Significance: The ability to culture and image microbes on the single-cell level has provided insight into many biological phenomena. Single-cell studies were made possible through the development of microfluidic devices, which have been restricted to culturing a handful of strains at a time. We developed a microfluidic microchemostat array capable of culturing 1,152 yeast strains and demonstrate that the platform is capable of large-scale analysis by imaging the entire yeast-GFP library under numerous environmental conditions. Aside from identifying novel regulatory mechanisms, large-scale single-cell analysis will be useful for cellular engineering.

Heart rate variability (HRV) provides significant information about the health status of an individual. Optical heart rate monitoring is a comfortable alternative to ECG based heart rate monitoring. However, most available optical heart rate monitoring devices do not supply beat-to-beat detection accuracy required by proper HRV analysis. We evaluate the beat-to-beat detection accuracy of a recent wrist-worn optical heart rate monitoring device, PulseOn (PO). Ten subjects (8 male and 2 female; 35.9±10.3 years old) participated in the study. HRV was recorded with PO and Firstbeat Bodyguard 2 (BG2) device, which was used as an ECG based reference. HRV was recorded during sleep. As compared to BG2, PO detected on average 99.57% of the heartbeats (0.43% of beats missed) and had 0.72% extra beat detection rate, with 5.94 ms mean absolute error (MAE) in beat-to-beat intervals (RRI) as compared to the ECG based RRI BG2. Mean RMSSD difference between PO and BG2 derived HRV was 3.1 ms. Therefore, PO provides an accurate method for long term HRV monitoring during sleep.

In recent years, there has been an increasing interest in getting a proper quantitative understanding of cellular and molecular processes [1], [2]. One of the major challenges of current biomedical research is to characterize not only the spatial organization of these complex systems but also their spatiotemporal relationships [3], [4]. Microscopy has matured to the point that it enables sensitive time-lapse imaging of cells in vivo and even of single molecules [5], [6]. Making microscopy more quantitative brings important scientific benefits in the form of improved performance and reproducibility. This has been fostered by the development of technological achievements such as high-throughput microscopy. A direct consequence is that the size and complexity of image data are increasing. Time-lapse experiments commonly generate hundreds to thousands of images, each containing hundreds of objects to be analyzed [7]. These data often cannot be analyzed manually because the manpower required would be too extensive, which calls for automated methods for the analysis of biomedical images. Such computerized extraction of quantitative information out of the rapidly expanding amount of acquired data remains a major challenge. The development of the related algorithms is nontrivial and is one of the most active fronts in the new field of bioimage informatics [8]?[11]. Segmenting thousands of individual biological objects and tracking them over time is remarkably difficult. A typical algorithm will need to be tuned to the imaging modality and will have to cope with the fact that cells can be tightly packed and may appear in various configurations, making them difficult to segregate.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human–computer interaction, data management, and communication in AI implementation projects.

We present a new class of continuously defined parametric snakes using a special kind of exponential splines as basis functions. We have enforced our bases to have the shortest possible support subject to some design constraints to maximize efficiency. While the resulting snakes are versatile enough to provide a good approximation of any closed curve in the plane, their most important feature is the fact that they admit ellipses within their span. Thus, they can perfectly generate circular and elliptical shapes. These features are appropriate to delineate cross sections of cylindrical-like conduits and to outline bloblike objects. We address the implementation details and illustrate the capabilities of our snake with synthetic and real data.