No abstract

Progress in the development of effective cancer treatments is limited by the availability of tumor models. For decades, established cancer cell lines represented the mainstay preclinical tumor model, but these tools have limitations, such as limited capacity to recapitulate inter-and intra-tumor heterogeneity, adaptation to grow in two-dimensional cultures and the lack of Research.

Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can influence the results of such methods, which could lead to additional uncertainties. Thus, techniques combining machine learning and statistical analysis applied directly to raw measurements are being discussed in recent researches. The purpose of this paper is to investigate statistical indicators, little explored in damage identification methods, to characterize acceleration measurements directly in the time domain. Hence, the present work compares two machine learning algorithms to identify structural changes using statistics obtained from raw dynamic data. The algorithms are based on Artificial Neural Networks and Support Vector Machines. They are initially evaluated through numerical simulations using a simply supported beam model. Then, they are assessed through experimental tests performed on a laboratory beam structure and an actual railway bridge, in France. For all cases, different damage scenarios were considered. The obtained results encourage the development of computational tools using statistical indicators of acceleration measurements for structural alteration assessment.

The present study investigated the accumulated oxygen deficit (AOD) method in breaststroke swimming with the aims to assess the reliability of the oxygen uptake/swimming velocity regression line and to quantify the precision of the AOD. Sixteen male swimmers performed two swimming tests in different days, with a 24-h recovery between tests: a graded swimming test and an all-out test. The all-out test was performed in one of two distances: 100 m (n = 7) or 200 m (n = 9). Through all testing, expired gases were collected breath by breath and analysed with a K4b2 Gas Analyser (Cosmed, Rome, Italy) connected to an AquaTrainer Valve (Cosmed, Rome, Italy). The standard error of the regression lines was approximately 5-6 ml kg(-1) min(-1) and the regressions allowed an extrapolation of the energy cost to higher intensities with a standard error of the predicted value that was lower in the 200-m bout (approximately 3.5 ml kg(-1) min(-1)) comparatively to the 100-m bout (approximately 6 ml kg(-1) min(-1)). The AOD imprecision was calculated as the square root of the sum of the oxygen uptake measurement error and the standard error of the predicted value for energy cost. AOD imprecision was smaller in the 200-m bout (approximately 9 ml kg(-1) min(-1)) comparatively to the 100-m bout (approximately 12 ml kg(-1) min(-1)). However, since the AOD values during the two distances were small, the AOD relative errors can be viewed as high. Additionally, the data variability was considerable (95% confidence intervals of the linear extrapolation larger than 20 ml kg(-1) min(-1)).