The crustal structure of South Eastern Europe in the new European Plate reference model

The new European Plate crustal model (EPcrust) represents a continental-scale, a priori, compilation of current knowledge on the structure of the upper layers of the earth, designed as a large-scale reference for further seismo- logical studies. Here we review some of the contributions used, and test and compare the model in detail for the Carpathians- Pannonian region with orogene, platform and basin structures (Hungary, Romania), Black Sea, Balkan area (Bul- garia, Greece and Turkey) and the western margin of the East European Platform (Ukraina). We specifically address thickness of sediments, Moho depth and Vp in upper and lower crust, and run comparisons with local compila- tions and individual studies mostly deriving from analysis of active source experiments. Among the most notable features in this region are Moho depths below the Carpathians range, generally between 32 and 37 km (but with some reported values as high as 45 km), and consolidated sediments in the Black Sea reaching thickness from 3 to 12 km. We compare maps and profiles based on the EPcrust model (cristalline basement and Moho surfaces) along great-circle cross-sections across the major tectonic structures in the SE Europe such as Pannonian-Carpathians system, with extension to the E in the East European Platform, from Balkans to the Black Sea or in the south from Greece towards Turkey. Along the profiles the local crustal parameters are mentioned as they were provided by local studies in each area

Spectral classification of young stars using conditional invertible neural networks I. Introducing and validating the method

Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional invertible neural network (cINN). Our networks allow us to infer the posterior distribution of each stellar parameter from the optical spectrum. Methods. We discuss cINNs trained on three different Phoenix grids: Settl, NextGen, and Dusty. We evaluate the performance of these cINNs on unlearned Phoenix synthetic spectra and on the spectra of 36 Class III template stars with well-characterised stellar parameters. Results. We confirm that the cINNs estimate the considered stellar parameters almost perfectly when tested on unlearned Phoenix synthetic spectra. Applying our networks to Class III stars, we find good agreement with deviations of at most 5--10 per cent. The cINNs perform slightly better for earlier-type stars than for later-type stars like late M-type stars, but we conclude that estimations of effective temperature and surface gravity are reliable for all spectral types within the network's training range. Conclusions. Our networks are time-efficient tools applicable to large amounts of observations. Among the three networks, we recommend using the cINN trained on the Settl library (Settl-Net), as it provides the best performance across the largest range of temperature and gravity.Comment: 29 pages, 19 figures, Accepted for publication by Astronomy & Astrophysics on 10. Apri

A deep learning approach for the 3D reconstruction of dust density and temperature in star-forming regions

Funding: The team in Heidelberg acknowledges funding from the European Research Council via the ERC Synergy Grant “ECOGAL” (project ID 855130), from the German Excellence Strategy via the Heidelberg Cluster of Excellence (EXC 2181 - 390900948) “STRUCTURES”, and from the German Ministry for Economic Affairs and Climate Action in project “MAINN” (funding ID 50OO2206). They also thank for computing resources provided by The Länd and DFG through grant INST 35/1134-1 FUGG and for data storage at SDS@hd through grant INST 35/1314-1 FUGG.Aims. We introduce a new deep learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (< 0.2 pc). Methods. We construct a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 µm. We simplify the task by reconstructing the cloud structure along individual lines of sight and train a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and is able to predict full posterior distributions for the target dust properties. We test different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluate the predictive performance of these models on synthetic test data. Results. We report an excellent reconstruction performance for the 23-wavelengths cINN model, achieving median absolute relative errors of about 1.8% in log(ndust/m−3) and 1% in log(Tdust/K), respectively. We identify trends towards overestimation at the low end of the density range and towards underestimation at the high end of both density and temperature, which may be related to a bias in the training data. Limiting coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 3.3% and 2.5% in log(ndust/m−3) and log(Tdust/K). Conclusions. This proof of concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and even feasible under realistic observational constraints.Peer reviewe

A elevada prevalência do índice de adiposidade corporal está associado a fatores sociodemográficos, hábitos comportamentais e perfil antropométrico?

Introdução: O estilo de vida não saudável aderido por uma parcela considerável da população propicia o acúmulo de massa corpórea que pode ser evidenciado pela aquisição de sobrepeso e obesidade além de outras comorbidades. Estimativa antropométrica da composição corpórea pode expandir não apenas a capacidade fidedigna de estudos epidemiológicos, mas também o direcionamento de políticas públicas eficazes que possam interferir positivamente na qualidade de vida dessas pessoas. O índice de Adiposidade Corporal (IAC), originado em 2011, emerge como método promissor a fim de viabilizar mais precisão nessas análises antropométricas frente a outros métodos. Objetivo: Estimar a prevalência do Índice de Adiposidade Corporal (IAC) e fatores associados em colaboradores técnicos de um Centro Universitário em Montes Claros-MG. Materiais e Métodos: Estudo epidemiológico, transversal e analítico, realizado no Centro Universitário FIPMoc (UNIFIPMoc), em Montes Claros-MG, no período de janeiro a dezembro de 2019. Resultados: A prevalência de adiposidade corporal foi de 45,3%, entre os colaboradores investigados. Considerando os aspectos sociodemográficos, os hábitos comportamentais e os perfis antropométricos investigados, as condições que demonstraram relação significativa com o IAC foram o sexo (p=0,000), o uso de cigarros (0,562) e o IMC (p=0,013). Discussão: Constatou-se uma alta prevalência de adiposidade corporal nos colaboradores técnicos que se mantiveram associados a fatores sociodemográficos, hábitos comportamentais e perfil antropométrico. Conclusão: A prevalência expressiva de adiposidade corporal evidenciada nos colaboradores técnicos pode possibilitar a implementação de medidas intervencionistas, a fim de promover a saúde e, de forma abrangente, potencializar a qualidade de vida dos participantes envolvidos

Characterization of S3Pvac Anti-Cysticercosis Vaccine Components: Implications for the Development of an Anti-Cestodiasis Vaccine

Background: Cysticercosis and hydatidosis seriously affect human health and are responsible for considerable economic loss in animal husbandry in non-developed and developed countries. S3Pvac and EG95 are the only field trial-tested vaccine candidates against cysticercosis and hydatidosis, respectively. S3Pvac is composed of three peptides (KETc1, GK1 and KETc12), originally identified in a Taenia crassiceps cDNA library. S3Pvac synthetically and recombinantly expressed is effective against experimentally and naturally acquired cysticercosis.Methodology/ Principal Findings: In this study, the homologous sequences of two of the S3Pvac peptides, GK1 and KETc1, were identified and further characterized in Taenia crassiceps WFU, Taenia solium, Taenia saginata, Echinococcus granulosus and Echinococcus multilocularis. Comparisons of the nucleotide and amino acid sequences coding for KETc1 and GK1 revealed significant homologies in these species. The predicted secondary structure of GK1 is almost identical between the species, while some differences were observed in the C terminal region of KETc1 according to 3D modeling. A KETc1 variant with a deletion of three C-terminal amino acids protected to the same extent against experimental murine cysticercosis as the entire peptide. on the contrary, immunization with the truncated GK1 failed to induce protection. Immunolocalization studies revealed the non stage-specificity of the two S3Pvac epitopes and their persistence in the larval tegument of all species and in Taenia adult tapeworms.Conclusions/ Significance: These results indicate that GK1 and KETc1 may be considered candidates to be included in the formulation of a multivalent and multistage vaccine against these cestodiases because of their enhancing effects on other available vaccine candidates

High Risk of Secondary Infections Following Thrombotic Complications in Patients With COVID-19

Background. This study’s primary aim was to evaluate the impact of thrombotic complications on the development of secondary infections. The secondary aim was to compare the etiology of secondary infections in patients with and without thrombotic complications. Methods. This was a cohort study (NCT04318366) of coronavirus disease 2019 (COVID-19) patients hospitalized at IRCCS San Raffaele Hospital between February 25 and June 30, 2020. Incidence rates (IRs) were calculated by univariable Poisson regression as the number of cases per 1000 person-days of follow-up (PDFU) with 95% confidence intervals. The cumulative incidence functions of secondary infections according to thrombotic complications were compared with Gray’s method accounting for competing risk of death. A multivariable Fine-Gray model was applied to assess factors associated with risk of secondary infections. Results. Overall, 109/904 patients had 176 secondary infections (IR, 10.0; 95% CI, 8.8–11.5; per 1000-PDFU). The IRs of secondary infections among patients with or without thrombotic complications were 15.0 (95% CI, 10.7–21.0) and 9.3 (95% CI, 7.9–11.0) per 1000-PDFU, respectively (P = .017). At multivariable analysis, thrombotic complications were associated with the development of secondary infections (subdistribution hazard ratio, 1.788; 95% CI, 1.018–3.140; P = .043). The etiology of secondary infections was similar in patients with and without thrombotic complications. Conclusions. In patients with COVID-19, thrombotic complications were associated with a high risk of secondary infections

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution