Abstract
Animal experiments have gained importance in human diseases studies, namely neurological diseases and its biomechanical and physiological aspects. As a model of human disease, the rat offers many advantages over other organisms. For the biomechanical aspects of locomotion these studies are based on the analysis of animals' kinetic parameters, accessed through a locomotion measurement system. However, these systems are not yet thoroughly developed, are still scarce and are also very expensive when developed for studies using small rodents. In this paper, a system capable of measuring contact forces of small rodents is presented. The platform hardware is based on a 5 x 3 matrix of ultra-sensitive force sensors that produce a set of signals acquired in a LabVIEW (TM) environment, used for data acquisition and processing. The post processing steps include the removal of null data, curve normalization related to the rat's weight and expressed as percentage of passage, resulting in a gait pattern. The proposed cost-effective system has achieved excellent results regarding the locomotion profile of healthy animals.

Abstract
The usage of small-sized unmanned aerial systems (UAS) has increased in the last years, in many different areas, being agriculture and forestry those who benefit the most from this relatively new remote sensing platform. Leaf area index, canopy and plant volume are among the parameters that can be determined using the very high resolution aerial data obtained by sensors coupled in unmanned aerial vehicles (UAV). This remote sensing technology affords the possibility of monitoring the vegetative development, identifying different types of issues, enabling the application of the most appropriated treatments in the affected areas. In this paper, a methodology allowing to perform multi-temporal UAS-based data analysis obtained by different sensors is proposed. A case study in vineyards and chestnuts is used to prove the benefits of continuous crop monitoring in its management and productivity of agroforestry parcels/activities. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
An emerging trend in industry 4.0 is to use wireless communication infrastructure and mesh networks in applications requiring high reliability and safety. Although not a typical industrial production process, railway vehicular networks are also an industrial application which come with stringent reliability and safety requirements. Current research is focusing on using vehicular networks as an enabling technology to actively control the separation between two consecutive vehicles, enforcing a safe distance which is nevertheless much shorter than currently used to maintain vehicle separation. In this respect, we analyze a hopping strategy for Time-Slotted Channel-Hopping (TSCH), which was introduced in the IEEE 802.15.4e amendment with a view of improving the reliability of IEEE 802.15.4 networks. We define a probability framework to estimate the chance of successful hopping assuming two previously merged vehicles, and we design a zero-sum game and propose a payoff function to always place communicating nodes in a Nash equilibrium by choosing whether to hop or not, and therefore maximizing the communication throughput by mitigating jamming signals.

Abstract
Sous vide cook-chill (SVCC) is characterized by vacuum-packaging raw or partially prepared foods before pasteurization, followed by rapid chilling and storage below 3C. The application of essential oils (EOs) to food products is a suitable strategy to control pathogens and to extend their shelf life by reducing microbial levels and oxidative processes. The aim of this study was to evaluate the efficacy of Rosmarinus officinalis L. (rosemary) and Thymus vulgaris L. (thyme) EOs against Listeria monocytogenes ATCC 679, inoculated in beef processed by SVCC stored at 2 and 8C for 1, 2, 3, 7, 14, 21 and 28 days. The composition of EOs was identified by gas chromatography-mass spectrometry analysis. The main compounds identified in rosemary EO were eucalyptol (13.05%), camphor (8.93%), verbenone (8.58%), endo-borneol (7.87%) and alpha-pinene (6.78%) and in thyme EO were linalool (18.18%), thymol (7.48%), limonene (6.49%), endo-borneol (5.86%) and terpinen-4-ol (5.66%). Using the minimum inhibitory concentration (MIC) method, L. monocytogenes was inhibited at 3.9 mu L/mL to thyme EO and at 62.5 mu L/mL to rosemary EO. Beef samples of M. longissimus thoracis et lumborum were packaged in bags, inoculated and one of each EO was added at MIC values. Bags were vacuum-sealed and samples were processed at 55C/65 min for 3log(10) CFU/g reduction. A reduction of the counts of L. monocytogenes was observed in all samples at 2C. At 8C counts of L. monocytogenes were almost similar in control samples and those with thyme EO with an increase of themicrobial counts since day 7. Inversely, counts of L. monocytogenes in beef samples with rosemary EO stored at 2 and 8C decreased about 2 log(10) CFU. These results support the possibility of using rosemary EO as natural preservative due to its antimicrobial effect against L. monocytogenes. Also, our results confirm that an adequate chilling storage is essential to guarantee the safety of SVCC product regarding L. monocytogenes to avoid foodborne outbreaks.

Abstract
A method to detect juxta-pleural nodules with radius smaller than 5mm is presented. The intensity difference between nodules and parenchymal tissue as well as the nodules' natural roundness are exploited. Solid nodules are detected by selecting an appropriate threshold over a sliding window, whereas sub-solid/non-solid nodules are enhanced using multi-scale Laplacian-of-Gaussian filters. The 2D-wise outputs are combined to 3D, producing a final candidate list. False positive reduction is achieved with fixed rules and supervised learning. The achieved sensitivity is 57% with 4 false positives/scan, increasing to 62% if only solid nodules are considered.

Abstract
In lung cancer diagnosis, the design of robust Computer Aided Diagnosis (CAD) systems needs to include an adequate differentiation of benign from malignant nodules. This paper presents a CAD system for the classification of lung nodules in chest Computed Tomography (CT) scans as the way to diagnose lung cancer. The proposed method measures a set of 295 heterogeneous characteristics, including morphology, intensity or texture features, that were used as input of different KNN and SVM classifiers. The system was modeled and trained using a groundtruth provided by specialists taken from a public lung image dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). This image dataset includes chest CT scans with lung nodule location together with information about the degree of malignancy, among other properties, provided by multiple expert clinicians. In particular, the computed degree of malignancy try to follow the manual labeling by the different radiologists. Promising results were obtained with a first order SVM with an exponential kernel achieving an area under the receiver operating characteristic curve of 96.2 +/- 0.5% when compared with the groundtruth provided in the public CT lung image dataset.