A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

Scheduling optimization of emergency supplies with multi-supply points based on variable length genotype genetic algorithm.

How to dispatch emergency supplies timely and efficiently and reduce the damage caused by emergencies has become the focus of social attention. On the premise of considering the characteristics of special emergencies such as the epidemic situation of COVID-19, this paper constructed a kind of emergency supplies scheduling network of multi-supply points and multimodal transportation. Taking the lowest transportation cost, the least time penalty and the minimum risk of infection of dispatchers as the optimization objectives, it established a kind of multi-objective optimal scheduling model. In view of the limitation of the optimization algorithm based on clustering in solving multi-supply points, especially multi-objective scheduling optimization problems, the paper proposed a kind of hybrid niche genetic algorithm for variable length genotypes considering the idea of full feasible regions, which could avoid the problem above by restoring the search range of the solution to the fully feasible regions. The experiment results of 23 benchmark instances show that the optimization performance of the algorithm is stronger and it can search better solutions than best-known solutions of some examples. On this basis, the simulation results of four kinds of genetic algorithms in emergency supplies scheduling examples of multi-supply points and multimodal transportation come to a conclusion that the improved strategies such as hybrid niche are superior. (English) [ FROM AUTHOR] 如何及时、高效地调度应急物资以减小突发事件带来的伤害成为社会关注的焦点问题。在综合考虑新 冠肺炎疫情这类特殊突发事件特点的前提下, 构建了一类多供应点多式联运应急物资调度网络, 并以运输成本 最低、时间惩罚最少、配送员被感染风险最小为优化目标建立了一类多目标调度优化模型。考虑到基于聚类思 想的优化算法在解决多供应点, 尤其是多目标调度优化问题中缩减可行域方法科学性存疑的局限性, 提出了一 类考虑完全可行域思想的变长基因型混合小生境遗传算法, 并借助 ２３个基准测试实例验证了这一算法的有效 性, 更新了部分实例的现有最优解。在此基础上, 通过比较多供应点应急物资多式联运算例中四类遗传算法的 仿真结果进一步验证了混合小生境等改进策略的优越性。 (Chinese) [ FROM AUTHOR] Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Smartwatch-Based Face-Touch Prediction Using Deep Representational Learning

World Health Organization (WHO) reported that viruses, including COVID-19, can be transmitted by touching the face with contaminated hands and advised people to avoid touching their face, especially the mouth, nose, and eyes. However, according to recent studies, people touch their faces unconsciously in their daily lives, and it is difficult to avoid such activities. Although many activity recognition methods have been proposed over the years, none of them target the prediction of face-touch (rather than detection) with other daily life activities. To address to problem, we propose TouchAlert: a system that automatically predict the occurrence of face-touch activity and warn the user before its occurrence. Specifically, TouchAlert utilizes commodity wearable devices’ sensors to train a deep learning-based model for predicting the variable length face-touching of different users at an early stage of its occurrence. Our experimental results show high accuracy of F1-score of 0.98 and prediction accuracy of 97.9%. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space.

The COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations.