Overview of Hybrid MANET-DTN Networking and its Potential for Emergency Response Operations

Communication networks for emergency response operations have to operate in harsh environments. As fixed infrastructures may be unavailable (e.g., they are destroyed or overloaded), mobile ad-hoc networks (MANETs) are a promising solution to establish communication for emergency response operations. However, networks for emergency responses may provide diverse connectivity characteristics which imposes some challenges, especially on routing. Routing protocols need to take transmission errors, node failures and even the partitioning of the network into account. Thus, there is a need for routing algorithms that provide mechanisms from Delay or Disruption Tolerant Networking (DTN) in order to cope with network disruptions but at the same time are as efficient as MANET routing schemes in order to preserve network resources. This paper reviews several hybrid MANET-DTN routing schemes that can be found in the literature. Additionally, the paper evaluates a realistic emergency response scenario and shows that MANET-DTN routing schemes have the potential to improve network performance as the resulting network is diverse in terms of connectivity. In particular, the network provides well-connected regions whereas other parts are only intermittently connected

A Multimedia Delivery System for Delay-/Disruption-Tolerant Networks

Abstract-Multimedia delivery systems and protocols usually assume end-to-end connections and low delivery delays between multimedia sources and consumers. However, neither of these two properties can always be achieved in hastily formed networks for emergency response operations. In particular, disruptions may break end-to-end connections, which makes it impossible to deliver multimedia content instantly. This work presents a multimedia delivery system that can operate in disrupted networks and hence may help improve the situational awareness in emergency response operations. The multimedia delivery system is based on HTTP adaptive streaming (HAS) and uses a modified version of HTTP which is able to deliver data in partitioned networks. The multimedia delivery system is evaluated in a realistic emergency response scenario

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article

An Experimental Analysis on Drone-Mounted Access Points for Improved Latency-Reliability

The anticipated densification of contemporary communications infrastructure expects the use of drone small cells (DSCs). Thus, we experimentally evaluate the capability of providing local and personalized coverage with a drone mounted Wi-Fi access point that uses the nearby LTE infrastructure as a backhaul in areas with mixed line of sight(LoS) and Non-LoS (NLoS) links to the local cellular infrastructure. To assess the potential of DSCs for reliable and low latency communication of outdoor users, we measure the channel quality and the total round trip latency of the system. For a drone following the ground user, the DSC-provided network extends the coverage for an extra 6.4% when compared to the classical LTE-direct link. Moreover, the DSC setup provides latencies that are consistently smaller than 50 msfor 95% of the experiment. Within the coverage of the LTE-direct connection, we observed a latency ceiling of 120ms for 95% reliability of the LTE-direct connection. The highest latency observed for the DSC system was 1200ms, while the LTE-direct link never exceeded 500 ms. As such, DSC setups are not only essential in NLoS situations, but consistently improve the latency of users in outdoor scenarios.Comment: To be published in proceedings of DroNet21. Winner of DroNet21's Best Paper Awar

RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina

<p>Abstract</p> <p>Background</p> <p>The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina.</p> <p>Description</p> <p>RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated.</p> <p>Conclusion</p> <p>The results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.</p

Community carriage of ESBL-producing Escherichia coli and Klebsiella pneumoniae: a cross-sectional study of risk factors and comparative genomics of carriage and clinical isolates

The global prevalence of infections caused by extended-spectrum βlactamase-producing Enterobacterales (ESBL-E) is increasing, and for Escherichia coli, observations indicate that this is partly driven by community-onset cases. The ESBL-E population structure in the community is scarcely described, and data on risk factors for carriage are conflicting. Here, we report the prevalence and population structure of fecal ESBL-producing E. coli and Klebsiella pneumoniae (ESBL-Ec/Kp) in a general adult population, examine risk factors, and compare carriage isolates with contemporary clinical isolates. Fecal samples obtained from 4,999 participants (54% women) ≥40 years in the seventh survey of the population-based Tromsø Study, Norway (2015, 2016), were screened for ESBL-Ec/Kp. In addition, we included 118 ESBL-Ec clinical isolates from the Norwegian surveillance program in 2014. All isolates were wholegenome sequenced. Risk factors associated with carriage were analyzed using multivariable logistic regression. ESBL-Ec gastrointestinal carriage prevalence was 3.3% [95% confidence interval (CI) 2.8%–3.9%, no sex difference] and 0.08% (0.02%–0.20%) for ESBL-Kp. For ESBL-Ec, travel to Asia was the only independent risk factor (adjusted odds ratio 3.46, 95% CI 2.18–5.49). E. coli ST131 was most prevalent in both collections. However, the ST131 proportion was significantly lower in carriage (24%) versus clinical isolates (58%, P < 0.001). Carriage isolates were genetically more diverse with a higher proportion of phylogroup A (26%) than clinical isolates (5%, P < 0.001), indicating that ESBL gene acquisition occurs in a variety of E. coli lineages colonizing the gut. STs commonly related to extraintestinal infections were more frequent in clinical isolates also carrying a higher prevalence of antimicrobial resistance, which could indicate clone-associated pathogenicity

Benchmarking a new semantic similarity measure using fuzzy clustering and reference sets: Application to cancer expression data

International audienceClustering algorithms rely on a similarity or distance measure that directs the grouping of similar objects into the same cluster and the separation of distant objects between distinct clusters. Our recently described semantic similarity measure (IntelliGO), that applies to functional comparison of genes, is tested here for the first time in clustering experiments. The dataset is composed of genes contained in a benchmarking collection of reference sets. Heatmap visualization of hierarchical clustering illustrates the advantages of using the IntelliGO measure over three other similarity measures. Because genes often belong to more than one cluster in functional clustering, fuzzy C-means clustering is also applied to the dataset. The choice of the optimal number of clusters and clustering performance are evaluated by the F-score method using the reference sets. Overlap analysis is proposed as a method for exploiting the matching between clusters and reference sets. Finally, our method is applied to a list of genes found dysregulated in cancer samples. In this case, the reference sets are provided by expression profiles. Overlap analysis between these profiles and functional clusters obtained with fuzzy C-means clustering leads to characterize subsets of genes displaying consistent function and expression profiles.Les algorithmes de classification (Clustering) reposent sur des mesures de similarité ou de distance qui dirigent le regroupement des objets similaires dans un même groupe et la séparation des objets différents entre des groupes distincts. Notre nouvelle mesure de similarité sémantique (IntelliGO), récemment décrite, qui s'applique à la comparaison fonctionnelle des gènes, est testée ici dans un processus de clustering. L'ensemble de test est composé des gènes contenus dans une collection de classes de référence (Pathways KEGG). La visualisation du clustering hiérarchique avec des cartes de densité (heatmaps) illustre les avantages de l'utilisation de la mesure IntelliGO, par rapport à trois autres mesures de similarité. Comme les gènes peuvent souvent appartenir à plus d'un cluster fonctionnel, la méthode C-means floue est également appliquée à l'ensemble des gènes de la collection. Le choix du nombre optimal de clusters et la performance du clustering sont évalués par la méthode F-score en utilisant les classes de référence. Une analyse de recouvrement entre clusters et classes de référence est proposée pour faciliter des analyses ultérieures. Enfin, notre méthode est appliquée à une liste de gènes dérégulés, concernant le cancer colorectal. Dans ce cas, les classes de référence sont les profils d'expression de ces gènes. L'analyse de recouvrement entre ces profils et les clusters fonctionnels obtenus avec la méthode C-means floue conduit à caractériser des sousensembles de gènes partageant à la fois des fonctions biologiques communes et un comportement transcriptionel identique