Bioprozessautomatisierung einer Algenanlage mithilfe eines Single-Board-Computers

Für die effektive Haltung von Mikroorganismen, kleinen Pflanzen oder Algen in Bioreaktoren ist die Aufrechterhaltung optimaler Kultivierungsbedingungen, wie beispielsweise pH-Wert, Temperatur oder Nährstoffgehalt, notwendig. Diese Parameter können sich während der Kultivierung ändern, weshalb sie regelmäßig kontrolliert und gegebenenfalls angepasst werden müssen. Wir präsentieren hier den technischen Aufbau und die Softwarerealisierung eines Automatisierungssystems zur autonomen Regulierung des pH-Wertes in Bioreaktoren, in denen die grüne Mikroalge Scenedesmus rubescens kultiviert wird. Dazu wurde ein System mit pH-Sensoren, Signalwandlern und Magnetventilen zur kontrollierten CO2-Begasung aufgebaut. Für die Steuerung und die Datenaufzeichnung diente ein Single-Board-Computer (Raspberry Pi) mit Webeserver. Die Anlage war voll funktionsfähig und konnte über mehrere Tage fehlerlos den pH-Wert auf einen vorgegebenen Wert regeln. Das System ist leicht auch auf Großanlagen und für andere Parameter erweiterbar. Durch die Nutzung eines Single-Board-Computers erfordert die Anlage nur minimalen Platz- und Energiebedarf und ist mit geringen Anschaffungskosten verbunden.The maintenance of stable internal conditions such as pH, temperature, or nutrients is essential for the cultivation of microorganisms, small plants and algae in bioreactors. These parameters can significantly vary during cultivation and have to be controlled and regulated. Here we present the technical construction and software implementation of an automation system for measurement and controlling pH in bioreactors for cultivating the green micro algae Scenedesmus rubescens. The system was built up with pH sensors, a signal transducing unit and magnetic valves that regulate the CO2 volume flow. A single-board-computer (Raspberry Pi) with web server was used as control unit and for data recording. The established system was stable for several days and approved for fulfilling all requirements. It is easily expandable for other parameters and can be used for larger systems. By using the Raspberry Pi as a low cost, very energy efficient credit-card sized computer with minimum space requirements the system can serve as an alternative for commercial automation systems

Diphenylstickstoffoxyd, ein neues organisches Radikal mit vierwertigem Stickstoff

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Streifzüge durch die Baltischen Provinzen : Schilderungen von Land und Leuten mit besondere Berücksichtigung der Walder und der Forstwirtchaft. Th. 1, Liv- und Kurland

http://tartu.ester.ee/record=b1735766~S1*es

Migrating curlews on schedule: departure and arrival patterns of a long-distance migrant depend on time and breeding location rather than on wind conditions

Background However, few studies have tried to validate the significance of these three concepts simultaneously, and long-term, high-resolution tagging datasets recording individual movements across consecutive years are scarce. We used such a dataset to explore intraspecific and intra-individual variabilities in departure and arrival decisions from/to wintering grounds in relation to these three different concepts in bird migration.We equipped 23 curlews (Numenius arquata) wintering in the Wadden Sea with Global Positioning System data loggers to record their spatio-temporal patterns of departure from and arrival at their wintering site, and the first part of their spring migration. We obtained data for 42 migrations over 6 years, with 12 individuals performing repeat migrations in consecutive years. Day of year of departure and arrival was related to 38 meteorological and bird-related predictors using the least absolute shrinkage and selection operator (LASSO) to identify drivers of departure and arrival decisions.Curlews migrated almost exclusively to Arctic and sub-Arctic Russia for breeding. Curlews breeding further away in areas with late snowmelt departed later. Departures dates varied by only < 4 days in individual curlews tagged over consecutive years.These results suggest that the trigger for migration in this long-distance migrant is largely independent of weather conditions but is subject to resource availability in breeding areas. The high intra-individual repeatability of departure days among subsequent years and the lack of relationship to weather parameters suggest the importance of genetic triggers in prompting the start of migration. Further insights into the timing of migration in immatures and closely related birds might help to further unravel the genetic mechanisms triggering migration patterns

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.Comment: This is a preprint version for IEEE I2MTC 202

Endogenous social distancing and its underappreciated impact on the epidemic curve

Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading

Modulation of mammalian cell behavior by nanoporous glass

The introduction of novel bioactive materials to manipulate living cell behavior is a crucial topic for biomedical research and tissue engineering. Biomaterials or surface patterns that boost specific cell functions can enable innovative new products in cell culture and diagnostics. This study investigates the influence of the intrinsically nano-patterned surface of nanoporous glass membranes on the behavior of mammalian cells. Three different cell lines and primary human mesenchymal stem cells (hMSCs) proliferate readily on nanoporous glass membranes with mean pore sizes between 10 and 124 nm. In both proliferation and mRNA expression experiments, L929 fibroblasts show a distinct trend toward mean pore sizes >80 nm. For primary hMSCs, excellent proliferation is observed on all nanoporous surfaces. hMSCs on samples with 17 nm pore size display increased expression of COL10, COL2A1, and SOX9, especially during the first two weeks of culture. In the upside down culture, SK-MEL-28 cells on nanoporous glass resist the gravitational force and proliferate well in contrast to cells on flat references. The effect of paclitaxel treatment of MDA-MB-321 breast cancer cells is already visible after 48 h on nanoporous membranes and strongly pronounced in comparison to reference samples, underlining the material's potential for functional drug screening