TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Avanços da cirurgia robotica no tratamento do Câncer

A cirurgia robótica tem sido almejada por profissionais cirurgiões, visto que, é um procedimento considerado de alta eficácia, assim como, apresenta reduzidos níveis de riscos ao paciente, o que configura significativamente a qualidade elevada do procedimento médico. Logo, tem sido utilizada no tratamento cirúrgico de diversos tipos de câncer, o qual tem demonstrado melhores resultados quando comparados com procedimentos mais invasivas ou tradicionais O principal objetivo do é discutir por meio de uma revisão sistematizada da literatura acerca dos avanços da cirurgia robótica no tratamento dos demais tipos de cânceres. O presente estudo trata-se de uma revisão sistemática da literatura, de modo que, realizou-se buscas de na Scielo, Periódico Capes e na Biblioteca Virtual em Saúde (BVS), através de termos específicos do Decs, o qual resultou-se em: “Neoplasias” AND “Procedimentos Cirúrgicos Robóticos” AND “Terapêutica”. Foram elegíveis um total de 8 estudos na presente revisão sistemática. Este procedimento é devidamente realizado com menores danos de incisão possível no paciente, de modo que, possibilitou a diminuição de dores pós-operatórios, além da redução de sangramentos, traumas, respostas inflamatórias, tempo de internação e até mesmo melhores resultados estéticos nos pacientes, quando comparados com os métodos mais tradicionais e invasivos

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

A Framework for Developing Distributed Cooperative Decision Support Systems - Inception Phase

This paper describes the This paper describes the inception phase of the development process of a Framework for Developing Distributed Cooperative Decision Support Systems (DSSs). It analyzes the reasons why the broad use of DSSs has not occurred yet and makes propositions to improve this situation. It shows that, for the most part, modern distributed computing architectures could solve many of the presented issues. In the first section, this paper gives an overview of DSSs, based on definitions, history, taxonomies and DSS architectures. In the second section, it covers three categories of problems in the DSS area: human factors, conceptual factors and technical factors. To finish, it proposes possible solutions to these problems using concepts borrowed from new distributed computing architectures.</jats:p

A New Vision for Distributed Decision Support Systems

Many mission-critical, decision-making situations happen in dynamic, rapidly changing, and often unpredictable distributed environments. Military, governmental, and medical contexts are examples of such situations, which can be characterized by highly decentralized, up-to-date data sets coming from various sources. Unlike other decisionmaking tools, DSSs designed for such situations are challenged by the need to access this decentralized data at any time, from anywhere, under tight time constraints

Developing Intelligent Decision Support Systems: A Bipartite Approach

The construction of an intelligent decision support system borrows concepts from two fields: software engineering and knowledge engineering. Yet the development processes of the purely technical components of the system and of the knowledge base of the DSS are very different in terms of development time, paradigms, tools, technical evolutions and the expertise required by the developer. In this paper, we propose an innovative approach taking these fundamental differences into consideration. The novelty of this bipartite approach lies in the clear and generic separation between the container of the DSS (responsible for the software engineering part) and the contents of the DSS (responsible for the knowledge engineering part)

THE VIRTUAL TWIN: A SOCIALIZATION AGENT FOR PEER-TO-PEER NETWORKS

As peer-to-peer computing finally reaches a critical mass, it triggers changes in the IT landscape that traditional network infrastructures based on centralized, client/server topologies cannot manage. Consequently, the ad hoc, self-organized and loosely controled nature of peer-to-peer networks needs to be supported by a new coordination layer representing the interests of the user