The seasonal variations of the rate and intensity of metacercarial infection of C. sinensis in P. parva were observed. The fish were collected at Sun-Am river which located in Kim-Hae City, Kyong-Sang-Nam Do(=Province), Korea, from March 1983 to February 1984 every month. A total of 788 fish was examined. The number of metacercariae in each fish was individually counted after the individual digestion by artificial gastric juice. The result was as follows: During one year, 513(65.1%) out of 788 fish were infected with metacercariae. In May, June, July and September, the infection rates ranged from 82. 0 % to 98. 6% whereas the rates was relatively low in March, April, November and February raning from 11. 4% to 64.7%. The intensity of infection was similar with those of infection rates. The mean intensity per infected fish was 103.0 and standard deviation was 118.9 throughout one year. The highest mean intenstiy was in June(294. 8) and the lowest in Novebmver(11.1). The observed frequency of fish with certain intensities of metcercariae were fitted to theoretical equations derived from negative binomial distribution in March, April, November and February(p>0.05). Meanwhile, the equation of lognormal distribution were fitted with the observed frequencies in May, June, July and September(p>0.05, p>0.75). The variance/mean ratio varied by month. The value was the highest in July(814.3) and the lowest in November(158.8). Unlike our hypothesis, the metacercarial density of Clonorchis sinensis in its the most favourable fish host, Pseudorasbora parva showed considerable seasonal variations in the hyperendemic area. The possible factors were discussed.
parasitology-helminth-trematoda ; Clonorchis sinensis ; epidemiology ; Pseudorasbora parva ; metacercaria ; negative binomial distribution ; intermediate host ; lognormal distribution

With the rapid development of MS technologiesy, the demands for a more sophisticated MS interpretation algorithm haves grown as well. We have developed a new protein fingerprinting method using a binomial distribution, (fBIND). With the fBIND, we improved the performance accuracy of protein fingerprinting up to the maximum 49% (more than MOWSE) and 2% than(at a previous binomial distribution approach studied by of Wool et al.) as compared to the established algorithms. Moreover, we also suggest a the statistical approach to define the significance of transcription factors and motifs in the identified proteins based on the Gene Ontology (GO).
Binomial Distribution ; Fungal Proteins* ; Gene Ontology ; Peptide Mapping ; Transcription Factors* ; Wool ; Yeasts*

OBJECTIVETo discuss the clustering of Yunnan unexplained sudden death (YUSD) in household and village.

METHODSFifty-two cases were found by YUSD surveillance system in 2005. Poisson distribution and beta-binomial distribution (BBD) were employed in studying the household distribution of the disease. Poisson distribution and negative binomial distribution (NBD) were employed in studying the village distribution of the disease.

RESULTSBBD were fitted household distribution of YUSD very well (chi(2) = 0.25, P = 0.62), while Poisson distribution was consistent with it (chi(2) = 46.01, P < 0.001). And NBD were fitted village distribution of YUSD very well (chi(2) = 0.05, P = 0.58), however the consistency in Poisson distribution was not observed (chi(2) = 110.57, P < 0.001).

CONCLUSIONHousehold and village clustering of YUSD does exist.

Bias ; Binomial Distribution ; Cause of Death ; China ; epidemiology ; Death, Sudden ; epidemiology ; Family ; Humans

OBJECTIVETo explore the application of nonlinear mixed models fitting logistic regression in clinical trials.

METHODSTwo clinical trials were selected to exemplify the method for fitting nonlinear logistic regression using nonlinear mixed models by running NLMIXED procedure in SAS.

RESULTSAll the parameters and their standard errors were estimated, and each factor could be properly interpreted.

CONCLUSIONNonlinear mixed models in which both fixed and random effects enter nonlinearly can fit nonlinear logistic regression. These models provide effective methods to analyze the binary data in clinical trials.

Binomial Distribution ; Clinical Trials as Topic ; methods ; Humans ; Logistic Models ; Models, Biological ; Nonlinear Dynamics

OBJECTIVE: To calculate the exclusion power of STR loci in motherless parentage testing and to discuss how to draw a conclusion if there are inconsistent loci. METHODS: Based on the law of inheritance and allele frequency, the powers of exclusion of STR loci in motherless parentage testing (PE(M)) were calculated. Based on the mean PE(M) and mutation rate of 13 CODIS loci. The probabilities of inconsistence under paternity and non-paternity were calculated respectively according to binomial theorem. RESULTS: The PE(M) of locus having co-dominate alleles could be calculated as: PE(M) = (i = 1)sigma (n) p i 2(1-p (i))2+ (i < j)sigma (n) 2p (i)p (j)(1-p (i)-p (j))2. According to the formula, the average PE(M) of 13 CODIS was 0.411. Based on the mean PE(M) and mutation rate, the likelihood ratio of true father to random man (paternity index) was got using binomial theorem. CONCLUSION The conclusion in motherless parentage testing could be drawn based on the likelihood ratio (paternity index) derived from mean PE(M) and mutation ratio.
Algorithms ; Alleles ; Binomial Distribution ; Forensic Genetics/methods* ; Gene Frequency ; Genetic Markers ; Humans ; Male ; Mutation ; Paternity ; Probability ; Tandem Repeat Sequences