Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree.

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy.

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public health. The current forecast models are generally useful, but they falter when long time series of data are managed. The emergence of new computational techniques such as the LSTM algorithms could constitute a significant improvement for the pollen risk assessment. In this study, several LSTM variants were applied to forecast monthly pollen integrals in Málaga (southern Spain) using meteorological variables as predictors. Olea and Urticaceae pollen types were modelled as proxies of different annual pollen curves, using data from the period 1992-2022. The aims of this study were to determine the LSTM variants with the highest accuracy when forecasting monthly pollen integrals as well as to compare their performance with the traditional pollen forecast methods. The results showed that the CNN-LSTM were the most accurate when forecasting the monthly pollen integrals for both pollen types. Moreover, the traditional forecast methods were outperformed by all the LSTM variants. These findings highlight the importance of implementing LSTM models in pollen forecasting for public health and research applications.

Climate change related phenological decoupling in species belonging to the Betulaceae family.

Betulaceae species are anemophilous, and allergens from their pollen are a major cause of respiratory allergies in temperate areas where they are widely distributed. It is expected that, due to the strong influence of temperature on Betulaceae phenology, global warming will impact both the distribution and phenology of these species during the coming decades. This study examines potential decoupling of flowering and leafing phenophases in Betulaceae species (i.e. Alnus glutinosa, Betula pendula and Corylus avellana) over long-term (1951-2015) and as shorter (15-year) periods. Phenological phases for flowering and leaf unfolding of Betulaceae species from the Pan-European Phenology (PEP725) database were examined along with maximum and minimum daily temperature data for the periods September-October-November (SON), December-January-February (DJF) and March-April-May (MAM). Significant increases in temperature since 1951 have been recorded in the relevant chilling and forcing periods. Both flowering and leaf unfolding phenophases are advancing, but flowering is advancing faster than leaf unfolding. This is increasing the time between phenophases, although analysis of 15-year periods shows that the pattern of change was not constant. The results presented here represent the most comprehensive analysis of flowering and leaf unfolding phenophases of Betulaceae species using the PEP725 database to date. It is expected that these changes to Betulaceae phenology will continue and that global warming-related phenological decoupling will increase plant stress in Betulaceae populations in central Europe.

Environmental drivers of the seasonal exposure to airborne Alternaria spores in Spain.

Alternaria conidia have high allergenic potential and they can trigger important respiratory diseases. Due to that and to their extensive detection period, airborne Alternaria spores are considered as a relevant airborne allergenic particle. Several studies have been developed in order to predict the human exposure to this aeroallergen and to prevent their negative effects on sensitive population. These studies revealed that some sampling locations usually have just one single Alternaria spore season while other locations generally have two seasons within the same year. However, the reasons of these two different seasonal patterns remain unclear. To understand them better, the present study was carried out in order to determine if there are any weather conditions that influence these different behaviours at different sampling locations. With this purpose, the airborne Alternaria spore concentrations of 18 sampling locations in a wide range of latitudinal, altitudinal and climate ranges of Spain were studied. The aerobiological samples were obtained by means of Hirst-Type volumetric pollen traps, and the seasonality of the airborne Alternaria spores were analysed. The optimal weather conditions for spore production were studied, and the main weather factor affecting Alternaria spore seasonality were analysed by means of random forests and regression trees. The results showed that the temperature was the most relevant variable for the Alternaria spore dispersion and it influenced both the spore integrals and their seasonality. The water availability was also a very significant variable. Warmer sampling locations generally have a longer period of Alternaria spore detection. However, the spore production declines during the summer when the temperatures are extremely warm, what splits the favourable period for Alternaria spore production and dispersion into two separate ones, detected as two Alternaria spore seasons within the same year.

Effects of future climate change on birch abundance and their pollen load.

Climate change impacts on the structure and function of ecosystems will worsen public health issues like allergic diseases. Birch trees (Betula spp.) are important sources of aeroallergens in Central and Northern Europe. Birches are vulnerable to climate change as these trees are sensitive to increased temperatures and summer droughts. This study aims to examine the effect of climate change on airborne birch pollen concentrations in Central Europe using Bavaria in Southern Germany as a case study. Pollen data from 28 monitoring stations in Bavaria were used in this study, with time series of up 30 years long. An integrative approach was used to model airborne birch pollen concentrations taking into account drivers influencing birch tree abundance and birch pollen production and projections made according to different climate change and socioeconomic scenarios. Birch tree abundance is projected to decrease in parts of Bavaria at different rates, depending on the climate scenario, particularly in current centres of the species distribution. Climate change is expected to result in initial increases in pollen load but, due to the reduction in birch trees, the amount of airborne birch pollen will decrease at lower altitudes. Conversely, higher altitude areas will experience expansions in birch tree distribution and subsequent increases in airborne birch pollen in the future. Even considering restrictions for migration rates, increases in pollen load are likely in Southwestern areas, where positive trends have already been detected during the last three decades. Integrating models for the distribution and abundance of pollen sources and the drivers that control birch pollen production allowed us to model airborne birch pollen concentrations in the future. The magnitude of changes depends on location and climate change scenario.

Medium- and long-range transport events of Alnus pollen in western Mediterranean.

Alnus pollen has been frequently detected in the atmosphere of different airborne sampling sites of Southern Spain. However, Alnus sp. populations are very scarce and fragmented in the area, being restricted to a few river valleys in the southwest, and other further away regions of the Iberian Peninsula. This leads to think that the airborne pollen detected could be mainly the result of a medium- or long-distant transport. So, the aim of this study was to characterize the annual patterns of airborne Alnus pollen detected at three different locations of Malaga province, as well as to determine its possible origin, the pollen dispersion potential of these Alnus isolated populations, and their possible reproductive connectivity. Pollen sampling was conducted by means of three Hirst-type volumetric pollen traps. Samples were mounted and counted following the recommendations of the Spanish Aerobiology Network and the European Aeroallergen Society. The possible pollen sources were detected by means of a combination of meteorological information and backward air trajectories analysis. A high inter-annual variability in the annual pollen integrals was found in all the stations, favouring certain meteorological conditions a long-range transport and, therefore, causing the high concentrations detected in some specific days. Alnus pollen seems to have a heterogeneous origin with prevalence of the long-distant transport, which would suggest a possible reproductive connection among distant populations.

Concordance of Genomic Variants in Matched Primary Breast Cancer, Metastatic Tumor, and Circulating Tumor DNA: The MIRROR Study.

PURPOSE: Genetic heterogeneity between primary tumors and their metastatic lesions has been documented in several breast cancer studies. However, the selection of therapy for patients with metastatic breast cancer and the search for biomarkers for targeted therapy are often based on findings from the primary tumor, mainly because of the difficulty of distant metastasis core biopsies. New methods for monitoring genomic changes in metastatic breast cancer are needed (ie, circulating tumor DNA [ctDNA] genomic analysis). The objectives of this study were to assess the concordance of genomic variants between primary and metastatic tumor tissues and the sensitivity of plasma ctDNA analysis to identify variants detected in tumor biopsies. PATIENTS AND METHODS: Next-generation sequencing technology was used to assess the genomic mutation profile of a panel of 54 cancer genes in matched samples of primary tumor, metastatic tumor, and plasma from 40 patients with metastatic breast cancer. RESULTS: Using Ion Torrent technology (ThermoFisher Scientific, Waltham, MA), we identified 110 variants that were common to the primary and metastatic tumors. ctDNA analysis had a sensitivity of 0.972 in detecting variants present in both primary and metastatic tissues. In addition, we identified 13 variants in metastatic tissue and ctDNA not present in primary tumor. CONCLUSION: We identified genomic variants present in metastatic biopsies and plasma ctDNA that were not present in the primary tumor. Deep sequencing of plasma ctDNA detected most DNA variants previously identified in matched primary and metastatic tissues. ctDNA might aid in therapy selection and in the search for biomarkers for drug development in metastatic breast cancer.

Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases.

OBJECTIVES: Different types of '-omics' data are becoming available in the post-genome era; still a single -omics assessment provides limited insights to understand the biological mechanism of complex diseases. Genomics, epigenomics and transcriptomics data provide insight into the molecular dysregulation of neoplastic diseases, among them urothelial bladder cancer (UBC). Here, we propose a detailed analytical framework necessary to achieve an adequate integration of the three sets of -omics data to ultimately identify previously hidden genetic mechanisms in UBC. METHODS: We built a multi-staged framework to study possible pair-wise combinations and integrated the data in three-way relationships. SNP genotypes, CpG methylation levels and gene expression levels were determined for a total of 70 individuals with UBC and with fresh tumour tissue available. RESULTS: We suggest two main hypothesis-based scenarios for gene regulation based on the -omics integration analysis, where DNA methylation affects gene expression and genetic variants co-regulate gene expression and DNA methylation. We identified several three-way trans-association 'hotspots' that are found at the molecular level and that deserve further studies. CONCLUSIONS: The proposed integrative framework allowed us to identify relationships at the whole-genome level providing some new biological insights and highlighting the importance of integrating -omics data.