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The issue of model explainability has gained significant attention in understanding the vulnerabilities and anonymous decision-making processes inherent in deep neural networks (DNN).While there has been considerable research on explainability for traditional DNN, there is a lack of exploration on the operation mechanism and explainability of reversible neural networks (RevNN).Additionally, the existing explanation methods for traditional DNN are not suitable for RevNN and suffer from issues such as excessive noise and gradient saturation.To address these limitations, a visual explanation method called visual explanation method for reversible neural network (VERN) was proposed for RevNN.VERN leverages the reversible property of RevNN and is based on the class-activation mapping mechanism.The correspondence between the feature map and the input image was explored by VERN, allowing for the mapping of classification weights of regional feature maps to the corresponding regions of the input image.The importance of each region for model decision-making was revealed through this process, which generates a basis for model decision-making.Experimental comparisons with other explanation methods on generalized datasets demonstrate that VERN achieves a more focused visual effect, surpassing suboptimal methods with up to 7.80% improvement in average drop (AD) metrics and up to 6.05% improvement in average increase (AI) metrics in recognition tasks.VERN also exhibits an 82.00% level of localization for the maximum point of the heat value.Furthermore, VERN can be applied to explain traditional DNN and exhibits good scalability, improving the performance of other methods in explaining RevNN.Furthermore, through adversarial attack analysis experiments, it is observed that adversarial attacks alter the decision basis of the model.This is reflected in the misalignment of the model’s attention regions, thereby aiding in the exploration of the operation mechanism of adversarial attacks.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

The site‐specific activation of bioorthogonal prodrugs has provided great opportunities for reducing the severe side effects of chemotherapy. However, the precise control of activation location, sustained drug production at the target site, and high bioorthogonal reaction efficiency in vivo remain great challenges. Here, we propose the construction of tumor cell membrane reactors in vivo to solve the above problems. Specifically, tumor‐targeted liposomes with efficient membrane fusion capabilities are generated to install the bioorthogonal trigger, the amphiphilic tetrazine derivative, on the surface of tumor cells. These predecorated tumor cells act as many living reactors, transforming the tumor into a "drug factory" that in situ activates an externally delivered bioorthogonal prodrug, for example intratumorally injected transcyclooctene‐caged doxorubicin. In contrast to the rapid elimination of cargo that is encapsulated and delivered by liposomes, these reactors permit stable retention of bioorthogonal triggers in tumor for 96 h after a single dose of liposomes via intravenous injection, allowing sustained generation of doxorubicin. Interestingly, an additional supplement of liposomes will compensate for the trigger consumed by the reaction and significantly improve the efficiency of the local reaction. This strategy provides a solution to the efficacy versus safety dilemma of tumor chemotherapy. [ABSTRACT FROM AUTHOR]

SUMMARY: Plant NAC transcription factors play a crucial role in enhancing cold stress tolerance, yet the precise molecular mechanisms underlying cold stress remain elusive. In this study, we identified and characterized CaNAC035, an NAC transcription factor isolated from pepper (Capsicum annuum) leaves. We observed that the expression of the CaNAC035 gene is induced by both cold and abscisic acid (ABA) treatments, and we elucidated its positive regulatory role in cold stress tolerance. Overexpression of CaNAC035 resulted in enhanced cold stress tolerance, while knockdown of CaNAC035 significantly reduced resistance to cold stress. Additionally, we discovered that CaSnRK2.4, a SnRK2 protein, plays an essential role in cold tolerance. In this study, we demonstrated that CaSnRK2.4 physically interacts with and phosphorylates CaNAC035 both in vitro and in vivo. Moreover, the expression of two ABA biosynthesis‐related genes, CaAAO3 and CaNCED3, was significantly upregulated in the CaNAC035‐overexpressing transgenic pepper lines. Yeast one‐hybrid, Dual Luciferase, and electrophoretic mobility shift assays provided evidence that CaNAC035 binds to the promoter regions of both CaAAO3 and CaNCED3 in vivo and in vitro. Notably, treatment of transgenic pepper with 50 μm Fluridone (Flu) enhanced cold tolerance, while the exogenous application of ABA at a concentration of 10 μm noticeably reduced cold tolerance in the virus‐induced gene silencing line. Overall, our findings highlight the involvement of CaNAC035 in the cold response of pepper and provide valuable insights into the molecular mechanisms underlying cold tolerance. These results offer promising prospects for molecular breeding strategies aimed at improving cold tolerance in pepper and other crops. [ABSTRACT FROM AUTHOR]