The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond

Data Mining Tool for Academic Data Exploitation : Publication Report on Engineering Students Profiles

This document aims to reflect the results obtained at SPEET project by means of the application of the deployed data mining tools. More specifically, the application of the student performance tools on the engineering degrees by the participating institutions. The analysis can be used twofold: Example on the use of the different performance analysis tools developed on the project and accessible at the project web tool. Example on the interpretation of the clustering and analysis tools in order to delimit a student profile. The provided analysis also extends the interpretations aiming at showing similarities country wise, so, finally we can conclude that the tools developed in this project can offer some significant information in detecting different profiles and the relationship between these profiles and categorical variables such as age, admission score, sex, previous studies

Direct venous inoculation of Plasmodium falciparum sporozoites for controlled human malaria infection: a dose-finding trial in two centres

BACKGROUND: Controlled human malaria infection (CHMI) accelerates development of anti-malarial interventions. So far, CHMI is done by exposure of volunteers to bites of five mosquitoes carrying Plasmodium falciparum sporozoites (PfSPZ), a technique available in only a few centres worldwide. Mosquito-mediated CHMI is logistically complex, exact PfSPZ dosage is impossible and live mosquito-based interventions are not suitable for further clinical development. METHODS: An open-labelled, randomized, dose-finding study in 18-45 year old, healthy, malaria-naive volunteers was performed to assess if intravenous (IV) injection of 50 to 3,200 aseptic, purified, cryopreserved PfSPZ is safe and achieves infection kinetics comparable to published data of mosquito-mediated CHMI. An independent study site verified the fully infectious dose using direct venous inoculation of PfSPZ. Parasite kinetics were assessed by thick blood smear microscopy and quantitative real time PCR. RESULTS: IV inoculation with 50, 200, 800, or 3,200 PfSPZ led to parasitaemia in 1/3, 1/3, 7/9, and 9/9 volunteers, respectively. The geometric mean pre-patent period (GMPPP) was 11.2 days (range 10.5-12.5) in the 3,200 PfSPZ IV group. Subsequently, six volunteers received 3,200 PfSPZ by direct venous inoculation at an independent investigational site. All six developed parasitaemia (GMPPP: 11.4 days, range: 10.4-12.3). Inoculation of PfSPZ was safe. Infection rate and pre-patent period depended on dose, and injection of 3,200 PfSPZ led to a GMPPP similar to CHMI with five PfSPZ-infected mosquitoes. The infectious dose of PfSPZ predicted dosage of radiation-attenuated PfSPZ required for successful vaccination. CONCLUSIONS: IV inoculation of PfSPZ is safe, well tolerated and highly reproducible. It shall further accelerate development of anti-malarial interventions through standardization and facilitation of CHMI. Beyond this, rational dose selection for whole PfSPZ-based immunization and complex study designs are now possible. TRIAL REGISTRATION: ClinicalTrials.gov NCT01624961 and NCT01771848

Data Mining Tool for Academic Data Exploitation : Graphical Data analysis and Visualization

This document aims to reflect the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The students' clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated

Data Mining Tool for Academic Data Exploitation : Graphical Data analysis and Visualization

This document aims to reflect the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The students' clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated

Data Mining Tool for Academic Data Exploitation : Webtool description and usage

This document is related to the Data Mining Tools for Academic Data Exploitation, and collects the overall architecture of the SPEET generated WEB-tool, the user manual for the use of WEB-tool and the overview of the output to establish clustering, dropout, and their dependency on students' characteristics. The ultimate goal of SPEET project is the development of an WEB-based tool to disseminate the main intellectual output in form of user-friendly and easily accessible software tool. The WEB-tool is accessible from speet.uab.cat is intended to make accessible by other faculties and schools outside of the SPEET consortium the possibility to make data analysis on students based on the proprietary data after these are organized accordingly. The WEB-tool developed within SPEET project represent a product that will remain after the end of the project to let European Universities accessing and comparing their performance metrics. Furthermore, the WEB-tool is a free-of-charge tool that contains the analyses of the SPEET institutions and another engineering institution can autonomously compare with the pre-existing ones to extract comparative analyses, or simply to investigate anomalous dropout situations

Data Mining Tool for Academic Data Exploitation : Selection of most suitable Algorithms

This document aims to reflect the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The students' clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated

Phosphofructo-1-Kinase Deficiency Leads to a Severe Cardiac and Hematological Disorder in Addition to Skeletal Muscle Glycogenosis

Mutations in the gene for muscle phosphofructo-1-kinase (PFKM), a key regulatory enzyme of glycolysis, cause Type VII glycogen storage disease (GSDVII). Clinical manifestations of the disease span from the severe infantile form, leading to death during childhood, to the classical form, which presents mainly with exercise intolerance. PFKM deficiency is considered as a skeletal muscle glycogenosis, but the relative contribution of altered glucose metabolism in other tissues to the pathogenesis of the disease is not fully understood. To elucidate this issue, we have generated mice deficient for PFKM (Pfkm−/−). Here, we show that Pfkm−/− mice had high lethality around weaning and reduced lifespan, because of the metabolic alterations. In skeletal muscle, including respiratory muscles, the lack of PFK activity blocked glycolysis and resulted in considerable glycogen storage and low ATP content. Although erythrocytes of Pfkm−/− mice preserved 50% of PFK activity, they showed strong reduction of 2,3-biphosphoglycerate concentrations and hemolysis, which was associated with compensatory reticulocytosis and splenomegaly. As a consequence of these haematological alterations, and of reduced PFK activity in the heart, Pfkm−/− mice developed cardiac hypertrophy with age. Taken together, these alterations resulted in muscle hypoxia and hypervascularization, impaired oxidative metabolism, fiber necrosis, and exercise intolerance. These results indicate that, in GSDVII, marked alterations in muscle bioenergetics and erythrocyte metabolism interact to produce a complex systemic disorder. Therefore, GSDVII is not simply a muscle glycogenosis, and Pfkm−/− mice constitute a unique model of GSDVII which may be useful for the design and assessment of new therapies