Selective oxytocin receptor activation prevents prefrontal circuit dysfunction and social behavioral alterations in response to chronic prefrontal cortex activation in male rats

IntroductionSocial behavioral changes are a hallmark of several neurodevelopmental and neuropsychiatric conditions, nevertheless the underlying neural substrates of such dysfunction remain poorly understood. Building evidence points to the prefrontal cortex (PFC) as one of the key brain regions that orchestrates social behavior. We used this concept with the aim to develop a translational rat model of social-circuit dysfunction, the chronic PFC activation model (CPA).MethodsChemogenetic designer receptor hM3Dq was used to induce chronic activation of the PFC over 10 days, and the behavioral and electrophysiological signatures of prolonged PFC hyperactivity were evaluated. To test the sensitivity of this model to pharmacological interventions on longer timescales, and validate its translational potential, the rats were treated with our novel highly selective oxytocin receptor (OXTR) agonist RO6958375, which is not activating the related vasopressin V1a receptor.ResultsCPA rats showed reduced sociability in the three-chamber sociability test, and a concomitant decrease in neuronal excitability and synaptic transmission within the PFC as measured by electrophysiological recordings in acute slice preparation. Sub-chronic treatment with a low dose of the novel OXTR agonist following CPA interferes with the emergence of PFC circuit dysfunction, abnormal social behavior and specific transcriptomic changes.DiscussionThese results demonstrate that sustained PFC hyperactivity modifies circuit characteristics and social behaviors in ways that can be modulated by selective OXTR activation and that this model may be used to understand the circuit recruitment of prosocial therapies in drug discovery

Parallel processing of radio signals and detector arrays in CORSIKA 8

This contribution describes some recent advances in the parallelization of the generation and processing of radio signals emitted by particle showers in CORSIKA 8. CORSIKA 8 is a Monte Carlo simulation framework for modeling ultra-high energy particle cascades in astroparticle physics. The aspects associated with the generation and processing of radio signals in antennas arrays are reviewed, focusing on the key design opportunities and constraints for deployment of multiple threads on such calculations. The audience is also introduced to Gyges, a lightweight, header-only and flexible multithread self-adaptive scheduler written compliant with C++17 and C++20, which is used to distribute and manage the worker computer threads during the parallel calculations. Finally, performance and scalability measurements are provided and the integration into CORSIKA 8 is commented

Hadron cascades in CORSIKA 8

We present characteristics of hadronic cascades from interactions of cosmic rays in the atmosphere, simulated by the novel CORSIKA 8 framework. The simulated spectra of secondaries, such as pions, kaons, baryons and muons, are compared with the cascade equations solvers MCEq in air shower mode, and full 3D air shower Monte Carlo simulations using the legacy CORSIKA 7. A novel capability of CORSIKA 8 is the simulation of cascades in media other than air, widening the scope of potential applications. We demonstrate this by simulating cosmic ray showers in the Mars atmosphere, as well as simulating a shower traversing from air into water. The CORSIKA 8 framework demonstrates good accuracy and robustness in comparison with previous results, in particular in those relevant for the production of muons in air showers. Furthermore, the impact of forward ρ$^{0}$ production on air showers is studied and illustrated

Fleet learning of thermal error compensation in machine tools

Thermal error compensation of machine tools promotes sustainable production. The thermal adaptive learning control (TALC) and machine learning approaches are the required enabling principals. Fleet learnings are key resources to develop sustainable machine tool fleets in terms of thermally induced machine tool error. The target is to integrate each machine tool of the fleet in a learning network. Federated learning with a central cloud server and dedicated edge computing on the one hand keeps the independence of each individual machine tool high and on the other hand leverages the learning of the entire fleet. The outlined concept is based on the TALC, combined with a machine agnostic and machine specific characterization and communication. The proposed system is validated with environmental measurements for two machine tools of the same type, one situated at ETH Zurich and the other one at TU Wien

Lead Generation: Sowing the Seeds for Future Success

Lead generation and the associated hit-to-lead process are key strategic elements in modern pharmaceutical research, and most companies have implemented this concept. Efficient lead generation is one of the main attempts to reduce the high attrition rates observed along the drug discovery process by focussing on the early developmental phases. The level of integration of the lead generation activities within the discovery organization, the flexibility in assessing and implementing new chemistries and new technologies, the high-quality standards set for the identification of the best possible chemical lead series will ultimately determine the future success in discovering new medicines