Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

The study of surface-layer evaporation ducts (EDs) maintains persistent scientific significance due to their critical role in modulating tropospheric propagation. While multiple ED quantification methodologies exist, the inherent complexity of these atmospheric structures radically constrains the observational completeness achievable through singular technologies. To address this limitation, we develop a novel Bayesian fusion framework that systematically integrates multi-source knowledge through probabilistic reasoning to estimate ED from point-to-point propagation loss (PL) observations. This approach reformulates the inverse problem as a maximum likelihood estimation challenge, systematically synthesizing prior ED distributions (derived from diverse knowledge sources) with PL-constrained likelihood functions through a rigorous Bayesian approach. This probabilistic integration enables robust determination of posterior ED distributions while inherently quantifying retrieval uncertainties. Validation experiments using a dedicated dataset confirms the framework’s viability, demonstrating sufficient accuracy to support real-world marine applications.

This study introduces RFCFormer, an innovative framework for retrieving evaporation duct refractivity from radar sea clutter, which synergistically integrates a dual-stream Transformer architecture with Gramian Angular Field (GAF) methodology. The framework first converts the input sea clutter into two distinct GAF matrices, effectively transforming the raw clutter into more manageable sparse matrix representations. This transformation enhances the model’s ability to identify underlying patterns within the sea clutter, improving the accuracy of the mapping between clutter characteristics and duct parameters. The dual-stream Transformer architecture then utilizes parallel processing pathways, where coordinated self-attention mechanisms process the GAF matrices in depth, facilitating the integration of multi-scale clutter features through cross-stream feature fusion. Experimental evaluations show that RFCFormer surpasses existing approaches in both inversion accuracy and generalization capability. Furthermore, the model achieves simultaneous retrieval of four duct parameters while maintaining adaptability to diverse sea states, which conclusively demonstrates its practical utility in real-world marine environments.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

To accurately cognize the long-term variations in evaporation duct height (EDH), this letter develops a novel environmental knowledge-driven prediction model based on the Informer for long-term EDH (EKDInformer-LTEDH). The model utilizes the Informer’s powerful time series (TS) processing abilities to capture long-term dependencies in EDH. Considering the influence of the marine environment on EDH, this letter adopts a knowledge-driven method, which incorporates multiple MEPs as prior knowledge inputs into the model. The results of testing the performance show that the EKDInformer-LTEDH model has significant advantages over other models in long-term EDH prediction. Additionally, integrating MEPs into the model as environmental priori knowledge reduces prediction errors and significantly improves its long-term EDH prediction performance.

Cardinality constraint, namely, constraining the number of nonzero outputs of models, has been widely used in structural learning. It can be used for modeling the dependencies between multidimensional labels. In hashing, the final outputs are also binary codes, which are similar to multidimensional labels. It has been validated that estimating how many 1's in a multidimensional label vector is easier than directly predicting which elements are 1 and estimating cardinality as a prior step will improve the classification performance. Hence, in this article, we incorporate cardinality constraint into the unsupervised image hashing problem. The proposed model is divided into two steps: 1) estimating the cardinalities of hashing codes and 2) then estimating which bits are 1. Unlike multidimensional labels that are known and fixed in the training phase, the hashing codes are generally learned through an iterative method and, therefore, their cardinalities are unknown and not fixed during the learning procedure. We use a neural network as a cardinality predictor and its parameters are jointly learned with the hashing code generator, which is an autoencoder in our model. The experiments demonstrate the efficiency of our proposed method.

Retrieving nearest neighbors across correlated data in multiple modalities, such as image-text pairs on Facebook and video-tag pairs on YouTube, has become a challenging task due to the huge amount of data. Multimodal hashing methods that embed data into binary codes can boost the retrieving speed and reduce storage requirements. Since unsupervised multimodal hashing methods are usually inferior to supervised ones and the supervised ones require too much manually labeled data, the proposed method in this paper utilizes partially available labels to design a semisupervised multimodal hashing method. The labels for unlabeled data are treated as an interval type-2 fuzzy set and estimated by the available labels. By defuzzifying the estimated labels using hard partitioning, a supervised multimodal hashing method is used to generate binary codes. Experiments show that the proposed semisupervised method with 50% labels can get a medium performance among the compared supervised ones and achieve approximate performance to the best supervised method with 90% labels. With only 10% labels, the proposed method can still compete with the worst compared supervised one. Furthermore, the proposed label estimation method has been experimentally proven to be more feasible for a multilabeled MIRFlickr data set in a hash lookup task.

In this paper, we investigate the problem of video-based parent-child relationship prediction via human face analysis. Most existing kinship verification methods predict the parent-child relationship from single images, which cannot effectively utilize videos of human faces for kinship verification. Recently, there have been a few methods for parent-child relationship prediction based on face videos, but all of them only perform pairwise comparisons between human faces between a single parent and a single child. Thus, they cannot effectively combine information about both the father's and the mother's faces when judging the kin relationship. In this paper, we propose a new dtaaset, Familyship Face Videos in the Wild (FFVW), which was captured both in wild conditions and standard reference, to deal with this issue. The inputs of FFVW are three separate videos of a family. To our best knowledge, our paper is the first attempt at addressing this problem. In our pre-processing step, we extract four key frames from each video, before doing facial recognition and alignment. Finally, we use a convolutional neural network to make the prediction. Overall, the effectiveness of this approach is verified by experimental results, which show that our dataset outperforms previous approaches to parent-child relationship prediction.

The purpose of this study is to describe a new hybrid method based on the reciprocity theorem, the physical optics (PO) and the Kirchhoff approximation (KA) for calculating the composite electromagnetic scattering from a target above a one-dimensional rough interface. The KA method is used to investigate characteristics of electromagnetic scattering from the rough interface (including the equivalent electric current densities and the scattered field from the rough interface). The scattered field from the isolated target was simulated by the PO method. Based on the reciprocity theorem, the multiple scattering up to 3rdorder by the target and the underlying randomly rough interface was considered. The validity of our methods is shown by comparing our results with that of Method of Moments (MoM). It is found that our methods are in good agreement with MoM and has a higher computational efficiency.

This new semi-deterministic facet model is aimed at providing a more accurate result in evaluating the radar cross section (RCS) from large-scope 2-D ocean like surface. The modified equivalent current approximation (MECA) method is blended with Bragg components in calculating the elementary radar returns from each facet. Compared with the Kirchhoff Approximation (KA) method which have used in the published relevant papers, the MECA can take the scattering from each facet into consideration. The result obtained by this new facet model would be more accurate than the conventional one in small and moderate scattering angles.