Nailfold Capillaroscopy and Autoimmune Connective Tissue Diseases in Patients From a Portuguese Nailfold Capillaroscopy Clinic

Raynaud's phenomenon (RP) is frequent in autoimmune connective tissue diseases (AICTD) and its approach includes nailfold capillaroscopy (NFC), as it is a non-invasive technique that permits direct visualization of the microcirculation. The aim of this study is to analyze and establish clinical correlations between NFC findings and particular aspects of autoimmune disorders. This is a retrospective study. Clinical data from patients attending our NFC clinic were reviewed. Inclusion criteria included AICTD previous diagnosis, which included systemic sclerosis (SSc), mixed connective tissue disease (MCTD), systemic lupus erythematosus (SLE), Sjögren syndrome, inflammatory idiopathic myopathies (IIM), rheumatoid arthritis, undifferentiated connective tissue disease and antiphospholipid syndrome (APS). Videocap® version 3.0 biomicroscope was used. NFC score was determined. For statistics, SPSS software was utilized. 384 patients were included; most of them were women, with mean age of 47 years. RP was present in 91% of the patients, with greater prevalence in SSc and MCTD. Scleroderma pattern was the most prevalent NFC pattern, mainly in SSc, MCTD and IIM. Mean capillary density was reduced in IIM, SSc and MCTD. NFC score was worse in SSc, IIM and MCTD. In patients with AICTD, RP is related to microvascular damage and worse NFC score. NFC scleroderma pattern correlates with SSc classification criteria score. In MCTD, scleroderma pattern relates to myositis. SLE and APS reveal significant hemorrhages, but not related to APS antibodies. This study highlights the possible role of NFC as biomarker of AICTD, particularly in SSc and IIM.info:eu-repo/semantics/publishedVersio

Raynaud's Phenomenon and Inflammatory Bowel Disease: the Possible Role of Microcirculation

info:eu-repo/semantics/publishedVersio

Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly Robotics, 10th International Workshop, Springer Proceedings in Advanced Robotics, vol 7. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly Robotics, 10th International Workshop,

A Review of Laboratory and Numerical Techniques to Simulate Turbulent Flows

Turbulence is still an unsolved issue with enormous implications in several fields, from the turbulent wakes on moving objects to the accumulation of heat in the built environment or the optimization of the performances of heat exchangers or mixers. This review deals with the techniques and trends in turbulent flow simulations, which can be achieved through both laboratory and numerical modeling. As a matter of fact, even if the term “experiment” is commonly employed for laboratory techniques and the term “simulation” for numerical techniques, both the laboratory and numerical techniques try to simulate the real-world turbulent flows performing experiments under controlled conditions. The main target of this paper is to provide an overview of laboratory and numerical techniques to investigate turbulent flows, useful for the research and technical community also involved in the energy field (often non-specialist of turbulent flow investigations), highlighting the advantages and disadvantages of the main techniques, as well as their main fields of application, and also to highlight the trends of the above mentioned methodologies via bibliometric analysis. In this way, the reader can select the proper technique for the specific case of interest and use the quoted bibliography as a more detailed guide. As a consequence of this target, a limitation of this review is that the deepening of the single techniques is not provided. Moreover, even though the experimental and numerical techniques presented in this review are virtually applicable to any type of turbulent flow, given their variety in the very broad field of energy research, the examples presented and discussed in this work will be limited to single-phase subsonic flows of Newtonian fluids. The main result from the bibliometric analysis shows that, as of 2021, a 3:1 ratio of numerical simulations over laboratory experiments emerges from the analysis, which clearly shows a projected dominant trend of the former technique in the field of turbulence. Nonetheless, the main result from the discussion of advantages and disadvantages of both the techniques confirms that each of them has peculiar strengths and weaknesses and that both approaches are still indispensable, with different but complementary purposes

On the identification and characterization of outdoor thermo-hygrometric stress events

Human thermal sensations are not controlled merely by the ambient temperature, but also by other biometeorological variables and personal factors. Therefore, thermo-hygrometric stress events need to be identified and monitored in addition to heat waves. The purpose of the present article is proposing a method for detection and characterization of thermo-hygrometric stress events, based on the rearrangement of heat waves indices and on new quantities. The Mediterranean Outdoor Thermal Comfort Index (MOCI) is used as a reference variable instead of the air temperature. The method is applied to Milan (Italy) for the 2022 summer, which: i) is the hottest in the period 1991–2020 with a temperature anomaly of 3.17 ◦C and ii) presents higher minimum temperatures (1.5 times higher) than those of the control period. The analysis of daytime values of MOCI demonstrates a cumulative MOCI higher than zero only in 2022. Hence, the lower fraction of data in the cold range determines a significant increase in the cumulative MOCI. The metrics on severe MOCI events in 2022 confirm the key-role of extreme temperatures. The proposed method is effective and, in this case, reveals the relevance of the cumulative thermal and thermo-hygrometric loads also in the absence of critical heating conditions

Snow metamorphism: a fractal approach

Snow is a porous disordered medium consisting of air and three water phases: ice, vapour and liquid. The ice phase consists of an assemblage of grains, ice matrix, initially arranged over a random load bearing skeleton. The quantitative relationship between density and morphological characteristics of different snow microstructures is still an open issue. In this work, a three-dimensional fractal description of density corresponding to different snow microstructure is put forward. First, snow density is simulated in terms of a generalized Menger sponge model. Then, a fully three-dimensional compact stochastic fractal model is adopted. The latter approach yields a quantitative map of the randomness of the snow texture, which is described as a three-dimensional fractional Brownian field with the Hurst exponent H varying as continuous parameter. The Hurst exponent is found to be strongly dependent on snow morphology and density. The approach might be applied to all those cases where the morphological evolution of snow cover or ice sheets should be conveniently described at a quantitative level