RESUMEN
BACKGROUND: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. METHODS: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. RESULTS: The best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. CONCLUSIONS: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.
Asunto(s)
Alérgenos , Predicción , Polen , Polen/inmunología , Predicción/métodos , Humanos , Cambio Climático , Modelos Teóricos , Monitoreo del Ambiente/métodosRESUMEN
PURPOSE OF THE REVIEW: Fungal sensitizations have been associated with hypersensitivity reactions with variable levels of evidence available to link types of fungi with human disease. We conducted systematic reviews of the literature to identify the strength of evidence linking lesser-studied fungi for which there are commercially available extracts to identify populations in which they were useful in clinical practice. RECENT FINDINGS: Excluding five fungi for which hundreds of articles were identified, there are 54 articles on the remaining fungi with clinical data. For 12 of the fungi, the prevalence of fungal sensitization varies in different hypersensitivity disorders due to factors related to geographic areas, age, and other underlying medical conditions. There were no studies linking seven genera to human disease. Most of the commercially available fungal extracts are uncommonly associated with hypersensitivity reactions in humans. Specific extracts may be useful in particular disease states such as allergic fungal sinusitis or allergic bronchopulmonary mycosis, or when routine testing fails to identify a cause of uncontrolled disease, such as in asthma.
Asunto(s)
Hongos , Hipersensibilidad , Humanos , Hongos/inmunología , Hipersensibilidad/inmunología , Antígenos Fúngicos/inmunología , Alérgenos/inmunología , Micosis/inmunologíaRESUMEN
PURPOSE OF REVIEW: To review current air sampling instruments and analysis methods and to describe new approaches being developed. RECENT FINDINGS: Spore trap sampling with analysis by microscopy remains the most widely used methods for aeroallergen determination even though there are often long delays from sample acquisition to data availability, as well as a need for specially-trained staff for sample analysis. The use of immunoassays and molecular biology to analyze outdoor or indoor samples has expanded in recent years and has provided valuable data on allergen exposure. New automated sampling devices capture pollen, analyze, and identify pollen grains by light scattering, laser-induced fluorescence, microscopy, or holography using signal or image processing to classify the pollen in real time or near real time. Air sampling data from current methods provide valuable information on aeroallergen exposure. The automated devices in use and under development show great potential but are not ready to replace existing aeroallergen networks.
Asunto(s)
Contaminantes Atmosféricos , Humanos , Contaminantes Atmosféricos/efectos adversos , Alérgenos/análisis , PolenRESUMEN
Monitoring aeroallergens has a long history within the American Academy of Allergy, Asthma & Immunology. The Aeroallergen Network of the National Allergy Bureau is composed mainly of members of the American Academy of Allergy, Asthma & Immunology, whose objectives are to enhance the knowledge of aerobiology and its relationship to allergy, increase the number of certified stations, maintain the standardization and quality of aerobiology data, improve the alert and forecast reporting system, and increase ties with other scientific entities inside and outside the United States. The public has a keen interest in pollen counts and pollen forecasts, as do many health professionals in the allergy community. In this review, we explore the past, present, and future of allergen monitoring with a focus on methods used for sampling, the training of those performing the analysis, and emerging technologies in the field. Although the development of automated samplers with machine intelligence offers great promise for meeting the goal of a fully automated system, there is still progress to be made regarding reliability and affordability.
Asunto(s)
Asma , Hipersensibilidad , Humanos , Estados Unidos/epidemiología , Reproducibilidad de los Resultados , Alérgenos , Hipersensibilidad/diagnóstico , Hipersensibilidad/epidemiología , PolenRESUMEN
The diagnosis and treatment of atopic disorders associated with specific aerobiological triggers require basic botanical training. However, the identification of specific pollen can often be confounded by broad naming conventions that range from categorized colloquial to scientific names based on either higher taxonomic levels or, in some cases, binomial nomenclature. Physicians specializing in allergy often lack a comprehensive understanding with respect to plant taxonomy and botanical nomenclature that are critical skills required for clinical practice and research programs evaluating pollen and airborne fungal spores. In addition, binomial and current family designation and synonyms, including author citation are often misused, causing a misinterpretation of existing plants species or pollen types. It is critical that the correct botanical name is linked to a validated specimen and scientific naming conventions are used where possible by the clinician and researcher. In relation to pollen identification, we propose that clinicians and researchers should provide the currently accepted binomial nomenclature, offer relevant synonyms, and use the Angiosperm Phylogeny Group names.
Asunto(s)
Plantas , Polen , HumanosRESUMEN
Climate change is having a significant effect on many allergenic plants resulting in increased pollen production and shifts in plant phenology. Although these effects have been well-studied in some areas of the world, few studies have focused on long-term changes in allergenic pollen in the South Central United States. This study examined airborne pollen, temperature, and precipitation in Tulsa, Oklahoma over 25 to 34 years. Pollen was monitored with a Hirst-type spore trap on the roof of a building at the University of Tulsa and meteorology data were obtained from the National Weather Service. Changes in total pollen intensity were examined along with detailed analyses of the eight most abundant pollen types in the Tulsa atmosphere. In addition to pollen intensity, changes in pollen season start date, end date, peak date and season duration were also analyzed. Results show a trend to increasing temperatures with a significant increase in annual maximum temperature. There was a non-significant trend toward increasing total pollen and a significant increase in tree pollen over time. Several individual taxa showed significant increases in pollen intensity over the study period including spring Cupressaceae and Quercus pollen, while Ambrosia pollen showed a significant decrease. Data from the current study also indicated that the pollen season started earlier for spring pollinating trees and Poaceae. Significant correlations with preseason temperature may explain the earlier pollen season start dates along with a trend toward increasing March temperatures. More research is needed to understand the global impact of climate change on allergenic species, especially from other regions that have not been studied.
RESUMEN
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in North America. Hence, estimating and predicting the daily atmospheric concentration of pollen (ragweed pollen in particular) is useful for both people with allergies and for the health professionals who care for them. In this study, we show that a suite of variables including meteorological and land surface parameters, as well as next-generation radar (NEXRAD) measurements together with machine learning can be used to estimate successfully the daily pollen concentration. The supervised machine learning approaches we used included random forests, neural networks, and support vector machines. The performance of the training is independently validated using 10% of the data partitioned using the holdout cross-validation method from the original dataset. The random forests (R= 0.61, R2= 0.37), support vector machines (R= 0.51, R2= 0.26), and neural networks (R= 0.46, R2= 0.21) effectively predicted the daily Ambrosia pollen, where the correlation coefficient (R) and R-squared (R2) values are given in brackets. Three independent approaches-the random forests, correlation coefficients, and interaction information-were employed to rank the relative importance of the available predictors.
Asunto(s)
Antígenos de Plantas/análisis , Monitoreo del Ambiente , Hipersensibilidad/inmunología , Aprendizaje Automático , Extractos Vegetales/análisis , Antígenos de Plantas/inmunología , Predicción , Humanos , Extractos Vegetales/inmunología , Radar , Tiempo (Meteorología)RESUMEN
Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.
Asunto(s)
Antígenos de Plantas , Predicción/métodos , Aprendizaje Automático , Redes Neurales de la Computación , Extractos Vegetales , Alérgenos/análisis , Ambrosia , Teorema de Bayes , Tiempo (Meteorología)RESUMEN
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind are known to affect the amount of pollen in the atmosphere. In the past, various regression techniques have been applied to estimate and forecast the daily pollen concentration in the atmosphere based on the weather conditions. In this research, machine learning methods were applied to the Next Generation Weather Radar (NEXRAD) data to estimate the daily Ambrosia pollen over a 300 km × 300 km region centered on a NEXRAD weather radar. The Neural Network and Random Forest machine learning methods have been employed to develop separate models to estimate Ambrosia pollen over the region. A feasible way of estimating the daily pollen concentration using only the NEXRAD radar data and machine learning methods would lay the foundation to forecast daily pollen at a fine spatial resolution nationally.
Asunto(s)
Alérgenos/análisis , Antígenos de Plantas/análisis , Atmósfera/química , Monitoreo del Ambiente/métodos , Aprendizaje Automático , Extractos Vegetales/análisis , Polen , Radar , Predicción , Oklahoma , Tiempo (Meteorología)RESUMEN
Weather and climate change are constant and ever-changing processes that affect allergy and asthma. The purpose of this report is to provide information since the last climate change review with a focus on asthmatic disease. PubMed and Internet searches for topics included climate and weather change, air pollution, particulates, greenhouse gasses, traffic, insect habitat, and mitigation in addition to references contributed by the individual authors. Changes in patterns of outdoor aeroallergens caused by increasing temperatures and amounts of carbon dioxide in the atmosphere are major factors linked to increased duration of pollen seasons, increased pollen production, and possibly increased allergenicity of pollen. Indoor air pollution threats anticipated from climate changes include microbial and mold growth secondary to flooding, resulting in displacement of persons and need for respiratory protection of exposed workers. Air pollution from indoor burning of mosquito repellants is a potential anticipatory result of an increase in habitat regions. Air pollution from fossil fuel burning and traffic-related emissions can alter respiratory defense mechanisms and work synergistically with specific allergens to enhance immunogenicity to worsen asthma in susceptible subjects. Community efforts can significantly reduce air pollution, thereby reducing greenhouse gas emission and improving air quality. The allergist's approach to weather pattern changes should be integrated and anticipatory to protect at-risk patients.
Asunto(s)
Contaminación del Aire/estadística & datos numéricos , Asma/epidemiología , Cambio Climático/estadística & datos numéricos , Exposición a Riesgos Ambientales/efectos adversos , Hipersensibilidad/epidemiología , Tiempo (Meteorología) , Contaminantes Atmosféricos/inmunología , Contaminación del Aire Interior , Alérgenos/inmunología , Humanos , Riesgo , Estados Unidos/epidemiologíaRESUMEN
Patients with pollen-related allergies are concerned about the species within their landscape that provoke their symptoms. Allergists are often asked for guidance but few information sources are available to aid patients in the recognition of allergenic plants and strategies to avoid personal exposure to them. Landscaping and horticultural workers also have few reliable guidance references, and what is available usually extols the virtues of the plants rather than their negative features. The aim of this article was to provide the results of the Landscape Allergen Working Group that was formed by the AAAAI Aerobiology Committee, which aimed to fill these existing knowledge gaps and develop guidance on producing a low-allergenic landscape. Within the context that complete pollen avoidance is unrealistic, the workgroup introduces selection criteria, avoidance strategies, and guidance on low-allergenic plants that could be selected by patients to reduce the overall pollen burden in their landscape environment. Specific focus is placed on entomophilous plants, which require insects as dispersal vectors and generally produce lower quantities of pollen, compared with anemophilous (wind-pollinated) species. Other biological hazards that can be encountered while performing landscaping activities are additionally reviewed and avoidance methods presented with the aim of protecting gardeners, and workers in the landscape and horticulture industries. The guidance presented in this article will ultimately be a helpful resource for the allergist and assist in engaging patients who are seeking to reduce the burden of allergen in their landscape environment.
Asunto(s)
Jardinería , Hipersensibilidad/prevención & control , Plantas , Animales , Humanos , Polen/inmunología , PolinizaciónRESUMEN
Emergomyces africanus is a thermally dimorphic fungus that causes a systemic mycosis in immunocompromised persons in South Africa. Infection is presumed to follow inhalation of airborne propagules. We developed a quantitative PCR protocol able to detect as few as 5 Es. africanus propagules per day. Samples were collected in Cape Town, South Africa over 50 weeks by a Burkard spore trap with an alternate orifice. We detected Es. africanus in air samples from 34 days (10%) distributed over 11 weeks. These results suggest environmental exposure to airborne Es. africanus propagules occurs more commonly in endemic areas than previously appreciated.
Asunto(s)
Microbiología del Aire , Técnicas Microbiológicas/métodos , Onygenales/aislamiento & purificación , Reacción en Cadena en Tiempo Real de la Polimerasa/métodos , Onygenales/genética , SudáfricaRESUMEN
In the Tulsa area, the Cupressaceae is largely represented by eastern red cedar (Juniperus virginiana L.). The encroachment of this species into the grasslands of Oklahoma has been well documented, and it is believed this trend will continue. The pollen is known to be allergenic and is a major component of the Tulsa atmosphere in February and March. This study examined airborne Cupressaceae pollen data from 1987 to 2016 to determine long-term trends, pollen seasonal variability, and influence of meteorological variables on airborne pollen concentrations. Pollen was collected through means of a Burkard sampler and analyzed with microscopy. Daily pollen concentrations and yearly pollen metrics showed a high degree of variability. In addition, there were significant increases over time in the seasonal pollen index and in peak concentrations. These increases parallel the increasing population of J. virginiana in the region. Pollen data were split into pre- and post-peak categories for statistical analyses, which revealed significant differences in correlations of the two datasets when analyzed with meteorological conditions. While temperature and dew point, among others were significant in both datasets, other factors, like relative humidity, were significant only in one dataset. Analyses using wind direction showed that southerly and southwestern winds contributed to increased pollen concentrations. This study confirms that J. virginiana pollen has become an increasing risk for individuals sensitive to this pollen and emphasizes the need for long-term aerobiological monitoring in other areas.
Asunto(s)
Contaminantes Atmosféricos/análisis , Alérgenos/análisis , Cupressaceae , Polen , Monitoreo del Ambiente/estadística & datos numéricos , Conceptos Meteorológicos , OklahomaRESUMEN
This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily Ambrosia abundance. The machine learning algorithms employed were Least Absolute Shrinkage and Selection Operator (LASSO), neural networks, and random forests. The best performance was obtained using random forests. The physical insights provided by the random forest are also discussed.
RESUMEN
Although considered rare, airborne pollen can be deposited far from its place of origin under a confluence of favorable conditions. Temporally anomalous records of Cupressacean pollen collected from January air samples in London, Ontario, Canada have been cited as a new case of long-distance transport. Data on pollination season implicated Juniperus ashei (mountain cedar), with populations in central Texas and south central Oklahoma, as the nearest source of the Cupressacean pollen in the Canadian air samples. This finding is of special significance given the allergenicity of mountain cedar pollen. While microscopy is used extensively to identify particles in the air spora, pollen from all members of the Cupressaceae, including Juniperus, are morphologically indistinguishable. Consequently, we implemented a molecular approach to characterize Juniperus pollen using PCR in order to test the long-distance transport hypothesis. Our PCR results using species-specific primers confirmed that the anomalous Cupressacean pollen collected in Canada was from J. ashei. Forward trajectory analysis from source areas in Texas and the Arbuckle Mountains in Oklahoma and backward trajectory analysis from the destination area near London, Ontario were completed using models implemented in HYSPLIT4 (Hybrid Single-Particle Lagrangian Integrated Trajectory). Results from these trajectory analyses strongly supported the conclusion that the J. ashei pollen detected in Canada had its origins in Texas or Oklahoma. The results from the molecular findings are significant as they provide a new method to confirm the long-distance transport of pollen that bears allergenic importance.
Asunto(s)
Juniperus/genética , Polen/genética , ADN de Plantas , Oklahoma , Ontario , Polinización , Reacción en Cadena de la Polimerasa , TexasRESUMEN
BACKGROUND: Pollen monitoring is a common and vital tool in the field of allergy, creating awareness in pollen sensitive individuals. Traditionally, pollen monitoring has been based on conventional microscopic counting techniques that are labor intensive and limited in the identification to the genus or family level. Molecular techniques provide an alternative approach that is less labor intensive and enable identification of any species by its genetic fingerprint. OBJECTIVE: To use quantitative polymerase chain reaction (qPCR) to evaluate pollen concentrations in air samples. METHODS: Juniperus pollen was selected as our model because of the importance of this pollen in the southcentral United States. We analyzed 105 air samples collected with a Burkard spore trap from 2013 to 2015 using species-specific primers and probes. To evaluate the feasibility of a molecular approach, we used duplicate air samples that allowed us to compare results from classical identification based on light microscopy with our qPCR results. RESULTS: Pollen concentrations from the qPCR data were significantly correlated with concentrations determined through light microscopy (R = 0.902, P < .001). We also confirmed an overlap in the pollination seasons between Juniperus ashei and Juniperus pinchotii and between J ashei and Juniperus virginiana. CONCLUSION: We found that this method correctly identified different Juniperus species present in mixed air samples in the southcentral United States, an accomplishment that cannot be achieved using microscopic identification. We conclude that the qPCR method is more accurate and sensitive than current pollen monitoring techniques and, therefore, has the potential to be used in various pollen monitoring stations.
Asunto(s)
Alérgenos/genética , Antígenos de Plantas/genética , Juniperus/genética , Polen/genética , Alérgenos/inmunología , Antígenos de Plantas/inmunología , Humanos , Juniperus/inmunología , Oklahoma , Polen/inmunología , Reacción en Cadena en Tiempo Real de la Polimerasa , Rinitis Alérgica Estacional/inmunologíaRESUMEN
The Kingdom Fungi contains diverse eukaryotic organisms including yeasts, molds, mushrooms, bracket fungi, plant rusts, smuts, and puffballs. Fungi have a complex metabolism that differs from animals and plants. They secrete enzymes into their surroundings and absorb the breakdown products of enzyme action. Some of these enzymes are well-known allergens. The phylogenetic relationships among fungi were unclear until recently because classification was based on the sexual state morphology. Fungi lacking an obvious sexual stage were assigned to the artificial, now-obsolete category, "Deuteromycetes" or "Fungi Imperfecti." During the last 20 years, DNA sequencing has resolved 8 fungal phyla, 3 of which contain most genera associated with important aeroallergens: Zygomycota, Ascomycota, and Basidiomycota. Advances in fungal classification have required name changes for some familiar taxa. Because of regulatory constraints, many fungal allergen extracts retain obsolete names. A major benefit from this reorganization is that specific immunoglobulin E (IgE) levels in individuals sensitized to fungi appear to closely match fungal phylogenetic relationships. This close relationship between molecular fungal systematics and IgE sensitization provides an opportunity to systematically look at cross-reactivity and permits representatives from each taxon to serve as a proxy for IgE to the group.
Asunto(s)
Hongos/clasificación , Alérgenos/inmunología , Hongos/inmunología , HumanosRESUMEN
Juniperus pollen is highly allergenic and is produced in large quantities across Texas, Oklahoma, and New Mexico. The pollen negatively affects human populations adjacent to the trees, and since it can be transported hundreds of kilometers by the wind, it also affects people who are far from the source. Predicting and tracking long-distance transport of pollen is difficult and complex. One parameter that has been understudied is the hygroscopic weight gain of pollen. It is believed that juniper pollen gains weight as humidity increases which could affect settling rate of pollen and thus affect pollen transport. This study was undertaken to examine how changes in relative humidity affect pollen weight, diameter, and settling rate. Juniperus ashei, Juniperus monosperma, and Juniperus pinchotii pollen were applied to greased microscope slides and placed in incubation chambers under a range of temperature and humidity levels. Pollen on slides were weighed using an analytical balance at 2- and 6-h intervals. The size of the pollen was also measured in order to calculate settling rate using Stokes' Law. All pollen types gained weight as humidity increased. The greatest settling rate increase was exhibited by J. pinchotii which increased by 24 %.
Asunto(s)
Contaminantes Atmosféricos/química , Humedad , Juniperus/química , Modelos Químicos , Polen/química , Humectabilidad , Adsorción , Contaminantes Atmosféricos/análisis , Simulación por Computador , Juniperus/clasificación , Ensayo de Materiales , Tamaño de la Partícula , Especificidad de la Especie , TemperaturaRESUMEN
BACKGROUND: Ambrosia pollen is an important aeroallergen in North America; the ability to predict daily pollen levels may provide an important benefit for sensitive individuals. OBJECTIVE: To analyze the long-term Ambrosia pollen counts and develop a forecasting model to predict the next day's pollen concentration. METHODS: Airborne pollen has been collected since December 1986 with a Burkard spore trap at the University of Tulsa. Summary statistics and season metrics were calculated for the 27 years of data. Concentration and previous-day meteorologic data from 1987 to 2011 were used to develop a multiple regression model to predict pollen levels for the following day. Model output was compared to 2012 and 2013 ragweed pollen data. RESULTS: The Tulsa ragweed season extends from the middle of August to late October. The mean start date is August 22, the mean peak date is September 10, and the mean end date is October 20. The mean cumulative season total is 11,599 pollen/m(3), and the mean daily concentration is 197 pollen/m(3). Previous-day meteorologic and phenologic data were positively related to pollen concentration (P < .001). Precipitation was modeled as a dichotomous variable. The final model included minimum temperature, dichotomous precipitation, dew point, and phenology variable (R = 0.7146, P < .001). Analysis of the model's accuracy revealed that the model was highly representative of the 2012 and 2013 seasons (R = 0.680, P < .001). CONCLUSION: Multiple regression models may be useful in explaining the variability of Ambrosia pollen levels. Further testing of the modeling parameters in different geographical areas is needed.