A two-phase approach for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

A two-phase strategy for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We previously evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach to delineate breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Detecting insomnia: comparison of four self-report measures of sleep in a young adult population

The sensitivity and specificity of four self-report measures of disordered sleep - the Sleep Impairment Index (SII), the Sleep Disorders Questionnaire (SDQ), the Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS) and the Sleep-Wake Activity Inventory (SWAI) - were compared in subjects with insomnia and normal sleep. Nineteen young adult subjects met DSM-IV criteria for primary insomnia and another 19 were normal control subjects. Discriminatory characteristics of each measure were assessed using receiver operator characteristic curve analyses. Discriminatory power was maximised for each measure to produce cut-scores applicable for identification of individuals with insomnia. The DBAS, SII and SDQ psychiatric DIMS subscale were found to correlate, and discriminated well between the two groups. The SWAI nocturnal sleep subscale was not found to be an accurate discriminator. The results suggest differences in the measures in their ability to detect insomnia, and offer guidelines as to the optimal use of test scores to identify young adults suspected of insomnia.

Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by 'orange rust' ( Puccinia kuehnii ) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660 nm) yielded increased separability of rust-affected areas. The newly formulated 'Disease-Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.

Detecting environmental impacts on metapopulations of mound spring invertebrates - Assessing an incidence function model

We use a stochastic patch occupancy model of invertebrates in the Mound Springs ecosystem of South Australia to assess the ability of incidence function models to detect environmental impacts on metapopulations. We assume that the probability of colonisation decreases with increasing isolation and the probability of extinction is constant across spring vents. We run the models to quasi-equilibrium, and then impose an impact by increasing the local extinction probability. We sample the output at various times pre- and postimpact, and examine the probability of detecting a significant change in population parameters. The incidence function model approach turns out to have little power to detect environmental impacts on metapopulations with small numbers of patches. (C) 2001 Elsevier Science Ltd. All rights reserved.

On surrogate methods for detecting lateral gene transfer

Surrogate methods for detecting lateral gene transfer are those that do not require inference of phylogenetic trees. Herein I apply four such methods to identify open reading frames (ORFs) in the genome of Escherichia coli K12 that may have arisen by lateral gene transfer. Only two of these methods detect the same ORFs more frequently than expected by chance, whereas several intersections contain many fewer ORFs than expected. Each of the four methods detects a different non-random set of ORFs. The methods may detect lateral ORFs of different relative ages; testing this hypothesis will require rigorous inference of trees. (C) 2001 Federation of European Microbiological Societies. Published by Elsevier Science BN. All rights reserved.

A new approach for detecting and mapping sewage impacts

Increased nitrogen loading has been implicated in eutrophication occurrences worldwide. Much of this loading is attributable to the growing human population along the world's coastlines. A significant component of this nitrogen input is from sewage effluent, and delineation of the distribution and biological impact of sewage-derived nitrogen is becoming increasingly important. Here, we show a technique that identifies the source, extent and fate of biologically available sewage nitrogen in coastal marine ecosystem. This method is based on the uptake of sewage nitrogen by marine plants and subsequent analysis of the sewage signature (elevated delta N-15) in plant tissues. Spatial analysis is used to create maps of delta N-15 and establish coefficient of variation estimates of the mapped values. We show elevated delta N-15 levels in marine plants near sewage outfalls in Moreton Bay, Australia, a semi-enclosed bay receiving multiple sewage inputs. These maps of sewage nitrogen distribution are being used to direct nutrient reduction strategies in the region and will assist in monitoring the effectiveness of environmental protection measures. (C) 2001 Elsevier Science Ltd. All rights reserved.

Comparison of identity by descent and identity by state for detecting genetic regions under selection in a sorghum pedigree breeding program

Seventy sorghum inbred lines which formed part of the Queensland Department of Primary Industries (QDPI) sorghum breeding program were screened with 104 previously mapped RFLP markers. The lines were related by pedigree and consisted of ancestral source lines, intermediate lines and recent releases from the program. We compared the effect of defining marker alleles using either identity by state (IBS) or identity by descent (IBD) on our capacity to trace markers through the pedigree and detect evidence of selection for particular alleles. Allelic identities defined using IBD were much more sensitive for detecting non-Mendelian segregation in this pedigree. Only one marker allele showed significant evidence of selection when IBS was used compared with ten regions with particular allelic identities when IBD was used. Regions under selection were compared with the location of QTLs for agronomic traits known to be under selection in the breeding program. Only two of the ten regions were associated with known QTLs that matched with knowledge of the agronomic characteristics of the ancestral lines. Some of the other regions were hypothesised to be associated with genes for particular traits based on the properties of the ancestral source lines.

Detecting multipartite entanglement

We discuss the problem of determining whether the state of several quantum mechanical subsystems is entangled. As in previous work on two subsystems we introduce a procedure for checking separability that is based on finding state extensions with appropriate properties and may be implemented as a semidefinite program. The main result of this work is to show that there is a series of tests of this kind such that if a multiparty state is entangled this will eventually be detected by one of the tests. The procedure also provides a means of constructing entanglement witnesses that could in principle be measured in order to demonstrate that the state is entangled.

Methods of detecting fouling caused by heating of milk

Fouling is the deposition of milk solids on heat transfer sur aces, particularly heat exchangers. It is a major industrial problem, which causes a decrease in heat transfer efficiency and shortens run times. The resultant effect is a decrease in process efficiency and economy. For studying and monitoring deposit formation, suitable fouling detectors or methods of measuring the deposit are required. This can be achieved through direct means, whereby the deposit is analyzed after a certain time, or indirectly through instrumentation for monitoring parameters such as temperature, pressure, flow rate, overall heat transfer coefficient, heat flux, and other physical properties. This article reviews the various reported fouling detection methods.