Accurate and robust alignment between LiDAR and visual sensors is essential for sensor fusion in perception-critical environments. However, it remains challenging due to confusing points, occlusion issues, and measurement noises inherent in mechanical LiDAR systems. Misalignments between 3D points and pixels can significantly impair system perception, underscoring the need for reliable calibration in automated tasks. This study addresses these challenges by introducing a specially designed calibration board that offers rich geometric elements, enabling adaptive constraints for more stable primitive extraction in both image and point cloud. Compared with existing checkerboards, the proposed board supports a wider effective calibration range and reduces sensitivity to sensor setup and viewing angles, especially for sparse LiDAR. A detailed analysis of the two fundamental assumptions underlying existing calibration frameworks is conducted. To address the associated limitations, an enhanced Region Growing (RG) algorithm is developed integrated with spatial feature analysis (SFA) is proposed, which improves the accuracy and robustness of geometric feature extraction under noisy or occluded conditions. Experimental results demonstrate that the proposed method achieves comparable rotation and translation accuracy to existing target-based techniques, while providing greater operational convenience and a wider effective range of 1.2 to 6.5 meters for the 16-line LiDAR and monocular camera setup.

Brain tumor segmentation (BTS) on magnetic resonance imaging (MRI) is essential for diagnosis yet remains challenging due to substantial inter-patient variations and the need to integrate cross-modal interactions. In this work, we propose a modality-aware BTS framework, MA-HybridBTS, to address these challenges through three key components. First, we introduce a modality-aware multi-scale feature extraction (MAMS) strategy that employs 3D dilated convolutions with tailored dilation rates to each sequence, concurrently refining tumor-core details while expanding global context. Second, we present a modality-aware hierarchical fusion (MAHF) module that explicitly leverages clinically established inter-modal dependencies to guide progressive feature fusion. Finally, we propose an adaptive loss function (ALF) that dynamically up-weights sub-regions with larger losses, thereby improving segmentation accuracy for smaller and harder-to-delineate tumor compartments. The proposed methods achieve state-of-the-art performance on the BraTS2021 dataset, delivering an average Dice score of 0.8984 and an average 95% Hausdorff distance (HD95) of 4.14 mm. Ablation studies further validate the effectiveness of each proposed component. The code is publicly available on GitHub: https://github.com/Jia7888/code.

Precise segmentation of pulmonary nodules is essential for the early detection of lung cancer, yet remains challenging due to the substantial diversity in nodule size, shape, and density. To tackle this problem, we propose a lightweight multi-scale 3D U-shaped convolutional network named MSU3D. It embeds a pooling-enhanced multi-scale extraction block (MSE3D) to enlarge receptive fields without extra parameters, and a channelwise 3D fusion module (MSF3D) that interleaves and re-weights cross-scale features for rich yet compact representations. The performance of the proposed method is evaluated on LUNA16 using standard 5-fold cross-validations. Experimental results show that MSU3D delivers competitive results, raising DSC to$\mathbf{9 1. 3 4 \%}$, PPV to$\mathbf{9 2. 9 5 \%}$, and SEN to$\mathbf{9 2. 0 7 \%}$, respectively.

Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations. Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods. Source code can be found at https://github.com/LZY-HZAU/PTM-CMGMS.

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://github.com/Chertuion/CHiMoGNN.

As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive results, it often fails to account for false DDI in the constructed DDI networks. Such inaccuracies caused by data errors, false alarms, or incorrect drug details can skew the network's structure and hinder the accuracy of GNN-based predictions. To tackle this challenge, we propose ANSM, a network-enhancement method specifically designed to identify and attenuate spurious links between drugs for ensuring the accuracy of DDI networks. ANSM integrates three key components: the feature extractor, the network optimizer, and the discriminative classifier. The feature extractor captures local structural features from drug node pairs, while the network optimizer leverages network information to improve feature extraction and reduce the impact of spurious DDI links. The discriminative classifier then identifies potential spurious links. Experimental results demonstrate that ANSM outperforms state-of-the-art methods in identifying spurious DDI.

Obtaining accurate and effective information for drug molecules is a crucial and challenging task, which relies on high-quality chemical knowledge. However, chemical knowledge has been accumulated over the past 100 years from various regions, laboratories, and experimental purposes, which contains a lot of noise and inconsistency, leading to the out-of-distribution (OOD) problem. OOD may results in weak robustness and unsatisfied performance. In order to solve OOD learning problem with noise, a novel benchmark: ADMEOOD is proposed, which is a systematic OOD dataset curator and specifically designed for drug property prediction. ADMEOOD screens 27 Absorption, Distribution, Metabolism and Excretion (ADME) drug properties from Chembl and relevant literature. This paper explicitly make distinctions between two kinds of OOD data shifts: Noise Shift and Concept Conflict Drift (CCD). Overall, ADME contains 6 domain annotations combined with noise, CCD and no shifts, resulting in 18 different splits in total. ADMEOOD provides performance results on a variety of SOTA OOD models. The results demonstrate a significant difference performance between in-distribution and OOD data. Moreover, Empirical Risk Minimization and other models exhibit distinct trends in different domains and measurement types. The ADMEOOD benchmark can be accessed via https://github.com/qweasdzxc-wsy/ADMEOOD/.

Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.

Mycobacterium tuberculosis (MTB) and Staphylococcus aureus (STA) are very common and complicated diseases that infect both humans and animals. The emergence of multidrug-resistant bacterium represents an urgent need to identify new drug targets and therapeutic drugs. In this study, we propose a systems chemical biology method to identify targets of anti-bacterial agents. This method integrates gene expression profiles of MTB upon chemical treatment and prior knowledge of protein-protein interactions (PPI) to predict antibacterial targets. We first validate the method by examining the targets of two approved anti-MTB drugs. Then, we predict the targets for Chelerythrine and Rhein, an anti-MTB natural product extracted from Chelidonium majus and a natural anti-bacterial agent for STA respectively. The identified targets are further evaluated visually by molecular docking and molecular dynamics simulation. The results show that our method is applicable for predicting the potential targets of anti-bacterial agents and has stable performance among multiple datasets across different species. Our strategy is expected to be used in target identification for other antibacterial.