Extraction of Vessels Networks over an Orientation Domain

This paper presents a new method to extract a network of vessels centerlines from a medical image. The network is composed of local geodesics over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the center of vessels and can deal robustly with crossings over the image plane. The vessel network is grown by an iterative algorithm that distributes seed points according to a geodesic saliency field. Numerical experiments on a database of synthetic and medical images show the superiority of our approach with respect to several methods based on shortest paths extractions. % With a minimum of user interaction, it allows to compute a complex network of vessels over noisy medical images

Extraction of Tubular Structures over an Orientation Domain

International audienceThis paper presents a new method to extract tubular structures from bi-dimensional images. The core of the proposed algorithm is the computation of geodesic curves over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the centerline of tubular structures, provide an estimation of the radius and can deal robustly with crossings over the image plane. Numerical experiments on a database of synthetic and natural images show the superiority of the proposed approach with respect to several method based on shortest paths extractions

Bioinformatic flowchart and database to investigate the origins and diversity of Clan AA peptidases

<p>Abstract</p> <p>Background</p> <p>Clan AA of aspartic peptidases relates the family of pepsin monomers evolutionarily with all dimeric peptidases encoded by eukaryotic LTR retroelements. Recent findings describing various pools of single-domain nonviral host peptidases, in prokaryotes and eukaryotes, indicate that the diversity of clan AA is larger than previously thought. The ensuing approach to investigate this enzyme group is by studying its phylogeny. However, clan AA is a difficult case to study due to the low similarity and different rates of evolution. This work is an ongoing attempt to investigate the different clan AA families to understand the cause of their diversity.</p> <p>Results</p> <p>In this paper, we describe in-progress database and bioinformatic flowchart designed to characterize the clan AA protein domain based on all possible protein families through ancestral reconstructions, sequence logos, and hidden markov models (HMMs). The flowchart includes the characterization of a major consensus sequence based on 6 amino acid patterns with correspondence with Andreeva's model, the structural template describing the clan AA peptidase fold. The set of tools is work in progress we have organized in a database within the GyDB project, referred to as Clan AA Reference Database <url>http://gydb.uv.es/gydb/phylogeny.php?tree=caard</url>.</p> <p>Conclusion</p> <p>The pre-existing classification combined with the evolutionary history of LTR retroelements permits a consistent taxonomical collection of sequence logos and HMMs. This set is useful for gene annotation but also a reference to evaluate the diversity of, and the relationships among, the different families. Comparisons among HMMs suggest a common ancestor for all dimeric clan AA peptidases that is halfway between single-domain nonviral peptidases and those coded by <it>Ty3/Gypsy </it>LTR retroelements. Sequence logos reveal how all clan AA families follow similar protein domain architecture related to the peptidase fold. In particular, each family nucleates a particular consensus motif in the sequence position related to the flap. The different motifs constitute a network where an alanine-asparagine-like variable motif predominates, instead of the canonical flap of the HIV-1 peptidase and closer relatives.</p> <p>Reviewers</p> <p>This article was reviewed by Daniel H. Haft, Vladimir Kapitonov (nominated by Jerry Jurka), and Ben M. Dunn (nominated by Claus Wilke).</p

Segtor: Rapid Annotation of Genomic Coordinates and Single Nucleotide Variations Using Segment Trees

Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes

Challenges in the use of NG2 antigen as a marker to predict MLL rearrangements in multi-center studies

AbstractRearrangements in MLL (MLL-r) are common within very young children with leukemia and affect the prognosis and treatment. Previous studies have suggested the use of the NG2 molecule as a marker for MLL-r but these studies were performed using a small number of infants. We analyzed 148 patients (all less than 24 months, 86 less than 12 months) from various centers in Brazil to determine the predictive power of NG2 within that cohort. We show that NG2 can be used for MLL-r prediction; however, proper staff training and standardized sampling procedures are essential when receiving samples from multiple centers as the accuracy of the prediction varies greatly on a per center basis

Reliability of P mode event classification using contemporaneous BiSON and GOLF observations

We carried out a comparison of the signals seen in contemporaneous BiSON and GOLF data sets. Both instruments perform Doppler shift velocity measurements in integrated sunlight, although BiSON perform measurements from the two wings of potassium absorption line and GOLF from one wing of the NaD1 line. Discrepancies between the two datasets have been observed. We show,in fact, that the relative power depends on the wing in which GOLF data observes. During the blue wing period, the relative power is much higher than in BiSON datasets, while a good agreement has been observed during the red period.Comment: 7 pages, HELAS II: Helioseismology, Asteroseismology, and MHD Connections, conference proceedin

Development and evaluation of new mask protocols for gene expression profiling in humans and chimpanzees

Abstract Background Cross-species gene expression analyses using oligonucleotide microarrays designed to evaluate a single species can provide spurious results due to mismatches between the interrogated transcriptome and arrayed probes. Based on the most recent human and chimpanzee genome assemblies, we developed updated and accessible probe masking methods that allow human Affymetrix oligonucleotide microarrays to be used for robust genome-wide expression analyses in both species. In this process, only data from oligonucleotide probes predicted to have robust hybridization sensitivity and specificity for both transcriptomes are retained for analysis. Results To characterize the utility of this resource, we applied our mask protocols to existing expression data from brains, livers, hearts, testes, and kidneys derived from both species and determined the effects probe numbers have on expression scores of specific transcripts. In all five tissues, probe sets with decreasing numbers of probes showed non-linear trends towards increased variation in expression scores. The relationships between expression variation and probe number in brain data closely matched those observed in simulated expression data sets subjected to random probe masking. However, there is evidence that additional factors affect the observed relationships between gene expression scores and probe number in tissues such as liver and kidney. In parallel, we observed that decreasing the number of probes within probe sets lead to linear increases in both gained and lost inferences of differential cross-species expression in all five tissues, which will affect the interpretation of expression data subject to masking. Conclusion We introduce a readily implemented and updated resource for human and chimpanzee transcriptome analysis through a commonly used microarray platform. Based on empirical observations derived from the analysis of five distinct data sets, we provide novel guidelines for the interpretation of masked data that take the number of probes present in a given probe set into consideration. These guidelines are applicable to other customized applications that involve masking data from specific subsets of probes