Schizostoma laceratum (Ehrenb. ex Fr.) Lév., a Catalunya

Schizostoma laceratum (Ehrenb. ex Fr.) Lév., a Catalunya. En aquest treball es descriu una interessant espècie del gènere Schizostoma (Agaricaeae, Basidiomycota), trobada a Catalunya, sobre dunes litorals.Schizostoma laceratum (Ehrenb. ex Fr.) Lév., in Catalonia. In this paper we describe one interesting specie of the genus Schizostoma (Agaricaeae, Basidiomycota), collected in Catalonia, on sand dunes.Schizostoma laceratum (Ehrenb. ex Fr.) Lév., en Cataluña. En este trabajo se describe una interesante especie del género Schizostoma (Agaricaeae, Basidiomycota), hallada en Cataluña, sobre dunas litorales

A local constitutive model for the discrete element method. Application to geomaterials and concrete

This paper presents a local constitutive model for modelling the linear and non linear behavior of soft and hard cohesive materials with the discrete element method (DEM). We present the results obtained in the analysis with the DEM of cylindrical samples of cement, concrete and shale rock materials under a uniaxial compressive strength (UCS) test, different triaxial tests, a uniaxial strain compaction (USC) test and a brazilian tensile strength (BTS) test. DEM results compare well with the experimental values in all cases

Advances in the DEM and coupled DEM and FEM techniques in non linear solid mechanics

Abstract In this chapter we present recent advances in the Discrete Element Method (DEM) and in the coupling of the DEM with the Finite Element Method (FEM) for solving a variety of problems in non linear solid mechanics involving damage, plasticity and multifracture situations.Preprin

Advances in the DEM and coupled DEM and FEM techniques in non linear solid mechanics

In this chapter we present recent advances in the Discrete Element Method (DEM) and in the coupling of the DEM with the Finite Element Method (FEM) for solving a variety of problems in non linear solid mechanics involving damage, plasticity and multifracture situations

Performance of comprehensive risk adjustment for the prediction of in-hospital events using administrative healthcare data: The queralt indices

Background: Accurate risk adjustment is crucial for healthcare management and benchmarking. Purpose: We aimed to compare the performance of classic comorbidity functions (Charlson's and Elixhauser's), of the All Patients Refined Diagnosis Related Groups (APR-DRG), and of the Queralt Indices, a family of novel, comprehensive comorbidity indices for the prediction of key clinical outcomes in hospitalized patients. Material and Methods: We conducted an observational, retrospective cohort study using administrative healthcare data from 156,459 hospital discharges in Catalonia (Spain) during 2018. Study outcomes were in-hospital death, long hospital stay, and intensive care unit (ICU) stay. We evaluated the performance of the following indices: Charlson's and Elixhauser's functions, Queralt's Index for secondary hospital discharge diagnoses (Queralt DxS), the overall Queralt's Index, which includes pre-existing comorbidities, in-hospital complications, and principal discharge diagnosis (Queralt Dx), and the APR-DRG. Discriminative ability was evaluated using the area under the curve (AUC), and measures of goodness of fit were also computed. Subgroup analyses were conducted by principal discharge diagnosis, by age, and type of admission. Results: Queralt DxS provided relevant risk adjustment information in a larger number of patients compared to Charlson's and Elixhauser's functions, and outperformed both for the prediction of the 3 study outcomes. Queralt Dx also outperformed Charlson's and Elixhauser's indices, and yielded superior predictive ability and goodness of fit compared to APR-DRG (AUC for in-hospital death 0.95 for Queralt Dx, 0.77- 0.93 for all other indices; for ICU stay 0.84 for Queralt Dx, 0.73- 0.83 for all other indices). The performance of Queralt DxS was at least as good as that of the APR-DRG in most principal discharge diagnosis subgroups. Conclusion: Our findings suggest that risk adjustment should go beyond pre-existing comorbidities and include principal discharge diagnoses and in-hospital complications. Validation of comprehensive risk adjustment tools such as the Queralt indices in other settings is needed

DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 hospitals—planification, implementation, and preliminary results

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.This project was funded by European Regional Development Funds, Programa operatiu FEDER de Catalunya 2014–2020 and SA18-014623 DIGIPATICS. UPC activity in this project was partially supported by PID2020-116907RB-I00 and funded by MCIN/AEI/10.13039/501100011033Peer ReviewedArticle signat per 18 autors/es: Jordi Temprana-Salvador (1), Pablo López-García (2), Josep Castellví Vives (1),Lluís de Haro (2), Eudald Ballesta (2), Matias Rojas Abusleme (3), Miquel Arrufat (4), Ferran Marques (5), Josep R. Casas (5),Carlos Gallego (6), Laura Pons (7), José Luis Mate (7), Pedro Luis Fernández (7), Eugeni López-Bonet (8), Ramon Bosch (9), Salomé Martínez (10), Santiago Ramón y Cajal (1), and Xavier Matias-Guiu (11,12) // (1) Department of Pathology, Vall d’Hebron University Hospital, CIBERONC, 08035 Barcelona, Spain; (2) Functional Competence Center, Information Systems, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (3) Center for Telecommunications and Information Technology (Centre de Telecomunicacions i Tecnologies de la Informació, CTTI), Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (4) Economic and Financial Management, Catalan Health Institute (Institut Català de la Salut), 08006 Barcelona, Spain; (5) Image Processing Group, Technical University of Catalonia (UPC), 08034 Barcelona, Spain; (6) Digital Medical Imaging System of Catalonia (SIMDCAT), TIC Salut, 08005 Barcelona, Spain, (7) Department of Pathology, Germans Trias i Pujol University Hospital, 08916 Badalona, Spain; (8) Department of Pathology, Doctor Josep Trueta Hospital of Girona, 17007 Girona, Spain; (9) Department of Pathology, Verge de la Cinta Hospital of Tortosa, 43500 Tarragona, Spain; (10) Department of Pathology, Joan XXIII University Hospital of Tarragona, 43005 Tarragona, Spain; (11) Department of Pathology, Arnau de Vilanova University Hospital, 25198 Lleida, Spain, (12) Department of Pathology, Bellvitge University Hospital, CIBERONC, 08907 Barcelona, SpainPostprint (published version

Aplicación de un modelo de mezcla total en acuífero cárstico

Por el fondo de un valle de la provincia de Girona mana un conjunto de surgencias cuyo caudal total anual se acercaa medio centenar de hectómetros cúbicos. Controlando no solo la cantidad de agua de salida sino también su calidad desde el punto de vista de su contenido en tritio y a partir de un modelo conceptual de mezcla uniforme de las aguas de recarga, ya com probado en otros sistemas, se acota la cadencia de participación en las surgencias de las distintas recargas anuales, as como el tiempo de renovación del volumen de agua del sistema hidrogeológic

A local constitutive model for the discrete element method: application to geomaterials and concrete

This paper presents a local constitutive model for modelling the linear and non linear behavior of soft and hard cohesive materials with the discrete element method (DEM). We present the results obtained in the analysis with the DEM of cylindrical samples of cement, concrete and shale rock materials under a uniaxial compressive strength test, different triaxial tests, a uniaxial strain compaction test and a Brazilian tensile strength test. DEM results compare well with the experimental values in all cases.Peer Reviewe

Advances in the DEM and coupled DEM and FEM techniques in non linear solid mechanics

Abstract In this chapter we present recent advances in the Discrete Element Method (DEM) and in the coupling of the DEM with the Finite Element Method (FEM) for solving a variety of problems in non linear solid mechanics involving damage, plasticity and multifracture situations

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients

Background: Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients' complexity. Purpose: To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity. Materials and methods: We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC). Results: Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices. Conclusion: In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting