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The swine industry annually suffers significant economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV). Because the available commercial vaccines have limited protective efficacy against epidemic PRRSV, there is an urgent need for innovative solutions. Nanoparticle vaccines induce robust immune responses and have become a promising direction in vaccine development. In this study, we designed and produced a self-assembling nanoparticle vaccine derived from thermophilic archaeal ferritin to combat epidemic PRRSV. First, multiple T cell epitopes targeting viral structural proteins were identified by IFN-γ screening after PRRSV infection. Three different self-assembled nanoparticles with epitopes targeting viral GP3, GP4, and GP5 proteins were constructed and mixed to generate a FeCocktail vaccine. Experiments showed that the FeCocktail vaccine effectively activated CD4+and CD8+T cells and effector memory T cells in mice. Piglets immunized with the FeCocktail vaccine generated specific antibodies and exhibited increased levels of PRRSV-specific IFN-γ produced by functional CD4+and CD8+cells. The FeCocktail also provided protective efficacy against PRRSV challenge, including mitigation of clinical symptoms, reduction of viral loads in serum and lungs, and the alleviation of lung tissue damage. In conclusion, this study offers a promising candidate vaccine for combating epidemic PRRSV, and affirms the utility of nanoparticle protein as a platform for next-generation PRRSV vaccine development.

Optical remote sensing images have extensive application value, but cloud contamination greatly limits their potential use in the field of geographic information. Cloud removal aims to restore clear, unobstructed images from cloud-covered ones for subsequent in-depth analysis. Due to severe cloud cover problems such as thick clouds in some areas of remote sensing images, cloud removal tasks have become challenging. Recently, many methods have attempted to incrementally fill in obscured regions by fusing cloud-free information from multitemporal data. However, most of these methods fail to effectively utilize the interaction among different temporal data, and some information of data is easily lost in the process of deep transmission, this causes problems such as inadequate cloud removal and blurred recovery of ground under the clouds. Therefore, we propose a multistream complementary generative adversarial network (MSC-GAN) for cloud removal using multitemporal data. First, it employs a multistream complementary (MSC) architecture in the down-sampling feature encoding stage to effectively promote the interaction of feature information across multitemporal data, alleviating information loss as network depth increases. Second, to reduce the feature blur, we design a group feature reweighting (GFR) module as a complementary connection of long-distance information, in which the grouping learning and multidimensional parallel architecture can cost-effectively enhance semantic fusion between low-level and high-level features. Moreover, a channel enhancement method is introduced to assist in processing the underlying transition information, minimizing the interference of invalid information. Experimental results on multiple benchmark datasets under a series of image quality assessment metrics demonstrate the effectiveness of the proposed method.

Deep Neural Networks (DNNs) have gained widespread adoption in diverse fields, including image classification, object detection, and natural language processing. However, training large-scale DNN models often encounters significant memory bottlenecks, which ask for efficient management of extensive tensors. Heterogeneous memory system, which combines persistent memory (PM) modules with traditional DRAM, offers an economically viable solution to address tensor management challenges during DNN training. However, existing memory management methods on heterogeneous memory systems often lead to low PM access efficiency, low bandwidth utilization, and incomplete analysis of model characteristics. To overcome these hurdles, we introduce an efficient tensor management approach, DeepTM, tailored for heterogeneous memory to alleviate memory bottlenecks during DNN training. DeepTM employs page-level tensor aggregation to enhance PM read and write performance and executes contiguous page migration to increase memory bandwidth. Through an analysis of tensor access patterns and model characteristics, we quantify the overall performance and transform the performance optimization problem into the framework of Integer Linear Programming. Additionally, we achieve tensor heat recognition by dynamically adjusting the weights of four key tensor characteristics and develop a global optimization strategy using Deep Reinforcement Learning. To validate the efficacy of our approach, we implement and evaluate DeepTM, utilizing the TensorFlow framework running on a PM-based heterogeneous memory system. The experimental results demonstrate that DeepTM achieves performance improvements of up to 36% and 49% compared to the current state-of-the-art memory management strategies AutoTM and Sentinel, respectively. Furthermore, our solution reduces the overhead by 18 times and achieves up to 29% cost reduction compared to AutoTM.