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.

Novel radionuclides for use in Nuclear Medicine in Europe: where do we stand and where do we go?

BACKGROUND: In order to support the ongoing research across Europe to facilitate access to novel radionuclides, the PRISMAP consortium (European medical radionuclides programme) was established to offer the broadest catalog of non-conventional radionuclides for medical and translational research. The aim of this article is to introduce readers with current status of novel radionuclides in Europe. MAIN BODY: A consortium questionnaire was disseminated through the PRISMAP consortium and user community, professional associations and preclinical/clinical end users in Europe and the current status of clinical end-users in nuclear medicine were identified. A total of 40 preclinical/clinical users institutions took part in the survey. Clinical end users currently use the following radionuclides in their studies: 177Lu, 68 Ga, 111In, 90Y, other alpha emitters, 225Ac, 64Cu and Terbium isotopes. Radionuclides that would be of interest for users within the next 2-5 years are 64Cu, Terbium radionuclide "family" and alpha emitters, such as 225Ac. CONCLUSIONS: Thanks to a questionnaire distributed by the PRISMAP consortium, the current status and needs of clinical end-users in nuclear medicine were identified.

Production and Supply of α-Particle-Emitting Radionuclides for Targeted α-Therapy.

Encouraging results from targeted α-therapy have received significant attention from academia and industry. However, the limited availability of suitable radionuclides has hampered widespread translation and application. In the present review, we discuss the most promising candidates for clinical application and the state of the art of their production and supply. In this review, along with 2 forthcoming reviews on chelation and clinical application of α-emitting radionuclides, The Journal of Nuclear Medicine will provide a comprehensive assessment of the field.

Functional Molecular Thin Films: Topological Templates for the Chemoselective Ligation of Antigenic Peptides to Self-Assembled Monolayers.

The combination of self-assembly and regioselective surface chemistry has made it possible to immobilize peptide recognition sites 1 on a template attached to a gold surface. Each of the seven individual reaction steps, including the final functional biomolecular recognition, was controlled in situ with surface-sensitive detection techniques. The presented strategy is of general importance for the formation of complex supramolecular structures with biologically interesting functionalities at the interfaces.

Diffusion-time distribution analysis reveals characteristic ligand-dependent interaction patterns of nuclear receptors in living cells.

Nuclear receptors initiate transcription, interact with regulatory proteins, and are influenced by hormones, drugs, and pollutants. Herein, we discover ligand-specific mobility patterns of human estrogen receptor-alpha (ER) in living cells using diffusion-time distribution analysis (DDA). This novel method, based on fluorescence correlation spectroscopy (FCS), is especially suited to unraveling multiple protein interactions in vivo at native expression levels. We found that ER forms a limited number of distinct complexes with a varying population by dynamic interaction with other nuclear components. Dose-response curves of different ligands could be obtained for each receptor interaction. The potential to identify interacting proteins was demonstrated by comparing DDA of the ER cofactor SRC-3 attached to yellow fluorescent protein (YFP) with those of YFP-ER. Our findings open up new routes to elucidating transcription regulation and to detecting and distinguishing pharmacologically and toxicologically active compounds in vivo. Moreover, DDA provides a general approach to monitoring biochemical networks in individual living cells.

Orientation modulation of a synthetic polypeptide in self-assembled monolayers: a TOF-SIMS study.

Structure and orientation of molecules are key properties of functionalized surfaces. Using time-of-flight secondary ion mass spectrometry (TOF-SIMS), here we investigate how to modulate these parameters upon the immobilization process varying the conditions of self-assembly. The molecule of interest, a template-assembled synthetic protein (TASP), consists of a central peptide ring with orthogonally arranged residues. Thioalkane chains allow the directed self-assembly of the molecule on a gold surface; four serine residues on the opposite side of the ring can be used as anchoring sites for various functional sensing molecules. The TASP conformation and its orientation in self-assembled monolayers (SAMs) play a central role for the accessibility of these serine residues. To study the influence of the self-assembly conditions, two series of samples were prepared. Pure TASP monolayers of different surface densities are compared to mixed TASP/alkanethiol monolayers prepared by sequential adsorption varying sequence and particular incubation times as well as by coadsorption modifying incubation times and TASP/alkanethiol mass ratios. Switching the TASP orientation from a state where the molecules are lying flat on the surface to an upright orientation turned out to be possible by inserting the TASP into a preformed alkanethiol monolayer of an appropriate surface density. This study demonstrates that TOF-SIMS is an excellent tool not only to investigate the surface composition, but also the molecular structure of functionalized surfaces.