Neuromechanical Analysis of Locust Jumping

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control, we need a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. We have developed a neuromechanical application named AnimatLab that addresses this problem through simulation. A computational model of a body and nervous system can be constructed from simple components and situated in a virtual world for testing. Simulations and live experiments were used to investigate questions about locust jumping. The neural circuitry and biomechanics of kicking in locusts have been extensively studied. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors, but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The SLP are bands of cuticle that store energy for use during jumping. The results of the model were compared to a variety of published data and were similar. The SLP significantly increased jump distance, power, total energy, and duration of the jump impulse. Locust can jump precisely to a target, but also exhibit tumbling. We proposed two mechanisms for controlling tumbling during the jump. The first was that locusts adjust the pitch of their body prior to the jump to move the center of mass closer to the thrust vector. The second was that contraction of the abdominal muscles during the jump produced torques that countered the torque due to thrust. There was a strong correlation relating increased pitch and takeoff angle. In simulations there was an optimal pitch-takeoff combination that minimized tumbling that was similar to the live data. The direction and magnitude of tumbling could be controlled by adjusting abdominal tension. Tumbling also influenced jump elevation. Neuromechanical simulation addressed problems that would be difficult to examine using traditional physiological approaches. It is a powerful tool for understanding the neural basis of behavior

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design