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Root rot disease poses a significant threat to canola (Brassica napus), underscoring the need for a comprehensive understanding of its causal agents for more effective disease mitigation. The composition and diversity of fungal pathogens associated with root rot of canola in Alberta, Canada, were evaluated from plant tissue samples collected in 2021 and 2022. The study revealed Fusarium spp. as the predominant pathogens found in almost all surveyed fields. Fusarium avenaceum, F. redolens, and F. solani were among the most frequently recovered species. Greenhouse trials confirmed their pathogenicity, with F. avenaceum and F. sporotrichioides found to be particularly aggressive. Additionally, F. sporotrichioides and F. commune were identified for the first time as canola root rot pathogens. Inoculation with isolates of most species resulted in significant reductions in seedling emergence, plant height, and shoot and root dry weights. Analysis of translation elongation factor 1-α (TEF-1α) and internal transcribed spacer (ITS) sequences confirmed the identity of the Fusarium spp., while concatenating the ITS and TEF-1α sequences enabled improved species differentiation. Geographic and year effects did not influence fungal diversity or aggressiveness, as determined by principal component analysis. This study emphasized the high diversity and impact of Fusarium spp. in causing canola root rot. [ABSTRACT FROM AUTHOR]

As two practical and important image processing tasks, color demosaicking (CDM) and super-resolution (SR) have been studied for decades. However, most literature studies these two tasks independently, ignoring the potential benefits of a joint solution. In this paper, aiming at efficient and effective joint demosaicking and super-resolution (JDSR), a well-designed two-stage convolutional neural network (CNN) architecture is proposed. For the first stage, by making use of the sampling-pattern information, a pattern-aware feature extraction (PFE) module extracts features directly from the Bayer-sampled low-resolution (LR) image, while keeping the resolution of the extracted features the same as the input. For the second stage, a dual-branch feature refinement (DFR) module effectively decomposes the features into two components with different spatial frequencies, on which different learning strategies are applied. On each branch of the DFR module, the feature refinement unit, namely, densely-connected dual-path enhancement blocks (DDEB), establishes a sophisticated nonlinear mapping from the LR space to the high-resolution (HR) space. To achieve strong representational power, two paths of transformations and the channel attention mechanism are adopted in DDEB. Extensive experiments demonstrate that the proposed method is superior to the sequential combination of state-of-the-art (SOTA) CDM and SR methods. Moreover, with much smaller model size, our approach also surpasses other SOTA JDSR methods. [ABSTRACT FROM AUTHOR]

• The BMISP framework models the ISP pipeline in both reverse and forward directions. • The nonlinear and linear transformation matrices are learned for reverse mapping. • Camera-style information is extracted and embedded in the forward mapping. • Experiments show the BMISP framework can facilitate image enhancement tasks well. After being processed by the image signal processing (ISP) pipeline in digital cameras, the sRGB images are nonlinear, and thus are not suitable for the computer vision tasks which work best in a linear color space. Therefore, mapping nonlinear sRGB images back to a linear color space is a highly valuable task. To achieve an accurate mapping, this paper proposes a framework based on convolutional neural networks, which models the ISP pipeline in both reverse and forward directions. In particular, for the reverse mapping, a U-net structure is applied to extract features from a given sRGB image, and the extracted features are utilized to estimate the linear and nonlinear transformations in the ISP pipeline. For the forward mapping, the original sRGB image is used as a guidance to embed the camera-style information. To incorporate the encoded prior knowledge, affine transformations are employed to modulate the features. Experiments demonstrate that the proposed framework is able to achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• Imposing low-rank property on the residual of patches is proved to be effective. • A shape-adaptive low-rank modeling method is proposed. • An external gradient prior is presented to exploit the HR information externally. • The joint-prior-based minimization problems are solved efficiently. • The proposed methods are superior to many state-of-the-art SR methods. Image super-resolution (SR) is a challenging task which aims to recover the high-resolution (HR) images from the degraded low-resolution (LR) observations. To address this ill-posed problem, properly exploiting the image prior is of great importance. In this paper, we propose a data-adaptive low-rank (DLR) model. Rather than directly assuming that the rank of a group of similar patches is low, the DLR model imposes the low-rank property on the residual of the grouped patches. In addition, the shape of the patches in our DLR model is adapted to the contents of images, so that the dissimilar pixels in a group of patches can be largely reduced. In order to further boost the performance, an external gradient prior (EGP), which is learned externally to capture gradient information, is combined with DLR to form a joint prior. When solving the DLR-based and the joint-prior-based minimization problems, the split Bregman method is adopted to speed up the convergence. The extensive experimental results show that our algorithms outperform many state-of-the-art single image SR methods in terms of both objective and subjective qualities. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SR) aims at generating a plausible and visually pleasing high-resolution (HR) image from a low-resolution (LR) input. In this paper, we propose an effective single image SR algorithm by using collaborative representation and exploiting non-local self-similarity of natural images. In particular, the collaborative-representation-based method is applied to build the so-called self-projection matrices from a training set of HR images. Then the learned self-projection matrices are used to establish the collaborative-representation-based regularization (CRR), which is responsible for introducing the external HR information. Furthermore, to guarantee a reliable estimation of the HR image, the non-local low-rank regularization (NLR) which exploits internal prior of images is also taken into consideration. Since the CRR term and NLR term are complementary, they are assembled together to form a new reconstruction-based framework for SR recovery. Finally, to implement the proposed framework, an iterative algorithm is designed to gradually improve the quality of the SR results. Extensive experimental results indicate that the proposed approach is capable of delivering higher quality of SR results than several state-of-the-art SR methods. [ABSTRACT FROM AUTHOR]

Color demosaicking is used to reconstruct full color images from incomplete color filter array samples captured by cameras with a single sensor array. In reconstructing natural-looking images, one key challenge is to model and respect the statistics of natural images. This paper presents a novel modeling strategy and an efficient color demosaicking algorithm. The approach starts with joint modeling of the color images, which supports simultaneous representation of inter-channel correlation and structural information in an image. The inter-channel correlation is explored by measuring the channel difference signals in the gradient domain, while the structural information is explored by nonlocal low-rank regularization. An efficient algorithm is then proposed to solve the joint formulation, by dividing the minimization problem into two sub-problems and solving them iteratively. The effectiveness of the proposed approach is demonstrated with extensive experiments on both noiseless and noisy datasets, with comparison with existing state-of-the-arts color demosaicking methods. [ABSTRACT FROM AUTHOR]

Employing correlation among images for improved reconstruction in compressive sensing is a conceptually attractive idea, although developing efficient modeling strategies and reconstruction algorithms are often the key to achieve any potential benefit. This paper presents a novel modeling strategy and an efficient reconstruction algorithm for processing a set of correlated images, jointly taking into consideration inter-image correlation, intra-image correlation and inter-channel correlation. The approach starts with joint modeling of the entire image set in the gradient domain, which supports simultaneous representation of local smoothness, nonlocal self-similarity of every single image, and inter-image correlation. Then an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. Furthermore, to support color image reconstruction, the proposed algorithm is extended by using the concept of group sparsity to explore inter-channel correlation. The effectiveness of the proposed approach is demonstrated with extensive experiments on both grayscale and color image sets. Results are also compared with recently proposed compressive sensing recovery algorithms. [ABSTRACT FROM AUTHOR]

This paper concentrates on the problem of image reconstruction from compressed sensing (CS) measurements in multi-view compressed imaging systems, where each view is acquired independently by CS technique. In order to take advantage of both the inter-view correlation and the spatial prior information in multi-view image sets, a weighted total variation (TV) regularized model, which combines the TV norm of a target view and the TV norm of the corresponding residual, is proposed. To efficiently solve the weighted TV regularization constrained problem, novel algorithms are presented for both the anisotropy TV and the isotropy TV cases. Given the multi-view CS measurements, a sliding window-based recovery framework is also developed to work with the weighted TV-based reconstruction algorithms and produce high-quality results. We show by experiments that the proposed methods greatly outperform the straight forward reconstruction which applies view by view image reconstruction independently, and also have significant advantages over other benchmark methods. [ABSTRACT FROM AUTHOR]

The thermal chemistry of tetrakis(ethylmethylamido)titanium (TEMAT) on (100)-oriented surfaces of silicon wafers was studied by using infrared absorption and X-ray photoelectron spectroscopies. Dissociative adsorption was identified starting at temperatures around 450 K, likely limited by the rate of an initial elimination of some of the amido groups. That adsorption is rapidly followed by a selective β-hydride elimination reaction from the ethyl moiety of the remaining ligands to produce N-methylethylidenimine adsorbed species. Long exposures of the Si(100) surface to TEMAT above the temperature of decomposition lead to the growth of a metal nitride film. Those films appear to grow in 3-D fashion, and contain high levels of C and O contaminants. [ABSTRACT FROM AUTHOR]

Previous methods for human motion generation have predominantly relied on skeleton representations to depict human poses and motion. These methods typically use a series of skeletons to represent the motion of a human. However, they are not directly suitable for handling the 3D point cloud sequences obtained from optical motion capture. To address this limitation, we propose a novel network called point cloud motion generation (PCMG) that can handle both skeleton-based motion representation and point cloud data from the human surface. PCMG is trained on finite point cloud sequences and is capable of generating infinite new point cloud sequences. By providing a predefined action label and shape label as input, PCMG generates a point cloud sequence that captures the semantics associated with these labels. PCMG achieves comparable results to state-of-the-art methods for action-conditional human motion generation, while outperforming previous approaches in terms of generation efficiency. The code for PCMG will be available at https://github.com/gxucg/PCMG [ABSTRACT FROM AUTHOR]

• The MWRB module is proposed to adaptively detect different scales of features. • The dilated convolutions with three different dilation factors are applied in MWRB. • The FCA module is designed to well exploit the multi-level features in MWRN. • Multi-level features are fused and the channel-wise fused features are rescaled. • The proposed MWRN is more efficient and effective than many CNN-based SR methods. In many recent image super-resolution (SR) methods based on convolutional neural networks (CNNs), the superior performance was achieved by training very large networks, which may not be suitable for real-world applications with limited computing resources. Therefore, it is necessary to develop more compact networks that achieve a better trade-off between the model size and the performance. In this paper, we propose an efficient and effective network called multi-path wide-activated residual network (MWRN). Firstly, as the basic building block of MWRN, the multi-path wide-activated residual block (MWRB) is presented to extract the multi-scale features. MWRB consists of three parallel wide-activated residual paths, where the dilated convolutions with different dilation factors are used to increase the receptive fields. Secondly, the fusional channel attention (FCA) module, which contains a bottleneck layer and a multi-path wide-activated residual channel attention (MWRCA) block, is designed to well exploit the multi-level features in MWRN. In each FCA, the MWRCA block refines the fused features by taking the interdependencies among feature channels into consideration. The experiments demonstrate that, compared with the state-of-the-art methods, the proposed MWRN model is able to provide very competitive performance with a relatively small number of parameters. [ABSTRACT FROM AUTHOR]

Recently, many super-resolution (SR) methods based on convolutional neural networks (CNNs) have achieved superior performance by utilizing deep and heavy models, which may not be suitable for real-world low-budget devices. To address this issue, we propose a novel lightweight SR network called a multi-scale feature selection network (MFSN). As the basic building block of MFSN, the multi-scale feature selection block (MFSB) is presented to extract the rich multi-scale features from a coarse-to-fine receptive field level. For a better representation ability, a wide-activated residual unit is adopted in each branch of MFSB except the last one. In MFSB, the scale selection module (SSM) is designed to effectively fuse the features from two adjacent branches by adjusting receptive field sizes adaptively. Further, a comprehensive channel attention mechanism (CCAM) is integrated into SSM to learn the dynamic selection weight by considering the local and global inter-channel dependencies. Extensive experimental results illustrate that the proposed MFSN is superior to other lightweight methods. • The MFSB module is proposed to extract multi-scale features from a coarse-to-fine receptive field level. • The SSM module is designed to well fuse multi-scale features by adjusting the receptive field adaptively. • The CCAM mechanism is built in the SSM to learn the selection weights by considering the global and local inter-channel dependencies. • The proposed MFSN is more efficient and effective than many lightweight CNN-based SR methods. [ABSTRACT FROM AUTHOR]

A universal method for constructing an N-port strictly non-blocking optical router based on 2×2 Mach-Zehnder optical switch for photonic networks-on-chip is proposed. By analyzing the routing table of the N-port optical router, the relationship between the optical links of port m → port n and port m → port n −1 is indicated, as well as the relationships between the block matrices of the N-port optical router. The strictly non-blocking property of the N-port optical router is proved by the contradiction method. The scale of the N-port optical router can be increased with the improvement of the performance of the Mach-Zehnder optical switch. [ABSTRACT FROM AUTHOR]

Aphanomyces root rot, caused by Aphanomyces euteiches, causes severe yield loss in field pea (Pisum sativum). The identification of a pea germplasm resistant to this disease is an important breeding objective. Polygenetic resistance has been reported in the field pea cultivar '00-2067'. To facilitate marker-assisted selection (MAS), bulked segregant RNA-seq (BSR-seq) analysis was conducted using an F8 RIL population derived from the cross of 'Carman' × '00-2067'. Root rot development was assessed under controlled conditions in replicated experiments. Resistant (R) and susceptible (S) bulks were constructed based on the root rot severity in a greenhouse study. The BSR-seq analysis of the R bulks generated 44,595,510~51,658,688 reads, of which the aligned sequences were linked to 44,757 genes in a reference genome. In total, 2356 differentially expressed genes were identified, of which 44 were used for gene annotation, including defense-related pathways (jasmonate, ethylene and salicylate) and the GO biological process. A total of 344.1 K SNPs were identified between the R and S bulks, of which 395 variants were located in 31 candidate genes. The identification of novel genes associated with partial resistance to Aphanomyces root rot in field pea by BSR-seq may facilitate efforts to improve management of this important disease. [ABSTRACT FROM AUTHOR]

Key message: A stable and major QTL, which mapped to an approximately 20.0 cM region on pea chromosome 4, was identified as the most consistent region conferring partial resistance to Aphanomyces euteiches. Aphanomyces root rot (ARR), caused by Aphanomyces euteiches Drechs., is a destructive soilborne disease of field pea (Pisum Sativum L.). No completely resistant pea germplasm is available, and current ARR management strategies rely on partial resistance and fungicidal seed treatments. In this study, an F8 recombinant inbred line population of 135 individuals from the cross 'Reward' (susceptible) × '00-2067' (tolerant) was evaluated for reaction to ARR under greenhouse conditions with the A. euteiches isolate Ae-MDCR1 and over 2 years in a field nursery in Morden, Manitoba. Root rot severity, foliar weight, plant vigor and height were used as estimates of tolerance to ARR. Genotyping was conducted with a 13.2 K single-nucleotide polymorphism (SNP) array and 222 simple sequence repeat (SSR) markers. Statistical analyses of the phenotypic data indicated significant (P < 0.001) genotypic effects and significant G × E interactions (P < 0.05) in all experiments. After filtering, 3050 (23.1%) of the SNP and 30 (13.5%) of the SSR markers were retained for linkage analysis, which distributed 2999 (2978 SNP + 21 SSR) of the markers onto nine linkage groups representing the seven chromosomes of pea. Mapping of quantitative trait loci (QTL) identified 8 major-effect (R2 > 20%), 13 moderate-effect (10% < R2 < 20%) effect and 6 minor-effect (R2 < 10%) QTL. A genomic region on chromosome 4, delimited by the SNP markers PsCam037549_22628_1642 and PsCam026054_14999_2864, was identified as the most consistent region responsible for partial resistance to A. euteiches isolate Ae-MDCR1. Other genomic regions important for resistance were of the order chromosome 5, 6 and 7. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging technology (HSI) can capture pathological tissue's spatial and spectral information simultaneously, with wide coverage and high accuracy characteristics, and is widely used in biomedical imaging. As an image-spectrum merging technology, HSI can obtain more practical information in disease diagnosis, which is helpful for pathological analysis. Focusing on the characteristics of scattered distribution of pathological areas, and combined with the advantages of HSI technology, a dimension-driven multi-path attention residual network (DDMARN) is proposed to pixel-level classification for membranous nephropathy (MN). To make full use of the space-spectrum information of hyperspectral data, dimension-driven multi-path attention residual block (DDMARB) is developed to effectively obtain the multi-scale features and differently treats these features containing different amounts of information through the channel attention (CA) mechanism, which makes the data depth features better expressed. The experimental results demonstrate that the proposed DDMARN performs very competitively in the PMN and HBV-MN classification tasks. The average of OA (%) ± standard deviation (%), AA (%) ± standard deviation (%), and Kappa coefficient ± standard deviation of 10 experiments results are 96.23 ± 0.17, 90.24 ± 0.18, and 0.8654 ± 0.0056, respectively, the optimal values in the comparison algorithm, and the model parameter is only 700K. • Propose a convolution neural network to classify membranous nephropathy. • The proposed block extracts multi-scale features from different information inputs. • The proposed model extracts data's spatial and spectral information in two stages. • The method is more efficient than many methods and has fewer parameters. [ABSTRACT FROM AUTHOR]

Genetic mapping for faba bean lags far behind other major crops. Density enhancement of the faba bean genetic linkage map was carried out by screening 5,325 genomic SSR primers and 2033 expressed sequence tag (EST)‐SSR primers on the parental cultivars '91825' and 'K1563'. Two hundred and fifteen genomic SSR and 133 EST‐SSR primer pairs that detected polymorphisms in the parents were used to screen 129 F2 individuals. This study added 337 more SSR markers and extended the previous linkage map by 2928.45 cM to a total of 4516.75 cM. The number of SSR markers in the linkage groups varied from 12 to 136 while the length of each linkage group ranged from 129.35 to 1180.21 cM. The average distance between adjacent loci in the enhanced genetic linkage map was 9.71 cM, which is 2.79 cM shorter than the first linkage map of faba bean. The density‐enhanced genetic map of faba bean will be useful for marker‐assisted selection and breeding in this important legume crop. [ABSTRACT FROM AUTHOR]

Mycosphaerella blight (Mycosphaerella pinodes) is the most important disease of field pea (Pisum sativum) in western Canada, occurring in almost every field each year. The objective of the study was to assess the impact of seed infection on epidemics of mycosphaerella blight. In preparation for the trial, seed lots of a susceptible and partially resistant cultivar with different levels of seed infection with M. pinodes were produced at a single site with uniform handling and storage conditions. Field trials were conducted in 2005 and 2006 at three sites in western Canada where levels of external inoculum were high (Vegreville, AB, Saskatoon, SK, Morden, MB), and one site (Bradford, ON) with little external inoculum. For each cultivar, the seed infection treatments were: (i) high seed infection (26-47% of seed infected with M. pinodes); (ii) intermediate seed infection (6-15% of seed infected); (iii) fungicide - intermediate seed lot treated with a fungicide to minimize seed transmission; and (iv) low seed infection (0-2% of seed infected). Seedling establishment was higher in the low seed infection treatment than the other treatments in four of eight station years, but there were no consistent differences among the other treatments. There were small differences in blight severity among treatments in several station years, but no differences were observed at Bradford, the site with minimal external inoculum. We conclude that seed infection with M. pinodes does not contribute substantially to above-ground symptoms in the year of planting. However, very high levels of seed infection reduced seedling establishment, so continued seed testing for germination is recommended. [ABSTRACT FROM AUTHOR]