Raazesh Sainudiin

Detecting Site-Specific Physicochemical Selective Pressures: Applications to the Class-I HLA of the Human Major Histocompatibility Complex and the SRK of the Plant Sporophytic Self-Incompatibility System

Models of codon substitution are developed that incorporate physicochemical properties of amino acids. When amino acid sites are inferred to be under positive selection, these models suggest the nature and extent of the physicochemical properties under selection. This is accomplished by first partitioning the codons on the basis of some property of the encoded amino acids. This partition is used to parametrize the rates of property-conserving and property-altering base substitutions at the codon level by means of finite mixtures of Markov models that also account for codon and transition:transversion biases. Here, we apply this method to two positively selected receptors involved in ligand-recognition: the class I alleles of the human major histocompatibility complex (MHC) of known structure and the S-locus receptor kinase (SRK) of the sporophytic self-incompatibility system (SSI) in cruciferous plants (Brassicaceae), whose structure is unknown. Through likelihood ratio tests we demonstrate that at some sites, the positively selected MHC and SRK proteins are under physicochemical selective pressures to alter polarity, volume, polarity and/or volume, and charge to various extents. An empirical Bayes approach is used to identify sites that may be important for ligand recognition in these proteins.

Identification of physicochemical selective pressure on protein encoding nucleotide sequences

BackgroundStatistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account.ResultsWe develop a new codon-based likelihood model for detecting site-specific selection pressures acting on specific physicochemical properties. Nonsynonymous substitutions are divided into substitutions that differ with respect to the physicochemical properties of interest, and those that do not. The substitution rates of these two types of changes, relative to the synonymous substitution rate, are then described by two parameters, γ and ω respectively. The new model allows us to perform likelihood ratio tests for positive selection acting on specific physicochemical properties of interest.The new method is first used to analyze simulated data and is shown to have good power and accuracy in detecting physicochemical selective pressure. We then re-analyze data from the class-I alleles of the human Major Histocompatibility Complex (MHC) and from the abalone sperm lysine.ConclusionOur new method allows a more flexible framework to identify selection pressure on particular physicochemical properties.

Human-assisted spread of a maladaptive behavior in a critically endangered bird.

Conservation management often focuses on counteracting the adverse effects of human activities on threatened populations. However, conservation measures may unintentionally relax selection by allowing the ‘survival of the not-so-fit’, increasing the risk of fixation of maladaptive traits. Here, we report such a case in the critically-endangered Chatham Island black robin (Petroica traversi) which, in 1980, was reduced to a single breeding pair. Following this bottleneck, some females were observed to lay eggs on the rims of their nests. Rim eggs left in place always failed to hatch. To expedite population recovery, rim eggs were repositioned inside nests, yielding viable hatchlings. Repositioning resulted in rapid growth of the black robin population, but by 1989 over 50% of all females were laying rim eggs. We used an exceptional, species-wide pedigree to consider both recessive and dominant models of inheritance over all plausible founder genotype combinations at a biallelic and possibly sex-linked locus. The pattern of rim laying is best fitted as an autosomal dominant Mendelian trait. Using a phenotype permutation test we could also reject the null hypothesis of non-heritability for this trait in favour of our best-fitting model of heritability. Data collected after intervention ceased shows that the frequency of rim laying has strongly declined, and that this trait is maladaptive. This episode yields an important lesson for conservation biology: fixation of maladaptive traits could render small threatened populations completely dependent on humans for reproduction, irreversibly compromising the long term viability of populations humanity seeks to conserve.

Artificial Neural Network–Based System Identification for a Single-Shaft Gas Turbine

—During recent decades, artificial intelligence has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines (GT). In this study, a methodology based on artificial neural network (ANN) techniques was developed for offline system identification of a low-power gas turbine. The processed data was obtained from a SIMULINK model of a gas turbine in MATLAB environment. A comprehensive computer program code was generated and run in MATLAB environment for creating and training different ANN models with two-layer feed-forward multi-layer perceptron (MLP) structure. The code consisted of various training functions, different number of neurons as well as a variety of transfer (activation) functions for hidden and output layers of the network. It was shown that the optimal model for a two-layer network with MLP structure, consisted of 20 neurons in its hidden layer and used trainlm as its training function, as well as tansig and logsid as its transfer functions for the hidden and output layers. It was also observed that trainlm has a superior performance in terms of minimum MSE, compared with each of the other training functions. The resulting model could predict performance of the system with high accuracy. The methodology provides a comprehensive view of the performance of over 18720 ANN models for system identification of single shaft GT. One can use the optimal ANN model from this study when training from real data obtained from this type of GT. This is particularly useful when real data is only available over a limited operational range.

Auto-validating von Neumann rejection sampling from small phylogenetic tree spaces

BackgroundIn phylogenetic inference one is interested in obtaining samples from the posterior distribution over the tree space on the basis of some observed DNA sequence data. One of the simplest sampling methods is the rejection sampler due to von Neumann. Here we introduce an auto-validating version of the rejection sampler, via interval analysis, to rigorously draw samples from posterior distributions over small phylogenetic tree spaces.ResultsThe posterior samples from the auto-validating sampler are used to rigorously (i) estimate posterior probabilities for different rooted topologies based on mitochondrial DNA from human, chimpanzee and gorilla, (ii) conduct a non-parametric test of rate variation between protein-coding and tRNA-coding sites from three primates and (iii) obtain a posterior estimate of the human-neanderthal divergence time.ConclusionThis solves the open problem of rigorously drawing independent and identically distributed samples from the posterior distribution over rooted and unrooted small tree spaces (3 or 4 taxa) based on any multiply-aligned sequence data.

Relative Contribution of Seed-Transmitted Inoculum to Foliar Populations of Phaeosphaeria nodorum.

ABSTRACT A marked-isolate, release-recapture experiment was conducted to assess the relative contributions of seed-transmitted (released isolates) versus all other inocula to foliar and grain populations of Phaeosphaeria nodorum in winter wheat rotated with nonsusceptible crops in New York and Georgia, United States. Seed infected with two distinct groups of marked isolates of P. nodorum containing rare alleles (identified by amplified fragment length polymorphisms [AFLPs]) and balanced for mating type were planted in experimental field plots in two locations in each state. Recapture was done by isolating P. nodorum from leaves showing necrotic lesions at spring tillering and flowering stages, and mature grains from spikes showing glume blotch. Isolates from these samples were genotyped by AFLPs and categorized as released or nonreleased to infer sources of inoculum. Both infected seed and other sources of the pathogen contributed significant primary inocula to populations recovered from leaves and harvested grain. Seed-transmitted genotypes accounted for a total of 57% of all isolates recovered from inoculated plots, with a range of 15 to 90% of the populations of P. nodorum collected over the season in individual, inoculated plots at the four locations. Plants in the noninoculated control plots also became diseased and 95% or more of the isolates recovered from these plots were nonreleased genotypes. Although other potential sources of P. nodorum within and adjacent to experimental plots were not ruled out, nonreleased genotypes likely were derived from immigrant ascospores potentially from sources at a considerable distance from the plots. Our results suggest that, although reduction of seedborne inoculum of P. nodorum may delay foliar epidemics, this strategy by itself is unlikely to result in high levels of control in eastern North America because of the additional contribution from alternative sources of inoculum.

Modelling and simulation of gas turbines

Today, gas turbines (GTs) are one of the major parts of modern industry. They have played a key role in aeronautical industry, power generation, and main mechanical drivers for large pumps and compressors. Modelling and simulation of GTs has always been a powerful tool for performance optimisation of this kind of equipment. Remarkable research activities have been carried out in this field and a variety of analytical and experimental models have been built so far to get in-depth understanding of the non-linear behaviour and complex dynamics of these systems. However, the need to develop accurate and reliable models of gas turbines for different objectives and applications has been a strong motivation for researchers to continue to work in this area. This paper focuses on major research activities which have been carried out so far in the field of modelling and simulation of gas turbines. It covers main white-box and black-box models and their applications in control systems. This study can be a good reference for current and prospective researchers who are working or planning to work in this fascinating area of research.

Experiments with the Site Frequency Spectrum

Evaluating the likelihood function of parameters in highly-structured population genetic models from extant deoxyribonucleic acid (DNA) sequences is computationally prohibitive. In such cases, one may approximately infer the parameters from summary statistics of the data such as the site-frequency-spectrum (SFS) or its linear combinations. Such methods are known as approximate likelihood or Bayesian computations. Using a controlled lumped Markov chain and computational commutative algebraic methods, we compute the exact likelihood of the SFS and many classical linear combinations of it at a non-recombining locus that is neutrally evolving under the infinitely-many-sites mutation model. Using a partially ordered graph of coalescent experiments around the SFS, we provide a decision-theoretic framework for approximate sufficiency. We also extend a family of classical hypothesis tests of standard neutrality at a non-recombining locus based on the SFS to a more powerful version that conditions on the topological information provided by the SFS.

An Iterative `Sudoku Style' Approach to Subgraph-based Word Sense Disambiguation

We introduce an iterative approach to subgraph-based Word Sense Disambiguation (WSD). Inspired by the Sudoku puzzle, it significantly improves the precision and recall of disambiguation. We describe how conventional subgraph-based WSD treats the two steps of (1) subgraph construction and (2) disambiguation via graph centrality measures as ordered and atomic. Consequently, researchers tend to focus on improving either of these two steps individually, overlooking the fact that these steps can complement each other if they are allowed to interact in an iterative manner. We tested our iterative approach against the conventional approach for a range of well-known graph centrality measures and subgraph types, at the sentence and document level. The results demonstrated that an average performing WSD system which embraces the iterative approach, can easily compete with state-ofthe-art. This alone warrants further investigation.

Posterior Expectation of Regularly Paved Random Histograms

We present a novel method for averaging a sequence of histogram states visited by a Metropolis-Hastings Markov chain whose stationary distribution is the posterior distribution over a dense space of tree-based histograms. The computational efficiency of our posterior mean histogram estimate relies on a statistical data-structure that is sufficient for nonparametric density estimation of massive, multidimensional metric data. This data-structure is formalized as statistical regular paving (SRP). A regular paving (RP) is a binary tree obtained by selectively bisecting boxes along their first widest side. SRP augments RP by mutably caching the recursively computable sufficient statistics of the data. The base Markov chain used to propose moves for the Metropolis-Hastings chain is a random walk that data-adaptively prunes and grows the SRP histogram tree. We use a prior distribution based on Catalan numbers and detect convergence heuristically. The performance of our posterior mean SRP histogram is empirically assessed for large sample sizes simulated from several multivariate distributions that belong to the space of SRP histograms.