Accurately predicting microRNA-disease associations (MDAs) is crucial for identifying biomarkers and therapeutic targets in complex diseases. However, experimental validation is costly, and existing computational methods, particularly Graph Neural Networks (GNNs), are limited by data sparsity. In such sparse graphs, the lack of topological connections hinders effective message passing, leading to poor performance for isolated nodes and in cold-start scenarios. To address these challenges, we propose the adaptive dynamic heterogeneous graph attention neural network (ADHGMDA), a framework for MDAs prediction and analysis. First, to resolve connectivity issues, we construct a dual-layer heterogeneous graph enhanced with virtual nodes derived via K-means clustering. These virtual nodes act as semantic bridges, integrating isolated microRNAs and diseases into the network. Second, we introduce a dynamic feature learning mechanism that simulates data sparsity during training, forcing the model to learn robust representations for cold-start prediction. Third, to mitigate the over-smoothing common in deep GNNs, we design a gradient conflict-aware multi-task optimization strategy with dynamic weight adaptation. Furthermore, addressing the issue that existing benchmarks rely on outdated data, we reconstructed a benchmark dataset based on the latest databases, integrated with visualization tools. Experimental results demonstrate that ADHGMDA significantly outperforms seven state-of-the-art methods, achieving area under the receiver operating characteristic curve scores of 0.9698 and 0.9730. In case studies on herpes simplex and interstitial nephritis, the model exhibits excellent performance in uncovering potential pathogenic pathways. Meanwhile, extensive validation experiments confirm that it has good robustness against label noise, class imbalance, and the cold-start problem of isolated nodes.

Leather is among the most widely traded textile materials worldwide, and detecting surface defects is crucial to ensuring its quality. Conventional manual inspection of leather surfaces is labor-intensive, time-consuming, and prone to human error. Advances in computer vision have facilitated the development of automated leather surface defect detection. However, vision-based methods remain sensitive to lighting conditions, particularly on high-gloss leather surfaces. In this paper, we introduce Le-Radio, an industrial IoT (IIoT) oriented RF-based system for automated leather defect detection. Le-Radio provides a gloss-robust, ubiquitous, and continuous inspection solution, which is crucial for integrating quality control into smart factory pipelines and advancing the digital transformation of the traditional leather industry. To extract informative representations of leather defects from wireless signals, we employ continuous radio snapshots and refined signal features to differentiate defect patterns. Specifically, we design an imaging algorithm that continuously visualizes defects on moving leather samples using a fixed radar. To enhance Le-Radio’s robustness in diverse environments, we train a transferable model that maintains consistent detection performance across different scenarios. We conduct extensive experiments in three rooms using 120 leather samples to evaluate Le-Radio. Experimental results demonstrate that Le-Radio accurately detects diverse defects in high-gloss leather. Beyond leather inspection, this work establishes Le-Radio as a practical IoT edge sensing node for material surface inspection in the IIoT. By integrating RF-based defect scanning capabilities and a robust radar imaging framework into a unified system, this work establishes RF sensing as a viable and novel paradigm for material surface inspection in the IIoT, particularly in scenarios where optical methods are fundamentally limited.

Unsupervised domain adaptation has emerged as an effective paradigm to address the domain shift issue in deep neural networks. Despite its success, achieving fully supervised performance remains a potential challenge. Recent efforts have introduced active learning to the domain adaptation problem, leveraging limited manual annotation costs to achieve near fully-supervised results. However, current methods still give rise to two main concerns: (1) annotation budget: existing methods tend to label excessive redundant regions within objects, potentially resulting in a waste of the annotation budget. (2) acquisition strategy: existing methods struggle to efficiently select representative samples, which could impede the network from learning information exclusive to the target domain. In light of these concerns, we propose a novel Slide-Window-Based Active Domain Adaptation (SWADA) method for cross-modality medical image segmentation. SWADA employs a slide-window mechanism to comprehensively explore the dependencies among different window regions, preventing redundant region selections and omissions of object boundaries. This ultimately minimizes the wastage of the annotation budget. Further, we present an innovative acquisition strategy that dynamically selects windows for annotation by considering inconsistency, uncertainty, and diversity. This guarantees the representative nature of the selected window regions, consequently enhancing the performance of cross-modality segmentation. Alongside this, we propose an inter-class distance optimization strategy to model and optimize the explicit category dependencies among various objects, strengthening the feature discriminability between different categories. Extensive empirical evidence demonstrates that SWADA consistently outperforms state-of-the-art methods across three public benchmarks.

Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction. To achieve this, we introduce a PCA-augmented multi-layer heterogeneous graph-based network that concentrates on key features throughout the encoding-decoding phase. Our approach initiates with the construction of a heterogeneous graph from various similarity metrics, which is then encoded via a graph neural network. We concatenate and integrate the resultant representation vectors to merge multi-level information. Subsequently, principal component analysis is applied to distill the most informative features, with the random forest algorithm employed for the final decoding of the integrated data. Our method outperforms six baseline models in terms of accuracy, as demonstrated by extensive experimentation. Comprehensive ablation studies, visualization of results, and in-depth case analyses further validate our framework's efficacy and interpretability, providing a novel tool for drug discovery that integrates multimodal features.

This study examines the effects and underlying mechanisms of algorithmic invasion and algorithmic transparency on users' privacy disclosure intentions within the context of electronic commerce platforms. The research consists of three studies: Studies 1 and 2 are controlled experiments with 120 participants. Study 3 is an empirical investigation, involving 400 online questionnaires. The findings reveal that algorithmic invasion negatively affects users' privacy disclosure intention, while algorithmic transparency has a positive influence. Psychological resistance and trust mediate the relationship between algorithmic invasion and privacy disclosure intention, whereas perceived risk and trust mediate the relationship between algorithmic transparency and privacy disclosure intention. Additionally, algorithmic invasion and transparency influence privacy disclosure intention sequentially through these mediators. This study contributes to understanding how algorithmic transparency and algorithmic invasion shape privacy-related behaviors.

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users’ active participation, making it impractical to monitor blood alcohol content (BAC) during driving. This article introduces BACFuse, a device-free, contactless, and noninvasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multimodal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse’s performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with an MAE of 2.13 mg/dl, showing promise for future in-car driving management paradigms.

Zero-shot text classification aims to handle the text classification task without any annotated training data, which can greatly alleviate the data scarcity problem. Current dominant approaches follow a novel text-image matching paradigm, reformulating zero-shot text classification into a text-image matching problem, which can capture the visual image information and show promising performance. Nevertheless, existing text-image matching approaches solely focus on the visual image information, ignoring the semantic knowledge embedded in the text labels. To address the challenge, in the work, we present a label-enhanced CLIP framework (Lab-CLIP) for zero-shot text classification to consider both the visual image and text label semantic information simultaneously. Specifically, LabCLIP first converts the label into the corresponding image, and then injects the text label into the corresponding label image to explicitly capture the label semantic knowledge. We conduct experiments on 8 publicly available zero-shot text classification datasets and experimental results indicate that LabCLIP outperforms previous approaches on all datasets (with 4.3% improvement on average). In addition, we provide extensive analysis on exploring how to effectively incorporate the text label information.

Assembly of the ribosome from its protein and RNA constituents has been studied extensively over the past 50 years, and experimental evidence suggests that prokaryotic ribosomal proteins undergo conformational changes during assembly. However, to date, no studies have attempted to elucidate these conformational changes. The present work utilizes computational methods to analyze protein dynamics and to investigate the linkage between dynamics and binding of these proteins during the assembly of the ribosome. Ribosomal proteins are known to be positively charged and we find the percentage of positive residues in r-proteins to be about twice that of the average protein: Lys+Arg is 18.7% for E. coli and 21.2% for T. thermophilus. Also, positive residues constitute a large proportion of RNA contacting residues: 39% for E. coli and 46% for T. thermophilus. This affirms the known importance of charge-charge interactions in the assembly of the ribosome. We studied the dynamics of three primary proteins from E. coli and T. thermophilus 30S subunits that bind early in the assembly (S15, S17, and S20) with atomic molecular dynamic simulations, followed by a study of all r-proteins using elastic network models. Molecular dynamics simulations show that solvent-exposed proteins (S15 and S17) tend to adopt more stable solution conformations than an RNA-embedded protein (S20). We also find protein residues that contact the 16S rRNA are generally more mobile in comparison with the other residues. This is because there is a larger proportion of contacting residues located in flexible loop regions. By the use of elastic network models, which are computationally more efficient, we show that this trend holds for most of the 30S r-proteins.

A cross-sensor calibration technique is developed and applied to improve upon the prelaunch radiance calibration and characterization for the Total Ozone Unit (TOU) onboard the FengYun-3/A satellite. The Level 3 products from the National Aeronautics and Space Administration Ozone Monitoring Instrument (OMI) onboard the Earth Observing System Aura are used as input to a radiative transfer model to predict the TOU radiances and characterize the biases for the measurements over the Pacific Ocean in low- and midlatitudes. The coefficients are derived from a regression algorithm to adjust the TOU radiances. It is shown that, after the measurement bias correction, the biases between the retrieved total column ozone products from the TOU with those from the OMI Total Ozone Mapping Spectrometer (TOMS)-Version 8 products and those from a set of ground-based station measurements are 3 % and 5% , respectively. The variations in the estimated total ozone amounts from the TOU are consistent with those derived from Solar Backscatter Ultraviolet Radiometer instruments and OMI for a period from January 2010 to February 2011.

In this paper, we develop an efficient moments-based permutation test approach to improve the system’s efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This approach involves the calculation of the first four moments of the permutation distribution. We propose a novel recursive method to derive these moments theoretically and analytically without any permutation. Experimental results using different test statistics are demonstrated using simulated data and real data. The proposed strategy takes advantage of nonparametric permutation tests and parametric Pearson distribution approximation to achieve both accuracy and efficiency.

There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.

It is a fascinating yet challenging problem to accurately and efficiently localize regionally distinct features between face groups in multi-dimensional signal processing and analysis. Given a data with unknown distribution and small sample size, we propose a new statistical analysis framework using hybrid randomization (i.e., permutation) tests to improve the system's efficiency in identifying distinct features. The proposed method fits the nonparametric distribution of the test statistic with Pearson distribution series. We bypass the tedious online randomization via calculating the first four moments of the permutation distribution. This can reduce the computational complexity from O(n!) to O(n2) over traditional methods for the modified Hotelling's T2test statistics. Experiments on simulated data and 3D face analysis demonstrate the efficiency, accuracy and robustness of the proposed approach.

This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points is generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second, to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, an intensity similarity measure, and statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model-based non-rigid registration and the two methods we present each have advantages under different conditions.

We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape, and combined models, respectively. Indeed, snakes, modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available.

We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model based non-rigid registration and the two methods we present each have advantages under different conditions.