Definition of a new multi-level early warning procedure for landslide risk management

The identification of potentially critical events involving unstable slopes is a major aspect in the field of natural hazards risk mitigation and management. In this framework, Early Warning Systems (EWS) exploiting advanced technologies represent an efficient approach to decrease the risk generated by landslide phenomena, allowing to reduce the possibility of damages and losses of human lives. EWS effectiveness has increased significantly in recent years, thanks to relevant advances in sensing technologies and data processing. In particular, the introduction of innovative monitoring instrumentation featuring automatic procedures and increased performances in terms of sampling rate and accuracy has permitted to develop EWS characterised by a near-real time approach. Among the several aspects involved in the development of a reliable Early Warning System, one of the most important is the ability to minimize the dissemination of false alarms, which should be avoided or identified in advance. The approach proposed in this study represents a new procedure aimed to assess the hazard level posed by a potentially critical event, previously identified by analysing displacement monitoring data. The process is implemented in a near-real time EWS and defines a total of five different hazard levels, on the basis of the results provided by two different models, namely an accelerating trend identification criterion and a failure forecasting model based on the Inverse Velocity Method (IVM). In particular, the forecasting analysis is performed only if the dataset elaborated by the onset-of-acceleration model highlights a potentially critical behaviour, which represents a first alert level. Following levels are determined by different conditions imposed on three parameters featured by the failure forecasting model, i.e. dataset dimension, coefficient of determination R-squared, and number of sensors displaying an accelerating trend. As these criteria get fulfilled, it is assumed that the monitored phenomenon is gradually evolving towards a more critical condition, thus reaching an increasing alert level depending on the analysis results. According to this classification, it is possible to set up for each single threshold a dedicated warning message, which could be automatically issued to authorities responsible of monitoring activities, in order to provide an adequate dissemination of information concerning the ongoing event. Moreover, the proposed procedure allows to customize the alert approach, giving the possibility to issue warning messages only if a certain Level is reached during the analysis

application of innovative monitoring tools for safety and alert procedures in road tunnels

Abstract Tunnels and underground structures are one of the most important components of road and railway networks, especially near urban areas. For this reason, it is particularly important to identify potentially hazardous conditions in order to guarantee the structure's durability and practicability. This paper presents a case study where a seismic event severely damaged a road tunnel located in Central Italy, impairing its accessibility and leading to its closure for safety reasons. Following the damage assessment, and given the importance of this specific structure, it was decided to perform a series of renovation works aimed to restore the tunnel's operability. In this context, an innovative automatic monitoring device, able to measure the structure deformation, was installed in a critical section of the road tunnel. This instrument, called Cir Array, is specifically designed for near-real time monitoring of convergence phenomena and localized deformations inside underground structures, obtaining accurate and reliable results during their operational phase. The instrumentation provided useful information about the structure's conditions, playing a major role into assessing the tunnel's accessibility and safety during the renovation works. Moreover, thanks to its automated and high frequency sampling process, it will allow the implementation of dedicated warning procedures related to the passage of the vehicles inside the tunnel

Prediction of functional outcome in young patients with a recent-onset psychiatric disorder: Beyond the traditional diagnostic classification system

A critical research goal is to identify modifiable risk factors leading to functional disabilities in young psychiatric patients. The authors developed a multidimensional trans-diagnostic predictive model of functional outcome in patients with the recent-onset of a psychiatric illness. Baseline clinical, psychosis-risk status, cognitive, neurological-soft-signs measures, and dopamine-related-gene polymorphisms (DRD1-rs4532, COMT-rs165599, and DRD4-rs1300955) were collected in 133 young non-psychotic outpatients. 116 individuals underwent follow-up (mean = 22 years, SD = 0.9) examination, A binary logistic model was used to predict low-functioning status at follow-up as defined by a score lower than 65 in the social occupational functioning assessment scale. A total of 54% of patients experiences low functioning at follow-up. Attention, Avolition, and Motor-Coordination subscale were significant predictors of low-functioning with an accuracy of 79.7%. A non-significant trend was found for a dopamine-related-gene polymorphism (DRD1-rs4532). The model was independent of psychotic risk status, DSM-diagnosis, and psychotic conversion. A trans-diagnostic approach taking into account specific neurocognitive, clinical, and neurological information has the potential to identify those individuals with low-functioning independent of DSM diagnosis or the level of psychosis-risk. Specific early interventions targeting modifiable risk factors and emphasize functional recovery in young psychiatric samples, independent of DSM-diagnosis and psychosis-risk, are essential. (C) 2016 Elsevier B.V. All rights reserved

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall

Sustained kidney biochemical derangement in treated experimental diabetes : a clue to metabolic memory.

The occurrence of biochemical alterations that last for a long period of time in diabetic individuals even after adequate handling of glycemia is an intriguing phenomenon named metabolic memory. In this study, we show that a kidney pathway is gradually altered during the course of diabetes and remains persistently changed after late glycemic control in streptozotocin-induced diabetic rats. This pathway comprises an early decline of uric acid clearance and pAMPK expression followed by fumarate accumulation, increased TGF-? expression, reduced PGC-1? expression, and downregulation of methylation and hydroxymethylation of mitochondrial DNA. The sustained decrease of uric acid clearance in treated diabetes may support the prolonged kidney biochemical alterations observed after tight glycemic control, and this regulation is likely mediated by the sustained decrease of AMPK activity and the induction of inflammation. This manuscript proposes the first consideration of the possible role of hyperuricemia and the underlying biochemical changes as part of metabolic memory in diabetic nephropathy development after glycemic control

EphA3 Expressed in the Chicken Tectum Stimulates Nasal Retinal Ganglion Cell Axon Growth and Is Required for Retinotectal Topographic Map Formation

BACKGROUND: Retinotopic projection onto the tectum/colliculus constitutes the most studied model of topographic mapping and Eph receptors and their ligands, the ephrins, are the best characterized molecular system involved in this process. Ephrin-As, expressed in an increasing rostro-caudal gradient in the tectum/colliculus, repel temporal retinal ganglion cell (RGC) axons from the caudal tectum and inhibit their branching posterior to their termination zones. However, there are conflicting data regarding the nature of the second force that guides nasal axons to invade and branch only in the caudal tectum/colliculus. The predominant model postulates that this second force is produced by a decreasing rostro-caudal gradient of EphA7 which repels nasal optic fibers and prevents their branching in the rostral tectum/colliculus. However, as optic fibers invade the tectum/colliculus growing throughout this gradient, this model cannot explain how the axons grow throughout this repellent molecule. METHODOLOGY/PRINCIPAL FINDINGS: By using chicken retinal cultures we showed that EphA3 ectodomain stimulates nasal RGC axon growth in a concentration dependent way. Moreover, we showed that nasal axons choose growing on EphA3-expressing cells and that EphA3 diminishes the density of interstitial filopodia in nasal RGC axons. Accordingly, in vivo EphA3 ectodomain misexpression directs nasal optic fibers toward the caudal tectum preventing their branching in the rostral tectum. CONCLUSIONS: We demonstrated in vitro and in vivo that EphA3 ectodomain (which is expressed in a decreasing rostro-caudal gradient in the tectum) is necessary for topographic mapping by stimulating the nasal axon growth toward the caudal tectum and inhibiting their branching in the rostral tectum. Furthermore, the ability of EphA3 of stimulating axon growth allows understanding how optic fibers invade the tectum growing throughout this molecular gradient. Therefore, opposing tectal gradients of repellent ephrin-As and of axon growth stimulating EphA3 complement each other to map optic fibers along the rostro-caudal tectal axis

Innovative Technologies for Monitoring Underground excavations during construction and usage

Tunnel design and excavation are complex problems which requires a lot of studies and data. Starting from the half of the last century, new methods like NATM and ADECO-RS were developed in order to better understand the behaviour of the rock mass and provide proper construction solutions. In particular, all these methods are now used worldwide and focus on the importance of the monitoring during the excavation processes to prevent instability or review the design assumptions. The paper describes the studies carried out on a new technology developed to monitor tunnel deformations during both construction and exercise phases. In particular, two different kinds of instrumentation have been developed using 3D MEMS based sensors. The first one, called CIR-Array, is a chain of nodes (Tunnel Link) equipped with an 3D accelerometer and a thermometer. It provides information about convergence of tunnels sections. The second one, called RAD-Array, is a mixed system that incorporates MEMS nodes (Radial Link) and multi-base extensometer. It provides information about the 3D deformations some meters inside the rock mass. The importance of this kind of instrumentation is related to the rapid and easy installation, to the high frequency of readings (which permits a statistical approach, increasing reliability and efficiency) and to the recording automation. Other important features are the possibility to extend the monitoring after the construction phase during tunnel operations and the relative low cost, if compared with other monitoring systems. The tests were conducted reproducing a theoretical real scale original tunnel shape in our laboratory using a fiber glass rod and then generate known deformation. Sensors have been installed along the section every 0.5 meters. Different deformations have been simulated and, for each one, data of different nature were collected using CIR-Array or RAD-Array, photogrammetry, laser distance meter and topography. Results are compared in order to understand the sensitivity and accuracy of every method and evaluate CIR-Array / RAD – Array solutions. New technologies have also been subjected to reliability test. The aim of this paper is to collect and analyse the preliminary results obtained to highlight the potential application of the technology