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Vegetable Gardening Support

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Free Download Pollen ((FULL))

Background and aims: Although many methods exist for quantifying the number of pollen grains in a sample, there are few standard methods that are user-friendly, inexpensive and reliable. The present contribution describes a new method of counting pollen using readily available, free image processing and analysis software.

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Methods: Pollen was collected from anthers of two species, Carduus acanthoides and C. nutans (Asteraceae), then illuminated on slides and digitally photographed through a stereomicroscope. Using ImageJ (NIH), these digital images were processed to remove noise and sharpen individual pollen grains, then analysed to obtain a reliable total count of the number of grains present in the image. A macro was developed to analyse multiple images together. To assess the accuracy and consistency of pollen counting by ImageJ analysis, counts were compared with those made by the human eye.

Key results and conclusions: Image analysis produced pollen counts in 60 s or less per image, considerably faster than counting with the human eye (5-68 min). In addition, counts produced with the ImageJ procedure were similar to those obtained by eye. Because count parameters are adjustable, this image analysis protocol may be used for many other plant species. Thus, the method provides a quick, inexpensive and reliable solution to counting pollen from digital images, not only reducing the chance of error but also substantially lowering labour requirements.

Currently, the principle and most reliable source of pollen concentration information in the United States comes from the National Allergy Bureau (NAB), part of the American Academy of Allergy Asthma and Immunology (AAAAI). Pollen data included in the NAB dataset are collected by certified counting stations, where specially trained and certified allied health workers count pollen under the direction of an allergist3. Data must meet certain quality metrics to be included. While the data in this dataset are collected and reported using standardized methods, there is variability in sampling strategies, the costs of data collection are borne by the participating organizations, and participation varies over time. The totality of pollen count stations data currently listed on the AAAAI website covers less than 70 geographic locations in the continental U.S.3. Given these concerns, other valid approaches to generating estimates of airborne pollen that could expand spatial coverage and address some of the issues related to variable sampling time frames would be welcome for gaining insight into pollen season dynamics in regions without pollen monitors.

Among seasonal allergy sufferers who are sensitive to airborne pollen, a majority self-identify and self-treat allergy symptoms4. For this reason, we explored the potential for using Google Trends (GT), a web-based tool for quantifying popular interest in specific search terms, as a proxy for pollen observations in observation-free zones and for predicting pollen season dynamics. Our study builds on prior work examining associations between online search queries and real-life phenomena. Specifically, GT search data has been shown to correlate to outbreaks of West Nile Virus and respiratory syncytial virus, while the most well-known GT offshoot, Google Flu Trends, correlates strongly with official influenza surveillance data and frequently predicts major flu outbreaks5,6,7.

Thus, the main goals of this study were to: (1) evaluate regional relationships between GT searches related to pollen and NAB pollen concentrations at diverse sites across the U.S., and (2) identify site-specific factors affecting association strength. In addition, we assess the potential for estimating the start of the pollen season using GT data. To these ends, we analyzed data from 40 location-matched GT Designated Market Areas (DMAs) and NAB stations both to determine the ability of GT data to accurately match NAB data, in the context of regional data availability, biogeography, and population. We restricted the scope of our investigation to early season pollen (January through June) when the largest peak in annual pollen concentration is commonly observed in the U.S. (mainly produced by trees); our analyses do not examine subsequent grass or weed pollen peak times which may have distinct characteristics14.

In the absence of academic consensus or regulatory guidelines to define pollen season start dates, current definitions used in the literature vary widely22. Most commonly, the start of the pollen season is the date upon which a predefined threshold is met, based on either: (1) a percentage of annual pollen, (2) a certain daily pollen concentration (or over a predefined period, such as three days), or (3) a number of consecutive days during which pollen grains are recorded23, 24.

In order to address this issue, we tested multiple definitions from recent literature and assessed their concordance when applied to NAB data. These were: the date that (1) cumulative pollen count reached 5% of annual total25,26,27, (2) cumulative pollen reached 2.5% of annual total28, 29, and (3) four consecutive days of pollen grains were recorded30. In addition, we examined the date that total daily pollen concentration exceeded 200 grains/m3, which is sometimes considered a threshold relevant to clinical symptoms, and thus potentially also relevant to the internet search activity of allergy sufferers31. However, defining symptom thresholds is itself a challenge, since both the ways in which the presence and severity of symptoms manifest, and are recorded, can change across individual experiences and study definitions32, 33.

Stata IC version 15.1 (College Station, TX, USA) was used for all data analyses. DMA characteristics and GT data were downloaded using Python version 2.7.10 (Python Software Foundation). All Stata code used for analysis and Python scripts used for GT downloads are available upon request.

Biogeography and population characteristics were assessed for their impact on data quality, specifically overall ecoregion classification, total annual precipitation and mean spring temperature (chosen for their likely impact pollen production and seasonality34), as well as TV-homes, a combinatorial metric for population size and media use.

NAB total pollen concentrations from the majority of station-years showed a bimodal seasonality consisting of one larger peak early in the year and one smaller peak later in the year (See Supplementary Fig. 1 for national seasonality). For correlation analyses, we focused specifically on the period from January to June to examine the extent to which GT data correlated to the larger, early season peak in total pollen. Of 246 GT location-matched NAB station-years, 24 (9.7%) had no NAB data recorded in the period of interest. In addition, the following station-years did not meet data quality inclusion criteria: 85 (34.5%) station-years had over 60% of days missing data during the pollen season, and an additional 32 (13.0%) station-years had over four consecutive days missing data within 10 days of the first high pollen concentration day of the year. A total of 105 station-years, representing 27 NAB stations, were ultimately included in correlation analyses. See the Supplement for visualizations of ecoregions (Supplementary Fig. 10) and geographical distribution (Supplementary Fig. 11; Interactive Map )36 of NAB stations represented in the included study sample.

Correlation between Google Trends searches and National Allergy Bureau pollen concentration data with respect to data quality and pattern. (a) Correlation by quartiles of annual percent of missing Google Trends data. (b) Signal to noise ratio (size of peak relative to smoothing function).

Overlay of lightly smoothed, normalized Google Trends search data (blue) and NAB pollen concentration data (orange) for representative station-years. Examples of (a) excellent, (b,c) good to moderate, and (d) poor correlation between GT and NAB data.

NAB station locations are limited due to the requirement of needing specially trained and certified allied health workers who must dedicate 2 hours a day, three times a week to counting pollen, as well as an allergist to oversee pollen counting. GT data can be a useful source of publicly available user-derived data related to pollen allergy patterns in regions where NAB are not available, though there are limitations on the extent to which GT can serve as a reliable proxy measure.

Summary of findings: covariates related to strength of correlation between regional internet searches and observed pollen, with representative examples. Line graphs show lightly smoothed normalized values for both Google Trends search volumes and daily observed pollen concentrations.

Of the top 50 Nielsen ranked DMAs, which are likely have adequate population size and media consumption for approximating NAB data, at least 25 could provide GT data in NAB observation-free zones (Supplementary Fig. 11; Interactive map may be accessed at )36. In addition, the utility of GT data as a proxy for observed pollen pattern information should improve over time as more search use leads to more search volume and less data exclusion, following our observed trends of improved data quality over time.

In addition to re-assessing the relationships outlined here as online search volumes increase over time, there are other potential applications based on our findings. For example, although currently using GT data to identify pollen season start dates tends to precede the NAB-derived start dates, the precision of GT-derived start dates appears to comparable to start dates calculated from previous year data. Thus, GT-derived data may be useful for approximating true season starts either with lead-time in mind or in more advanced modeling that includes factors affecting lead time such as climate-based data. In this way, GT data may be helpful in the future for estimating a large number of historical and current location-specific data points for annual pollen season start dates over time, and this data could be used to investigate varied trends alongside other data, including weather, plant phenology, health, or other GT data to assess co-variance between variations in pollen season start dates and other trends like the effects of climate change over time (comparing to local temperatures or extended spring indices, for example).


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