Predicting protein secondary structure using neural net and statistical methods.

A comparison of neural network methods and Bayesian statistical methods is presented for prediction of the secondary structure of proteins given their primary sequence. The Bayesian method makes the unphysical assumption that the probability of an amino acid occurring in each position in the protein is independent of the amino acids occurring elsewhere. However, we find the predictive accuracy of the Bayesian method to be only minimally less than the accuracy of the most sophisticated methods used to date. We present the relationship of neural network methods to Bayesian statistical methods and show that, in principle, neural methods offer considerable power, although apparently they are not particularly useful for this problem. In the process, we derive a neural formalism in which the output neurons directly represent the conditional probabilities of structure class. The probabilistic formalism allows introduction of a new objective function, the mutual information, which translates the notion of correlation as a measure of predictive accuracy into a useful training measure. Although a similar accuracy to other approaches (utilizing a mean-square error) is achieved using this new measure, the accuracy on the training set is significantly and tantalizingly higher, even though the number of adjustable parameters remains the same. The mutual information measure predicts a greater fraction of helix and sheet structures correctly than the mean-square error measure, at the expense of coil accuracy, precisely as it was designed to do. By combining the two objective functions, we obtain a marginally improved accuracy of 64.4%, with Matthews coefficients C alpha, C beta and Ccoil of 0.40, 0.32 and 0.42, respectively. However, since all methods to date perform only slightly better than the Bayes algorithm, which entails the drastic assumption of independence of amino acids, one is forced to conclude that little progress has been made on this problem, despite the application of a variety of sophisticated algorithms such as neural networks, and that further advances will require a better understanding of the relevant biophysics.     

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

The 2015 epidemic of Middle East respiratory syndrome (MERS) in the Republic of Korea has been the largest outbreak outside Middle East. This epidemic had caused 185 laboratory-confirmed cases and 36 deaths in the Republic of Korea until September 2, 2015, which attracted public’s attention. Based on the detailed data of patients released by World Health Organization (WHO) and actual propagation of the epidemic, we construct two dynamical models to simulate the propagation processes from May 20 to June 8 and from June 9 to July 10, 2015, respectively and find that the basic reproduction number R ₀ reaches up to 4.422. The numerical analysis shows that the reasons of the outbreak spread quickly are lack of self-protection sense and targeted control measures. Through partial correction analysis, the parameters β ₁ and γ have strong correlations with R ₀, i.e., the infectivity and proportion of the asymptomatic infected cases have much influence on the spread of disease. By sensitivity analysis, strengthening self-protection ability of susceptible and quickly isolating or monitoring close contacts are effective measures to control the disease.     

Models and Algorithms for Hub and Spoke Locations for Emergency Service Facilities in Response to Serious Emergency Incidents

This article examines the location-allocation of emergency service facilities as a research subject. The research presents the setup of the single allocation set covering location-allocation models for emergency service facilities under strong time constraints, in view of the shortage of hub & spoke network bypass. The article also presents an extension to the single allocation set covering location-allocation model (SASCP) and the SASCP model with bypass constraints (γ-SASCP) for emergency service facilities under large-scale emergency requirements. For the two models, an improved genetic algorithm was designed and the two models were respectively solved, with the effectiveness of the algorithm verified by a specific example. The impacts of change of parameters such as time discount rate, maximum time constraints, and bypass ratio on the model's results are compared and analyzed, based on solved results by the specific example.     

Laboratory and field assessment of molluscicidal activity of B-2 against Oncomelania hupensis, the vector snail of schistosomiasis in China

Molluscicidal activity of B-2 (sodium 2,5-dichloro-4-bromophenol; called as Phebrol and registered in WHO as OMS 3012) was evaluated in a laboratory and the field trials were performed in two different localities in Yueyang city, China, for control of Oncomelania hupensis. B-2 was effective against O. hupensis both in the laboratory and in the field. A dosage of 50 g/m2 in 10% granular form or 20 ml/m2 in 25% liquid form of B-2 would be recommendable as a standard mollusciciding dose for control of O. hupensis.     

微生物异源合成咖啡酸及其酯类衍生物研究进展

咖啡酸及其酯类衍生物如绿原酸、迷迭香酸和咖啡酸苯乙酯等具有天然抗氧化、抗肿瘤、抗病毒和抗炎等重要的药理活性,具有广阔的药用开发前景。从天然药物中提取或者化学合成咖啡酸及其酯类衍生物,存在含量低、提取效率不高、催化成本高昂以及环境污染等问题。随着咖啡酸及其酯类衍生物合成途径解析和合成生物学的快速发展,微生物异源合成咖啡酸及其酯类衍生物的研究已逐渐展开。对微生物异源合成咖啡酸及其酯类衍生物合成途径的最新进展以及代谢工程策略进行了综述,并讨论了目前存在的问题和未来的发展趋势。