Simulation of titration experiments

Nowadays computer simulation is used in various fields, particularly in laboratories where it is used for the exploration data which are sometimes experimentally inaccessible. In less developed countries where there is a need for up to date laboratories for the realization of practical lessons in chemistry, especially in secondary schools and some higher institutions of learning, it may permit learners to carryout experiments such as titrations without the use of laboratory materials and equipments. Computer simulations may also permit teachers to better explain the realities of practical lessons, given that computers have now become very accessible and less expensive compared to the acquisition of laboratory materials and equipments. This work is aimed at coming out with a virtual laboratory that shall permit the simulation of an acid-base titration and an oxidation-reduction titration with the use of synthetic images. To this effect, an appropriate numerical method was used to obtain appropriate organigram, which were further transcribed into source codes with the help of a programming language so as to come out with the software.

Estimating Multiple Tumor Transition Rates Based on Data from Survival/Sacrifice Experiments

In this paper, we focus on the model for two types of tumors. Tumor development can be described by four types of death rates and four tumor transition rates. We present a general semi-parametric model to estimate the tumor transition rates based on data from survival/sacrifice experiments. In the model, we make a proportional assumption of tumor transition rates on a common parametric function but no assumption of the death rates from any states. We derived the likelihood function of the data observed in such an experiment, and an EM algorithm that simplified estimating procedures. This article extends work on semi-parametric models for one type of tumor (see Portier and Dinse and Dinse) to two types of tumors.

Estimation of Transition Probabilities in Animal Carcinogenicity Experiments

This paper discusses estimation of the tumor incidence rate, the death rate given tumor is present and the death rate given tumor is absent using a discrete multistage model. The model was originally proposed by Dewanji and Kalbfleisch (1986) and the maximum likelihood estimate of the tumor incidence rate was obtained using EM algorithm. In this paper, we use a reparametrization to simplify the estimation procedure. The resulting estimates are not always the same as the maximum likelihood estimates but are asymptotically equivalent. In addition, an explicit expression for asymptotic variance and bias of the proposed estimators is also derived. These results can be used to compare efficiency of different sacrifice schemes in carcinogenicity experiments.

On the Synthesis of Microarray Experiments

With many different investigators studying the same disease and with a strong commitment to publish supporting data in the scientific community, there are often many different datasets available for any given disease. Hence there is substantial interest in finding methods for combining these datasets to provide better and more detailed understanding of the underlying biology. We consider the synthesis of different microarray data sets using a random effects paradigm and demonstrate how relatively standard statistical approaches yield good results. We identify a number of important and substantive areas which require further investigation.