Avaliação e perspectivas da abordagem à conservação do patrimônio organístico no Brasil

Órgãos de tubos são construídos no Brasil, ou importados do exterior, desde as primeiras décadas do período colonial. Estes instrumentos, apesar de constituírem um conjunto relativamente exíguo, representam um testemunho histórico e musical de valor inestimável. No entanto, de um modo geral, encontram-se em condições precárias de funcionamento e, em parte, mais ou menos descaracterizados profundamente, quanto à sua estrutura e configuração original, às vezes em estado de abandono e, por incrível que pareça, ainda sujeitos ao risco de intervenções arbitrárias e desprovidas de bases técnicas e de preocupação histórica e musicológica. Neste artigo, busca-se percorrer o caminho que levou à atual situação e discutem-se os princípios e os critérios de processos de recuperação, manutenção e conservação preventiva desse patrimônio, partindo dos pressupostos do respeito à sua originalidade e do direito a restauros segundo os rigorosos cânones já definidos para objetos com plena condição de bem cultural, cujo reconhecimento para os órgãos de qualquer época deve ser urgentemente garantido.Pipe organs have been built in Brazil or imported from elsewhere since the very first decades of the colonial period. Albeit relatively small in number, such instruments have inestimable historical and musical value. Notwithstanding, they are generally in very poor working condition and some have lost much of their original structure and configuration; they are often found in a state of abandonment and, incredible as it may seem, still under the threat of arbitrary interventions performed with no technical expertise or any concern for their historical and musicological significance. This article is an effort to understand how this state of affairs came to be, and discusses principles and criteria for the restoration, maintenance and preventive conservation of this legacy, based on assumptions regarding its original conditions and entitlement to restoration in accordance with the stringent standards already set for artifacts that enjoy the full status of cultural assets, which should also be urgently granted to pipe organs of any time and age

Image registration via stochastic gradient markov chain monte carlo

We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates

A Closest Point Proposal for MCMC-based Probabilistic Surface Registration

We propose to view non-rigid surface registration as a probabilistic inference problem. Given a target surface, we estimate the posterior distribution of surface registrations. We demonstrate how the posterior distribution can be used to build shape models that generalize better and show how to visualize the uncertainty in the established correspondence. Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence. We introduce the closest-point proposal for the Metropolis-Hastings algorithm. Our proposal overcomes the limitation of slow convergence compared to a random-walk strategy. As the algorithm decouples inference from modeling the posterior using a propose-and-verify scheme, we show how to choose different distance measures for the likelihood model. All presented results are fully reproducible using publicly available data and our open-source implementation of the registration framework

ATP release from the human ureter on distension and P2X3 receptor expression on suburothelial sensory nerves

It is not clear how the increase in intraluminal pressure behind an obstructing ureteric calculus causes an increase in action potential frequency in ureteric sensory nerves so the pain messages are transmitted to the brain. It has been proposed that ureteric distension causes urothelial release of ATP, which activates purinoceptors on suburothelial nociceptive sensory nerves. The purpose of this study was to determine whether distension of the human ureter results in the release of ATP and whether the nociceptive P2 receptor, P2X3, is expressed on suburothelial sensory nerves in the human ureter. Human ureter segments were perfused with Krebs solution and intermittently distended to a range of pressures. Samples of perfusate were collected throughout and the ATP concentration ([ATP]) was determined using a luciferin-luciferase assay. Sections of ureter were stained using antibodies against P2X3 and capsaicin receptors (TRPV1). [ATP] rose to more than 10 times baseline levels after distension beyond a threshold of 25–30 cmH2O. Immunofluorescence studies on consecutive frozen sections showed that suburothelial nerves stained positively for P2X3 and capsaicin receptors, with no staining in controls. These findings are consistent with the hypothesis that purinergic signalling is involved in human ureteric mechanosensory transduction, leading to nociception

Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification

© Springer Nature Switzerland AG 2018. This paper presents a novel approach to modeling the posterior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric mapping (LDDMM). We develop a Laplace approximation of Bayesian registration models entirely in a bandlimited space that fully describes the properties of diffeomorphic transformations. In contrast to current methods, we compute the inverse Hessian at the mode of the posterior distribution of diffeomorphisms directly in the low dimensional frequency domain. This dramatically reduces the computational complexity of approximating posterior marginals in the high dimensional imaging space. Experimental results show that our method is significantly faster than the state-of-the-art diffeomorphic image registration uncertainty quantification algorithms, while producing comparable results. The efficiency of our method strengthens the feasibility in prospective clinical applications, e.g., real-time image-guided navigation for brain surgery