Fine and ultrafine particles from indoor sources – Effects on healthy humans in a controlled exposure study and on lung epithelial cells in vitro

In recent years increasing concern has been expressed about the potential adverse health effects of particles from indoor sources. The aims of the EPIA project were: (1) to characterize potentially relevant indoor sources of (ultra)fine particles with respect to their emission levels and composition and (2) to investigate their adverse health effects. We investigated the effects of emissions from candle burning (CB), toasting of bread (TB) and sausage frying (FS) in a randomized, cross-over sham-controlled exposure study in healthy adults as well as in vitro in A549 human lung epithelial cells. Participants were exposed for 2 h to each of these sources at two different exposure levels, and examined before, during and after the exposures at defined time-intervals. We found transient associations between exposures and several respiratory and cardiovascular effects as well as inflammatory changes (e.g. lung function, blood pressure, arterial stiffness, interleukin-8 in nasal lavage/blood). Specific effects were found to depend strongly on the emission source and the selected exposure metric (e.g. size-specific particle mass concentration, size-specific particle number concentration, lung deposited surface area concentration). Evaluation of PM2.5 samples in the A549 cells, revealed an increased interleukin-8 release and DNA strand breakage induction for toasting, whereas candle burning only resulted in DNA damage. The results from our project demonstrate that elevated concentrations from certain indoor emission sources may lead to changes in the lung and cardiovascular systems as well as possibly induce inflammation

A novel feature selection-based sequential ensemble learning method for class noise detection in high-dimensional data

© 2018, Springer Nature Switzerland AG. Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method

Development of intermediate layer systems for direct deposition of thin film solar cells onto low cost steel substrates

The functionalisation of low-cost steel over large areas with low cost intermediate layers (ILs) for utilisation as substrates in thin film solar modules is reported. Three approaches for the deposition of ILs are demonstrated and evaluated; a thick SiOx sol–gel based on a one-step acidic catalysis applied by spray technique, a commercial screen-printable dielectric ink, and an epoxy-based material (SU8) deposited by screen printing or bar coating. These ILs demonstrated the properties of surface levelling (quantified by mechanical profilometry), electric insulation (tested using breakdown voltage and leakage current) and acted as an anti-diffusion barrier (demonstrated with glow discharge mass spectrometry). Moreover, the performances of amorphous silicon (a-Si:H) and organic photovoltaic (OPV) thin film solar cells grown on carbon and stainless steels (a-Si:H: 5.53% and OPV: 2.40%) show similar performances as those obtained using a reference glass substrate (a-Si:H: 5.51% and OPV: 2.90%). Finally, a cost analysis taking into account both the SiOx sol–gel and the dielectric ink IL was reported to demonstrate the economic feasibility of the steel/IL prototypes

AdaBoost algorithm with random forests for predicting breast cancer survivability

In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity, and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction. © 2008 IEEE

Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction

Finding and removing misclassified instances are important steps in data mining and machine learning that affect the performance of the data mining algorithm in general. In this paper, we propose a C-Support Vector Classification Filter (C-SVCF) to identify and remove the misclassified instances (outliers) in breast cancer survivability samples collected from Srinagarind hospital in Thai- land, to improve the accuracy of the prediction models. Only instances that are correctly classified by the filter are passed to the learning algorithm. Perform- ance of the proposed technique is measured with accuracy and area under the re- ceiver operating characteristic curve (AUC), as well as compared with several popular ensemble filter approaches including AdaBoost, Bagging and ensemble of SVM with AdaBoost and Bagging filters. Our empirical results indicate that C-SVCF is an effective method for identifying misclassified outliers. This ap- proach significantly benefits ongoing research of developing accurate and robust prediction models for breast cancer survivability

An Active Noise Correction Graph Embedding Method Based on Active Learning for Graph Noisy Data

© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. In various scenarios of the real world, there are various graph data. Most graph structures are confronted with the problems of complex structure and large consumption of memory space. Graph embedding is an effective method to overcome such challenges, which converts graph structure into a low-dimensional dense vector space. In the real world, label acquisition is expensive, and there may be noise in the data. Therefore, it is important to find valuable noise nodes as much as possible to improve the performance of downstream task. In this paper, we propose a novel active sampling strategy for graph noisy data named Active Noise Correction Graph Embedding method (ANCGE). Given the label budget, the proposed method aims to use semi-supervised graph embedding algorithm to find valuable mislabeled nodes. ANCGE measures the value of noise nodes according to their representativeness and influence on the graph. The experimental results on three open datasets demonstrate the effectiveness of our method and its stability under different noise rates

Silver nanoparticles induce hormesis in A549 human epithelial cells

Despite the gaps in our knowledge on the toxicity of silver nanoparticles (AgNPs), the application of these materials is fast expanding, from medicine, to food as well as the use in consumer products. It has been reported that prolonged exposure might make cells more resistant to AgNPs. This prompted us to investigate if AgNPs may give rise to a hormetic response. Two types of AgNPs were used, i.e. colloidal AgNPs and an AgNP powder. For both types of nanosilver it was found that a low dose pretreatment of A549 human epithelial cells with AgNPs induced protection against a toxic dose of AgNPs and acrolein. This protection was more pronounced after pretreatment with the colloidal AgNPs. Interestingly, the mechanism of the hormetic response appeared to differ from that of acrolein. Adaptation to acrolein is related to Nrf2 translocation, increased mRNA expression of gamma GCS, HO-1 and increased GSH levels and the increased GSH levels can explain the hormetic effect. The adaptive response to AgNPs was not related to an increase in nIRNA expression of gamma GCS and GSH levels. Yet, HO-1 mRNA expression and Nrf2 immunoreactivity were enhanced, indicating that these processes might be involved. So, AgNPs induce adaptation, but in contrast to acrolein GSH plays no role. (C) 2017 The Authors. Published by Elsevier Ltd