This investigation is an attempt to test the common supposition that postpartum emotional disturbance is related to hormone changes. A group of 27 normal pregnant women were assessed three times before delivery and sixteen times in the six weeks following delivery. During the first two interviews baseline data on personality and other personal variables were obtained. On each occasion blood was taken and three measures of clinical status and mood were completed. Plasma LH, FSH, total oestrogen and progesterone results are presented in detail and the results of prolactin assays mentioned more briefly. An attempt to correlate hormone findings and clinical findings is described. This failed to produce any strong evidence that hormones are related to mood at this time, although hormone changes were correlated weakly with a few specific symptoms. Some of the unexpected clinical findings and technical difficulties of the study are discussed, with special reference to possible further research in this area.

This study investigated the role of the 5-HT2/1C receptor antagonist ritanserin on d-fenfluramine (d-FF) induced changes in food intake, prolactin (PRL) secretion and oral temperature in 12 healthy male volunteers. The study was double blind and placebo controlled. Food intake was measured using an automated food dispenser. d-FF (30 mg) significantly reduced fat intake. While ritanserin (5 mg) had no effect when given alone it abolished the d-FF induced reduction in fat intake. In addition, ritanserin abolished the d-FF induced rise in PRL and oral temperature. The results suggest that 5-HT2 or 5-HT1C receptors mediate the effects of d-fenfluramine on appetite, prolactin secretion and temperature in humans.

Abstract-Precise control of industrial automation systems with non-linear kinematics due to joint elasticity, variation in cable tensioning, or backlash is challenging; especially in systems that can only be controlled through an interface with an imprecise internal kinematic model. Cable-driven Robotic Surgical Assistants (RSAs) are one example of such an automation system, as they are designed for master-slave teleoperation. We consider the problem of learning a function to modify commands to the inaccurate control interface such that executing the modified command on the system results in a desired state. To achieve this, we must learn a mapping that accounts for the non-linearities in the kinematic chain that are not accounted for by the system's internal model. Gaussian Process Regression (GPR) is a data-driven technique that can estimate this non-linear correction in a task-specific region of state space, but it is sensitive to corruption of training examples due to partial occlusion or lighting changes. In this paper, we extend the use of GPR to learn a non-linear correction for cable-driven surgical robots by using i) velocity as a feature in the regression and ii) removing corrupted training observations based on rotation limits and the magnitude of velocity. We evaluate this approach on the Raven II Surgical Robot on the task of grasping foam "damaged tissue" fragments, using the PhaseSpace LED-based motion capture system to track the Raven end-effector. Our main result is a reduction in the norm of the mean position error from 2.6 cm to 0.2 cm and the norm of the mean angular error from 20.6 degrees to 2.8 degrees when correcting commands for a set of held-out trajectories. We also use the learned mapping to achieve a 3.8× speedup over past results on the task of autonomous surgical debridement. Further information on this research, including data, code, photos, and video, is available at http: //rll.berkeley.edu/surgical.

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