A New Search Algorithm for Feature Selection in Hyperspectral Remote Sensing Images

A new suboptimal search strategy suitable for feature selection in very high-dimensional remote-sensing images (e.g. those acquired by hyperspectral sensors) is proposed. Each solution of the feature selection problem is represented as a binary string that indicates which features are selected and which are disregarded. In turn, each binary string corresponds to a point of a multidimensional binary space. Given a criterion function to evaluate the effectiveness of a selected solution, the proposed strategy is based on the search for constrained local extremes of such a function in the above-defined binary space. In particular, two different algorithms are presented that explore the space of solutions in different ways. These algorithms are compared with the classical sequential forward selection and sequential forward floating selection suboptimal techniques, using hyperspectral remote-sensing images (acquired by the AVIRIS sensor) as a data set. Experimental results point out the effectiveness of both algorithms, which can be regarded as valid alternatives to classical methods, as they allow interesting tradeoffs between the qualities of selected feature subsets and computational cost

Self field measurements by Hall sensors on the SeCRETS short sample CICC's subjected to cyclic load

An imbalance in the transport current among the strands of a Cable-in-Conduit conductors (CICC) can be associated with the change of their performance. In order to understand and improve the performance of CICC's, it is essential to study the current imbalance. This paper focuses on the study of the current imbalance in two short samples of the SeCRETS (Segregated Copper Ratio Experiment on Transient Stability) conductors subjected to a cyclic load in the SULTAN facility. The self field around the conductors was measured on four locations by 32 miniature Hall sensors for a reconstruction of the current distribution. The results of the self field measurements in the DC tests are presented and discussed

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

Class II ADP-ribosylation factors are required for efficient secretion of Dengue viruses

This article is available open access through the publisher’s website.Identification and characterization of virus-host interactions are very important steps toward a better understanding of the molecular mechanisms responsible for disease progression and pathogenesis. To date, very few cellular factors involved in the life cycle of flaviviruses, which are important human pathogens, have been described. In this study, we demonstrate a crucial role for class II Arf proteins (Arf4 and Arf5) in the dengue flavivirus life cycle. We show that simultaneous depletion of Arf4 and Arf5 blocks recombinant subviral particle secretion for all four dengue serotypes. Immunostaining analysis suggests that class II Arf proteins are required at an early pre-Golgi step for dengue virus secretion. Using a horseradish peroxidase protein fused to a signal peptide, we show that class II Arfs act specifically on dengue virus secretion without altering the secretion of proteins through the constitutive secretory pathway. Co-immunoprecipitation data demonstrate that the dengue prM glycoprotein interacts with class II Arf proteins but not through its C-terminal VXPX motif. Finally, experiments performed with replication-competent dengue and yellow fever viruses demonstrate that the depletion of class II Arfs inhibits virus secretion, thus confirming their implication in the virus life cycle, although data obtained with West Nile virus pointed out the differences in virus-host interactions among flaviviruses. Our findings shed new light on a molecular mechanism used by dengue viruses during the late stages of the life cycle and demonstrate a novel function for class II Arf proteins.Research Fund for Control of Infectious Diseases of Hong Kong and BNP Paribas Corporate and Investment Banking

Definition of the cellular interactome of the highly pathogenic avian influenza H5N1 virus: identification of human cellular regulators of viral entry, assembly, and egress.

This study was supported by the Research Fund for the Control of Infectious Diseases, Food and Health Bureau, Hong Kong SAR Government (#09080892), University Grants Committee Area of Excellence Scheme (AoE/M-12/-06), French Ministry of Health, RESPARI Pasteur network, and the Li Ka Shing Foundation

The M, E, and N structural proteins of the severe acute respiratory syndrome coronavirus are required for efficient assembly, trafficking, and release of virus-like particles

Copyright @ 2008 American Society for Microbiology.The production of virus-like particles (VLPs) constitutes a relevant and safe model to study molecular determinants of virion egress. The minimal requirement for the assembly of VLPs for the coronavirus responsible for severe acute respiratory syndrome in humans (SARS-CoV) is still controversial. Recent studies have shown that SARS-CoV VLP formation depends on either M and E proteins or M and N proteins. Here we show that both E and N proteins must be coexpressed with M protein for the efficient production and release of VLPs by transfected Vero E6 cells. This suggests that the mechanism of SARS-CoV assembly differs from that of other studied coronaviruses, which only require M and E proteins for VLP formation. When coexpressed, the native envelope trimeric S glycoprotein is incorporated onto VLPs. Interestingly, when a fluorescent protein tag is added to the C-terminal end of N or S protein, but not M protein, the chimeric viral proteins can be assembled within VLPs and allow visualization of VLP production and trafficking in living cells by state-of-the-art imaging technologies. Fluorescent VLPs will be used further to investigate the role of cellular machineries during SARS-CoV egress.The University of Hong Kong and the French Ministry of Health

Evaluation of extraction-free RT-qPCR methods for SARS-CoV-2 diagnostics

Extraction-based real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) is currently the “gold standard” in SARS-CoV-2 diagnostics. However, some extraction-free RT-qPCR techniques have recently been developed. In this study, we compared the sensitivity of traditional extraction-based, heated extraction-free, and unheated extraction-free RT-qPCR methods for SARS-CoV-2 detection in nasopharyngeal swabs from symptomatic individuals. The unheated extraction-free method showed perfect agreement with the standard extraction-based RT-qPCR. By contrast, the heat-treated technique was associated with an 8.2% false negativity rate. Unheated extraction-free RT-qPCR for the molecular diagnosis of SARS-CoV-2 is a valuable alternative to the traditional extraction-based methods and may accelerate turnaround times by about two hours