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Abstract The emergence of adapted variants of the SARS-CoV-2 virus has led to a surge in breakthrough infections worldwide. A recent analysis of immune responses in people who received inactivated vaccines has revealed that individuals with no prior infection have limited resistance to Omicron and its sub-lineages, while those with previous infections exhibit a significant amount of neutralizing antibodies and memory B cells. However, specific T-cell responses remain largely unaffected by the mutations, indicating that T-cell-mediated cellular immunity can still provide protection. Moreover, the administration of a third dose of vaccine has resulted in a marked increase in the spectrum and duration of neutralizing antibodies and memory B cells in vivo, which has enhanced resistance to emerging variants such as BA.2.75 and BA.2.12.1. These results highlight the need to consider booster immunization for previously infected individuals and the development of novel vaccination strategies. The rapid spread of adapted variants of the SARS-CoV-2 virus presents a significant challenge to global health. The findings from this study underscore the importance of tailoring vaccination strategies based on individual immune backgrounds and the potential need for booster shots to combat emerging variants. Continued research and development are crucial to discovering new immunization strategies that will effectively protect public health against the evolving virus.

Abstract Variants of concern (VOCs) like Delta and Omicron, harbor a high number of mutations, which aid these viruses in escaping a majority of known SARS-CoV-2 neutralizing antibodies (NAbs). In this study, Rhesus macaques immunized with 2-dose inactivated vaccines (Coronavac) were boosted with an additional dose of homologous vaccine or an RBD-subunit vaccine, or a bivalent inactivated vaccine (Beta and Delta) to determine the effectiveness of sequential immunization. The booster vaccination significantly enhanced the duration and levels of neutralizing antibody titers against wild-type, Beta, Delta, and Omicron. Animals administered with an indicated booster dose and subsequently challenged with Delta or Omicron variants showed markedly reduced viral loads and improved histopathological profiles compared to control animals, indicating that sequential immunization could protect primates against Omicron. These results suggest that sequential immunization of inactivated vaccines or polyvalent vaccines could be a potentially effective countermeasure against newly emerging variants.

At present, differential power analysis (DPA) is one of the most important threats to the security of block ciphers in chips.When the collected power trace is insufficient, DPA is vulnerable to ghost peak caused by the difference mean value generated by the wrong key.Based on DPA, a incidence matrix differential power analysis (IMDPA) was proposed which could effectively resist ghost peak.The prediction difference mean matrix was constructed to avoid the influence of the non leaking interval on the key guessing of the leaking interval by using the weak correlation of the guessing key in the non leaking interval.The proposed IMDPA was tested in different leak intervals of AES-128 algorithm.The results show that compared with traditional DPA, IMDPA requires less (up to 85%) power trace to guess the correct key.At the same time, the key guessing efficiency of AES-128 under the implementation of protective measures by IMDPA still has obvious advantages.In order to further verify the universality of IMDPA in block ciphers, experimental verification is conducted on SM4 algorithm.Compared with traditional DPA, IMDPA requires less (up to 87.5%) power traces to guess the correct key.

Existing chaotic systems have many defects in engineering applications, such as discontinuous chaotic parameter range, weak chaos, uneven output of chaotic sequences and dynamic degradation.Therefore, a generalized 2D polynomial chaotic mapping model was proposed.By setting different control parameters and the highest degree of polynomial, a series of 2D robust chaotic maps with specific Lyapunov exponent could be obtained.In order to avoid the output of the second state equation collapsing to a fixed value, a random disturbance variable which did not change with time was introduced.Finally, a numerical example was given to verify the effectiveness of the proposed model, and the dynamic analysis showed that the mapping has complex dynamic behavior.Finally, the system was applied to information transmission technology.Compared with other chaotic maps, the system can achieve lower bit error rate, which indicates that the chaotic map is more suitable for chaotic information transmission.

PurposeThe lack of finely annotated pathologic data has limited the application of deep learning systems (DLS) to the automated interpretation of pathologic slides. Therefore, this study develops a robust self-supervised learning (SSL) pathology diagnostic system to automatically detect malignant melanoma (MM) in the eyelid with limited annotation.DesignDevelopment of a self-supervised diagnosis pipeline based on a public dataset, then refined and tested on a private, real-world clinical dataset.SubjectsA. Patchcamelyon (PCam)-a publicly accessible dataset for the classification task of patch-level histopathologic images. B. The Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) dataset – 524,307 patches (small sections cut from pathologic slide images) from 192 H&E-stained whole-slide-images (WSIs); only 72 WSIs were labeled by pathologists.MethodsPatchcamelyon was used to select a convolutional neural network (CNN) as the backbone for our SSL-based model. This model was further developed in the ZJU-2 dataset for patch-level classification with both labeled and unlabeled images to test its diagnosis ability. Then the algorithm retrieved information based on patch-level prediction to generate WSI-level classification results using random forest. A heatmap was computed for visualizing the decision-making process.Main outcome measure(s)The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the algorithm in identifying MM.ResultsResNet50 was selected as the backbone of the SSL-based model using the PCam dataset. This algorithm then achieved an AUC of 0.981 with an accuracy, sensitivity, and specificity of 90.9, 85.2, and 96.3% for the patch-level classification of the ZJU-2 dataset. For WSI-level diagnosis, the AUC, accuracy, sensitivity, and specificity were 0.974, 93.8%, 75.0%, and 100%, separately. For every WSI, a heatmap was generated based on the malignancy probability.ConclusionOur diagnostic system, which is based on SSL and trained with a dataset of limited annotation, can automatically identify MM in pathologic slides and highlight MM areas in WSIs by a probabilistic heatmap. In addition, this labor-saving and cost-efficient model has the potential to be refined to help diagnose other ophthalmic and non-ophthalmic malignancies.

Application of algicidal bacteria is a potential bio-technique to inhibit harmful algal growth and remove algal blooms. At present, algicidal bacteria are mainly used in the form of freely suspended cells. In this study, the bacterium Brevundimonas sp. AA06 against Microcystis aeruginosa was isolated from soil and the algicidal effects of immobilized strain AA06 were investigated. The spherically immobilized beads (φ = 3.0~4.0 mm) were prepared by polyvinyl alcohol (PVA) and sodium alginate (SA). Scanning electron microscopy (SEM) images of polyvinyl alcohol-sodium alginate (PVA-SA) beads showed that there were a large number of pores inside and that the bacterial cells adhered to the skeleton. Cyanobacterial dose-relationship tests showed that the PVA-SA beads exhibited weaker algicidal effects than freely suspended cells. At an initial M. aeruginosa cell density of 2.0 × 109 cells/L, PVA-SA beads inhibited the growth of M. aeruginosa for up to 60 days, which was much longer than freely suspended cells (34 days). When M. aeruginosa was exposed to PVA-SA beads at EC70,96h (concentrations for 70% of maximal algicidal effect after 96 h), the total microcystin-LR (MC-LR) concentrations (intra and extra cellular) remained stable at 40.0±5 μg/L, with intracellular MC-LR concentrations declining. [ABSTRACT FROM AUTHOR]

We propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the condition that nonlinear uncertainties and external disturbances are completely unknown. Stability analysis showed that the error system asymptotically tended to zero in combination with the relevant lemma. Numerical simulation results show the effectiveness of the controller.

In recent years, side-channel analysis technology has been one of the greatest threats to information security. SCA decrypts the key information in the encryption device by establishing an appropriate leakage model. As one of many leakage models, the XOR operation leakage proposed by linear regression has typical representative significance in side-channel analysis. However, linear regression may have the problem of irreversibility of a singular matrix in the modeling stage of template analysis and the problem of poor data fit in the template analysis after the cryptographic algorithm is masked. Therefore, this paper proposes a second-order template analysis method based on orthogonal transformation nonlinear regression. The irreversibility of a singular matrix and the inaccuracy of the model are solved by orthogonal transformation and adding a negative direction to the calculation of the regression coefficient matrix. In order to verify the data fitting effect of the constructed template, a comparative experiment of template analysis based on regression, Gaussian, and clustering was carried out on SAKURA-G. The experimental results show that the second-order template analysis based on orthogonal transformation nonlinear regression can complete key recovery without sacrificing the performance of regression estimation. Under the condition of high noise and high order template analysis, the established template has good universality.

Chaos is considered as a natural candidate for encryption systems owing to its sensitivity to initial values and unpredictability of its orbit. However, some encryption schemes based on low-dimensional chaotic systems exhibit various security defects due to their relatively simple dynamic characteristics. In order to enhance the dynamic behaviors of chaotic maps, a novel 3D infinite collapse map (3D-ICM) is proposed, and the performance of the chaotic system is analyzed from three aspects: a phase diagram, the Lyapunov exponent, and Sample Entropy. The results show that the chaotic system has complex chaotic behavior and high complexity. Furthermore, an image encryption scheme based on 3D-ICM is presented, whose security analysis indicates that the proposed image encryption scheme can resist violent attacks, correlation analysis, and differential attacks, so it has a higher security level.

An S-box is the most important part of a symmetric encryption algorithm. Various schemes are put forward by using chaos theory. In this paper, a construction method of S-boxes with good cryptographic properties is proposed. The output of an S-box can be regarded as a group of Boolean functions. Therefore, we can use the different properties of chaos and Bent functions to generate a random Bent function with a high nonlinearity. By constructing a set of Bent functions as the output of an S-box, we can create an S-box with good cryptological properties. The nonlinearity, differential uniformity, strict avalanche criterion and the independence criterion of output bits are then analyzed and tested. A security analysis shows that the proposed S-box has excellent cryptographic properties.

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.