B-GOOD: Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making

A key to healthy beekeeping is the Health Status Index (HIS) inspired by EFSA’s Healthy-B toolbox which we will make fully operational, with the active collaboration of beekeepers, by facilitating the coordinated and harmonised flow of data from various sources and by testing and validating each component thoroughly. We envisage a step-by-step expansion of participating apiaries, and will eventually cover all EU biogeographic regions. The key to a sustainable beekeeping is a better understanding of its socio-economics, particularly within local value chains, its relationship with bee health and the human-ecosystem equilibrium of the beekeeping sector and to implement these insights into the data processing and decision making. We will fully integrate socio-economic analyses, identify viable business models tailored to different contexts for European beekeeping and determine the carrying capacity of the landscape. In close cooperation with the EU Bee Partnership, an EU-wide bee health and management data platform and affiliated project website will be created to enable sharing of knowledge and learning between scientists and stakeholders within and outside the consortium. We will utilise and further expand the classification of the open source IT-application for digital beekeeping, BEEP, to streamline the flow of data related to beekeeping management, the beehive and its environment (landscape, agricultural practices, weather and climate) from various sources. The dynamic bee health and management data platform will allow us to identify correlative relationships among factors impacting the HSI, assess the risk of emerging pests and predators, and enable beekeepers to develop adaptive management strategies that account for local and EU-wide issues. Reinforcing and establishing, where necessary, new multi-actor networks of collaboration will engender a lasting learning and innovation system to ensure social-ecological resilient and sustainable beekeeping

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

In vivo imaging of hepatobiliary transport function mediated by multidrug resistance associated protein and P-glycoprotein

Multidrug resistance associated proteins (MRPs) and P-glycoprotein (P-gp) are involved in hepatobiliary transport of various compounds. Our aim was (1) to define transporter specificity of the cholescintigraphic agents 99mTc-HIDA and 99mTc-MIBI, which are used clinically for myocardial perfusion measurements; and (2) to deduce MRP and P-gp functions in vivo from hepatic 99mTc kinetics. Accumulation of radioactivity was measured in the human tumor cell lines GLC4, GLC4/ADR 150x (MRP1-overexpressing/ P-gp-negative) and GLC4/P-gp (P-gp-overexpressing). Bile secretion was quantified in untreated and in glutathione-depleted control and MRP2-deficient (GY/TR-) rats. Hepatobiliary transport was measured using a gamma camera in both types of rats. 99mTc-HIDA accumulated 5.8-fold less in GLC4/ADR 150x calls than in GLC4 or GLC4/P-gp cells. In GLC4/ADR150x, the cellular 99mTc-HIDA content was increased 3.4-fold by the MRP1,2 inhibitor MK571 (50 μM), while MK571 had no measurable effect in GLC4 and GLC4/P-gp cells. 99mTc-MIBI accumulated less in GLC4/P-gp and GLC 4/ ADR150x cells than in GLC4 cells. Bile secretion of 99mTc-HIDA was impaired in GY/TR- compared to control rats and not affected by glutathione depletion in GY/TR- rats. Hepatic secretion of 99mTc-HIDA was slower in GY/TR- (t1/2 40 min) than in control rats (t1/2 7 min). Bile secretion of 99mTc-MIBI was similar in both rat strains and impaired by glutathione depletion in control rats only, indicating compensatory activity of additional transporter(s) in GY/TRT- rats. 99mTc-HIDA is transported only by MRP1,2 only, while 99mTc-MIBI is transported by P-gp and MRP1,2. The results indicate that hepatic P-gp and MRP1,2 function can be assessed in vivo by sequential use of both radiopharmaceuticals