Search

Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum, Cercospora zeina, and Bipolaris maydis, respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent.

Virtual Learning Environments (VLEs) are becoming commonplace in Higher Education. Amongst the latest VLE developments is the design and use of Virtual Microscopes (VMs) that allow for viewing and manipulation of online images by multiple students. Although students are found to be generally satisfied with the use of VMs, it is yet not known what teaching and learning conditions better support their use and lead to enhanced learning outcomes. The aim of this paper is to compare the usage patterns and perceptions of two different cohorts of undergraduate students that made use of the VM in blended and online only learning conditions respectively, and draw conclusions about the pedagogy that better supports teaching and learning with VMs. Data collected from a survey with 139 students and 11 semi-structured interviews revealed that blended learning better caters for students' engagement and satisfaction due to the systematic use of the VM in course design, its complementary use with a physical microscope, and the ongoing provision of tutors' support and guidance. Equally good perceived learning gains were reported by both blended and online only students. Implications for the design of learning environments around virtual microscopy are discussed.

The article explains the need for farmers and ranchers in Idaho to complete their estate planning to avoid disputes and problems arising from the Idaho Uniform Probate Code. Topics discussed include the necessity for a spousal conflict waiver when there is disagreement between spouses, alternative decisions in case a client does not have adequate non-farm or non-ranch assets to equalize inheritances, consequences of leaving the farm of ranch to only one of the children, and federal estate tax.

Seed quality plays an important role in the establishment of healthy crop stands. The aim of this study was to identify the mycoflora associated with maize grain collected over two growing seasons, one experiencing severe drought, from smallholder farms in KwaZulu-Natal (KZN) and the Eastern Cape (EC). These are two adjacent provinces in South Africa with many maize-producing smallholder farmers. Asymptomatic maize ears were collected at harvest during the 2014/2015 and 2015/2016 maize growing seasons from farms located in Hlanganani (KZN), Ntabamhlophe (KZN), KwaNxamalala (KZN), Bizana (EC) and Tabankulu (EC). Maize grain was subjected to seed health tests using the agar plate method. The percentage incidence of fungal species isolated from maize grain was determined with species identities confirmed by ITS sequencing. Eleven fungal genera were identified with Fusarium species and Stenocarpella maydis the most prevalent. Fusarium verticillioides, Fusarium graminearum and S. maydis were isolated from all sites in both seasons. No fungal species exhibited a higher incidence in the drought season across all sites. F. graminearum and S. maydis had higher incidences in the wetter season at four and three sites, respectively. F. verticillioides had a greater incidence at the EC sites, particularly the coastal Bizana site. We conclude that local factors that affect the inoculum levels of each species, such as land preparation, previous crop, amount of debris from previous season, fertilizer application, and the micro-environment at field scale had a greater impact than the drought season on the population structure of ear-rot pathogens. The widespread presence of fungi that are potentially mycotoxin-producing in asymptomatic maize grain poses health risks to consumers and is worthy of further investigation.

Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum, Cercospora zeina, and Bipolaris maydis, respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent. [ABSTRACT FROM AUTHOR]

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Maize underpins food security in South Africa. An annual production of more than 10 million tons is a combination of the output of large-scale commercial farms plus an estimated 250 000 ha cultivated by smallholder farmers. Maize leaves are a rich source of nutrients for fungal pathogens. Farmers must limit leaf blighting by fungi to prevent sugars captured by photosynthesis being ‘stolen’ instead of filling the grain. This study aimed to fill the knowledge gap on the prevalence and impact of fungal foliar diseases in local smallholder maize fields. A survey with 1124 plant observations from diverse maize hybrids was conducted over three seasons from 2015 to 2017 in five farming communities in KwaZulu-Natal Province (Hlanganani, Ntabamhlophe, KwaNxamalala) and Eastern Cape Province (Bizana, Tabankulu). Northern leaf blight (NLB), common rust, Phaeosphaeria leaf spot, and grey leaf spot had overall disease incidences of 75%, 77%, 68% and 56%, respectively, indicating high disease pressure in smallholder farming environments. NLB had the highest disease severity (LSD test, p

The Canadian healthcare system serves as an example of equity and federal service to citizens across the world. However, it is not without its challenges. Prosthetic coverage across Canada is highly variable and largely unable to provide equal coverage for Canadian persons living with amputation. Many persons with limb loss are forced to rely upon personal resources, fundraising, or the charity of non-governmental organizations in order to meet this basic healthcare need. This disparity in the Canadian healthcare system is unusual and largely undescribed in the literature. We thus explore the nature of Canadian healthcare prosthetic coverage across Canada, investigating the variability in coverage, presence of prosthetic coverage policies, clarity of policy, eligibility criteria, and interval of prosthetic replacement. Our findings highlight potential areas for improvement within current Canadian healthcare policy. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/33489/25953 How To Cite: Howard C.W, Saraswat D.K, McLeod G, Yeung A, Jeong D, Lam J. Canada’s prosthetic coverage: a review of provincial prosthetic policy. Canadian Prosthetics & Orthotics Journal. 2019;Volume2, Issue2, No.4. https://doi.org/10.33137/cpoj.v2i2.33489 Corresponding Author: Calvin W. Howard, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Phone: +1 (587) 987-7723 E-mail: calvin.howard@ucalgary.ca ORCID: https://orcid.org/0000-0001-5576-9608

Gray leaf spot (GLS) disease in maize, caused by the fungus Cercospora zeina, is a threat to maize production globally. Understanding the molecular basis for quantitative resistance to GLS is therefore important for food security. We developed a de novo assembly pipeline to identify candidate maize resistance genes. Near-isogenic maize lines with and without a QTL for GLS resistance on chromosome 10 from inbred CML444 were produced in the inbred B73 background. The B73-QTL line showed a 20% reduction in GLS disease symptoms compared to B73 in the field (p = 0.01). B73-QTL leaf samples from this field experiment conducted under GLS disease pressure were RNA sequenced. The reads that did not map to the B73 or C. zeina genomes were expected to contain novel defense genes and were de novo assembled. A total of 141 protein-coding sequences with B73-like or plant annotations were identified from the B73-QTL plants exposed to C. zeina. To determine whether candidate gene expression was induced by C. zeina, the RNAseq reads from C. zeina-challenged and control leaves were mapped to a master assembly of all of the B73-QTL reads, and differential gene expression analysis was conducted. Combining results from both bioinformatics approaches led to the identification of a likely candidate gene, which was a novel allele of a lectin receptor-like kinase named L-RLK-CML that (i) was induced by C. zeina, (ii) was positioned in the QTL region, and (iii) had functional domains for pathogen perception and defense signal transduction. The 817AA L-RLK-CML protein had 53 amino acid differences from its 818AA counterpart in B73. A second “B73-like” allele of L-RLK was expressed at a low level in B73-QTL. Gene copy-specific RT-qPCR confirmed that the l-rlk-cml transcript was the major product induced four-fold by C. zeina. Several other expressed defense-related candidates were identified, including a wall-associated kinase, two glutathione s-transferases, a chitinase, a glucan beta-glucosidase, a plasmodesmata callose-binding protein, several other receptor-like kinases, and components of calcium signaling, vesicular trafficking, and ethylene biosynthesis. This work presents a bioinformatics protocol for gene discovery from de novo assembled transcriptomes and identifies candidate quantitative resistance genes.

Gray leaf spot (GLS), caused by the sibling species Cercospora zeina or Cercospora zeae-maydis, is cited as one of the most important diseases threatening global maize production. C. zeina fails to produce cercosporin in vitro and, in most cases, causes large coalescing lesions during maize infection, a symptom generally absent from cercosporin-deficient mutants in other Cercospora spp. Here, we describe the C. zeina cercosporin toxin biosynthetic (CTB) gene cluster. The oxidoreductase gene CTB7 contained several insertions and deletions as compared with the C. zeae-maydis ortholog. We set out to determine whether complementing the defective CTB7 gene with the full-length gene from C. zeae-maydis could confer in vitro cercosporin production. C. zeina transformants containing C. zeae-maydis CTB7 were generated by Agrobacterium tumefaciens–mediated transformation and were evaluated for in vitro cercosporin production. When grown on nitrogen-limited medium in the light—conditions conducive to cercosporin production in other Cercospora spp.—one transformant accumulated a red pigment that was confirmed to be cercosporin by the KOH assay, thin-layer chromatography, and ultra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. Our results indicated that C. zeina has a defective CTB7, but all other necessary machinery required for synthesizing cercosporin-like molecules and, thus, C. zeina may produce a structural variant of cercosporin during maize infection.