Determination of some dominant genetic characteristics and morpho-meristic features for selection of rainbow trout (Oncorhynchus mykiss) broodstocks

In this study, Iranian and French male and female Oncorhynchus mykiss broodstocks were divided into two groups 50 and 24 respectively in Research center of genetic and breeding of coldwater fishes, Yasouj, Iran and the genetic structure of them was investigated using 6 microsatellite markers. Then 19 morphometric and 5 meristic of broodstock were measured and compared in two populations. Along with broodstock maturation, fertilization 1:1(female:male) were randomly assigned and occurred in 25 of 12 Iranian and French treatment respectively. Reproductive parameters were recorded for the whole family. Average number of observed alleles in Iranian and French stocks was 6.68 and 6.83, respectively. Average number of effective alleles in Iranian and French stocks was 3.13 and 3.45 respectively. Fixation index Fst was calculated based on allelic frequency between two stocks was 0.058 with significant difference between 2 stocks. Morphometric analysis showed significant difference between two stocks in 8 characteristics. Meristic characters was without significant difference in broodstock groups. Eyed percentage for french broodstock calculated zero and deleted. Fertilization rate (100-0), the eyed percentage (98- 0), The hatch rate (98-0), the average fecundity 4114.708, the average eggs size 4.88 mm, Survival in the first three months 19-73% calculated for Iranian broodstocks. Considering the quality of eggs and larvae at different stages and selection between the different family and the within family remained 10 treatments and are kept as future broodstocks. The relationship between fecundity - egg size, fecundity - weight , fecundity - length, egg size- weight was performed using regression. The results showed that Fecundity was influenced more by weight and productive length. The research is beginning to ID the broodstock in our country.

Effects of testosterone undecanoate as a male contraceptive candidate on rat immunological features

Testosterone undecanoate (TU) is under phase III clinical trial as a hormonal male contraceptive in China. Sex hormones can modulate the immune system. Female hormonal contraceptives may affect SIV/HIV-1 transmission. To evaluate the safety of TU and to understand whether long-term use of TU for a male contraceptive affects users' immunological features, adult male rats were treated for a 32-week TU-treated phase at the dose of 20 mg TU/kg body weight and a 24-week recovery phase. The reproductive and immunological parameters of 4-6 rats in each subgroup were examined at the stated time point. The mean sperm count and viability in the treated rats were significantly suppressed (p < 0.01). In the TU-treated group: the mean blood leukocyte and lymphocyte counts; the proliferation indexes of T cells from peripheral blood mononuclear cells (PBMC) and spleen; and, of B cells from spleen, as well as the mean counts of blood T, NK, and B cells decreased in comparison with those of control group. These decreases were not significant (p > 0.01). Similarly, the mean serum IgM, IgG, and IgA levels and complement activity in TU-treated rats were lower than those in control group (p > 0.01), and the changes in the antibody levels of the examined genital secretions were not significant (p > 0.01). The changes in the thickness of urethra epithelium, and in secretory component (SC) expression in genitals were not observed in the treated group. These results demonstrated that long-term supraphysiological TU injection did not obviously affect the examined rat immunological parameters.

Statistical analysis of sequence features of introns with positive transcriptional regulation in yeast genes

A great deal of experimental studies have shown that many introns of eukaryotic genes function as regulators of transcription. However, comprehensive studies of this problem have not yet been conducted. After checking the transcription frequencies of some Saccharomyces cerevisiae (yeast), genes and their introns, a remarkable phenomenon was discovered that generally the introns of the genes with higher transcription frequencies are longer, and the introns of the genes with lower transcription frequencies are shorter. This suggests that the longer introns of genes with higher transcription frequencies may contain some characteristic sequence structures, which could enhance the transcription of genes. Therefore, two sets of introns of yeast genes were chosen for further study. The transcription frequencies of the first set of genes are higher (>30), and those of the second set of genes are lower (less than or equal to10). Some oligonucleotides are detected by statistically comparative analyses of the occurrence frequencies of oligonucleotides (mainly tetranucleotides and pentanucleotides), whose occurrence frequencies in the first set of introns; are significantly higher than those in the second set of introns, and are also significantly higher than those in the exons flanking the introns of the first set. Some of these extracted oligonucleotides are the same as the regulatory elements of transcription revealed by experimental analyses. Besides, the distributions of these extracted oligonucleotides in the two sets of introns and the exons show that the sequence structures of the first set of introns are favorable for transcription of genes.

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Recovering 3D Structure of Poorly Textured Infrastructure Scenes Using Point and Line Features

Most of the existing automated machine vision-based techniques for as-built documentation of civil infrastructure utilize only point features to recover the 3D structure of a scene. However it is often the case in man-made structures that not enough point features can be reliably detected (e.g. buildings and roofs); this can potentially lead to the failure of these techniques. To address the problem, this paper utilizes the prominence of straight lines in infrastructure scenes. It presents a hybrid approach that benefits from both point and line features. A calibrated stereo set of video cameras is used to collect data. Point and line features are then detected and matched across video frames. Finally, the 3D structure of the scene is recovered by finding 3D coordinates of the matched features. The proposed approach has been tested on realistic outdoor environments and preliminary results indicate its capability to deal with a variety of scenes.

ChESS - Quick and Robust Detection of Chess-board Features

Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the `Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in Structured Light 3D reconstruction. Evidence is presented showing its robustness, accuracy, and efficiency in comparison to other commonly used detectors both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects

ChESS - Quick and robust detection of chess-board features

Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the 'Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in structured light 3D reconstruction. Evidence is presented showing its superior robustness, accuracy, and efficiency in comparison to other commonly used detectors, including Harris & Stephens and SUSAN, both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects. © 2013 Elsevier Inc. All rights reserved.

MRI features of cellular angiomyofibroma with pathologic correlation.

Within the spectrum of extratesticular mesenchymal tumors in the scrotum and perineum lies cellular angiofibroma, also known as angiomyofibroblastoma-like tumor, a rare lesion originally described to almost exclusively occur in the vulva, perineum, and pelvis of women. We report a case of this tumor, with an adjacent scrotal lipoma, occurring in a 60-year-old male who presented to our department with a firm palpable scrotal mass. To our knowledge, the MRI findings of this entity have yet to be described in the radiological literature. We present the MRI features of cellular angiofibroma that are consistent with the pathological characteristics of this entity-a benign cellular and fibrous tumor with prominent vascularity.