O4OA Specification

This document is the reference ontology specification for the Ontology for Ontological Analysis (O4OA) version 2.6.This work has been developed under the project Digital Knowledge Graph – Adaptable Analytics API with the financial support of Accenture LTD, the Generalitat Valenciana through the CoMoDiD project (CIPROM/2021/023), the Spanish State Research Agency through the DELFOS (PDC2021-121243-I00) and SREC (PID2021-123824OB-I00) projects, MICIN/AEI/10.13039/501 100011033 and co-financed with ERDF and the European Union Next Generation EU/PRTR.Franco Martins Souza, B.; Guizzardi, R.; Pastor López, O. (2023). O4OA Specification. http://hdl.handle.net/10251/19672

Towards the Consolidation of Cybersecurity Standardized Definitions

[ES] La ciberseguridad es un dominio vasto y complejo, por lo que las empresas están buscando activamente soluciones eficientes en este área. Los Knowledge Graphs (KG) son uno de los mecanismos que utilizan las organizaciones para explorar la seguridad entre activos y posibles ataques. Sin embargo, la gran cantidad de información puede generar una mala interpretación de los conceptos representados en estos modelos conceptuales. Como un Knowledge Graph puede considerarse una implementación de una conceptualización, la base de los conceptos es fundamental. De ahí, el apoyo de las mejores prácticas de Modelado Conceptual, especialmente de la rama de Ontologías. En este informe se lleva a cabo un estudio piloto para descubrir el estado del arte en ”Ontologías de Ciberseguridad”. A partir de este estudio, proponemos una encuesta para ampliar nuestro enfoque terminológico. La encuesta produjo una gran cantidad de datos, por lo que desarrollamos una API REST para la manipulación de datos y una base de datos NoSQL para almacenarlos, que es la principal contribución de este documento. Nuestro objetivo es proporcionar una herramienta de análisis ontológico para ayudar a las partes interesadas a evitar malas interpretaciones durante el desarrollo y la implementación de los KG.[EN] Cybersecurity is a vast and complex domain, therefore enterprises are actively seeking efficient solutions in this matter. Knowledge Graphs (KG) are one of the mechanisms that organizations use to explore the security among assets and possible attacks. However, the great amount of information can create misinterpretation of concepts represented in these structures of conceptualizations. As a KG may be considered an implementation of a conceptualization, the grounding of concepts is fundamental. Therefore, the support of Conceptual Modeling best-practices, especially regarding the branch of Ontologies. We made a pilot study that finds out the state-of-art in ”Cybersecurity Ontologies”. From this study, we propose a survey to extend our terminological approach. The survey produced a huge amount of data, thus we develop a REST API for data manipulation and a NoSQL database to store them which is the main contribution of this document. Our goal is to provide an ontological analysis tool to help stakeholders avoid misinterpretations during KGs development and implementation.This work has been developed under the project Digital Knowledge Graph – Adaptable Analytics API with the financial support of Accenture LTD.Franco Martins Souza, B.; Serrano Gil, LJ.; Reyes Román, JF.; Panach Navarrete, JI.; Pastor López, O. (2021). Towards the Consolidation of Cybersecurity Standardized Definitions. Universitat Politècnica de València. http://hdl.handle.net/10251/16389

Incorporating biodiversity responses to land use change scenarios for preventing emerging zoonotic diseases in areas of unknown host-pathogen interactions

The need to reconcile food production, the safeguarding of nature, and the protection of public health is imperative in a world of continuing global change, particularly in the context of risks of emerging zoonotic disease (EZD). In this paper, we explored potential land use strategies to reduce EZD risks using a landscape approach. We focused on strategies for cases where the dynamics of pathogen transmission among species were poorly known and the ideas of “land-use induced spillover” and “landscape immunity” could be used very broadly. We first modeled three different land-use change scenarios in a region of transition between the Cerrado and the Atlantic Forest biodiversity hotspots. The land-use strategies used to build our scenarios reflected different proportions of native vegetation cover, as a proxy of habitat availability. We then evaluated the effects of the proportion of native vegetation cover on the occupancy probability of a group of mammal species and analyzed how the different land-use scenarios might affect the distribution of species in the landscape and thus the risk of EZD. We demonstrate that these approaches can help identify potential future EZD risks, and can thus be used as decision-making tools by stakeholders, with direct implications for improving both environmental and socio-economic outcomes

Convalescent plasma for COVID-19 in hospitalised patients : an open-label, randomised clinical trial

Background: The effects of convalescent plasma (CP) therapy in hospitalised patients with coronavirus disease 2019 (COVID-19) remain uncertain. This study investigates the effect of CP on clinical improvement in these patients. Methods: This is an investigator-initiated, randomised, parallel arm, open-label, superiority clinical trial. Patients were randomly (1:1) assigned to two infusions of CP plus standard of care (SOC) or SOC alone. The primary outcome was the proportion of patients with clinical improvement 28 days after enrolment. Results: A total of 160 (80 in each arm) patients (66.3% critically ill, 33.7% severely ill) completed the trial. The median (interquartile range (IQR)) age was 60.5 (48–68) years; 58.1% were male and the median (IQR) time from symptom onset to randomisation was 10 (8–12) days. Neutralising antibody titres >1:80 were present in 133 (83.1%) patients at baseline. The proportion of patients with clinical improvement on day 28 was 61.3% in the CP+SOC group and 65.0% in the SOC group (difference −3.7%, 95% CI −18.8–11.3%). The results were similar in the severe and critically ill subgroups. There was no significant difference between CP+SOC and SOC groups in pre-specified secondary outcomes, including 28-day mortality, days alive and free of respiratory support and duration of invasive ventilatory support. Inflammatory and other laboratory marker values on days 3, 7 and 14 were similar between groups. Conclusions: CP+SOC did not result in a higher proportion of clinical improvement on day 28 in hospitalised patients with COVID-19 compared to SOC alone

Reinfecção da Covid-19 em neonatos e crianças: revisão de literatura : Reinfection of Covid-19 in neonates and children: literature review

Em dezembro de 2019, um novo vírus respiratório foi detectado na China, sendo denominado posteriormente como COVID-19, provindo do vírus SARS-CoV-2. Este vírus se espalhou rapidamente gerando uma pandemia mundial. Vários foram e ainda são os problemas decorrentes deste vírus, que atingiu desde crianças aos idosos, dentre eles, cita-se a reinfeção dos indivíduos, causando como consequência, problemas de saúde inacabáveis. Assim, devido aos estudos elencarem menores sinais e sintomas nas crianças e, ainda, menores taxas de infecção, passou-se a questionar quanto as reinfecções em crianças e neonatos. Por isso, o estudo objetivou realizar uma revisão de literatura sobre reinfecção de COVID-19 em crianças e neonatos. Para isso, realizou-se uma revisão sistemática de literatura, através de uma busca nas bases de dados Latino-Americana e do Caribe em Ciências da Saúde, Google Scholar e Scientific Electronic Library Online, utilizando-se os descritores: Reinfecção COVID-19; Reinfecção COVID-19 em crianças; Reinfecção COVID-19 em neonatos; COVID-19 e reinfecção. Com isso, foram selecionados 10 artigos que compunham os critérios de inclusão e exclusão do presente estudo. Dessa forma, destaca-se que os estudos evidenciaram que o número de reinfecções em crianças e neonatos é baixo, porém, são escassas as literaturas sobre o tema, possuindo, assim diversas lacunas a serem sanadas para uma compreensão melhor do assunto

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

Combined fit to the spectrum and composition data measured by the Pierre Auger Observatory including magnetic horizon effects

The measurements by the Pierre Auger Observatory of the energy spectrum and mass composition of cosmic rays can be interpreted assuming the presence of two extragalactic source populations, one dominating the flux at energies above a few EeV and the other below. To fit the data ignoring magnetic field effects, the high-energy population needs to accelerate a mixture of nuclei with very hard spectra, at odds with the approximate E$^{-2}$ shape expected from diffusive shock acceleration. The presence of turbulent extragalactic magnetic fields in the region between the closest sources and the Earth can significantly modify the observed CR spectrum with respect to that emitted by the sources, reducing the flux of low-rigidity particles that reach the Earth. We here take into account this magnetic horizon effect in the combined fit of the spectrum and shower depth distributions, exploring the possibility that a spectrum for the high-energy population sources with a shape closer to E$^{-2}$ be able to explain the observations

Studies of the mass composition of cosmic rays and proton-proton interaction cross-sections at ultra-high energies with the Pierre Auger Observatory

In this work, we present an estimate of the cosmic-ray mass composition from the distributions of the depth of the shower maximum (Xmax) measured by the fluorescence detector of the Pierre Auger Observatory. We discuss the sensitivity of the mass composition measurements to the uncertainties in the properties of the hadronic interactions, particularly in the predictions of the particle interaction cross-sections. For this purpose, we adjust the fractions of cosmic-ray mass groups to fit the data with Xmax distributions from air shower simulations. We modify the proton-proton cross-sections at ultra-high energies, and the corresponding air shower simulations with rescaled nucleus-air cross-sections are obtained via Glauber theory. We compare the energy-dependent composition of ultra-high-energy cosmic rays obtained for the different extrapolations of the proton-proton cross-sections from low-energy accelerator data