Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Gapless Genome Assembly of the Potato and Tomato Early Blight Pathogen Alternaria solani.

The Alternaria genus consists of saprophytic fungi as well as plant-pathogenic species that have significant economic impact. To date, the genomes of multiple Alternaria species have been sequenced. These studies have yielded valuable data for molecular studies on Alternaria fungi. However, most of the current Alternaria genome assemblies are highly fragmented, thereby hampering the identification of genes that are involved in causing disease. Here, we report a gapless genome assembly of A. solani, the causal agent of early blight in tomato and potato. The genome assembly is a significant step toward a better understanding of pathogenicity of A. solani.

The Population Structure of Phytophthora infestans from the Toluca Valley of Central Mexico Suggests Genetic Differentiation Between Populations from Cultivated Potato and Wild Solanum spp.

ABSTRACT The population structure of Phytophthora infestans in the Toluca Valley of central Mexico was assessed using 170 isolates collected from cultivated potatoes and the native wild Solanum spp., S. demissum and S. xendinense. All isolates were analyzed for mitochondrial DNA (mtDNA) haplotype and amplified fragment length polymorphism (AFLP) multi-locus fingerprint genotype. Isolate samples were monomorphic for mtDNA haplotype because all isolates tested were of the Ia haplotype. A total of 158 multilocus AFLP genotypes were identified among the 170 P. infestans isolates included in this study. P. infestans populations sampled in the Toluca Valley in 1997 were highly variable and almost every single isolate represented a unique genotype based on the analysis of 165 AFLP marker loci. Populations of P. infestans collected from the commercial potato-growing region in the valley, the subsistence potato production area along the slopes of the Nevado de Toluca, and the native Solanum spp. on the forested slopes of the volcano showed a high degree of genetic diversity. The number of polymorphic loci varied from 20.0 to 62.4% for isolates collected from the field station and wild Solanum spp. On average, 81.8% (135) of the AFLP loci were polymorphic. Hetero-zygosity varied between 7.7 and 19.4%. Significant differentiation was found at the population level between strains originating from cultivated potatoes and wild Solanum spp. (P = 0.001 to 0.022). Private alleles were observed in individual isolates collected from all three populations, with numbers of unique dominant alleles varying from 9 to 16 for isolates collected from commercial potato crops and native Solanum spp., respectively. Four AFLP markers were exclusively found present in isolates collected from S. demissum. Indirect estimation of gene flow between populations indicated restricted gene flow between both P. infestans populations from cultivated potatoes and wild Solanum hosts. There was no evidence found for the presence of substructuring at the subpopulation (field) level. We hypothesize that population differentiation and genetic isolation of P. infestans in the Toluca Valley is driven by host-specific factors (i.e., R-genes) widely distributed in wild Solanum spp. and random genetic drift.