1,626 research outputs found
Studies on the Codling Moth (Lepidoptera: Tortricidae) Response to Different Codlemone Release Rates
[EN] The response of the codling moth (Cydia pomonella L. (Lepidoptera: Tortricidae)) to different emission values of its main pheromone component, 8E,10E-dodecadien-1-ol (codlemone), was investigated in three field trials conducted in plots without mating disruption treatments. Moth catches obtained in traps baited with pheromone dispensers were correlated with the corresponding codlemone release rates by multiple regression analysis. In a preliminary trial conducted in Lleida (NE Spain), a decreasing trend of captures was observed based on increasing pheromone levels. After this, the pheromone release profiles of the pheromone dispensers were studied, in parallel with the field trials, by residual codlemone extraction and gas chromatography quantification. In the trials carried out in Asturias (NW Spain), a correlation between trap catches and emission levels (within the range from 11 to 1,078 microg/d) was found and fitted a logarithmic model. Captures followed a decreasing linear trend in the range of emission rates from 11 to 134 microg/d. Given that release values comprised between 11 and 67 mcrog/d did not lead to significantly different catches in traps, this emission range could be considered to develop effective formulations for attraction purposes when mating disruption is not acting in the environment.Vacas Gonzålez, S.; Miñarro, M.; Bosch, M.; Primo Millo, J.; Navarro Llopis, V. (2013). Studies on the Codling Moth (Lepidoptera: Tortricidae) Response to Different Codlemone Release Rates. ENVIRONMENTAL ENTOMOLOGY. 42(6):1383-1389. doi:10.1603/EN13114S1383138942
CoVITEST: A Fast and Reliable Method to Monitor Anti-SARS-CoV-2 Specific T Cells From Whole Blood
Cellular and humoral immune responses are essential for COVID-19 recovery and protection against SARS-CoV-2 reinfection. To date, the evaluation of SARS-CoV-2 immune protection has mainly focused on antibody detection, generally disregarding the cellular response, or placing it in a secondary position. This phenomenon may be explained by the complex nature of the assays needed to analyze cellular immunity compared with the technically simple and automated detection of antibodies. Nevertheless, a large body of evidence supports the relevance of the T cell's role in protection against SARS-CoV-2, especially in vulnerable individuals with a weakened immune system (such as the population over 65 and patients with immunodeficiencies). Here we propose to use CoVITEST (Covid19 anti-Viral Immunity based on T cells for Evaluation in a Simple Test), a fast, affordable and accessible in-house assay that, together with a diagnostic matrix, allows us to determine those patients who might be protected with SARS-CoV-2-reactive T cells. The method was established using healthy SARS-CoV-2-naĂŻve donors pre- and post-vaccination (n=30), and further validated with convalescent COVID-19 donors (n=51) in a side-by-side comparison with the gold standard IFN-? ELISpot. We demonstrated that our CoVITEST presented reliable and comparable results to those obtained with the ELISpot technique in a considerably shorter time (less than 8 hours). In conclusion, we present a simple but reliable assay to determine cellular immunity against SARS-CoV-2 that can be used routinely during this pandemic to monitor the immune status in vulnerable patients and thereby adjust their therapeutic approaches. This method might indeed help to optimize and improve decision-making protocols for re-vaccination against SARS-CoV-2, at least for some population subsets.Copyright © 2022 Egri, OlivĂ©, HernĂĄndez-RodrĂguez, Castro, De Guzman, Heredia, Segura, Fernandez, de Moner, Torradeflot, BallĂșs, Martinez, Vazquez, Costa, Dobaño, Mazza, Mazzotti, Pascal, Juan, GonzĂĄlez-Navarro and CalderĂłn
Higher education studentsâ achievement emotions and their antecedents in e-learning amid COVID-19 pandemic: A multi-country survey
The outbreak of the COVID-19 pandemic has had a wide range of negative consequences for higher education students. We explored the generalizability of the control-value theory of achievement emotions for e-learning, focusing on their antecedents. We involved 17019 higher education students from 13 countries, who completed an online survey during the first wave of the pandemic. A structural equation model revealed that proximal antecedents (e-learning self-efficacy, computer self-efficacy) mediated the relation between environmental antecedents (cognitive and motivational quality of the task) and positive and negative achievement emotions, with some exceptions. The model was invariant across country, area of study, and gender. The rates of achievement emotions varied according to these same factors. Beyond their theoretical relevance, these findings could be the basis for policy recommendations to support stakeholders in coping with the challenges of e-learning and the current and future sequelae of the pandemic.info:eu-repo/semantics/publishedVersio
A review of elliptical and disc galaxy structure, and modern scaling laws
A century ago, in 1911 and 1913, Plummer and then Reynolds introduced their
models to describe the radial distribution of stars in `nebulae'. This article
reviews the progress since then, providing both an historical perspective and a
contemporary review of the stellar structure of bulges, discs and elliptical
galaxies. The quantification of galaxy nuclei, such as central mass deficits
and excess nuclear light, plus the structure of dark matter halos and cD galaxy
envelopes, are discussed. Issues pertaining to spiral galaxies including dust,
bulge-to-disc ratios, bulgeless galaxies, bars and the identification of
pseudobulges are also reviewed. An array of modern scaling relations involving
sizes, luminosities, surface brightnesses and stellar concentrations are
presented, many of which are shown to be curved. These 'redshift zero'
relations not only quantify the behavior and nature of galaxies in the Universe
today, but are the modern benchmark for evolutionary studies of galaxies,
whether based on observations, N-body-simulations or semi-analytical modelling.
For example, it is shown that some of the recently discovered compact
elliptical galaxies at 1.5 < z < 2.5 may be the bulges of modern disc galaxies.Comment: Condensed version (due to Contract) of an invited review article to
appear in "Planets, Stars and Stellar
Systems"(www.springer.com/astronomy/book/978-90-481-8818-5). 500+ references
incl. many somewhat forgotten, pioneer papers. Original submission to
Springer: 07-June-201
Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial
IMPORTANCE: Secretory phospholipase A2(sPLA2) generates bioactive phospholipid products implicated in atherosclerosis. The sPLA2inhibitor varespladib has favorable effects on lipid and inflammatory markers; however, its effect on cardiovascular outcomes is unknown. OBJECTIVE: To determine the effects of sPLA2inhibition with varespladib on cardiovascular outcomes. DESIGN, SETTING, AND PARTICIPANTS: A double-blind, randomized, multicenter trial at 362 academic and community hospitals in Europe, Australia, New Zealand, India, and North America of 5145 patients randomized within 96 hours of presentation of an acute coronary syndrome (ACS) to either varespladib (n = 2572) or placebo (n = 2573) with enrollment between June 1, 2010, and March 7, 2012 (study termination on March 9, 2012). INTERVENTIONS: Participants were randomized to receive varespladib (500 mg) or placebo daily for 16 weeks, in addition to atorvastatin and other established therapies. MAIN OUTCOMES AND MEASURES: The primary efficacy measurewas a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), nonfatal stroke, or unstable angina with evidence of ischemia requiring hospitalization at 16 weeks. Six-month survival status was also evaluated. RESULTS: At a prespecified interim analysis, including 212 primary end point events, the independent data and safety monitoring board recommended termination of the trial for futility and possible harm. The primary end point occurred in 136 patients (6.1%) treated with varespladib compared with 109 patients (5.1%) treated with placebo (hazard ratio [HR], 1.25; 95%CI, 0.97-1.61; log-rank P = .08). Varespladib was associated with a greater risk of MI (78 [3.4%] vs 47 [2.2%]; HR, 1.66; 95%CI, 1.16-2.39; log-rank P = .005). The composite secondary end point of cardiovascular mortality, MI, and stroke was observed in 107 patients (4.6%) in the varespladib group and 79 patients (3.8%) in the placebo group (HR, 1.36; 95% CI, 1.02-1.82; P = .04). CONCLUSIONS AND RELEVANCE: In patients with recent ACS, varespladib did not reduce the risk of recurrent cardiovascular events and significantly increased the risk of MI. The sPLA2inhibition with varespladib may be harmful and is not a useful strategy to reduce adverse cardiovascular outcomes after ACS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01130246. Copyright 2014 American Medical Association. All rights reserved
Observation of associated near-side and away-side long-range correlations in âsNN=5.02ââTeV proton-lead collisions with the ATLAS detector
Two-particle correlations in relative azimuthal angle (ÎÏ) and pseudorapidity (Îη) are measured in âsNN=5.02ââTeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1ââÎŒb-1 of data as a function of transverse momentum (pT) and the transverse energy (ÎŁETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Îη|<5) ânear-sideâ (ÎÏâŒ0) correlation that grows rapidly with increasing ÎŁETPb. A long-range âaway-sideâ (ÎÏâŒÏ) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ÎŁETPb, is found to match the near-side correlation in magnitude, shape (in Îη and ÎÏ) and ÎŁETPb dependence. The resultant ÎÏ correlation is approximately symmetric about Ï/2, and is consistent with a dominant cosâĄ2ÎÏ modulation for all ÎŁETPb ranges and particle pT
Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and
healthcare, the deployment and adoption of AI technologies remain limited in
real-world clinical practice. In recent years, concerns have been raised about
the technical, clinical, ethical and legal risks associated with medical AI. To
increase real world adoption, it is essential that medical AI tools are trusted
and accepted by patients, clinicians, health organisations and authorities.
This work describes the FUTURE-AI guideline as the first international
consensus framework for guiding the development and deployment of trustworthy
AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and
currently comprises 118 inter-disciplinary experts from 51 countries
representing all continents, including AI scientists, clinicians, ethicists,
and social scientists. Over a two-year period, the consortium defined guiding
principles and best practices for trustworthy AI through an iterative process
comprising an in-depth literature review, a modified Delphi survey, and online
consensus meetings. The FUTURE-AI framework was established based on 6 guiding
principles for trustworthy AI in healthcare, i.e. Fairness, Universality,
Traceability, Usability, Robustness and Explainability. Through consensus, a
set of 28 best practices were defined, addressing technical, clinical, legal
and socio-ethical dimensions. The recommendations cover the entire lifecycle of
medical AI, from design, development and validation to regulation, deployment,
and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which
provides a structured approach for constructing medical AI tools that will be
trusted, deployed and adopted in real-world practice. Researchers are
encouraged to take the recommendations into account in proof-of-concept stages
to facilitate future translation towards clinical practice of medical AI
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI
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