Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

Bayesian Estimation of the Distribution Function of the Poisson Model

This paper presents the Bayes estimators of the Poisson distribution function based on complete and truncated data under a natural conjugate prior. Laplace transform of the incomplete gamma function and the Gauss hypergeometric function have been employed in order to overcome the intractability of the integrals. Numerical examples from biosciences are given to illustrate the results. A Monte Carlo study has been carried out to compare Bayes estimators under complete data with the corresponding maximum liklihood estimators.     

Evaluation of statistical precision and design of efficient sampling for the population estimation based on frequency of occurrence

The binomial sampling to estimate population density of an organism based simply upon the frequency of its occurrence among sampled quadrats is a labour-saving technique which is potentially useful for small animals like insects and has actually been applied occasionally to studies of their populations. The present study provides a theoretical basis for this convenient technique, which makes it statistically reliable and tolerable for consistent use in intensive as well as preliminary population censuses. Firs, the magnitude of sampling error in relation to sample size is formulated mathematically for the estimate to be obtained by this indirect method of census, using either of the two popular models relating frequency of occurrence (p) to mean density (m), i.e. the negative binomial model, p=1−(1+m/k)^−k, and the empirical model, p=1−exp(−am^b). Then, the equations to calculate sample size and census cost that are necessary to attain a given desired level of precision in the estimation are derived for both models. A notable feature of the relationship of necessary sample size (or census cost) to mean density in the frequency method, in constrast to that in the ordinary census, is that it shows a concave curve which tends to rise sharply not only towards lower but also towards higher levels of density. These theoretical results make it also possible to design sequential estimation procedures based on this convenient census technique, which may enable us with the least necessary cost to get a series of population estimates with the desired precision level. Examples are presented to explain how to apply these programs to acutal censuses in the field.     

Statistical treatment of VAM infection data

Summary The amount of vesicular-arbuscular mycorrhizal (VAM) infection, when expressed as length of infected roots, is commonly quite variable among replicate pots within an experimental treatment. In this paper we show that frequency distributions of VAM infection parameters are often non-normal in form and may follow the negative binomial, a distribution commonly associated with aggregated organisms in nature. The lack of normality means that statistical procedures should either be non-parametric or should include data transformations.     

A statistical model of the reproduction of aphids

1. The logistic function has been generally used to describe the reproductive process of a “population” of animal. However, this model can not give us any information about the reproductive process of “individuals” in the population. In this study a statistical model on the basis of the reproduction of individuals of barley aphid is presented to find the proportion of the mature individuals, the heterogeneity in reproductive ability of the aphids, etc.
2. The model is constructed as follows:
3. The probability that j insects are found on a plant at time t₀ is represented as Q(j).
4. The probability that h individuals of j have reproductive ability, say, mature individuals, in the period t₀ to t₁ is represented as B(h/j)=_jC_hw^h(1−w)^j−h, where w is the proportion of mature individuals.
5. In a population with a homogeneous reproductive ability, the probability that each parent lays i offspring in the period t₀ to t₁ is represented as P(i/m)=e^−mmⁱ/i!, where m is mean. And, in a population, m changes according to the gamma distribution. Hence the probability that a parent lays i offspring between t₀ and t₁ is represented as , where p and k are parameters of negative binomial distribution. The probability that h parents on a plant lays s offspring is represented as .
6. From the assumptions mentioned above, the probability that s offspring are to be found at time t₁ on a plant with the original j individuals at time t₀ is represented by
7. The experimental populations were demonstrated to fit well to the model.

     

Gene-directed mutagenesis on the chromosome of Bacillus subtilis 168

Summary We have devised a method whereby any mutagenized cloned DNA from Bacillus subtilis can be reinserted at the original site on the B. subtilis chromosome. The procedure depends on the accuracy and high frequency of homologous recombination between the B. subtilis chromosome and the DNA taken up by the cell. The method makes use of two drug resistance selection markers (the chloramphenicol resistance gene and the neomycin resistance gene) and a marker gene which functions as a catalyst. The utility of the method has been demonstrated using leuB and pro of B. subtilis as target gene and catalyst, respectively, and mutations such as leuB: : cat, leuB ^–, and pro: : neo constructed in vitro on the cloned DNA fragments. Transformation in sequential steps as (leuB ⁺ pro⁺)(leuB: : cat pro ⁺) (leuB ^– pro: : neo)(leuB ^– pro ⁺) resulted in a leuB ^– single mutant without affecting other regions of the B. subtilis chromosome (gene-directed mutagenesis). We also demonstrate that other single mutations such as metD ^– and pro ^–, as well as the double mutation leuB ^– pro ^– can be introduced by the same procedure. In principle, true isogenies with multiple mutations can be constructed by the method described in this paper. Furthermore, the procedure should be generally applicable to any organisms in which homologous recombination is proficient.     

The spatial distribution pattern of the brown planthopperNilaparvata lugens Stål (Homoptera: Delphacidae) in west Java,Indonesia

Spatial distribution pattern of the brown planthopper (BPH) was analyzed at 9 experimental fields in the northern part of West Java during two consecutive rice cropping seasons, i.e., wet and dry seasons. The population of each developmental stage and wing form of BPH at each location showed consistent departure from the random (Poisson) distribution, the variances of the densities in most cases exceeding their means. Namely, the distribution pattern of BPH per hill of rice plant was found to have a general tendency to be aggregated or contagious and to fit fairly well to the negative binomial model. The tendency for aggregation was further confirmed by both the β-values of -m regression being larger than unity and the C_A-values being larger than zero for each developmental stage. Although significant variations in the distribution pattern as measured by β- or C_A-value were observed between different developmental stages, between wing forms and among locations, the degree of aggregation for a given developmental stage at each experimental field remained fairly stable throughout the crop period, despite wide temporal changes in population density. Possible factors to explain these characteristics of the spatial distribution pattern of the BPH in West Java were discussed with reference to the process generating it.     

The Sec1 Family: A Novel Family of Proteins Involved in Synaptic Transmission and General Secretion

Abstract: The Sec1 family, a novel family of proteins involved in synaptic transmission and general secretion, is described. To date, 14 members of this family have been identified: four yeast proteins, Sec1, Sly1, Slp1/Vps33, and Vps45/Stt10; three nematode proteins, Unc-18 and the homologues of Sly1 and Slp1; the Drosophila Rop; and six mammalian proteins, the rat Munc-18/n-Sec1/rbSec1A and rbSec1B, the mouse Munc-18b/muSec1 and Munc-18c, and the bovine Munc-18 and mSec1. The mammalian proteins share 44–63% sequence identity with the nematode Unc-18 and Drosophila Rop proteins and 20–29% with the yeast proteins and their nematode homologues. The Sec1 proteins are mostly hydrophilic and lack a transmembrane domain. Nevertheless, Sec1 proteins are found as membrane-bound proteins. Some of them are also found as soluble, cytoplasmic proteins. Binding of the rat brain Sec1 to the presynaptic membrane may be due to strong interaction with syntaxin, an integral component of this membrane. The rat brain Sec1 is also bound to Cdk5, a neural cyclin-dependent kinase. The Sec1 proteins play a positive role in exocytosis. Loss of function mutations in SEC1 , SLY1 , or SLP1 result in blocking of protein transport between distinct yeast subcellular compartments. Inactivation of unc-18 and rop results in inhibition of neurotransmitter release and, in the case of rop , inhibition of general secretion as well. In addition, studies of Rop and n-Sec1 indicate that they also play a negative role in synaptic transmission, mediated by their interaction with syntaxin. A working model addressing the dual regulative role of the Sec1 proteins in secretion is presented.