Abstract
This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter's support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization.

Abstract
The study of the optical properties of biological tissues for a wide spectral range is necessary for the development and planning of noninvasive optical methods to be used in clinical practice. In this study, we propose a new method to calculate almost all optical properties of tissues as a function of wavelength directly from spectral measurements. Using this method, and with the exception of the reduced scattering coefficient, which was obtained by traditional simulation methods, all the other optical properties were calculated in a simple and fast manner for human and pathological colorectal tissues. The obtained results are in good agreement with previous published data, both in magnitude and in wavelength dependence. Since this method is based on spectral measurements and not on discrete-wavelength experimental data, the calculated optical properties contain spectral signatures that correspond to major tissue chromophores such as DNA and hemoglobin. Analysis of the absorption bands of hemoglobin in the wavelength dependence of the absorption spectra of normal and pathological colorectal mucosa allowed to identify differentiated accumulation of a pigment in these tissues. The increased content of this pigment in the pathological mucosa may be used for the future development of noninvasive diagnostic methods for colorectal cancer detection.

Abstract
The interest of using light in clinical practice is increasing strongly and many applications work at various wavelengths from the ultraviolet to the infrared. Due to this great range of applications, the determination of the optical properties of biological tissues in a wide spectral range becomes of interest. The liver is an important organ, since it has a major role in the human body and various pathologies are known to develop within it. For these reasons, this study concerns the estimation of the optical properties of human normal and pathological (metastatic carcinoma) liver tissues between 200 and 1000 nm. The obtained optical properties present the expected wavelength dependencies for both tissues - the refractive index, the absorption and the scattering coefficients decrease with the wavelength and the anisotropy and light penetration depth increase with the wavelength. Although similar behavior was observed for the various properties between the normal and pathological tissues, evidence of smaller blood content in the pathological tissues was found. A possible explanation is that the cancer cells destroy liver's vasculature and internal architecture, providing though a reduction in the blood content. For low wavelengths, it was observed a matching between the scattering and the reduced scattering coefficients, which implies a nearly zero anisotropy in that range. The scattering coefficient decreases from nearly 140 cm(-1) (at 200 nm) to 80 cm(-1) (at 1000 nm) for the normal liver and from nearly 140 cm(-1) (at 200 nm) to 95 cm(-1) (at 1000 nm) for the pathological tissue.

Abstract
System identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies.

Abstract
Biometric authentication (i.e., verification of a given subject’s identity using biological characteristics) relying on gait characteristics obtained in a non-intrusive way can be very useful in the area of security, for smart surveillance and access control. In this contribution, we investigated the possibility of carrying out subject identification based on a predictive model built using machine learning techniques, and features extracted from 3-D body joint data provided by a single low-cost RGB-D camera (Microsoft Kinect v2). We obtained a dataset including 400 gait cycles from 20 healthy subjects, and 25 anthropometric measures and gait parameters per gait cycle. Different machine learning algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines, multilayer perceptron, and multilayer perceptron ensemble. The algorithm that led to the model with best trade-off between the considered evaluation metrics was the random forest: overall accuracy of 99%, class accuracy of 100±Â0%, and F 1 score of 99±Â2%. These results show the potential of using a RGB-D camera for subject identification based on quantitative gait analysis. © 2020, Springer Nature Switzerland AG.

Abstract
The concept of 'tissue optical windows' and method of optical clearing (OC) based on controllable and reversible modification of tissue optical properties by their soaking with a biocompatible optical clearing agent (OCA) are prsented. Fundamentals and major mechanisms of OC allowing one to enhance optical imaging facilities and laser treatment efficiency of living tissues are described. Perspectives of immersion optical clearing/contrasting technique aiming to enhance optical imaging of living tissues by using different imaging modalities working in the ultra-broad wavelength range from deep UV to terahertz waves are discussed. It demonstrated that immersion OC method can be applied to evaluate the characteristic diffusion properties of water and OCA in various tissues and even discriminate between the mobile water content in normal and pathological tissues.