Refine
Year of publication
Document Type
- Article (29)
- Postprint (11)
- Monograph/Edited Volume (8)
- Part of Periodical (5)
- Conference Proceeding (1)
- Doctoral Thesis (1)
- Other (1)
Keywords
- Brandenburg (3)
- Digitalisierung (3)
- Governance (3)
- digitalization (3)
- governance (3)
- local governance (3)
- Lehrerbildung (2)
- PLFA (2)
- agroecosystem (2)
- algae (2)
Institute
- Institut für Biochemie und Biologie (9)
- Institut für Geowissenschaften (7)
- Department Psychologie (5)
- Kommunalwissenschaftliches Institut (5)
- Extern (4)
- Institut für Chemie (4)
- Department Sport- und Gesundheitswissenschaften (3)
- Mathematisch-Naturwissenschaftliche Fakultät (3)
- Zentrum für Lehrerbildung und Bildungsforschung (ZeLB) (3)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (2)
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age-and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
We investigated inflow of a wastewater treatment plant and sediment of an urban lake for the presence of Clostridioides difficile by cultivation and PCR. Among seven colonies we sequenced the complete genomes of three: two non-toxigenic isolates from wastewater and one toxigenic isolate from the urban lake. For all obtained isolates, a close genomic relationship with human-derived isolates was observed. (C) 2019 Elsevier Ltd. All rights reserved.
Zum dreißigjährigen Bestehen des Kommunalwissenschaftlichen Instituts an der Universität Potsdam vereint dieser Jubiläumsband kurze Aufsätze von ehemaligen und aktuellen Vorstandsmitgliedern, von Ehrenmitgliedern des Vorstands, langjährigen wissenschaftlichen Mitarbeitern des Instituts und aktuellen wissenschaftlichen Kooperationspartnern. Die insgesamt zwölf Beiträge befassen sich mit den Kommunalwissenschaften und der Geschichte des Kommunalwissenschaftlichen Instituts, mit aktuellen kommunalwissenschaftlichen Fragestellungen und wissenschaftlichen Kooperationen des KWI. Der vom KWI-Vorstand herausgegebene Band soll einen breiten Blick auf 30 Jahre Kommunalwissenschaften in Brandenburg und an der Universität Potsdam werfen und einen Ausblick auf zukünftige kommunalwissenschaftliche Forschung geben.