Creation and Validation of Chronojump-Boscosystem: A Free Tool to Measure Vertical Jumps

Measuring the height of the vertical jump is an indicator of the strength and power of the lower body. The technological tools available to measure the vertical jump are black boxes and are not open to third-party verification or adaptation. We propose the creation of a measurement system called Chronojump-Boscosystem, consisting of open hardware and free software. Methods: A microcontroller was created and validated using a square wave generator and an oscilloscope. Two types of contact platforms were developed using different materials. These platforms were validated by the minimum pressure required for activation at different points by a strain gauge, together with the on/off time of our platforms in respect of the Ergojump-Boscosystem platform by a sample of 8 subjects performing submaximal jumps with one foot on each platform. Agile methodologies were used to develop and validate the software. Results: All the tools fall under the free software / open hardware guidelines and are, in that sense, free. The microcontroller margin of error is 0.1%. The validity of the fiberglass platform is 0.95 (ICC). The management software contains nearly 113.000 lines of code and is available in 7 languages.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Sub- or intertrochanteric fracture following screw fixation of an intracapsular proximal femoral fracture: true complication or technical error?

PURPOSE: To review, retrospectively, the possible causes of sub- or intertrochanteric fractures after screw fixation of intracapsular fractures of the proximal femur. METHODS: Eighty-four patients with an intracapsular fracture of proximal femur were operated between 1995 and 1998 by using three cannulated 6.25 mm screws. The screws were inserted in a triangular configuration, one screw in the upper part of the femoral neck and two screws in the inferior part. Between 1999 and 2001, we use two screws proximally and one screw distally. RESULTS: In the first series, two patients died within one week after operation. Sixty-four fractures healed without problems. Four patients developed an atrophic non-union; avascular necrosis of the femoral head was found in 11 patients. Three patients (3.6%) suffered a sub- and/or intertrochanteric fracture after a mean postoperative time of 30 days, in one case without obvious trauma. In all three cases surgical revision was necessary. Between 1999 and 2001 we did not observe any fracture after screwing. CONCLUSION: Two screws in the inferior part of the femoral neck create a stress riser in the subtrochanteric region, potentially inducing a fracture in the weakened bone. For internal fixation for proximal intracapsular femoral fracture only one screw must be inserted in the inferior part of neck.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.