Suncus etruscus (Soricomorpha, Soricidae): A new species for Elba Island (Tuscan Archipelago, Italy)

The following study contains a report regarding the first record of presence of Suncus etruscus on the island of Elba. We considered original and literature data obtained from the analysis of Barn owl (Tyto alba) pellets. Three roosts located in different areas of Elba (Marciana: Colle d'Orano-Patresi; Campo nell'Elba: La Grotta; Portoferraio: Casa Rossa) have been monitored since 1968. The presence of the Pygmy white-toothed shrew has only been observed at one roost (Casa Rossa) since 2004. We performed a biometrical analysis of 15 skull characters on 67 specimens of 'S. etruscus. From the results, we conclude that a colonization of the island by S. etruscus is in progress, but long-term monitoring is needed in order to control the status of the population

The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of those operations that are typically carried out by humans (e.g., building inspection, photo collection). The potential of drone applications can be pushed even further when they are operated in fleets and in a fully autonomous manner, acting de facto as a drone swarm. Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members themselves, as well as by the field agents operating within the area of interest. By considering the practical example of a swarm-powered 3D reconstruction application, we present a new optimization problem for the efficient generation and execution, on top of swarm-powered ad-hoc cloud infrastructure, of multi-node computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the inter-drone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones

Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones

On the Use of Electrooculogram for Efficient Human Computer Interfaces

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as “clean-up” could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes

Least squares optimization: From theory to practice

Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios

Using urban climate modelling and improved land use classifications to support climate change adaptation in urban environments: A case study for the city of Klagenfurt, Austria

This study outlines the results of current and future climate scenarios, and potentially realizable climate adaptation measures, for the city of Klagenfurt, Austria. For this purpose, we used the microscale urban climate model (MUKLIMO_3), in conjunction with the cuboid method, to calculate climate indices such as the average number of summer and hot days per year. For the baseline simulation, we used meteorological measurements from 1981 to 2010 from the weather station located at Klagenfurt Airport. Individual building structures and canopy cover from several land monitoring services were used to derive accurate properties for land use classes in the study domain. To characterize the effectiveness of climate adaptation strategies, we compared changes in the climate indices for several (future) climate adaptation scenarios to the reference simulation. Specifically, we considered two major adaptation pathways: (i) an increase in the albedo values of sealed areas (i.e., roofs, walls and streets) and (ii) an increase in green surfaces (i.e., lawns on streets and at roof level) and high vegetated areas (i.e., trees). The results indicate that some climate adaptation measures show higher potential in mitigating hot days than others, varying between reductions of 2.3 to 11.0%. An overall combination of adaptation measures leads to a maximum reduction of up to 44.0%, indicating a clear potential for reduction/mitigation of urban heat loads. Furthermore, the results for the future scenarios reveal the possibility to remain at the current level of urban heat load during the daytime over the next three decades for the overall combination of measures

Guaranteed clustering and biclustering via semidefinite programming

Identifying clusters of similar objects in data plays a significant role in a wide range of applications. As a model problem for clustering, we consider the densest k-disjoint-clique problem, whose goal is to identify the collection of k disjoint cliques of a given weighted complete graph maximizing the sum of the densities of the complete subgraphs induced by these cliques. In this paper, we establish conditions ensuring exact recovery of the densest k cliques of a given graph from the optimal solution of a particular semidefinite program. In particular, the semidefinite relaxation is exact for input graphs corresponding to data consisting of k large, distinct clusters and a smaller number of outliers. This approach also yields a semidefinite relaxation for the biclustering problem with similar recovery guarantees. Given a set of objects and a set of features exhibited by these objects, biclustering seeks to simultaneously group the objects and features according to their expression levels. This problem may be posed as partitioning the nodes of a weighted bipartite complete graph such that the sum of the densities of the resulting bipartite complete subgraphs is maximized. As in our analysis of the densest k-disjoint-clique problem, we show that the correct partition of the objects and features can be recovered from the optimal solution of a semidefinite program in the case that the given data consists of several disjoint sets of objects exhibiting similar features. Empirical evidence from numerical experiments supporting these theoretical guarantees is also provided

Move and countermove: the integrated stress response in picorna- and coronavirus-infected cells

Viruses, when entering their host cells, are met by a fierce intracellular immune defense. One prominent antiviral pathway is the integrated stress response (ISR). Upon activation of the ISR - typically though not exclusively upon detection of dsRNA - translation-initiation factor eukaryotic initiation factor 2 (eIF2) becomes phosphorylated to act as an inhibitor of guanine nucleotide-exchange factor eIF2B. Thus, with the production of ternary complex blocked, a global translational arrest ensues. Successful virus replication hinges on effective countermeasures. Here, we review ISR antagonists and antagonistic mechanisms employed by picorna- and coronaviruses. Special attention will be given to a recently discovered class of viral antagonists that inhibit the ISR by targeting eIF2B, thereby allowing unabated translation initiation even at exceedingly high levels of phosphorylated eIF2

Dissecting distinct proteolytic activities of FMDV Lpro implicates cleavage and degradation of RLR signaling proteins, not its deISGylase/DUB activity, in type I interferon suppression

Author summary Outbreaks of the picornavirus foot-and-mouth disease virus (FMDV) have significant consequences for animal health and product safety and place a major economic burden on the global livestock industry. Understanding how this notorious animal pathogen suppresses the antiviral type I interferon (IFN-alpha/beta) response may help to develop countermeasures to control FMDV infections. FMDV suppresses the IFN-alpha/beta response through the activity of its Leader protein (L-pro), a protease that can cleave host cell proteins. L(pro)was also shown to have deubiquitinase and deISGylase activity, raising the possibility that L(pro)suppresses IFN-alpha/beta by removing ubiquitin and/or ISG15, two posttranslational modifications that can regulate the activation, interactions and localization of (signaling) proteins. Here, we show that TBK1 and MAVS, two signaling proteins that are important for activation of IFN-alpha/beta gene transcription, are cleaved by L-pro. By generating L(pro)mutants lacking either of these two activities, we demonstrate that L-pro's ability to cleave signaling proteins, but not its deubiquitination/deISGylase activity, correlates with suppression of IFN-beta gene transcription. The type I interferon response is an important innate antiviral pathway. Recognition of viral RNA by RIG-I-like receptors (RLRs) activates a signaling cascade that leads to type I interferon (IFN-alpha/beta) gene transcription. Multiple proteins in this signaling pathway (e.g. RIG-I, MDA5, MAVS, TBK1, IRF3) are regulated by (de)ubiquitination events. Most viruses have evolved mechanisms to counter this antiviral response. The leader protease (L-pro) of foot-and-mouth-disease virus (FMDV) has been recognized to reduce IFN-alpha/beta gene transcription; however, the exact mechanism is unknown. The proteolytic activity of L(pro)is vital for releasing itself from the viral polyprotein and for cleaving and degrading specific host cell proteins, such as eIF4G and NF-kappa B. In addition, L(pro)has been demonstrated to have deubiquitination/deISGylation activity. L-pro's deubiquitination/deISGylation activity and the cleavage/degradation of signaling proteins have both been postulated to be important for reduced IFN-alpha/beta gene transcription. Here, we demonstrate that TBK1, the kinase that phosphorylates and activates the transcription factor IRF3, is cleaved by L(pro)in FMDV-infected cells as well as in cells infected with a recombinant EMCV expressing L-pro.In vitrocleavage experiments revealed that L(pro)cleaves TBK1 at residues 692-694. We also observed cleavage of MAVS in HeLa cells infected with EMCV-L-pro, but only observed decreasing levels of MAVS in FMDV-infected porcine LFPK alpha V beta 6 cells. We set out to dissect L-pro's ability to cleave RLR signaling proteins from its deubiquitination/deISGylation activity, to determine their relative contributions to the reduction of IFN-alpha/beta gene transcription. The introduction of specific mutations, of which several were based on the recently published structure of L(pro)in complex with ISG15, allowed us to identify specific amino acid substitutions that separate the different proteolytic activities of L-pro. Characterization of the effects of these mutations revealed that L-pro's ability to cleave RLR signaling proteins but not its deubiquitination/deISGylation activity correlates with the reduced IFN-beta gene transcription