian J. , Quick Harald H. , Timmann Dagmar
Published: November 2024

The key elements for fear extinction learning are unexpected omissions of expected aversive events, which are considered to be rewarding. Given its reception of reward information, we tested the hypothesis that the cerebellum contributes to reward prediction error processing driving extinction learning via its connections with the ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day fear conditioning paradigm in a 7T MR scanner. The cerebellum and VTA were active during unexpected omissions of aversive unconditioned stimuli, particularly during initial extinction trials. Increased functional connectivity was observed between the cerebellum and VTA, indicating that the cerebellum could positively modulate VTA activity, which in turn might facilitate dopaminergic signaling during fear extinction learning. These results imply that an interaction between the cerebellum and VTA should be incorporated into the existing model of the fear extinction network.
Induction of pluripotent oncogenic stem cells from mouse fibroblasts
Authors: Shabrish Snehal , Devi Naorem Leimarembi , Mohanty Shubham , Kumar Rohit , Lopes Relestina , Akhter Nabila , Khare Naveen Kumar , Raghuram Gorantla V , Jadhav Vishalkumar , Shinde Sushma , Chandrani Pratik , Mittra Indraneel
Published: November 2024

Natural biological agents that can transform normal somatic cells into cancer stem cells, have not been identified. We earlier reported that cell free chromatin particles (cfChPs) that circulate in blood of cancer patients can horizontally transfer themselves to healthy cells to induce dsDNA breaks and inflammation. Here we show that a single cell clone D5 developed from NIH3T3 mouse fibroblast cells treated with cfChPs isolated from sera of cancer patients exhibited upregulation of stemness related transcription factors and surface markers, and the ability to form spheroids in appropriate culture medium. Transcriptome analysis revealed upregulation of cancer related pathways including invasion, metastasis and stemness. Subcutaneous inoculation into SCID mice resulted in development of malignant tumors which expressed all three germline markers. Our results suggest the cfChPs that circulate in blood of cancer patients are oncogenic and can transform susceptible somatic cells into cancer stem cells with the potential to promote metastasis.

Graphical Abstract
HumanTestisDB: A Comprehensive Atlas of Testicular Transcriptomics and Cellular Interactions
Authors: Wang Mengjie , Li Laihua , Cheng Qing , Zhang Hao , Liu Zhaode , Cui Yiqiang , Sha Jiahao , Yuan Yan
Published: November 2024

Advances in single-cell technology have enabled the detailed mapping of testicular cell transcriptomes, which is essential for understanding spermatogenesis. However, the fragmented nature of age-specific data from various literature sources has hindered comprehensive analysis. To overcome this, the Human Testis Database (HumanTestisDB) was developed, consolidating multiple human testicular sequencing datasets to address this limitation. Through extensive investigation, 38 unique cell types were identified, providing a detailed perspective on cellular variety. Furthermore, the database systematically categorizes samples into eight developmental stages, offering a structured framework to comprehend the temporal dynamics of testicular development. Each stage features comprehensive maps of cell-cell interactions, elucidating the complex communication network inside the testicular microenvironment at particular developmental stages. Moreover, by facilitating comparisons of interactions among various cell types at different stages, the database permits examining alterations that transpire during critical transitions in spermatogenesis. HumanTestisDB, available athttps://shalab.njmu.edu.cn/humantestisdb, offers vital insights into testicular transcriptomics and interactions, serving as an essential resource for advancing research in reproductive biology.
Enhanced extracellular matrix production provides protection to cell wall-deficientEscherichia coli
Authors: Crooijmans Marjolein E. , Willemse Joost J. , de Winde Johannes H. , Claessen Dennis
Published: November 2024

Escherichia coli-induced recurrent urinary tract infections (rUTIs) present a complicated challenge within the medical field. Most first-line antibiotic treatments primarily target cell-wall synthesis, which can lead to the formation of cell wall-deficient cells. To investigate how such cells can sustain, we obtained anE. colistrain capable of efficiently proliferating without its cell wall. One of the mutations lead to enhanced expression ofrcsA, encoding an important regulator involved in responding to cell envelope stress. RNA sequencing demonstrated an upregulation of genes associated with the production of extracellular matrix components, and this increased extracellular matrix production was confirmed using various imaging techniques. Remarkably, a subsequent long-term evolution experiment on this strain revealed a further augmentation in extracellular matrix production, coinciding with an enhanced ability to withstand harsh conditions. These findings demonstrate howE. coliadapts to loss of its cell wall and that an increased synthesis of matrix constituents can compensate for the protective properties of the cell wall.

Significance

The cell envelope is crucial for the protection ofE. coli. During bacterial infections such as urinary tract infections, antibiotics that disrupt cell wall synthesis are commonly prescribed. However, this can stimulate the formation of wall-deficient bacteria that are still able to proliferate despite the presence of these drugs. Our findings reveal that loss of the cell wall inE. coliincreases the production of extracellular matrix, a mechanism found in other unicellular organism too. This adaptation allows the bacteria to maintain their structural integrity and survive, highlighting a potential challenge in the successful treatment of bacterial infections.
The neural mechanisms of concept formation over time in music
Authors: Bonetti L. , Fernández-Rubio G. , Szabó S.A. , Carlomagno F. , Vuust P. , Kringelbach M.L. , Brattico E.
Published: November 2024

The interplay between abstraction, learning and memory is crucial for humans to interact with the environment. Abstraction in relation to concept formation is typically studied with Gestalt stimuli varying in their physical properties while maintaining abstract internal relations. The role of temporal integration in recognising abstract concepts has been, however, overlooked even in a sensory domain relying on temporal item succession such as audition. Here, we investigated the neural mechanisms underlying abstract concept generalisation over time using a musical adaptation of the old/new recognition paradigm. During magnetoencephalography (MEG) recordings, 108 participants categorised five-tone sequences as previously learned or novel or as musical or not. The sequences varied in pace, ranging from very fast (125 ms per sound) to very slow (5000 ms per sound). On the behavioural level, sequence duration exerted a substantial impact on the recognition and musicality judgments of the novel sequences. The differences in neural responses between memorised and novel sequences especially involved regions in the auditory cortex, medial cingulate gyrus and medial temporal lobe and were most pronounced at the highest musical pace, diminishing proportionally as the pace decreased. These findings contribute to our understanding of how the brain generalises and derives meaning from abstract concepts that unfold over time learned through the auditory system.
Dynamic Causal Modeling in Probabilistic Programming Languages
Authors: Baldy Nina , Woodman Marmaduke , Jirsa Viktor , Hashemi Meysam
Published: November 2024

Understanding the intricate dynamics of brain activities necessitates models that incorporate causality and nonlinearity. Dynamic Causal Modelling (DCM) presents a statistical framework that embraces causal relationships among brain regions and their responses to experimental manipulations, such as stimulation. In this study, we perform Bayesian inference on a neurobiologically plausible generative model that simulates event-related potentials observed in magne-to/encephalography data. This translates into probabilistic inference of latent and observed states of a system driven by input stimuli, described by a set of nonlinear ordinary differential equations (ODEs) and potentially correlated parameters. We provide a guideline for reliable inference in the presence of multimodality, which arises from parameter degeneracy, ultimately enhancing the predictive accuracy of neural dynamics. Solutions include optimizing the hyperparameters, leveraging initialization with prior information, and employing weighted stacking based on predictive accuracy. Moreover, we implement the inference and conduct comprehensive model comparison in several probabilistic programming languages to streamline the process and benchmark their efficiency. Our investigation shows that model inversion in DCM extends beyond variational approximation frameworks, demonstrating the effectiveness of gradient-based Markov Chain Monte Carlo methods. We illustrate the accuracy and efficiency of posterior estimation using a self-tuning variant of Hamiltonian Monte Carlo and the automatic Laplace approximation, effectively addressing parameter degeneracy challenges. This technical endeavor holds the potential to advance the inversion of state-space ODE models, and contribute to neuro-science research and applications in neuroimaging through automatic DCM.