Silencing the rhythm gene AgCLK-1 reduced feeding of Aphis gossypii.

The cotton aphid Aphis gossypii Glover is an important cotton pest, and means of controlling this insect is a primary research focus. Although biological rhythm is an important mechanism that regulates numerous insect processes and activities, its role in cotton aphid has not been elucidated. In the present study, four highly-expressed circadian rhythm genes were selected from the cotton aphid genome database and their physicochemical properties and protein structures were analyzed. These genes were in the Takeout, Timeless, and Timeless interacting-related families, and the corresponding proteins contained highly-conserved Swis and TIMELESS domains. Gene expression analysis at multiple developmental stages revealed differing expression patterns between the four genes. AgCLK-1 had the highest relative expression of the four, especially during the nymph period. Silencing AgCLK-1 caused a significant refusal of the cotton aphids to feed at 1, 3, and 5 d of treatment. These results demonstrated that AgCLK-1 played a key role in regulating the feeding behavior of cotton aphid. This new functional understanding provides novel insights into cotton aphid biology and suggests new targeting strategies for agricultural pest control.

The MLE of the uniform distribution with right-censored data.

We carry out parametric inferences to a breast cancer data set which is right censored using the uniform distribution U(a, b). Under right censoring, it is rare that one can find the explicit solution to the maximum likelihood estimator (MLE) under the parametric set-up, except for the exponential distribution [Formula: see text]. We show that the MLE of a has a closed form solution, whereas the MLE of b has a closed form solution in some sense. We further propose a diagnostic plotting method and test for U(a, b). The asymptotic properties of the MLE are also investigated. It turns out that this breast cancer data set fits both U(a, b) and [Formula: see text]. Moreover, U(a, b) leads to more useful and reasonable inferences than those using the product-limit estimator or using the MLE of [Formula: see text].

A consistent NPMLE of the joint distribution function with competing risks data under the dependent masking and right-censoring model.

Dinse (Biometrics, 38:417-431, 1982) provides a special type of right-censored and masked competing risks data and proposes a non-parametric maximum likelihood estimator (NPMLE) and a pseudo MLE of the joint distribution function [Formula: see text] with such data. However, their asymptotic properties have not been studied so far. Under the extention of either the conditional masking probability (CMP) model or the random partition masking (RPM) model (Yu and Li, J Nonparametr Stat 24:753-764, 2012), we show that (1) Dinse's estimators are consistent if [Formula: see text] takes on finitely many values and each point in the support set of [Formula: see text] can be observed; (2) if the failure time is continuous, the NPMLE is not uniquely determined, and the standard approach (which puts weights only on one element in each observed set) leads to an inconsistent NPMLE; (3) in general, Dinse's estimators are not consistent even under the discrete assumption; (4) we construct a consistent NPMLE. The consistency is given under a new model called dependent masking and right-censoring model. The CMP model and the RPM model are indeed special cases of the new model. We compare our estimator to Dinse's estimators through simulation and real data. Simulation study indicates that the consistent NPMLE is a good approximation to the underlying distribution for moderate sample sizes.

A generalized log-rank test for interval-censored failure time data via multiple imputation.

Interval-censored (IC) failure time data often occur in follow-up studies where subjects can only be followed periodically and the failure time can only be known to lie in an interval. In this paper we propose a modified log-rank test for the problem of comparing two or more IC samples. Our log-rank statistic and covariance matrix are calculated by a multiple imputation technique. Through simulation studies, we find that the performance of the proposed test is comparable to that of the test proposed by Finkelstein (Biometrics 1986; 42(4):845-854) and is better than that of the two existing log-rank type tests proposed by Sun (Lifetime Data Anal. 2001; 7:363-375) and Zhao and Sun (Stat. Med. 2004; 23(10):1621-1629) due to the differences in the method of multiple imputation and the covariance matrix estimation. Our covariance matrix estimator is similar to the estimator used by Follmann et al. (Biometrics 2003; 59:420-429) for clustered data. The proposed method is illustrated by means of an example involving patients with breast cancer.

A minimum distance estimation approach to the two-sample location-scale problem.

As reported by Kalbfleisch and Prentice (1980), the generalized Wilcoxon test fails to detect a difference between the lifetime distributions of the male and female mice died from Thymic Leukemia. This failure is a result of the test's inability to detect a distributional difference when a location shift and a scale change exist simultaneously. In this article, we propose an estimator based on the minimization of an average distance between two independent quantile processes under a location-scale model. Large sample inference on the proposed estimator, with possible right-censorship, is discussed. The mouse leukemia data are used as an example for illustration purpose.