High-Density Microwell Chip for Culture and Analysis of Stem Cells

With recent findings on the role of reprogramming factors on stem cells, in vitro screening assays for studying (de)-differentiation is of great interest. We developed a miniaturized stem cell screening chip that is easily accessible and provides means of rapidly studying thousands of individual stem/progenitor cell samples, using low reagent volumes. For example, screening of 700,000 substances would take less than two days, using this platform combined with a conventional bio-imaging system. The microwell chip has standard slide format and consists of 672 wells in total. Each well holds 500 nl, a volume small enough to drastically decrease reagent costs but large enough to allow utilization of standard laboratory equipment. Results presented here include weeklong culturing and differentiation assays of mouse embryonic stem cells, mouse adult neural stem cells, and human embryonic stem cells. The possibility to either maintain the cells as stem/progenitor cells or to study cell differentiation of stem/progenitor cells over time is demonstrated. Clonality is critical for stem cell research, and was accomplished in the microwell chips by isolation and clonal analysis of single mouse embryonic stem cells using flow cytometric cell-sorting. Protocols for practical handling of the microwell chips are presented, describing a rapid and user-friendly method for the simultaneous study of thousands of stem cell cultures in small microwells. This microwell chip has high potential for a wide range of applications, for example directed differentiation assays and screening of reprogramming factors, opening up considerable opportunities in the stem cell field

Beiträge zum 28. Darmstädter Geotechnik-Kolloquium am 09. März 2022

Themenschwerpunkte: 1. Modell- und Feldversuche 2. Digitalisierung und künstliche Intelligenz in der Geotechnik 3. Nationale und internationale Großprojekte 4. Normung und Rechtliche

Exploring the Dotterel Mountains : Improving the understanding of breeding habitat characteristics of an Arctic-breeding specialist bird

Arctic-breeding birds are of particular conservation concern since their habitats are subject to severe changes and shifts upwards in both altitude and latitude due to global warming. Detailed knowledge on habitat characteristics of those species is required to understand how specialized Arctic-breeding species deal with changing habitat conditions. Therefore, sufficient data and methods to assess habitat suitability on large spatial scales in a time- and cost-efficient way are needed. The Eurasian Dotterel Charadrius morinellus is a specialist highaltitude and Arctic-breeding wader and can serve as an ideal model species for addressing habitat requirements of Arctic-breeding birds and consequences for conservation. We combined field surveys with remote sensing data to develop a distribution model for the breeding habitat of the Eurasian Dotterel in the Vindelfjällen Nature Reserve in northern Sweden. The remote sensing data comprised 211 spectral, structural and topographic indices derived from freely available satellite images and digital elevation models. For species distribution modeling we used MaxEnt with an advanced variable and parameter selection method for model training. The trained model produced excellent results (AUC = 0.99) with seven resulting predictor variables reflecting the habitat requirements of the Dotterel: Sparsely vegetated mountain tops with dry ground which are very open. This study further highlights the potential of combining survey data with freely available remote sensing data for detailed area-wide population predictions and the monitoring of habitat change as a tool in species conservation

Impact of glycan nature on structure and viscoelastic properties of glycopeptide hydrogels

Mucus is a complex biological hydrogel that acts as a barrier for almost everything entering or exiting the body. It is therefore of emerging interest for biomedical and pharmaceutical applications. Besides water, the most abundant components are the large and densely glycosylated mucins, a family of glycoproteins with sizes of up to 20 MDa and a carbohydrate content of up to 80 wt%. Here, we designed and explored a library of glycosylated peptides to deconstruct the complexity of mucus. By using the well characterised hFF03 coiled-coil system as a hydrogel-forming peptide scaffold, we systematically probed the contribution of single glycans to the secondary structure as well as the formation and the viscoelastic properties of the resulting hydrogels. We show that glycan-decoration does not affect α helix and coiled-coil formation while it alters gel stiffness. By using oscillatory macrorheology, dynamic light scattering microrheology and fluorescence lifetime-based nanorheology, we characterised the glycopeptide materials over several length scales. Molecular simulations revealed that the glycosylated linker may extend into the solvent, but more frequently interacts with the peptide, thereby likely modifying the stability of the self-assembled peptide fibres. The results of this systematic study highlight the interplay between glycan structure and hydrogel properties and may guide the development of synthetic mucus mimetics

Classifying the activity states of small vertebrates using automated VHF telemetry

Abstract The most basic behavioural states of animals can be described as active or passive. While high‐resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (3 million signals from very‐high‐frequency (VHF) telemetry from two forest‐dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF‐tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98). We provide an ecological case‐study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night‐time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method. Our approach can classify VHF‐signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state‐of‐the‐art open‐source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation

Classifying the activity states of small vertebrates using automated VHF telemetry

The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98). The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation

The SCIP optimization suite 5.0

This article describes new features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite. In its central component, the constraint integer programming solver SCIP, remarkable performance improvements have been achieved for solving mixed-integer linear and nonlinear programs. On MIPs, SCIP 5.0 is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve. For MINLP, SCIP 5.0 is about 17 % faster overall and 23 % faster on instances that take at least 100 seconds to solve. This boost is due to algorithmic advances in several parts of the solver such as cutting plane generation and management, a new adaptive coordination of large neighborhood search heuristics, symmetry handling, and strengthened McCormick relaxations for bilinear terms in MINLPs. Besides discussing the theoretical background and the implementational aspects of these developments, the report describes recent additions for the other software packages connected to SCIP, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solver SCIP-SDP, and the parallelization framework UG