A Review of the Technical and Socio-Organizational Components of Earthquake Early Warning Systems

Every year, natural hazards affect millions of people around the world, causing significant economic and life losses. The rapid progress of technology and advances in understanding of the highly complex physical phenomena related to various natural hazards have promoted the development of new disaster-mitigation tools, such as earthquake early warning (EEW) systems. However, there is a general lack of integration between the multi- and cross-disciplinary elements of EEW, limiting its effectiveness and applications for end users. This paper reviews the current state-of-the-art in EEW, exploring both the technical components (i.e., seismological and engineering) as well as the socio-organizational components (i.e., social science, policy, and management) of EEW systems. This includes a discussion of specific evidence from case studies of Italy, United States’ West Coast, Japan, and Mexico, where EEW systems have reached varying levels of maturity. Our aim is to highlight necessary improvements for increasing the effectiveness of the technical aspects of EEW in terms of their implications on operational, political/legal, social, behavioral, and organizational drivers. Our analysis suggests open areas for research, associated with: 1) the information that needs to be included in EEW alerts to implement successful mitigation actions at both individual and organizational levels; 2) the need for response training to the community by official bodies, such as civil protection; 3) existing gaps in the attribution of accountability and development of liability policies involving EEW implementation; 4) the potential for EEW to increase seismic resilience of critical infrastructure and lifelines; 5) the need for strong organizational links with first responders and official EEW bodies; and 6) the lack of engineering-related (i.e., risk and resilience) metrics currently used to support decision making related to the triggering of alerts by various end users

Allosteric Inhibitors of the NS3 Protease from the Hepatitis C Virus

The nonstructural protein 3 (NS3) from the hepatitis C virus processes the non-structural region of the viral precursor polyprotein in infected hepatic cells. The NS3 protease activity has been considered a target for drug development since its identification two decades ago. Although specific inhibitors have been approved for clinical therapy very recently, resistance-associated mutations have already been reported for those drugs, compromising their long-term efficacy. Therefore, there is an urgent need for new anti-HCV agents with low susceptibility to resistance-associated mutations. Regarding NS3 protease, two strategies have been followed: competitive inhibitors blocking the active site and allosteric inhibitors blocking the binding of the accessory viral protein NS4A. In this work we exploit the intrinsic Zn+2-regulated plasticity of the protease to identify a new type of allosteric inhibitors. In the absence of Zn+2, the NS3 protease adopts a partially-folded inactive conformation. We found ligands binding to the Zn+2-free NS3 protease, trap the inactive protein, and block the viral life cycle. The efficacy of these compounds has been confirmed in replicon cell assays. Importantly, direct calorimetric assays reveal a low impact of known resistance-associated mutations, and enzymatic assays provide a direct evidence of their inhibitory activity. They constitute new low molecular-weight scaffolds for further optimization and provide several advantages: 1) new inhibition mechanism simultaneously blocking substrate and cofactor interactions in a non-competitive fashion, appropriate for combination therapy; 2) low impact of known resistance-associated mutations; 3) inhibition of NS4A binding, thus blocking its several effects on NS3 protease

Fluctuation geometry: A counterpart approach of inference geometry

Starting from an axiomatic perspective, \emph{fluctuation geometry} is developed as a counterpart approach of inference geometry. This approach is inspired on the existence of a notable analogy between the general theorems of \emph{inference theory} and the the \emph{general fluctuation theorems} associated with a parametric family of distribution functions $dp(I|\theta)=\rho(I|\theta)dI$, which describes the behavior of a set of \emph{continuous stochastic variables} driven by a set of control parameters $\theta$. In this approach, statistical properties are rephrased as purely geometric notions derived from the \emph{Riemannian structure} on the manifold $\mathcal{M}_{\theta}$ of stochastic variables $I$. Consequently, this theory arises as an alternative framework for applying the powerful methods of differential geometry for the statistical analysis. Fluctuation geometry has direct implications on statistics and physics. This geometric approach inspires a Riemannian reformulation of Einstein fluctuation theory as well as a geometric redefinition of the information entropy for a continuous distribution.Comment: Version submitted to J. Phys. A. 26 pages + 2 eps figure

Deconvolution analysis for classifying gastric adenocarcinoma patients based on differential scanning calorimetry serum thermograms

Recently, differential scanning calorimetry (DSC) has been acknowledged as a novel tool for diagnosing and monitoring several diseases. This highly sensitive technique has been traditionally used to study thermally induced protein folding/unfolding transitions. In previous research papers, DSC profiles from blood samples of patients were analyzed and they exhibited marked differences in the thermal denaturation profile. Thus, we investigated the use of this novel technology in blood serum samples from 25 healthy subjects and 30 patients with gastric adenocarcinoma (GAC) at different stages of tumor development with a new multiparametric approach. The analysis of the calorimetric profiles of blood serum from GAC patients allowed us to discriminate three stages of cancer development (I to III) from those of healthy individuals. After a multiparametric analysis, a classification of blood serum DSC parameters from patients with GAC is proposed. Certain parameters exhibited significant differences (P < 0.05) and allowed the discrimination of healthy subjects/patients from patients at different tumor stages. The results of this work validate DSC as a novel technique for GAC patient classification and staging, and offer new graphical tools and value ranges for the acquired parameters in order to discriminate healthy from diseased subjects with increased disease burden

Geometry of random interactions

It is argued that spectral features of quantal systems with random interactions can be given a geometric interpretation. This conjecture is investigated in the context of two simple models: a system of randomly interacting d bosons and one of randomly interacting fermions in a j=7/2 shell. In both examples the probability for a given state to become the ground state is shown to be related to a geometric property of a polygon or polyhedron which is entirely determined by particle number, shell size, and symmetry character of the states. Extensions to more general situations are discussed

Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstral-domain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank. As next step, we select the best features using the Fast Correlation-Based Filtering method. Since our dataset has 129 users, then as next step, we augmented the training data by randomly selecting a percentage of the training data and adding a small random Gaussian noise to the extracted features. We then train a radial basis SVM model using the Leave-One-Out (LOO) methodology. The experimental results show an average accuracy classification improvement ranging of 15%, and an accuracy classification improvement ranging from 4% to 34% compared with baseline (state of the art) for specific emotions such as depression, anxiety, stress, and typical emotional states

Role of ejecta clumping and back-reaction of accelerated cosmic rays in the evolution of Type Ia supernova remnants

We investigate the role played by initial clumping of ejecta and by efficient acceleration of cosmic rays (CRs) in determining the density structure of the post-shock region of a Type Ia supernova remnant (SNR) through detailed 3D MHD modeling. Our model describes the expansion of a SNR through a magnetized interstellar medium (ISM), including the initial clumping of ejecta and the effects on shock dynamics due to back-reaction of accelerated CRs. The model predictions are compared to the observations of SN 1006. We found that the back-reaction of accelerated CRs alone cannot reproduce the observed separation between the forward shock (FS) and the contact discontinuity (CD) unless the energy losses through CR acceleration and escape are very large and independent of the obliquity angle. On the contrary, the clumping of ejecta can naturally reproduce the observed small separation and the occurrence of protrusions observed in SN 1006, even without the need of accelerated CRs. We conclude that FS-CD separation is a probe of the ejecta structure at the time of explosion rather than a probe of the efficiency of CR acceleration in young SNRs.Comment: 12 pages, 11 Figures; accepted for publication on ApJ. Version with full resolution images can be found at http://www.astropa.unipa.it/~orlando/PREPRINTS/sorlando_clumping.pd

Regional Anesthesia in the Prevention of Chronic Postoperative Pain

Chronic postsurgical pain (CPSP) develops after a surgical procedure but increases its intensity and persists beyond the healing process without another cause to explain it. The incidence ranges from 5–85%, according to the type of surgery. Patients who develop CPSP may have a protracted ambulation, cardiac and pulmonary complications and increased morbidity and mortality. Several risk factors have been found related to the development of CPSP: female gender, young age, genetic predisposition, and psychosocial problems, hence prevention, early identification and treatment of these factors is essential. Several guidelines recommend the use of multimodal analgesia to treat postoperative pain, and the perioperative management seems to have a preventive role in the development of CPSP. Regional anesthesia (RA) either neuraxial or peripheral nerve blocks, by modulating signaling created by a surgical incision, play a key role in the prevention of CPSP. Local anesthetics have anti-inflammatory properties which decrease sensitization, reduce ectopic firing of neurons, cytokines expression and decrease neutrophil priming. RA reduces pain signals to the spinal cord and supraspinal and cortical nociceptive centers. RA along with other pharmacologic interventions can improve the CPSP as well as the physical and social functionality