Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult t...Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.展开更多
The method for malware detection based on Application Programming Interface(API)call sequences,as a primary research focus within dynamic detection technologies,currently lacks attention to subsequences of API calls,t...The method for malware detection based on Application Programming Interface(API)call sequences,as a primary research focus within dynamic detection technologies,currently lacks attention to subsequences of API calls,the variety of API call types,and the length of sequences.This oversight leads to overly complex call sequences.To address this issue,a dynamic malware detection approach based on multiple subsequences is proposed.Initially,APIs are remapped and encoded,with the introduction of percentile lengths to process sequences.Subsequently,a combination of One-Dimensional Convolutional Neural Network(1D-CNN)and Bidirectional Long Short-Term Memory(Bi-LSTM)networks,along with an attention mechanism,is employed to extract features from subsequences of varying lengths for feature fusion and classification.Experiments conducted on two widely used public API-based datasets,namelyMalBehavD-V1 and Alibaba Cloud,demonstrate that the proposedmethod reduces the number of API call types by approximately 20%compared to representative deep learning–based API sequence detection methods,while achieving a peak accuracy of 98.70%.Additionally,experimental results indicate that sequence length at the 95th percentile represents the optimal solution that balances classification performance and computational efficiency.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2023YFC2410403。
文摘Background:Rats are often used to prepare skin defect models.However,the skin defect sizes of the models prepared by researchers are different,and the lack of consensus on the critical-size defect makes it difficult to compare their research results.Methods:The time for wound closure was evaluated and recorded through gross observation.The regression equation between the healing time and the diameter of skin defect was established,which can be used to predict the healing time for a certain skin defect size in rats.Histochemical and immunohistochemical staining was used to observe the regeneration and reconstruction of skin appendages,and the functional skin repair was quantitatively scored.Results:The critical-size defect of rats was determined based on the maximum capacity of structural skin repair,and the functional skin repair was quantitatively scored based on the regeneration and reconstruction of skin appendages.The allowable range of critical-size skin defect of SD rats lies between 45 and 50 mm in diameter.The concept of structural repair and the category of functional repair of injured skin are put forward.The regression equation between the structural skin healing time and defect diameters is established.Conclusion:The allowable range of skin critical-size defect of SD rats lies between 45 and 50 mm in diameter.The regression equation between the structural skin healing time and defect diameters can be used to predict the healing time for a certain skin defect size in rats.
基金supported by the National Natural Science Foundation of China(62262020)the Graduate Education Innovation Project of Hubei Minzu University(MYK2024025).
文摘The method for malware detection based on Application Programming Interface(API)call sequences,as a primary research focus within dynamic detection technologies,currently lacks attention to subsequences of API calls,the variety of API call types,and the length of sequences.This oversight leads to overly complex call sequences.To address this issue,a dynamic malware detection approach based on multiple subsequences is proposed.Initially,APIs are remapped and encoded,with the introduction of percentile lengths to process sequences.Subsequently,a combination of One-Dimensional Convolutional Neural Network(1D-CNN)and Bidirectional Long Short-Term Memory(Bi-LSTM)networks,along with an attention mechanism,is employed to extract features from subsequences of varying lengths for feature fusion and classification.Experiments conducted on two widely used public API-based datasets,namelyMalBehavD-V1 and Alibaba Cloud,demonstrate that the proposedmethod reduces the number of API call types by approximately 20%compared to representative deep learning–based API sequence detection methods,while achieving a peak accuracy of 98.70%.Additionally,experimental results indicate that sequence length at the 95th percentile represents the optimal solution that balances classification performance and computational efficiency.
