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

Is Age an Independent Factor in Assessing Renal Health and Function in Healthy Individuals? A Pilot Study

Estimated glomerular filtration rate (eGFR) is a measure of renal filtration and clearance of serum creatinine and is conventionally used to characterize the progressive decline in renal function. Assessment of renal function and health is traditionally believed to be age-dependent. However, in the absence of cardiometabolic diseases (hypertension, diabetes, hyperlipemia, etc.), this may not be the case. Recently, novel markers of renal health and function support the notion that age is a secondary factor influencing renal decline. PURPOSE: To determine the magnitude of age as an influencing factor involved in the decline of renal function with novel markers of renal health and function in the absence of cardiometabolic risk factors. METHODS: Thirty-nine participants (n = 18 men; n = 21 women; age 32.5 + 12.6 yr; height 171.1 + 11.4 cm; weight 78.7 + 15.6 kg; BMI 27.1 + 5.8; SBP 120 + 11.2; DBP 78 + 6.6; CHOL 173 + 30; and GLU 96 + 7) completed a single health assessment to quantify renal health and function. Blood and urine samples were collected by the same technician under standardized conditions and stored at -60 ºC until project completion. Serum creatinine (sCR), urine creatinine (uCr), urine epidermal growth factor (uEGF), uEGF/uCr ratio (uEGFR), cystatin C (CyC) and eGFR - modification of diet in renal disease (MDRD) and the CKD-EPI - responses were analyzed and compared in age groups (20s, 30s, 40s, 50s) using 4 (group) by 1 (sample) ANOVAs. RESULTS: There were no significant differences in markers of renal health and function between any age group. sCR (p = 0.90), uCr (p = 0.17), uEGF (p = 0.15), CyC (p = 0.32), uEGFR (p = 0.28), MDRD (p = 0.17), and CKD-EPI (p = 0.83). CONCLUSION: In healthy individuals, changes in renal health and function appear to be independent of age in the absence of cardiometabolic diseases. Indicating renal health and function could potentially be maintained throughout adulthood, middle age, and possibly attenuated in the senior years with the continued absence of cardiometabolic diseases