Abstract
The aim of this work was to investigate the potential of Fourier transform infrared spectroscopy (FTIR) to detect and predict the bacterial load of salmon fillets (Salmo salar) stored at 3, 8 and 30 degrees C under three packaging conditions: air packaging (AP) and two modified atmospheres constituted by a mixture of 50%N-2/40%CO2/10%O-2 with lemon juice (MAPL) and without lemon juice (MAP). Fresh salmon samples were periodically examined for total viable counts (TVC), specific spoilage organisms (SSO) counts, pH, FTIR and sensory assessment of freshness. Principal components analysis (PCA) allowed identification of the wavenumbers potentially correlated with the spoilage process. Linear discriminant analysis (LDA) of infrared spectral data was performed to support sensory data and to accurately identify samples freshness. The effect of the packaging atmospheres was assessed by microbial enumeration and LDA was used to determine sample packaging from the measured infrared spectra. It was verified that modified atmospheres can decrease significantly the bacterial load of fresh salmon. Lemon juice combined with MAP showed a more pronounced delay in the growth of Brochothrix thermosphacta, Photobacterium phosphoreum, psychrotrophs and H2S producers. Partial least squares regression (PLS-R) allowed estimates of TVC and psychrotrophs, lactic acid bacteria, molds and yeasts, Brochothrix thermosphacta, Enterobacteriaceae, Pseudomonas spp. and H2S producer counts from the infrared spectral data: For TVC, the root mean square error of prediction (RMSEP) value was 0.78 log cfu g(-1) for an external set of samples. According to the results, FTIR can be used as a reliable, accurate and fast method for real time freshness evaluation of salmon fillets stored under different temperatures and packaging atmospheres.

Abstract

Abstract
One of the most common ways of collecting ocean data is to deploy sensors from the surface, allowing to understand the variation of water properties with depth. Autonomous vertical profilers are robotic vehicles that replace human operators in this task. They form a particular class of autonomous underwater vehicles that move predominantly along the vertical axis, typically with reduced control on the horizontal axis. This paper describes a propeller driven autonomous underwater profiler, optimized for shallow waters. The vehicle has no fins or other control surfaces, and uses four independent thrusters to provide both vertical and horizontal motion, including hovering in the water column. The paper describes the main subsystems, including the hardware implementation, the software structure, and the motion controllers, with experimental data from the first trials. © 2017 Marine Technology Society.

Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Abstract
Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study aims at characterizing the complexity of FHR features captured by entropy and compression, using as reference international guidelines. Single and multi-scale approaches were considered in the computation of entropy and compression. The following physiologic-based features were considered: FHR baseline; percentage of abnormal long (% abLTV) and short (% abSTV) term variability; average short term variability; and, number of acceleration and decelerations. All of the features were computed on a set of 68 intrapartum FHR tracings, divided as normal, mildly, and moderately-severely acidemic born fetuses. The correlation between entropy/compression features and the physiologic-based features was assessed. There were correlations between compressions and accelerations and decelerations, but neither accelerations nor decelerations were significantly correlated with entropies. The % abSTV was significantly correlated with entropies (ranging between 0.54 and 0.62), and to a higher extent with compression (ranging between 0.80 and 0.94). Distinction between groups was clearer in the lower scales using entropy and in the higher scales using compression. Entropy and compression are complementary complexity measures.

Abstract
Distributed Generation (DG) using renewable technologies is increasing due to their benefits including energy security and emission reduction. However, installing new DGs in distributed networks is limited due to network constraints such as feeder capacity and short circuit level, as well as higher investment costs. In this paper, network reconfiguration and reactive power planning are used to maximize DG penetration level and to minimize annual loss for DGs with biomass technologies. In order to model the problem uncertainties, 96 scenarios considering ten different network load levels are studied. A multi objective method is applied for solving this optimization problem by using Pareto front. The numerical results indicate the positive impacts of the proposed approach on improving the network security.