Identification and Fine-Mapping of Clubroot (Plasmodiophora brassicae) Resistant QTL in Brassica rapa

European fodder turnips (Brassica rapa ssp. rapifera) were identified as sources of clubroot resistance (CR) and have been widely used in Brassica resistance breeding. An F2 population derived from a cross between a resistant turnip and a susceptible Chinese cabbage was used to determine the inheritance and locating the resistance Quantitative Trait Loci (QTLs). The parents showed to be very resistant/susceptible to the field isolates (pathotype 4) of clubroot from Henan in China. After inoculation, 27 very resistant or susceptible individuals were selected to construct bulks, respectively. Next-generation-sequencing-based Bulk Segregant Analysis Sequencing (BSA-Seq) was used and located resistance QTL on chromosome A03 (3.3–7.5 Mb) and A08 (0.01–6.5 Mb), named Bcr1 and Bcr2, respectively. Furthermore, an F3 population including 180 families derived from F2 individuals was phenotyped and used to verify and narrow candidate regions. Ten and seven Kompetitive Allele-Specific PCR (KASP) markers narrowed the target regions to 4.3–4.78 Mb (A03) and 0.02–0.79 Mb (A08), respectively. The phenotypic variation explained (PVE) of the two QTLs were 33.3% and 13.3% respectively. The two candidate regions contained 99 and 109 genes. In the A03 candidate region, there were three candidate R genes, namely Bra006630, Bra006631 and Bra006632. In the A08 candidate region, there were two candidate R genes, namely Bra030815 and Bra030846.

Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

Mining massive genomic data of two Swiss Braunvieh cattle populations reveals six novel candidate variants that impair reproductive success

Background This study was carried out on the two Braunvieh populations reared in Switzerland, the dairy Brown Swiss (BS) and the dual-purpose Original Braunvieh (OB). We performed a genome-wide analysis of array data of trios (sire, dam, and offspring) from the routine genomic selection to identify candidate regions showing missing homozygosity and phenotypic associations with five fertility, ten birth, and nine growth-related traits. In addition, genome-wide single SNP regression studies based on 114,890 single nucleotide polymorphisms (SNPs) for each of the two populations were performed. Furthermore, whole-genome sequencing data of 430 cattle including 70 putative haplotype carriers were mined to identify potential candidate variants that were validated by genotyping the current population using a custom array. Results Using a trio-based approach, we identified 38 haplotype regions for BS and five for OB that segregated at low to moderate frequencies. For the BS population, we confirmed two known haplotypes, BH1 and BH2. Twenty-four variants that potentially explained the missing homozygosity and associated traits were detected, in addition to the previously reported TUBD1:p.His210Arg variant associated with BH2. For example, for BS we identified a stop-gain variant (p.Arg57*) in the MRPL55 gene in the haplotype region on chromosome 7. This region is associated with the ‘interval between first and last insemination’ trait in our data, and the MRPL55 gene is known to be associated with early pregnancy loss in mice. In addition, we discuss candidate missense variants in the CPT1C, MARS2, and ACSL5 genes for haplotypes mapped in BS. In OB, we highlight a haplotype region on chromosome 19, which is potentially caused by a frameshift variant (p.Lys828fs) in the LIG3 gene, which is reported to be associated with early embryonic lethality in mice. Furthermore, we propose another potential causal missense variant in the TUBGCP5 gene for a haplotype mapped in OB. Conclusions We describe, for the first time, several haplotype regions that segregate at low to moderate frequencies and provide evidence of causality by trait associations in the two populations of Swiss Braunvieh. We propose a list of six protein-changing variants as potentially causing missing homozygosity. These variants need to be functionally validated and incorporated in the breeding program.

An evolutionary functional genomics approach identifies novel candidate regions involved in isoniazid resistance in Mycobacterium tuberculosis

Efforts to eradicate tuberculosis are hampered by the rise and spread of antibiotic resistance. Several large-scale projects have aimed to specifically link clinical mutations to resistance phenotypes, but they were limited in both their explanatory and predictive powers. Here, we combine functional genomics and phylogenetic associations using clinical strain genomes to decipher the architecture of isoniazid resistance and search for new resistance determinants. This approach has allowed us to confirm the main target route of the antibiotic, determine the clinical relevance of redox metabolism as an isoniazid resistance mechanism and identify novel candidate genes harboring resistance mutations in strains with previously unexplained isoniazid resistance. This approach can be useful for characterizing how the tuberculosis bacilli acquire resistance to new antibiotics and how to forestall them.

Mitochondrial genomes of two parasitic Cuscuta species lack clear evidence of horizontal gene transfer and retain unusually fragmented ccmFC genes

Background The intimate association between parasitic plants and their hosts favours the exchange of genetic material, potentially leading to horizontal gene transfer (HGT) between plants. With the recent publication of several parasitic plant nuclear genomes, there has been considerable focus on such non-sexual exchange of genes. To enhance the picture on HGT events in a widely distributed parasitic genus, Cuscuta (dodders), we assembled and analyzed the organellar genomes of two recently sequenced species, C. australis and C. campestris, making this the first account of complete mitochondrial genomes (mitogenomes) for this genus. Results The mitogenomes are 265,696 and 275,898 bp in length and contain a typical set of mitochondrial genes, with 10 missing or pseudogenized genes often lost from angiosperm mitogenomes. Each mitogenome also possesses a structurally unusual ccmFC gene, which exhibits splitting of one exon and a shift to trans-splicing of its intron. Based on phylogenetic analysis of mitochondrial genes from across angiosperms and similarity-based searches, there is little to no indication of HGT into the Cuscuta mitogenomes. A few candidate regions for plastome-to-mitogenome transfer were identified, with one suggestive of possible HGT. Conclusions The lack of HGT is surprising given examples from the nuclear genomes, and may be due in part to the relatively small size of the Cuscuta mitogenomes, limiting the capacity to integrate foreign sequences.

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.

Evidence for a long-r ange RNA-RNA interaction between ORF8 and the downstream region of the Spike polybasic insertion of SARS-CoV-2

SARS-CoV-2 has affected people all over the world as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV responsible for the 2002-2003 SARS outbreak in Asia. Intense research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Similar to other betacoronaviruses, long-range RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into different viral proteins. An important difference between both viruses is a polybasic insertion in the Spike region of SARS-CoV-2, absent in SARS-CoV. Here we show that a 26-base-pair long-range RNA-RNA interaction occurs between the genomic region downstream of the Spike insertion and ORF8 in SARS-CoV-2. Predictions suggest that the corresponding ORF8 region forms the most energetically favorable interaction with that of Spike region from amongst all possible candidate regions within SARS-CoV-2 genomic RNA. We also found signs of sequence covariation in the predicted interaction using a large dataset with 27,592 full-length SARS-CoV-2 genomes. In particular, a synonymous mutation in ORF8 accommodated for base pairing with Spike [G23675 C28045U], and a non-synonymous mutation in Spike accommodated for base pairing with ORF8 [C23679U G28042] both of which were in close proximity of one another. The predicted interactions can potentially be related to regulation of sub-genomic RNA production rates.

Neural Attention-driven Non-Maximum Suppression for Person Detection

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (ROIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning. A typical case of failure with high application relevance is pedestrian/person detection in dense human crowds, where GreedyNMS doesn't provide accurate results. This paper proposes an efficient deep neural architecture for NMS in the person detection scenario, by capturing relations of neighbouring ROIs and aiming to ideally assign precisely one detection per person. The presented Seq2Seq-NMS architecture assumes a sequence-to-sequence formulation of the NMS problem, exploits the Multihead Scale-Dot Product Attention mechanism and jointly processes both geometric and visual properties of the input candidate ROIs. Thorough experimental evaluation on three public person detection datasets shows favourable results against competing methods, with acceptable inference runtime requirements and good behaviour for large numbers of raw candidate ROIs per image.

Neural Attention-driven Non-Maximum Suppression for Person Detection

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (ROIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning. A typical case of failure with high application relevance is pedestrian/person detection in dense human crowds, where GreedyNMS doesn't provide accurate results. This paper proposes an efficient deep neural architecture for NMS in the person detection scenario, by capturing relations of neighbouring ROIs and aiming to ideally assign precisely one detection per person. The presented Seq2Seq-NMS architecture assumes a sequence-to-sequence formulation of the NMS problem, exploits the Multihead Scale-Dot Product Attention mechanism and jointly processes both geometric and visual properties of the input candidate ROIs. Thorough experimental evaluation on three public person detection datasets shows favourable results against competing methods, with acceptable inference runtime requirements and good behaviour for large numbers of raw candidate ROIs per image.