Recognition of Protein Pupylation Sites by Adopting Resampling Approach.

With the in-depth study of posttranslational modification sites, protein ubiquitination has become the key problem to study the molecular mechanism of posttranslational modification. Pupylation is a widely used process in which a prokaryotic ubiquitin-like protein (Pup) is attached to a substrate through a series of biochemical reactions. However, the experimental methods of identifying pupylation sites is often time-consuming and laborious. This study aims to propose an improved approach for predicting pupylation sites. Firstly, the Pearson correlation coefficient was used to reflect the correlation among different amino acid pairs calculated by the frequency of each amino acid. Then according to a descending ranked order, the multiple types of features were filtered separately by values of Pearson correlation coefficient. Thirdly, to get a qualified balanced dataset, the K-means principal component analysis (KPCA) oversampling technique was employed to synthesize new positive samples and Fuzzy undersampling method was employed to reduce the number of negative samples. Finally, the performance of our method was verified by means of jackknife and a 10-fold cross-validation test. The average results of 10-fold cross-validation showed that the sensitivity (Sn) was 90.53%, specificity (Sp) was 99.8%, accuracy (Acc) was 95.09%, and Matthews Correlation Coefficient (MCC) was 0.91. Moreover, an independent test dataset was used to further measure its performance, and the prediction results achieved the Acc of 83.75%, MCC of 0.49, which was superior to previous predictors. The better performance and stability of our proposed method showed it is an effective way to predict pupylation sites.

Lessons from China's Experience: Legal Analysis of Health Risks and Medical Obstacles of Personnel in COVID-19 Control Areas.

Purpose: Individuals in controlled areas often face restrictions on their personal freedom, and if they are unable to receive medical treatment when needed, it can significantly increase their health risks. However, current epidemic prevention and control policies do not provide clear guidelines on how to ensure individuals in controlled areas to seek medical attention when faced with health problems. By implementing specific measures that local governments must take in order to protect the health of those in controlled areas, the risks to their health can be greatly reduced. Patients and Methods: Our research utilizes a comparative approach to analyze the measures adopted by various regions for safeguarding the health of individuals in control areas, and the diverse outcomes they produce. We conduct empirical analysis and present examples of severe health risks that individuals in control areas face due to inadequate health protection measures. Furthermore, we conduct a critical evaluation of China's legal management of control areas, identifying both its principles and shortcomings. Results: The lack of unified legal regulations has led some local governments to make some shortcomings in making decisions on epidemic prevention and control. Specifically, some governments have neglected to provide adequate medical protection for individuals in controlled areas, limited the authority of specific implementers of prevention policies, and failed to establish fair punishment mechanisms. These shortcomings have a direct impact on the health of those in controlled areas and can even lead to tragic outcomes. Conclusion: Effective management of individuals in control areas during public health emergencies is crucial in reducing health risks. To achieve this, China needs to establish unified regulations and requirements, particularly with regards to medical protection, for individuals in control areas. Such measures can be achieved through the improvement of legislation, which can significantly reduce health risks faced by individuals in control areas during public health emergencies.

Obstacles to Exercising Reproductive Rights for Single Women in China and Legal Recommendations.

Purpose: Reproductive rights represent an intrinsic and pivotal human entitlement, encompassing legal protection for procreation. Essential to this framework is the recognition that single women equally deserve reproductive rights. Although Chinese legislation refrains from overtly disallowing reproductive rights for single women, the interplay of conventional marriage norms and family planning policies has inadvertently tied these rights to marital status, consequently constraining single women's ability to assert them. The establishment of a robust legal structure to ensure reproductive rights for single women would profoundly contribute to advancing a harmonious evolution of China's population dynamics. Patients and Methods: We employ meticulous textual scrutiny to analyze comprehensively the stipulations concerning women's reproductive liberties within the framework of Chinese jurisprudence. Furthermore, we engage in empirical inquiry to enumerate and elucidate the multifarious constraints placed upon the reproductive freedoms of unmarried women in the Chinese context. This endeavor entails a detailed exposition and incisive examination of China's limitations imposed upon the reproductive rights of single women, encompassing both legal strictures and policy dimensions. Results: The absence of legal endorsement and safeguarding has given rise to substantial impediments to the exercise of reproductive rights among single women in China. Not only do endeavors to assert reproductive rights on behalf of single women encounter intricate challenges in judicial implementation, but they also encounter manifold barriers within national policies. This predicament not only subjects single women to considerable psychological strain but also contradicts the overarching objective of achieving a harmonized population development trajectory in China. Conclusion: China should expedite the development of a legal framework for protecting reproductive rights that includes provisions for supporting single women to have children. This legal apparatus should efficaciously enshrine and shield the reproductive rights of single women, thus playing a pivotal role in advancing a more equitable and well-balanced trajectory of population development.

Safeguarding the Right to Health of the Elderly in Rural China: A Legal Analysis.

Older people in rural China enjoy the right to health under Chinese law. However, the problems of economic difficulties, the unbalanced allocation of medical resources between urban and rural areas and the weakening of the traditional function of the family as a provider of old-age care in reality make the elderly in rural areas face a higher risk of health problems. The law is an important tool to mitigate these problems. Although China has made great efforts to legally guarantee the right to health of the rural elderly, it has not yet fully implemented the requirements of Article 12 of the International Covenant on Economic, Social and Cultural Rights, Article 14 of the Madrid Political Declaration and International Plan of Action on Ageing. China needs to further improve the relevant legislation to provide adequate legal support for the exercise of the right to health of the elderly in rural areas.

Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier.

Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub- support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.

Identification of S-nitrosylation sites based on multiple features combination.

Protein S-nitrosylation (SNO) is a typical reversible, redox-dependent and post-translational modification that involves covalent modification of cysteine residues with nitric oxide (NO) for the thiol group. Numerous experiments have shown that SNO plays a major role in cell function and pathophysiology. In order to rapidly analysis the big sets of data, the computing methods for identifying the SNO sites are being considered as necessary auxiliary tools. In this study, multiple features including Parallel correlation pseudo amino acid composition (PC-PseAAC), Basic kmer1 (kmer1), Basic kmer2 (kmer2), General parallel correlation pseudo amino acid composition (PC-PseAAC_G), Adapted Normal distribution Bi-Profile Bayes (ANBPB), Double Bi-Profile Bayes (DBPB), Bi-Profile Bayes (BPB), Incorporating Amino Acid Pairwise (IAAPair) and Position-specific Tri-Amino Acid Propensity(PSTAAP) were employed to extract the sequence information. To remove information redundancy, information gain (IG) was applied to evaluate the importance of amino acids, which is the information entropy of class after subtracting the conditional entropy for the given amino acid. The prediction performance of the SNO sites was found to be best by using the cross-validation and independent tests. In addition, we also calculated four commonly used performance measurements, i.e. Sensitivity (Sn), Specificity (Sp), Accuracy (Acc), and the Matthew's Correlation Coefficient (MCC). For the training dataset, the overall Acc was 83.11%, the MCC was 0.6617. For an independent test dataset, Acc was 73.17%, and MCC was 0.3788. The results indicate that our method is likely to complement the existing prediction methods and is a useful tool for effective identification of the SNO sites.