A stop-signal task for sheep:Introduction and validation of a direct measure for the stop-signal reaction time

Huntington?s disease (HD) patients show reduced flexibility in inhibiting an already-started response. This can be quantified by the stop-signal task. The aim of this study was to develop and validate a sheep version of the stop-signal task that would be suitable for monitoring the progression of cognitive decline in a transgenic sheep model of HD. Using a semi-automated operant system, sheep were trained to perform in a two-choice discrimination task. In 22% of the trials, a stop-signal was presented. Upon the stop-signal presentation, the sheep had to inhibit their already-started response. The stopping behaviour was captured using an accelerometer mounted on the back of the sheep. This set-up provided a direct read-out of the individual stop-signal reaction time (SSRT). We also estimated the SSRT using the conventional approach of subtracting the stop-signal delay (i.e., time after which the stop-signal is presented) from the ranked reaction time during a trial without a stop-signal. We found that all sheep could inhibit an already-started response in 91% of the stop-trials. The directly measured SSRT (0.974 ? 0.04 s) was not significantly different from the estimated SSRT (0.938 ? 0.04 s). The sheep version of the stop-signal task adds to the repertoire of tests suitable for investigating both cognitive dysfunction and efficacy of therapeutic agents in sheep models of neurodegenerative disease such as HD, as well as neurological conditions such as attention deficit hyperactivity disorder.publishersversionPeer reviewe

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Durchmischt und umgedreht: innovative didaktische Konzepte im anwendungsorientierten Pythonkurs für Ingenieure

Am Anfang standen die unabhängigen und übereinstimmenden Beobachtungen von drei Doktoranden aus verschiedenen Teildisziplinen der Ingenieurwissenschaften: Nahezu alle der von ihnen in Projekt- und Abschlussarbeiten betreuten Studierenden hatten keine oder unzureichende Kenntnisse in den Feldern Simulation, symbolisches und numerisches Rechnen, Datenverarbeitung und -auswertung sowie Programmierung. Aus diesen Beobachtungen leitete sich die Idee für eine Lehrveranstaltung ab, die entsprechendes Handwerkszeug anhand von Beispielen mit möglichst hohem Realitätsbezug vermittelt. Neben den konkreten Kenntnissen und Fertigkeiten wird dabei implizit die Botschaft transportiert: Auch zunächst sehr kompliziert erscheinende Methoden lassen sich am Ende auf einige Zeilen Quellcode reduzieren und zur Beantwortung konkreter, realistischer Fragestellungen nutzen – es lohnt sich also sie zu verstehen. Zielgruppe für eine solche Lehrveranstaltung sind Studierende aller Ingenieursdisziplinen genauso wie der Naturwissenschaften

Hyperosmotic stress enhances cytotoxicity of SMAC mimetics

Inhibitors of apoptosis (IAP) proteins contribute to cell death resistance in malignancies and emerged as promising targets in cancer therapy. Currently, small molecules mimicking the IAP-antagonizing activity of endogenous second mitochondria-derived activator of caspases (SMAC) are evaluated in phase 1/2 clinical trials. In cancer cells, SMAC mimetic (SM)-mediated IAP depletion induces tumor necrosis factor (TNF) secretion and simultaneously sensitizes for TNF-induced cell death. However, tumor cells lacking SM-induced autocrine TNF release survive and thus limit therapeutic efficacy. Here, we show that hyperosmotic stress boosts SM cytotoxicity in human and murine cells through hypertonicity-induced upregulation of TNF with subsequent induction of apoptosis and/or necroptosis. Hypertonicity allowed robust TNF-dependent killing in SM-treated human acute lymphoblastic leukemia cells, which under isotonic conditions resisted SM treatment due to poor SM-induced TNF secretion. Mechanistically, hypertonicity-triggered TNF release bypassed the dependency on SM-induced TNF production to execute SM cytotoxicity, effectively reducing the role of SM to TNF-sensitizing, but not necessarily TNF-inducing agents. Perspectively, these findings could extend the clinical application of SM

Hypoxia regulates TRAIL sensitivity of colorectal cancer cells through mitochondrial autophagy

The capacity of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) to selectively induce cell death in malignant cells triggered numerous attempts for therapeutic exploitation. In clinical trials, however, TRAIL did not live up to the expectations, as tumors exhibit high rates of TRAIL resistance in vivo. Response to anti-cancer therapy is determined not only by cancer cell intrinsic factors (e.g. oncogenic mutations), but also modulated by extrinsic factors such as the hypoxic tumor microenvironment. Here, we address the effect of hypoxia on pro-apoptotic TRAIL signaling in colorectal cancer cells. We show that oxygen levels modulate susceptibility to TRAIL-induced cell death, which is severely impaired under hypoxia (0.5% O-2). Mechanistically, this is attributable to hypoxia-induced mitochondrial autophagy. Loss of mitochondria under hypoxia restricts the availability of mitochondria-derived pro-apoptotic molecules such as second mitochondria-derived activator of caspase ( SMAC), thereby disrupting amplification of the apoptotic signal emanating from the TRAIL death receptors and efficiently blocking cell death in type-II cells. Moreover, we identify strategies to overcome TRAIL resistance in low oxygen environments. Counteracting hypoxia-induced loss of endogenous SMAC by exogenous substitution of SMAC mimetics fully restores TRAIL sensitivity in colorectal cancer cells. Alternatively, enforcing a mitochondria-independent type-I mode of cell death by targeting the type-II phenotype gatekeeper X-linked inhibitor of apoptosis protein ( XIAP) is equally effective. Together, our results indicate that tumor hypoxia impairs TRAIL efficacy but this limitation can be overcome by combining TRAIL with SMAC mimetics or XIAPtargeting drugs. Our findings may help to exploit the potential of TRAIL in cancer therapy

A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles