Mathematical Modeling of Host - Pest Interactions in Stage-Structured Populations: A Case of False Codling Moth [Thaumatotibia leucotreta]

False codling moth (FCM) (Thaumatotibia lucotreta) is a significant pest due to its potential economic impact on many susceptible fruits in most temperate regions of the world. Efforts to control the codling moth in the past mostly relied on the use of broad spectrum insecticide sprays, which has resulted in the development of insecticide resistance, and the disruption of the control of secondary pests. Understanding the dynamic of this pest is of great in importance in order to effectively employ the most effective control strategies. In this study, a mathematical model of host-false codling moth interactions is developed and qualitatively analysed using stability theory of system of differential equations. The basic offspring number with respect to FCM free equilibrium is obtain using next generation matrix. The condition for local and global asymptotic stability of FCM free and coexistence equilibria are established. The model is analysed numerically and graphically represented to justify the analytical results

Assessment of onion farming practices and purple blotch disease knowledge among farmers in varied agro-ecological zones of Nyeri County, Kenya

Onion (Allium cepa L.) is the second most produced vegetable globally, following tomato and plays a vital role in both cuisines and daily diets. However, the threat of diseases, such as purple blotch caused by Alternaria porri, poses a substantial risk to onion production, particularly in Nyeri County, Kenya. Despite its critical impact on farming, there is a lack of information on farmers' knowledge of purple blotch in this region. This study aimed to assess the onion farming practices and farmers' understanding of purple blotch disease across various agro-ecological zones (AEZs) in Nyeri County. Specifically, the study examines farmers' demographics, cultivated onion varieties, and their knowledge of purple blotch disease. Farms were selected using cluster random sampling. Data were collected from 100 onion farmers through semi-structured questionnaires, and statistical analysis was performed using the chi-square test in Scientific Analysis System (SAS) version 9.4 at α=0.05. The findings revealed that while the Rucet F1 onion variety was popular among the farmers (52%), there is no significant association (X2 (6, 100) = 11.947, p = 0.063) between the choice of variety and AEZs. Similarly, the preferred source of onion seeds, mainly Agroshop (84%), showed insignificant association (X2 (9, 100) = 7.153, p = 0.621) with AEZs. Despite 65% of farmers reporting knowledge about onion diseases, there is no significant association (p > 0.05) between their awareness of purple blotch and AEZs. In conclusion, the study highlights a significant gap in farmers' understanding of purple blotch disease, emphasizing the need for training programs to enhance disease identification skills. Early detection can empower farmers to implement proactive measures, ultimately improving onion productivity. This study recommends diversifying onion varieties for disease resilience, promoting awareness and training on purple blotch identification, engaging women and youths in farming, and fostering collaborative networks for ongoing knowledge exchange and improvement in onion cultivation in Nyeri County

Towards a graph-theoretic approach to hybrid performance prediction from large-scale phenotypic data

High-throughput biological data analysis has received a large amount of interest in the last decade due to pioneering technologies that are able to automatically generate large-scale datasets by performing millions of analytical tests on a daily basis. Here we present a new network-based approach to analyze a high-throughput phenomic dataset that was collected on maize inbreds and hybrids by an automated phenotyping facility. Our dataset consists of 1600 biological samples from 600 different genotypes (200 inbred and 400 hybrid lines). On each sample, 141 phenotypic traits were observed for 33 days. We apply a graph-theoretic approach to address two important problems: (i) to discover meaningful patterns in the dataset and (ii) to predict hybrid performance in terms of biomass based on automatically collected phenotypic traits. We propose a modelling framework in which the prediction problem becomes transformed into finding the shortest path in a correlation-based network. Preliminary results show small but encouraging correlations between predicted and observed biomass. Extensions of the algorithm and applications of the modelling framework to other types of biological data are discussed

Targeted Sequencing Reveals Large-Scale Sequence Polymorphism in Maize Candidate Genes for Biomass Production and Composition

<div><p>A major goal of maize genomic research is to identify sequence polymorphisms responsible for phenotypic variation in traits of economic importance. Large-scale detection of sequence variation is critical for linking genes, or genomic regions, to phenotypes. However, due to its size and complexity, it remains expensive to generate whole genome sequences of sufficient coverage for divergent maize lines, even with access to next generation sequencing (NGS) technology. Because methods involving reduction of genome complexity, such as genotyping-by-sequencing (GBS), assess only a limited fraction of sequence variation, targeted sequencing of selected genomic loci offers an attractive alternative. We therefore designed a sequence capture assay to target 29 Mb genomic regions and surveyed a total of 4,648 genes possibly affecting biomass production in 21 diverse inbred maize lines (7 flints, 14 dents). Captured and enriched genomic DNA was sequenced using the 454 NGS platform to 19.6-fold average depth coverage, and a broad evaluation of read alignment and variant calling methods was performed to select optimal procedures for variant discovery. Sequence alignment with the B73 reference and <i>de novo</i> assembly identified 383,145 putative single nucleotide polymorphisms (SNPs), of which 42,685 were non-synonymous alterations and 7,139 caused frameshifts. Presence/absence variation (PAV) of genes was also detected. We found that substantial sequence variation exists among genomic regions targeted in this study, which was particularly evident within coding regions. This diversification has the potential to broaden functional diversity and generate phenotypic variation that may lead to new adaptations and the modification of important agronomic traits. Further, annotated SNPs identified here will serve as useful genetic tools and as candidates in searches for phenotype-altering DNA variation. In summary, we demonstrated that sequencing of captured DNA is a powerful approach for variant discovery in maize genes.</p></div

Variant detection performance.

<p>*'CornFed Target Diversity' (CTD) is the final set of VPs in chromosomes.</p><p>Comprehensive overview of eight evaluated variant detection tools. Predicted VPs of each variant caller are compared to the four control data sets (50k, GBS, RNAseq, and HapMap2) in terms of sensitivity (‘S_e’), specificity (‘S_p’), and the F₁-score (‘F₁’). In addition the final set of variants detected in this study (CTD) is showing the proportion each variant caller is capturing.</p

Distribution of 4,785 candidate genes over 10 maize chromosomes.

<p>Selected genes are predicted to control various aspects of plant growth, biomass production, and composition; bar graph depicts their distribution in the maize genome.</p

SNP based phylogenetic tree.

<p>Phylogenetic tree depicting the SNP distance between 21 maize inbred lines, emphasizing the diversity in this collection. Over 265,000 homozygous SNP calls have been processed to construct this dendrogram.</p

Evaluation of read alignment and variant calling methods.

<p><b>(A)</b> Comprehensive illustration of 441 evaluated read alignment results. Each method is referenced in standard, and in two additional, parameter settings. The plots show the number of aligned reads, where the range for each bar illustrates the observed variability when different lines were used. <b>(B)</b> Heat map depicts the true positive sites in the 50k array. A total of 504 combinations of read alignment and variant calling methods were evaluated to identify recommended or less optimal applications (genotype NC358). <b>(C)</b> Variant caller performance compared to the 50k array. The total number of identified SNPs, as well as the number of unique SNPs, is depicted for each of the eight evaluated methods (genotype NC358).</p

Global diversity classification per genotype.

<p>Variant positions in captured genes (on target) identified after basic filtering (at least 5-fold coverage of read depth at SNP position) and off-target regions for different maize inbred lines.</p

Venn diagram of marker/variants in maize.

<p>Illustration of the overlapping intersections between four diversity data sets (50K, GBS, RNAseq, and HapMap2) and the ‘CornFed Target Diversity’ (CTD).</p