This manuscript reports on the development of a capacitive sensor for the detection of imidacloprid (IMD) in water samples based on molecularly imprinted polymers (MIPs). MIPs used as recognition elements were synthesized via a photo-initiated emulsion polymerization. read more The particles were carefully washed using a methanol (MeOH) /acetic acid mixture to ensure complete template removal and were then dried. The average size of the obtained particles was less than 1 µm. The imprinting factor (IF) for IMD was 6 and the selectivity factor (α) for acetamiprid, clothianidin, thiacloprid and thiamethoxam were 14.8, 6.8, 7.1 and 8.2, respectively. The particles were immobilized on the surface of a gold electrode by electropolymerization. The immobilized electrode could be spontaneously regenerated using a mixture of MeOH/10 mM of phosphate buffer (pH = 7.2)/triethylamine before each measurement and could be reused for 32 times. This is the first-time that automated regeneration was introduced as part of a sensing platform for IMD detection. The developed sensor was validated by the analysis of artificially spiked water samples. Under the optimal conditions, the linearity was in the range of 5-100 µM, with a limit of detection (LOD) of 4.61 µM.Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group’s robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.Here, we analyzed patterns of taxon richness and endemism of freshwater protists in Europe. Even though the significance of physicochemical parameters but also of geographic constraints for protist distribution is documented, it remains unclear where regional areas of high protist diversity are located and whether areas of high taxon richness harbor a high proportion of endemics. Further, patterns may be universal for protists or deviate between taxonomic groups. Based on amplicon sequencing campaigns targeting the SSU and ITS region of the rDNA we address these patterns at two different levels of phylogenetic resolution. Our analyses demonstrate that protists have restricted geographical distribution areas. For many taxonomic groups the regions of high taxon richness deviate from those having a high proportion of putative endemics. In particular, the diversity of high mountain lakes as azonal habitats deviated from surrounding lowlands, i.e. many taxa were found exclusively in high mountain lakes and several putatively endemic taxa occurred in mountain regions like the Alps, the Pyrenees or the Massif Central. Beyond that, taxonomic groups showed a pronounced accumulation of putative endemics in distinct regions, e.g. Dinophyceae along the Baltic Sea coastline, and Chrysophyceae in Scandinavia. Many other groups did not have pronounced areas of increased endemism but geographically restricted taxa were found across Europe.Freshwater mussels (order Unionida) are among the world’s most biodiverse but imperiled taxa. Recent unionid mass mortality events around the world threaten ecosystem services such as water filtration, nutrient cycling, habitat stabilization, and food web enhancement, but causes have remained elusive. To examine potential infectious causes of these declines, we studied mussels in Clinch River, Virginia and Tennessee, USA, where the endemic and once-predominant pheasantshell (Actinonaias pectorosa) has suffered precipitous declines since approximately 2016. Using metagenomics, we identified 17 novel viruses in Clinch River pheasantshells. However, only one virus, a novel densovirus (Parvoviridae; Densovirinae), was epidemiologically linked to morbidity. Clinch densovirus 1 was 11.2 times more likely to be found in cases (moribund mussels) than controls (apparently healthy mussels from the same or matched sites), and cases had 2.7 (log10) times higher viral loads than controls. Densoviruses cause lethal epidemic disease in invertebrates, including shrimp, cockroaches, crickets, moths, crayfish, and sea stars.