Loss of second and sixth conserved cysteine residues from trypsin inhibitor-like cysteine-rich domain-type protease inhibitors in Bombyx mori may induce activity against microbial proteases

Previous studies have indicated that most trypsin inhibitor-like cysteine-rich domain (TIL)-type protease inhibitors, which contain a single TIL domain with ten conserved cysteines, inhibit cathepsin, trypsin, chymotrypsin, or elastase. Our recent findings suggest that Cys^2nd and Cys^6th were lost from the TIL domain of the fungal-resistance factors in Bombyx mori, BmSPI38 and BmSPI39, which inhibit microbial proteases and the germination of Beauveria bassiana conidia. To reveal the significance of these two missing cysteines in relation to the structure and function of TIL-type protease inhibitors in B. mori, cysteines were introduced at these two positions (D36 and L56 in BmSPI38, D38 and L58 in BmSPI39) by site-directed mutagenesis. The homology structure model of TIL domain of the wild-type and mutated form of BmSPI39 showed that two cysteine mutations may cause incorrect disulfide bond formation of B. mori TIL-type protease inhibitors. The results of Far-UV circular dichroism (CD) spectra indicated that both the wild-type and mutated form of BmSPI39 harbored predominantly random coil structures, and had slightly different secondary structure compositions. SDS-PAGE and Western blotting analysis showed that cysteine mutations affected the multimerization states and electrophoretic mobility of BmSPI38 and BmSPI39. Activity staining and protease inhibition assays showed that the introduction of cysteine mutations dramaticly reduced the activity of inhibitors against microbial proteases, such as subtilisin A from Bacillus licheniformis, protease K from Engyodontium album, protease from Aspergillus melleus. We also systematically analyzed the key residue sites, which may greatly influence the specificity and potency of TIL-type protease inhibitors. We found that the two missing cysteines in B. mori TIL-type protease inhibitors might be crucial for their inhibitory activities against microbial proteases. The genetic engineering of TIL-type protease inhibitors may be applied in both health care and agricultural industries, and could lead to new methods for breeding fungus-resistant transgenic crops and antifungal transgenic silkworm strains.     

The Treatment of Missing Values in Logistic Regression

The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.     

Adjusting for Nonignorable Missingness When Estimating Generalized Additive Models

Generalized additive models (GAMs) have been widely used for flexible modeling of various types of outcomes. When the outcome in a GAM is subject to missing, practical analyses often assume that missingness is missing at random (MAR). This assumption can be of suspicion when the missingness is not by design. Evaluating the potential effects of alternative nonignorable missing data mechanism on the MAR inference from a GAM can be important but often challenging due to the complicatedness of alternative nonignorable models. We apply the index approach to local sensitivity (Troxel, Ma, and Heitjan 2004 (2004). Statistica Sinica 14 , 1221–1237) to evaluate the potential changes of the GAM estimates in the neighborhood of the MAR model. The approach avoids fitting any complicated nonignorable GAM. Only MAR estimates are required to calculate the resulting sensitivity index and adjust the GAM estimates to account for nonignorable missingness. Thus the proposed approach is considerably simpler to conduct, as compared with the alternative methods. The simulation study shows that the index provides valid assessment of the local sensitivity of the GAM estimates to nonignorable missingness. We then illustrate the method using a rheumatoid arthritis clinical trial data set.     

How do Araucaria angustifolia trees grow in overstocked stands?

The objective of this study was to evaluate growth along the stem of Araucaria angustifolia (Bertol.) Kuntze trees competing in overstocked stands, in order to identify periods when growth and trunk shape are differentiated during the trees' lifespan. The research was carried out in a planted forest of Araucaria angustifolia established in 1946 in the Açungui National Forest in Campo Largo, Paraná, Brazil, when these trees were 65 years old. One thinning was recorded, at some time between 1970 and 1980. Forty-six trees were selected and divided into three development classes (DC) at 65 years of age; these classes considered diameter at 1.30 m (breast height, dbh) with a range of 20 cm (from 10 cm to 70 cm). In addition to dbh, total tree height, and crown height and diameter were measured in the field. From each tree, 14 disks were removed to analyze growth rings and confirm the age of the stand. Some trees in the smallest DC (10 ≤ dbh < 30 cm) were the product of natural regeneration (younger trees that grew after the initial planting). In 63% of the trees, at least one growth ring was missing at breast height. Missing rings at breast height were more common in trees with smaller dbh and crown diameters. The need for more growing space was observed at different periods during the studied lifespan of the trees from three DCs. It resulted in changes in stems shape from conical to cylindrical. Different growth patterns could be observed during the lives of some trees as they outgrew their competition.     

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.