An Arbitrary Lagrangian-Eulerian SPH-MLS Method for the Computation of Compressible Viscous Flows

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] In this work we present a high-accurate discretization to solve the compressible Navier-Stokes equations using an Arbitrary Lagrangian-Eulerian meshless method (SPH-MLS), which can be seen as a general formulation that includes some well-known meshfree methods as a particular case. The formulation is based on the use of Moving Least Squares (MLS) approximants as weight functions on a Galerkin formulation and to accurate discretize the convective and viscous fluxes. This formulation also verifies the discrete partition of unity and reproduces the zero-gradient condition for constant functions. Convective fluxes are discretized using Riemann solvers. In order to obtain high accuracy MLS is also used for the high-order reconstruction of the Riemann states. The accuracy and performance of the proposed method is demonstrated by solving different steady and unsteady benchmark problems.This work has been partially supported by Ministerio de Ciencia, Innovación y Universidades of the Spanish Government (grant #RTI2018-093366-B-I00) and by the Consellería de Educación e Ordenación Universitaria of the Xunta de Galicia (grant# ED431C 2018/41), cofinanced with FEDER funds of the European Union. Luis Ramírez also acknowledges the funding provided by the Xunta de Galicia through the program Axudas para a mellora, creación, recoñecemento e estruturación de agrupacións estratéxicas do Sistema universitario de Galicia (reference # ED431E 2018/11)Xunta de Galicia; ED431C 2018/41Xunta de Galicia; ED431E 2018/1

MLS-SPH-ALE: A Review of Meshless-FV Methods and a Unifying Formulation for Particle Discretizations

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.[Abstract:] Mesh-based and particle methods were conceived as two different discretization strategies to solve partial differential equations. In the last two decades computational methods have diversified and a myriad of hybrid formulations that combine elements of these two approaches have been developed to solve Computational fluid dynamics problems. In this work we present a review about the meshless-FV family of methods, an analysis is carried out showing that the MLS-SPH-ALE method can be considered as a general formulation from which a set of particle-based methods can be recovered. Moreover, we show the relations between the MLS-SPH-ALE method and the finite volume method. The MLS-SPH-ALE method is a versatile particle-based method that was developed to circumvent the consistency issues of particle methods caused by the use of the kernel approximation. The MLS-SPH-ALE method is developed from the differential equation in ALE form using the partition unity property which is automatically fulfilled by the Moving Least Squares approximation.The authors gratefully acknowledge the support provided by the [Grant PID2021-125447OB-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, and the funds by [Grant TED2021–129805B-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. They also acknowledge the funding provided by the Xunta de Galicia (Grant #ED431C 2022/06). J. Fernández-Fidalgo acknowledges the support provided by “Ayudas para la recualificación del sistema universitario español para 2021–2023. Modalidad Margarita Salas RSU.UDC.MS20" by the Ministerio de Universidades of the Spanish Government and European Union through the NextGenerationEU funds.Xunta de Galicia; ED431C 2022/0

Mecanismos químicos e mineralógicos de transformação da magnesioferrita de solo derivado de tufito, da região do Alto Paranaíba, MG

Magnetic soils forming on tuffite of the region of Alto Paranaíba, Minas Gerais, Brazil, usually contain iron-rich spinels exceptionally rich in magnesium and titanium. In this work, samples of the magnetically separated portion from the sand fraction of a Brunizém (Chernossolo) and from its mother-rock material were analyzed with synchrotron X-ray diffraction and 57Fe-Mössbauer spectroscopy. Magnesioferite (MgFe2O4) and maghemite (its pure non-stoichiometric spinel structure, Fe8/3 ⊕ 1/3 O4, where ⊕ = cation vacancy, corresponds to γFe2O3) were the magnetic iron oxides so identified. Basing on these data, a consistent chemical-mineralogical model is proposed for the main transformation steps involving these iron oxides in the pedosystem, starting on magnesioferrite to finally render hematite (αFe2O3), passing through maghemite as an intermediate specie

Antibody persistence and booster responses 24-36 months after different 4CMenB vaccination schedules in infants and children: A randomised trial.

This phase IIIb, open-label, multicentre, extension study (NCT01894919) evaluated long-term antibody persistence and booster responses in participants who received a reduced 2 + 1 or licensed 3 + 1 meningococcal serogroup B vaccine (4CMenB)-schedule (infants), or 2-dose catch-up schedule (2-10-year-olds) in parent study NCT01339923. Children aged 35 months to 12 years (N = 851) were enrolled. Follow-on participants (N = 646) were randomised 2:1 to vaccination and non-vaccination subsets; vaccination subsets received an additional 4CMenB dose. Newly enrolled vaccine-naïve participants (N = 205) received 2 catch-up doses, 1 month apart (accelerated schedule). Antibody levels were determined using human serum bactericidal assay (hSBA) against MenB indicator strains for fHbp, NadA, PorA and NHBA. Safety was also evaluated. Antibody levels declined across follow-on groups at 24-36 months versus 1 month post-vaccination. Antibody persistence and booster responses were similar between infants receiving the reduced or licensed 4CMenB-schedule. An additional dose in follow-on participants induced higher hSBA titres than a first dose in vaccine-naïve children. Two catch-up doses in vaccine-naïve participants induced robust antibody responses. No safety concerns were identified. Antibody persistence, booster responses, and safety profiles were similar with either 2 + 1 or 3 + 1 vaccination schedules. The accelerated schedule in vaccine-naïve children induced robust antibody responses.Novartis Vaccines DivisionGlaxoSmithKline Biologicals S

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost