Possibility Study of Scale Invariant Feature Transform (SIFT) Algorithm Application to Spine Magnetic Resonance Imaging

The purpose of this study is an application of scale invariant feature transform (SIFT) algorithm to stitch the cervical-thoracic-lumbar (C-T-L) spine magnetic resonance (MR) images to provide a view of the entire spine in a single image. All MR images were acquired with fast spin echo (FSE) pulse sequence using two MR scanners (1.5 T and 3.0 T). The stitching procedures for each part of spine MR image were performed and implemented on a graphic user interface (GUI) configuration. Moreover, the stitching process is performed in two categories; manual point-to-point (mPTP) selection that performed by user specified corresponding matching points, and automated point-to-point (aPTP) selection that performed by SIFT algorithm. The stitched images using SIFT algorithm showed fine registered results and quantitatively acquired values also indicated little errors compared with commercially mounted stitching algorithm in MRI systems. Our study presented a preliminary validation of the SIFT algorithm application to MRI spine images, and the results indicated that the proposed approach can be performed well for the improvement of diagnosis. We believe that our approach can be helpful for the clinical application and extension of other medical imaging modalities for image stitching.     

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.     

Primitive Fitting Based on the Efficient multiBaySAC Algorithm

Although RANSAC is proven to be robust, the original RANSAC algorithm selects hypothesis sets at random, generating numerous iterations and high computational costs because many hypothesis sets are contaminated with outliers. This paper presents a conditional sampling method, multiBaySAC (Bayes SAmple Consensus), that fuses the BaySAC algorithm with candidate model parameters statistical testing for unorganized 3D point clouds to fit multiple primitives. This paper first presents a statistical testing algorithm for a candidate model parameter histogram to detect potential primitives. As the detected initial primitives were optimized using a parallel strategy rather than a sequential one, every data point in the multiBaySAC algorithm was assigned to multiple prior inlier probabilities for initial multiple primitives. Each prior inlier probability determined the probability that a point belongs to the corresponding primitive. We then implemented in parallel a conditional sampling method: BaySAC. With each iteration of the hypothesis testing process, hypothesis sets with the highest inlier probabilities were selected and verified for the existence of multiple primitives, revealing the fitting for multiple primitives. Moreover, the updated version of the initial probability was implemented based on a memorable form of Bayes’ Theorem, which describes the relationship between prior and posterior probabilities of a data point by determining whether the hypothesis set to which a data point belongs is correct. The proposed approach was tested using real and synthetic point clouds. The results show that the proposed multiBaySAC algorithm can achieve a high computational efficiency (averaging 34% higher than the efficiency of the sequential RANSAC method) and fitting accuracy (exhibiting good performance in the intersection of two primitives), whereas the sequential RANSAC framework clearly suffers from over- and under-segmentation problems. Future work will aim at further optimizing this strategy through its application to other problems such as multiple point cloud co-registration and multiple image matching.     

Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs

Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k-mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/～kmahmood/afree. EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/～kmahmood/EGM2.     

Genomic Organization Around the Centromeric End of the HLA Class I Region: Large-Scale Sequence Analysis

We previously sequenced two regions around the centromeric end of HLA class I and the boundary between class I and class III. In this paper we analyze the two regions of about 385 kb and confirm, giving a new line of evidence, that the following two pairs of the genomic segments were duplicated in evolution: (i) a 43-kb genomic segment including the HLA-B gene showing the highest polymorphism among the classical HLA class I loci (class Ia) and a 40-kb segment including the HLA-C locus showing the lowest polymorphism and (ii) a 52-kb segment including the MIC (MHC class I chain related gene) B and a 35-kb segment including MICA. We also found that repetitive elements such as SINEs, LINEs, and LTRs occupy as much as 47% of nucleotides in this 385-kb region. This unusually high content of repetitive elements indicates that repeat-mediated rearrangements have frequently occurred in the evolutionary history of the HLA class Ia region. Analysis of LINE compositions within the two pairs of duplicated segments revealed that (i) LINEs in these regions had been dispersed prior to both the duplication of the HLA-B and -C loci and the duplication of the MICB and MICA loci, and (ii) the divergence of the HLA-B and -C loci occurred prior to the duplication of the MICA and MICB loci. To find novel genes responsible for HLA class I-associated or other diseases, we performed computer analysis applying GenScan and GRAIL to GenBank's dbEST. As a result, at least five as yet uncharacterized genes were newly mapped on the HLA class I centromeric region studied. These novel genes should be analyzed further to determine their relationships to diseases associated with this region. Received: 16 June 1998 / Accepted: 18 August 1998     

Linkage of interactions in sickle hemoglobin fiber assembly: inhibitory effect emanating from mutations in the AB region of the alpha-chain is annulled by a mutation at its EF corner

The AB and GH regions of the alpha-chain are located in spatial proximity and contain a cluster of intermolecular contact residues of the sickle hemoglobin (HbS) fiber. We have examined the role of dynamics of AB/GH region on HbS polymerization through simultaneous replacement of non-contact Ala(19) and Ala(21) of the AB corner with more flexible Gly or rigid alpha-aminoisobutyric acid (Aib) residues. The polymerization behavior of HbS with Aib substitutions was similar to the native HbS. In contrast, Gly substitutions inhibited HbS polymerization. Molecular dynamics simulation studies of alpha-chains indicated that coordinated motion of AB and GH region residues present in native (Ala) as well as in Aib mutant was disrupted in the Gly mutant. The inhibitory effect due to Gly substitutions was further explored in triple mutants that included mutation of an inter-doublestrand contact (alphaAsn(78) --> His or Gln) at the EF corner. Although the inhibitory effect of Gly substitutions in the triple mutant was unaffected in the presence of alphaGln(78), His at this site almost abrogated its inhibitory potential. The polymerization studies of point mutants (alphaGln(78) --> His) indicated that the inhibitory effect due to Gly substitutions in the triple mutant was synergistically compensated for by the polymerization-enhancing activity of His(78). Similar synergistic coupling, between alphaHis(78) and an intra-double-strand contact point (alpha16) mutation located in the AB region, was also observed. Thus, two conclusions are made: (i) Gly mutations at the AB corner inhibit HbS polymerization by perturbing the dynamics of the AB/GH region, and (ii) perturbations of AB region (through changes in dynamics of the AB/GH region or abolition of a specific fiber contact site) that influence HbS polymerization do so in concert with alpha78 site at the EF corner. The overall results provide insights about the interaction-linkage between distant regions of the HbS tetramer in fiber assembly.     

Divergent selection and heterogeneous genomic divergence

Levels of genetic differentiation between populations can be highly variable across the genome, with divergent selection contributing to such heterogeneous genomic divergence. For example, loci under divergent selection and those tightly physically linked to them may exhibit stronger differentiation than neutral regions with weak or no linkage to such loci. Divergent selection can also increase genome‐wide neutral differentiation by reducing gene flow (e.g. by causing ecological speciation), thus promoting divergence via the stochastic effects of genetic drift. These consequences of divergent selection are being reported in recently accumulating studies that identify: (i) ‘outlier loci’ with higher levels of divergence than expected under neutrality, and (ii) a positive association between the degree of adaptive phenotypic divergence and levels of molecular genetic differentiation across population pairs [‘isolation by adaptation’ (IBA)]. The latter pattern arises because as adaptive divergence increases, gene flow is reduced (thereby promoting drift) and genetic hitchhiking increased. Here, we review and integrate these previously disconnected concepts and literatures. We find that studies generally report 5–10% of loci to be outliers. These selected regions were often dispersed across the genome, commonly exhibited replicated divergence across different population pairs, and could sometimes be associated with specific ecological variables. IBA was not infrequently observed, even at neutral loci putatively unlinked to those under divergent selection. Overall, we conclude that divergent selection makes diverse contributions to heterogeneous genomic divergence. Nonetheless, the number, size, and distribution of genomic regions affected by selection varied substantially among studies, leading us to discuss the potential role of divergent selection in the growth of regions of differentiation (i.e. genomic islands of divergence), a topic in need of future investigation.     

Population genetic structure across dissolved oxygen regimes in an African cichlid fish

Ecological isolation is a process whereby gene flow between selective environments is reduced due to selection against maladapted dispersers, migrant alleles, or hybrids. Although ecological isolation has been documented in several systems, gene flow can often be high among selective regimes, and more studies are thus needed to better understand the conditions under which ecological gradients or divergent selective regimes should influence population structure. We test for ecological isolation in a system in which high plasticity occurs with respect to traits that are adaptive in alternate forms under different environmental conditions. Pseudocrenilabrus multicolor victoriae is a widespread haplochromine cichlid fish in East Africa that exploits both normoxic (normal oxygen) rivers/lakes and hypoxic (low oxygen) swamps. Here, we examine population structure, using mitochondrial DNA and microsatellites, to determine if genetic divergence is significantly increased between dissolved oxygen regimes relative to within them, while controlling for geographical structure. Our results indicate that geographical separation influences population structure, while no effects of divergent selection with respect to oxygen regimes were detected. Specifically, we document (i) genetic clustering according to geographical region, but no clustering according to oxygen regime; (ii) higher genetic variation among than within regions, but no effect of oxygen regime on genetic variation; (iii) isolation by distance within one region; and (iv) decreasing genetic variability with increasing geographical distance from Lake Victoria. We speculate that plasticity may be facilitating gene flow between oxygen regimes in this system.     

HapScope: a software system for automated and visual analysis of functionally annotated haplotypes

We have developed a software analysis package, HapScope, which includes a comprehensive analysis pipeline and a sophisticated visualization tool for analyzing functionally annotated haplotypes. The HapScope analysis pipeline supports: (i) computational haplotype construction with an expectation-maximization or Bayesian statistical algorithm; (ii) SNP classification by protein coding change, homology to model organisms or putative regulatory regions; and (iii) minimum SNP subset selection by either a Brute Force Algorithm or a Greedy Partition Algorithm. The HapScope viewer displays genomic structure with haplotype information in an integrated environment, providing eight alternative views for assessing genetic and functional correlation. It has a user-friendly interface for: (i) haplotype block visualization; (ii) SNP subset selection; (iii) haplotype consolidation with subset SNP markers; (iv) incorporation of both experimentally determined haplotypes and computational results; and (v) data export for additional analysis. Comparison of haplotypes constructed by the statistical algorithms with those determined experimentally shows variation in haplotype prediction accuracies in genomic regions with different levels of nucleotide diversity. We have applied HapScope in analyzing haplotypes for candidate genes and genomic regions with extensive SNP and genotype data. We envision that the systematic approach of integrating functional genomic analysis with population haplotypes, supported by HapScope, will greatly facilitate current genetic disease research.     

Efficient algorithms and software for detection of full-length LTR retrotransposons

LTR retrotransposons constitute one of the most abundant classes of repetitive elements in eukaryotic genomes. In this paper, we present a new algorithm for detection of full-length LTR retrotransposons in genomic sequences. The algorithm identifies regions in a genomic sequence that show structural characteristics of LTR retrotransposons. Three key components distinguish our algorithm from that of current software--(i) a novel method that preprocesses the entire genomic sequence in linear time and produces high quality pairs of LTR candidates in run-time that is constant per pair, (ii) a thorough alignment-based evaluation of candidate pairs to ensure high quality prediction, and (iii) a robust parameter set encompassing both structural constraints and quality controls providing users with a high degree of flexibility. We implemented our algorithm into a software program called LTR_par, which can be run on both serial and parallel computers. Validation of our software against the yeast genome indicates superior results in both quality and performance when compared to existing software. Additional validations are presented on rice BACs and chimpanzee genome.     

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.     

Use of monoclonal antibodies to detect Mn(II)-peroxidase in birch wood degraded by Phanerochaete chrysosporium

Summary A monoclonal antibody (Mab) produced to purified Mn(II)-peroxidase was visualized on and within cell corners of birch wood degraded by Phanerochaete chrysosporium using colloidal gold immuno-transmission electron microscopy techniques. Labelling of the fungal cell membrane and cell wall was also observed. The same Mab was used to visualize the penetration of extracellular fungal metabolite extracts, infiltrated into previously decayed wood. Binding of antibodies to the lignin-rich cell corner region of the middle lamella in wood decayed by P. chrysosporium was observed in sectioned wood blocks and in wood infiltrated with crude extracellular extracts from P. chrysospirium liquid cultures. When a control monoclonal antiserum, produced to extracellular metabolites of Postia (Poria) placenta and cross-reactive with fungal cellulase, was used in labelling, the cellulose rich region of the wood cell walls were labelled. Labelling in the middle lamella cell corners was only noted in what has been described as nonor poorly lignified cell corner regions. Offprint requests to: G. Daniel     

Using multiple sequence correlation analysis to characterize functionally important protein regions

Protein co-evolution under structural and functional constraints necessitates the preservation of important interactions. Identifying functionally important regions poses many obstacles in protein engineering efforts. In this paper, we present a bioinformatics-inspired approach (residue correlation analysis, RCA) for predicting functionally important domains from protein family sequence data. RCA is comprised of two major steps: (i) identifying pairs of residue positions that mutate in a coordinated manner, and (ii) using these results to identify protein regions that interact with an uncommonly high number of other residues. We hypothesize that strongly correlated pairs result not only from contacting pairs, but also from residues that participate in conformational changes involved during catalysis or important interactions necessary for retaining functionality. The results show that highly mobile loops that assist in ligand association/dissociation tend to exhibit high correlation. RCA results exhibit good agreement with the findings of experimental and molecular dynamics studies for the three protein families that are analyzed: (i) DHFR (dihydrofolate reductase), (ii) cyclophilin, and (iii) formyl-transferase. Specifically, the specificity (percentage of correct predictions) in all three cases is substantially higher than those obtained by entropic measures or contacting residue pairs. In addition, we use our approach in a predictive fashion to identify important regions of a transmembrane amino acid transporter protein for which there is limited structural and functional information available.     

Genome-wide patterns of divergence during speciation: the lake whitefish case study

The nature, size and distribution of the genomic regions underlying divergence and promoting reproductive isolation remain largely unknown. Here, we summarize ongoing efforts using young (12 000 yr BP) species pairs of lake whitefish (Coregonus clupeaformis) to expand our understanding of the initial genomic patterns of divergence observed during speciation. Our results confirmed the predictions that: (i) on average, phenotypic quantitative trait loci (pQTL) show higher F(ST) values and are more likely to be outliers (and therefore candidates for being targets of divergent selection) than non-pQTL markers; (ii) large islands of divergence rather than small independent regions under selection characterize the early stages of adaptive divergence of lake whitefish; and (iii) there is a general trend towards an increase in terms of numbers and size of genomic regions of divergence from the least (East L.) to the most differentiated species pair (Cliff L.). This is consistent with previous estimates of reproductive isolation between these species pairs being driven by the same selective forces responsible for environment specialization. Altogether, dwarf and normal whitefish species pairs represent a continuum of both morphological and genomic differentiation contributing to ecological speciation. Admittedly, much progress is still required to more finely map and circumscribe genomic islands of speciation. This will be achieved through the use of next generation sequencing data but also through a better quantification of phenotypic traits moulded by selection as organisms adapt to new environmental conditions.     

3D surface analysis and classification in neuroimaging segmentation

This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes.     

The tyrosine corner: A feature of most greek key β-barrel proteins

The Tyr corner is a conformation in which a tyrosine (residue “Y”) near the beginning or end of an antiparallel β-strand makes an H bond from its side-chain OH group to the backbone NH and/or CO of residue Y – 3, Y – 4, or Y – 5 in the nearby connection. The most common “classic” case is a Δ4 Tyr corner (more than 40 examples listed), in which the H bond is to residue Y – 4 and the Tyr x1 is near ?60°. Y – 2 is almost always a glycine, whose left-handed β or very extended β conformation helps the backbone curve around the Tyr ring. Residue Y – 3 is in polyproline II conformation (often Pro), and residue Y – 5 is usually a hydrophobic (often Leu) that packs next to the Tyr ring. The consensus sequence, then, is LxPGxY, where the first x (the H-bonding position) is hydrophilic. Residues Y and Y – 2 both form narrow pairs of β-sheet H-bonds with the neighboring strand, Δ5 Tyr corners have a 1-residue insertion between the Gly and the Tyr, forming a β-bulge. One protein family has a Δ4 corner formed by a His rather than a Tyr, and several examples use Trp in place of Tyr. For almost all these cases, the protein or domain is a Greek key β-barrel structure, the Tyr corner ends a Greek key connection, and it is well-conserved in related proteins. Most low-twist Greek key β-barrels have 1 Tyr corner. “Reverse” Δ4 Tyr corners (H bonded to Y + 4) and other variants are described, all less common and less conserved. It seems likely that the more classic Tyr corners (Δ4, Δ5, and Δ3 Tyr, Trp, or His) contribute to the stability of a Greek key connection over a hairpin connection, and also that they may aid in the process of folding up Greek key structures.     

Perspective: matching, mate choice, and speciation

Matching was developed in the 1960s to match such entities as residents and hospitals, colleges and students, or employers and employees. This approach is based on "preference lists," whereby each participant ranks potential partners according to his/her preferences and tries to match with the highest-ranking partner available. Here, we discuss the implications of matching for the study of mate choice and speciation. Matching differs from classic approaches in several respects, most notably because under this theoretical framework, the formation of mating pairs is context-dependant (i.e., it depends on the configuration of pairings in the entire population), because the stability of mating pairs is considered explicitly, and because mate choice is mutual. The use of matching to study mate choice and speciation is not merely a theoretical curiosity; its application can generate counter-intuitive predictions and lead to conclusions that differ fundamentally from classic theories about sexual selection and speciation. For example, it predicts that when mate choice is mutual and the stability of mating pairs is critical for successful reproduction, sympatric speciation is a robust evolutionary outcome. Yet the application of matching to the study of mate choice and speciation has been largely dominated by theoretical studies. We present the hamlets, a group of brightly colored Caribbean coral reef fishes in the genus Hypoplectrus (Serranidae), as a particularly apt system to test empirically specific predictions generated by the application of matching to mate choice and speciation.