Manganese-containing mixed oxide electrodes as anode materials for degradation of model organic pollutants

Mixed oxide thin film electrodes have been prepared by thermal decomposition from alcoholic solution on Pt substrate. In particular, three different anodes have been obtained by co-deposition of Ru (Ruthenium) and Mn (Manganese) oxides, Ru, Mn and Cu (Copper) oxides and co-deposition of Ru, Mn and Co (Cobalt) oxides. The electrochemical behaviour of the prepared electrodes was evaluated by potentiodynamic polarization curves and cyclic voltammetry tests. We also tested and compared their oxidizing ability in the degradation of aqueous solutions containing methyl orange as model compound and small amount of chloride. Galvanostatic experiments were conducted in a membrane-free reactor. The treatment extent was assessed by detection of color and TOC decay. The electrogeneration of active chlorine, chlorate and perchlorate was also monitored. The preliminary results show that ternary oxides coated electrodes exhibit enhanced electrocatalytic activity without producing undesired chlorinated by-products

Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples

Machine Learning algorithms provide astonishing performance in a wide range of tasks, including sensitive and critical applications. On the other hand, it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated to alter the model output. In the last decade, the research community put a strong effort into this field, proposing new evasion attacks and methods to defend against them. With this thesis, we propose different approaches that can be applied to Deep Neural Networks to detect and reject adversarial examples that present an anomalous distribution with respect to training data. The first leverages the domain knowledge of the relationships among the considered classes integrated through a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the classifier is able to reject samples that violate the domain knowledge constraints. This approach can be applied in both single and multi-label classification settings. The second one is based on a Deep Neural Rejection (DNR) mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. To this end, we exploit RBF SVM classifiers, which provide decreasing confidence values as samples move away from the training data distribution. Despite technical differences, this approach shares a common backbone structure with other proposed methods that we formalize in a unifying framework. As all of them require comparing input samples against an oversized number of reference prototypes, possibly at different representation layers, they suffer from the same drawback, i.e., high computational overhead and memory usage, that makes these approaches unusable in real applications. To overcome this limitation, we introduce FADER (Fast Adversarial Example Rejection), a technique for speeding up detection-based methods by employing RBF networks as detectors: by fixing the number of required prototypes, their runtime complexity can be controlled. All proposed methods are evaluated in both black-box and white-box settings, i.e., against an attacker unaware of the defense mechanism, and against an attacker who knows the defense and adapts the attack algorithm to bypass it, respectively. Our experimental evaluation shows that the proposed methods increase the robustness of the defended models and help detect adversarial examples effectively, especially when the attacker does not know the underlying detection system

prevention of tuberculosis transmission through medical surveillance systems

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Better data, more tailored tuberculosis therapies

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Is tuberculosis elimination a reality?

Multidrug-resistant (MDR) tuberculosis is a public health emergency and a challenging scenario for both patients and clinicians. In 2012, there were more than 450 000 incident cases and 170 000 deaths worldwide. Treatment of MDR tuberculosis is complex and expensive (€100 000 or more for drugs for one patient), especially its most severe, extensively drug-resistant forms. Treatment is long (at least 2 years), the drugs are toxic (specific expertise is needed to manage adverse reactions), and the outcomes are poor (with low success and high death rates). New drugs will soon be available that will probably shorten and simplify treatment for MDR tuberculosis and increase effectiveness, and public health strategies have been developed to prevent the occurrence of drug resistance. The traditional approach of national tuberculosis programmes, focused on tuberculosis control (ie, rapid diagnosis and early, effective treatment of newly detected infectious cases), which was advocated by the WHO Stop TB Strategy, will soon be replaced by the post-2015 strategy focused on the concept of tuberculosis elimination (ie, fewer than one new sputum smear-positive tuberculosis case per 1 million population). Whereas traditional contact tracing (eg, looking for the contacts of individuals with tuberculosis and MDR tuberculosis in progressive circles) recommends identification and treatment of latently infected individuals and additional tuberculosis cases, new approaches recommend genotypic identification of the causative strain, monitoring of the epidemic, and initiation of adequate measures to manage it. One such approach is mycobacterial interspersed repetitive-unit-variable-number tandem repeat (MIRU-VNTR) strain typing. In The Lancet Infectious Diseases, Laura F Anderson and colleagues report an assessment of transmission of MDR tuberculosis in the UK between 2004 and 2007, using the 24-loci MIRU-VNTR method together with epidemiological data collected through the national surveillance system and an ad-hoc cluster investigation questionnaire. The scope was to identify the relative frequency of MDR tuberculosis cases transmitted nationwide. 204 patients were diagnosed with MDR tuberculosis in the study period of whom 189 (92·6%) had an MIRU-VNTR profile. 15% of these cases were clustered. Furthermore, Anderson and colleagues analysed the risk factors associated with MDR tuberculosis transmission: being born in the UK (odds ratio 4·81; 95% CI 2·03—11·36, p=0·0005) and having a history of illicit drug use (4·75; 1·19—18·96, p=0·026) significantly increased the probability of transmission. Most cases (21 of 22) were transmitted in the household. The occurrence of MDR tuberculosis transmission in the UK is lower than in other European and non-European settings, probably as a consequence of scarce transmission occurring between specific population groups. The study is an excellent example of nationwide implementation of one of the European Centre for Disease Prevention and Control (ECDC) recommendations to eliminate tuberculosis in the European Union. Moreover, the identification of risk factors allows the prioritisation of the public health investigations, reducing the probability of transmission related to health-care system delay. The core strength of the study is the high proportion of MDR tuberculosis cases assessed with the novel diagnostic approach (ie, 24-loci MIRU-VNTR) and the ability to increase sensitivity compared with the traditional epidemiological investigations. However, other more sensitive techniques such as whole genome sequencing analysis could also have increased the ability to identify additional epidemiological links, which means that a potential underestimation of MDR tuberculosis transmission should be considered. Low culture confirmation (about 60% in the UK) could also have underestimated the true prevalence of transmission. Molecular methods have several public health applications, including identification of outbreaks, population groups at highest risk of transmission, transmission across jurisdictions, transmission chains, reinfected and relapsing cases, and laboratory cross-contamination. However, several technical problems currently hinder their integration into national tuberculosis programmes, including the absence of a gold standard to effectively assess their discriminatory power. The ECDC recommends monitoring and assessment of transmission of drug-susceptible and drug-resistant mycobacterial strains by adoption of sensitive and specific molecular methods. Molecular fingerprinting, alongside classic epidemiological studies, will be helpful to discriminate real clusters of tuberculosis cases (ie, individuals infected by the same genotypes) and to (indirectly) assess the efficacy of a tuberculosis control programme implemented at a national or regional level. If we are to make tuberculosis elimination a reality, this UK experience needs to be followed up in other European Union countries. The implementation of molecular methods with increased sensitivity allows the bypassing of low discriminatory power associated with traditional contact tracing procedures, scaling up part of the European Union's tuberculosis elimination package.</br

Effect of the short-course regimen on the global epidemic of multidrug-resistant tuberculosis

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Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Global tuberculosis prevention: should we start from the beginning?

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