A Frame Element Model for the Nonlinear Analysis of FRP-Strengthened Masonry Panels Subjected to In-Plane Loads

A frame element model for evaluating the nonlinear response of unstrengthened and FRP-strengthened masonry panels subjected to in-plane vertical and lateral loads is presented. The proposed model, based on some assumptions concerning the constitutive behaviour of masonry and FRP material, considers the panel discretized in frame elements with geometrical and mechanical properties derived on the basis of the different states characterizing the sectional behaviour. The reliability of the proposed model is assessed by considering some experimental cases deduced from the literature

Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via Adaptive Stochastic Optimization

This dissertation builds on two lines of research that are related to the task of community detection on large-scale network data. Our first contribution is a novel generator for large-scale networks with overlapping communities. Synthetic generators are essential for algorithm testing and simulation studies for networks, as these data are scarce and constantly evolving. We propose a generator based on a flexible random graph model that allows for the control of two complementary measures of centrality -- the degree centrality and the eigencentrality. For an arbitrary centrality target and community structure, we study the problem of recovering the model parameters that enforce such targets in expectation. We find that this problem always admits a solution in the parameter space, which is also unique for large graphs. We propose to recover this solution via a properly initialized multivariate-Newton Raphson algorithm. The resulting benchmark generator is able to simulate networks with a billion edges and hundreds of millions of nodes in 30 seconds, while reproducing a wide spectrum of network topologies -- including assortative mixing and power-law centrality distributions. Our second contribution involves variance reduction techniques for stochastic variational inference (SVI). SVI scales approximate inference to large-scale data -- including massive networks -- via stochastic optimization. SVI is efficient because, at each iteration, it only uses a random minibatch of the data to produce a noisy estimate of the gradient. However, such estimates can suffer from high variance, which slows down convergence. One strategy to reduce the variance of the gradient is to use importance sampling, biasing the distribution of data for each minibatch towards the data points that are most influential to the inference at hand. Here, we develop an importance sampling strategy for SVI. Our adaptive stochastic variational inference algorithm (AdaSVI) reweights the sampling distribution to minimize the variance of the stochastic natural gradient. We couple the importance sampling strategy with an adaptive learning rate providing a parameter-free stochastic optimization algorithm where the only user input required is the minibatch size. We study AdaSVI on a matrix factorization model and find that it significantly improves SVI, leading to faster convergence on synthetic data

Just a Flexible Linker? the Structural and Dynamic Properties of CBP-ID4 Revealed by NMR Spectroscopy

Here, we present a structural and dynamic description of CBP-ID4 at atomic resolution. ID4 is the fourth intrinsically disordered linker of CREB-binding protein (CBP). In spite of the largely disordered nature of CBP-ID4, NMR chemical shifts and relaxation measurements show a significant degree of α-helix sampling in the protein regions encompassing residues 2-25 and 101-128 (1852-1875 and 1951-1978 in full-length CBP). Proline residues are uniformly distributed along the polypeptide, except for the two α-helical regions, indicating that they play an active role in modulating the structural features of this CBP fragment. The two helical regions are lacking known functional motifs, suggesting that they represent thus-far uncharacterized functional modules of CBP. This work provides insights into the functions of this protein linker that may exploit its plasticity to modulate the relative orientations of neighboring folded domains of CBP and fine-tune its interactions with a multitude of partners. © 2016 Biophysical Society

Effectiveness of sealing active proximal caries lesions with an adhesive system: 1-year clinical evaluation

The objective of this study was to evaluate the effectiveness of a therapeutic sealant to arrest non-cavitated proximal carious lesion progression. The study population comprised 44 adolescents who had bitewing radiographs taken for caries diagnosis. Non-cavitated lesions extending up to half of dentin thickness were included in the sample. In the experimental group (n = 33), the proximal caries-lesion surfaces were sealed with an adhesive (OptiBond Solo, Kerr) after tooth separation. The control group (n = 11) received no treatment, except for oral hygiene instructions including use of dental floss. Follow-up radiographs were taken after one year and were analyzed in comparison with baseline radiographs. In a blind study setting, visual readings were performed by two examiners, blinded to whether the examined radiograph was baseline or follow-up, and whether it concerned a test or control lesion. The efficacy of sealing treatment was evaluated by the McNemar test (0.05). About 22% of the sealed lesions showed reduction, 61% showed no change and 16% showed progression. For the control lesions, the corresponding values were 27%, 36% and 36% respectively. The number of lesions that showed reduction and no changes were merged and therefore 83.3% of the sealed lesions and 63.6% of the control lesions were considered clinically successful. No statistical significance was detected (p > 0.05). In the course of 1 year, sealing proximal caries lesions was not shown to be superior to lesion monitoring.Dr. Priscila Bresciani, Dr. Bernardo Hahn and Dr. Franciele Loeblein for the help provided in the clinical part of this stud

Transrectal ultrasonography in infertile patients with persistently elevated bacteriospermia

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Mortality Related to Chronic Obstructive Pulmonary Disease during the COVID-19 Pandemic: An Analysis of Multiple Causes of Death through Different Epidemic Waves in Veneto, Italy

Mortality related to chronic obstructive pulmonary disease (COPD) during the COVID-19 pandemic is possibly underestimated by sparse available data. The study aimed to assess the impact of the pandemic on COPD-related mortality by means of time series analyses of causes of death data. We analyzed the death certificates of residents in Veneto (Italy) aged ≥40 years from 2008 to 2020. The age-standardized rates were computed for COPD as the underlying cause of death (UCOD) and as any mention in death certificates (multiple cause of death-MCOD). The annual percent change (APC) in the rates was estimated for the pre-pandemic period. Excess COPD-related mortality in 2020 was estimated by means of Seasonal Autoregressive Integrated Moving Average models. Overall, COPD was mentioned in 7.2% (43,780) of all deaths. From 2008 to 2019, the APC for COPD-related mortality was -4.9% (95% CI -5.5%, -4.2%) in men and -3.1% in women (95% CI -3.8%, -2.5%). In 2020 compared to the 2018-2019 average, the number of deaths from COPD (UCOD) declined by 8%, while COPD-related deaths (MCOD) increased by 14% (95% CI 10-18%), with peaks corresponding to the COVID-19 epidemic waves. Time series analyses confirmed that in 2020, COPD-related mortality increased by 16%. Patients with COPD experienced significant excess mortality during the first year of the pandemic. The decline in COPD mortality as the UCOD is explained by COVID-19 acting as a competing cause, highlighting how an MCOD approach is needed