Estimation of PM10-bound As, Cd, Ni and Pb levels by means of statistical modelling: PLSR and ANN approaches

Air quality assessment regarding metals and metalloids using experimental measurements is expensive and time consuming due to the cost and time required for the analytical determination of the levels of these pollutants. According to the European Union (EU) Air Quality Framework Directive (Directive 2008/50/EC), other alternatives, such as objective estimation techniques, can be considered for ambient air quality assessment in zones and agglomerations where the level of pollutants is below a certain concentration value known as the lower assessment threshold. These conditions occur in urban areas in Cantabria (northern Spain). This work aims to estimate the levels of As, Cd, Ni and Pb in airborne PM10 at two urban sites in the Cantabria region (Castro Urdiales and Reinosa) using statistical models as objective estimation techniques. These models were developed based on three different approaches: partial least squares regression (PLSR), artificial neural networks (ANNs) and an alternative approach consisting of principal component analysis (PCA) coupled with ANNs (PCA-ANN). Additionally, these models were externally validated using previously unseen data. The results show that the models developed in this work based on PLSR and ANNs fulfil the EU uncertainty requirements for objective estimation techniques and provide an acceptable estimation of the mean values. As a consequence, they could be considered as an alternative to experimental measurements for air quality assessment regarding the aforementioned pollutants in the study areas while saving time and resources.The authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project CMT2010-16068. The authors also thank the Regional Environment Ministry of the Cantabria Government for providing the PM10 samples at the Castro Urdiales and Reinosa sites

Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour

A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions. Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth

Analysis and Forecasting of Airborne Pollen-induced Symptoms with the Aid of Computational Intelligence Methods

Allergies due to airborne pollen affect a considerable percentage of Europeans; thus, the provision of health-related information services concerning pollen-induced symptoms can improve the overall quality of life. In this paper, we demonstrate the development of personalized, health-related, quality-of-life information services by adopting a data-driven approach. The data we use consist of allergic symptoms reported by people as well as detailed pollen count information of the most allergenic taxa. We apply computational intelligence methods in order to analyze symptoms, identify possible interrelationships with several pollen taxa and develop models that associate pollen count levels with allergic symptoms on a personal level. The results for the case of Austria show that this approach can lead to accurate personalized symptom forecasting models; we report an average correlation coefficient of r = 0.70 for a sample of 102 users of the Patients Hayfever Diary. We conclude that some of these models could serve as the basis for personalized health information services

Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour

A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype. In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions. Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth

The Patient’s Hay-fever Diary: Three Years of Results from Germany

The patient’s hay-fever diary (PHD) is a newly developed, internet-based tool for self-documentation of pollen-induced symptoms (eyes, nose and airways), general well-being and medication use. In Germany, more than 1,600 users made over 60,500 reports in 3 years (2009–2011). An analysis of these reports reveal that the nose symptom “sneezing” is the most commonly reported (3/10 of reports), followed by eye symptom “itching” and nose “blocked”. In addition, medication use follows a similar pattern every year, with tablets being the most commonly used medication type (up to 60 % of the reports made in the years 2009 and 2011). Temporal variations in overall symptoms and organ-specific symptom scores are found to be associated with atmospheric concentrations of birch and grass pollen. Data from the PHD can be analysed with the aid of various mathematical methods and may provide information about symptoms and their severity for pollen-allergic sufferers. They may also be valuable for clinical studies in immunotherapy with pollen extracts

A New Feature Selection Methodology for Environmental Modelling Support: The Case of Thessaloniki Air Quality

Part 2: Air and ClimateInternational audienceEnvironmental systems status is described via a (usually big) set of parameters. Therefore, relevant models employ a large feature space, thus making feature selection a necessity towards better modelling results. Many methods have been used in order to reduce the number of features, while safeguarding environmental model performance and resulting to low computational time. In this study, a new feature selection methodology is presented, making use of the Self Organizing Maps (SOM) method. SOM visualization values are used as a similarity measure between the parameter that is to be forecasted, and parameters of the feature space. The method leads to the smallest set of parameters that surpass a similarity threshold. Results obtained, for the case of Thessaloniki air quality forecasting, are comparable to what feature selection methods offer

Endothelial cell death after ionizing radiation does not impair vascular structure in mouse tumor models

The effect of radiation therapy on tumor vasculature has long been a subject of debate. Increased oxygenation and perfusion have been documented during radiation therapy. Conversely, apoptosis of endothelial cells in irradiated tumors has been proposed as a major contributor to tumor control. To examine these contradictions, we use multiphoton microscopy in two murine tumor models: MC38, a highly vascularized, and B16F10, a moderately vascularized model, grown in transgenic mice with tdTomato-labeled endothelium before and after a single (15 Gy) or fractionated (5 × 3 Gy) dose of radiation. Unexpectedly, even these high doses lead to little structural change of the perfused vasculature. Conversely, non-perfused vessels and blind ends are substantially impaired after radiation accompanied by apoptosis and reduced proliferation of their endothelium. RNAseq analysis of tumor endothelial cells confirms the modification of gene expression in apoptotic and cell cycle regulation pathways after irradiation. Therefore, we conclude that apoptosis of tumor endothelial cells after radiation does not impair vascular structure