A sensitivity analysis for nonrandomly missing categorical data arising from a national health disability survey

Using data from 145,007 adults in the Disability Supplement to the National Health Interview Survey, we investigated the effect of balance difficulties on frequent depression after controlling for age, gender, race, and other baseline health status information. There were two major complications: (i) 80% of subjects were missing data on depression and the missing-data mechanism was likely related to depression, and (ii) the data arose from a complex sample survey. To adjust for (i) we investigated three classes of models: missingness in depression, missingness in depression and balance, and missingness in depression with an auxiliary variable. To adjust for (ii) we developed the first linearization variance formula for nonignorable missing-data models. Our sensitivity analysis was based on fitting a range of ignorable missing-data models along with nonignorable missing-data models that added one or two parameters. All nonignorable missing-data models that we considered fit the data substantially better than their ignorable missing-data counterparts. Under an ignorable missing-data mechanism, the odds ratio for the association between balance and depression was 2.0 with a 95% CI of (1.8, 2.2). Under 29 of the 30 selected nonignorable missing-data models, the odds ratios ranged from 2.7 with 95% CI of (2.3, 3.1) to 4.2 with 95% CI of (3.9, 4.6). Under one nonignorable missing-data model, the odds ratio was 7.4 with 95% CI of (6.3, 8.6). This is the first analysis to find a strong association between balance difficulties and frequent depression.     

A heuristic approach to handling missing data in biologics manufacturing databases

Bioprocess and Biosystems Engineering - The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and...     

A new approach to the species classification problem in floristic analysis

A new method of species (inverse) classification of vegetation data, i.e. classification of species into groups with similar ecological tolerances, is presented which overcomes the problems of species abundance distorting the results. The algorithm TWO-STEP is based on the use of an asymmetric measure of dissimilarity: where i, j are species, h is the stand, n is the total number of stands, and x_ih is the amount of species i in stand h. The algorithm uses the rows of the asymmetric dissimilarity matrix generated as above to form a second symmetric dissimilarity matrix using the measure: where m is the number of species and k the species. Flexible sorting is applied to generate a species classification. Comparison of results after applying the TWO-STEP algorithm and a standard alternative to an artificial data set demonstrates its efficacy. TWO-STEP also shows considerable advantages over previous analyses for a Queensland rainforest data set (quantitative) and an English heath (qualitative) data set. Normalization of species data appears advantageous for quantitative data only.     

A new DNA combing method for biochemical analysis

A simple molecular combing method for analysis of biochemical reactions, called the moving droplet method, has been developed. In this method, small droplets containing DNA molecules run down a sloped glass substrate, and this creates a moving interface among the air, droplet, and substrate that stretches the DNA molecules. This method requires a much smaller volume of sample solution than other established combing methods, allowing wider application in various fields. Using this method, λDNA molecules were stretched and absorbed to a glass substrate, and single-molecule analysis of DNA synthesis by DNA polymerases was performed.     

Genetic relationships among some new cat populations sampled in Europe: A spatial autocorrelation analysis

The allelic frequencies of nine Mendelizing genetic characteristics that control coat colour, tabby and length and some skeletal abnormalities have been studied in four feral domestic cat populations, two in the north of Catalonia (Girona and Roses & L’Estartit, northeastern Spain) and two Adriatic Italian populations (Rimini and Venice). Using different genetic and multivariate analyses (Nei’s and Cavalli-Sforza and Edwards’s genetic distances, phenograms and cladograms using different algorithms, strict consensus trees, canonical population, principal coordinates and nonmetric multidimensional scaling analyses), I show the genetic relationships between these populations and other Western European cat populations previously studied. In the Western European area comprising Catalonia, Italy, France and Great Britain, I found significant spatial structure for thet ^b, l andW alleles and for the average correlogram for the seven alleles studied as a whole using a spatial autocorrelation analysis. The genetic distance matrices between these European cat populations also showed a significant correlation with the geographical distance between these populations using Mantel’s test. These analyses showed that in each of these countries, local cat populations have characteristic genetic profiles which were different to neighbouring populations in nearby countries. At least in this area of Western Europe, the geographical distances between cat populations (although the gene flow can be relatively high) is an important factor which can explain differences in allele frequencies between these populations.     

A new method for dealing with residual spatial autocorrelation in species distribution models

Species distribution modelling (SDM) is a widely used tool and has many applications in ecology and conservation biology. Spatial autocorrelation (SAC), a pattern in which observations are related to one another by their geographic distance, is common in georeferenced ecological data. SAC in the residuals of SDMs violates the ‘independent errors’ assumption required to justify the use of statistical models in modelling species’ distributions. The autologistic modelling approach accounts for SAC by including an additional term (the autocovariate) representing the similarity between the value of the response variable at a location and neighbouring locations. However, autologistic models have been found to introduce bias in the estimation of parameters describing the influence of explanatory variables on habitat occupancy. To address this problem we developed an extension to the autologistic approach by calculating the autocovariate on SAC in residuals (the RAC approach). Performance of the new approach was tested on simulated data with a known spatial structure and on strongly autocorrelated mangrove species’ distribution data collected in northern Australia. The RAC approach was implemented as generalized linear models (GLMs) and boosted regression tree (BRT) models. We found that the BRT models with only environmental explanatory variables can account for some SAC, but applying the standard autologistic or RAC approaches further reduced SAC in model residuals and substantially improved model predictive performance. The RAC approach showed stronger inferential performance than the standard autologistic approach, as parameter estimates were more accurate and statistically significant variables were accurately identified. The new RAC approach presented here has the potential to account for spatial autocorrelation while maintaining strong predictive and inferential performance, and can be implemented across a range of modelling approaches.     

A new bioassay method for quantitative analysis of tetracyclines

A new method is described for quantitative analysis of tetracycline, based on the decrease in external pH of bacterial suspensions after the addition of a glucose pulse. The decrease in external pH of these suspensions was inversely proportional to the concentration of tetracycline. The correlation coefficient of standard response lines derived from the bioassay was 0.99. Tetracycline potency was determined in six tetracycline HCl samples by the sugar pulse bioassay and a turbidimetric method. The turbidimetric assay result varied from the glucose-pulse data by no more than 7 and 3% at 3 and 7 min, respectively. The procedure is rapid, precise and quantitative, and requires minimal preparation and use of media, with savings in laboratory resources and time.     

A new method for sequence analysis of oligodeoxyribonucleotides.

The structure of an oligodeoxyribonucleotide may be determined by a simple two-dimensional separation on a polyethyleneimine-cellulose thin layer sheet. Chromatography in the first dimension fractionates by chain length a nested set of fragments that are generated by subjecting the oligomer to partial spleen phosphodiesterase degradation and then labelling their non-common ends with 32P using polynucleotide kinase. A subsequent in situ treatment with nuclease Bal 31 produces labelled mononucleotides, and these are identified by chromatography in the second dimension. Since the method does not identify the 3' terminal nucleotide, a convenient procedure involving 3' end labelling followed by enzymatic digestion to monomers has been developed for this purpose. This approach to sequence analysis also has the advantage of permitting assignment of the identity and location of any modified or unusual bases within the oligonucleotide.     

A nonrestrictive method for maintaining grouped swine for blood sampling applicable to physiologic assessment of stress

Studies of the stress response in swine require repeated blood sampling to determine diurnal variations of measured parameters. Most of these methods require elaborate artificial habitats such as metabolism crates and/or physical restraint for venipuncture. In either case, the aversive nature of the sampling procedure induces activation of the hypothalamic-hypophyseal-adrenal cortical axis. The development of a nonaversive procedure for repeated blood sampling of swine is described in this paper. The pigs were unrestrained and maintained in groups in a variety of habitats.     

A new method for nonhomogeneous time course expression analysis

Time course expression analysis constitutes a large portion of applications of microarray experiments. One primary goal of such experiments is to detect genes with the temporal changes over a period of time or at some interested time points. Difficulties arising from data with small number of replicates over only a few unaligned time points in multiple groups pose challenges for efficient statistical analysis. Some known methods are limited by the unverifiable assumptions or by the scope of applications for only two groups. We present a new method for detecting differentially expressed genes under nonhomogeneous time course experiments in multiple groups. The new method first models the time course curve of one gene by a Gaussian process to align the nonhomogeneous time course data and to compute the gradient of the time course curve as well, the latter of which is used as directional information to enhance the sensitivity of detection for temporal changes. Second, we adopt a nonparametric method to test a surrogate hypothesis based on the augmented data from the Gaussian process model. The proposed method is robust in terms of model fitting and testing. It does not require any distributional assumption for the observations or the test statistic and the method works for the case with as few as triplicate samples over four or five time points under multiple groups. We show the effectiveness and superiority of the new method in comparison with some existing methods using simulated models and two real data sets.     

A new method for disulfide analysis of peptides.

A simple, sensitive, reliable method for determining disulfide groups in peptides is presented. The disulfides are cleaved in a brief treatment with strong alkali. Following neutralization with phosphoric acid, thiol resulting from the alkaline cleavage is estimated colorimetrically with 5,5′-dithio-bis(2-nitrobenzoic acid). In the presence of EDTA, the color yield is stable and is linear with the concentration of oxidized glutathione. The stoichiometry with other peptide disulfides appears to be somewhat variable but not so as to interfere with detection of peptide disulfides in chromatographic fractions. The present method compares favorably with two other proposed disulfide analytical methods. The cleavage assay is chromogenic with disulfides, thiols, and with certain blocked thiols but is not chromogenic with methionine and lanthionine.     

A new efficient method for analyzing fungi species using correlations between nucleotides

Background

In recent years, DNA barcoding has become an important tool for biologists to identify species and understand their natural biodiversity. The complexity of barcode data makes it difficult to analyze quickly and effectively. Manual classification of this data cannot keep up to the rate of increase of available data.

Results

In this study, we propose a new method for DNA barcode classification based on the distribution of nucleotides within the sequence. By adding the covariance of nucleotides to the original natural vector, this augmented 18-dimensional natural vector makes good use of the available information in the DNA sequence. The accurate classification results we obtained demonstrate that this new 18-dimensional natural vector method, together with the random forest classifier algorthm, can serve as a computationally efficient identification tool for DNA barcodes. We performed phylogenetic analysis on the genus Megacollybia to validate our method. We also studied how effective our method was in determining the genetic distance within and between species in our barcoding dataset.

Conclusions

The classification performs well on the fungi barcode dataset with high and robust accuracy. The reasonable phylogenetic trees we obtained further validate our methods. This method is alignment-free and does not depend on any model assumption, and it will become a powerful tool for classification and evolutionary analysis.