Low-Dose CT (LDCT) denoising is critical for minimizing radiation exposure while maintaining diagnostic image quality. Conventional model-based and transform domain approaches often result in over-smoothed images with diminished structural details. To overcome these limitations, deep learning techniques have emerged as powerful alternatives, leveraging data-driven models to achieve superior noise suppression and structural preservation. In this work, we propose a novel model named FD-GAN that incorporates dual discriminators operating in both the image and Fourier domains. By jointly optimizing spatial and Fourier domain information, FD-GAN addresses common issues such as over-smoothing, loss of high-frequency details, and training instability observed in traditional Generative Adversarial Network (GAN) models. Our framework integrates pixel-wise reconstruction loss with adversarial and Fourier domain losses to ensure global consistency and local detail enhancement. Extensive experiments on the NIHAAPM-Mayo Clinic LDCT dataset demonstrate that FD-GAN outperforms conventional Mean Squared Error (MSE)-based and single-domain GAN models in both quantitative metrics and visual quality. Furthermore, FD-GAN offers improved stability during training and robustness against artifacts, making it a promising solution for clinical LDCT denoising applications.

The incidence of multi-drug resistant bacterial species is rapidly increasing. To avoid antibiotic misuse and further exacerbation of this health crisis, clinicians should obtain a swift and accurate diagnosis of bacterial resistance. Existing laboratory-based antibiotic susceptibility testing (AST) methods are slow and laborious. Our study puts forward an efficient end-to-end image classification network using deep learning for predicting antibiotic resistance from fluorescence microscopy images of bacteria. Our resulting PAS-Net model integrates two parallel branches: the convolution branch (C-branch), employing ConvNeXt, and the Transformer branch (T-branch), utilizing Vision Transformer (ViT). To bridge these branches effectively, we introduce the feature interaction unit (FIU), which facilitates the integration of local features generated by the C-branch and global representations by the T-branch interactively. Additionally, we present a novel attention mechanism which reduces computational costs while maintaining the global representation capability. Experimental results, conducted on a fluorescence image dataset and public open datasets, suggest the promising potential of our method for rapidly diagnosing antibiotic susceptibility in bacteria and achieving high prediction performance. The code is publicly available at:https://github.com/Td270/PAS-NetNote to Practitioners—The rise of multi-drug resistant bacteria poses a significant challenge to public health, requiring faster and more accurate methods to predict bacterial antibiotic susceptibility. Current laboratory methods are slow and often impractical for time-sensitive clinical decisions. This work introduces PAS-Net, a deep learning framework designed to predict antibiotic resistance directly from fluorescence microscopy images of bacteria. Practitioners can apply this method in clinical and laboratory settings to significantly reduce diagnosis time, potentially enabling real-time decision-making. By combining convolutional and Transformer-based architectures, PAS-Net captures both detailed local patterns and global structural information, leading to improved predictive accuracy. This method is particularly suited for labs with access to fluorescence imaging equipment but limited by the slow throughput of traditional assays. However, the approach requires high-quality imaging and computational resources, which could limit its immediate application in low-resource settings. Future research should focus on adapting the model for broader imaging modalities and reducing its dependency on computational infrastructure. Beyond clinical diagnostics, the approach could be extended to other microscopy-based applications, such as microbial ecology or industrial bioprocess monitoring, where rapid and accurate phenotypic assessments are required.

Federated learning (FL), which enables collaborative learning without revealing raw data, is an emerging topic in privacy-preserving machine learning. Based on our experiences in thousands of real-world applications, time-series feature extraction plays a significant role in improving model quality. In this work, we propose a system automatically integrating time series feature extraction for training FL models. Our experiments show that by adopting time series feature extraction, the model accuracy (AUC) is improved by 3% on average, and recall is increased by 10% in recommender systems. We have open-sourced the project https://github.com/4paradigm/tsfe and provided a step by step demonstration on how audiences can use our system to create their own FL pipeline that extracts time series features. Demonstration video at: https://youtu.be/UW27dWT-ays

Object handover is one of the basic abilities for the robot to interact with the human. Most of the previous works only focus on the limited handover scenarios where the robot uses one hand to give small objects to the human. In this article, we design a bidirectional bimanual handover system that enables the robot to both give and receive the big planar object with vertical grasp posture. In addition to the basic object handover function, the designed handover system also integrates a position adjustment mechanism to improve the human experience. According to different task states, the system is divided into four modes. In each mode, the robot performs a subtask and switches to the next mode at an appropriate time. We propose a two-finger grip force controller and a dual-arm admittance NN controller to control the robot to generate actual motions. By applying specific locating, trajectory planning, and signal identifying methods, we implement the designed handover system on a Baxter robot. The system is tested on two wooden plates with different widths, thicknesses, and weights. The results show that the robot can perform the handover task safely and effectively with the designed handover system.Note to Practitioners—This article aims to solve the limitation that the robot can only hand over small objects with one hand in the human–robot handover systems. In daily life, especially in carrying tasks, many objects, such as windows, wooden boards, and big frames, may also be handed over to each other. These objects can be classified as big planar objects. To enable the robot to hand over this kind of object with the human, we design a bidirectional bimanual human–robot handover system. The designed system has three main functions. First, the robot can receive the big planar object from the human and hold the object safely with two hands, which is impossible in a single-hand handover system because the weight and size of the object are large. Then, the robot can help the human hold the object or transport it to other places. Second, the robot can adjust the object handover position according to the human’s intentions while holding the object. Because the size of objects and the height of humans may different, or some tasks require that the human position be higher or lower than the robot, the current object holding position may be hard for the current human operator to take over the object. With this function, the human can move the object to an appropriate position and then take it comfortably. Third, the robot releases the object only when it gets a clear signal. Before that, the robot always grasps the object safely, and the human can freely adjust his posture. With the designed handover system, the robot can cooperate with the human to complete more tasks indoors or in factories.

The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.

Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and clinical medicine. However, designing biological experiments to validate disease-related miRNAs is usually time-consuming and expensive. Therefore, it is urgent to design effective computational methods for predicting potential miRNA-disease associations. Inspired by the great progress of graph neural networks in link prediction, we propose a novel graph auto-encoder model, named GAEMDA, to identify the potential miRNA-disease associations in an end-to-end manner. More specifically, the GAEMDA model applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes' neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information. Then, the embeddings of miRNA and disease nodes are fed into a bilinear decoder to identify the potential links between miRNA and disease nodes. The experimental results indicate that GAEMDA achieves the average area under the curve of $93.56\pm 0.44\%$ under 5-fold cross-validation. Besides, we further carried out case studies on colon neoplasms, esophageal neoplasms and kidney neoplasms. As a result, 48 of the top 50 predicted miRNAs associated with these diseases are confirmed by the database of differentially expressed miRNAs in human cancers and microRNA deregulation in human disease database, respectively. The satisfactory prediction performance suggests that GAEMDA model could serve as a reliable tool to guide the following researches on the regulatory role of miRNAs. Besides, the source codes are available at https://github.com/chimianbuhetang/GAEMDA.

BACKGROUND: The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades. RESULTS: Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets. CONCLUSIONS: All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.

Modern multiprocessor systems-on-chip (MPSoCs) are expected to handle multi-application use cases. As the number and complexity of these applications scale, resource allocation to meet the application throughput requirement is becoming quite a challenge. In this paper, a complete design flow is proposed for partially reconfigurable heterogeneous MPSoC platforms. The proposed flow determines the minimum resources required to map and guarantee the throughput of applications in all use-cases. Further, a suitable mapping for each application is chosen so that energy consumption is minimized. Experiments conducted with a set of synthetic benchmarks and real-life applications clearly demonstrate the advantage of our approach over homogeneous or fully reconfigurable designs. The proposed design flow achieves more than 50% energy savings when the number of configurations is not optimized. With configuration-optimization, our flow results in 75% reduction in the number of configurations with 5% reduction in energy.