Abstract
We quantitatively evaluated the localizing and lateralizing characteristics of ictal upper limb automatisms (ULAs) in patients with temporal lobe epilepsy (TLE; n = 38) and frontal lobe epilepsy (FLE; n = 20). Movement speed, extent, length, and duration of ULAs were quantitatively analyzed with motion capturing techniques. Upper limb automatisms had a larger extent (p < 0.001), covered more distance (p < 0.05), and were faster (p < 0.001) in FLE than in TLE. In TLE, the maximum speed of ULAs was higher ipsilaterally than contralaterally (173 vs. 84 pixels/s; p = 0.02), with no significant difference in FLE (511 vs. 428). The duration of ictal automatisms in relation to the total seizure duration was shorter in TLE than in FLE (median 36% vs. 63%; p < 0.001), with no difference in the absolute duration (26 s vs. 27 s). These results demonstrate that quantitative movement analysis of ULAs differentiates FLE from TLE, which may aid in the localization of the epileptogenic zone.

Abstract
With the objective to study the variation of optical properties of rat muscle during optical clearing, we have performed a set of optical measurements from that kind of tissue. The measurements performed were total transmittance, collimated transmittance, specular reflectance and total reflectance. This set of measurements is sufficient to determine diffuse reflectance and absorbance of the sample, also necessary to estimate the optical properties. All the performed measurements and calculated quantities will be used later in inverse Monte Carlo (IMC) simulations to determine the evolution of the optical properties of muscle during treatments with ethylene glycol and glucose. The results obtained with the measurements already provide some information about the optical clearing treatments applied to the muscle and translate the mechanisms of turning the tissue more transparent and sequence of regimes of optical clearing.

Abstract
Electrical impedance spectroscopy, EIS, has been proving efficacy and utility in a wide range of areas, from the characterization of biological tissues to living organisms. Several commercial solutions, with high precision and resolution, are available. Nonetheless, the typical equipments are expensive, unfeasible for in vivo and in field applications and unspecific for concrete applications. These features, together with the lately demands in the vegetal field, fundament this work. Actually, the fast spread of asymptomatic forest diseases, with no cure available to date, such as the pinewood disease, PWD, constitute a problem of economical and forestall huge proportions. Herein is proposed a portable EIS system, for biological applications, able to perform AC current or voltage scans within a selectable frequency range. The procedure and the results obtained for a population of 24 young pine trees (Pinus pinaster Aiton) are also presented. Pine trees were kept in a controlled environment and were inoculated with the nematode (Bursaphelenchus xylophilus Nickle), that causes the PWD, and also with bark beetles (Tomicus destruens Wollaston). Some degree of discrimination between different physiological states was achieved. These results may constitute a first innovative approach to the diagnosis of such types of diseases.

Abstract
The study of agent diffusion in biological tissues is very important to understand and characterize the optical clearing effects and mechanisms involved: tissue dehydration and refractive index matching. From measurements made to study the optical clearing, it is obvious that light scattering is reduced and that the optical properties of the tissue are controlled in the process. On the other hand, optical measurements do not allow direct determination of the diffusion properties of the agent in the tissue and some calculations are necessary to estimate those properties. This fact is imposed by the occurrence of two fluxes at optical clearing: water typically directed out of and agent directed into the tissue. When the water content in the immersion solution is approximately the same as the free water content of the tissue, a balance is established for water and the agent flux dominates. To prove this concept experimentally, we have measured the collimated transmittance of skeletal muscle samples under treatment with aqueous solutions containing different concentrations of glucose. After estimating the mean diffusion time values for each of the treatments we have represented those values as a function of glucose concentration in solution. Such a representation presents a maximum diffusion time for a water content in solution equal to the tissue free water content. Such a maximum represents the real diffusion time of glucose in the muscle and with this value we could calculate the corresponding diffusion coefficient.

Abstract
The laser diode self-mixing technique is a well-known and powerful interferometric technique that has been used in biomedical applications, namely for the extraction of cardiovascular parameters. However, to construct an optical probe using the self-mixing principle which is able to acquire signals in the human carotid artery, some problems are expected. The laser diode has a small aperture area, which means that, for physiological sensing purposes, it can be considered as a point-like detector. This feature imparts difficulties to quality recording of physiological signals since the number of photons collected and mixed in the cavity of the photodiode is very small. In order to overcome this problem, a new mixing geometry based on an external large area planar photodiode (PD) is used in the probe, enabling a much larger number of photons to be collected, hence improving the quality of the signal. In this work, the possibility to obtain the mixing effect outside the laser cavity using an external photodetector, such as a planar photodiode, is demonstrated. Two test benches were designed, both with of two reflectors. The first one, which reflects the light beam with the same frequency of the original one is fixed, and the second one, is movable, reflecting the Doppler shifted light to the photodetector. The first test bench has a fixed mirror in front of the movable mirror, creating an umbra and penumbra shadow above the movable mirror. To avoid this problem, another test bench was constructed using a wedged beam splitter (WSB) instead of a fixed mirror. This new assembly ensures the separation of a single input beam into multiple copies that undergo successive reflections and refractions. Some light waves are reflected by the planar surface of WSB, while other light beams are transmitted through the WSB, reaching the movable mirror. Also in this case, the movable mirror reflects the light with a Doppler frequency shift, and the PD receives both beams. The two test benches were designed to demonstrate that it is possible to obtain mixing effect outside the laser cavity, using a planar photodiode. The Doppler spectrograms from the signals acquired in the test benches show that the signal frequency changes along time which correspond to the modulus of the derivative of the mirror movement, as expected in the self-mixing signals. Nevertheless, the test bench A showed better results than the test bench B. This fact probably results from the attenuation that the original beam suffers in each reflection and refraction in the WBS. Tests developed in the test benches opened the possibility to construct a probe that uses a planar photodiode with a large area to collect medical signals, and improve the quality of the acquisition with a better SNR.

Abstract
The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation. © 2013 by the authors; licensee MDPI, Basel, Switzerland.