bluuebunny/crossref_metadata_2025 huggingface dataset:
Adolescent alcohol consumption produces long term alterations in response inhibition and orbitofrontal-striatal activity in a sex-specific manner
Authors: McCane Aqilah M , Kronheim Lo , Moghaddam Bita
Published: January 2025

Alcohol use disorder (AUD) is strongly associated with initiation of drinking during adolescence. Little is known about neural mechanisms that produce the long-term detrimental effects of adolescent drinking. A critical feature of AUD is deficits in response inhibition, or the ability to withhold a reward-seeking response. Here we sought to determine if adolescent drinking affects response inhibition and encoding of neural events by the orbitofrontal cortex (OFC) and dorsomedial striatum (DMS), two regions critical for expression of response inhibition. Adolescent male and female rats were given access to alcohol for four hours a day for five consecutive days. Then, rats were tested in a cued response inhibition task as adolescents or adults while we recorded concomitantly from the OFC and DMS. Adolescent voluntary alcohol drinking impaired response inhibition and increased alcohol drinking in adult male but not female rats. Adolescent alcohol drinking also resulted in sex-specific effects on both unit firing and local field potential measures in the OFC and DMS during premature and correct actions. Collectively, these data suggest sex-specific effects of adolescent alcohol drinking on response inhibition and corresponding alterations in cortical-striatal circuitry.

Highlights

Moderate adolescent alcohol drinking disrupts adult response inhibition

Action encoding in the OFC and DMS changes after adolescent alcohol drinking

OFC-DMS connectivity is altered in males after adolescent alcohol drinking

Adolescent alcohol drinking promotes increased alcohol intake in adult males
Large Language Model Symptom Identification from Clinical Text: A Multi-Center Study
Authors: McMurry Andrew J. , Phelan Dylan , Dixon Brian E. , Geva Alon , Gottlieb Daniel , Jones James R. , Terry Michael , Taylor David , Callaway Hannah Grace , Mahoharan Sneha , Miller Timothy , Mandl Kenneth D.
Published: January 2025

Recognition of patient symptoms is core to medicine, research, and public health. We tested four large language models (LLMs) identifying 11 symptoms of infectious respiratory diseases from emergency department notes (N=204). Each LLM outperformed ICD-10-based identification. GPT-4 had highest tested accuracy, F1 score 91.4% vs. 45.1% for ICD-10. GPT-4 performance in an independent validation cohort (N=308) was even higher with an F1 score of 94.0%.
Psilocybin causes sex, time, and dose dependent alterations in brain signaling pathways
Authors: Barnett J. Hudson , Todd Kennedi T. , Benetatos Joseph , Rabichow Benjamin E. , Gibson Katelin A. , Olney Kimberly C. , Fryer John D.
Published: January 2025

Psilocybin is a psychedelic tryptamine that has emerged as a potential candidate for the treatment of a variety of conditions, including treatment resistant depression and post-traumatic stress disorder. Clinical trials which have assessed the efficacy of psilocybin for these conditions report a rapid and sustained improvement in patient- and clinician-rated depression scores. The established mechanism of action for psychedelics such as psilocybin is agonism of the serotonin 2A receptor (5HT2AR), however, the downstream events that mediate their therapeutic effects remain uncertain. As high doses of psychedelics are known to induce strong perceptual alterations, an additional outstanding question is whether subperceptual doses induce similar molecular effects as psychoactive dosages. Here, we report the first analysis of dose- and sex-dependent transcriptional changes in forebrains of female and male mice at 3 timepoints (8 hours, 24 hours, and 7 days) following a single administration of psilocybin at low (0.25 mg/kg) or high (1 mg/kg) doses. Grouped analysis of both sexes reveals dose- and time-dependent transcriptomic alterations. We report more rapid transcriptional changes and attenuation of such changes in females following a single low-dose relative to males treated identically. Females also responded more robustly to high-dose administration relative to males at 8 and 24 hours, with signal attenuation in both sexes by 7 days. A notable observation was the persistent transcriptional effect of low-dose psilocybin at 7 days, which outlasted high-dose changes, and which suggests that low doses may have prolonged biological effects. A myriad of pathways were altered depending on sex and timepoint, but common features included functions related to neuronal differentiation, neurogenesis, and changes in receptor signaling. These data reveal dose- and sex-dependent molecular effects of psilocybin and support previous studies demonstrating its effect on dendritogenesis. Given ongoing clinical interest in psilocybin for treating mental health disorders, our results suggest that these sexually divergent changes should be considered when weighing treatment strategies. Additional consideration should be given to temporal effects of low vs high dosages on gene transcription, especially when timing psilocybin with adjuvant cognitive behavioral therapy.
Synergistic Neural Circuits for Novelty and Goal-Directed Behavior
Authors: Elliott Blake L. , O’Brien Katey , Fain Matthew , Ellman Lauren M. , Murty Vishnu P.
Published: January 2025

The ability to adapt in a dynamic world relies on detecting, learning, and responding to environmental changes. The detection of novelty serves as a critical indicator of such changes, prompting mechanisms to detect and respond to goal-relevant information. However, neural regions that support novelty detection and learning (hippocampus), salience detection (dopaminergic midbrain [VTA]), and executive function (prefrontal cortex [PFC]) have yet to be described as a sequential process that unfolds over time. Using functional magnetic resonance imaging (fMRI) we explored interactions between the hippocampus, VTA, and PFC in humans performing a novelty-imbued target-detection task. Hippocampal novelty activation predicted subsequent goal-directed VTA activity, enhancing readiness to detect goal-relevant information. Concurrently, goal-directed PFC activation modulated VTA target activation, refining focus on behaviorally significant cues. These circuits function both synergistically and independently, promoting subsequent hippocampal activity. This work provides new insights into how distributed circuits coordinate to optimize adaptive behavior.
Integrating Support Vector Machines and Deep Learning Features for Oral Cancer Histopathology Analysis
Authors: Pham Tuan D.
Published: January 2025

This study introduces an approach to classifying histopathological images for detecting dys- plasia in oral cancer through the fusion of support vector machine (SVM) classifiers trained on deep learning features extracted from InceptionResNet-v2 and vision transformer (ViT) models. The classification of dysplasia, a critical indicator of oral cancer progression, is of- ten complicated by class imbalance, with a higher prevalence of dysplastic lesions compared to non-dysplastic cases. This research addresses this challenge by leveraging the comple- mentary strengths of the two models. The InceptionResNet-v2 model, paired with an SVM classifier, excels in identifying the presence of dysplasia, capturing fine-grained morphological features indicative of the condition. In contrast, the ViT-based SVM demonstrates superior performance in detecting the absence of dysplasia, effectively capturing global contextual information from the images. A fusion strategy was employed to combine these classifiers through class selection: the majority class (presence of dysplasia) was predicted using the InceptionResNet-v2-SVM, while the minority class (absence of dysplasia) was predicted us- ing the ViT-SVM. The fusion approach significantly outperformed individual models and other state-of-the-art methods, achieving superior balanced accuracy, sensitivity, precision, and area under the curve. This demonstrates its ability to handle class imbalance effectively while maintaining high diagnostic accuracy. The results highlight the potential of integrating deep learning feature extraction with SVM classifiers to improve classification performance in complex medical imaging tasks. This study underscores the value of combining comple- mentary classification strategies to address the challenges of class imbalance and improve diagnostic workflows.
Attention-based whole-slide image compression achieves pathologist-level pre-screening of multi-organ routine histopathology biopsies
Authors: Aswolinskiy Witali , van der Post Rachel S , Simons Michiel , Munari Enrico , Campora Michela , Baronchelli Carla , Ardighieri Laura , Vatrano Simona , Nagtegaal Iris , van der Laak Jeroen , Ciompi Francesco
Published: January 2025

Screening programs for early detection of cancer such as colorectal and cervical cancer have led to an increased demand for histopathological analysis of biopsies. Advanced image analysis with Deep Learning has shown the potential to automate cancer detection in digital pathology whole-slide images. Particularly, techniques of weakly supervised learning can achieve whole-slide image classification without the need for tedious, manual annotations, using only slide-level labels. Here, we used data from n=12,580 whole-slide images from n=9,141 tissue blocks to train and validate a deep learning approach based on Neural Image Compression with Attention (NIC-A) and show how it can be leveraged to pre-screen (pre)malignant lesions in colorectal and cervical biopsies and to analyze duodenal biopsies for celiac disease. Our NIC-A classifies normal tissue, low-grade dysplasia, high-grade dysplasia and cancer in colon and uterine cervix, and identifies celiac disease in duodenal biopsies. We validated NIC-A for colon and cervix against a panel of four and three pathologists, respectively, on cohorts from two European centers. We show that the proposed approach reaches pathologist-level performance at detecting and classifying abnormalities, suggesting its potential to assist pathologists in pre-screening workflows by reducing workload in digital pathology routine diagnostics.
Minimal Role of Hamstring Hypertrophy in Strength Transfer Between Nordic Hamstring and Stiff-Leg Deadlift: A Blinded Randomized Controlled Trial
Authors: Morin Titouan , Doguet Valentin , Nordez Antoine , Caillet Arnault , Lacourpaille Lilian
Published: January 2025

Strength transfer refers to the gain in strength in an untrained exercise resulting from training another exercise. This study aims to determine whether whole and selective hamstring hypertrophy influence the magnitude of strength transfer between the Nordic hamstring exercise (NHE) and the stiff-leg Deadlift (SDL). Using a blinded randomized controlled design, thirty-six resistance-untrained individuals were assigned either to a control group (CON), a NHE group, and a SDL group, the two resistance training programs being performed three times per week for nine weeks. Semimembranosus (SM), semitendinosus (ST), and biceps femoris (BF) hypertrophy was estimated from changes in the muscle volume. Strength transfer was measured by changes in the one-repetition maximum (1RM) of the non-trained exercise. After the resistance training programs, similar and significant whole hypertrophy was observed in both training groups (NHE: 11.4±6.5%, and SDL: 7.0±8.1%). The NHE group and SDL group, however, presented a selective hypertrophy of ST (24.3±10.8%) and SM (11.2±12.7%), respectively. Conversely, no difference in strength transfer was observed between the NHE group (10.7±8.5%) and the SDL group (20.7±15.0%) (p=0.06). Finally, non-significant correlations were found between strength transfer and both whole and selective hamstring hypertrophy (r≤0.3), except for the BF in SDL (r=0.6). We conclude that hamstring hypertrophy has a minimal role in strength transfer in resistance-untrained individuals. These findings suggest that, even in presence of hypertrophy, prevention and rehabilitation programs should include both Nordic hamstring and stiff-leg Deadlift exercises in the same training block to increase hamstring strength across both knee flexion-oriented and hip extension-oriented tasks.
Outer membrane tube formation byFrancisella novicidainvolves extensive envelope modifications and is linked with type VI secretion and alterations to the host phagosomal membrane
Authors: Rashid Maheen , Tachiyama Shoichi , Zhu Shiwei , Zhao Hang , McCaig William D. , Sun Jingchuan , Li Hulin , Liu Jun , Thanassi David G.
Published: January 2025

Francisella tularensisis a Gram-negative, intracellular pathogen that causes the zoonotic disease tularemia. Due to its ease of dissemination and high lethality,F. tularensisis classified as a Tier 1 Select Agent with potential for misuse as a bioweapon. The mechanisms by whichFrancisellareplicates intracellularly and interacts with the host during infection are not well understood.Francisellaproduces spherical outer membrane vesicles (OMV) and novel tubular extensions of its cell surface that are also released extracellularly. These OMV and outer membrane tubes (OMT) containFrancisellavirulence factors and are produced in response to amino acid starvation and during infection of macrophages. To investigate how the OMT are formed, we used cryogenic electron tomography to examine the modelFrancisellaspecies,F. novicida, duringin vitroculture and within the macrophage phagosome. OMT formation involved progressive alterations of the bacterial envelope, resulting in extensions of both the inner and outer membranes. A dynamic cytoplasmic structure was present at the base of the OMT that extended into the tubes during elongation, together with cytoplasmic material. OMT produced within the macrophage phagosome were associated with changes to the phagosomal membrane, suggesting a role in phagosomal escape. Consistent with this, using confocal microscopy, we observed colocalization of theFrancisellatype VI secretion system with the OMT, both within bacteria and in released tubular vesicles. These findings reveal the cellular transformations that occur during membrane tubulation byFrancisellaand provide insights into the function of membrane-derived structures during host-pathogen interactions.
Structural basis for collagen recognition by theStreptococcus pyogenesM3 protein and its involvement in biofilm
Authors: Wojnowska Marta , Wajima Takeaki , Yelland Tamas , Ludewig Hannes , Hagan Robert M. , Watt Grant , Hamaia Samir W. , Bihan Dominique , Malcor Jean-Daniel , Bonna Arkadiusz , Bergsten Helena , Svensson Mattias , Oppegaard Oddvar , Skrede Steinar , Arnell Per , Hyldegaard Ole , Farndale Richard W. , Norrby-Teglund Anna , Schwarz-Linek Ulrich
Published: January 2025

The M protein is an essential virulence factor ofStreptococcus pyogenes, or group A streptococci (GAS), one of the most common and dangerous human pathogens. Molecular and functional characterization of M protein variants and their interactions with host components is crucial for understanding streptococcal pathogenesis and vaccine development. The M3 protein is produced by the prevalentemm3 GAS serotype, which is frequently associated with severe invasive diseases. Here we characterize the interaction of M3 with human collagens through detailed structural and biochemical binding analysis. High-resolution structures of the N-terminal M3 domain in the free state as well as bound to a collagen peptide derived from the Collagen Ligands Collection reveal a novel T-shaped protein fold that presents binding sites complementing the characteristic topology of collagen triple helices. The structure of the M3/collagen peptide complex explains howemm3 GAS and related streptococci, such as the emerging human pathogenStreptococcus dysgalactiaesubsp.equisimilis, can target collagens to enable colonization of various tissues. In line with this, we demonstrate that the M3/collagen interaction promotes enhanced biofilm formation ofemm3 GAS in anemmtype specific manner, which can be inhibited with the recombinant M3 N-terminal domain fragment. Further,emm3 GAS, but not anemm1 strain, are shown to colocalize with collagen in tissue biopsies from patients with necrotizing soft tissue infections, where GAS biofilms are common. This observation is reproduced in organotypic skin models. Together, these data provide detailed molecular insights into an important streptococcal virulence mechanism with implications for the understanding of invasive infections, strategies for treating biofilm and M-protein based vaccine design.
Differential roles of NaV1.2 and NaV1.6 in neocortical pyramidal cell excitability
Authors: Garcia Joshua D. , Wang Chenyu , Banks Emmie , Fenton Timothy , DeKeyser Jean-Marc , Abramova Tatiana V. , George Alfred L. , Ben-Shalom Roy , Hackos David H. , Bender Kevin J.
Published: January 2025

Mature neocortical pyramidal cells functionally express two sodium channel (NaV) isoforms: NaV1.2 and NaV1.6. These isoforms are differentially localized to pyramidal cell compartments, and as such are thought to contribute to different aspects of neuronal excitability. But determining their precise roles in pyramidal cell excitability has been hampered by a lack of tools that allow for selective, acute block of each isoform individually. Here, we leveraged aryl sulfonamide-based molecule (ASC) inhibitors of NaVchannels that exhibit state-dependent block of both NaV1.2 and NaV1.6, along with knock-in mice with changes in NaV1.2 or NaV1.6 structure that prevents ASC binding. This allowed for acute, potent, and reversible block of individual isoforms that permitted dissection of the unique contributions of NaV1.2 and NaV1.6 in pyramidal cell excitability. Remarkably, block of each isoform had contrasting—and in some situations, opposing—effects on neuronal action potential output, with NaV1.6 block decreasing and NaV1.2 block increasing output. Thus, NaVisoforms have unique roles in regulating different aspects of pyramidal cell excitability, and our work may help guide development of therapeutics designed to temper hyperexcitability through selective NaVisoform blockade.
Targeting de novo cholesterol synthesis in rhabdomyosarcoma induces cell cycle arrest and triggers apoptosis through ER stress-mediated pathways
Authors: Gizaw Nebeyu Yosef , Kolari Kalle , Alitalo Kari , Kivelä Riikka
Published: January 2025

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, but the outcomes of high-grade RMS patients remain poor, underscoring the critical need for novel therapeutic strategies. Although metabolic pathways in RMS are incompletely characterized, emerging evidence suggests that metabolic adaptations in RMS resemble those in other malignancies. Here, we identify elevated cholesterol biosynthesis driven by the PROX1 transcription factor as a defining feature of RMS. Our findings demonstrate that the cholesterol biosynthesis pathway is essential for RMS cell growth, proliferation, and survival. Blocking this pathway through genetic or pharmacological inhibition of the key cholesterol biosynthesis enzymes significantly impairs RMS cell proliferation, halts cell cycle progression, and triggers apoptosis through activation of endoplasmic reticulum stress pathways. We furthermore validate the critical role of cholesterol biosynthesis in RMS progression in tumor xenograft models, demonstrating that silencing of the DHCR7 gene significantly suppresses tumor growth. Transcriptomic analysis revealed widespread downregulation of cell cycle-related genes following DHCR7 silencing, further supporting the role of cholesterol metabolism in cell cycle regulation. These results highlight the vulnerability of RMS cells to cholesterol biosynthesis inhibition and suggest that targeting this metabolic pathway as a promising therapeutic approach for improving RMS outcomes. Our findings provide a rationale for the development of novel therapies targeted to cholesterol biosynthesis in this aggressive cancer.

Significance

This study reveals that targeting cholesterol biosynthesis in rhabdomyosarcoma induces ER-stress, apoptosis and cell cycle arrest, highlighting a potential therapeutic strategy for treating this aggressive pediatric cancer.
Heat stress drives transcription of LTR retrotransposons in the regenerative flatwormMacrostomum lignano
Authors: Ustyantsev Kirill , Mouton Stijn , Biryukov Mikhail , Wudarski Jakub , Glazenburg Lisa , Berezikov Eugene
Published: January 2025

The evolutionary arms race between transposable elements (TEs) and their hosts contributes to genomic complexity. As TEs mobilization is deleterious for individual cells and organisms, their activity is restricted. During stress, TEs can be reactivated; however, the exact mechanisms vary. We discovered that in the flatwormMacrostomum lignano, LTR retrotransposons hijack the heat shock response pathway to boost their transcription at elevated temperatures. While it has been well-described in cruciferous plants, this is the first report of this mechanism in animal LTR retrotransposons. Our results suggest a convergent evolution of the heat stress response in LTR retrotransposons from animals and plants.
Remote-Controlled Wireless Bioelectronics for Fluoxetine Therapy to Promote Wound Healing in a Porcine Model
Authors: Li Houpu , Asefifeyzabadi Narges , Schorger Kaelan , Baniya Prabhat , Tebyani Maryam , Barbee Alexie , Yang Hsin-ya , Hee Wan Shen , Gallegos Anthony , Zhu Kan , Recendez Cynthia , Luka George , Kim Sujung , Devarajan Koushik , Nguyen Tiffany , Figuerres Sydnie , Franco Celeste , Aslankoohi Elham , Zhao Min , Isseroff Roslyn Rivkah , Teodorescu Mircea , Rolandi Marco
Published: January 2025

Wound healing presents a significant challenge in biomedical science, requiring precise therapeutic delivery and real-time monitoring. Bioelectronic systems offer a promising solution but remain largely unexplored for wound care, particularly in large animal models that reflect human healing dynamics. This study introduces a remote controlled wireless bioelectronic platform equipped with an iontophoretic pump to deliver fluoxetine, a selective serotonin reuptake inhibitor that promotes wound repair. In vitro and ex-vivo testing validated efficient on demand fluoxetine delivery. In vivo experiments in a porcine wound model demonstrated clear therapeutic efficacy over 3-day and 7-day periods. The system enhanced healing outcomes, increasing re-epithelialization by 37% (H&E staining), reducing the M1/M2 macrophage ratio by 33%, and stimulating neuronal growth at the wound site. This bioelectronic platform delivers fluoxetine in a controlled, remotely-controlled manner while allowing for wound direct wound imaging that can be used to monitor wound healing progress. Additionally, it allows precise dose and temporal delivery of treatment to enhance the outcome of future large animal wound healing studies.
An integrated and scalable rodent cage system enabling continuous computer vision-based behavioral analysis and AI-enhanced digital biomarker development
Authors: Robertson Timothy L. , Ellis Michael , Bratcher-Petersen Natalie , Ruidiaz Manuel E. , Harada Kevin , Toburen Debra , Oberhauser Juan Pablo , Grzenda Daniel , Peltier Nicole E. , Raza Madison , Benway Jan , Kiros Jamie , Kumar Vivek
Published: January 2025

1

Home cage monitoring enables continuous observation of animals in familiar environments. It has large utility in preclinical testing, mechanistic studies, animal husbandry, and the general practice of the Replacement, Reduction, Refinement (3R) principles. Despite its acknowledged utility, the broad adoption of home cage monitoring has not been broadly adopted. This is mainly due to the complexity of the tasks that must be solved to have a successful system that includes hardware and sensor development, data management, machine vision expertise, behavioral expertise, support, and user training. Here, we describe the Digital In Vivo System (DIV Sys), a modern end-to-end system for video-based rodent home cage monitoring. The DIV Sys consists of a cloud-based study design, monitoring, display, and visualization app (DIV App), local hardware for data acquisition cages (DAX), a machine learning model for tracking mice (mHydraNet) optimized for speed and accuracy, a study display and visualization app, and an advanced behavior quantification workbench (DIV Data). The platform seamlessly manages terabytes of video data in the cloud and is built around enterprise-level security and data standards. Collaborative tools enable teams across geographical locations to work together. As a demonstration of its utility, we used DIV Sys to analyze over a century of mouse videos across multiple geographic locations. We also characterized home cage behavior of 8 mouse strains and carried out customized video analysis. Together, we present a scalable home cage monitoring system for advanced behavior quantification for the rodent research community.
Known unknowns and model selection in ecological evidence synthesis
Authors: Blowes Shane A.
Published: January 2025

Quantitative evidence synthesis is a prominent path towards generality in ecology. Generality is typically discussed in terms of central tendencies, such as an average effect across a compilation of studies, and the role of heterogeneity for advancing understanding is not as well developed. Heterogeneity quantifies the consistency of ecological effects across studies, and between-study heterogeneity is typically assumed as constant. Here, I relax the assumption of constant heterogeneity, and show how cross validation can further the generality goals of quantitative evidence synthesis. First, I examine scale-dependent heterogeneity for a meta-analysis of plant native-exotic species richness relationships, quantifying the relationships among unexplained effect size variation, spatial grain and extent. Second, I examine relationships among patch size, study-level covariates and unexplained variation in species richness using a database of fragmentation studies. Both case studies show that relaxing the assumption of constant unexplained variation can provide a more detailed description of available evidence - for example, where effects can be transferred with more or less certainty. Moreover, cross validation shows for both case studies that assuming constant heterogeneity can constrain model predictive performance, and in particular, the ability of model predictions to generalise to new studies.
Detection and quantification of hepatitis A virus titers from wastewater in South Africa and comparison with clinical data from the National Surveillance Database
Authors: Subramoney Kathleen , Gwala Sipho , Phalane Emmanuel , Macheke Mokgaetji , Singh Natasha , Mangena Thabo , Ndlovu Nkosenhle , Msomi Nosihle , Maposa Sibonginkosi , Sankar Chenoa , Els Fiona , Ntuli Phindile , Motloung Mantshali , Mabasa Victor , McCarthy Kerrigan , Yousif Mukhlid
Published: January 2025

Wastewater surveillance is useful for monitoring the prevalence of hepatitis A virus (HAV). We developed and optimized HAV detection and quantification methods for wastewater samples, and applied them to samples collected through a national wastewater surveillance program. Previously identified 5’-untranslated region-targeting primers and probes were used to develop the assay. Serial dilutions of HAV-positive clinical samples were used to validate and determine limits of quantification (LOQ). Retrospective testing of weekly wastewater samples collected through the SARS-CoV-2 wastewater surveillance program at 26 sites in Gauteng (August 2021 to March 2024) were undertaken using ultrafiltration-based concentration, and nucleic acids were extracted using the KingFisher Flex purification system with a wastewater isolation kit. A digital PCR assay was used for HAV detection and quantification (as genome copies/μL). Clinical data from the Surveillance Database Warehouse of the National Health Laboratory Service were compared with wastewater data, epidemiological week-wise and district-wise, to determine correlations between the datasets. Based on the validation results, one partition on the digital PCR (dPCR) platform was equivalent to an LOQ of 0.4 genome copies/μL. In total, 2013 wastewater samples were tested, of which 349 were positive for HAV (17.3%), wherein the majority (304, 87.1%) had the lowest HAV concentration (2.0-2.7 gc/μL, 1-5 partitions), followed by 20 samples (5.7%) with concentrations of 2.8-3.0 gc/μL (6-10 partitions). HAV was detected in 17.1% (241/1170) of the Gauteng samples, and a 26.1% correlation between anti-HAV IgM in clinical samples and HAV in wastewater samples was detected. We successfully developed and optimized a dPCR method to detect and quantify HAV in wastewater samples, and determined its LOQ. Further analysis is required to compare the wastewater data with clinical surveillance data to facilitate appropriate interpretation of the results.
DifferentiatingPeromyscus leucopusbone marrow-derived macrophages for characterization of responses toBorrelia burgdorferiand lipopolysaccharide
Authors: Wells Christopher C. , Petnicki-Ocwieja Tanja , Tan Shumin , Bunnell Stephen C. , Telford Sam R. , Hu Linden T. , Bourgeois Jeffrey S.
Published: January 2025

Currently, most tools utilized in host-pathogen interaction studies depend on the use of human or mouse (Mus musculus) cells and tissues. While these species have led to countless breakthroughs in our understanding of infectious disease, there are undoubtably important biological processes that are missed by limiting studies to these two vertebrate species. For instance, it is well-established that a common North American deermouse,Peromyscus leucopus, has unique interactions with microbes, which likely shape its ability to serve as a critical reservoir for numerous zoonotic pathogens, including a Lyme disease spirochete,Borrelia burgdorferi. In this work, we expand the immunological toolkit to studyP. leucopusbiology by performing the first differentiation of deermouse bone marrow to macrophages usingP. leucopusM-CSF producing HEK293T cells. We find thatP. leucopusBMDMs generated through this method behave broadly very similarly to C57BL/6J macrophages generated with L-929 supernatant, though RNA sequencing revealed modest differences in transcriptomic responses toB. burgdorferiand lipopolysaccharide. In particular, differences inIl10induction and caspase expression were observed between the species.
Role of the clathrin adaptor AP-1 in cell wall integrity and virulence factor secretion in the plant pathogen Botrytis cinerea
Authors: Calvar Glen , Hamandjian Adrien , Crumière Mélanie , Dupuy Jean-William , Moriscot Christine , Gallet Benoit , Gillet François-Xavier , de Vallée Amélie , Rascle Christine , Choquer Mathias , Bruel Christophe , Poussereau Nathalie
Published: January 2025

In filamentous fungi, the secretory pathway plays a key role in polarized growth through the dlivery of vesicles carrying enzymes and components necessary to cell wall synthesis and fungal nutrition. In pathogenic species, it moreover supports the delivery of virulence factors. In metazoa and yeast, the formation of several secretory vesicles occurs at the Golgi apparatus and endosomes through a complex and partially characterized machinery involving clathrin and adaptor proteins such as AP-1. Data on the molecular mechanisms controlling this process are still scarce in filamentous fungi. Using a mutant under-expressing the β-subunit of the heterotetrameric AP-1 clathrin adaptor complex, we investigated the importance of intracellular vesicle trafficking in the phytopathogenic fungusBotrytis cinerea. The mutant exhibits pleiotropic developmental defects, an abnormality in cell wall integrity associated with a mislocalization of the plasma-membrane chitine synthase BcCHSIIIa. Moreover, the AP-1 mutant is affected in the secretion of hydrolytic enzymes and displays a severe defect in pathogenicity. This study confirms the importance of the AP1/clathrin machinery in the secretory pathway ofB. cinerea.
Kun-peng: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all
Authors: Chen Qiong , Zhang Boliang , Peng Chen , Huang Jiajun , Shen Xiaotao , Jiang Chao
Published: January 2025

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchmarked on mock communities (Amos HiLo, Mixed, and NIST) against Kraken2, Centrifuge, and Sylph. Kun-peng matched Sylph, achieving the highest precision and lowest false-positive rates while demonstrating superior time and memory efficiency among all tested tools. Furthermore, Kun-peng’s efficient database architecture enables the practical utilization of large-scale reference databases that were previously computationally prohibitive. In comprehensive testing across 586 air, water, soil, and human metagenomic samples using an expansive pan-domain database (204,477 genomes, 4.3TB), Kun-peng classified 69.78-94.29% of reads, achieving 38-43% higher classification rates than Kraken2 with the standard database. Unexpectedly, Sylph failed to classify any reads in air samples and left > 99.85% of reads unclassified in water and soil samples. In terms of computational efficiency, Kun-peng processed each sample in 0.2∼11.2 minutes using only 4.0∼35.4GB peak memory. Remarkably, these processing times were comparable to Kraken2 using the standard database (81GB, 5% of the pan-domain database). Memory-wise, Kun-peng required only 35.4GB peak memory with the pan-domain database, representing a 473-fold reduction compared to Kraken2. When compared to Sylph, Kun-peng processes samples up to 46.3 times faster while using up to 20.6 times less memory. Overall, Kun-peng offers an ultra-memory-efficient, fast, and accurate solution for pan-domain metagenomic classifications.
Frequency-tagged fMRI: A platform for fine-grained spatiotemporal analysis of cortical function
Authors: Ngo Geoffrey N. , Rafeh Reebal W. , Muller Lyle E. , Khan Ali R. , Menon Ravi S. , Schmitz Taylor W. , Mur Marieke
Published: January 2025

Frequency tagging with functional MRI (ft-fMRI) enables precise mapping of neural dynamics by synchronizing oscillatory stimuli to stimulus-driven blood-oxygen-level-dependent (BOLD) responses. We developed and validated a dual-frequency tagging protocol to dissociate fundamental, multiplexed, and nonlinear intermodulation frequency responses across the human visual cortex at high spatial resolution. Using 3T and 7T fMRI, we reliably detected frequency-tagged BOLD responses at the level of individual vertices, revealing fine-grained cortical topographies and robust temporal synchronization to driving frequencies. Multiplexed responses, encoding multiple frequencies simultaneously, and nonlinear intermodulation components, were spatially dissociable and exhibited reproducible dynamics within and across experimental sessions. These findings establish ft-fMRI as a powerful tool for investigating fine-grained cortical computations, previously inaccessible to traditional fMRI. By bridging the spatiotemporal resolution gap between electrophysiology and fMRI, ft-fMRI provides a versatile platform for studying perception, attention, and multisensory integration in health and disease.
Retinotopic mapping data permit accurate matching of participants across different datasets
Authors: Schwarzkopf D. Samuel , Morgan Catherine A. , Roberts Reece P. , Dakin Steven C.
Published: January 2025

Public sharing of neuroimaging data is becoming increasingly common for the advancement and validation of scientific research. However, this sharing poses challenges regarding privacy and data safety, and associated questions about the ownership of research data. Here we show that a simple pattern correlation algorithm can match nominally deidentified participants across two separate functional magnetic resonance imaging (fMRI) experiments. This re-identification procedure is effective despite functional maps being spatially warped to a common template brain. This work highlights the need for appropriate safeguards against possible misuse of shared neuroimaging data.
A Deep Learning Approach for Culture-Free Bacterial Meningitis Diagnosis and ICU Outcome Prediction
Authors: Chen Rui , Cai Yu , Zhang Sitong , Huo Zirong , Song Mingming , Li Wenqing , Yang Dongyan , Zhang Xi
Published: January 2025

Background

Cerebrospinal fluid (CSF) culture is essential for diagnosing neuroinfectious diseases such as meningitis, where clinical symptoms alone cannot reliably confirm the diagnosis or differentiate bacterial from non-bacterial causes. However, traditional CSF cultures are time-consuming and often lack sensitivity, particularly after antibiotic treatment. This study applies machine learning to expedite diagnosis and enhance the accuracy of outcome predictions for patients with suspected neuroinfectious diseases.

Methods

Training and validation datasets were derived from the MIMIC-III and MIMIC-IV databases, respectively, focusing on patients undergoing CSF bacterial culture. CNN, FCN, and RF classifiers were trained using integrated test results and clinical data to predict CSF culture outcomes. CSF culture result labels were redefined based on clinical guidelines for bacterial meningitis diagnosis to address false negatives caused by prior antibiotic treatment. Unstructured text data were encoded using BioBERT embeddings, combined with structured laboratory data, and processed through CNN, FCN, or LSTM layers to develop multimodal neural networks for predicting clinical outcomes. Class imbalance was mitigated, and text data sources were optimized for interchangeability.

Results

For CSF culture diagnostic predictions, 9261 cases were analyzed, including 245 initial culture-positive and 9016 culture-negative results. Label correction increased the positive cases to 404, with 8857 negatives. The FCN model showed the best performance post-label correction, achieving an AUC of 0.891, accuracy of 0.867, sensitivity of 0.833, and specificity of 0.868. In survival prediction analysis (5795 cases: 5500 survivors, 295 in-hospital deaths), the optimized multimodal neural network with CNN layers achieved an AUC of 0.933, accuracy of 0.850, sensitivity of 0.847, and specificity of 0.850. Models trained without text data showed diminished performance. Independent validation datasets confirmed the model’s robustness, yielding comparable results.

Conclusions

The multimodal machine learning models demonstrated strong efficacy in predicting CSF culture outcomes, supporting the diagnosis of bacterial meningitis, and in forecasting ICU survival outcomes. Integrating these models into a clinical decision support system could empower ICU clinicians with timely, data-driven insights, enabling culture-independent diagnostics and enhancing patient management.
Silaffins-Driven Genetic Engineering of Diatom Cell Walls: Insight into Biosilica Morphology and Nanomaterial Design
Authors: Qiao Tengsheng , Wang Lulu , Zhao Yan , Li Yun , Yang Guanpin , Zhu Baohua , Pan Kehou
Published: January 2025

Diatoms synthesize silica cell walls (frustules) with genetically encoded morphologies, ranging from nanopatterns to micropatterns, that far exceed current synthetic chemistry. Silaffins, a family of phosphoproteins undergoing complex post-translational modifications, have been isolated from frustules and shown to facilitate and regulate biosilica formationin vitrowith long-chain polyamines. However, their particular role in frustule morphogenesis and functionality remains unclear. In this study, functions of two representativesilaffins,TpSil1andTpSil3, were investigated in the model organismThalassiosira pseudonanausing gene overexpression and CRISPR/Cas9-mediated knockout approaches. Due to high sequence homology,TpSil2was concurrently disrupted inTpSil1knockout strains, while the homozygous knockout ofTpSil3proved to be lethal. Quantitative morphological analysis revealed distinct yet complementary roles: TpSil3 governs both microscale overall size and mesoscale features, including macropore (fultoportula) density and mesopore (cribrum pore) pattern, whereas TpSil1/2 exclusively contribute to macropore morphogenesis and mesopore density. Overexpression ofsilaffinsincreased silica deposition, while knockouts exhibited reduced silicification but enhanced cell growth and photosynthetic efficiency. Furthermore, these genetic modifications significantly influenced the physicochemical and optical properties of bulk frustules, potentially enhancing the hemostatic, catalytic and photonic performances, thereby positioning them as versatile candidates for a wide range of biotechnological and industrial applications. Collectively, our findings elucidate the distinct roles ofTpSil1/2andTpSil3in diatom physiology and frustule morphology, highlighting a promising pathway for engineering nanostructured silica materials with tailored properties through synthetic biology.
RGB color indices as proxy for symbiont cell density and chlorophyll content during coral bleaching
Authors: Ferrara Erik Francesco , Bauer Lavinia , Puntin Giulia , Bautz Friederike , Celayir Sibel , Do Marie-Sa , Eck Frederik , Heider Melissa , Wissel Pia , Arnold Angelina , Wilke Thomas , Reichert Jessica , Ziegler Maren
Published: January 2025

Coral bleaching, the breakdown of the symbiosis between the coral host and endosymbiotic microalgae, is the main cause of widespread coral reef degradation. Current methods for assessing coral health based on visual appearance, such as the use of color reference cards, are limited by subjective human color perception and low resolution. Digital photography with RGB (Red, Green, Blue) color channel analyses offers a fast, non-invasive, and standardized alternative to estimate physiological parameters. However, the link between coral color and physiological parameters during bleaching may vary depending on the type of stressor. While such approaches are extensively used in plant studies, their application in estimating Symbiodiniaceae cell density and chlorophyll content in corals requires further attention. In this study, we analyzed the correlation between Symbiodiniaceae cell density and chlorophyll content across three coral species (Acropora muricata,Pocillopora verrucosa,andStylophora pistillata) with 19 color indices derived from the RGB channels currently established as predictors of chlorophyll content in plants. Corals were exposed to three bleaching conditions (acute short-term and chronic long-term heat stress and menthol bleaching) to identify the best color indices for assessing coral health through image analysis. We found that the Red index had the strongest linear correlation with symbiont cell density and chlorophyll content across species (R2up to 0.97), so that relative changes in this color index can be directly interpreted as corresponding changes in tissue parameters. To train a model that predicts symbiont densities of a distinct sample set using the Red index, we found 10 to 12 samples to be sufficient to achieve an accuracy of > 95 % of the models trained on the full datasets. This research contributes to improved image analysis as a reliable and non-invasive tool for monitoring, by providing guidelines for a systematic use of RGB data to interpret coral health.
Performance of eDNA capture methods for monitoring fish biodiversity in a hyper-tidal estuary
Authors: Jackman Jake M , Sales Naiara Guimaraes , Benvenuto Chiara , Drewitt Andrea , Wolfenden Andrew , Robins Peter , Coscia Ilaria , McDevitt Allan
Published: January 2025

Environmental DNA (eDNA) has become an established and efficient method for monitoring biodiversity in aquatic systems. However, there is a need to compare and standardise sampling methods across ecosystem types, particularly complex ecosystems such as estuaries where unique challenges for monitoring fish populations are present due to fluctuating environmental factors. Here, we compare fish biodiversity metrics obtained from eDNA metabarcoding data using four different eDNA filtering methods: three manual filtering methods with different pore sizes (0.45, 1.2 and 5 µm) and a newly established passive method, the metaprobe. The study was applied across a salinity gradient in a hyper-tidal estuarine ecosystem. Overall, 44 fish species were detected across the four methods used. The 0.45 µm filter recovered the highest richness (39 species), then the metaprobe method (35), followed by the 1.2 µm (34) and 5 µm (33) filters. Filter performance between salinity gradients revealed that the 0.45 µm and the 1.2 µm methods recovered the highest species richness across all sampled zones. The 0.45 µm also had the most consistent detection probabilities using representative species from each zone. While the 0.45 µm method appeared to be the optimal method, each of the methods can be considered as a viable and comparable option for biomonitoring in dynamic ecosystems such as estuaries and rivers. In particular, the passive metaprobe (used in a freshwater system for the first time here) performed well in comparison to the manual filtering methods despite a short deployment time. This study provides critical insights for optimising fish biodiversity assessments using eDNA metabarcoding in estuarine ecosystems, providing a valuable framework for future monitoring efforts in similar systems worldwide.