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The traditional prescribing approaches in clinical therapies, such as “one drug fits all”, have limits due to consideration of drug effectiveness and safety. It is now well recognized that the Single Nucleotide Polymorphism (SNP) plays an important role in pharmacogenomics and personalized medicine by serving as the predictive biomarker for patient stratification and dose selection. Considering the economic efficiency of drug development, searching for the single best predictive SNP has just begun and has great potential. Current statistical methods for the best predictive biomarker selection rely on a variety of ranking procedures. However, these ordering approaches face three main issues: (1) they can potentially fail to distinguish predictive biomarkers from prognostic biomarkers; (2) the ordering is not necessarily correlated with the true significance, especially when the signal-to-noise is small; (3) such ranking approaches provide no control on the incorrect assertion in terms of the best predictive SNP detection.In this paper, we propose to overcome the first issue by quantifying the predictive ability of each candidate SNP in two parallel approaches: (1) adjusting the model with Least Square mean (LSmean); (2) adjusting the data with Virtual Matching (VM) before doing estimation. For the selection of the best predictive SNP, we propose to apply the Multiple Comparisons with the Best (MCB) to the ranking and selection procedures. Specifically, we will do the indifference zone selection with MCB lower bounds; and the subset selection with MCB upper bounds (suppose a larger response is better). The probability that both inferences are correct is controlled. Simulation studies show that our proposed method is more reliable in distinguishing between predictive and prognostic effects, and has a larger chance to detect the true best predictive SNP than varieties of methods in both multiple testing and machine learning approaches. Furthermore, the adjusting-model approach is generally better than the adjusting-data approach when we assume there is one single predictive SNP; however, the latter one is easier to be extended to the scenario with multiple predictive SNPs.

Yaping Li,1 Muqi Wang,1 Wenjun Wang,1 Dandan Feng,1 Huiling Deng,1,2 Yufeng Zhang,2 Shuangsuo Dang,1 Song Zhai1 1Department of Infectious Diseases, Xi’an Jiaotong University Second Affiliated Hospital, Xi’an 710004, People’s Republic of China; 2Department of Infectious Diseases, Xi’an Children’s Hospital, Xi’an 710003, People’s Republic of ChinaCorrespondence: Shuangsuo Dang; Song ZhaiDepartment of Infectious Diseases, Xi’an Jiaotong University Second Affiliated Hospital, Xi’an 710004, People’s Republic of ChinaTel/Fax +86-29-87679688; +86 18991307953Fax +86 29-87679273Email dangshuangsuo123@xjtu.edu.cn; zhaisong1103@xjtu.edu.cnIntroduction: Severe hand, foot, and mouth disease (HFMD) may lead to serious complications, which cause child mortality during outbreaks. The aim of this study was to determine whether neutrophil-to-lymphocyte ratio (NLR) can predict death risk in severe HFMD.Methods: Medical records for 664 severe HFMD patients were retrospectively examined, and NLR was calculated from blood counts. Youden’s index was calculated to determine the optimal NLR cutoff. Uni- and multivariate logistic regression were used to determine death risk factors associated with severe HFMD.Results: An NLR cutoff value of 2.01 and 2.50 respectively predicted mortality among all 664 severe HFMD and 137 critical HFMD. Among all 664 patients, the multivariate model identified the following as independently associated with death risk: high fever (OR 3.342, 95% CI 1.736– 6.432), EV71 infection (OR 3.200, 95% CI 1.529– 6.698), fasting glucose (OR 37.343, 95% CI 18.616– 74.909), and NLR (> 2.01) (OR 2.142, 95% CI 1.125– 4.079). Among 137 critical HFMD, EV71 infection (OR 3.441, 95% CI 1.132– 10.462), fasting glucose (OR 14.173, 95% CI 4.920– 40.827), and NLR (> 2.50) (OR 4.166, 95% CI 1.570– 11.051) were associated with death risk.Conclusion: In conclusion, NLR (> 2.01) in severe HFMD and NLR (> 2.50) in critical HFMD patients may be associated with increased death risk.Keywords: enterovirus 71, hand, foot and mouth disease, neutrophil-to-lymphocyte ratio, risk factor

In view of the functional demands of the automatic train operation (ATO) system of urban rail transit, under the conditions of train operation safety, speed limit and train dynamics performance constraints, the multi-objective optimization model for the train operation is built with low energy consumption, stopping accuracy, punctuality and passenger comfort as control objectives. Under the MATLAB environment, first comparing the Pareto optimal solution of NSGA-III algorithm with Non-dominated Sorting Genetic Algorithm III based on Elite opposition-based Learn (EOL- NSGA-III) algorithm, then based on the Beijing Yizhuang Line interval route data, the EOL-NSGA-III algorithm is applied to solve the multi-objective optimization model. The simulation results confirm the feasibility of the EOL-NSGA-III algorithm and the effectiveness of the multi-objective optimization model, thereby designing an efficient multi-objective operation of urban rail transit trains control strategy.

ObjectiveTo investigate the expression of glucose-regulated protein 78 (GRP78) in HBV-related hepatocellular carcinoma (HBV-HCC) and its association with clinicopathological features, as well as its regulatory effect on mitochondrial genesis proteins in hepatoma cells, and to provide a basis for new strategies for the prevention and treatment of HCC. MethodsTissue samples were collected from 54 patients with HBV-HCC, and immunohistochemistry and Western blot were used to measure the expression of GRP78, Lon, TFAM, and cytochrome C oxidase Ⅳ (COXⅣ). The expression of GRP78 in hepatoma cells was interfered by siRNA, and then the expression of GRP78, Lon, mitochondrial transcription factor A (TFAM), and COX Ⅳ was measured. Quantitative real-time PCR was used to measure the level of mitochondrial DNA (mtDNA) in clinical specimens and HCC cells after GRP78 expression was interfered with. A statistical analysis was performed for clinical and experimental data. The t-test was used for comparison of continuous data between groups, the Fisher′s exact test was used for comparison of categorical data between groups, and the Kaplan-Meier method was used for survival analysis. Results Compared with the adjacent tissues, HBV-HCC tissues had significantly higher expression of GRP78 and Lon (t=9.135 and 5523, both P＜0.0001) and significantly lower expression of the mitochondrial genesis proteins TFAM and COX Ⅳ and mtDNA level (t=2.765, 4260, and 12.280, P=0.011, ＜0.001, and ＜0.001). There were significant increases in the expression of the mitochondrial genesis proteins TFAM and COX Ⅳ and mtDNA level after the interference with GRP78 expression in hepatoma cells (all P＜0.05). There were significant differences in the expression of GRP78 between patients with different numbers of tumors, patients with and without portal vein tumor thrombus, and patients with different tumor stages (P=0.016， 0.003, and 0.045). The patients with low GRP78 expression had significantly longer overall survival and recurrence-free survival after surgery than those with high GRP78 expression (χ２=5006 and 4995， P=0.025 and 0.026). ConclusionGRP78 has a potential regulatory effect on mitochondrial genesis and maintenance and has important clinical guiding significance in establishing new strategies for the prevention and treatment of HCC.

Trehalose (α-D-glucopyranosyl-1,1-α-D-glucopyranoside) is a non-reducing disaccharide. It is currently thought that just trace level of trehalose was detected in plants, and that trehalose metabolic pathway was significantly related to stress tolerance. In this study, we report that expression levels of three genes with regard to trehalose metabolic pathway were measured in Arabidopsis thaliana, including AtTPS1, AtTPPA and AtTRE1. As a result, transcriptional levels of these genes are the highest in floral organ, and the expression of AtTRE1 is much more than AtTPS1 and AtTPPA. Additionally, we present transcriptional response analyses in drought and heat stresses, which have shown the changes of these genes expression from tolerance in early stress to senescence in later stress.