RFamide-related peptide (RFRP), the mammalian homolog of avian gonadotropin-inhibitory hormone, has a pronounced suppressive action on the reproductive axis across species. In mammals, RFRP acts directly on GnRH neurons, and likely at the level of the pituitary, to inhibit gonadotropin secretion. In the present study, we examined whether RFRP might act outside of mammalian brain on reproductive tissues directly. Using RT-PCR and in situ hybridization, we found that both RFRP and its receptors [G protein-coupled receptor (GPR) 147 and GPR74] are expressed in the testis of Syrian hamster. These results were confirmed and extended using double- and triple-label immunohistochemistry. RFRP expression was observed in spermatocytes and in round to early elongated spermatids. Significant expression of RFRP was not seen in Leydig cells. GPR147 protein was observed in myoid cells in all stages of spermatogenesis, pachytene spermatocytes, maturation division spermatocytes, and in round and late elongated spermatids. GPR74 proteins only appeared in late elongated spermatids. Additionally, we found that RFRP and its receptor mRNA are markedly altered by day length and reproductive condition. These findings highlight a possible novel autocrine and/or paracrine role for RFRP in Syrian hamster testis, potentially contributing to the differentiation of spermatids during spermiogenesis.

In this paper, we report several advances in the Sugar2.0 MEMS system simulation package, including reduced-order modeling techniques, simple hierarchical description of complex structures, synthesis tools, a variety of models, and a web-based interface. Examples include the modeling of a torsional micromirror with lateral actuators compared to experiment, and the prototyping of a microrobot.

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This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme centered around the simultaneous actuation of multiple cables, the robot demonstrates robust climbing on inclined surfaces in hardware experiments and speeds significantly faster than previous spherical tensegrity models. This robot is an improvement over other iterations in the TT-series and the first tensegrity to achieve reliable locomotion on inclined surfaces of up to 24 • . We analyze locomotion in simulation and hardware under single and multicable actuation, and introduce two novel multi-cable actuation policies, suited for steep incline climbing and speed, respectively. We propose compelling justifications for the increased dynamic ability of the robot and motivate development of optimization algorithms able to take advantage of the robot's increased control authority.

Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics. To overcome these issues, this work presents two controllers for tensegrity spine robots, using model-predictive control (MPC), and demonstrates the first closed-loop control of such structures. The first of the two controllers is formulated using only state tracking with smoothing constraints. The second controller, newly introduced in this work, tracks both state and input reference trajectories without smoothing. The reference input trajectory is calculated using a rigid-body reformulation of the inverse kinematics of tensegrity structures, and introduces the first feasible solutions to the problem for certain tensegrity topologies. This second controller significantly reduces the number of parameters involved in designing the control system, making the task much easier. The controllers are simulated with 2D and 3D models of a particular tensegrity spine, designed for use as the backbone of a quadruped robot. These simulations illustrate the different benefits of the higher performance of the smoothing controller versus the lower tuning complexity of the more general input-tracking formulation. Both controllers show noise insensitivity and low tracking error, and can be used for different control goals. The reference input tracking controller is also simulated against an additional model of a similar robot, thereby demonstrating its generality.