Microsatellite markers uncover cryptic species of Odontotermes (Termitoidae: Termitidae) from Peninsular Malaysia

Termites from the genus Odontotermes are known to contain numerous species complexes that are difficult to tell apart morphologically or with mitochondrial DNA sequences. We developed markers for one such cryptic species complex, that is, Odontotermes srinakarinensis sp. nov. from Maxwell Hill Forest Reserve (Perak, Malaysia), and characterised them using a sample of 41 termite workers from three voucher samples from the same area. We then genotyped 150 termite individuals from 23 voucher samples/colonies of this species complex from several sites in Peninsular Malaysia. We analysed their population by constructing dendograms from the proportion of shared-alleles between individuals and genetic distances between colonies; additionally, we examined the Bayesian clustering pattern of their genotype data. All methods of analysis indicated that there were two distinct clusters within our data set. After the morphologies of specimens from each cluster were reexamined, we were able to separate the two species morphologically and found that a single diagnostic character found on the mandibles of its soldiers could be used to separate the two species quite accurately. The additional species in the clade was identified as Odontotermes denticulatus after it was matched to type specimens at the NHM London and Cambridge Museum of Zoology

Spanning tree approximations for conditional random fields

Abstract In this work we show that one can train Conditional Random Fields of intractable graphs effectively and efficiently by considering a mixture of random spanning trees of the underlying graphical model. Furthermore, we show how a maximum-likelihood estimator of such a training objective can subsequently be used for prediction on the full graph. We present experimental results which improve on the state-of-the-art. Additionally, the training objective is less sensitive to the regularization than pseudo-likelihood based training approaches. We perform the experimental validation on two classes of data sets where structure is important: image denoising and multilabel classification

Concentrations of heavy metal in different parts of the gastropod, Faunus ater (linnaeus), collected from intertidal areas of Peninsular Malaysia.

Marine gastropods, Faunus ater (Linnaeus), were collected from Pantai Sri Tujoh (Kelantan), Pantai Bisikan Bayu (Kelantan), Kg. Telaga Nenas (Perak) and Kesang Laut (Johor). Soft tissues of gastropods were dissected into digestive caecum (DC), foot, remainder, muscle, and operculum. The shell and dissected parts were analyzed for Cd, Cu, Ni, and Pb. It was found that the DC and the remainder accumulated high concentrations of Cu ranging between 159.1 and 290.2 μg/g dw. The shell was shown to highly accumulate non-essential Pb, Ni, and Cd compared to the soft tissues. Meanwhile, higher bioavailabilities of Cd and Cu were found in Pantai Sri Tujoh, whereas higher bioavailabilities of Ni and Pb were found in Pantai Bisikan Bayu compared to other sampling sites. The present results suggested that F. ater could be used as a potential biomonitor of heavy metal contamination. However, further studies are still needed in order to validate the use of F. ater as a good biomonitor of heavy metal pollution

Near-optimal experimental design for model selection in systems biology

Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. Results: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Availability: Toolbox ‘NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Inferring structural variant cancer cell fraction.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity

Use of dynamic contrast-enhanced MRI to measure subtle blood-brain barrier abnormalities

There is growing interest in investigating the role of subtle changes in blood–brain barrier (BBB) function in common neurological disorders and the possible use of imaging techniques to assess these abnormalities. Some studies have used dynamic contrast-enhanced MR imaging (DCE-MRI) and these have demonstrated much smaller signal changes than obtained from more traditional applications of the technique, such as in intracranial tumors and multiple sclerosis. In this work, preliminary results are presented from a DCE-MRI study of patients with mild stroke classified according to the extent of visible underlying white matter abnormalities. These data are used to estimate typical signal enhancement profiles in different tissue types and by degrees of white matter abnormality. The effect of scanner noise, drift and different intrinsic tissue properties on signal enhancement data is also investigated and the likely implications for interpreting the enhancement profiles are discussed. No significant differences in average signal enhancement or contrast agent concentration were observed between patients with different degrees of white matter abnormality, although there was a trend towards greater signal enhancement with more abnormal white matter. Furthermore, the results suggest that many of the factors considered introduce uncertainty of a similar magnitude to expected effect sizes, making it unclear whether differences in signal enhancement are truly reflective of an underlying BBB abnormality or due to an unrelated effect. As the ultimate aim is to achieve a reliable quantification of BBB function in subtle disorders, this study highlights the factors which may influence signal enhancement and suggests that further work is required to address the challenging problems of quantifying contrast agent concentration in healthy and diseased living human tissue and of establishing a suitable model to enable quantification of relevant physiological parameters. Meanwhile, it is essential that future studies use an appropriate control group to minimize these influences

pncA mutations in clinical Mycobacterium tuberculosis isolates from Korea

BACKGROUND: Pyrazinamide (PZA) is among the first-line drugs for the treatment of tuberculosis. In vitro, it kills semidormant mycobacteria only at low pH. The purpose of this study was to compare PZA resistance with pyrazinamidase (PZase) activity and the genotype to better understand the molecular basis of PZA resistance and to expand the profile of pncA mutations worldwide. RESULTS: Of the 28 tested strains of Mycobacterium tuberculosis, 6 were susceptible to PZA and positive for PZase activity and had no pncA mutations. Twenty-one strains were resistant to PZA and negative for PZase activity and had mutations in the pncA gene, including 15 point mutations, 5 insertions, and 2 deletions. One strain had no mutation in the pncA gene, even though it was resistant to PZA and negative for PZase activity. Three isolates had adenine to guanine point mutations in the -11 upstream region, making this the most common type of pncA mutations in this study, with at least two different RFLP patterns. CONCLUSION: These data help in the understanding of the molecular basis of PZA resistance. An adenine to guanine point mutation in the -11 upstream region was the most common type of pncA mutation in our isolates. The results of pncA mutation analyses should be carefully interpreted for epidemiologic purposes

Nonmonotone Barzilai-Borwein Gradient Algorithm for ℓ1\ell_1-Regularized Nonsmooth Minimization in Compressive Sensing

This paper is devoted to minimizing the sum of a smooth function and a nonsmooth $\ell_1$-regularized term. This problem as a special cases includes the $\ell_1$-regularized convex minimization problem in signal processing, compressive sensing, machine learning, data mining, etc. However, the non-differentiability of the $\ell_1$-norm causes more challenging especially in large problems encountered in many practical applications. This paper proposes, analyzes, and tests a Barzilai-Borwein gradient algorithm. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the $\ell_1$-norm. Moreover, a nonmonotone line search technique is incorporated to find a suitable stepsize along this direction. The algorithm is easily performed, where the values of the objective function and the gradient of the smooth term are required at per-iteration. Under some conditions, the proposed algorithm is shown to be globally convergent. The limited experiments by using some nonconvex unconstrained problems from CUTEr library with additive $\ell_1$-regularization illustrate that the proposed algorithm performs quite well. Extensive experiments for $\ell_1$-regularized least squares problems in compressive sensing verify that our algorithm compares favorably with several state-of-the-art algorithms which are specifically designed in recent years.Comment: 20 page

Complete Killing of Caenorhabditis elegans by Burkholderia pseudomallei Is Dependent on Prolonged Direct Association with the Viable Pathogen

Background: Burkholderia pseudomallei is the causative agent of melioidosis, a disease of significant morbidity and mortality in both human and animals in endemic areas. Much remains to be known about the contributions of genotypic variations within the bacteria and the host, and environmental factors that lead to the manifestation of the clinical symptoms of melioidosis. Methodology/Principal Findings: In this study, we showed that different isolates of B. pseudomallei have divergent ability to kill the soil nematode Caenorhabditis elegans. The rate of nematode killing was also dependent on growth media: B. pseudomallei grown on peptone-glucose media killed C. elegans more rapidly than bacteria grown on the nematode growth media. Filter and bacteria cell-free culture filtrate assays demonstrated that the extent of killing observed is significantly less than that observed in the direct killing assay. Additionally, we showed that B. pseudomallei does not persistently accumulate within the C. elegans gut as brief exposure to B. pseudomallei is not sufficient for C. elegans infection. Conclusions/Significance: A combination of genetic and environmental factors affects virulence. In addition, we have also demonstrated that a Burkholderia-specific mechanism mediating the pathogenic effect in C. elegans requires proliferating B

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups