Flux-based ozone risk assessment for a plant injury index (pii) in three european cool-temperate deciduous tree species

This study investigated visible foliar ozone (O3) injury in three deciduous tree species with different growth patterns (indeterminate, Alnus glutinosa (L.) Gaertn.; intermediate, Sorbus aucuparia L.; and determinate, Vaccinium myrtillus L.) from May to August 2018. Ozone effects on the timing of injury onset and a plant injury index (PII) were investigated using two O3 indices, i.e., AOT40 (accumulative O3 exposure over 40 ppb during daylight hours) and PODY (phytotoxic O3 dose above a flux threshold of Y nmol m−2 s−1). A new parameterization for PODY estimation was developed for each species. Measurements were carried out in an O3 free-air controlled exposure (FACE) experiment with three levels of O3 treatment (ambient, AA; 1.5 × AA; and 2.0 × AA). Injury onset was found in May at 2.0 × AA in all three species and the timing of the onset was determined by the amount of stomatal O3 uptake. It required 4.0 mmol m−2 POD0 and 5.5 to 9.0 ppm·h AOT40. As a result, A. glutinosa with high stomatal conductance (gs) showed the earliest emergence of O3 visible injury among the three species. After the onset, O3 visible injury expanded to the plant level as confirmed by increased PII values. In A. glutinosa with indeterminate growth pattern, a new leaf formation alleviated the expansion of O3 visible injury at the plant level. V. myrtillus showed a dramatic increase of PII from June to July due to higher sensitivity to O3 in its flowering and fruiting stage. Ozone impacts on PII were better explained by the flux-based index, PODY, as compared with the exposure-based index, AOT40. The critical levels (CLs) corresponding to PII = 5 were 8.1 mmol m−2 POD7 in A. glutinosa, 22 mmol m−2 POD0 in S. aucuparia, and 5.8 mmol m−2 POD1 in V. myrtillus. The results highlight that the CLs for PII are species-specific. Establishing species-specific O3 flux-effect relationships should be key for a quantitative O3 risk assessment

Impacts of ground-level ozone on sugarcane production

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Sugarcane is a vital commodity crop often grown in (sub)tropical regions which have been experiencing a recent deterioration in air quality. Unlike for other commodity crops, the risk of air pollution, specifically ozone (O3), to this C4 crop has not yet been quantified. Yet, recent work has highlighted both the potential risks of O3 to C4 bioenergy crops, and the emergence of O3 exposure across the tropics as a vital factor determining global food security. Given the large extent, and planned expansion of sugarcane production in places like Brazil to meet global demand for biofuels, there is a pressing need to characterize the risk of O3 to the industry. In this study, we sought to a) derive sugarcane O3 dose-response functions across a range of realistic O3 exposure and b) model the implications of this across a globally important production area. We found a significant impact of O3 on biomass allocation (especially to leaves) and production across a range of sugarcane genotypes, including two commercially relevant varieties (e.g. CTC4, Q240). Using these data, we calculated dose-response functions for sugarcane and combined them with hourly O3 exposure across south-central Brazil derived from the UK Earth System Model (UKESM1) to simulate the current regional impact of O3 on sugarcane production using a dynamic global vegetation model (JULES vn 5.6). We found that between 5.6 % and 18.3 % of total crop productivity is likely lost across the region due to the direct impacts of current O3 exposure. However, impacts depended critically on the substantial differences in O3 susceptibility observed among sugarcane genotypes and how these were implemented in the model. Our work highlights not only the urgent need to fully elucidate the impacts of O3 in this important bioenergetic crop, but the potential implications air quality may have upon tropical food production more generally.Natural Environment Research Council (NERC)FAPESPCNRMet Office Hadley Centre Climate ProgrammeMet Offic

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p