Development of a Virtual Laparoscopic Trainer using Accelerometer Augmented Tools to Assess Performance in Surgical training

Previous research suggests that virtual reality (VR) may supplement conventional training in laparoscopy. It may prove useful in the selection of surgical trainees in terms of their dexterity and spatial awareness skills in the near future. Current VR training solutions provide levels of realism and in some instances, haptic feedback, but they are cumbersome by being tethered and not ergonomically close to the actual surgical instruments for weight and freedom of use factors. In addition, they are expensive hence making them less accessible to departments than conventional box trainers. The box trainers in comparison, although more economical, lack tangible feedback and realism for handling delicate tissue structures. We have previously reported on the development of a modified digitally enhanced surgical instrument for laparoscopic training, named the Parkar Tool. This tool contains wireless accelerometer and gyroscopic sensors integrated into actual laparoscopic instruments. By design, it alleviates the need for both tethered and physically different shaped tools thereby enhancing the realism when performing surgical procedures. Additionally the software (Valhalla) has the ability to digitally record surgical motions, thereby enabling it to remotely capture surgical training data to analyse and objectively evaluate performance. We have adapted and further developed our initial single training tool method as used with a laparoscopic pyloromyotomy scenario, to an enhanced method using multiple Parkar wireless tools simultaneously, for use in several different case scenarios. This allows the use and measurement of right and left handed dexterity with the benefit of using several tasks of differing complexity. The development of a 3D tissue-surface deformations solution written in OpenGL gives us several different virtual surgical training scenario approximations to use with the instruments. The trainee can start with learning simple tasks e.g. incising tissue, grasping, squeezing and stretching tissue, to more complex procedures such as suturing, herniotomies, bowel anastomoses, as well as the original pyloromyotomy as used in the first model

Integrated control-system design via generalized LQG (GLQG) theory

Thirty years of control systems research has produced an enormous body of theoretical results in feedback synthesis. Yet such results see relatively little practical application, and there remains an unsettling gap between classical single-loop techniques (Nyquist, Bode, root locus, pole placement) and modern multivariable approaches (LQG and H infinity theory). Large scale, complex systems, such as high performance aircraft and flexible space structures, now demand efficient, reliable design of multivariable feedback controllers which optimally tradeoff performance against modeling accuracy, bandwidth, sensor noise, actuator power, and control law complexity. A methodology is described which encompasses numerous practical design constraints within a single unified formulation. The approach, which is based upon coupled systems or modified Riccati and Lyapunov equations, encompasses time-domain linear-quadratic-Gaussian theory and frequency-domain H theory, as well as classical objectives such as gain and phase margin via the Nyquist circle criterion. In addition, this approach encompasses the optimal projection approach to reduced-order controller design. The current status of the overall theory will be reviewed including both continuous-time and discrete-time (sampled-data) formulations

Feno de capim-de-Rhodes ( Chloris gayana Kunth) e o capim elefante (Pennisetum purpureum Schum) var. Napier verde picado na alimentação de equideos em crescimento.

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Extension of PRISM by Synthesis of Optimal Timeouts in Fixed-Delay CTMC

We present a practically appealing extension of the probabilistic model checker PRISM rendering it to handle fixed-delay continuous-time Markov chains (fdCTMCs) with rewards, the equivalent formalism to the deterministic and stochastic Petri nets (DSPNs). fdCTMCs allow transitions with fixed-delays (or timeouts) on top of the traditional transitions with exponential rates. Our extension supports an evaluation of expected reward until reaching a given set of target states. The main contribution is that, considering the fixed-delays as parameters, we implemented a synthesis algorithm that computes the epsilon-optimal values of the fixed-delays minimizing the expected reward. We provide a performance evaluation of the synthesis on practical examples

The Postoperative Morbidity Survey was validated and used to describe morbidity after major surgery.

OBJECTIVES: To describe the reliability and validity of the Postoperative Morbidity Survey (POMS). To describe the level and pattern of short-term postoperative morbidity after major elective surgery using the POMS. STUDY DESIGN AND SETTING: This was a prospective cohort study of 439 adults undergoing major elective surgery in a UK teaching hospital. The POMS, an 18-item survey that address nine domains of postoperative morbidity, was recorded on postoperative days 3, 5, 8, and 15. RESULTS: Inter-rater reliability was perfect for 11/18 items (Kappa=1.0), with Kappa=0.94 for 6/18 items. A priori hypotheses that the POMS would discriminate between patients with known measures of morbidity risk, and predict length of stay were generally supported through observation of data trends, and there was statistically significant evidence of construct validity for all but the wound and neurological domains. POMS-defined morbidity was present in 325 of 433 patients (75.1%) remaining in hospital on postoperative day 3 after surgery, 231 of 407 patients (56.8%) on day 5, 138 of 299 patients (46.2%) on day 8, and 70 of 111 patients (63.1%) on day 15. Gastrointestinal (47.4%), infectious (46.5%), pain-related (40.3%), pulmonary (39.4%), and renal problems (33.3%) were the most common forms of morbidity. CONCLUSION: The POMS is a reliable and valid survey of short-term postoperative morbidity in major elective surgery. Many patients remain in hospital without any morbidity as recorded by the POMS

The experiences of carers looking after people with Parkinson's disease who exhibit impulsive and compulsive behaviours: an exploratory qualitative study

BACKGROUND: Impulsive and compulsive behaviours (ICBs) are a serious complication in Parkinson disease (PD) strongly associated with dopamine replacement therapy used to treat patients. These behaviours comprise abnormal activities such as pathological gambling, binge eating, compulsive shopping, and hypersexuality. These behaviours place a considerable burden on patients and on their carers and families. The aim of this qualitative study is to understand the experiences of carers who were confronted by the development of these behaviours. DESIGN: An exploratory qualitative study. METHODS: Using a convenience sampling approach, 13 carers were recruited to participate in semi-structured interviews. Interviews were conducted over the telephone. Verbatim transcripts were analysed used a thematic analysis approach. COREQ guidelines were adhered to in the reporting of this study. RESULTS: Five main themes were identified: (1) realisation - developing awareness of ICB symptoms and their causes; (2) reacting - confronting and attempts to manage ICBs; (3) reaching out - help-seeking and selective disclosure; (4) reframing - shifting perspectives on ICBs over time; and (5) resignation - impact on relationships and facing the future. CONCLUSIONS: The profound impact of ICBs on quality of life, relationships, and economic stability was clear in the carers' accounts. Possible avenues for future clinical research are suggested. RELEVANCE TO CLINICAL PRACTICE: The potentially devastating effects of ICBs provide a strong imperative for nurses and other health professionals to ensure that close monitoring for symptom development together with patient education are always part of practice

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics