Desert dust intrusions and their incidence on airborne biological content. Review and case study in the Iberian Peninsula.

Desert dust intrusions cause the transport of airborne particulate matter from natural sources, with important consequences for climate regulation, biodiversity, ecosystem functioning and dynamics, human health, and socio-economic activities. Some effects of desert intrusions are reinforced or aggravated by the bioaerosol content of the air during these episodes. The influence of desert intrusions on airborne bioaerosol content has been very little studied from a scientific point of view. In this study, a systematic review of scientific literature during 1970-2021 was carried out following the standard protocol Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). After this literature review, only 6% of the articles on airborne transport from desert areas published in the last 50 years are in some way associated with airborne pollen, and of these, only a small proportion focus on the study of pollen-related parameters. The Iberian Peninsula is affected by Saharan intrusions due to its proximity to the African continent and is seeing an increasing trend the number of intrusion events. There is a close relationship among the conditions favouring the occurrence of intrusion episodes, the transport of particulate matter, and the transport of bioaerosols such as pollen grains, spores, or bacteria. The lack of linearity in this relationship and the different seasonal patterns in the occurrence of intrusion events and the pollen season of most plants hinders the study of the correspondence between both phenomena. It is therefore important to analyse the proportion of pollen that comes from regional sources and the proportion that travels over long distances, and the atmospheric conditions that cause greater pollen emission during dust episodes. Current advances in aerobiological techniques make it possible to identify bioaerosols such as pollen and spores that serve as indicators of long-distance transport from remote areas belonging to other bioclimatic and biogeographical units. A greater incidence of desert intrusion episodes may pose a challenge for both traditional systems and for the calibration and correct validation of automatic aerobiological monitoring methods.

Methods for interpolating missing data in aerobiological databases.

Missing data is a common problem in scientific research. The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.

Predicting the Olea pollen concentration with a machine learning algorithm ensemble.

Air pollution in large cities produces numerous diseases and even millions of deaths annually according to the World Health Organization. Pollen exposure is related to allergic diseases, which makes its prediction a valuable tool to assess the risk level to aeroallergens. However, airborne pollen concentrations are difficult to predict due to the inherent complexity of the relationships among both biotic and environmental variables. In this work, a stochastic approach based on supervised machine learning algorithms was performed to forecast the daily Olea pollen concentrations in the Community of Madrid, central Spain, from 1993 to 2018. Firstly, individual Light Gradient Boosting Machine (LightGBM) and artificial neural network (ANN) models were applied to predict the day of the year (DOY) when the peak of the pollen season occurs, resulting the estimated average peak date 149.1 ± 9.3 and 150.1 ± 10.8 DOY for LightGBM and ANN, respectively, close to the observed value (148.8 ± 9.8). Secondly, the daily pollen concentrations during the entire pollen season have been calculated using an ensemble of two-step GAM followed by LightGBM and ANN. The results of the prediction of daily pollen concentrations showed a coefficient of determination (r2) above 0.75 (goodness of the model following cross-validation). The predictors included in the ensemble models were meteorological variables, phenological metrics, specific site-characteristics, and preceding pollen concentrations. The models are state-of-the-art in machine learning and their potential has been shown to be used and deployed to understand and to predict the pollen risk levels during the main olive pollen season.

Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.

Betula pollen is frequently found in the atmosphere of central and northern Europe. Betula pollen are health relevant as they cause severe allergic reactions in the population. We developed models of thermal requirements to predict start, peak and end dates of the Betula main pollen season for Bavaria (Germany). Betula pollen data of one season from 19 locations were used to train the models. Estimated dates were compared with observed dates, and the errors were spatially represented. External validation was carried out with time series datasets of 3 different locations (36years in total). RESULTS: The temperature requirements to detonate the main pollen season proved non-linear. For the start date model (error of 8,75days during external validation), daily mean temperatures above a threshold of 10°C from 28th of February onwards were the most relevant. The peak model (error of 3.58days) takes into account mean daily temperatures accumulated since the first date of the main pollen season in which the daily average temperature exceeded 11°C. The end model (error of 3.75days) takes into account all temperatures accumulated since the start of the main pollen season. CONCLUSION: These models perform predictions that enable the allergic population to better manage their disease. With the established relationship between temperatures and pollen season dates, changes in the phenological behaviour of Betula species due to climate change can be also estimated in future studies by taking into account the different climate scenarios proposed by previous climate change studies.

Modeling olive pollen intensity in the Mediterranean region through analysis of emission sources.

Aerobiological monitoring of Olea europaea L. is of great interest in the Mediterranean basin because olive pollen is one of the most represented pollen types of the airborne spectrum for the Mediterranean region, and olive pollen is considered one of the major cause of pollinosis in this region. The main aim of this study was to develop an airborne-pollen map based on the Pollen Index across a 4-year period (2008-2011), to provide a continuous geographic map for pollen intensity that will have practical applications from the agronomical and allergological points of view. For this purpose, the main predictor variable was an index based on the distribution and abundance of potential sources of pollen emission, including intrinsic information about the general atmospheric patterns of pollen dispersal. In addition, meteorological variables were included in the modeling, together with spatial interpolation, to allow the definition of a spatial model of the Pollen Index from the main olive cultivation areas in the Mediterranean region. The results show marked differences with respect to the dispersal patterns associated to the altitudinal gradient. The findings indicate that areas located at an altitude above 300ma.s.l. receive greater amounts of olive pollen from shorter-distance pollen sources (maximum influence, 27km) with respect to areas lower than 300ma.s.l. (maximum influence, 59km).

ETP-46321, a dual p110α/δ class IA phosphoinositide 3-kinase inhibitor modulates T lymphocyte activation and collagen-induced arthritis.

Class IA phosphoinositide 3-kinases (PI3Ks) are essential to function of normal and tumor cells, and to modulate immune responses. T lymphocytes express high levels of p110α and p110δ class IA PI3K. Whereas the functioning of PI3K p110δ in immune and autoimmune reactions is well established, the role of p110α is less well understood. Here, a novel dual p110α/δ inhibitor (ETP-46321) and highly specific p110α (A66) or p110δ (IC87114) inhibitors have been compared concerning T cell activation in vitro, as well as the effect on responses to protein antigen and collagen-induced arthritis in vivo. In vitro activation of naive CD4(+) T lymphocytes by anti-CD3 and anti-CD28 was inhibited more effectively by the p110δ inhibitor than by the p110α inhibitor as measured by cytokine secretion (IL-2, IL-10, and IFN-γ), T-bet expression and NFAT activation. In activated CD4(+) T cells re-stimulated through CD3 and ICOS, IC87114 inhibited Akt and Erk activation, and the secretion of IL-2, IL-4, IL-17A, and IFN-γ better than A66. The p110α/δ inhibitor ETP-46321, or p110α plus p110δ inhibitors also inhibited IL-21 secretion by differentiated CD4(+) T follicular (Tfh) or IL-17-producing (Th17) helper cells. In vivo, therapeutic administration of ETP-46321 significantly inhibited responses to protein antigen as well as collagen-induced arthritis, as measured by antigen-specific antibody responses, secretion of IL-10, IL-17A or IFN-γ, or clinical symptoms. Hence, p110α as well as p110δ Class IA PI3Ks are important to immune regulation; inhibition of both subunits may be an effective therapeutic approach in inflammatory autoimmune diseases like rheumatoid arthritis.