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Touch-response experimentation in zebrafish helps researchers better understand the link between genetics, drug effects, and behaviors. However, commonly manually conducted experimentation cannot fulfill a high-throughput screening and often delivers low accuracy and lacks reproducibility. Thus, the main aim of this work is to establish a fully automated robot-assisted experimentation system with minimal human participation to conduct the touch-response experimentation with freely swimming zebrafish larvae. Our designed system is able to undertake the role of repeated touch-response experiments at predefined specific location of the larvae in different ages and under different conditions, with high accuracy, robustness, and repeatability, and can also get comparable experimental results. The errors of the detection methods are less than 3 pixels and the offset errors of the touching points are less than 5%. Designed for high-efficiency experimentation, this system will promisingly release a great amount of the burden for the biological operators from touch-response experiments and may also have potential applications in other organisms for touch-evoked response analysis.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

The opportunistic yeastCandida albicansis the most common cause of candidiasis. With only four classes of antifungal drugs on the market, resistance is becoming a problem in the treatment of fungal infections, especially in immunocompromised patients. The development of novel antifungal drugs with different modes of action is urgent. In 2016, we developed a groundbreaking new medium-throughput method to distinguish the effects of antibacterial agents. Using small-angle X-ray scattering for biological samples (BioSAXS), it is now possible to screen hundreds of new antibacterial compounds and select those with the highest probability for a novel mode of action. However, yeast (eukaryotic) cells are highly structured compared to bacteria and the action of an antifungal drug might leave most structures unchanged. In the pioneering work described here, we explored the possibility if BioSAXS can be used to measure the ultrastructural changes ofCandida albicansdirectly or indirectly induced by antifungal compounds. For this exploratory study, we used the well-characterized antifungal drug flucytosine. BioSAXS measurements were performed on the synchrotron P12 BioSAXS beamline, EMBL (DESY, Hamburg) on treated and untreated yeastC. albicans. BioSAXS curves were analysed using principal component analysis (PCA). The PCA showed that flucytosine-treated and untreated yeast were separated. Based on that success further measurements were performed on five antifungal peptides (1. Cecropin A-melittin hybrid [CA(1-7)M(2-9)], KWKLFKKIGAVLKVL; 2. Lasioglossin LL-III, VNWKKILGKIIKVVK; 3. Mastoparan M, INLKAIAALAKKLL; 4. Bmkn2, FIGAIARLLSKIFGKR; and 5. optP7, KRRVRWIIW). The ultrastructural changes ofC. albicansindicate that the peptides may have different modes of action compared to Flucytosine as well as to each other, except for the Cecropin A-melittin hybrid [CA(1-7)M(2-9)] and optP7, showing very similar effects onCandida albicans. This very first study demonstrates that BioSAXS shows great promise to be used for antifungal drug development. The use of such a tool like BioSAXS could accelerate and de-risk antifungal drug development, however, further experiments are necessary to establish this application.

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. How- ever, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user- centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile- based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different proba- bilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty