We present a best-practices protocol for targeted DNA methylation sequencing analysis that integrates both bisulfite- and enzymatic-conversion approaches using Twist Bioscience technology. Building on established tools and vendor-recommended workflows, this protocol provides a standardized, fully reproducible framework that addresses current gaps in the implementation, documentation, and downstream integration of targeted methylation sequencing analyses. The workflow incorporates widely used open-source tools across two main stages: preprocessing (adapter trimming with Trim Galore and quality control via FastQC/MultiQC) and methylation calling (alignment with BWA-meth, duplicate marking using Picard, and methylation extraction with MethylDackel). Importantly, we extend existing workflows by introducing a structured third stage for downstream statistical analysis in R, including β-value calculation, differential methylation analysis with methylKit, principal component analysis, and tiling-based regional profiling, all embedded in a unified, reproducible pipeline. A key contribution of this protocol is the integration of multi-stage quality control checkpoints, harmonized parameterization, and explicit reporting of intermediate outputs, enabling transparency and reproducibility across all analytical steps. The modular design allows tool substitution and adaptation to different experimental contexts, including enzyme-based methylation methods, while maintaining consistency in data processing and interpretation. This guide provides step-by-step instructions, troubleshooting strategies, and examples of expected outputs, along with built-in statistical routines that generate comprehensive results, including methylation matrices, correlation analyses, differentially methylated region annotations, and publication-ready visualizations. The complete workflow requires approximately 9-27 hr for 10 samples (approximately 134.2 million reads per sample) on Linux systems and assumes moderate proficiency in command-line environments and R. In summary, this protocol offers a practical, transparent, and reproducible framework that standardizes targeted DNA methylation analysis and facilitates its application across diverse biological and clinical research settings. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Alzheimer's disease (AD) is characterized by a progressive cognitive decline and pathological cascade involving amyloid-β (Aβ) plaque, hyperphosphorylated tau (pTau), neuroinflammation, and neurodegeneration. Although anti-amyloid antibody therapies have recently been approved, their clinical benefits remain limited and often accompanied with significant adverse effects. Physical activity has emerged as a promising non-pharmacological intervention for AD, with both aerobic and resistance physical activity showing beneficial effects in preclinical studies. However, the effects of different types of physical activity remain insufficiently understood. In this study, we investigated the effects of a forced treadmill running and ladder climbing physical activity procedure on cognitive behaviors, gliosis, and protein aggregate pathology in aged 3xTg-AD mice. Thirteen to fourteen months old 3xTg-AD mice were subjected to a six-week forced treadmill running and ladder climbing physical activity protocol. Cognitive functions were evaluated using the novel object recognition, Y-maze, and social behavior tests. Hippocampal and cortical tissues were analyzed by immunostaining for Aβ, pTau, Glial Fibrillary Acidic Protein (GFAP), Ionized Calcium-Binding Adapter Molecule 1 (IBA1), and inhibitory neuron markers. Our study demonstrated that the six-week forced treadmill running and ladder climbing physical activity procedure reduced age-associated cognitive declines in spatial working memory, object recognition memory, and sociability in aged 3xTg-AD mice. Such physical activity procedure also reduced Thioflavin S (Thio S) labeled misfolded protein aggregates and reactive gliosis in the cortex and/or hippocampus. Notably, forced treadmill running and ladder climbing physical activity procedure decreased Thio S positive neuropathology within somatostatin-positive interneurons of the hippocampus and cortex, indicating a cell-specific neuroprotective effect. Our results demonstrated that forced treadmill running and ladder climbing physical activity procedure produced significant behavioral and neuropathological benefits in aged 3xTg-AD mice, supporting physical activity as a promising non-pharmacological intervention for modulating gliosis and protein aggregate pathology in aging and AD.

Sensory processing is dynamically shaped by neural adaptation, allowing the brain to adjust to recent sensory input. Phi and reverse-phi motion provide a framework for probing adaptive processes and ON-OFF pathway interactions in visual motion processing. We recorded 64-channel EEG while 30 participants completed a rapid motion-adaptation paradigm. Participants adapted to contrast-preserving phi motion or contrast-reversing reverse-phi motion for a short (188 msec) or long (752 msec) duration and then judged the direction of an ambiguous counterphase-flickering test grating. Behaviourally, adaptation duration primarily determined perceptual bias: short adaptation produced a modest same-direction, priming-like bias, whereas long adaptation produced an opposite-direction motion aftereffect. Test-locked event-related potentials (ERPs) revealed a robust Duration × Adaptation Type interaction over early posterior scalp regions (128-160 msec), primarily expressed in the N1 range. This interaction reflected greater differentiation between phi and reverse-phi adaptation after long than short adaptation. Exploratory whole-scalp topographic analyses indicated that the interaction involved changes in scalp-response configuration rather than uniform amplitude modulation. Adapter-locked analyses further showed that phi and reverse-phi motion elicited distinguishable ERP responses during the initial adaptation period and separable multivariate EEG patterns from approximately 50 msec after adapter onset, indicating early neural divergence between the two motion types. Together, these findings provide electrophysiological evidence that rapid adaptation to phi and reverse-phi motion leaves distinct neural traces that differentially influence subsequent processing of an ambiguous motion stimulus, advancing our understanding of rapid sensory adaptation and ON-OFF pathway interactions in human motion perception.

This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation. In general-domain summarization on the XLSum Bengali dataset, mT5 achieved stronger overall performance than Flan-T5, with ROUGE-1 and BERTScore values of 0.21 and 0.72, respectively. The Flan-T5-XLSUM model outperformed other LLMs, achieving a ROUGE score of 0.79, a BLEU score of 0.13 and a BERTScore of 0.86. A human evaluation involving 28 participants was conducted to assess summary fluency, adequacy, and factual consistency across domains, confirming the qualitative effectiveness of the proposed models. A custom dataset is developed with manually annotated categories for domain adaptation purposes, contributing to domain-specific Bengali data. The ablation study illustrated that pre-training and fine-tuning on domain-specific data significantly enhanced model performance, with Flan-T5 fine-tuned on sports data. Three explainability methods (LIME, SHAP, and BertViz) revealed that geographically and contextually meaningful tokens strongly influenced the domain-adaptive Flan-T5 Bengali summarization model. The adapter-based Flan-T5 architecture enables effective multi-domain fine-tuning with less than 1% of trainable parameters while preserving model performance.

Toll-like receptor 2/1 (TLR2/1) heterodimers detect triacylated lipoproteins. Although TLR2/1 complexes localize to both the plasma membrane and endosome, the functional contributions of compartment-specific signaling are not completely defined. A common human TLR1 polymorphism (rs5743618), at which one allele substitutes isoleucine for serine adjacent to the transmembrane domain, restricts TLR1 intracellularly and is associated with an increased risk of antibiotic-refractory Lyme arthritis. This mis-localization provides a model to examine how receptor positioning influences downstream signaling. Here, we investigated the rs5743618 polymorphism's effects on TLR1 localization and compartment-specific NF-κB activation. Using a CD14+ TLR2+ HEK cell reporter system that enables transient expression of both allelic variants, we confirmed that the polymorphism is excluded from the cell surface and retained intracellularly, whereas the ancestral allele localizes to both compartments. Functionally, the ancestral allele signaled from both compartments, while the derived allele signaled exclusively from endosomes. Spatially restricted signaling exhibited influence on IkBα degradation and NF-κB activation compared with the ancestral allele. This activation was dependent on MyD88 localized at ligand-containing endosomes, establishing a role for this adapter in intracellular TLR2/1 signaling. These findings provide insight into how this common polymorphism impacts inflammatory outcomes.

Federated learning enables privacy-preserving collaborative healthcare, while digital twins can represent institution-specific anatomical and modality states. However, federated multimodal medical image segmentation remains challenged by cross-center heterogeneity and missing modalities, while existing methods largely overlook the anatomical and modality complementarity of client updates. To address this problem, we propose TwinFedSeg, a federated learning framework guided by an anatomical twin state for multimodal medical image segmentation. Each client maintains a lightweight state comprising dynamic class prototypes, structural statistics, modality availability, and reliability. These states guide client selection, model aggregation, and global state updating, while the updated global state is fed back through a lightweight feature modulation adapter for cross-center adaptation under missing modalities. Extensive experiments on BraTS 2023-GLi and ISLES 2022 show that TwinFedSeg consistently outperforms representative federated methods, achieving average Dice scores of 80.77% and 72.58%, respectively, with gains of 3.52 and 4.06 percentage points over FedAvg.

Peripheral intravenous catheters (PIVCs) are the preferred vascular access for apheresis; however, veins that initially appear suitable may develop venous spasms, resulting in poor inlet flow and frequent pressure alarms. Ports used for apheresis can also experience inlet pressure alarms due to catheter or venous occlusion. When conservative measures failed, we implemented a novel double-draw technique in which a second draw line was connected via a four-way stopcock and male-to-male adapter to the initial draw access (either a PIVC or port) to improve inlet flow. The double-draw technique was performed in 26 procedures (therapeutic plasma exchange [TPE], n = 10 and lipoprotein apheresis [LA], n = 16) in 8 patients. During TPE, median negative inlet pressure improved from -179 mmHg (IQR -218 to -69) with single-draw access to -71 mmHg (IQR -78 to -62) following placement of a second draw line (p = 0.0162). Inlet flow rate during TPE improved from median 47 mL/min (IQR, 40-58 mL/min) with single-draw access to 70 mL/min (IQR, 60-75 mL/min) (p = 0.008). During LA, median negative inlet pressure improved from -136 mmHg (IQR -193 to -109) to -59 mmHg (IQR -66 to -47) (p < 0.0001). Median inlet flow rate during LA improved from 70 mL/min (IQR 59-71 mL/min) with single-draw access to median 80 mL/min (IQR 70-80 mL/min) (p = 0.023). Overall 25 of 26 procedures (96.2%) demonstrated an improvement in inlet pressure following implementation of the double-draw technique. All procedures were completed successfully without abandonment of the original access site. The double-draw technique is a novel option for managing inlet pressure alarms during apheresis when conservative measures fail.

Non-contact inline Raman spectroscopy is used as a fast, flexible, and safe method for monitoring and optimizing continuous-flow processes involving highly hazardous and corrosive materials. The synthesis of nitroguanidine (NQ), an energetic material, was characterized and optimized as a model for hazardous reactions. The contactless, flexible, and real-time analysis in this environment was implemented using a clip-on adapter for a Raman probe downstream of the microflow reactor. Initial experiments focus on characterizing the in situ formation of the nitration agent, the nitronium ion (NO2[+]), and identifying an optimal nitric acid volume fraction between 10 and 20% v/v for maximum NO2[+] yield. The PAT setup was then used to rapidly optimize reaction parameters (residence time, reactor temperature, and stoichiometry) to enhance reactor output while maintaining high yield. Increasing the reaction temperature to 70 °C effectively reduces system pressure due to the lower viscosity of the reaction stream, while simultaneously increasing yield. Reduced residence times enable higher output with only minimal yield loss (6.3 min, 97%). The analysis of the molar ratio of the reagents further revealed that using a slight excess of nitric acid maintains the yield while avoiding product losses due to back reaction during subsequent processing. To verify the long-term repeatability of the reactor for synthesis, the reaction was performed several times under optimized conditions.

Accurate 3D neuron segmentation from large-scale electron microscopy (EM) volumes is a cornerstone of connectomics, yet it is hampered by a trade-off between accuracy and computational efficiency. While 3D Convolutional Neural Networks (CNNs) offer high accuracy by predicting 3D affinity maps, their high computational cost and limited input size impede their application to large-scale data. To overcome this, we introduce EM-SliceCrafter, a novel framework that employs a lightweight 2D CNN for both efficient and precise 3D neuron segmentation. Our method generates a 3D affinity map by computing distances between embedding maps of adjacent 2D slices. To enrich the 2D network's contextual understanding, we propose a dual-distillation strategy. First, Cross-Dimension Affinity Distillation (CAD) transfers inter-slice structural knowledge from a 3D teacher network to enhance 3D continuity. Complementarily, Internal-Dimension Affinity Distillation (IAD) leverages a powerful teacher built on the pre-trained Segment Anything Model 2 (SAM2) encoder, using a learnable adapter, to distill fine-grained intra-slice boundary details. Furthermore, a Feature Grafting Interaction (FGI) module deepens knowledge transfer by integrating embeddings across the student and both teacher networks. Extensive experiments on diverse EM datasets, featuring various imaging modalities and resolutions, show that EM-SliceCrafter improves segmentation accuracy over state-of-the-art methods while achieving a 33× speedup in GPU forward inference. The code is available at https://github.com/liuxy1103/EM-SliceCrafter.

The regeneration of skeletal muscle involves precisely regulated intercellular communication, including extracellular vesicle (EV)-mediated processes. In this study, we show that during in vitro myogenic differentiation of human skeletal muscle progenitor cells (myoblasts), miR-193b-3p accumulates within small EVs (sEVs) in a temporally regulated manner. Both sEV-mediated and synthetic delivery of miR-193b-3p enhanced myogenic differentiation in vitro and promoted histological recovery in a murine volumetric muscle loss model. Mechanistically, we demonstrate that miR-193b-3p directly suppresses adapter-related protein complex 2 subunit mu 1 (AP2M1), a clathrin-associated adapter protein that has not been well characterized in muscle biology, and identify it as a negative regulator of myogenic progression. AP2M1 knockdown phenocopied the effects of miR-193b-3p, suggesting that this regulatory axis may contribute to muscle regeneration. In addition, transcriptomic analyses of aged rodent and sarcopenic human datasets revealed conserved upregulation of AP2M1 in degenerative muscle states, supporting its potential clinical relevance. Collectively, these findings reveal a myogenic differentiation-dependent, sEV-mediated miRNA regulatory mechanism that contributes to muscle regeneration. Based on these results, we propose the miR-193b-3p-AP2M1 axis as a potential therapeutic target for muscle-wasting disorders.

Vision-language models show promise in zero-shot semantic segmentation, but a key challenge is the disconnect between text and visual features. While text embeddings can roughly localize unseen objects, they often lack the fine-grained detail necessary for accurate segmentation, leading to oversegmentation or undersegmentation. To address this, we propose MODdapter, a Multimodal Adapter with Proportional-Integral-Derivative (PID) control for precise dual-space alignment of vision and language features. Our core idea is to leverage visual cues obtained during testing to complement textual information, improving the alignment between text and visual features. MODdapter extracts visual semantic cues from coarse segmentation results and integrates them as supplementary textual data, allowing for a more accurate projection of text features into the feature space and enhancing fine-grained recognition. To maintain stability and counteract potential oscillations caused by deviations in the extracted instance cues, we incorporate a PID control module that regulates the alignment process. PID control module leverages the combined joint constraints of short-term (derivative) and long-term (integral) memory, achieving fine-grained recognition of unseen target. This strategy significantly enhances the recognition of unseen objects, achieving state-of-the-art performance across multiple datasets. On the challenging COCO-20[i] dataset, our method outperforms the current state-of-the-art by a significant improvements of 6.9%.

Large-scale vision-language pretrained models like CLIP, paired with parameter-efficient fine-tuning techniques, have emerged as promising solutions for image-to-video transfer in video action recognition. However, existing methods often prioritize strong supervised performance at the expense of transferability and generalization. To address this challenge, we propose a novel multimodal, multi-task CLIP adaptation framework, M$^{2}$-CLIP, which effectively balances high supervised performance with robust generalization. Our approach begins by enhancing individual modality architectures with multimodal adapters tailored for both the visual and textual branches. Specifically, we propose TED-Adapter, which improves temporal modeling by capturing both global temporal enhancements and local temporal differences within the visual encoder. We also integrate adapters into the text encoder to strengthen the semantic alignment between visual and textual modalities. To further boost performance, we incorporate a multi-task decoder that leverages diverse supervisory signals, including contrastive learning, cross-modal classification, masked language modeling, and visual classification tasks. This multi-task decoder ensures robust supervised performance while preserving multimodal consistency. Building on the success of M$^{2}$-CLIP, we introduce M$^{2}$-CLIP++, which enhances I2V feature modeling through advanced visual feature integration. Conventional fusion strategies typically adopt a sequential paradigm or rely on elementary operations (e.g., addition), which fail to capture the complex, non-linear correlations between spatial appearance and temporal dynamics. To address this limitation, M$^{2}$-CLIP++ is explicitly designed to enable higher-order temporal and spatial feature fusion, allowing the model to better interpret how spatial elements evolve over time. Specifically, it updates the visual adapter to TED-Adapter++, which employs a dual learnable bilinear fusion strategy to achieve this sophisticated interaction modeling. Combined with multi-head processing and low-rank decomposition, our approach optimizes computational efficiency while maintaining effective representation capacity. Additionally, M$^{2}$-CLIP++ incorporates multi-scale feature aggregation to effectively merge features across different semantic levels. Extensive experiments demonstrate that our models achieve state-of-the-art supervised learning performance while maintaining exceptional generalization capabilities. Code will be made available at https://github.com/sallymmx/m2clip.

Coxiella burnetii (Cb), the causative agent of Q fever, replicates within host macrophages by modulating innate immune responses through its type IVB secretion system (T4SS). Host cells sense pathogens via Pattern recognition receptors (PRRs), including Toll-like receptors (TLRs), to recognize pathogen-associated molecular patterns (PAMPs) and initiate signaling pathways that drive pro-inflammatory cytokine and interferon responses. Toll-like receptor 3 (TLR3) triggers the nuclear translocation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and Interferon regulatory factors (IRFs) resulting in pro-inflammatory cytokine production and type I interferon (IFN-I) induction. Here we demonstrate that Cb requires T4SS to suppress TLR3-induced NF-κB and IRF transcriptional responses. Furthermore, RNA purified from both virulent and avirulent Cb is sufficient to activate TLR3, identifying pathogen RNA as a relevant PAMP in this context. Using a pulmonary infection model with virulent Cb, we found that TLR3/ Toll-Interleukin-1 Receptor Domain-Containing Adapter Protein Inducing Interferon Beta (TRIF)-dependent innate immune pathway inhibits cachexia. In contrast, signaling through the interferon-α/β receptor (IFNAR) restricts bacterial dissemination, indicating that these pathways play distinct but complementary roles in host defense. Mechanistically, Cb suppresses TLR3/TRIF signaling in a T4SS-dependent manner by preventing host TLR3 recruitment to the Coxiella-containing vacuole (CCV) and disrupting TRIF-TNF Receptor-Associated Factor 6 (TRAF6) interactions, thereby selectively inhibiting impairing NF-κB activation. Consistent with this, we identify five T4SS effector proteins that attenuate TLR3-induced NF-κB signaling, including CBU1292, which suppresses both NF-κB and IRF-dependent responses downstream of TLR3.