Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures

We report the largest and most comprehensive comparison of protein structural alignment methods. Specifically, we evaluate six publicly available structure alignment programs: SSAP, STRUCTAL, DALI, LSQMAN, CE and SSM by aligning all 8,581,970 protein structure pairs in a test set of 2930 protein domains specially selected from CATH v.2.4 to ensure sequence diversity. We consider an alignment good if it matches many residues, and the two substructures are geometrically similar. Even with this definition, evaluating structural alignment methods is not straightforward. At first, we compared the rates of true and false positives using receiver operating characteristic (ROC) curves with the CATH classification taken as a gold standard. This proved unsatisfactory in that the quality of the alignments is not taken into account: sometimes a method that finds less good alignments scores better than a method that finds better alignments. We correct this intrinsic limitation by using four different geometric match measures (SI, MI, SAS, and GSAS) to evaluate the quality of each structural alignment. With this improved analysis we show that there is a wide variation in the performance of different methods; the main reason for this is that it can be difficult to find a good structural alignment between two proteins even when such an alignment exists. We find that STRUCTAL and SSM perform best, followed by LSQMAN and CE. Our focus on the intrinsic quality of each alignment allows us to propose a new method, called "Best-of-All" that combines the best results of all methods. Many commonly used methods miss 10-50% of the good Best-of-All alignments. By putting existing structural alignments into proper perspective, our study allows better comparison of protein structures. By highlighting limitations of existing methods, it will spur the further development of better structural alignment methods. This will have significant biological implications now that structural comparison has come to play a central role in the analysis of experimental work on protein structure, protein function and protein evolution.     

Potential role of regucalcin as a specific biochemical marker of chronic liver injury with carbon tetrachloride administration in rats

The potential sensitivity of liver specific protein regucalcin as a biochemical marker of chronic liver injury with carbon tetrachloride (CCl₄) administration in rats was investigated. CCl₄ (10%; 1.0 ml/100 g body wt) was orally given 5 times at 3-day intervals to rats, and the animals were killed by bleeding at 3, 6, 18, and 30 days after the first administration of CCl₄. The body weight of rats was significantly lowered 3 and 6 days after CCl₄ administration as compared with that of control rats administered with corn oil, and then the weight was restored at 18 and 30 days. Serum glutamate-oxaloacetate transaminase (GOT) and glutamate-pyruvate transaminase (GPT) activities were significantly increased 3 days after the administration, while a significant increase in serum -glutamyltranspeptidase (-GTP) activity was seen at 3 and 6 days after the administration. Serum GOT, GPT, and -GTP activities were restored to control levels at 18 and 30 days after CCl₄ administration. Serum albumin, -fetoprotein, and ammonium levels were not changed by CCl₄ administration. Meanwhile, serum regucalcin concentration was markedly increased 3 and 6 days after CCl₄ administration, and a significant increase in serum regucalcin concentration was observed 18 and 30 days after the administration. Liver regucalcin mRNA and liver cytosolic regucalcin levels were significantly decreased 18 and 30 days after CCl₄ administration. Liver content of calcium, which intracellular calcium homeostasis is maintained, was significantly increased between 3 and 30 days after CCl₄ administration. Hepatic mitochondrial succinate dehydrogenase activity was significantly increased 30 days after the administration. The present study demonstrates that serum regucalcin has a potential sensitivity as a specific biochemical marker of chronic liver injury with CCl₄ administration in rats.     

Strategies to fit pattern-mixture models

Whereas most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years (Little, 1993, 1994). In this paper, we outline several strategies to fit pattern-mixture models, including the so-called identifying restrictions strategy. Multiple imputation is used to apply this strategy to realistic settings, such as quality-of-life data from a longitudinal study on metastatic breast cancer patients.     

菜豆病虫害及其防治

对危害菜豆的病虫害进行了研究,根据病虫害的种类及发生规律,提出了相应的防治措施.     

Hypermethylation of 5′-Region of the Human Calcitonin Gene in Leukemias: Structural Features and Diagnostic Significance

Methylation of the 5-region of the calcitonin gene was investigated in bone marrow and peripheral blood cells of 27 healthy volunteers and 25 leukemic patients. In all patients suffering from various forms of myeloid and lymphoid leukemia, hypermethylation of CpG sequences was observed in this region of the calcitonin gene. Cytosine hypermethylation in the CpG sequence did not involve cytosines of adjacent CpNpG sequences (where N is any nucleoside). The 5-region of the calcitonin gene lacked CpNpG methylation both in healthy controls and in leukemic patients; this apparently represents specific non-alternative type of CpG methylation in the extended DNA sequence. Methylation of the calcitonin gene was monitored in 18 leukemic patients during malignant progression and medical treatment. Hypermethylation of the calcitonin gene was not observed on long-term clinical hematological remission. In ten patients characterized by unstable (or incomplete) remission hypermethylation of the calcitonin gene persisted through the whole period of observation. In relapses, hypermethylation of the calcitonin gene appeared again and in six patients, this molecular relapse being registered 1-8 months before onset of clinical and laboratory signs of disease progression. The leukemia-specific hypermethylation of CpG sequences of the 5-region of the calcitonin gene is a promising prognostic and diagnostic marker of leukemias and might be useful for monitoring of this disease.     

Multivariate multilevel nonlinear mixed effects models for timber yield predictions

Nonlinear mixed effects models have become important tools for growth and yield modeling in forestry. To date, applications have concentrated on modeling single growth variables such as tree height or bole volume. Here, we propose multivariate multilevel nonlinear mixed effects models for describing several plot-level timber quantity characteristics simultaneously. We describe how such models can be used to produce future predictions of timber volume (yield). The class of models and methods of estimation and prediction are developed and then illustrated on data from a University of Georgia study of the effects of various site preparation methods on the growth of slash pine (Pinus elliottii Engelm.).     

Estimating the responsiveness of an instrument using more than two repeated measures

The responsiveness of a measuring instrument is its ability to detect change over time. A commonly used index of responsiveness is the effect size for paired differences. This paper generalizes the effect size for paired differences to more than two repeated observations per subject. The sampling distribution of the generalized responsiveness statistic, Rt, is simulated for a range of plausible parameter values and for a range of sample sizes varying both the number of subjects (n) and the number of observations per subject (t). The coverage properties of confidence intervals constructed by four methods are compared. Confidence intervals based on jackknife estimates of the standard error and bias of Rt have good coverage properties even when n and t are small. The methods are used to determine which of two standard quality-of-life measures is more responsive to improvements in quality of life following surgery for early-stage breast cancer.     

Evaluating decision rules for nitrogen fertilization

It is important, both for farmer profit and for the environment, to correctly dose nitrogen fertilizer for crop growth. Fertilizer recommendations are embodied in decision rules, which give a recommended dose of nitrogen (N) as a function of information available at the time the decision is made. In this paper, we first propose a criterion for evaluating decision rules. The proposed criterion is the expectation of the objective function when the decision rule is implemented. The major problem here is the estimation of this criterion. Two estimators are considered, a model-based and a nonparametric estimator. A simulation study shows that, in essentially all cases, the nonparametric estimator is better or no worse than the model-based estimator. The bias in the nonparametric estimator is always very small.     

Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout

Pattern mixture models are frequently used to analyze longitudinal data where missingness is induced by dropout. For measured responses, it is typical to model the complete data as a mixture of multivariate normal distributions, where mixing is done over the dropout distribution. Fully parameterized pattern mixture models are not identified by incomplete data; Little (1993, Journal of the American Statistical Association 88, 125-134) has characterized several identifying restrictions that can be used for model fitting. We propose a reparameterization of the pattern mixture model that allows investigation of sensitivity to assumptions about nonidentified parameters in both the mean and variance, allows consideration of a wide range of nonignorable missing-data mechanisms, and has intuitive appeal for eliciting plausible missing-data mechanisms. The parameterization makes clear an advantage of pattern mixture models over parametric selection models, namely that the missing-data mechanism can be varied without affecting the marginal distribution of the observed data. To illustrate the utility of the new parameterization, we analyze data from a recent clinical trial of growth hormone for maintaining muscle strength in the elderly. Dropout occurs at a high rate and is potentially informative. We undertake a detailed sensitivity analysis to understand the impact of missing-data assumptions on the inference about the effects of growth hormone on muscle strength.     

Nonparametric models and methods for designs with dependent censored data: part I

We consider a nonparametric (NP) approach to the analysis of repeated measures designs with censored data. Using the NP model of Akritas and Arnold (1994, Journal of the American Statistical Association 89, 336-343) for marginal distributions, we present test procedures for the NP hypotheses of no main effects, no interaction, and no simple effects. This extends the existing NP methodology for such designs (Wei and Lachin, 1984, Journal of the American Statistical Association 79, 653-661). The procedures do not require any modeling assumptions and should be useful in cases where the assumptions of proportional hazards or location shift fail to be satisfied. The large-sample distribution of the test statistics is based on an i.i.d. representation for Kaplan-Meier integrals. The testing procedures apply also to ordinal data and to data with ties. Useful small-sample approximations are presented, and their performance is examined in a simulation study. Finally, the methodology is illustrated with two real life examples, one with censored and one with missing data. It is indicated that one of the data sets does not conform to any set of assumptions underlying the available methods and also that the present method provides a useful additional analysis even when data sets conform to modeling assumptions.