Atomic-scale representation and statistical learning of tensorial properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule

Predictors of Mortality and Cardiovascular Outcome at 6 Months after Hospitalization for COVID-19

Clinical outcome data of patients discharged after Coronavirus disease 2019 (COVID-19) are limited and no study has evaluated predictors of cardiovascular prognosis in this setting. Our aim was to assess short-term mortality and cardiovascular outcome after hospitalization for COVID-19. A prospective cohort of 296 consecutive patients discharged after COVID-19 from two Italian institutions during the first wave of the pandemic and followed up to 6 months was included. The primary endpoint was all-cause mortality. The co-primary endpoint was the incidence of the composite outcome of major adverse cardiac and cerebrovascular events (MACCE: cardiovascular death, myocardial infarction, stroke, pulmonary embolism, acute heart failure, or hospitalization for cardiovascular causes). The mean follow-up duration was 6 ± 2 months. The incidence of all-cause death was 4.7%. At multivariate analysis, age was the only independent predictor of mortality (aHR 1.08, 95% CI 1.01–1.16). MACCE occurred in 7.2% of patients. After adjustment, female sex (aHR 2.6, 95% CI 1.05–6.52), in-hospital acute heart failure during index hospitalization (aHR 3.45, 95% CI 1.19–10), and prevalent atrial fibrillation (aHR 3.05, 95% CI 1.13–8.24) significantly predicted the incident risk of MACCE. These findings may help to identify patients for whom a closer and more accurate surveillance after discharge for COVID-19 should be considered

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Change over time of COVID-19 hospital presentation in Northern Italy

none40After the first autochthonous case described on February 19, also in Italy the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV-2) infection rapidly circulated, mainly in the Northern regions of the country. The earliest reports on Coronavirus disease-19 (COVID-19) have described worldwide a high prevalence of severe respiratory illness [1]. A suggestive feature of COVID-19 has been a rapid progression of the respiratory impairment, leading to acute respiratory distress syndrome (ARDS) and often requiring ventilation support [2]. To date, whether clinical features at hospital presentation and outcome of COVID-19 have changed over the outbreak course is unknown. We explored this issue in a multicenter cohort of patients hospitalized for COVID-19 in Northern Italy.mixedPatti G.; Mennuni M.; Della Corte F.; Spinoni E.; Sainaghi P. P.; COVID-UPO Clinical Team; Azzolina D; Hayden E; Rognon A; Grisafi L; Colombo C; Lio V; Pirisi M; Vaschetto R; Aimaretti G; Krengli M; Avanzi GC; Balbo PE; Capponi A; Castello LM; Bellan M; Malerba M; Garavelli PL; Zeppegno P; Savoia P; Chichino G; Olivieri C; Re R; Maconi A; Comi C; Roveta A; Bertolotti M; Carriero A; Betti M; Mussa M; Borrè S; Cantaluppi V; Cantello R; Bobbio F; GavellI F.Patti, G.; Mennuni, M.; Della Corte, F.; Spinoni, E.; Sainaghi, P. P.; COVID-UPO Clinical, Team; Azzolina, D; Hayden, E; Rognon, A; Grisafi, L; Colombo, C; Lio, V; Pirisi, M; Vaschetto, R; Aimaretti, G; Krengli, M; Avanzi, Gc; Balbo, Pe; Capponi, A; Castello, Lm; Bellan, M; Malerba, M; Garavelli, Pl; Zeppegno, P; Savoia, P; Chichino, G; Olivieri, C; Re, R; Maconi, A; Comi, C; Roveta, A; Bertolotti, M; Carriero, A; Betti, M; Mussa, M; Borrè, S; Cantaluppi, V; Cantello, R; Bobbio, F; Gavelli, F

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions

Use of hydroxychloroquine in hospitalised COVID-19 patients is associated with reduced mortality: Findings from the observational multicentre Italian CORIST study

Background: Hydroxychloroquine (HCQ) was proposed as potential treatment for COVID-19. Objective: We set-up a multicenter Italian collaboration to investigate the relationship between HCQ therapy and COVID-19 in-hospital mortality. Methods: In a retrospective observational study, 3,451 unselected patients hospitalized in 33 clinical centers in Italy, from February 19, 2020 to May 23, 2020, with laboratory-confirmed SARS-CoV-2 infection, were analyzed. The primary end-point in a time-to event analysis was in-hospital death, comparing patients who received HCQ with patients who did not. We used multivariable Cox proportional-hazards regression models with inverse probability for treatment weighting by propensity scores, with the addition of subgroup analyses. Results: Out of 3,451 COVID-19 patients, 76.3% received HCQ. Death rates (per 1,000 person-days) for patients receiving or not HCQ were 8.9 and 15.7, respectively. After adjustment for propensity scores, we found 30% lower risk of death in patients receiving HCQ (HR=0.70; 95%CI: 0.59 to 0.84; E-value=1.67). Secondary analyses yielded similar results. The inverse association of HCQ with inpatient mortality was particularly evident in patients having elevated C-reactive protein at entry. Conclusions: HCQ use was associated with a 30% lower risk of death in COVID-19 hospitalized patients. Within the limits of an observational study and awaiting results from randomized controlled trials, these data do not discourage the use of HCQ in inpatients with COVID-19

Activation of Regulatory T Cells during Inflammatory Response Is Not an Exclusive Property of Stem Cells

BACKGROUND: Sepsis and systemic-inflammatory-response-syndrome (SIRS) remain major causes for fatalities on intensive care units despite up-to-date therapy. It is well accepted that stem cells have immunomodulatory properties during inflammation and sepsis, including the activation of regulatory T cells and the attenuation of distant organ damage. Evidence from recent work suggests that these properties may not be exclusively attributed to stem cells. This study was designed to evaluate the immunomodulatory potency of cellular treatment during acute inflammation in a model of sublethal endotoxemia and to investigate the hypothesis that immunomodulations by cellular treatment during inflammatory response is not stem cell specific. METHODOLOGY/PRINCIPAL FINDINGS: Endotoxemia was induced via intra-peritoneal injection of lipopolysaccharide (LPS) in wild type mice (C3H/HeN). Mice were treated with either vital or homogenized amniotic fluid stem cells (AFS) and sacrificed for specimen collection 24 h after LPS injection. Endpoints were plasma cytokine levels (BD™ Cytometric Bead Arrays), T cell subpopulations (flow-cytometry) and pulmonary neutrophil influx (immunohistochemistry). To define stem cell specific effects, treatment with either vital or homogenized human-embryonic-kidney-cells (HEK) was investigated in a second subset of experiments. Mice treated with homogenized AFS cells showed significantly increased percentages of regulatory T cells and Interleukin-2 as well as decreased amounts of pulmonary neutrophils compared to saline-treated controls. These results could be reproduced in mice treated with vital HEK cells. No further differences were observed between plasma cytokine levels of endotoxemic mice. CONCLUSIONS/SIGNIFICANCE: The results revealed that both AFS and HEK cells modulate cellular immune response and distant organ damage during sublethal endotoxemia. The observed effects support the hypothesis, that immunomodulations are not exclusive attributes of stem cells