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Immunological memory is the key source of protective immunity against pathogens. At the current stage of the COVID-19 pandemic, heterologous combinations of exposure to viral antigens during infection and/or vaccination shape a distinctive immunological memory. Immune imprinting, the downside of memory, might limit the generation of de novo immune response against variant infection or the response to the next-generation vaccines. Here, we review mechanistic basis of immune imprinting by focusing on B cell immunobiology and discuss the extent to which immune imprinting is harmful, as well as its effect on SARS-CoV-2 infection and vaccination. [ABSTRACT FROM AUTHOR]

The advancements in hardware have drawn more attention than ever to high-quality offline rendering with modern stream processors, both in the industry and in research fields. However, the graphics APIs are fragmented and existing shading languages lack high-level constructs such as polymorphism, which adds complexity to developing and maintaining cross-platform high-performance renderers. We present LuisaRender1, a high-performance rendering framework for modern stream-architecture hardware. Our main contribution is an expressive C++-embedded DSL for kernel programming with JIT code generation and compilation. We also implement a unified runtime layer with resource wrappers and an optimized Monte Carlo renderer. Experiments on test scenes show that LuisaRender achieves much higher performance than existing research renderers on modern graphics hardware, e.g., 5--11× faster than PBRT-v4 and 4--16× faster than Mitsuba 3. [ABSTRACT FROM AUTHOR]

Patrol missiles are a common type of unmanned aerial vehicle, which can be efficiently used for reconnaissance and sensing. In this work, target detection and the damage assessment of typical mobile ground targets by patrol missiles are studied, and a new method, combining the YOLO v3 with the VGG networks, is proposed for the problem. Specifically, with YOLO v3 as the precursor, the proposed method can detect, classify, and localize ground targets accurately and quickly. Then, the image blocks of detected targets are fed into the lightweight VGG networks, which can evaluate their damage level coarsely. Meanwhile, based on class activation mapping (CAM) and deconvolution, we further analyse the activation intensity of clustered convolution kernels, which helps determine whether the targets' key components are destroyed. Unlike traditional image change detection methods, which require images before and after a strike for comparison, the proposed method learns the target model through extensive training and can assess the target's damage status in a timely and online manner. Compared to previous learning-based methods, our detailed analysis with convolutional feature visualization of the damaged targets and their components gives a more interpretable perspective. Finally, Unity simulation experiments prove the proposed method's effectiveness, which improves the accuracy of damage level assessment by 16.0% and 8.8% compared with traditional image-change-detection-based methods and the two-CNN learning-based method. The convolutional feature clustering method evaluates the status of the targets' key components with an accuracy of 72%. [ABSTRACT FROM AUTHOR]

Spherical Harmonics (SH) have been proven as a powerful tool for rendering, especially in real-time applications such as Precomputed Radiance Transfer (PRT). Spherical harmonics are orthonormal basis functions and are efficient in computing dot products. However, computations of triple product and multiple product operations are often the bottlenecks that prevent moderately high-frequency use of spherical harmonics. Specifically state-of-the-art methods for accurate SH triple products of order n have a time complexity of O(n5), which is a heavy burden for most real-time applications. Even worse, a brute-force way to compute k-multiple products would take O(n2k) time. In this paper, we propose a fast and accurate method for spherical harmonics triple products with the time complexity of only O(n3), and further extend it for computing k-multiple products with the time complexity of O(kn3 + k2n2log(kn)). Our key insight is to conduct the triple and multiple products in the Fourier space, in which the multiplications can be performed much more efficiently. To our knowledge, our method is theoretically the fastest for accurate spherical harmonics triple and multiple products. And in practice, we demonstrate the efficiency of our method in rendering applications including mid-frequency relighting and shadow fields. [ABSTRACT FROM AUTHOR]

This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. Then, the graph importance consistency is also proposed, which is one of the critical goals of the flocking task. Moreover, the importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and structure-related aggregation of graph data. Next, by employing IDGCNs-based imitation learning, a distributed and scalable flocking strategy is obtained, and its performance is very close to the centralized strategy template while considering communication constraints. To speed up and simplify the training process, we train the flocking strategy with a small number of agents and set restrictions on communication. Finally, various simulation experiments are executed to verify the advantages of the obtained strategy in terms of realizing the swarm consistency and improving the swarm robustness. The results also show that the performance is well maintained while the scale of agents expands (tested with 20, 30, 40 robots). [ABSTRACT FROM AUTHOR]

Deep learning techniques such as convolutional neural network and capsule network have attained good results in iris recognition. However, due to the influence of eyelashes, skin, and background noises, the model often needs many iterations to retrieve informative iris patterns. Also because of some nonideal situations, such as reflection of glasses and facula on the eyeball, it is hard to detect the boundary of pupil and iris perfectly. Under such a circumstance, discarding the rest parts beyond the boundary may cause losing useful information. Hence, we use Gaussian, triangular fuzzy average, and triangular fuzzy median smoothing filters to preprocess the image by fuzzifying the region beyond the boundary to improve the signal-to-noise ratios. We applied the enhanced images through fuzzy operations to train deep learning methods, which speeds up the process of convergence and also increases the recognition accuracy rate. The saliency maps show that fuzzified image filters make the images more informative for deep learning. The proposed fuzzy operation of images may be a robust technique in many other deep-learning applications of image processing, analysis, and prediction. [ABSTRACT FROM AUTHOR]

In recent years, the widespread use of antibiotics in intensive grouper mariculture has resulted in the ineffectiveness of antibiotic treatment, leading to an increasing incidence of diseases caused by bacteria, viruses, and parasites, causing serious economic losses. Hence, it is crucial to develop alternative strategies to antibiotics for healthy and sustainable development of the mariculture industry. Here, we aimed to screen host gut-derived probiotics and evaluate its effects on growth and immunity of grouper. In this study, 43 bacterial strains were isolated from the intestine of the hybrid grouper (Epinephelus fuscoguttatus♀ × E. lanceolatus♂), and a potential probiotic strain G1-26, which can efficiently secrete amylase, protease, and lipase, was obtained using different screening mediums. Based on 16S rDNA sequencing, the potential probiotic strain G1-26 was identified as Vibrio fluvialis. The results of a biological characteristic evaluation showed that V. fluvialis G1-26 could grow at 25–45 °C, pH 5.5–7.5, salinity 10–40, and bile salt concentration 0–0.030%, and produce amylase, lipase, and protease under different culture conditions. Additionally, V. fluvialis G1-26 is sensitive to many antibiotics and does not exhibit aquatic biotoxicity. Subsequently, hybrid groupers were fed diets containing V. fluvialis G1-26 at different concentrations (0, 106, 108, and 1010 CFU/g) for 60 d. The results showed that V. fluvialis G1-26 at 108 CFU/g did not significantly affect the growth performance of the hybrid grouper (P > 0.05). V. fluvialis G1-26 supplementation at 108 and 1010 CFU/g significantly promoted the relative expression of immune-related genes in hybrid groupers (TLR3 , TLR5 , IL-1β , IL-8 , IL-10 , CTL , LysC , TNF-2 , and MHC-2) and improved the activities of alkaline phosphatase, acid phosphatase, total superoxide dismutase, and total protein in the liver. In conclusion, V. fluvialis G1-26, a potential probiotic strain isolated from the intestine of the hybrid grouper, can be used as an effective immunopotentiator at an optimal dose of 108 CFU/g diet. Our results provide a scientific basis for the development and utilization of probiotics in the grouper mariculture industry. • 43 bacterial strains were isolated from the intestine of the hybrid grouper. • A strain G1-26 identified as Vibrio fluvialis with strong digestive enzyme production ability was isolated. • The G1-26 feeding at 108 and 1010 CFU/g could enhance the immune performance and the optimal dosage is 108 CFU/g diet. [ABSTRACT FROM AUTHOR]