Federated cINN Clustering for Accurate Clustered Federated Learning

Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks. To address this challenge, we propose the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into different groups, avoiding mutual interference between clients with data heterogeneity, and thereby enhancing the performance of the global model. Specifically, FCCA utilizes a global encoder to transform each client's private data into multivariate Gaussian distributions. It then employs a generative model to learn encoded latent features through maximum likelihood estimation, which eases optimization and avoids mode collapse. Finally, the central server collects converged local models to approximate similarities between clients and thus partition them into distinct clusters. Extensive experimental results demonstrate FCCA's superiority over other state-of-the-art clustered federated learning algorithms, evaluated on various models and datasets. These results suggest that our approach has substantial potential to enhance the efficiency and accuracy of real-world federated learning tasks

Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without experimental data. However, their predictions are limited in accuracy as well as interpretability. Furthermore, such deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity. By combining the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. The experiments across 87 deep mutational scanning datasets underscore its superiority over both unsupervised and supervised approaches, revealing its potential in facilitating AI-guided protein design

The progress of pulmonary artery denervation

Pulmonary arterial hypertension (PAH) is a chronic pulmonary vascular disease characterized by increased pulmonary arterial pressure and pulmonary arterioles remodeling. Some studies have discovered the relationship between sympathetic nerves (SNs) and pathogenesis of PAH. This review is aimed to illustrate the location and components of SNs in the pulmonary artery, along with different methods and effects of pulmonary artery denervation (PADN). Studies have shown that the SNs distributed mainly around the main pulmonary artery (MPA) and pulmonary artery (PA) bifurcation. And the SNs could be destroyed by three ways: the chemical way, the surgical way and the catheter-based way. PADN can significantly decrease pulmonary arterial pressure rapidly, improve hemodynamic varieties, and then palliate PAH. PADN has been recognized as a prospective and effective therapy for PAH patients, especially for those with medication-refractory PAH. However, further enlarged clinical studies are needed to confirm accurate distribution of SNs in the pulmonary artery and the efficacy of PADN

PD1-based DNA vaccine amplifies HIV-1 GAG-specific CD8+ T cells in mice

Viral vector-based vaccines that induce protective CD8+ T cell immunity can prevent or control pathogenic SIV infections, but issues of preexisting immunity and safety have impeded their implementation in HIV-1. Here, we report the development of what we believe to be a novel antigen-targeting DNA vaccine strategy that exploits the binding of programmed death-1 (PD1) to its ligands expressed on dendritic cells (DCs) by fusing soluble PD1 with HIV-1 GAG p24 antigen. As compared with non-DC-targeting vaccines, intramuscular immunization via electroporation (EP) of the fusion DNA in mice elicited consistently high frequencies of GAG-specific, broadly reactive, polyfunctional, long-lived, and cytotoxic CD8+ T cells and robust anti-GAG antibody titers. Vaccination conferred remarkable protection against mucosal challenge with vaccinia GAG viruses. Soluble PD1-based vaccination potentiated CD8+ T cell responses by enhancing antigen binding and uptake in DCs and activation in the draining lymph node. It also increased IL-12-producing DCs and engaged antigen cross-presentation when compared with anti-DEC205 antibody-mediated DC targeting. The high frequency of durable and protective GAG-specific CD8+ T cell immunity induced by soluble PD1-based vaccination suggests that PD1-based DNA vaccines could potentially be used against HIV-1 and other pathogens.published_or_final_versio

Development of attractants and repellents for Tuta absoluta based on plant volatiles from tomato and eggplant

IntroductionTuta absoluta is currently considered one of the most devastating invasive pests of solanaceous plants worldwide, causing severe damage to the tomato industry. Insects use volatile organic compounds (VOCs) to locate host plant for feeding and oviposition. Those VOCs could be developed as lures for pest monitoring and control.MethodsIn this study, the differentially accumulated VOCs between the preferred host (tomato) and non-preferred host (eggplant) were analyzed by GC–MS method, and their roles on female T. absoluta host selection and egg laying behaviors were investigated using electroantennography (EAG), olfactometer and cage experiments.ResultsA total of 39 differentially accumulated VOCs were identified in tomato and eggplant. Strong EAG signals were obtained in 9 VOCs, including 5 VOCs highly accumulated in tomato and 4 VOCs highly accumulated in eggplant. Further olfactometer bioassays showed that 4 compounds (1-nonanol, ethyl heptanoate, ethyl octanoate and o-nitrophenol) were attractive to T. absoluta females, while 5 compounds (2-phenylethanol, 2-pentylfuran, trans,trans-2,4-nonadienal, 2-ethyl-5-methylpyrazine and trans-2-nonenal) were repellent, indicating that VOCs from host plants play important roles in host plant preferences. The attractive activities of 1-nonanol and ethyl octanoate, as well as the repellent activities of trans,trans-2,4-nonadienal and trans-2-nonenal, were further confirmed in cage experiments.DiscussionIn this study, two attractants and two repellents for T. absoluta were developed from plant released VOCs. Our results could be useful to enhance the development of eco-friendly and sustainable pest management strategies for T. absoluta

Hyperspectral Multispectral Image Fusion via Fast Matrix Truncated Singular Value Decomposition

Recently, methods for obtaining a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) and high spatial resolution multispectral image (HR-MSI) have become increasingly popular. However, most fusion methods require knowing the point spread function (PSF) or the spectral response function (SRF) in advance, which are uncertain and thus limit the practicability of these fusion methods. To solve this problem, we propose a fast fusion method based on the matrix truncated singular value decomposition (FTMSVD) without using the SRF, in which our first finding about the similarity between the HR-HSI and HR-MSI is utilized after matrix truncated singular value decomposition (TMSVD). We tested the FTMSVD method on two simulated data sets, Pavia University and CAVE, and a real data set wherein the remote sensing images are generated by two different spectral cameras, Sentinel 2 and Hyperion. The advantages of FTMSVD method are demonstrated by the experimental results for all data sets. Compared with the state-of-the-art non-blind methods, our proposed method can achieve more effective fusion results while reducing the fusing time to less than 1% of such methods; moreover, our proposed method can improve the PSNR value by up to 16 dB compared with the state-of-the-art blind methods

Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning

Hyperspectral image (HSI) clustering is a major challenge due to the redundant spectral information in HSIs. In this paper, we propose a novel deep subspace clustering method that extracts spatial–spectral features via contrastive learning. First, we construct positive and negative sample pairs through data augmentation. Then, the data pairs are projected into feature space using a CNN model. Contrastive learning is conducted by minimizing the distances of positive pairs and maximizing those of negative pairs. Finally, based on their features, spectral clustering is employed to obtain the final result. Experimental results gained over three HSI datasets demonstrate that our proposed method is superior to other state-of-the-art methods

Fast and Stable Hyperspectral Multispectral Image Fusion Technique Using Moore–Penrose Inverse Solver

Fusion low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI) are important methods for obtaining high-resolution hyperspectral images (HR-HSI). Some hyperspectral image fusion application areas have strong real-time requirements for image fusion, and a fast fusion method is urgently needed. This paper proposes a fast and stable fusion method (FSF) based on matrix factorization, which can largely reduce the computational workloads of image fusion to achieve fast and efficient image fusion. FSF introduces the Moore–Penrose inverse in the fusion model to simplify the estimation of the coefficient matrix and uses singular value decomposition (SVD) to simplify the estimation of the spectral basis, thus significantly reducing the computational effort of model solving. Meanwhile, FSF introduces two multiplicative iterative processes to optimize the spectral basis and coefficient matrix to achieve stable and high-quality fusion. We have tested the fusion method on remote sensing and ground-based datasets. The experiments show that our proposed method can achieve the performance of several state-of-the-art algorithms while reducing execution time to less than 1% of such algorithms

FPGA-Based BNN Architecture in Time Domain with Low Storage and Power Consumption

With the increasing demand for convolutional neural networks (CNNs) in many edge computing scenarios and resource-limited settings, researchers have made efforts to apply lightweight neural networks on hardware platforms. While binarized neural networks (BNNs) perform excellently in such tasks, many implementations still face challenges such as an imbalance between accuracy and computational complexity, as well as the requirement for low power and storage consumption. This paper first proposes a novel binary convolution structure based on the time domain to reduce resource and power consumption for the convolution process. Furthermore, through the joint design of binary convolution, batch normalization, and activation function in the time domain, we propose a full-BNN model and hardware architecture (Model I), which keeps the values of all intermediate results as binary (1 bit) to reduce storage requirements by 75%. At the same time, we propose a mixed-precision BNN structure (model II) based on the sensitivity of different layers of the network to the calculation accuracy; that is, the layer sensitive to the classification result uses fixed-point data, and the other layers use binary data in the time domain. This can achieve a balance between accuracy and computing resources. Lastly, we take the MNIST dataset as an example to test the above two models on the field-programmable gate array (FPGA) platform. The results show that the two models can be used as neural network acceleration units with low storage requirements and low power consumption for classification tasks under the condition that the accuracy decline is small. The joint design method in the time domain may further inspire other computing architectures. In addition, the design of Model II has certain reference significance for the design of more complex classification tasks