Somatostatin agonist pasireotide inhibits exercise stimulated growth in the male Siberian hamster (Phodopus sungorus)

R.Dumbell was supported by a University of Aberdeen PhD studentship and a research visit grant awarded by the British Society of Neuroendocrinology. Further support was provided by the Scottish Government Rural and Environment Science and Analytical Services Division (Barrett and the German Research Foundation (DFG; STE 331/8-1; Steinlechner lab). We are grateful for technical assistance from Dana Wilson at RINH and Siegried Hiliken at UVMH, and thank Dr Claus-Dieter Mayer of Biomathematics & Statistics Scotland for valuable advice on statistical analysis.Peer reviewedPostprin

Analysis and Comparison of new Downlink Technologies for Earth Observation Satellites

New generation of Earth observation sensors are creating an increasing amount of data which has to be delivered from space-to-ground. Additionally, many applications require timely availability of this sensor data. As new link technologies have been made available in the last years and data rate requirements are still increasing a revise of the conventional direct-downlink technology at X-band frequencies is essential. This work aims in a trade-off of the available direct-downlink technologies for satellites in low, polar orbits. Generally, there are two approaches to fulfill the requirement of timely delivery of a huge amount of data from space-to-ground. This is either increasing space-to-ground contact time resulting in a more complex ground station network or increasing carrier frequency whereas link reliability is limited by atmospheric effects. In this work different approaches like using K$_a$-band or utilizing ground station network with additional locations are compared against each other

Pathogenic Escherichia coli strain discrimination using laser-induced breakdown spectroscopy

A pathogenic strain of bacteria, Escherichia coli O157:H7 (enterohemorrhagic E. coli or EHEC), has been analyzed by laser-induced breakdown spectroscopy (LIBS) with nanosecond pulses and compared to three nonpathogenic E. coli strains: a laboratory strain of K-12 (AB), a derivative of the same strain termed HF4714, and an environmental strain, E. coli C (Nino C). A discriminant function analysis (DFA) was performed on the LIBS spectra obtained from live colonies of all four strains. Utilizing the emission intensity of 19 atomic and ionic transitions from trace inorganic elements, the DFA revealed significant differences between EHEC and the Nino C strain, suggesting the possibility of identifying and discriminating the pathogenic strain from commonly occurring environmental strains. EHEC strongly resembled the two K-12 strains, in particular, HF4714, making discrimination between these strains difficult. DFA was also used to analyze spectra from two of the nonpathogenic strains cultured in different media: on a trypticase soy (TS) agar plate and in a liquid TS broth. Strains cultured in different media were identified and effectively discriminated, being more similar than different strains cultured in identical media. All bacteria spectra were completely distinct from spectra obtained from the nutrient medium or ablation substrate alone. The ability to differentiate strains prepared and tested in different environments indicates that matrix effects and background contaminations do not necessarily preclude the use of LIBS to identify bacteria found in a variety of environments or grown under different conditions

Diagnosis driven Anomaly Detection for CPS

In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models