Grain size distribution uncertainty quantification in volcanic ash dispersal and deposition from weak plumes

We present the results of uncertainty quantification and sensitivity analysis applied to volcanic ash dispersal from weak plumes with focus on the uncertainties associated to the original grain size distribution of the mixture. The Lagrangian particle model Lagrangian Particles Advection Code is used to simulate the transport of inertial particles under the action of realistic atmospheric conditions. The particle motion equations are derived by expressing the particle acceleration as the sum of forces acting along its trajectory, with the drag force calculated as a function of particle diameter, density, shape, and Reynolds number. Simulations are representative of a weak plume event of Mount Etna (Italy) and aimed at quantifying the effect on the dispersal process of the uncertainty in the mean and standard deviation of a lognormal function describing the initial grain size distribution and in particle sphericity. In order to analyze the sensitivity of particle dispersal to these uncertain variables with a reasonable number of simulations, response surfaces in the parameter space are built by using the generalized polynomial chaos expansion technique. The mean diameter and standard deviation of particle size distribution, and their probability density functions, at various distances from the source, both airborne and on ground, are quantified. Results highlight that uncertainty ranges in these quantities are drastically reduced with distance from source, making them largely dependent just on the location. Moreover, at a given distance from source, the distribution is mostly controlled by particle sphericity, particularly on the ground, whereas in air also mean diameter and sorting play a main role

Une approche computationnelle du cadastre napoléonien de Venise

Au début du xixᵉ siècle, l’administration napoléonienne impose à la ville de Venise la mise en place d’un nouveau système de description standardisé pour rendre compte de manière objective de la forme et des fonctions du tissu urbain. Le cadastre, déployé à l’échelle européenne, offre pour la première fois une vue articulée et précise de la structure de la ville et de ses activités grâce à une approche méthodique et à des catégories standardisées. Les techniques numériques, basées notamment sur l’apprentissage profond, permettent aujourd’hui d’extraire de ces documents une représentation à la fois précise et dense de la ville et de ses habitants. En s’attachant à vérifier systématiquement la cohérence de l’information extraite, ces techniques évaluent aussi la précision et la systématicité du travail des arpenteurs et des sondeurs de l’Empire et qualifient par conséquent, de façon indirecte, la confiance à accorder aux informations extraites. Cet article revient sur l’histoire de ce protosystème computationnel, décrit la manière dont les techniques numériques offrent non seulement une documentation systématique, mais aussi des perspectives d’extraction d’informations latentes, encore non explicitées, mais implicitement présentes dans ce système d’information du passé.At the beginning of the 19th century, the Napoleonic administration introduced a new standardised description system to give an objective account of the form and functions of the city of Venice. The cadastre, deployed on a European scale, was offering for the first time an articulated and precise view of the structure of the city and its activities, through a methodical approach and standardised categories. With the use of digital techniques, based in particular on deep learning, it is now possible to extract from these documents an accurate and dense representation of the city and its inhabitants. By systematically checking the consistency of the extracted information, these techniques also evaluate the precision and systematicity of the surveyors’ work and therefore indirectly qualify the trust to be placed in the extracted information. This article reviews the history of this computational protosystem and describes how digital techniques offer not only systematic documentation, but also extraction perspectives for latent information, as yet uncharted, but implicitly present in this information system of the past

Retrieval and intercomparison of volcanic SO2 injection height and eruption time from satellite maps and ground-based observations

Syneruptive gas flux time series can, in principle, be retrieved from satellite maps of SO2 collected during and immediately after volcanic eruptions, and used to gain insights into the volcanic processes which drive the volcanic activity. Determination of the age and height of volcanic plumes are key prerequisites for such calculations. However, these parameters are challenging to constrain using satellite-based techniques. Here, we use imagery from OMI and GOME-2 satellite sensors and a novel numerical procedure based on back-trajectory analysis to calculate plume height as a function of position at the satellite measurement time together with plume injection height and time at a volcanic vent location. We applied this new procedure to three Etna eruptions (12 August 2011, 18 March 2012 and 12 April 2013) and compared our results with independent satellite and ground-based estimations. We also compare our injection height time-series with measurements of volcanic tremor, which reflects the eruption intensity, showing a good match between these two datasets. Our results are a milestone in progressing towards reliable determination of gas flux data from satellite-derived SO2 maps during volcanic eruptions, which would be of great value for operational management of explosive eruptions.1) European Research Council under the European Union's Seventh Framework Programme (FP/2.007-2013)/ERC Grant Agreement no. 279802, project 283 CO2Volc. 2) MEDiterranean SUpersite Volcanoes 280 (MED-SUV) WP 3.3.3Published79-915V. Dinamica dei processi eruttivi e post-eruttiviJCR Journa

Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model

Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset

A Process Calculus for Molecular Interaction Maps

We present the MIM calculus, a modeling formalism with a strong biological basis, which provides biologically-meaningful operators for representing the interaction capabilities of molecular species. The operators of the calculus are inspired by the reaction symbols used in Molecular Interaction Maps (MIMs), a diagrammatic notation used by biologists. Models of the calculus can be easily derived from MIM diagrams, for which an unambiguous and executable interpretation is thus obtained. We give a formal definition of the syntax and semantics of the MIM calculus, and we study properties of the formalism. A case study is also presented to show the use of the calculus for modeling biomolecular networks.Comment: 15 pages; 8 figures; To be published on EPTCS, proceedings of MeCBIC 200

Double-strand break repair and colorectal cancer: gene variants within 3' UTRs and microRNAs binding as modulators of cancer risk and clinical outcome

Genetic variations in 3' untranslated regions of target genes may affect microRNA binding, resulting in differential protein expression. microRNAs regulate DNA repair, and single-nucleotide polymorphisms in miRNA binding sites (miRSNPs) may account for interindividual differences in the DNA repair capacity. Our hypothesis is that miRSNPs in relevant DNA repair genes may ultimately affect cancer susceptibility and impact prognosis.In the present study, we analysed the association of polymorphisms in predicted microRNA target sites of double-strand breaks (DSBs) repair genes with colorectal cancer (CRC) risk and clinical outcome. Twenty-one miRSNPs in non-homologous end-joining and homologous recombination pathways were assessed in 1111 cases and 1469 controls. The variant CC genotype of rs2155209 in MRE11A was strongly associated with decreased cancer risk when compared with the other genotypes (OR 0.54, 95% CI 0.38-0.76, p = 0.0004). A reduced expression of the reporter gene was observed for the C allele of this polymorphism by in vitro assay, suggesting a more efficient interaction with potentially binding miRNAs. In colon cancer patients, the rs2155209 CC genotype was associated with shorter survival while the TT genotype of RAD52 rs11226 with longer survival when both compared with their respective more frequent genotypes (HR 1.63, 95% CI 1.06-2.51, p = 0.03 HR 0.60, 95% CI 0.41-0.89, p = 0.01, respectively). miRSNPs in DSB repair genes involved in the maintenance of genomic stability may have a role on CRC susceptibility and clinical outcome

Clinical and dopaminergic imaging characteristics of the FARPRESTO cohort of trial-ready idiopathic rapid eye movement sleep behavior patients

Introduction: Idiopathic/isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is considered the prodromal stage of alpha-synucleinopathies. Thus, iRBD patients are the ideal target for disease-modifying therapy. The risk FActoRs PREdictive of phenoconversion in iRBD Italian STudy (FARPRESTO) is an ongoing Italian database aimed at identifying risk factors of phenoconversion, and eventually to ease clinical trial enrollment of well-characterized subjects.Methods: Polysomnography-confirmed iRBD patients were retrospectively and prospectively enrolled. Baseline harmonized clinical and nigrostriatal functioning data were collected at baseline. Nigrostriatal functioning was evaluated by dopamine transporter-single-photon emission computed tomography (DaT-SPECT) and categorized with visual semi-quantification. Longitudinal data were evaluated to assess phenoconversion. Cox regressions were applied to calculate hazard ratios.Results: 365 patients were enrolled, and 289 patients with follow-up (age 67.7 & PLUSMN; 7.3 years, 237 males, mean follow-up 40 & PLUSMN; 37 months) were included in this study. At follow-up, 97 iRBD patients (33.6%) phenoconverted to an overt synucleinopathy. Older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression, and visual semi-quantification of nigrostriatal functioning predicted phenoconversion. The remaining 268 patients are in follow-up within the FARPRESTO project.Conclusions: Clinical data (older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression) predicted phenoconversion in this multicenter, longitudinal, observational study. A standardized visual approach for semi-quantification of DaT-SPECT is proposed as a practical risk factor for phenoconversion in iRBD patients. Of note, non-converted and newly diagnosed iRBD patients, who represent a trial-ready cohort for upcoming disease-modification trials, are currently being enrolled and followed in the FARPRESTO study. New data are expected to allow better risk characterization

Globally Significant CO2 Emissions From Katla, a Subglacial Volcano in Iceland

Volcanoes are a key natural source of CO2, but global estimates of volcanic CO2 flux are predominantly based on measurements from a fraction of world's actively degassing volcanoes. We combine high-precision airborne measurements from 2016 and 2017 with atmospheric dispersion modeling to quantify CO2 emissions from Katla, a major subglacial volcanic caldera in Iceland that last erupted 100 years ago but has been undergoing significant unrest in recent decades. Katla's sustained CO2 flux, 12–24 kt/d, is up to an order of magnitude greater than previous estimates of total CO2 release from Iceland's natural sources. Katla is one of the largest volcanic sources of CO2 on the planet, contributing up to 4% of global emissions from nonerupting volcanoes. Further measurements on subglacial volcanoes worldwide are urgently required to establish if Katla is exceptional, or if there is a significant previously unrecognized contribution to global CO2 emissions from natural sources