Candidate Regions Research Articles

Multiple Genetic Loci Associated with Pug Dog Thoracolumbar Myelopathy.

Pug dogs with thoracolumbar myelopathy (PDM) present with a specific clinical phenotype that includes progressive pelvic limb ataxia and paresis, commonly accompanied by incontinence. Vertebral column malformations and lesions, excessive scar tissue of the meninges, and central nervous system inflammation have been described. PDM has a late onset and affects more male than female dogs. The breed-specific presentation of the disorder suggests that genetic risk factors are involved in the disease development. To perform a genome-wide search for PDM-associated loci, we applied a Bayesian model adapted for mapping complex traits (BayesR) and a cross-population extended haplotype homozygosity test (XP-EHH) in 51 affected and 38 control pugs. Nineteen associated loci (harboring 67 genes in total, including 34 potential candidate genes) and three candidate regions under selection (with four genes within or next to the signal) were identified. The multiple candidate genes identified have implicated functions in bone homeostasis, fibrotic scar tissue, inflammatory responses, or the formation, regulation, and differentiation of cartilage, suggesting the potential relevance of these processes to the pathogenesis of PDM.

Anti-Interference Recognition for Aerial Infrared Object Based on Convolutional Feature Inference Network

The infrared decoy countermeasure technology develops along with the infrared imaging guidance technology. The release of infrared decoys can block the object and destroy the integrity of the object features. The traditional statistical approach based on feature fusion matching for pattern recognition is not effective in handling this problem. To improve the characterization ability of the invariant detail features in the infrared object domain and improve the classification accuracy of the probability identification model, this article proposes an inference recognition model that combines the convolutional features and the Bayesian network. The proposed model exploits the convolutional neural network to extract the detailed feature information of the object in the infrared image. Then, it uses the 2DPCANet screening mechanism to reduce the dimensionality and select more obvious detailed information in the candidate region. Based on the combination of human prior knowledge and posterior data, an infrared object recognition probability model is established through the application of the Bayesian network. The performance of the proposed algorithm is tested on the complex air combat simulation image dataset. The experimental results indicate that the recognition rate of the proposed anti-interference recognition algorithm reaches 94.17%.

Intelligent Tumor Cell Detection Method Based on Circulating Tumor Cell (CTC) Technology

<p>Cancer has become one of the greatest threats for human life. Doctors can get original images of the sick organs with the assistance of medical technology. However, manual interpretation of the original images is time consuming and labor consuming. Nowadays, the intelligent detection of tumor cell images is commonly adopted in cancer diagnosis. In this paper, we propose Imporved Selective Search (ISS) algorithm and CTCNet based on Circulating Tumor Cell (CTC) technology to improve the cancer images’ detection efficiency. CTCs can be collected by a sampling needle with EpCAM antibody which can specifically bind to tumor cells. After fluorescent staining process, images obtained from sampling needle will be processed by the ISS algorithm for candidate region preselection. All of the eligible areas are evaluated with a self-designed neural network called CTCNet, resulting in efficient recognition of circulating tumor cells. During this process, all algorithms are accelerated through the GPU and NPU hardware platform, which further improves the detection speed of the system. In the experiment, we first verify the efficiency of proposed ISS algorithm by compared with Original Selective Search (OSS), we found that the number of candidate boxes reduced from 549 to 16 and the time consuming reduced by 0.3s after adopting the ISS algorithm. In order to evaluate the performance of the proposed 7-layer CTCNet, we compared CTCNet with SVM, BP neural network, AlexNet and VGGNet by using the dataset of 12312 samples from 30 patients, among them, there were 12 patients with early cancer and 18 patients with advanced cancer. And we got the highest recognition accuracy of 97.95% on CTCNet, which even beyond the VGGNet with deeper layers. In contrast to other combinations, we detect CTC in diverse clinical CTC images, the joint application of ISS algorithm and CTCNet achieves an outstanding system performance with accuracy up to 94.03% in the whole view images. Meanwhile, because of the application of a lightweight network in different hardware acceleration platform, the detection time of the CTC image in a single view can be less than 12s.</p> <p> </p>

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification.

Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.

Multiallelic Copy Number Variation in ORM1 is Associated with Plasma Cell-Free DNA Levels as an Intermediate Phenotype for Venous Thromboembolism.

Venous thromboembolism (VTE) is a common disease with high heritability. However, only a small portion of the genetic variance of VTE can be explained by known genetic risk factors. Neutrophil extracellular traps (NETs) have been associated with prothrombotic activity. Therefore, the genetic basis of NETs could reveal novel risk factors for VTE. A recent genome-wide association study of plasma cell-free DNA (cfDNA) levels in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT-2) Project showed a significant associated locus near ORM1. We aimed to further explore this candidate region by next-generation sequencing, copy number variation (CNV) quantification, and expression analysis using an extreme phenotype sampling design involving 80 individuals from the GAIT-2 Project. The RETROVE study with 400 VTE cases and 400 controls was used to replicate the results. A total of 105 genetic variants and a multiallelic CNV (mCNV) spanning ORM1 were identified in GAIT-2. Of these, 17 independent common variants, a region of 22 rare variants, and the mCNV were significantly associated with cfDNA levels. In addition, eight of these common variants and the mCNV influenced ORM1 expression. The association of the mCNV and cfDNA levels was replicated in RETROVE (p-value = 1.19 × 10-6). Additional associations between the mCNV and thrombin generation parameters were identified. Our results reveal that increased mCNV dosages in ORM1 decreased gene expression and upregulated cfDNA levels. Therefore, the mCNV in ORM1 appears to be a novel marker for cfDNA levels, which could contribute to VTE risk.

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN

Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.

Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering

The velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth, and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on the Chan–Vese (CV) model and mean-shift clustering algorithm was proposed. It can be divided into three steps. First, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noises. Then, the velocity trend band is applied to the Chan–Vese model as the initial time condition to segment the velocity spectrum and obtain the velocity candidate region. Finally, mean-shift clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from the Chan–Vese model. When implementing the mean-shift clustering algorithm, the Gaussian kernel function and the energy of the velocity spectrum are utilized to control the efficiency and accuracy of the cluster. The tests of the model and real data prove that the proposed method can dramatically improve the accuracy and efficiency of velocity picking compared with the K-means and manual picking method.

Automatic detection of river capture based on planform pattern and χ-plot of the stream network

River capture is a surficial process that can lead to drainage reorganization and have significant impacts on sediment dispersal and biotic evolution. Discovery and study of river capture events are helpful in revealing the history of drainage basins, however, the present-day identification of river capture mainly depends on field and map observations by geomorphologists, who lack an automatic method. In the case of a large-scale drainage system with a complicated stream network, a field investigation is too time-consuming and costly. This study aims to develop a novel method for automatic river capture detection based on planform morphology and χ-plots of the stream network. The whole method can be described as a workflow including three steps: (1) searching candidate regions where river capture may have occurred; (2) iterating over each candidate region and roughly detecting the captor, captured, reversal, and beheaded rivers using the planform pattern of the stream network; (3) verifying the river capture with a χ-elevation plot in the candidate region found by rough detection. The description of the method is followed by two case studies from China and Spain, demonstrating the ability of the method to identify the location where the river capture might occur. We also discuss the parameters that must be optimized before extracting the river capture efficiently from the stream network.

Primary mapping of quantitative trait loci regulating multivariate horticultural phenotypes of watermelon (Citrullus lanatus L.).

Watermelon fruits exhibit a remarkable diversity of important horticultural phenotypes. In this study, we initiated a primary quantitative trait loci (QTL) mapping to identify the candidate regions controlling the ovary, fruit, and seed phenotypes. Whole genome sequencing (WGS) was carried out for two differentiated watermelon lines, and 350 Mb (96%) and 354 Mb (97%) of re-sequenced reads covered the reference de novo genome assembly, individually. A total of 45.53% non-synonymous single nucleotide polymorphism (nsSNPs) and 54.47% synonymous SNPs (sSNPs) were spotted, which produced 210 sets of novel SNP-based cleaved amplified polymorphism sequence (CAPS) markers by depicting 46.25% co-dominant polymorphism among parent lines and offspring. A biparental F2:3 mapping population comprised of 100 families was used for trait phenotyping and CAPS genotyping, respectively. The constructed genetic map spanned a total of 2,398.40 centimorgans (cM) in length and averaged 11.42 cM, with 95.99% genome collinearity. A total of 33 QTLs were identified at different genetic positions across the eight chromosomes of watermelon (Chr-01, Chr-02, Chr-04, Chr-05, Chr-06, Chr-07, Chr-10, and Chr-11); among them, eight QTLs of the ovary, sixteen QTLs of the fruit, and nine QTLs of the seed related phenotypes were classified with 5.32-25.99% phenotypic variance explained (PVE). However, twenty-four QTLs were identified as major-effect and nine QTLs were mapped as minor-effect QTLs across the flanking regions of CAPS markers. Some QTLs were exhibited as tightly localized across the nearby genetic regions and explained the pleiotropic effects of multigenic nature. The flanking QTL markers also depicted significant allele specific contributions and accountable genes were predicted for respective traits. Gene Ontology (GO) functional enrichment was categorized in molecular function (MF), cellular components (CC), and biological process (BP); however, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were classified into three main classes of metabolism, genetic information processing, and brite hierarchies. The principal component analysis (PCA) of multivariate phenotypes widely demonstrated the major variability, consistent with the identified QTL regions. In short, we assumed that our identified QTL regions provide valuable genetic insights regarding the watermelon phenotypes and fine genetic mapping could be used to confirm them.

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification.

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

Slc9a6 mutation causes Purkinje cell loss and ataxia in the shaker rat.

The shaker rat carries a naturally occurring mutation leading to progressive ataxia characterized by Purkinje cell (PC) loss. We previously reported on fine-mapping the shaker locus to the long arm of the rat X chromosome. In this work, we sought to identify the mutated gene underlying the shaker phenotype and confirm its identity by functional complementation. We fine-mapped the candidate region and analyzed cerebellar transcriptomes, identifying a XM_217630.9 (Slc9a6):c.[191_195delinsA] variant in the Slc9a6 gene that segregated with disease. We generated an adeno-associated virus (AAV) targeting Slc9a6 expression to PCs using the mouse L7-6 (L7) promoter. We administered the AAV prior to the onset of PC degeneration through intracerebroventricular injection and found that it reduced the shaker motor, molecular and cellular phenotypes. Therefore, Slc9a6 is mutated in shaker and AAV-based gene therapy may be a viable therapeutic strategy for Christianson syndrome, also caused by Slc9a6 mutation.

Integrating BSA-Seq with RNA-Seq Reveals a Novel Fasciated Ear5 Mutant in Maize

Increasing grain yield is required to meet the rapidly expanding demands for food, feed, and fuel. Inflorescence meristems are central to plant growth and development. However, the question concerning whether inflorescence development can be regulated to improve grain yield remains unclear. Here, we describe a naturally occurring single recessive mutation called fea5 that can increase grain yield in maize. Using bulk segregant analysis sequencing (BSA-seq), the candidate region was initially mapped to a large region on chromosome 4 (4.68 Mb-11.26 Mb). Transcriptome sequencing (RNA-seq) revealed a total of 1246 differentially expressed genes (DEGs), of which 835 were up-regulated and 411 were down-regulated. Further analysis revealed the enrichment of DEGs in phytohormone signal transduction. Consistently, phytohormone profiling indicated that auxin (IAA), jasmonic acid (JA), ethylene (ETH), and cytokinin (CK) levels increased significantly, whereas the gibberellin (GA) level decreased significantly in fea5. By integrating BSA-seq with RNA-seq, we identified Zm00001d048841 as the most likely candidate gene. Our results provide valuable insight into this new germplasm resource and the molecular mechanism underlying fasciated ears that produce a higher kernel row number in maize.

Genetic mapping and regional association analysis revealed a CYTOKININ RESPONSE FACTOR 10 gene controlling flowering time in Brassica napus L.

Oilseed rape (Brassica napus L.), as an important source of renewable energy and an industrial crop, has received extensive attention. Breeding cultivars with appropriate flowering time (FT) is of high significance for crop yields, but it is currently impeded by incomplete understanding of genetic control for flowering time in B. napus. In this study, ebm, an ethyl methanesulfonate mutagenesis (EMS) mutant with early blooming, was derived from B. napus cv. Zhongshuang9 (ZS9). Genetic analysis showed that the early blooming of ebm was controlled by a dominant nuclear gene. The Ebm locus was delimitated in a 905-kb region on chromosome A02 by bulked segregant analysis sequencing and fine mapping. Based on the expression and variation analysis of the genes in the candidate region, and gene conservation analysis in pan-genome, a potential gene BnaA02.CRF10 was pinpointed, which encodes a cytokinin response factor involved in the cytokinin signaling pathway. Regional association and haplotype analyses of the initial and blooming FT in a worldwide bio-panel of 263 re-sequenced B. napus accessions further confirmed the candidate gene and mined favorable allelic variations. Additionally, differentially expressed genes (DEGs) related to ethylene and cytokinin genetic factors were significantly induced in the ebm mutant by comparative transcriptome analysis. Moreover, phylogenetic and synteny network analyses provided strong evidence for the phylogenetic conservation of the CRF subfamily during the evolutionary history from Chlorophyta to dicotyledonous plants. Taken together, a novel conserved gene with favorable alleles conferring early flowering trait in B. napus was identified, which lays a foundation for the genetic improvement and cultivar breeding of this important crop with shortened growth periods in the future.

Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications’ needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.

MaLAdapt Reveals Novel Targets of Adaptive Introgression From Neanderthals and Denisovans in Worldwide Human Populations.

Adaptive introgression (AI) facilitates local adaptation in a wide range of species. Many state-of-the-art methods detect AI with ad-hoc approaches that identify summary statistic outliers or intersect scans for positive selection with scans for introgressed genomic regions. Although widely used, approaches intersecting outliers are vulnerable to a high false-negative rate as the power of different methods varies, especially for complex introgression events. Moreover, population genetic processes unrelated to AI, such as background selection or heterosis, may create similar genomic signals to AI, compromising the reliability of methods that rely on neutral null distributions. In recent years, machine learning (ML) methods have been increasingly applied to population genetic questions. Here, we present a ML-based method called MaLAdapt for identifying AI loci from genome-wide sequencing data. Using an Extra-Trees Classifier algorithm, our method combines information from a large number of biologically meaningful summary statistics to capture a powerful composite signature of AI across the genome. In contrast to existing methods, MaLAdapt is especially well-powered to detect AI with mild beneficial effects, including selection on standing archaic variation, and is robust to non-AI selective sweeps, heterosis from deleterious mutations, and demographic misspecification. Furthermore, MaLAdapt outperforms existing methods for detecting AI based on the analysis of simulated data and the validation of empirical signals through visual inspection of haplotype patterns. We apply MaLAdapt to the 1000 Genomes Project human genomic data and discover novel AI candidate regions in non-African populations, including genes that are enriched in functionally important biological pathways regulating metabolism and immune responses.

HD-Zip Transcription Factor is Responsible for No-Lobed Leaf in Watermelon (Citrullus lanatus L.)

Leaf is a vital organ of plants that plays an essential role in photosynthesis and respiration. As an important agronomic trait in leaf development, leaf shape is classified into lobed, entire (no-lobed), and serrated in most crops. In this study, two-lobed leaf watermelon inbred lines WT2 and WCZ, and a no-lobed leaf watermelon inbred line WT20 were used to create two F2 populations. Segregation analysis suggested that lobed leaves were dominant over the no-lobed leaves, and it was controlled by a signal gene. A locus on watermelon chromosome 4 controlling watermelon lobed/no-lobed leaves was identified through BSA-seq strategy combined with linkage analysis. The candidate gene was fine-mapped to a 61.5 kb region between 21,224,481 and 21,285,957 bp on watermelon chromosome 4 using two F2 populations. Four functional genes were annotated in the candidate region, while sequences blast showed that there was a single-base deletion (A/-) only in the exon of Cla018360, which resulted in premature termination of translation in the no-lobed leaf lines. Function prediction showed that Cla018360 encodes an HD-Zip protein that has been reported to regulate the development of leaf shape. The single-base deletion also occurred in the HD-Zip domain. We inferred that the Cla018360 gene is the candidate gene for regulating the development of lobed/no-lobed leaves in watermelon. Gene expression analysis showed that Cla018360 was highly expressed in young leaves. Phylogenetic analysis showed that Cla018360 had a close genetic relationship with AtHB51, which had been reported to regulate the formation of leaf shape in Arabidopsis. Furthermore, transcriptome analysis showed that a total of 333 differentially expressed genes were identified between WT2 and WT20, of which 115 and 218 genes were upregulated and downregulated in no-lobed leaved watermelon WT20. This study not only provides a good entry point for studying leaf development but also provides foundational insights into breeding for special plant architecture in watermelon.

Identification of six genomic regions associated with pigment intensity in alpacas

Context Currently, there is a growing consumer demand for more ecologically sustainable practices in the textile industry. Fabric dyeing is highly pollutive, and one way to avoid dyeing is to use naturally coloured fibres. Alpacas exhibit a wide range of fleece colours, thereby making them a good source of fibre for sustainable textile production. Our understanding of the colour genetics of alpacas is improving, but there is still no explanation for all the variation seen in alpaca coat colours. Aims To identify a region or regions in the alpaca genome that contribute to differences in pigment intensity. Methods Colorimetric analysis using L*a*b* colourspace of fibre from white and black alpacas, that had been genotyped using the Neogen Australasia alpaca coat colour test, was used to classify the samples into intense and dilute groups for each colour. Illumina short-read genotyping by sequencing of the DNA from these alpacas was used to identify single nucleotide polymorphisms that were subsequently used in a case–control genome-wide association study (GWAS) comparing the extreme dilute and intensely pigmented animals. Key results Intense eumelanic fibre is darker (P = 0.0003), less red (P = 0.004), and more blue (P = 0.001) than is dilute eumelanic fibre. Intense pheomelanic fibre is darker (P = 1 × 10−7), more red (P = 3 × 10−20), and more yellow (P = 2 × 10−6) than is dilute pheomelanic fibre. The GWAS showed six regions of genome-wide significance. After manual inspection of these six regions, the best candidate region was upstream of KITLG, a gene previously associated with pigment intensity in dogs. Conclusions In combination with ASIP genotype, a regulatory mutation in a region upstream of KITLG in alpacas potentially has a major effect on pigment intensity in the species. Implications Successful identification of a marker for pigment intensity will allow breeders to select more precisely for breeding and production animals that will assist them in supplying the desired fibre colours to the textile industry.

Detection and Classification of Hemorrhages in Retinal Images

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy (DR). Hemorrhages is the first clinically visible symptoms of DR. This paper presents a new technique to extract and classify the hemorrhages in fundus images. The normal objects such as blood vessels, fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages. For masking blood vessels, thresholding that separates blood vessels and background intensity followed by a new filter to extract the border of vessels based on orientations of vessels are used. For masking optic disc, the image is divided into sub-images then the brightest window with maximum variance in intensity is selected. Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques. Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features. Three different types of Support Vector Machine (SVM), Linear SVM, Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemorrhages or healthy. The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico. The performance of the method is measured based on average sensitivity, specificity, F-score and accuracy. Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.

Identification of genomic regions associated with total and progressive sperm motility in Italian Holstein bulls.

Sperm motility is directly related to the ability of sperm to move through the female reproductive tract to reach the ovum. Sperm motility is a complex trait that is influenced by environmental and genetic factors and is associated with male fertility, oocyte penetration rate, and reproductive success of cattle. In this study we carried out a GWAS in Italian Holstein bulls to identify candidate regions and genes associated with variations in progressive and total motility (PM and TM, respectively). After quality control, the final data set consisted of 5,960 records from 949 bulls having semen collected in 10 artificial insemination stations and genotyped at 412,737 SNPs (call rate >95%; minor allele frequency >5%). (Co)variance components were estimated using single trait mixed models, and associations between SNPs and phenotypes were assessed using a genomic BLUP approach. Ten windows that explained the greatest percentage of genetic variance were located on Bos taurus autosomes 1, 2, 4, 6, 7, 23, and 26 for TM and Bos taurus autosomes 1, 2, 4, 6, 8, 16, 23, and 26 for PM. A total of 150 genes for TM and 72 genes for PM were identified within these genomic regions. Gene Ontology enrichment analyses identified significant Gene Ontology terms involved with energy homeostasis, membrane functions, sperm-egg interactions, protection against oxidative stress, olfactory receptors, and immune system. There was significant enrichment of quantitative trait loci for fertility, calving ease, immune response, feed intake, and carcass weight within the candidate windows. These results contribute to understanding the architecture of the genetic control of sperm motility and may aid in the development of strategies to identify subfertile bulls and improve reproductive success.