Tecnologia da realidade aumentada na propaganda: avaliação da eficácia com base em entendimento, risco e resposta afetiva

A Realidade Virtual, técnica que permite a interação entre pessoas e computadores, tem tido sua interface melhorada avançando para a tecnologia da Realidade Aumentada (RA), que permite uma interação “usuário-ambiente” mais eficiente e rica. Este estudo questiona como os consumidores avaliam a eficácia da propaganda em ambiente de Realidade Aumentada tendo como base o seu entendimento, a resposta afetiva e risco de compra. O uso da tecnologia da Realidade Aumentada foi avaliado neste estudo quanto à sua eficácia em propaganda via internet, no intuito de descobrir o quão eficazmente este recurso é percebido, com base nas influências dos constructos sobre percepção de risco do consumidor em relação ao produto, resposta afetiva ao experimentar o recurso e entendimento geral da propaganda. Utilizou-se como base teórica os conhecimentos a respeito dos usos da Realidade Aumentada, além do resgate de pesquisas que envolvessem os quatro pilares teóricos do estudo: risco percebido, entendimento, resposta afetiva e eficácia da propaganda. Foi realizada uma pesquisa de caráter exploratório, predominantemente quantitativa, com aplicação de questionários com 261 respondentes, abordados de forma não probabilística. Os resultados demonstraram que com o uso da tecnologia da Realidade Aumentada, o entendimento da propaganda apresenta uma relação positiva e significativa estatisticamente com eficácia da propaganda. Da mesma forma que a resposta afetiva também está positivamente relacionada com a essa eficácia. Entretanto, o risco percebido apresenta relação negativa com a eficácia, confirmando as hipóteses testadas pela pesquisa e a literatura existente

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK

Background A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. Methods This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. Findings Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0–75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4–97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8–80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3–4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. Interpretation ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials

Comparison between subjective and quantitative methods for assessing the resolution limit of radiographic systems

The aim of this study was to compare two ways of measuring the resolution limit of radiographic systems, one subjective and one quantitative. To this end, nine images were acquired with different radiographic techniques using a pattern of bars and aluminum plates. With these images were acquired modulation transfer function (MTF) through the edge image obtained by the aluminum plate — the MTF 10% was measured on all images — and the variation of these points, which was faced with the evaluation obtained by the resolution limit of the standard bar. Although we have observed a greater variation between measurements obtained using the bar-pattern, the simplicity of this measuring technique favors the common use of the same. We concluded that, to optimize the quality control of radiographic equipment, it is suggested to measure the MTF at least in periods of time while the annual pattern of bars to be used in shorter time periods to measure changes in resolution of the system.O objetivo deste estudo foi comparar duas formas de aferição da resolução limite de sistemas radiográficos, uma subjetiva e outra quantitativa. Para tal, foram adquiridas nove imagens com diferentes técnicas radiográficas utilizando um padrão de barras e placas de alumínio. Com estas imagens, foram adquiridas a função de transferência modulada (FTM) através da imagem da borda obtida pela placa de alumínio — a FTM foi aferida 10% em todas as imagens — e a variação destes pontos — que foi confrontada com a avaliação da resolução limite obtida através do padrão de barras. Apesar de termos observado uma maior variação entre as medidas obtidas com a utilização do padrão de barras, a simplicidade de medição desta técnica favorece o uso corriqueiro da mesma. Concluí-se que, visando a otimização do controle de qualidade de equipamentos radiográficos, sugere-se fazer a medição da FTM pelo menos em períodos de tempo anuais, enquanto que o padrão de barras seja utilizado em períodos de tempo menores para a aferição de mudanças na resolução do sistema

Desenvolvimento de algoritmos computacionais para quantificação e estruturas pulmonares

The high-resolution computed tomography has become the imaging diagnostic exam most commonly used for the evaluation of the squeals of Paracoccidioidomycosis. The subjective evaluations the radiological abnormalities found on HRCT images do not provide an accurate quantification. The computer-aided diagnosis systems produce a more objective assessment of the abnormal patterns found in HRCT images. Thus, this research proposes the development of algorithms in Matlab® computing environment can quantify semi-automatically pathologies such as pulmonary fibrosis and emphysema. The algorithm consists in selecting a region of interest (ROI), and by the use of masks, filter densities and morphological operators, to obtain a quantification of the injured area to the area of a healthy lung. The proposed method was tested on ten HRCT scans of patients with confirmed PCM. The results of semi-automatic measurements were compared with subjective evaluations performed by a specialist in radiology, falling to a coincidence of 80% for emphysema and 58% for fibrosis.A tomografia computadorizada de alta resolução se tornou o exame de diagnóstico por imagem mais utilizado para avaliação das sequelas da Paracoccidioidomicose. As avaliações subjetivas das anormalidades radiológicas encontradas nas imagens de TCAR não proporcionam uma quantificação acurada. O diagnóstico auxiliado por sistemas computacionais produzem uma avaliação mais objetiva dos padrões anormais encontrados nas imagens de TCAR. Desse modo, nesta pesquisa propôs-se o desenvolvimento de algoritmos em ambiente computacional Matlab®, capaz de quantificar semiautomaticamente as patologias pulmonares, tais como fibrose e enfisema. O algoritmo consiste em selecionar a região de interesse (ROI), e por meio da utilização de máscaras, filtros de densidades e operadores morfológicos obter a quantificação da área lesionada em relação à área sadia do pulmão. O método proposto foi testado em dez exames de TCAR de pacientes com PCM confirmada. Os resultados das quantificações semiautomáticas foram comparados com as avaliações subjetivas realizadas por especialista na área de radiologia, recaindo a uma coincidência de 80% para enfisema e 58% para fibrose.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES