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

The Functions of Mediator in Candida albicans Support a Role in Shaping Species-Specific Gene Expression

The Mediator complex is an essential co-regulator of RNA polymerase II that is conserved throughout eukaryotes. Here we present the first study of Mediator in the pathogenic fungus Candida albicans. We focused on the Middle domain subunit Med31, the Head domain subunit Med20, and Srb9/Med13 from the Kinase domain. The C. albicans Mediator shares some roles with model yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe, such as functions in the response to certain stresses and the role of Med31 in the expression of genes regulated by the activator Ace2. The C. albicans Mediator also has additional roles in the transcription of genes associated with virulence, for example genes related to morphogenesis and gene families enriched in pathogens, such as the ALS adhesins. Consistently, Med31, Med20, and Srb9/Med13 contribute to key virulence attributes of C. albicans, filamentation, and biofilm formation; and ALS1 is a biologically relevant target of Med31 for development of biofilms. Furthermore, Med31 affects virulence of C. albicans in the worm infection model. We present evidence that the roles of Med31 and Srb9/Med13 in the expression of the genes encoding cell wall adhesins are different between S. cerevisiae and C. albicans: they are repressors of the FLO genes in S. cerevisiae and are activators of the ALS genes in C. albicans. This suggests that Mediator subunits regulate adhesion in a distinct manner between these two distantly related fungal species

Searching for the proverbial needle in a haystack: advances in mosquito-borne arbovirus surveillance

Abstract Surveillance is critical for the prevention and control of mosquito-borne arboviruses. Detection of elevated or emergent virus activity serves as a warning system to implement appropriate actions to reduce outbreaks. Traditionally, surveillance of arboviruses has relied on the detection of specific antibodies in sentinel animals and/or detection of viruses in pools of mosquitoes collected using a variety of sampling methods. These methods, although immensely useful, have limitations, including the need for a cold chain for sample transport, cross-reactivity between related viruses in serological assays, the requirement for specialized equipment or infrastructure, and overall expense. Advances have recently been made on developing new strategies for arbovirus surveillance. These strategies include sugar-based surveillance, whereby mosquitoes are collected in purpose-built traps and allowed to expectorate on nucleic acid preservation cards which are submitted for virus detection. New diagnostic approaches, such as next-generation sequencing, have the potential to expand the genetic information obtained from samples and aid in virus discovery. Here, we review the advancement of arbovirus surveillance systems over the past decade. Some of the novel approaches presented here have already been validated and are currently being integrated into surveillance programs. Other strategies are still at the experimental stage, and their feasibility in the field is yet to be evaluated