“…The inclusion of autocorrelation is particularly relevant in time-series data sets, where each datapoint is clearly correlated to (and therefore dependent on) the previous and the next datapoints. Therefore, this analysis is particularly useful to data sets characterized by dynamic and time-series data (Boswijk et al, 2020).…”
Section: Gas Exchange Curve Fitting Using Gam(m)mentioning
confidence: 99%
“…These models need to be capable of capturing dynamic data, often time series, as well as the interaction and effects between multiple factors (Ohana-Levi et al, 2020). Among these, generalized additive mixed models (GAMMs) have been successfully featured in several applied science fields (Murase et al, 2009;Zuur et al, 2009;Pedersen et al, 2019;Boswijk et al, 2020;Ohana-Levi et al, 2020) including ecology at large and plant ecophysiology. In particular, Ohana-Levi et al (2020) highlighted the potential of GAMs to model non-linear relationships between evapotranspiration (ETc) drivers and evaluating their impacts on grapevine transpiration.…”
Biostimulants are emerging as a feasible tool for counteracting reduction in climate change-related yield and quality under water scarcity. As they are gaining attention, the necessity for accurately assessing phenotypic variables in their evaluation is emerging as a critical issue. In light of this, high-throughput phenotyping techniques have been more widely adopted. The main bottleneck of these techniques is represented by data management, which needs to be tailored to the complex, often multifactorial, data. This calls for the adoption of non-linear regression models capable of capturing dynamic data and also the interaction and effects between multiple factors. In this framework, a commercial glycinebetaine- (GB-) based biostimulant (Vegetal B60, ED&F Man) was tested and distributed at a rate of 6 kg/ha. Exogenous application of GB, a widely accumulated and documented stress adaptor molecule in plants, has been demonstrated to enhance the plant abiotic stress tolerance, including drought. Trials were conducted on tomato plants during the flowering stage in a greenhouse. The experiment was designed as a factorial combination of irrigation (water-stressed and well-watered) and biostimulant treatment (treated and control) and adopted a mixed phenotyping-omics approach. The efficacy of a continuous whole-canopy multichamber system coupled with generalized additive mixed modeling (GAMM) was evaluated to discriminate between water-stressed plants under the biostimulant treatment. Photosynthetic performance was evaluated by using GAMM, and was then correlated to metabolic profile. The results confirmed a higher photosynthetic efficiency of the treated plants, which is correlated to biostimulant-mediated drought tolerance. Furthermore, metabolomic analyses demonstrated the priming effect of the biostimulant for stress tolerance and detoxification and stabilization of photosynthetic machinery. In support of this, the overaccumulation of carotenoids was particularly relevant, given their photoprotective role in preventing the overexcitation of photosystem II. Metabolic profile and photosynthetic performance findings suggest an increased effective use of water (EUW) through the overaccumulation of lipids and leaf thickening. The positive effect of GB on water stress resistance could be attributed to both the delayed onset of stress and the elicitation of stress priming through the induction of H2O2-mediated antioxidant mechanisms. Overall, the mixed approach supported by a GAMM analysis could prove a valuable contribution to high-throughput biostimulant testing.
“…The inclusion of autocorrelation is particularly relevant in time-series data sets, where each datapoint is clearly correlated to (and therefore dependent on) the previous and the next datapoints. Therefore, this analysis is particularly useful to data sets characterized by dynamic and time-series data (Boswijk et al, 2020).…”
Section: Gas Exchange Curve Fitting Using Gam(m)mentioning
confidence: 99%
“…These models need to be capable of capturing dynamic data, often time series, as well as the interaction and effects between multiple factors (Ohana-Levi et al, 2020). Among these, generalized additive mixed models (GAMMs) have been successfully featured in several applied science fields (Murase et al, 2009;Zuur et al, 2009;Pedersen et al, 2019;Boswijk et al, 2020;Ohana-Levi et al, 2020) including ecology at large and plant ecophysiology. In particular, Ohana-Levi et al (2020) highlighted the potential of GAMs to model non-linear relationships between evapotranspiration (ETc) drivers and evaluating their impacts on grapevine transpiration.…”
Biostimulants are emerging as a feasible tool for counteracting reduction in climate change-related yield and quality under water scarcity. As they are gaining attention, the necessity for accurately assessing phenotypic variables in their evaluation is emerging as a critical issue. In light of this, high-throughput phenotyping techniques have been more widely adopted. The main bottleneck of these techniques is represented by data management, which needs to be tailored to the complex, often multifactorial, data. This calls for the adoption of non-linear regression models capable of capturing dynamic data and also the interaction and effects between multiple factors. In this framework, a commercial glycinebetaine- (GB-) based biostimulant (Vegetal B60, ED&F Man) was tested and distributed at a rate of 6 kg/ha. Exogenous application of GB, a widely accumulated and documented stress adaptor molecule in plants, has been demonstrated to enhance the plant abiotic stress tolerance, including drought. Trials were conducted on tomato plants during the flowering stage in a greenhouse. The experiment was designed as a factorial combination of irrigation (water-stressed and well-watered) and biostimulant treatment (treated and control) and adopted a mixed phenotyping-omics approach. The efficacy of a continuous whole-canopy multichamber system coupled with generalized additive mixed modeling (GAMM) was evaluated to discriminate between water-stressed plants under the biostimulant treatment. Photosynthetic performance was evaluated by using GAMM, and was then correlated to metabolic profile. The results confirmed a higher photosynthetic efficiency of the treated plants, which is correlated to biostimulant-mediated drought tolerance. Furthermore, metabolomic analyses demonstrated the priming effect of the biostimulant for stress tolerance and detoxification and stabilization of photosynthetic machinery. In support of this, the overaccumulation of carotenoids was particularly relevant, given their photoprotective role in preventing the overexcitation of photosystem II. Metabolic profile and photosynthetic performance findings suggest an increased effective use of water (EUW) through the overaccumulation of lipids and leaf thickening. The positive effect of GB on water stress resistance could be attributed to both the delayed onset of stress and the elicitation of stress priming through the induction of H2O2-mediated antioxidant mechanisms. Overall, the mixed approach supported by a GAMM analysis could prove a valuable contribution to high-throughput biostimulant testing.
“…Com as séries normalizadas, procedeu-se a centralizar os diferentes registros em torno do momento no qual cada grupo escutou uma das cinco palavras-alvo (cuidado, muitos, oito, respeito e muito), e usando o pacote ggplot2 [Wickham 2011] estimou-se uma linha de regressão para suavizar a série temporal usando modelos aditivos generalizados seguindo a proposta feita por [Boswijk et al 2020].…”
Section: Pupilometriaunclassified
“…Uma das maneiras de observar se uma estrutura é mais saliente do que outra é quanto ao dispêndio de esforc ¸o de processamento. Na abordagem sociolinguística, no entanto, nem sempre formas salientes do ponto de vista cognitivo e de frequência são necessariamente salientes do ponto de vista social (e vice-versa), o que torna a mensurac ¸ão do efeito de saliência muito difícil [Kerswill e Williams 2011, Kecskes 2011, Boswijk et al 2020.…”
Section: Introduc ¸ãOunclassified
“…O estudo do processamento da variac ¸ão linguística tem se valido de medidas de rastreamento ocular, com resultados que evidenciam respostas emocionais após a exposic ¸ão a um estímulo saliente [Foucart et al 2019, Boswijk et al 2020. Neste texto, apresentamos um estudo exploratório em contextos de exposic ¸ão a diferentes variantes de uma variável linguística saliente do ponto de vista social, a palatalizac ¸ão progressiva de oclusivas alveolares, a fim de subsidiar o desenvolvimento de técnicas online não invasivas, como o rastreamento ocular, para o estudo do processamento da variac ¸ão linguística.…”
Um estudo exploratório de exposição de participantes às variantes de uma variável linguística saliente do ponto de vista social, a palatalização progressiva, foi realizado com o uso de rastreamento ocular, para examinar o processamento da variação linguística. Os resultados mostram que exposição à variante estigmatizada captou a atenção e aumentou a dilatação da pupila dos participantes, o que pode ser interpretado como evidência de uma resposta emocional.
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