Revista de biometría y bioestadística

Background: Data preparation, such as missing values imputation and transformation, is the first step in any data analysis and requires crucial attention. We take advantage of availability of replication samples to identify the empirical distribution of missing values through utilization of statistical techniques. We apply these techniques to metabolomics data for imputation. Results: Using replication samples, we obtained the empirical distribution of missing values. After application of the techniques on metabolites, we observed that the rate of missing values is approximately distributed uniformly across metabolite range. Therefore, the missing values cannot be imputed with the lowest values. To have a realistic simulation, we designed a simulation study based on empirical distribution of missing values to find an optimal imputation approach. Our findings validated the optimal approach introduced previously for metabolomics. Conclusions: Our analysis utilized replication samples as a new approach to metabolite imputation and found empirical distribution of missing values, designed a simulation study close to reality, and compared different approaches for selecting an optimal imputation approach. The result of this study validated the optimal approach for metabolite imputation through a different data set and different approach, and the aim was to encourage researchers to pay more attention to metabolite imputation since imputing metabolomic missing values with lowest value is going to be a common approach, for example in genomic-metabolomic data analysis.

The structures of the human hands and feet are shaped by evolution and its effects on the brain, skeleton and other structures, and on behavior. We used measurements obtained of hands and feet from living humans in Europe, the Americas (South and North) and Australia and images of hands and feet in cave art, paintings, and photographs obtained from the Web including some from Africa. We used the ratios of the third finger/width of hand and second toe/width of foot. We hypothesized that hand ratios would not have changed over millennia whereas, because of the use of footwear and mechanical locomotion, the ratios obtained from feet could have changed significantly. Here we report that statistical analyses and modeling confirmed our initial hypothesis.

After the fall of the central government and the emergence of the recurrent civil conflict in Somalia, many new sociocultural phenomenons that have appeared across the country contributed to the spread of many infectious diseases including TB. One of these social phenomena is a wide spread use of the illicit drug Khat which is predominately used by the Somali males. Mostly khat is chewed in small overcrowded, unhygienic and unventilated makeshift huts known as Marfishes. These Marfishes became the launch pad and the breeding grounds of many infectious diseases that have affected the lives of many Somalis including children. Under-five mortality in somalia is estimated at 200 deaths per 1000 births, which is one of the highest in the world. Approximately one third of these are neonatal deaths, occurring during the first month of life, pneumonia and diarrhea are the main killers each contributing 20-25 percent of all under-five mortality. While these diseases still remained the top major killers, communicable diseases including TB are also a leading cause of death. This paper investigates the incidence and the trends of tuberculosis among the Somali children by using time series statistical models.

In this paper, an algorithm of directed graph replacement by acyclic directed graph is constructing and is applying for a study of the key players required for connecting ABA signaling and ABA-mediated drought and thermo tolerance.

When analyzing time-to-event data in a non-parametric setting without considering covariates, the Kaplan-Meier estimator is widely used to estimate the survival function. When considering covariates, the Cox proportional hazards model is widely used to account for covariates effects. In this setting, for the baseline survival function, the most commonly used approach is the Breslow method, which estimates the baseline survival function as an exponential function of the cumulative baseline hazard function. However, an unnatural and undesirable feature of the Breslow es timator is that, its estimated survival probability will never reaches zero even if the last observation is an event. In this article, we consider an less commonly used alternative, the Kalbfleisch Prentice estimator for the baseline survival function. It is the counterpart of the Kaplan-Meier estimator in a setting with covariates, and thus similarly as the Kaplan Meier estimator, it will reach zero if the last observation is an event. To evaluate the usefulness of the Kalbfleisch Prentice estimator and its relative performance comparing with the Breslow estimator, we conduct simulation studies across a range of conditions by varying the true survival time distribution, sample size, censoring rate and covariate values. We compare the performance of the two estimators regarding bias, mean squared error and relative mean squared error. In most situations in our study, the Kalbfleisch Prentice estimator results in less bias and smaller mean squared error than the Breslow estimator. Their differences are especially clear at the tail of the distribution. The implications of such differences in applications are discussed. We advocate the use of Kalbfleisch Prentice estimator in practice, and further research on its properties.

In this study, first of all, a definition of sets will be explained. In addition to the definition of the set known in mathematics, new approaches will be presented. Ten rules about the set definition model will be proposed. 1. It is necessary to determine the title and descriptive identity of the set. 2. It is necessary to determine the address of the set. 3. Specifying the appropriate boundary of the set of creature. 4. Specifying the time of the cluster. 5. The status of the cluster action must be specified. 6. The type and group of the cluster should be specified. 7. The status of live or non-live clusters should be indicated. 8. The load of the cluster element must be specified. 9. If possible, the gender of the cluster element must be specified. 10. The scientific name of the cluster elements must be specified. Out of these ten rules: the cluster must be specified if there is a special case. Later, the definitions of these rules will be presented with examples. Also gender, load, location, action, group, live and inanimate etc., cluster properties will be defined. Examples of animate and inanimate creature sets will be presented. After all, we gain mathematical cluster consciousness of all animate and inanimate creatures and systems in the nature. In addition to this; Natural, artificial, virtual, objective, mental, light etc., examples of sets will be given.