Dynamic modal identification of telecommunication towers using ground based radar interferometry

This work presents a methodology to monitor the dynamic behaviour of tall metallic towers based on ground-based radar interferometry, and apply it to the case of telecommunication towers. Ground-based radar displacement measurements of metallic towers are acquired without installing any Corner Reflector (CR) on the structure. Each structural element of the tower is identified based on its range distance with respect to the radar. The interferometric processing of a time series of radar profiles is used to measure the vibration frequencies of each structural element and estimate the amplitude of its oscillation. A methodology is described to visualize the results and provide a useful tool for the real-time analysis of the dynamic behaviour of metallic towers

Erratum: “Seed layer technique for high quality epitaxial manganite films” [AIP Advances 6, 085109 (2016)]

No abstract available

Mechanical Properties of Animal Tendons: A Review and Comparative Study for the Identification of the Most Suitable Human Tendon Surrogates

The mechanical response of a tendon to load is strictly related to its complex and highly organized hierarchical structure, which ranges from the nano-to macroscale. In a broader context, the mechanical properties of tendons during tensile tests are affected by several distinct factors, due in part to tendon nature (anatomical site, age, training, injury, etc.) but also depending on the experimental setup and settings. This work aimed to present a systematic review of the mechanical properties of tendons reported in the scientific literature by considering different anatomical regions in humans and several animal species (horse, cow, swine, sheep, rabbit, dog, rat, mouse, and foal). This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The literature research was conducted via Google Scholar, PubMed, PicoPolito (Politecnico di Torino’s online catalogue), and Science Direct. Sixty studies were selected and analyzed. The structural and mechanical properties described in different animal species were reported and summarized in tables. Only the results from studies reporting the strain rate parameter were considered for the comparison with human tendons, as they were deemed more reliable. Our findings showed similarities between animal and human tendons that should be considered in biomechanical evaluation. An additional analysis of the effects of different strain rates showed the influence of this parameter

Surface nanostructures in manganite films

Ultrathin manganite films are widely used as active electrodes in organic spintronic devices. In this study, a scanning tunnelling microscopy (STM) investigation with atomic resolution revealed previously unknown surface features consisting of small non-stoichiometric islands. Based upon this evidence, a new mechanism for the growth of these complex materials is proposed. It is suggested that the non-stoichiometric islands result from nucleation centres that are below the critical threshold size required for stoichiometric crystalline growth. These islands represent a kinetic intermediate of single-layer growth regardless of the film thickness, and should be considered and possibly controlled in manganite thin-film applications

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model. Still, the traditional multivariate model requires stringent assumptions that may not be answered in the clinical setting. Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features predicting the outcome. These features, collected at time of diagnosis, should be capable to classify patients who will develop duodenal atrophy from those who will remain potential. Four ML methods were adopted to select features predictive of the outcome; the feature selection procedure was indeed capable to reduce the number of overall features from 85 to 19. ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%. The specificity score was always more than 75% also, with two of the considered methods over 98%, while the best performance of sensitivity was 60%. The best model, optimized Boosted Trees, was able to classify PCD starting from the selected 19 features with an accuracy of 0.80, sensitivity of 0.58 and specificity of 0.84. Finally, with this work, we are able to categorize PCD patients that can more likely develop overt CD using ML. ML techniques appear to be an innovative approach to predict the outcome of PCD, since they provide a step forward in the direction of precision medicine aimed to customize healthcare, medical therapies, decisions, and practices tailoring the clinical management of PCD children

Neuronal pentraxins mediate synaptic refinement in the developing visual system

Peer reviewedPublisher PD

Effect of Cigarette Smoking on Clinical and Molecular Endpoints in COPD Patients

Cigarette smoking is a primary contributor to mortality risks and is associated with various diseases. Among these, COPD represents a significant contributor to global mortality and disability. The objective of this study is to investigate the effect of smoking on a selected battery of variables, with an emphasis on DNA damage. A total of 87 elderly patients diagnosed with COPD, divided into three groups based on their smoking history (current, former, never-smokers), were evaluated using a cross-sectional approach. Clinical features including mortality and inflammatory/oxidative parameters (Lymphocytes/Monocytes, Neutrophils/Lymphocytes, Platelets/Lymphocytes ratio), SII, MDA, 8-Oxo-dG, and IL6 (ELISA assay), as well as DNA damage (comet assay), were investigated. Virus infection, i.e., influenza A virus subtype H1N1, JC polyomavirus (JCPyV), BK polyomavirus (BKPyV), and Torquetenovirus (TTV), was also tested. Current smokers exhibit higher levels of comorbidity (CIRS; p < 0.001), Platelets/Lymphocytes ratio (p < 0.001), systemic immune inflammation (p < 0.05), and DNA damage (p < 0.001). Former smokers also showed higher values for parameters associated with oxidative damage and showed a much lower probability of surviving over 5 years compared to never- and current smokers (p < 0.0017). This study showed a clear interaction between events which are relevant to the oxidative pathway and cigarette smoking. A category of particular interest is represented by former smokers, especially for lower survival, possibly due to the presence of more health problems. Our findings raise also the attention to other parameters which are significantly affected by smoking and are useful to monitor COPD patients starting a program of pulmonary rehabilitation (DNA damage, inflammation parameters, and selected viral infections)