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Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that makes use of GIS technology for the digitization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure (a) increases the interpreter's overall efficiency by a factor of 2, (b) reduces significantly the subjectivity introduced by the visual (manual) transfer of the landslide information to the digital database, resulting in more accurate LIMs. With the new procedure, the landslide positional error decreases with increasing landslide size, following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.

Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km 2 in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km 2 . All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.

Inundations and landslides are widespread phenomena in Italy, where they cause severe damage and pose a threat to the population. Little is known about the public perception of landslide and flood risk. This is surprising, as an accurate perception is important for the successful implementation of many risk reduction or adaptation strategies. In an attempt to address this gap, we have conducted two national surveys to measure the perception of landslide and flood risk amongst the population of Italy. The surveys were conducted in 2012 and 2013, and consisted of approximately 3100 computer-assisted telephone interviews for each survey. The samples of the interviewees were statistically representative for a national-scale quantitative assessment. The interviewees were asked questions designed to obtain information on (i) their perception of natural, environmental, and technological risks, (ii) direct experience or general knowledge of the occurrence of landslides and floods in their municipality, (iii) perception of the possible threat posed by landslides and floods to their safety, (iv) general knowledge on the number of victims affected by landslides or floods, and on (v) the factors that the interviewees considered important for controlling landslide and flood risks in Italy. The surveys revealed that the population of Italy fears technological risks more than natural risks. Of the natural risks, earthquakes were considered more dangerous than floods, landslides, and volcanic eruptions. Examination of the temporal and geographical distributions of the responses revealed that the occurrence of recent damaging events influenced risk perception locally, and that the perception persisted longer for earthquakes and decreased more rapidly for landslides and floods. We explain the difference by the diverse consequences of the risks. The interviewees considered inappropriate land management the main cause of landslide and food risk, followed by illegal construction, abandonment of the territory, and climate change. Comparison of the risk perception with actual measures of landslide and flood risk, including the number of fatal events, the number of fatalities, and the mortality rates, revealed that in most of the Italian regions, the perception of the threat did not match the long-term risk posed to the population by landslides and floods. This outcome points to a need to foster an understanding of the public towards landslide and flood hazards and risks in Italy.

We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

We describe a semi-automatic procedure for the classification of satellite imagery into landslide or no landslide categories, aimed at preparing event landslide inventory maps. The two-steps procedure requires knowledge of the occurrence of a landslide event, availability of a pre- and post- event pseudo-stereo pair and a digital elevation model. The first step consists in the evaluation of a discriminant function, applied to a combination of well-known change detection indices tuned on landslide spectral response. The second step is devoted to discriminant function classification, aimed at distinguishing the only landslide class, through an improvement of the usual 'thresholding' method. We devised a multi-threshold classification, in which thresholding is applied separately in small subsets of the scene. We show that using slope units as topographic-aware subsets produces best classification performance when compared to the ground truth of a landslide inventory prepared by visual interpretation. The method proved to be superior to the use of a single threshold and to any multi-threshold procedure based on topography-blind subdivisions of the scene, especially in the validation stage. We argue that the improved classification performance and limited training requirements represent a step forward towards an automatic, real-time landslide mapping from satellite imagery.

We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the \"non-susceptible\" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia – IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.

Several bacterial pathogens have TIR domain-containing proteins that contribute to their pathogenesis. We identified a second TIR-containing protein in Brucella spp. that we have designated BtpB. We show it is a potent inhibitor of TLR signaling, probably via MyD88. BtpB is a novel Brucella effector that is translocated into host cells and interferes with activation of dendritic cells. In vivo mouse studies revealed that BtpB is contributing to virulence and control of local inflammatory responses with relevance in the establishment of chronic brucellosis. Together, our results show that BtpB is a novel Brucella effector that plays a major role in the modulation of host innate immune response during infection.

, the causative agent of bovine brucellosis, invades and replicates within cells inside a membrane-bound compartment known as the containing vacuole (BCV). After trafficking along the endocytic and secretory pathways, BCVs mature into endoplasmic reticulum-derived compartments permissive for bacterial replication. Type IV Secretion System (VirB) is a major virulence factor essential for the biogenesis of the replicative organelle. Upon infection, uses the VirB system to translocate effector proteins from the BCV into the host cell cytoplasm. Although the functions of many translocated proteins remain unknown, some of them have been demonstrated to modulate host cell signaling pathways to favor intracellular survival and replication. BPE123 (BAB2_0123) is a VirB-translocated effector protein recently identified by our group whose function is yet unknown. In an attempt to identify host cell proteins interacting with BPE123, a pull-down assay was performed and human alpha-enolase (ENO-1) was identified by LC/MS-MS as a potential interaction partner of BPE123. These results were confirmed by immunoprecipitation assays. In bone-marrow derived macrophages infected with , ENO-1 associates to BCVs in a BPE123-dependent manner, indicating that interaction with translocated BPE123 is also occurring during the intracellular phase of the bacterium. Furthermore, ENO-1 depletion by siRNA impaired intracellular replication in HeLa cells, confirming a role for α-enolase during the infection process. Indeed, ENO-1 activity levels were enhanced upon infection of THP-1 macrophagic cells, and this activation is highly dependent on BPE123. Taken together, these results suggest that interaction between BPE123 and host cell ENO-1 contributes to the intracellular lifestyle of .

Inundations and landslides are common phenomena that cause serious damage and pose a severe threat to the population of Italy. The societal and economic impact of landslides and floods in Italy is particularly severe, and strategies that target the mitigation of the effects of these events are essential. Although, in the last few years, the scientific community has wanted to communicate information on research activities regarding geo-hydrological hazards and the associated risks to society through thematic websites, very often, communication achieves specific technical purposes for experts. To address the problem posed by the lack of communication on geo-hydrological hazards with potential human consequences in Italy to the broader society, we designed the POLARIS website. The POLARIS website publishes accurate and detailed information on geo-hydrological risks, including periodical reports on landslide and flood risk to the population of Italy, data and analysis on specific damaging events and blog posts on landslide and flood events that able to encourage mass media and citizens' engagement. By monitoring the access of users to POLARIS since January 2014, when the website was published, we registered maximum access during the occurrence of the worst geo-hydrological events and for the promotion of relevant new content through press releases. In particular, in the latter case, we noted the highest access value when journalists promoted the website through television channels. The POLARIS initiative demonstrates how the scientific community can implement suitable communication strategies that address different societal audiences by exploiting the role of mass media and social media. These strategies can help these audiences to understand how risks can be reduced through appropriate measures and behaviors; thus, they can contribute to increasing the resilience of the population to geo-hydrological events.

Automatic subdivision of landscapes into terrain units remains a challenge. Slope units are terrain units bounded by drainage and divide lines, but their use in hydrological and geomorphological studies is limited because of the lack of reliable software for their automatic delineation. We present the r.slopeunits software for the automatic delineation of slope units, given a digital elevation model and a few input parameters. We further propose an approach for the selection of optimal parameters controlling the terrain subdivision for landslide susceptibility modeling. We tested the software and the optimization approach in central Italy, where terrain, landslide, and geo-environmental information was available. The software was capable of capturing the variability of the landscape and partitioning the study area into slope units suited for landslide susceptibility modeling and zonation. We expect r.slopeunits to be used in different physiographical settings for the production of reliable and reproducible landslide susceptibility zonations.