An in vivo Assay for Simultaneous Monitoring of Neuronal Activity and Behavioral Output in the Stomatogastric Nervous System of Decapod Crustaceans

Central pattern generators (CPGs) generate rhythmic output patterns and drive vital behaviors such as breathing, swallowing, locomotion and chewing 1-10. While most insights into the rhythm generating mechanisms of CPGs have been derived from isolated nervous system preparations, the relationship between neural activity and corresponding behavioral expression is often unclear. The stomatogastric system of decapod crustaceans is one of the best characterized neural system for motor pattern generation 9-12 and many mechanisms of motor pattern generation and selection have been discovered in this system. Since most studies are limited to the isolated nervous system, little is known about the actual behavioral output of this system. For example, it is unknown whether the observed flexibility in the motor patterns is present in vivo and whether distinct motor activities drive corresponding behavioral patterns. We present a method which allows electrophysiological recordings of CPG neurons and the simultaneous monitoring of the behavioral output of the stomatogastric nervous system. For this, we use extracellular hook electrodes either for recording or stimulation of neurons in the gastric mill CPG that drive the chewing movements of three teeth in the foregut of the animal. Electrodes are applied in tethered, but otherwise fully intact crabs (Cancer pagurus) and an endoscope is used to monitor tooth movements. Nerve and video recordings of the endoscopic camera are synchronized and motion tracking techniques are used to analyze gastric mill movements. This approach thus allows testing the behavioral relevance of the neural activity patterns produced by central pattern generators

Diffractive Interactions: Theory Summary

I review various theory issues in diffraction that have been presented and discussed in the working group on diffractive interactions, and a few points concerning the comparison of theory with data.Comment: 9 pages, 3 figures. Talk given at the 7th International Workshop on Deep Inelastic Scattering and QCD (DIS 99), Zeuthen, Germany, 19-23 April 199

Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

A dual-control effect preserving formulation for nonlinear output-feedback stochastic model predictive control with constraints

In this paper we propose a formulation for approximate constrained nonlinear output-feedback stochastic model predictive control. Starting from the ideal but intractable stochastic optimal control problem (OCP), which involves the optimization over output-dependent policies, we use linearization with respect to the uncertainty to derive a tractable approximation which includes knowledge of the output model. This allows us to compute the expected value for the outer functions of the OCP exactly. Crucially, the dual control effect is preserved by this approximation. In consequence, the resulting controller is aware of how the choice of inputs affects the information available in the future which in turn influences subsequent controls. Thus, it can be classified as a form of implicit dual control

Evolution of colour correlated double parton distributions: a quantitative study

Double parton distributions satisfy the same evolution equations as ordinary single-parton densities, provided that the colour state of each parton is summed over individually. This is no longer the case when the two partons are correlated in their colour. Evolving such colour correlated distributions to higher scales results in a suppression by Sudakov double logarithms. We perform a detailed study of evolution for this case, both analytically and numerically, at lowest order and beyond. When the two observed partons originate from the perturbative splitting of a single one, the Sudakov suppression of colour correlations at the cross section level is not as strong as one might expect.Comment: 61 pages, 32 figure

The Stomatogastric Nervous System as a Model for Studying Sensorimotor Interactions in Real-Time Closed-Loop Conditions

The perception of proprioceptive signals that report the internal state of the body is one of the essential tasks of the nervous system and helps to continuously adapt body movements to changing circumstances. Despite the impact of proprioceptive feedback on motor activity it has rarely been studied in conditions in which motor output and sensory activity interact as they do in behaving animals, i.e., in closed-loop conditions. The interaction of motor and sensory activities, however, can create emergent properties that may govern the functional characteristics of the system. We here demonstrate a method to use a well-characterized model system for central pattern generation, the stomatogastric nervous system, for studying these properties in vitro. We created a real-time computer model of a single-cell muscle tendon organ in the gastric mill of the crab foregut that uses intracellular current injections to control the activity of the biological proprioceptor. The resulting motor output of a gastric mill motor neuron is then recorded intracellularly and fed into a simple muscle model consisting of a series of low-pass filters. The muscle output is used to activate a one-dimensional Hodgkin–Huxley type model of the muscle tendon organ in real-time, allowing closed-loop conditions. Model properties were either hand tuned to achieve the best match with data from semi-intact muscle preparations, or an exhaustive search was performed to determine the best set of parameters. We report the real-time capabilities of our models, its performance and its interaction with the biological motor system

Regulation und Funktion von Homeobox-Transkriptionsfaktoren in Endothelzellen

Die funktionelle Integrität des Endothels ist von essentieller Bedeutung für den Organismus. Die Entstehung und Progression vaskulärer Erkrankungen, wie z.B. der Atherosklerose, ist daher oftmals ursächlich mit einer Dysfunktion des Endothels verbunden. Vor diesem Hintergrund ist insbesondere die Aufklärung der molekularen Grundlagen der Regulation von Endothelzellfunktionen, ein zentraler Aspekt heutiger Forschung. Homeobox- (Hox) Transkriptionsfaktoren nehmen eine Schlüsselposition bei der Regulation einer Vielzahl zellulärer Prozesse, wie Proliferation, Migration und Gewebe-spezifischer Differenzierung ein. Die Identifikation sowie die Analyse der Funktion und Regulation von Hox-Transkriptionsfaktoren in Endothelzellen, leistet deshalb einen wichtigen Beitrag zum Verständnis der Endothelzellbiologie. Als ein zentraler Befund dieser Arbeit, konnte mit der Histon-Methyltransferase MLL erstmals die funktionelle Rolle eines epigenetischen Hox-Regulators auch in differenzierten Endothelzellen nachgewiesen werden. MLL erwies sich hierbei von essentieller Bedeutung für pro-angiogene Endothelzell-Funktionen. Die bedeutende Rolle von MLL bei der Migration von Endothelzellen konnte mit der transkriptionellen Regulation der beiden Hox-Transkriptionsfaktoren HoxA9 und HoxD3 in Verbindung gebracht werden, die hier erstmals als direkte Zielgene von MLL in Endothelzellen beschrieben wurden. Als funktionelle Mediatoren der MLLabhängigen Migration konnten zudem der EphB4-Rezeptor sowie die Integrine αVβ3 und α5β1, als Zielgene von HoxA9 bzw. HoxD3 nachgewiesen werden. Neben der Migration konnte für MLL auch eine essentielle Rolle für das Sprouting von Endothelzellen nachgewiesen werden, die sich im Gegensatz zur Migration, nicht auf die Regulation von HoxA9 oder HoxD3 zurückführen ließ. Diese Beobachtung lässt auf die Involvierung zusätzlicher MLLabhängiger Faktoren schließen, und verdeutlicht damit die zentrale Rolle von MLL bei der Regulation komplexer, pro-angiogener Prozesse in Endothelzellen. Über die genannte Rolle von MLL hinaus konnte im Rahmen dieser Arbeit das Wissen um Hox-Transkriptionsfaktoren mit funktioneller Relevanz für Endothelzellen, um die beiden Hox-Transkriptionsfaktoren HoxB4 und HoxB5 erweitert werden. Hier konnte für HoxB4 eine Rolle für die Fähigkeit von Endothelzellen zur Ausbildung zwei- und 3-dimensionaler Gefäßstrukturen nachgewiesen werden, während HoxB5 in die Proliferation, die Expression des endothelialen Markergens eNOS sowie die morphologische Beschaffenheit von Endothelzellen eingreift. Zusätzlich konnte die Rolle von transkriptionellen Hox Co-Faktoren, als Modulatoren von Hox-Funktionen, am Beispiel der Interaktion von Meis1 und HoxA9 bei der Transaktivierung des eNOS-Promoters aufgezeigt werden. Zusammenfassend leisten die hier gezeigten Daten einen Beitrag zum Verständnis der Rolle von Hox-Transkriptionsfaktoren als molekulare Regulatoren endothelialer Zellfunktionen

Implicit and Explicit Dual Model Predictive Control with an Application to Steel Recycling

We present a formulation for both implicit and explicit dual model predictive control with chance constraints. The formulation is applicable to systems that are affine in the state and disturbances, but possibly nonlinear in the controls. Awareness of uncertainty and dual control effect is achieved by including the covariance of a Kalman Filter state estimate in the predictions. For numerical stability, these predictions are obtained from a square-root Kalman filter update based on a QR decomposition. In the implicit formulation, the incentive for uncertainty reduction is given indirectly via the impact of active constraints on the objective, as large uncertainty leads to large safety backoffs from the constraint set boundary. The explicit formulation additionally uses a heuristic cost term on uncertainty to encourage its active exploration. We evaluate the methods based on numerical simulation of a simplified but representative industrial steel recycling problem. Here, new steel needs to be produced by choosing a combination of several different steel scraps with unknown pollutant content. The pollutant content can only be measured after a scrap combination is molten, allowing for inference on the pollutants in the different scrap heaps. The cost should be minimized while ensuring high quality of the product through constraining the maximum amount of pollutant. The numerical simulations demonstrate the superiority of the two dual formulations with respect to a robustified but non-dual formulation. Specifically we achieve lower cost for the closed-loop trajectories while ensuring constraint satisfaction with a given probability.Comment: Submitted to IEEE Conference on Decision and Control 2022 (CDC 22

An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars

This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.Comment: ECC 202