Discriminant function analysis for evaluating the status of coronary heart disease risk

We investigated to discriminate those individuals categorized by 1. obesity, 2. hypercholesterolemia, 3. hypertension, 4. low maximal oxygen uptake, 5. an abnormal electrocardiogram reflecting ischemic patterns, and/or 6. real sedentary life, from relatively healthier individuals without coronary heart disease (CHD) risk factors. One hundred and six Japanese women, aged 30 to 72 years, all of whom were in the postabsorptive state, were recruited in a series of tests for anthropometric and physiologic profiles both during the resting state and during the submaximal-maximal cycling exercise. Subjects were categorized into two groups--those who possessed four or more of the above 1, 2, 3, 4, 5, and 6 (high-CHD-risk group, n = 15) and apparently healthy individuals with a minimum number of risk factors (low-CHD-risk group, n = 83). Analyses of the data revealed that a combination of 8 variables extracted from among original 25 variables accurately classified 13/15 (87%) of high-CHD-risk group and 77/83 (93%) of low-CHD-risk group (mean = 90/98 or 92%) into their respective groups. The 8 variables were double product, Katsura index, waist girth, chest girth, TG, TC, and skinfold thicknesses at the subscapular and abdominal sites. Subsequent t-test identified significant differences between groups not only for VO2max, SBP and TC but also for DBP, LDLC, TG, Hb, HR, and HRmax. Most of these differences were of a much greater magnitude compared to the existing difference in chronological age. These findings suggest the usefulness and importance of anthropometric and blood lipid variables in the explanation of differences in the health status between high-CHD-risk women and their counterparts.     

MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification

Background

Sequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.

Results

Thus, we introduce a hierarchical multivariate Poisson model (MP) and the associated optimal Bayesian classifier (OBC) for classifying samples using sequencing data. Lacking closed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification. We demonstrate superior or equivalent classification performance compared to typical classifiers for two synthetic datasets and over a range of classification problem difficulties. We also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator and demonstrate its computation over the feature space. In addition, we demonstrate superior or leading class performance over an RNA-Seq dataset containing two lung cancer tumor types from The Cancer Genome Atlas (TCGA).

Conclusions

Through model-based, optimal Bayesian classification, we demonstrate superior classification performance for both synthetic and real RNA-Seq datasets. A tutorial video and Python source code is available under an open source license at http://bit.ly/1gimnss.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0401-3) contains supplementary material, which is available to authorized users.     

Choice of function for mortality analysis: Effective forecasting depends on a minimum parameter representation

For nearly two centuries actuaries, statisticians and demographers have sought a parameter space in which the mortality pattern of a population could be located, that would be linked to the larger space of age-specific rates by an analytical formula. Gompertz, Makeham, Brillinger, Wolfenden, Pollard and others have made contributions to this end. Model or reference tables perform a similar task, usually with fewer parameters, but are less easily manipulated. A common means of reducing dimensionality is to map the original space of mortality rates in five-year groups on a straight line in which are located the expectations of life at age zero (ė₀). A small-space representation is advantageous for filling gaps in data. If the only fact known about a country is the fraction of girls who have a living mother, then a one-dimensional set can be indexed on this to provide the full detail of mortality, assuming the unknown mortality is part of that set. For forecasting one would like the succession of life tables for a given population over past times to be representable by points moving in a simple way through a parameter space—ideally in a straight line—over a succession of calendar years. The simpler the curve, the more realistic is likely to be its projection into the future. The Brass relational method provides a trajectory well suited to extrapolation in a space of only two parameters. Some useful extensions of this have recently been devised.     

Development and implementation of a reduced risk peach arthropod management program in New Jersey

We implemented a 2-yr program to reduce organophosphorus and carbamate insecticide use and mitigate their associated risks as they relate to peach production in New Jersey and elsewhere. The main thrust integrated mating disruption with ground cover management practices to reduce oriental fruit moth, Grapholita molesta (Busck) (Lepidoptera: Tortricidae), and tarnished plant bug, Lygus lineolaris (Palisot de Beauvois) (Heteroptera: Miridae), abundance and damage. This Reduced Risk Peach Arthropod Management Program was compared with adjacent conventionally managed peach orchards. In 1999, we found 2.3 times fewer L. lineolaris and stink bugs (Euschistus servus (Say), E. tristigmus (Say), Acrostemum hilare (Say) (Heteroptera: Pentatomidae) and 2.0 times less heteropteran damaged peaches in reduced-risk orchards when compared with conventionally managed orchards. In 2000, we observed 4.9 times fewer heteropteran insects in reduced-risk orchards but damage levels were not significantly different between the two programs. In both years, G. molesta mating disruption gave at least 4 mo of noninsecticidal control of this major pest. The reduced-risk program provided a level of pest control that was equal to or better than conventional peach pest management programs while using fewer organophosporus and carbamate insecticides.     

MScanner: a classifier for retrieving Medline citations

Background  

Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains.     

Feature-similarity protein classifier as a ligand engineering tool

Kinases have been often targeted in drug therapy aimed at blocking signaling pathways. However, the conservation of protein structure across homologs often leads to uncontrolled cross-reactivity. On the other hand, sticky packing defects in proteins are typically not conserved across homologs, making them ligand-anchoring sites potentially important to enhance selectivity. Thus, we introduce a hierarchical clustering of PDB-reported kinases according to packing differences. This kinome partitioning is highly correlated with proximity relations arising from the pharmacological profiling of kinases. A variable packing sensitivity is observed for individual drugs, with highly promiscuous ligands being the most insensitive to packing differences. Our classifier enables a strategy to design selective inhibitors.     

Nearest-neighbor classifier as a tool for classification of protein families

Knowledge about protein function is essential in understanding the biological processes. A specific class or family of protein shares common structural and chemical properties amongst its member sequences. The set of properties that display its unique characteristics for clearly classifying a protein sequence into its corresponding protein family needs to be studied. Our study of these important properties conducted on four major classes of proteins namely Globins, Homeoboxes, Heat Shock proteins (HSP) and Kinase have shown that frequency of twenty naturally occurring amino acids, hydrophobic content of protein, molecular weight of protein, isoelectric point of protein, secondary structure composition of amino acid residues as helices, coils and sheets and the composition of helices, coils and sheets in the secondary structure topology plays a significant role in correctly classifying the protein into its corresponding class or family as indicated by the overall efficiency of Nearest Neighbor Classifier as 84.92%.     

Dynamics of the public concern and risk communication program implementation

Abstract

The public concern about electromagnetic field (EMF) exposure varies due to different reasons. A part of them are connected with the better and higher quality of information that people receive from science, media, Internet, social networks, industry, but others are based on good communication programs performed by the responsible institutions, administration and persons. Especially, in Bulgaria, public concern follows interesting changes, some of them in correlation with the European processes of concern, but others following the economic and political processes in the country. Here, we analyze the dynamics of the public concern over the last 10 years. Our explanation of the decrease of the people’s complaints against EMF exposure from base stations for mobile communication is as a result of our risk communication program that is in implementation for >10 years.     

The chemical logic of a minimum protocell

Traditional schemes for the origin of cellular life on earth generally suppose that the chance assembly of polymer synthesis systems was the initial event, followed by incorporation into a membrane-enclosed volume to form the earliest cells. Here we discuss an alternative system consisting of replicating membrane vesicles, which we define as minimum protocells. These consist of vesicular bilayer membranes that self-assemble from relatively rare organic amphiphiles present in the prebiotic environment. If some of the amphiphiles are primitive pigment molecules asymmetrically oriented in the bilayer, light energy can be captured in the form of electrochemical ion gradients. This energy could then be used to convert relatively common precursor molecules into membrane amphiphiles, thereby providing an initial photosynthetic growth process, as well as an appropriate microenvironment for incorporation and evolution of polymer synthesis systems.     

Decreased skin function may be a risk factor for metabolic syndrome

The metabolic syndrome, a cluster of risk factors for cardiovascular disease, is closely related to environmental and lifestyle risk factors. Increasing evidence suggests that environmental risk factors may involve an increase in xenobiotic exposure, for example due to environmental toxins, medications, high meat intake, food additives and supplements; while lifestyle risk factors, such as sedentary lifestyles, may involve a decrease in the detoxification and elimination of xenobiotics. The skin, the body's largest organ, plays a distinct role in the detoxification and elimination of xenobiotics and the body lipid homeostasis, which is affected by sedentary lifestyle and physical activity, as well as by ambient temperature. Thus, it seems that decreased skin biotransformation and excretion, for example due to low ambient temperature and sedentary lifestyle, may be an important risk factor for metabolic syndrome. This review aims to provide insight into the role of the skin in the development of metabolic syndrome.