The basic pattern of activity underlying stepping in mammals is generated by a neural network located in the caudal spinal cord. Within this network, the specific circuitry coordinating left-right alternation has been shown to involve several groups of molecularly defined interneurons. Here we characterize a population of spinal neurons that express the Wilms' tumor 1 () gene and investigate their role during locomotor activity in mice of both sexes. We demonstrate that -expressing cells are located in the ventromedial region of the spinal cord of mice and are also present in the human spinal cord. In the mouse, these cells are inhibitory, project axons to the contralateral spinal cord, terminate in close proximity to other commissural interneuron subtypes, and are essential for appropriate left-right alternation during locomotion. In addition to identifying-expressing interneurons as a key component of the locomotor circuitry, this study provides insight into the manner in which several populations of molecularly defined interneurons are interconnected to generate coordinated motor activity on either side of the body during stepping. In this study, we characterize -expressing spinal interneurons in mice and demonstrate that they are commissurally projecting and inhibitory. Silencing of this neuronal population during a locomotor task results in a complete breakdown of left-right alternation, whereas flexor-extensor alternation was not significantly affected. Axons of neurons are shown to terminate nearby commissural interneurons, which coordinate motoneuron activity during locomotion, and presumably regulate their activity. Finally, the gene is shown to be present in the spinal cord of humans, raising the possibility of functional homology between these species. This study not only identifies a key component of the locomotor circuitry but also begins to unravel the connectivity among the growing number of molecularly defined interneurons that comprise this neural network.

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The basic rhythmic activity characteristic of locomotion in mammals is generated by a neural network, located in the spinal cord, known as the locomotor central pattern generator (CPG). Although a great deal of effort has gone into the study of this neural circuit over the past century, identification and characterization of its component interneurons has proven to be challenging, largely due to their location and distribution. Recent work incorporating a molecular approach has provided a great deal of insight into the genetic identity of interneurons that make up this neural circuit, as well as the specific roles that they play during stepping. Despite this progress we still know relatively little regarding the manner in which these neuronal populations are interconnected. In this article we review the interneuronal populations shown to be involved in locomotor activity, briefly summarize their specific function, and focus on experimental work that provides insight into their synaptic connectivity. Finally, we discuss how recently developed viral approaches can potentially be incorporated to provide further insight into the network structure of this neural circuit.

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This paper addresses the dynamic modeling and simulation of the AIRBUS A380 wing. The governing equations of motion which describe the vibrational motion of the wing were derived using the extended Hamilton’s principle. The elastic wing structure is assumed to follow a Bernoulli-Euler cantilever beam clamped on the moving fuselage and carrying two Trent-900 engines on its span which were treated as lumped masses. The obtained equations of motion of the wing model were analyzed by means of the unconstrained modal analysis and the system natural frequencies were estimated. In order to verify the obtained results, the dynamic response was simulated using the Finite Element (FE) computational software by which the vibrational response was generated under several types of excitation. The obtained results showed good match between the natural frequencies extracted from the mathematical model and the corresponding ones generated by FE simulation. Also the developed computational model in this investigation was found successful in capturing the vibrational motions of a wide spectrum of relevant aerodynamic and unbalance loading conditions.

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