Abstract
This article presents a medical simulation solution. This solution allows a physician to train a clinical scenario by interacting with a graphical interface. The aim is to instigate the learning and internalization of clinical procedures. Currently these resources have been intensifying in the most diverse areas, being our focus, Medicine. Within this area, the focus is on medical simulation. There are numerous biomedical simulation centers, whose main objective is to create realistic simulations to aid health professionals. Thus, it is intended to optimize its performance, to meet the needs detected and to anticipate unexpected situations (critical or complex events). However, current simulation systems face some limitations, since they have enough difficulties in the development of new scenarios, since they are restricted to the level of modularity and the number of simulated situations. The training of these professionals is limited to simulation centers. The goal is to create a platform to simulate real scenarios and develop serious games that simulate various clinical situations, in order to facilitate access to this type of training and training.

Abstract
Knowledge of the optical properties of tissues is necessary, since they change from tissue to tissue and can differ between normal and pathological conditions. These properties are used in light transport models with various areas of application. In general, tissues have significantly high scattering coefficient when compared to the absorption coefficient and such difference usually increases with decreasing wavelength. The study of the wavelength dependence of the optical properties has been already made for several animal and human tissues, but extensive research is still needed in this field. Considering that most of the Biophotonics techniques used in research and clinical practice use visible to NIR light, we have estimated the optical properties of colorectal muscle (muscularis propria) between 400 and 1000 nm. The samples used were collected from patients undergoing resection surgery for colorectal carcinoma. The estimated scattering coefficient for colorectal muscle decreases exponentially with wavelength from 122 cm(-1) at 400 nm to 95 cm(-1) at 650 nm and to 91 cm(-1) at 1000 nm. The absorption coefficient shows a wavelength dependence according to the behavior seen for other tissues, since it decreases from 8 cm(-1) at 400 nm to 2.6 cm(-1) at 650 nm and to 1.3 cm(-1) at 1000 nm. The estimated optical properties differ from the ones that we have previously obtained for normal and pathological colorectal mucosa. The data obtained in this study covers an extended spectral range and it can be used for planning optical clearing treatments for some wavelengths of interest.

Abstract
To characterize the optical clearing treatments in human colorectal tissues and possibly to differentiate between treatments of normal and pathological tissues, we have used a simple indirect method derived from Mie scattering theory to estimate the kinetics of the reduced scattering coefficient. A complementary method to estimate the kinetics of the scattering coefficient is also used so that the kinetics of the anisotropy factor and of the refractive index are also calculated. Both methods rely only on the thickness and collimated transmittance measurements made during treatment. The results indicate the expected time dependencies for the optical properties of both tissues: an increase in the refractive index and anisotropy factor and a decrease in the scattering coefficients. The similarity in the kinetics obtained for normal and pathological tissues indicates that optical clearing treatments can be applied also in pathological tissues to produce similar effects. The estimated time dependencies using experimental spectral data in the range from 400 to 1000 nm allowed us to compare the kinetics of the optical properties between different wavelengths. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
With the objective of developing a diagnostic tool, we have used the immersion optical clearing method and studied normal and pathological tissues (cancer, diabetes) under treatment by optical clearing agents (OCAs). In order to quantify pathology status OCA diffusion properties in different tissues were measured. We have demonstrated that free water content in cancerous tissues is higher than in normal.

Abstract
In this paper, a new system identification algorithm is proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, the identification problem is reduced to the estimation of the system input matrix and the observer gain which can be performed by a simple Least Square Estimator. The quality of the estimator depends on the observer state matrix. In the proposed algorithm, this matrix is found by an iterative process where, in each iteration, a state matrix called curiosity is generated. A weight depending on the value of the Least Square Cost is associated to each curiosity. The optimal state matrix is the barycenter of the curiosities. This iterative process is a free derivative optimization algorithm with its roots in non-iterative barycenter methods previously introduced to solve adaptive control and system identification problems. Although the Barycenter iterative version was recently proposed as an optimization method, here it will be implemented in an identification algorithm for the first time.

Abstract
Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered. We propose two distance-based integrative clustering frameworks based on bi-level and bi-objective extensions of the p-median problem. A hybrid branch-and-cut method is developed to find global optimal solutions to the bi-level p-median model. As to the bi-objective problem, an epsilon-constraint algorithm is proposed to generate an approximation to the Pareto optimal set. Every solution found by any of the frameworks corresponds to an integrative clustering. We present an application of our approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles. We demonstrate that the proposed mathematical optimization-based approaches outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques.