Statistical evidence for remnants of the primordial code in the acceptor stem of prokaryotic transfer RNA

Summary The specificity of interaction of amino acids with triplets in the acceptor helix stem of tRNA was investigated by means of a statistical analysis of 1400 tRNA sequences. The imprint of a prototypic genetic code at position 3–5 of the acceptor helix was detected, but only for those major amino acids, glycine, alanine, aspartic acid, and valine, that are formed by spark discharges of simple gases in the laboratory. Although remnants of the code at position 3–5 are typical for tRNAs of archaebacteria, eubacteria, and chloroplasts, eukaryotes do not seem to contain this code, and mitochondria take up an intermediary position. A duplication mechanism for the transposition of the original 3–5 code toward its present position in the anticodon stern of tRNA is proposed. From this viewpoint, the mode of evolution of mRNA and functional ribosomes becomes more understandable.Offprint requests to: W. Moller     

Statistical analysis for the spatial validity of a model to forecast the daily number of forest fires

In earlier papers a qualitative and quantitative model was developed for predicting the number of forest fires occurring per day. This model permits the forecast at 00.00 hours Universal Time Convention (UTC) of any day (d), the number of forest fires per day for a range of several days (d tod+5) over a particular region. Input data are the number of forest fires in the region during two preceding days (d–2 andd–1) and the type of day (real and evaluated from radiosonde ford–2,d–1,d and predicted from meteorological medium-range forecasts, i.e. of European Centre, ford+1,d+2,d+3,d+4 andd+5. As this model requires data obtained by radiosonde, particularly temperatures and geopotentials at 850 and 700 hPa and dew points (or specific humidity) at 850 hPa, this study investigates the spatial validity of the model in relation to the distance from the radiosonde station (RS). The highest quality forecast is obtained for the region immediately surrounding the RS, and diminishes with increasing distance from it, this being due to the data obtained from the RS not being representative of the atmospheric column over the region. Hence, the derivation of the critical distance for a particular quality level of measurement. Conversely, fixed quality level implies a specific separation between RS and the region for the prediction, with a higher predictive quality implying a shorter distance.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance,

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Highlights

• •Bayesian Beta-Binomial model integrates ion statistics with peptide ratio agreement.
• •Model appropriately interprets information from low signal peptides.
• •Confidence can be assigned even without replicates.
• •Model adds sensitivity to detection of small changes.

     

Analysis of Protein Three-Dimension Structure Using Amino Acids Depths

The issue of amino acid depth in proteins gives important insights to our understanding of protein’s three-dimensional structure. There has already been much research done in mathematical and statistical sciences regarding the general definitions, properties and algorithms describing the particle depth of spatially extended systems. We constructed a method of calculating the amino acids depths and applied it to a set of 527 protein structures. We propose the introduction of amino acid depth tendency factors for three-dimensional structures of proteins. The depth tendency factors relate not only to the hydrophobicity indices but also to the electrostatic charge. We found a relationship between the protein size and the number of residues using the distance between the deepest residue and surface residues. We made a prediction regarding the number of residues on the surface of a protein, the deepest amino acid, and the average depth, all of which are fitted well to a linear functional relationship with the length of the protein. Finally, we have predicted the depths of multiple peptides in protein’s three-dimension structure. Electronic supplementary material The online version of this article () contains supplementary material, which is available to authorized users.     

The effects of non-homology in RAPD bands on similarity and multivariate statistical ordination in Brassica and Helianthus

 In order to estimate the impact of mis-coding non-homologous, co-migrating DNA bands as homologous, two sets of data were utilized. Analyses were conducted using three Helianthus species in which each co-migrating band had previously been confirmed. Comparisons of the similarities between these three Helianthus species using the original 177 RAPD bands and the corrected, homology verified, 197 RAPD band data set revealed that the triangular relationship among these three species was almost identical in both data sets. The non-homology errors in the Helianthanus data sets were found to be random. These random errors merely reduced the absolute similarities, but not the relative similarities nor the relationships among the taxa, in principal-coordinate-analysis ordination. Analyses of RAPDs for the classical Brassica U triangle were made by inserting random non-homologies for 5, 10, 15 and 20% of the original 220 RAPD bands. These analyses revealed a progressive decrease in similarities and less loading on the first two axes in principal coordinate analysis (PCO). However, the basic U triangle of relationships among these six Brassica species was maintained. It appears that if errors in homology of co-migrating DNA bands are random, this will have little effect on the relative similarities and on PCO ordination. This helps explain the successful use of RAPDs at the specific level. Received: 6 December 1997 / Accepted: 11 December 1997     

Some model based considerations on observing generation times for communicable diseases

The generation time of an infectious disease is usually defined as the time from the moment one person becomes infected until that person infects another person. The concept is similar to “generation gap” in demography, with new infections replacing births in a population. Originally applied to diseases such as measles where at least the first generations are clearly discernible, the concept has recently been extended to other diseases, such as influenza, where time order of infections is usually much less apparent.By formulating the relevant statistical questions within a simple yet basic mathematical model for infection spread, it is possible to derive theoretical properties of observations in various situations e.g. in “isolation”, in households, or during large outbreaks. In each case, it is shown that the sampling distribution of observations depends on a number of factors, usually not considered in the literature and that must be taken into account in order to achieve unbiased inference about the generation time distribution. Some implications of these findings for statistical inference methods in epidemic spread models are discussed.     

Quantifying change in distributions: a new departure index that detects,measures and describes change in distributions from population structures,size-classes and other ordered data

Many statistics are available to compare distributions. Some are limited to nominal data while others, such as skew, Kullback-Leibler, Kolmogorov-Smirnov and the Gini coefficient, are useful for providing information about ordered distributions. While many of these tests are useful for determining properties of data in histograms, there has not been a test until now that allows for the detection of differences between distributions, describes the difference and is sensitive to the location of the departures. Such a test could be critical for comparing pre-and post-event distributions, such as a change in the distribution of biomass due to fire, for example, or for comparing data from different locations, such as soil size distributions, and even for evaluating economic disparity or examining differences in age demographics. We present a new statistic, a departure index, which allows a test distribution to be compared with any reference distribution. The resulting index contains information about the location, magnitude and direction of departure from the reference distribution to the test distribution. The departure index in turn provides a standardized response range that allows for a comparison of results from different analyses. A case study of actual fire data demonstrates the sensitivity and range of the test.     

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field. An erratum to this article can be found at