This paper analyzes the recently epidemic status of schistosomiasis,the change of natural and social factors,and field survey and evaluation data of schistosomiasis in Ya’an City after Lushan Earthquake on April 20,2013,and proposes that it is necessary to strengthen the conventional schistosomiasis control measures,the control of exogenous infection sources,the con-trol of Oncomelania hupensis snails and health education for ensuring no major epidemics after the disaster. This paper also recom-mends the direction and suggestions for future schistosomiasis control in Ya’an City.

Objective: To investigate the abnormalities of the blood system in landfill workers. Methods: A cohort study was conducted for 224 landfill workers who were followed up for 6 consecutive years with abnormal routine blood test results and a low platelet count as the outcome events. The life-table method was used to analyze the incidence rates of these two outcome events, and the incidence rates were compared between first-and second-line workers. Results: A total of 71 workers had abnormal routine blood test results, among whom 29 had abnormal leukocyte count, 14 had abnormal erythrocyte count, 40 had abnormal platelet count, 17 had abnormal hemoglobin, and 29 had a reduction in platelet count. For these landfill workers, the 6-year abnormal rate of routine blood test results was 43.2%, and the incidence rate of low platelet count within 6 years was 13.5%. The first-line workers had a significantly lower abnormal rate of routine blood test results than the second-line workers (P=0.020) , and the relative risk of abnormal routine blood test results in the first-and second-line workers was 0.592. There was no significant difference in the incidence rate of low platelet count between the two groups of workers (P≥0.05) . Conclusion The landfill has an adverse effect on the blood system of landfill workers, and the second-line workers have greater impairment than the first-line workers.

Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.