Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

Fast and sensitive supercritical fluid chromatography - tandem mass spectrometry multi-class screening method for the determination of doping agents in urine.

This study shows the possibility offered by modern ultra-high performance supercritical fluid chromatography combined with tandem mass spectrometry in doping control analysis. A high throughput screening method was developed for 100 substances belonging to the challenging classes of anabolic agents, hormones and metabolic modulators, synthetic cannabinoids and glucocorticoids, which should be detected at low concentrations in urine. To selectively extract these doping agents from urine, a supported liquid extraction procedure was implemented in a 48-well plate format. At the tested concentration levels ranging from 0.5 to 5 ng/mL, the recoveries were better than 70% for 48-68% of the compounds and higher than 50% for 83-87% of the tested substances. Due to the numerous interferences related to isomers of steroids and ions produced by the loss of water in the electrospray source, the choice of SFC separation conditions was very challenging. After careful optimization, a Diol stationary phase was employed. The total analysis time for the screening assay was only 8 min, and interferences as well as susceptibility to matrix effect (ME) were minimized. With the developed method, about 70% of the compounds had relative ME within the range ±20%, at a concentration of 1 and 5 ng/mL. Finally, limits of detection achieved with the above-described strategy including 5-fold preconcentration were below 0.1 ng/mL for the majority of the tested compounds. Therefore, LODs were systematically better than the minimum required performance levels established by the World anti-doping agency, except for very few metabolites.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

Towards the Construction of a Multi-agent Approach for Discovering the Meaning of Natural Language Collaborative Conversations

Peer-reviewed

Determinações multi-elementares de metais, Sulfato e Cloreto em amostras de águas por espectrometria de massas com fonte de plasma (ICP-MS)

The multi-element determination of Al, Cr, Mn, Ni, Cu, Zn, Cd, Ba, Pb, SO4= and Cl- in riverine water samples was accomplished by inductively coupled plasma mass spectrometry (ICP-MS). The sample passed through a column containing the anionic resin AG1-X8 and the metals were determined directly. The retained anionic species were eluted and SO4= and Cl- were determined at m/z 48 and 35 correspondent to the ions SO+ and Cl+ formed at the plasma. Accuracy for metals was assessed by analysing the certified reference TM-26 (National Water Research Institute of Canada). Results for SO4= and Cl- were in agreement with those obtained by turbidimetry and spectrophotometry. LOD's of 0.1 µg l-1 for Cd, Ba and Pb; 0.2 µg l-1 for Al, Mn and Cu; 0.5 µg l-1 for Cr; 0.9 for Zn; 2.0 µg l-1for Ni , 60 µg l-1 for S and 200 µg l-1 Cl were attained.

Multi-sensor Simultaneous Localization and Mapping

Simultaneous localization and mapping(SLAM) is a very important problem in mobile robotics. Many solutions have been proposed by different scientists during the last two decades, nevertheless few studies have considered the use of multiple sensors simultane¬ously. The solution is on combining several data sources with the aid of an Extended Kalman Filter (EKF). Two approaches are proposed. The first one is to use the ordinary EKF SLAM algorithm for each data source separately in parallel and then at the end of each step, fuse the results into one solution. Another proposed approach is the use of multiple data sources simultaneously in a single filter. The comparison of the computational com¬plexity of the two methods is also presented. The first method is almost four times faster than the second one.

Social Networking on Mobile Environment on top of PeerHood

Social networking and social networking sites have gained popularity among internet users during the past few years. Social networks fulfill the need of users to stay connected to friends and other people interested in the same issues. Combining social networks to the mobile environment is a growing interest of mobile device users as it allows the users to be in their online social community despite their mobility. This thesis highlights the basics of mobile environment, social networking and PeerHood and introduces a new approach of social networking on mobile environment, which is a new concept in mobile social networking. This approach is based on dynamic group discovery in accordance to some common user interests and management in the PeerHood environment. A reference implementation of a social networking application built on top of PeerHood is presented and it is tested and analyzed to understand the social networking on mobile environment and the new concept of dynamic group discovery in it.

Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input-output approach

Global warming mitigation has recently become a priority worldwide. A large body of literature dealing with energy related problems has focused on reducing greenhouse gases emissions at an engineering scale. In contrast, the minimization of climate change at a wider macroeconomic level has so far received much less attention. We investigate here the issue of how to mitigate global warming by performing changes in an economy. To this end, we make use of a systematic tool that combines three methods: linear programming, environmentally extended input output models, and life cycle assessment principles. The problem of identifying key economic sectors that contribute significantly to global warming is posed in mathematical terms as a bi criteria linear program that seeks to optimize simultaneously the total economic output and the total life cycle CO2 emissions. We have applied this approach to the European Union economy, finding that significant reductions in global warming potential can be attained by regulating specific economic sectors. Our tool is intended to aid policymakers in the design of more effective public policies for achieving the environmental and economic targets sought.