Candidate Regions Research Articles

Population genetics provides insights into the important agronomic traits of Ganoderma cultivation varieties in China

This study aimed to investigate the species diversity and genetic differentiation of the genome of the main cultivated strains of Ganoderma in China. Population genomics analysis was conducted based on 150 cultivated strains of Ganoderma collected nationwide. The results indicated that the main species currently cultivated in China were Ganoderma sichuanense and Ganoderma lucidum, with a minor proportion of Ganoderma sessile, Ganoderma weberianum, Ganoderma sinense, Ganoderma gibbosum and Ganoderma australe. A total of 336,506 high-quality single nucleotide polymorphism (SNP) loci were obtained through population evolution analysis. The Fst values were calculated using a 5-kb sliding window, which ranged from 0.11 to 0.74. This suggests varying degrees of genetic differentiation between populations and genetic exchange among varieties. On this basis, the genes related to the stipe length, cap color and branch phenotypes of Ganoderma were excavated, and the region with the top 1% ZFst value region was used as a candidate region. A total of 137, 270 and 222 candidate genes were identified in the aforementioned 3 phenotypes, respectively. Gene annotation revealed that genes associated with stipe length were mainly related to cell division and differentiation, including proteins such as Nse4 protein and DIM1 protein. The genes related to Ganoderma red color were mainly related to the metabolism of tryptophan and flavonoids. The genes related to the branch were mainly related to cytokinin synthesis, ABC transporter and cytochrome P450. This study provided 150 valuable genome resequencing data in assessing the diversity and genetic differentiation of Ganoderma and laid a foundation for agronomic trait analysis and the development of new varieties of Ganoderma.

Review of Object Detection Algorithms for Sonar Images based on Deep Learning

Background: Deep learning object detection algorithm is widely used in the field of image classification and has become an indispensable part. With the improvement of image classification accuracy, sonar image target detection algorithm based on deep learning has gradually become the focus of more and more people's research. Objective: This article aims to provide a summary and analysis of deep learning-based sonar image object detection algorithms, with the hope of offering insights for future research in the field of sonar target detection technology. Method: This paper systematically summarizes sonar image target detection algorithms based on deep learning. According to the method principle, the existing deep learning target detection algorithms are divided into four categories: target detection algorithm based on candidate region, deep target detection method based on regression, Anchor Free deep learning target detection algorithm, and search-based target detection and recognition algorithm. Then, the performance of algorithms based on COCO data sets is compared, and the standard sonar data sets and formats are introduced. Results: The sonar image object detection algorithm based on deep learning has made significant progress. The combination of deep learning and object detection methods has been applied to sonar images, resulting in the emergence of excellent performing algorithms. However, most algorithms are still in the developmental stage and face challenges in practical applications. Subsequently, several invention patents have been developed based on the aforementioned algorithms, including a feature extraction method for side-scan sonar images based on fully convolutional neural networks, an underwater sonar image target detection method based on improved YOLOv3-tiny, and more. Conclusion: Sonar image object detection technology based on deep learning has a wide range of application needs but also faces many difficulties and challenges, we still need to continue to learn and explore in future research, and we believe that we can make greater breakthroughs in the future.

On the causality paradox and the Karch-Randall braneworld as an EFT

Holography on cutoff surfaces can appear to be in tension with causality. For example, as argued by Omiya and Wei [1], double holography seemingly allows for superluminal signalling. In this paper we argue that the brane description of double holography should be treated as an effective theory and demonstrate that causality violations due to faster-than-light communication are not visible above the associated cutoff length scale. This suggests that end-of-the-world brane models are consistent with causality and that the apparent superluminal signalling is a UV effect. Moreover, we argue that short distance non-localities generically give rise to apparent faster-than-light propagation of signals in Anti-de Sitter space.Nonetheless, superluminal signalling indicates that the causal structure on holographic cutoff surfaces needs to be modified. We propose and study three different candidate regions that might replace the domain of dependence in the brane EFT of the Karch-Randall model. These regions are defined by unitarity on the brane, through bulk entanglement wedges and through the nice slice criterion, respectively. In all dimensions, these candidate regions exclude those parts of the domain of dependence which are affected by superluminal signalling. While all three definitions agree in two dimensions, they are different in higher dimensions.

Genome-Wide Association Study of Fiber Diameter in Alpacas.

The aim of this study was the identification of candidate genomic regions associated with fiber diameter in alpacas. DNA samples were collected from 1011 female Huacaya alpacas from two geographical Andean regions in Peru (Pasco and Puno), and three alpaca farms within each region. The samples were genotyped using an Affymetrix Custom Alpaca genotyping array containing 76,508 SNPs. After the quality controls, 960 samples and 51,742 SNPs were retained. Three association study methodologies were performed. The GWAS based on a linear model allowed us to identify 11 and 35 SNPs (-log10(p-values) > 4) using information on all alpacas and alpacas with extreme values of fiber diameter, respectively. The haplotype and marker analysis method allowed us to identify nine haplotypes with standardized haplotype heritability higher than six standard deviations. The selection signatures based on cross-population extended haplotype homozygosity (XP-EHH) allowed us to identify 180 SNPs with XP-EHH values greater than |3|. Four candidate regions with adjacent SNPs identified via two association methods of analysis are located on VPA6, VPA9, VPA29 and one chromosomally unassigned scaffold. This study represents the first analysis of alpaca whole genome association with fiber diameter, using a recently assembled alpaca SNP microarray.

Molecular basis of ClC-6 function and its impairment in human disease.

ClC-6 is a late endosomal voltage-gated chloride-proton exchanger that is predominantly expressed in the nervous system. Mutated forms of ClC-6 are associated with severe neurological disease. However, the mechanistic role of ClC-6 in normal and pathological states remains largely unknown. Here, we present cryo-EM structures of ClC-6 that guided subsequent functional studies. Previously unrecognized ATP binding to cytosolic ClC-6 domains enhanced ion transport activity. Guided by a disease-causing mutation (p.Y553C), we identified an interaction network formed by Y553/F317/T520 as potential hotspot for disease-causing mutations. This was validated by the identification of a patient with a de novo pathogenic variant p.T520A. Extending these findings, we found contacts between intramembrane helices and connecting loops that modulate the voltage dependence of ClC-6 gating and constitute additional candidate regions for disease-associated gain-of-function mutations. Besides providing insights into the structure, function, and regulation of ClC-6, our work correctly predicts hotspots for CLCN6 mutations in neurodegenerative disorders.

Large dam candidate region identification from multi-source remote sensing images via a random forest and spatial analysis approach

ABSTRACTThe extraction of large dam candidate regions is critical for broad-scale efforts to rapidly detect large-area dams. The framework proposed in this paper attempts to combine random forest classification models and spatial analysis methods with large dam candidate area extraction methods for large-scale areas. First, we studied the combination of optical, microwave, texture, and topographic features of the dam and constructed a multisource remote sensing and topographic feature vector of the dam. Secondly, we constructed random forest classifiers in different study areas and evaluate their performance. Then we explored the geographic characteristics of the dams and their relationships with other features. Finally, we introduced the spatial analysis method to constrain the large dam candidate area. The proposed framework was tested in a total area of 968,533 km2 in five countries and achieved promising results, which constrained the candidate area to less than 1.06% of the total area. We calculated the completeness rate of large dams using the multi-source dam datasets. The framework achieved a completeness rate of more than 97.62%. Our results show that the entire framework is reliable for automated and fast large dam candidate area acquisition based on data from open remote sensing products.

Ascertaining the genetic background of the Celtic-Iberian pig strain: A signatures of selection approach.

Celtic-Iberian pig breeds were majority in Spain and Portugal until the first half of the 20th century. In the 1990s, they were nearly extinct as a result of the introduction of foreign improved pig breeds. Despite its historical importance, the genetic background of the Celtic-Iberian pig strain is poorly documented. In this study, we have identified genomic regions that might contain signatures of selection peculiar of the Celtic-Iberian genetic lineage. A total of 153 DNA samples of Celtic-Iberian pigs (Spanish Gochu Asturcelta and Portuguese Bísara breeds), Iberian pigs (Spanish Iberian and Portuguese Alentejano breeds), Cinta Senese pig, Korean local pig and Cosmopolitan pig (Hampshire, Landrace and Large White individuals) were analysed. A pairwise-comparison approach was applied: the Gochu Asturcelta and the Bísara samples as test populations and the five other pig populations as reference populations. Three different statistics (XP-EHH, FST and ΔDAF) were computed on each comparison. Strict criteria were used to identify selection sweeps in order to reduce the noise brought on by the Gochu Asturcelta and Bísara breeds' severe population bottlenecks. Within test population, SNPs used to construct potential candidate genomic areas under selection were only considered if they were identified in four of ten two-by-two pairwise comparisons and in at least two of three statistics. Genomic regions under selection constructed within test population were subsequently overlapped to construct candidate regions under selection putatively unique to the Celtic-Iberian pig strain. These genomic regions were finally used for enrichment analyses. A total of 39 candidate regions, mainly located on SSC5 and SSC9 and covering 3130.5 kb, were identified and could be considered representative of the ancient genomic background of the Celtic-Iberian strain. Enrichment analysis allowed to identify a total of seven candidate genes (NOL12, LGALS1, PDXP, SH3BP1, GGA1, WIF1, and LYPD6). Other studies reported that the WIF1 gene is associated with ear size, one of the characteristic traits of the Celtic-Iberian pig strain. The function of the other candidate genes could be related to reproduction, adaptation and immunity traits, indirectly fitting with the rusticity of a non-improved pig strain traditionally exploited under semi-extensive conditions.

A whole genome scan reveals distinct features of selection in Zhaotong cattle of Yunnan province.

Over the years, indigenous cattle have not only played an essential role in securing primary food sources but have also been utilized for labor by humans, making them invaluable genetic resources. The Zhaotong cattle, a native Chinese breed from the Yunnan province, possess excellent meat quality and resistance to heat and humidity. Here we used whole genome sequencing data of 104 animals to delve into the population structure, genomic diversity and potential positive selection signals in Zhaotong cattle. The findings of this study demonstrate that the genetic composition of Zhaotong cattle was primarily derived from Chinese indicine cattle and East Asian cattle. The nucleotide diversity of Zhaotong cattle was only lower than that of Chinese indicine cattle, which was much higher than that of other taurine cattle. Genome-wide selection scans detected a series of positive candidate regions containing multiple key genes related to bone development and metabolism (CA10, GABRG3, GLDN and NOTUM), meat quality traits (ALG8, LINGO2, MYO5B, PRKG1 and GABRB1), immune response (ADA2, BMF, LEF1 and PAK6) and heat resistance (EIF2AK4 and LEF1). In summary, this study supplies essential genetic insights into the genome diversity within Zhaotong cattle and provides a foundational framework for comprehending the genetic basis of indigenous cattle breeds.

A Feedback-Driven DNN Inference Acceleration System for Edge-Assisted Video Analytics

With the proposal of edge computing, lots of intelligence applications have made significant progress. For enormous video analysis, how to further accelerate the process is still a major challenge. To overcome the challenge, researchers propose various video frame filtering systems to reduce the data transmission. In this work, we propose a Feedback-Driven DNN Inference Acceleration system (FDDIA). FDDIA is committed to further reducing the latency of DNN inference and the transmission according to the feedback information. Specifically, on the device side, FDDIA first uses the detection results of prior frames as feedback to determine the key candidate regions and uses the inter-frame difference to determine the new object regions. Those regions containing large objects are down-sampled to further reduce the frame. On the edge side, FDDIA first integrates all candidate regions into a smaller new image. Then it remaps the detection result for the new image back to the original frame and returns the result to the device. We evaluate FDDIA on different video benchmarks for three object detection tasks. The results show FDDIA improves the average end-to-end latency by 44% and average bandwidth usage by 41% than the existing advanced method while maintaining a high accuracy.

A purebred South American breed showing high effective population size and independent breed ancestry: The Chilean Terrier.

The Chilean Terrier is a known breed in Chile that has not been genetically assessed despite its distinctive color patterns, agility, and hardiness across the diversity of climates encountered within the Chilean landscape. The population structure and its relatedness with other breeds, as well as the actual origin of the breed, remain unknown. We estimated several population parameters using samples from individuals representing the distribution of the Chilean Terrier across the country. By utilizing the Illumina HD canine genotyping array, we computed the effective population size (Ne ), individual inbreeding, and relatedness to evaluate the genetic diversity of the breed. The results show that linkage disequilibrium was relatively low and decayed rapidly; in fact, Ne was very high when compared to other breeds, and similar to other American indigenous breeds (such as the Chihuahua with values of Ne near 500). These results are in line with the low estimates of genomic inbreeding and relatedness and the relatively large number of effective chromosome segments (Me = 2467) obtained using the properties of the genomic relationship matrix. Between population analysis (cross-population extended haplotype homozygosity, di ) with other breeds such as the Jack Russell Terrier, the Peruvian-Inca Orchid, and the Chihuahua suggested that candidate regions harboring FGF5, PAX3, and ASIP, probably explained some morphological traits, such as the distinctive color pattern characteristic of the breed. When considering Admixture estimates and phylogenetic analysis, together with other breeds of American and European origin, the Chilean Terrier does not have a recent European ancestry. Overall, the results suggest that the breed has evolved independently in Chile from other terrier breeds, from an unknown European terrier ancestor.

Reconstructing 3D Human Pose from RGB‐D Data with Occlusions

AbstractWe propose a new method to reconstruct the 3D human body from RGB‐D images with occlusions. The foremost challenge is the incompleteness of the RGB‐D data due to occlusions between the body and the environment, leading to implausible reconstructions that suffer from severe human‐scene penetration. To reconstruct a semantically and physically plausible human body, we propose to reduce the solution space based on scene information and prior knowledge. Our key idea is to constrain the solution space of the human body by considering the occluded body parts and visible body parts separately: modeling all plausible poses where the occluded body parts do not penetrate the scene, and constraining the visible body parts using depth data. Specifically, the first component is realized by a neural network that estimates the candidate region named the “free zone”, a region carved out of the open space within which it is safe to search for poses of the invisible body parts without concern for penetration. The second component constrains the visible body parts using the “truncated shadow volume” of the scanned body point cloud. Furthermore, we propose to use a volume matching strategy, which yields better performance than surface matching, to match the human body with the confined region. We conducted experiments on the PROX dataset, and the results demonstrate that our method produces more accurate and plausible results compared with other methods.

Genomic data of two chickpea populations sharing a potential Ascochyta blight resistance region.

Chickpea (Cicer arietinum L.) is one of the most important crops worldwide and a valuable nutritional source. The availability of data from different genotypes and populations is important for the comprehension of the biology and trait control of chickpea. Tissue from young leaves was collected from adult plants and sequenced using an Illumina HiSeq X platform, which provided sequencing data for a total of 169 individuals from two different populations. Furthermore, functional annotation was performed with BLAST2GO software in a candidate region for resistance to Ascochyta blight, a devastating disease that produces huge yield reductions if the growth conditions are optimal for the fungus. A total of 273 different genes in a region spanning ∼4.67 Mb in chromosome 4 were fully annotated. The raw DNA sequences and functional annotation data can be reused by the scientific community for the analysis of different agronomic traits of interest in chickpea.

Targeting the insula with transcranial direct current stimulation; A simulation study

Insula is considered an important region of the brain in the generation and maintenance of a wide range of psychiatric symptoms, possibly due to being key in fundamental functions such as interoception and cognition in general. Investigating the possibility of targeting this area using non-invasive brain stimulation techniques can open new possibilities to probe the normal and abnormal functioning of the brain and potentially new treatment protocols to alleviate symptoms of different psychiatric disorders. In the current study, COMETS2, a MATLAB based toolbox was used to simulate the magnitude of the current density and electric field in the brain caused by different transcranial direct current stimulation (tDCS) protocols to find an optimum montage to target the insula and its 6 subregions for three different current intensities, namely 2, 3, and 4 mA. Frontal and occipital regions were found to be optimal candidate regions.. The results of the current study showed that it is viable to reach the insula and its individual subregions using tDCS.

A multitiered haplotype strategy to enhance phased assembly and fine mapping of a disease resistance locus.

Fine mapping of quantitative trait loci (QTL) to dissect the genetic basis of traits of interest is essential to modern breeding practice. Here, we employed a multitiered haplotypic marker system to increase fine mapping accuracy by constructing a chromosome-level, haplotype-resolved parental genome, accurate detection of recombination sites, and allele-specific characterization of the transcriptome. In the first tier of this system, we applied the preexisting panel of 2,000 rhAmpSeq core genome markers that is transferable across the entire Vitis genus and provides a genomic resolution of 200 kb to 1 Mb. The second tier consisted of high-density haplotypic markers generated from Illumina skim sequencing data for samples enriched for relevant recombinations, increasing the potential resolution to hundreds of base pairs. We used this approach to dissect a novel Resistance to Plasmopara viticola-33 (RPV33) locus conferring resistance to grapevine downy mildew, narrowing the candidate region to only 0.46 Mb. In the third tier, we used allele-specific RNA-seq analysis to identify a cluster of 3 putative disease resistance RPP13-like protein 2 genes located tandemly in a nonsyntenic insertion as candidates for the disease resistance trait. In addition, combining the rhAmpSeq core genome haplotype markers and skim sequencing-derived high-density haplotype markers enabled chromosomal-level scaffolding and phasing of the grape Vitis × doaniana 'PI 588149' assembly, initially built solely from Pacific Biosciences (PacBio) high-fidelity (HiFi) reads, leading to the correction of 16 large-scale phasing errors. Our mapping strategy integrates high-density, phased genetic information with individual reference genomes to pinpoint the genetic basis of QTLs and will likely be widely adopted in highly heterozygous species.

Candidate signatures of positive selection for environmental adaptation in indigenous African cattle: A review.

Environmental adaptation traits of indigenous African cattle are increasingly being investigated to respond to the need for sustainable livestock production in the context of unpredictable climatic changes. Several studies have highlighted genomic regions under positive selection probably associated with adaptation to environmental challenges (e.g. heat stress, trypanosomiasis, tick and tick-borne diseases). However, little attention has focused on pinpointing the candidate causative variant(s) controlling the traits. This review compiled information from 22 studies on signatures of positive selection in indigenous African cattle breeds to identify regions under positive selection. We highlight some key candidate genome regions and genes of relevance to the challenges of living in extreme environments (high temperature, high altitude, high infectious disease prevalence). They include candidate genes involved in biological pathways relating to innate and adaptive immunity (e.g. BoLAs, SPAG11, IL1RL2 and GFI1B), heat stress (e.g. HSPs, SOD1 and PRLH) and hypoxia responses (e.g. BDNF and INPP4A). Notably, the highest numbers of candidate regions are found on BTA3, BTA5 and BTA7. They overlap with genes playing roles in several biological functions and pathways. These include but are not limited to growth and feed intake, cell stability, protein stability and sweat gland development. This review may further guide targeted genome studies aiming to assess the importance of candidate causative mutations, within regulatory and protein-coding genome regions, to further understand the biological mechanisms underlying African cattle's unique adaption.

Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning.

The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset's character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset's characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images.

OS-DS tracker: Orientation-variant Siamese 3D tracking with Detection based Sampling

LIDAR point clouds are less affected by the weather and possess more depth of field than 2D images. However, sparsity and disorder of point clouds bring challenges for 3D object tracking due to the difficulty in detecting and encoding. In addition, ineffective candidate proposals increase the phenomenon of the loss of target tracking or wrong target tracking. In this paper, we propose an Orientation-variant Siamese 3D object tracking network that utilizes a Detection based Sampling module to generate candidate proposals (OS-DS tracker). The Detection based Sampling module is used to cut the excessive and useless proposals. And the multivariate Gaussian sampling generates the candidate proposals when the objects are not detected. Concretely, we first pre-train PointRCNN Network to globally detect objects from LIDAR point clouds. Then the 3D detected objects are refined by Candidate Regions Sampling to generate candidate proposals. Meanwhile, to make the feature vectors more discriminative, we design an Orientation-variant Siamese Auto-encoder, i.e., tracking loss regress to the intersection over union (IOU) of the template box and the candidate region boxes. Our method is tested on the KITTI dataset and the SHIP Tracking dataset. Compared with the previous state-of-the-arts, the proposed method outperforms in vessel tracking with 5.2%/2.1% improvement in Success and Precision.

MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

This study aims to develop a deep learning model to improve the accuracy of identifying tiny targets on high resolution remote sensing (HRS) images. We propose a novel multi-level weighted depth perception network, which we refer to as MwdpNet, to better capture feature information of tiny targets in HRS images. In our method, we introduce a new group residual structure, S-Darknet53, as the backbone network of our proposed MwdpNet, and propose a multi-level feature weighted fusion strategy that fully utilizes shallow feature information to improve detection performance, particularly for tiny targets. To fully describe the high-level semantic information of the image, achieving better classification performance, we design a depth perception module (DPModule). Following this step, the channel attention guidance module (CAGM) is proposed to obtain attention feature maps for each scale, enhancing the recall rate of tiny targets and generating candidate regions more efficiently. Finally, we create four datasets of tiny targets and conduct comparative experiments on them. The results demonstrate that the mean Average Precision (mAP) of our proposed MwdpNet on the four datasets achieve 87.0%, 89.2%, 78.3%, and 76.0%, respectively, outperforming nine mainstream object detection algorithms. Our proposed approach provides an effective means and strategy for detecting tiny targets on HRS images.

Crosshap: R package for local haplotype visualization for trait association analysis.

Genome-wide association studies (GWAS) excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present crosshap, an R package that performs robust density-based clustering of variants based on their linkage profiles to capture haplotype structures in a local genomic region of interest. Following this, crosshap is equipped with visualization tools for choosing optimal clustering parameters (ɛ) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotype, and metadata traits. The crosshap package is freely available under the MIT license and can be downloaded directly from CRAN with R >4.0.0. The development version is available on GitHub alongside issue support (https://github.com/jacobimarsh/crosshap). Tutorial vignettes and documentation are available (https://jacobimarsh.github.io/crosshap/).

Characterization and Gene Mapping of an Open-Glume Oryza sativa L. Mutant.

Floral organ development determines agricultural productivity by affecting seed development, seed quality, and final yield. In this study, we described the novel ogl mutant in rice (Oryza sativa L.), which is characterized by an open-glume phenotype, increased pistil number, reduced stamen number, decreased seed setting rate, and smaller rice grains. Genetic analysis showed that the open-glume phenotype might be controlled by a recessive qualitative trait locus. Employing bulked segregant analysis (BSA), one candidate region was identified on rice chromosome 1. The glume opening phenotype cosegregated with SNP (Chr1:1522703), which was located at the start codon of one transcript of OsJAG, resulting in partial loss of OsJAG function. cDNA analysis revealed that OsJAG encodes two transcript variants. Compared to normal plants, the expression of OsJAG.1 was upregulated in open-glume plants. When investigating the glume phenotype, we found that the expression of genes related to floral development changed greatly in open-glume plants. Taken together, this work increases our understanding of the developmental role of OsJAG in rice floral development.