Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Application of compositional data analysis to geochemical data of marine sediments

In an earlier investigation (Burger et al., 2000) five sediment cores near the Rodrigues Triple Junction in the Indian Ocean were studied applying classical statistical methods (fuzzy c-means clustering, linear mixing model, principal component analysis) for the extraction of endmembers and evaluating the spatial and temporal variation of geochemical signals. Three main factors of sedimentation were expected by the marine geologists: a volcano-genetic, a hydro-hydrothermal and an ultra-basic factor. The display of fuzzy membership values and/or factor scores versus depth provided consistent results for two factors only; the ultra-basic component could not be identified. The reason for this may be that only traditional statistical methods were applied, i.e. the untransformed components were used and the cosine-theta coefficient as similarity measure. During the last decade considerable progress in compositional data analysis was made and many case studies were published using new tools for exploratory analysis of these data. Therefore it makes sense to check if the application of suitable data transformations, reduction of the D-part simplex to two or three factors and visual interpretation of the factor scores would lead to a revision of earlier results and to answers to open questions . In this paper we follow the lines of a paper of R. Tolosana- Delgado et al. (2005) starting with a problem-oriented interpretation of the biplot scattergram, extracting compositional factors, ilr-transformation of the components and visualization of the factor scores in a spatial context: The compositional factors will be plotted versus depth (time) of the core samples in order to facilitate the identification of the expected sources of the sedimentary process. Kew words: compositional data analysis, biplot, deep sea sediments

Is there a common genetic basis for autoimmune diseases?

Autoimmune diseases (ADs) represent a diverse collection of diseases in terms of their demographic profile and primary clinical manifestations. The commonality between them however, is the damage to tissues and organs that arises from the response to self-antigens. The presence of shared pathophysiological mechanisms within ADs has stimulated searches for common genetic roots to these diseases. Two approaches have been undertaken to sustain the “common genetic origin” theory of ADs. Firstly, a clinical genetic analysis showed that autoimmunity aggregates within families of probands diagnosed with primary Sjögren's (pSS) syndrome or type 1 diabetes mellitus (T1D). A literature review supported the establishment of a familiar cluster of ADs depending upon the proband's disease phenotype. Secondly, in a same and well-defined population, a large genetic association study indicated that a number of polymorphic genes (i.e. HLA-DRB1, TNF and PTPN22) influence the susceptibility for acquiring different ADs. Likewise, association and linkage studies in different populations have revealed that several susceptibility loci overlap in ADs, and clinical studies have shown that frequent clustering of several ADs occurs. Thus, the genetic factors for ADs consist of two types: those which are common to many ADs (acting in epistatic pleitropy) and those that are specific to a given disorder. Their identification and functional characterization will allow us to predict their effect as well as to indicate potential new therapeutic interventions. Both autoimmunity family history and the co-occurrence of ADs in affected probands should be considered when performing genetic association and linkage studies.

Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk production

Genetic parameters and breeding values for dairy cow fertility were estimated from 62 443 lactation records. Two-trait analysis of fertility and milk yield was investigated as a method to estimate fertility breeding values when culling or selection based on milk yield in early lactation determines presence or absence of fertility observations in later lactations. Fertility traits were calving interval, intervals from calving to first service, calving to conception and first to last service, conception success to first service and number of services per conception. Milk production traits were 305-day milk, fat and protein yield. For fertility traits, range of estimates of heritability (h(2)) was 0.012 to 0.028 and of permanent environmental variance (c(2)) was 0.016 to 0.032. Genetic correlations (r(g)) among fertility traits were generally high ( > 0.70). Genetic correlations of fertility with milk production traits were unfavourable (range -0.11 to 0.46). Single and two-trait analyses of fertility were compared using the same data set. The estimates of h(2) and c(2) were similar for two types of analyses. However, there were differences between estimated breeding values and rankings for the same trait from single versus multi-trait analyses. The range for rank correlation was 0.69-0.83 for all animals in the pedigree and 0.89-0.96 for sires with more than 25 daughters. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended. (C) 2002 Elsevier Science B.V. All rights reserved.

Genetic analysis of complex demographic scenarios: Spatially expanding populations of the cane toad, Bufo marinus

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

A genetic linkage map of microsatellite, gene-specific and morphological markers in diploid Fragaria

Diploid Fragaria provide a potential model for genomic studies in the Rosaceae. To develop a genetic linkage map of diploid Fragaria, we scored 78 markers (68 microsatellites, one sequence-characterised amplified region, six gene-specific markers and three morphological traits) in an interspecific F2 population of 94 plants generated from a cross of F.vesca f. semperflorens × F. nubicola. Co-segregation analysis arranged 76 markers into seven discrete linkage groups covering 448 cM, with linkage group sizes ranging from 100.3 cM to 22.9 cM. Marker coverage was generally good; however some clustering of markers was observed on six of the seven linkage groups. Segregation distortion was observed at a high proportion of loci (54%), which could reflect the interspecific nature of the progeny and, in some cases, the self-incompatibility of F. nubicola. Such distortion may also account for some of the marker clustering observed in the map. One of the morphological markers, pale-green leaf (pg) has not previously been mapped in Fragaria and was located to the mid-point of linkage group VI. The transferable nature of the markers used in this study means that the map will be ideal for use as a framework for additional marker incorporation aimed at enhancing and resolving map coverage of the diploid Fragaria genome. The map also provides a sound basis for linkage map transfer to the cultivated octoploid strawberry.

A novel method for the transport and analysis of genetic material from polyps and zooxanthellae of scleractinian corals

We have developed a new simple method for transport, storage, and analysis of genetic material from the corals Agaricia agaricites, Dendrogyra cylindrica, Eusmilia ancora, Meandrina meandrites, Montastrea annularis, Porites astreoides, Porites furcata, Porites porites, and Siderastrea siderea at room temperature. All species yielded sufficient DNA from a single FTA(R) card (19 mug-43 ng) for subsequent PCR amplification of both coral and zooxanthellar DNA. The D1 and D2 variable region of the large Subunit rRNA gene (LSUrDNA) was amplified from the DNA of P. furcata and S. siderea by PCR. Electrophoresis yielded two major DNA bands: an 800-base pair (bp) DNA, which represented the coral ribosomal RNA (rRNA) gene, and a 600-bp DNA, which represented the zooxanthellar srRNA gene. Extraction of DNA from the bands yielded between 290 mug total DNA (S. siderea coral DNA) and 9 mug total DNA (P. furcata zooxanthellar DNA). The ability to transport and store genetic material from scleractinian corals without resort to laboratory facilities in the field allows for the molecular Study of a far wider range and variety of coral sites than have been studied to date. (C) 2003 Elsevier Science B.V. All rights reserved.

Genetic analysis of heat tolerance at anthesis in rice

Genetic analysis of heat tolerance will help breeders produce rice (Oryza sativa L.) varieties adapted to future climates. An F6 population of 181 recombinant inbred lines of Bala (tolerant) × Azucena (susceptible) was screened for heat tolerance at anthesis by measuring spikelet fertility at 30°C (control) and 38°C (high temperature) in experiments conducted in the Philippines and the United Kingdom. The parents varied significantly for absolute spikelet fertility under control (79–87%) and at high temperature (2.9–47.1%), and for relative spikelet fertility (high temperature/control) at high temperature (3.7–54.9%). There was no correlation between spikelet fertility in control and high-temperature conditions and no common quantitative trait loci (QTLs) were identified. Two QTLs for spikelet fertility under control conditions were identified on chromosomes 2 and 4. Eight QTLs for spikelet fertility under high-temperature conditions were identified on chromosomes 1, 2, 3, 8, 10, and 11. The most significant heat-responsive QTL, contributed by Bala and explaining up to 18% of the phenotypic variation, was identified on chromosome 1 (38.35 mega base pairs on the rice physical genome map). This QTL was also found to influence plant height, explaining 36.6% of the phenotypic variation. A comparison with other studies of abiotic (drought, cold, salinity) stresses showed QTLs at similar positions on chromosomes 1, 3, 8, and 10, suggesting common underlying stress-responsive regions of the genome.

Genetic expression of an amyloid peptide fragment and analysis of formylated products

The model amyloid peptide AAKLVFF was expressed as a His-tagged fusion protein with the immunoglobulin-binding domain B1 of streptococcal protein G (GB1), a small (56 residues), stable, single-domain protein. It is shown that expression of this model amyloid peptide is possible and is not hindered by aggregation. Formylation side reactions during the CNBr cleavage are investigated via synthesis of selectively formylated peptides.

Modelling niches of arbitrary shape in genetic algorithms using Niche Linkage in the Dynamic Niche Clustering framework

We present some additions to a fuzzy variable radius niche technique called Dynamic Niche Clustering (DNC) (Gan and Warwick, 1999; 2000; 2001) that enable the identification and creation of niches of arbitrary shape through a mechanism called Niche Linkage. We show that by using this mechanism it is possible to attain better feature extraction from the underlying population.