Clustering via minimum volume ellipsoids

We propose minimum volume ellipsoids (MVE) clustering as an alternative clustering technique to k-means for data clusters with ellipsoidal shapes and explore its value and practicality. MVE clustering allocates data points into clusters in a way that minimizes the geometric mean of the volumes of each cluster’s covering ellipsoids. Motivations for this approach include its scale-invariance, its ability to handle asymmetric and unequal clusters, and our ability to formulate it as a mixed-integer semidefinite programming problem that can be solved to global optimality. We present some preliminary empirical results that illustrate MVE clustering as an appropriate method for clustering data from mixtures of “ellipsoidal” distributions and compare its performance with the k-means clustering algorithm as well as the MCLUST algorithm (which is based on a maximum likelihood EM algorithm) available in the statistical package R. Research of the first author was supported in part by a Discovery Grant from NSERC and a research grant from Faculty of Mathematics, University of Waterloo. Research of the second author was supported in part by a Discovery Grant from NSERC and a PREA from Ontario, Canada.     

An efficient sampling scheme for dynamic generalized models

A multimove sampling scheme for the state parameters of non-Gaussian and nonlinear dynamic models for univariate time series is proposed. This procedure follows the Bayesian framework, within a Gibbs sampling algorithm with steps of the Metropolis–Hastings algorithm. This sampling scheme combines the conjugate updating approach for generalized dynamic linear models, with the backward sampling of the state parameters used in normal dynamic linear models. A quite extensive Monte Carlo study is conducted in order to compare the results obtained using our proposed method, conjugate updating backward sampling (CUBS), with those obtained using some algorithms previously proposed in the Bayesian literature. We compare the performance of CUBS with other sampling schemes using two real datasets. Then we apply our algorithm in a stochastic volatility model. CUBS significantly reduces the computing time needed to attain convergence of the chains, and is relatively simple to implement.     

MPKin-YSTR: Interpretation of Y chromosome STR haplotypes for missing persons cases

Y chromosome Short Tandem Repeat (STR) haplotypes have been used in assisting forensic investigations primarily for identification and male lineage determination. The current SWGDAM interpretation guidelines for Y-STR typing provide helpful guidance on those purposes but do not address the issue of kinship analysis with Y-STR haplotypes. Because of the high mutation rate of Y-STRs, there are complex missing person cases in which inconsistent Y-STR haplotypes between true paternal lineage relatives will arise and cases with two or more male references in the same lineage and yet differ in their haplotypes. Therefore, more useful methods are needed for interpreting the Y-STR haplotype data. Computational methods and interpretation guidelines have been developed specifically addressing this issue, either using a mismatch-based counting method or a pedigree likelihood ratio method. In this study, a software program, MPKin-YSTR, was developed by implementing those more sophisticated methods. This software should be able to improve the interpretation of complex cases with Y-STR haplotype evidence. Thus, more biological evidence will be interpreted, which in turn will result in more investigation leads to help solve crimes.     

Functional proteomics: application of mass spectrometry to the study of enzymology in complex mixtures

This review covers recent developments in mass spectrometry-based applications dealing with functional proteomics with special emphasis on enzymology. The introduction of mass spectrometry into this research field has led to an enormous increase in knowledge in recent years. A major challenge is the identification of “biologically active substances” in complex mixtures. These biologically active substances are, on the one hand, potential regulators of enzymes. Elucidation of function and identity of those regulators may be accomplished by different strategies, which are discussed in this review. The most promising approach thereby seems to be the one-step procedure, because it enables identification of the functionality and identity of biologically active substances in parallel and thus avoids misinterpretation. On the other hand, besides the detection of regulators, the identification of endogenous substrates for known enzymes is an emerging research field, but in this case studies are quite rare. Moreover, the term biologically active substances may also encompass proteins with diverse biological functions. Elucidation of the functionality of those—so far unknown—proteins in complex mixtures is another branch of functional proteomics and those investigations will also be discussed in this review.     

Plantazolicin A and B: structure elucidation of ribosomally synthesized thiazole/oxazole peptides from Bacillus amyloliquefaciens FZB42

The structures of the ribosomally synthesized peptide antibiotics from Bacillus amyloliquefaciens FZB42, plantazolicin A and B, have been elucidated by high resolving ESI-MSMS, 2D (1)H-(13)C-correlated NMR spectroscopy as well as (1)H-(15)N-HMQC/(1)H-(15)N-HMBC NMR experiments. (15)N-labeling prior to the experiments facilitated the structure determination, unveiling a hitherto unusual number of thiazoles and oxazoles formed from a linear 14mer precursor peptide. This finding further extends the number of known secondary metabolites from B. amyloliquefaciens and represents a new type of secondary metabolites from the genus Bacillus.     

Microprobe sampling—Photo ionization-time-of-flight mass spectrometry for in situ chemical analysis of pyrolysis and combustion gases: Examination of the thermo-chemical processes within a burning cigarette

A microprobe sampling device (μ-probe) has been developed for in situ on-line photo ionization mass spectrometric analysis of volatile chemical species formed within objects consisting of organic matter during thermal processing. With this approach the chemical signature occurring during heating, pyrolysis, combustion, roasting and charring of organic material within burning objects such as burning fuel particles (e.g., biomass or coal pieces), lit cigarettes or thermally processed food products (e.g., roasting of coffee beans) can be investigated. Due to its dynamic changes between combustion and pyrolysis phases the cigarette smoking process is particularly interesting and has been chosen as first application. For this investigation the tip of the μ-probe is inserted directly into the tobacco rod and volatile organic compounds from inside the burning cigarette are extracted and real-time analyzed as the glowing front (or coal) approaches and passes the μ-probe sampling position. The combination of micro-sampling with photo ionization time-of-flight mass spectrometry (PI-TOFMS) allows on-line intrapuff-resolved analysis of species formation inside a burning cigarette. Monitoring volatile smoke compounds during cigarette puffing and smoldering cycles in this way provides unparalleled insights into formation mechanisms and their time-dependent change. Using this technique the changes from pyrolysis conditions to combustion conditions inside the coal of a cigarette could be observed directly. A comparative analysis of species formation within a burning Kentucky 2R4F reference cigarette with μ-probe analysis reveals different patterns and behaviors for nicotine, and a range of semi-volatile aromatic and aliphatic species.