Clinical imaging protocols designed to accelerate acquisition or reduce radiation dose often lead to degraded image quality, compromising diagnostic confidence. The heterogeneity in degradation types and severities across imaging modalities further challenges the development of generalized restoration solutions. In this work, we introduce a unified framework that formulates medical image restoration as posterior sampling from self-supervised Latent Diffusion Models (LDMs), pretrained on multi-modal high-quality images. At the core of our method is an Estimation Posterior Sampling (EPS) strategy, which enhances both data fidelity and anatomical detail retention. EPS incorporates two key components: (i) estimated diffusion initialization to constrain sampling within the measurement-consistent solution space, and (ii) gradient-balanced optimization to adaptively trade off denoising strength and detail preservation throughout the diffusion trajectory. Unlike traditional task-specific models, our approach enables Plug-and-Play (PnP) deployment, supporting diverse degradations without retraining. Extensive experiments conducted on deterministic degradations (e.g., under-sampled MRI, sparse-view CT) and blind degradations (e.g., low-dose PET) across multiple degradation levels demonstrate superior quantitative and qualitative performance compared to both supervised baselines and state-of-the-art posterior sampling methods. Notably, our method achieves PSNR improvements of up to +2.9 dB (MRI), +1.1 dB (CT), and +0.9 dB (PET) in PnP mode. These results highlight the robustness and broad applicability of our framework for clinical deployment.

Maize is a key global food crop, with China being a major producer vital for global maize supply and food security. Accurately analysing the relationships between grain yields of maize and influencing factors is crucial for enhancing crop production, evaluating arable land quality, and optimizing planting structure. However, when modelling those relationships, the coefficients of each influencing factor vary spatially and have different spatial scales, suggesting that the importance of the influencing factors is multiscale. Traditional global and local modelling methods such as Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models cannot accurately explain those multiscale spatial relationships. The Multiscale GWR (MGWR) model, an extension of GWR, addresses these limitations by allowing each explanatory variable to have a unique spatial scale. By aligning the neighbourhood structure of each variable with its corresponding spatial scale, MGWR improves the accuracy of local regression coefficient estimations, providing a more refined analysis of spatial heterogeneity. In this paper, the multiscale importance of influencing factors on maize grain yield of the Chinese mainland was apportioned via MGWR model. Our findings verified that the relationships between maize grain yield and influencing factors differ at multiple spatial scales. MGWR model can comprehensively apportion the importance of influencing factors at multiple scale, while global and local modelling methods provide biased estimations, with OLS method leaving large residues and GWR model attributing part contribution to spatially varying intercept terms. With the MGWR model, organic fertilizer and terrain aspect are globally important and their relationships with yields keep stationary; the relationships between yields and soil pH value, GDP, DEM and slope vary on a medium-scale, presenting obvious regional differences; cultivation convenience and hydrothermal conditions affect yields at small scales. The comprehensive apportionment of multiscale relationships is an important guideline for the scientific management of agriculture and arable land resources.

Significant wave height (SWH) plays a critical role in marine operations, ship navigation, and climate prediction. The X-band radar is widely used for SWH inversion due to its short wavelength and high attenuation rate. Current inversion methods mainly fall into two categories: spatiotemporal-domain methods based on deep learning, and frequency-domain methods based on physical models. However, traditional deep learning methods typically emphasize spatial features while neglecting sequential information in radar image processing, which results in the loss of temporal features. Additionally, physics-based inversion methods in frequency-domain rely on manual feature design and cannot adaptively learn the nonlinear mapping between frequency feature and SWH, which hinders the improvement of inversion accuracy. To solve these problems, this paper proposes a bimodal cross-attention spatiotemporal-frequency fusion inversion network (BiCross-STFNet) to estimate SWH. First, the radar image sequences are simultaneously input into the spatiotemporal feature extraction module and frequency feature extraction module, where spatiotemporal and frequency features are extracted using the 3D spatiotemporal aware residual block. These features are then aligned and fused through the bimodal cross-attention fusion module, and finally, SWH is estimated. Experimental results demonstrate that BiCross-STFNet achieves a correlation coefficient of 0.964, a root mean square deviation (RMSD) as low as 0.04, and a mean absolute percentage error (MAPE) of 5.59%, outperforming existing methods.

LiDAR and camera are two critical sensors that can provide complementary information for accurate 3D object detection. Most works are devoted to improving the detection performance of fusion models on the clean and well-collected datasets. However, the collected point clouds and images in real scenarios may be corrupted to various degrees due to potential sensor malfunctions, which greatly affects the robustness of the fusion model and poses a threat to safe deployment. In this paper, we first analyze the shortcomings of most fusion detectors, which rely mainly on the LiDAR branch, and the potential of the bird’s eye-view (BEV) paradigm in dealing with partial sensor failures. Based on that, we present a robust LiDAR-camera fusion pipeline in unified BEV space with two novel designs under four typical LiDAR-camera malfunction cases. Specifically, a mutual deformable attention is proposed to dynamically model the spatial feature relationship and reduce the interference caused by the corrupted modality, and a temporal aggregation module is devised to fully utilize the rich information in the temporal domain. Together with the decoupled feature extraction for each modality and holistic BEV space fusion, the proposed detector, termed RobBEV, can work stably regardless of single-modality data corruption. Extensive experiments on the large-scale nuScenes dataset under robust settings demonstrate the effectiveness of our approach.

BIM standard development is central to the performance and behavior of BIM model application across transmission, visualization, and information management perspectives. Tremendous effort has been made to ease the implementation of IFC data model in practice. Yet, the complexity of IFC data model hurdles the implementation of the import and export functionality by software vendors. To overcome this, buildingSMART introduced the concept of Model View Definitions to define which parts of an IFC data model need to be implemented for a specific data exchange scenario. With such, the certification of compatibility for software products with the IFC standard is formed. The Model View Definition is use case orientated to determine whether the specific information should be included in an IFC partial model. With the creation of ad-hoc, project-specific Exchange Requirements increasing, associated MVD development requires much more work to incorporate standard development. To resolve this issue, this paper attempts to exploit the potential of information entropy which has proven itself extremely crucial in many other industries in terms of information management, and then integrates it with sandpile simulation to propose a Top-down hierarchy to structure as well as interpret IFC partial model via Model View Definition. The proposed information entropy shifted MVD development approach would manage to unify the MVD development process that enables the reduction on confusion for various end users, specific organization, or project needs. Moreover, to better translate the BIM standard topology into sandpile simulations, a new notion system is proposed. Sandpile simulations are further implemented to prove their applicability, during the simulation, self-organized criticality is identified, and the existence of chaos is observed.

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naïve Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the 'majority voting strategy' led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS.