Fattori che influenzano la formazione di corpuscoli in soggetti esposti ad amianto

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Measuring the spectral index of turbulent gas with deep learning from projected density maps

Turbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of their initial conditions, better constraints on turbulence can result in windfalls in Galactic archaeology, star cluster dynamics and star formation. Observationally, constraining the spectral index of turbulent gas usually involves computing spectra from velocity maps. Here we suggest that information on the spectral index might be directly inferred from column density maps (possibly obtained by dust emission/absorption) through deep learning. We generate mock density maps from a large set of adaptive mesh refinement turbulent gas simulations using the hydro-simulation code RAMSES. We train a convolutional neural network (CNN) on the resulting images to predict the turbulence index, optimize hyper-parameters in validation and test on a holdout set. Our adopted CNN model achieves a mean squared error of 0.024 in its predictions on our holdout set, over underlying spectral indexes ranging from 3 to 4.5. We also perform robustness tests by applying our model to altered holdout set images, and to images obtained by running simulations at different resolutions. This preliminary result on simulated density maps encourages further developments on real data, where observational biases and other issues need to be taken into account.Comment: 7 pages, 7 figures, 1 tabl

Interpretable machine learning for finding intermediate-mass black holes

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to realistically simulate GCs, the simulation physics or initial conditions may fail to fully reflect reality. Therefore our training data may be biased, leading to a failure in generalization on observational data. Both the first issue -- explainability/interpretability -- and the second -- out of distribution generalization and fairness -- are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an XGBoost classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost and a global rule from CORELS.Comment: ApJ accepte

Effect of Trandolapril on Regression of Retinopathy in Hypertensive Patients with Type 2 Diabetes: A Prespecified Analysis of the Benedict Trial

Background. The effect of angiotensin converting enzyme inhibitors (ACEi) on regression of retinopathy in type 2 diabetics is still ill defined. Methods. We compared the incidence of retinopathy regression in 90 hypertensive type 2 diabetics randomized to at least 3-year blinded ACEi with trandolapril (2 mg/day) or non-ACEi therapy who had preproliferative or proliferative retinopathy at baseline. Results. Over a median (interquartile range) follow-up period of 35.8 (12.4–60.7) months, retinopathy regressed in 27 patients (30.0%). Regression occurred in 18 of 42 patients (42.9%) on ACEi and in 9 of 48 (18.8%) on non-ACEi therapy (adjusted for predefined baseline covariates HR (95% CI): 2.75 (1.18–6.42), P = .0193). Concomitant treatment with or without Non-Dihydropyridine Calcium Channel Blockers (ndCCBs) did not appreciably affect the incidence of retinopathy regression. Conclusions. Unlike ndCCB, ACEi therapy may have an additional effect to that of intensified BP and metabolic control in promoting regression of diabetic retinopathy

Allogenic tissue-specific decellularized scaffolds promote long-term muscle innervation and functional recovery in a surgical diaphragmatic hernia model

Congenital diaphragmatic hernia (CDH) is a neonatal defect in which the diaphragm muscle does not develop properly, thereby raising abdominal organs into the thoracic cavity and impeding lung development and function. Large diaphragmatic defects require correction with prosthetic patches to close the malformation. This treatment leads to a consequent generation of unwelcomed mechanical stress in the repaired diaphragm and hernia recurrences, thereby resulting in high morbidity and significant mortality rates. We proposed a specific diaphragm-derived extracellular matrix (ECM) as a scaffold for the treatment of CDH. To address this strategy, we developed a new surgical CDH mouse model to test the ability of our tissue-specific patch to regenerate damaged diaphragms. Implantation of decellularized diaphragmatic ECM-derived patches demonstrated absence of rejection or hernia recurrence, in contrast to the performance of a commercially available synthetic material. Diaphragm-derived ECM was able to promote the generation of new blood vessels, boost long-term muscle regeneration, and recover host diaphragmatic function. In addition, using a GFP\u202f+\u202fSchwann cell mouse model, we identified re-innervation of implanted patches. These results demonstrated for the first time that implantation of a tissue-specific biologic scaffold is able to promote a regenerating diaphragm muscle and overcome issues commonly related to the standard use of prosthetic materials

Clinical Study Effect of Trandolapril on Regression of Retinopathy in Hypertensive Patients with Type 2 Diabetes: A Prespecified Analysis of the Benedict Trial

Background. The effect of angiotensin converting enzyme inhibitors (ACEi) on regression of retinopathy in type 2 diabetics is still ill defined. Methods. We compared the incidence of retinopathy regression in 90 hypertensive type 2 diabetics randomized to at least 3-year blinded ACEi with trandolapril (2 mg/day) or non-ACEi therapy who had preproliferative or proliferative retinopathy at baseline. Results. Over a median (interquartile range) follow-up period of 35.8 (12.4-60.7) months, retinopathy regressed in 27 patients (30.0%). Regression occurred in 18 of 42 patients (42.9%) on ACEi and in 9 of 48 (18.8%) on non-ACEi therapy (adjusted for predefined baseline covariates HR (95% CI): 2.75 (1.18-6.42), P = .0193). Concomitant treatment with or without Non-Dihydropyridine Calcium Channel Blockers (ndCCBs) did not appreciably affect the incidence of retinopathy regression. Conclusions. Unlike ndCCB, ACEi therapy may have an additional effect to that of intensified BP and metabolic control in promoting regression of diabetic retinopathy

La rotazione delle stelle di campo e in ammassi

La dissipazione del momento angolare, sia nella formazione che nell' evoluzione della stella, dovuta in particolare al frenamento magnetico nelle stelle di piccola massa provoca il rallentamento della velocità di rotazione. Questo processo permette l'uso della girocronologia i.e. la misura dell'età a partire dalla rotazione della stella. Questa tecnica è applicabile sia a stelle di campo che in ammassi. Vengono infine confrontate le eà ottenute dalla rotazione con altri metodi (attività cromosferica e isocrone). Dal confronto risulta come le età, per stelle di campo, ricavate con la girocronologia risultano molto minori rispetto a quelle derivate dalle isocrone