HiChIP enables high-resolution chromatin interaction mapping, but existing methods generally overlook restriction enzyme (RE) cut site density or treat it as a technical bias requiring normalization or removal, discarding chromatin accessibility information that distinguishes functional regulatory elements. Here we introduce sintHiChIP to address this methodological gap. sintHiChIP explicitly models RE cut site density as a biological signal and integrates Gaussian kernel smoothing with distance-dependent statistics, allowing detection of chromatin loops while capturing local regulatory heterogeneity. Moreover, the algorithm employs adaptive probability distributions to resolve inherent data overdispersion and sparsity dynamically. Validation against independent datasets and comparison with existing methods demonstrate that sintHiChIP reliably recovers canonical chromatin loops, exhibiting distinct superiority in regulatory H3K27ac environments and comparable accuracy in structural cohesin contexts. Notably, sintHiChIP achieves exceptional precision in predicting CRISPRi experiments and reveals highly coherent cell-type-specific genetic regulatory networks. Executing efficiently on standard workstations, our method delivers a promising analytical framework for functional 3D genomic studies.

The distribution biases and scarcity of samples in multi-source data present significant challenges for few-shot learning (FSL) tasks based on brain-computer interface (BCI). Recent efforts have explored the application of diffusion mechanisms in FSL, typically utilizing labeled data to augment the support set. However, this approach has not effectively utilized unlabeled data nor addressed distribution biases. Inspired by the latest advancements in FSL, we propose the manhattan self-attention diffusion residual networks (MSADiff-Resnet) with dynamic bias rectification. This model explicitly adds the manhattan self-attention diffusion layer to resnet, using attention mechanisms and manhattan distance-based decay function to control local diffusion intensity, and adjusts the global diffusion strength through the parameter. This diffusion mechanism bridges labeled and unlabeled data, addressing the limitations associated with sample availability. Additionally, we effectively tackle the distribution biases of multi-source data through inter-class bias rectification and dynamic intra-class bias rectification. Moreover, this study presents for the first time a universal deep learning framework specifically designed for BCI-based FSL tasks. Extensive experiments on multi-source BCI task datasets have validated the effectiveness of proposed method.

Emotion recognition tasks based on physiological signals require the simultaneous capture of local features and global correlations of these signals. Although transformer-based models are widely used due to their superior ability to integrate information, their quadratic computational complexity limits their efficiency in processing large-scale or high-resolution data. Recently, state space models (SSM) with efficient hardware-aware designs have demonstrated significant potential in modeling long sequences. However, existing SSMs face limitations in processing global information due to window constraints. Therefore, this paper introduces a novel Global Context (GC) MambaVision model, which combines the linear time complexity advantage of SSMs with a new type of local-global attention mechanism. GC MambaVision maintains high computational efficiency in emotion recognition tasks while providing a more comprehensive understanding of the dynamic changes in local and global emotional states. Experimental results on the DEAP and SEED-V datasets show that GC MambaVision achieves superior performance compared to current state-of-the-art models, with accuracies reaching 98.62% and 85.88%, respectively.

We performed a meta-analysis to evaluate the association with immune-related adverse events (irAEs), comparing immune checkpoint inhibitors (ICIs) 10 mg/kg versus other doses. A total of 2030 participants (985 cases treated with dose 10 mg/kg of ICIs and 1045 controls treated with other doses of ICIs) were included in the meta-analysis. We Selected four key irAEs, including increased AST, colitis, hypothyroidism and rash, from treatments with approved cytotoxic T-lymphocyte–associated protein 4 (CTLA-4) and programmed cell death 1 (PD-1) inhibitors (ipilimumab, nivolumab and pembrolizumab, respectively). Fixed effect model was used to calculate odds ratios (OR) with 95 confidence intervals (CI) by R software. Compared to other doses, 10 mg/kg dose of ICIs was associated with more increased AST (OR=1.7, CI=1.08-2.69), colitis (OR=2.07, CI=1.20-3.58) and rash (OR=1.57, 1.25-1.97) in all-grade irAEs. We did not find significant heterogeneity in all groups.