Impact of weather variables and season on sporulation of Phytophthora pluvialis and Phytophthora kernoviae

2020 ◽  
Vol 50 (2) ◽  
pp. e12588
Author(s):  
Stuart Fraser ◽  
Mireia Gomez-Gallego ◽  
Judy Gardner ◽  
Lindsay S. Bulman ◽  
Sandra Denman ◽  
...  
Keyword(s):  
Weather Variables ◽  
Phytophthora Kernoviae ◽  
Phytophthora Pluvialis

scholarly journals Validating outsourced high throughput automated qPCR for increased research outputs from forest pathology trials

2018 ◽  
Vol 71 ◽  
pp. 355 ◽  
Author(s):  
Renelle O'Neill ◽  
Rebecca McDougal ◽  
Stuart Fraser ◽  
Catherine Banham ◽  
Mike Cook ◽  
...  
Keyword(s):  
High Throughput ◽  
Forest Pathology ◽  
Robotic Platform ◽  
Diagnostic Screening ◽  
Qpcr Analysis ◽  
Trial Analysis ◽  
Phytophthora Kernoviae ◽  
Potential Alternative ◽  
Phytophthora Pluvialis ◽  
Red Needle Cast

Needle diseases of Pinus radiata caused by Phytophthora pluvialis and Phythophthora kernoviae have been increasingly recognised since the discovery of red needle cast in 2008. There is a need for rapid diagnostic screening of numerous samples, but sample processing time, equipment and staff availability limit the throughput and utilisation of diagnostic qPCR analysis in the research environment. Automated and high-throughput capable DNA extraction and real-time PCR provides the opportunity to expand the capacity of research trial analysis and a potential alternative to laborious isolation and plating but must be thoroughly validated before results can be used with confidence. The use of a high-throughput format for qPCR assays targeting Phytophthora pluvialis and Phythophthora kernoviae was validated on a robotic platform, proving to be consistently more sensitive than isolation, achieving qPCR detection down to 1% diluted inoculated material for Phytophthora kernoviae and 10% for Phytophthora pluvialis. Plating results yielded a 60% detection rate of Phythophthora pluvialis in inoculated needle fragments, whereas qPCR yielded a 100% detection on the same material. High throughout automated qPCR can therefore be utilised with confidence in forest pathology research trial analyses in future.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

scholarly journals The epidemiology of the common cold IV. The effect of weather

1965 ◽  
Vol 63 (3) ◽  
pp. 427-439 ◽  
Author(s):  
O. M. Lidwell ◽  
R. W. Morgan ◽  
R. E. O. Williams
Keyword(s):  
Common Cold ◽  
Partial Correlation ◽  
Water Vapour Pressure ◽  
Outdoor Temperature ◽  
Weather Variables ◽  
Smooth Curves ◽  
Correlation Studies ◽  
The Common ◽  
Expected Values ◽  
Time Of Year

An investigation has been made of the association between weather and the numbers of colds reported on a given day. The seasonal trends were eliminated by working with the differences between the observed values on any day and the expected values derived from smooth curves fitted to the averages for the time of year.Examination of nine weather variables for the day on which the colds were reported and for each of the 29 preceding days showed that only two, mean day temperature and water-vapour pressure at 9 a.m., were significantly correlated with the numbers of colds. Partial correlation studies showed that the strongest association was with lowered mean day temperature between 2 and 4 days before the reported onset of symptoms.Regression analysis demonstrated that the magnitudes of the associations were sufficient to account for the greater part of the seasonal variation in the incidence of the common cold in both London and Newcastle. A small effect of atmospheric pollution appeared in this analysis.These results suggest that some effect of low outdoor temperature promotes transmission of the virus or the development of disease.

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