Browse Articles

The Impact of Age-Associated Lymph Node Microenvironment Changes On Dendritic Cell:CD4+ T Cell Interactions During Germinal Center Initiation 2255775

Abstract Introduction Germinal center (GC) formation in lymph node (LN) follicles is essential for generating high-affinity antibodies following infection or vaccination. This response in aging is impaired, resulting in smaller and fewer GCs that generate less abundant antibodies with reduced antigen affinities as compared to young adults. Early GC formation depends on dendritic cell (DC)-mediated activation of naïve CD4+ T cells, which differentiate into CD4+ T follicular helper (TFH) cells that drive B cell responses. Antigen-specific TFH induction is mediated by type 2 conventional DCs, which localize to the T:B cell interfollicular boundaries. Live-cell 2-photon microscopy has previously revealed that T cell priming by DCs occurs in distinct phases of lasting versus intermittent interactions, though the quality and duration of DC:CD4+ T cell interactions has not been characterized in aged hosts. We hypothesize that age-associated alterations in LN microenvironments disrupt these interactions, leading to impaired GC formation. Methods Young and aged mice were subcutaneously immunized in the flank with nitrophenyl hapten conjugated to ovalbumin in alum adjuvant, and naïve OT-II CD4+ T cells were adoptively transferred via retroorbital injection. 48 hours after immunization, draining LNs and naïve controls were analyzed by multiplex immunofluorescence microscopy to map the spatial organization of DCs and stromal elements. Results Structural mapping showed disrupted alignment of stromal networks, suggesting impaired spatial compartmentalization of the aged microenvironment. Moreover, within aged immunized mice the localization of DCs within the interfollicular region and T:B cell zone borders of the LNs were altered. Conclusion Our findings suggest age-associated disruption of the LN microarchitecture diminishes the quality of DC:CD4+ T cell interactions essential for initiating GCs. These insights highlight tissue microenvironmental remodeling as a key contributor to age-related immune decline. Funding Source NIH Topic Categories Lymphocyte Differentiation and Peripheral Maintenance (LYM)

Uncovering a role of aryl hydrocarbon receptor (AhR) in the differentiation dynamics of human B cells 2258222

Abstract Introduction Upon activation, B cells differentiate into plasmablasts (PBs), germinal center (GC) B cells, and memory B cells (MBCs). Key transcription factors, including BACH2, IRF4, IRF8, PRDM1, and BCL6, play critical roles in regulating these distinct fates. Methods Using single-cell profiling, we identified the aryl hydrocarbon receptor (AhR) and its target genes as inducibly and differentially expressed in activated human B cells differentiating into pre-GC and PB populations. Additionally, by intersecting open chromatin regions with AhR chromatin immunoprecipitation sequencing datasets, we predicted AhR molecular activity in activated and pre-GC B cells. To investigate the function of AhR, we modulated its activity using agonist and antagonist ligands, as well as CRISPR editing, in the human B cell culture system. Results Activation of AhR with three different ligands (FICZ, Kynurenine, and TCDD) promoted PB generation, while AhR antagonism had the opposite effect. Furthermore, CRISPR editing revealed that FICZ-induced PB differentiation was AhR-dependent and could be further enhanced by knocking down GC-promoting transcription factors (IRF8, BATF, or PAX5). Multiome analysis reveals that AhR activation rapidly induces a PB gene expression module within activated B cells. Conclusion These findings identify AhR as a previously unrecognized regulator of human B cell fate dynamics and PB differentiation. This pathway represents a mechanism by which dietary, commensal, or environmental AhR ligands can modulate humoral B cell responses. These insights have broad implications for understanding vaccine efficacy and developing therapeutic strategies to enhance or suppress PB generation. Funding Source NHGRI UO1HG012041 NIAID R01AI170108 Topic Categories Immune Response Regulation: Molecular Mechanisms (IRM)

eQTL analysis of SLE B cells reveals functional genomic regulatory variation associated with autoimmunity 2253669

Abstract Introduction Systemic lupus erythematosus (SLE) is a complex and heterogenous autoimmune disease characterized by the expansion of extrafollicular pathogenic B cells derived from newly activated naïve cells. Ultimately the production and deposition of autoantibodies results in damage to multiple organ systems. Therefore, further defining the molecular drivers of extrafollicular B cell differentiation and antibody production is important to ultimately prevent and treat SLE. Genetic variants uncovered by GWAS studies only explain a portion of SLE heritability, with most polymorphisms located in non-coding regulatory elements. Expression Quantitative Trait Loci (eQTL) approaches functionally associate non-coding variant alleles with gene expression and cell type abundance changes to facilitate functional annotation of disease associated genetic variation and uncover alterations in gene regulatory networks that contribute to autoimmunity. However, no eQTL study to date has focused on SLE individuals of African ancestry (AFR), despite the higher disease prevalence and burden in this group. Methods Here, we integrated whole genome sequencing with scRNA-seq and scATAC-seq data on B cells obtained from 58 individuals with SLE, including 46 AFR, 7 European ancestry, and 5 healthy controls. Results We identified thousands of eQTLs where genetic variation correlated with changes in transcription in B cells. Analysis of B cell subsets including extrafollicular, naive, and switched memory cells, revealed eQTLs that were unique to distinct cell types and may promote the altered differentiation profile of B cells observed in SLE. Conclusion These analyses provide risk alleles and cellular pathways specific to SLE that may contribute to pathogenic B cell responses in autoimmunity. Funding Source NIH/NIAID P01 AI125180 and LRA GTSA 821168 Topic Categories Immune Mechanisms of Human Disease (HUM)

Similar systemic but differential vaginal soluble mediator levels in injectable contraceptive users and impact of study site and STI prevalence

Anti-obesity effects of heat-killed Apilactobacillus kosoi, a FLAB-derived postbiotic, in high-fat diet-induced obese mice

Abstract Apilactobacillus kosoi is a fructophilic lactic acid bacterium (FLAB) isolated from a Japanese fermented food, kôso liquid. As a postbiotic, heat-killed A. kosoi has previously been reported to exhibit immunomodulatory and antitumor activities; however, its effects on obesity remain unknown. In this study, we investigated the anti-obesity effects of heat-killed A. kosoi in a high-fat diet-induced obese mouse model. Heat-killed A. kosoi significantly suppressed body weight gain and energy intake, with a tendency toward reduced fat accumulation, without causing adverse changes in blood biochemical parameters. It also partially alleviated the high-fat diet-induced increase in the relative abundance of Bacillota, mitigated the reduction in serum IgA levels, and restored fecal concentrations of both formate and butyrate. Modest alterations in the expression of hepatic genes involved in fatty acid, cholesterol, and bile acid metabolism further suggested a contribution of hepatic metabolic regulation to its anti-obesity effects. Collectively, these findings demonstrate that heat-killed A. kosoi is the first FLAB-derived postbiotic reported to exhibit anti-obesity activity. The restoration of formate, together with butyrate, highlights a potential role for microbial metabolites in host energy metabolism and expands current understanding of the physiological functions of FLAB-derived postbiotics.

Green synthesis of gold nanoparticles from Lilium ciliatum leaf and flower extracts through phenolic composition, process optimization, and colloidal properties

Design and evaluation of IntuNav and EdgeNav navigation methods in desktop multi-browser virtual environments

Abstract Desktop virtual reality (Desktop VR) is increasingly used as a software platform for information exploration, productivity, and learning, particularly when immersive head-mounted displays are not feasible. In these systems, navigation serves as a fundamental software interaction mechanism that directly influences usability, efficiency, and technology acceptance. This research empirically evaluates two mouse-only, first-person navigation techniques (IntuNav and EdgeNav) implemented as software-level interaction models within a desktop Multi-Browser virtual environment. A controlled between-subjects experiment involved 111 participants engaging in structured information-seeking and content-creation tasks that reflect real-world software workflows. Objective performance metrics were gathered alongside validated subjective measures, including the System Usability Scale (SUS), Technology Acceptance Model (TAM), Igroup Presence Questionnaire (IPQ), and the raw NASA Task Load Index (NASA-TLX). The findings indicate that IntuNav markedly enhances task efficiency, usability, technology acceptance, and presence relative to EdgeNav, whereas perceived cognitive load shows no significant differences between the navigation models. The findings provide empirical evidence that navigation design is a crucial factor in software interaction in Desktop VR systems, highlighting practical implications for usability-focused software development in three-dimensional navigation.

A multidimensional epidemiological study of 905 canine leptospirosis cases, metropolitan France, 2020–2024

Radar detection of small UAVs in severe ground clutter using 2D spatial-temporal matched filtering

Abstract Based on the empirical characterization of radar signatures from three distinct Unmanned Aerial Vehicles (UAVs)—the DJI Mini 4 Pro, Mavic 3 Pro, and Phantom 4 Pro—this paper proposes a detection framework applicable to both Frequency-Modulated Continuous Wave (FMCW) and pulsed radar architectures. Unlike conventional approaches that rely on theoretical point-target models, we design a two-dimensional spatial-temporal matched filter. This approach addresses the challenge of detecting low Radar Cross-Section (RCS) targets in severe ground clutter, where standard Constant False Alarm Rate (CFAR) algorithms frequently suffer from threshold breakdown. By shifting the paradigm from amplitude thresholding to 2D pattern matching via Normalized Cross-Correlation (NCC), the proposed method integrates target energy across both fast-time (range) and slow-time dimensions. The study presents validation comprising both Monte Carlo simulations and outdoor field trials. Simulation results demonstrate sensitivity gain of 10 to 14 dB over standard CA-, GO-, SO-, and OS-CFAR methods in Rayleigh and Weibull clutter. Crucially, experimental validation on the outdoor field radar data, for the three small UAVs, confirms the robustness of the 2D matched filter, which consistently achieves a Precision-Recall Area Under the Curve (AUC) between 0.93 and 0.99, effectively suppressing false alarms even for low Signal-to-Clutter Ratio (SCR).

Genetic parser and deep residual bivariate Pascal-based energy-aware routing and scheduling for edge-enabled smart grids

Abstract Edge-enabled smart grid communication systems operate under tight energy budgets and fluctuating traffic conditions, making it essential to use routing and scheduling methods that control energy use without compromising delay or reliability. Many existing approaches treat routing and scheduling as separate tasks, which often leads to poor coordination—especially when traffic surges unexpectedly or when edge devices face uneven energy availability. In this work, a unified solution is developed that couples a Genetic Parser, used to generate energy-aware routing and slot assignments, with a deep residual model that leverages Bivariate Pascal statistics to anticipate short-term traffic and energy behavior. The Genetic Parser selects paths that distribute load efficiently, while the predictive module estimates the likelihood of delay increases or energy spikes so the scheduler can adjust in advance. Tests carried out on representative smart grid scenarios show clear gains: energy use drops by roughly 18–24%, network lifetime increases by about 27%, and end-to-end latency falls by 15–20% when compared with established methods. Packet delivery also remains consistently high, reaching 98.4% even when traffic conditions vary rapidly. These results indicate that combining evolutionary search with lightweight predictive modeling can improve both stability and overall efficiency in next-generation smart grid communication networks.

Ultrashort wave therapy promotes the repair of jaw defect by regulating the function of jaw bone marrow mesenchymal stromal cells

Multi-objective automatic optimization design of low-carbon courtyard landscape based on NSGA-II algorithm: a case study of the Guangdong-Hong Kong-Macao greater bay area

Metal-dependent inhibition of germination and seedling growth in two onion cultivars under in vitro conditions

Individual differences in speech categorization and perceptual cue reliance across phonological contrasts

A streamlined identification method for position-independent geometric errors of rotary axes in five-axis machine tools considering setup errors

Changes in agricultural commodity prices and household consumption of skilled workforce after COVID-19 movement restrictions in Bangladesh

An integrated modelling approach for assessing stock status of data-limited Indian Ocean yellowfin tuna

Morpho-physical and biochemical traits associated with potato (Solanum tuberosum L.) genotypes conferring resistance to potato tuber moth, Phthorimaea operculella (Zeller)

Bacterial wilt in melon caused by a complex of novel pathogens

A miniature piezoelectric-thermomagnetic generator for low-grade waste heat recovery

Abstract This paper presents the experimental and model-based investigation of a compact miniature-size generator for conversion of low-grade waste heat at temperatures near 100 °C to electricity leveraging the combined transducer effects of a thermomagnetic (TM) and a piezoelectric (P) layer. TM energy conversion is achieved via the recently developed effect of resonant self-actuation of a cantilever using the large temperature-induced change of magnetization at the ferromagnetic transition of a Ni-Mn-Ga film. The resulting kinetic energy is converted into electrical energy via the piezoelectric effect of a thin lead-zirconium-titanate (PZT) layer, which enables rectification of the harvested energy using standard electrical circuitry. The performance of the generator is characterized with respect to the effects of tip mass and length of the piezoelectric layer as well as heat transfer and damping. A lumped element model (LEM) of the coupled system shows that heat intake and heat dissipation have to be balanced to achieve resonant self-actuation, while damping limits the oscillation frequency of the cantilever. A single P-TM generator generates an average power output of 0.8 µW at 105 °C. After rectification, this corresponds to 1.25 µW/cm² of DC power at a voltage of 2 V DC.