Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Atmos. Meas. Tech., 10, 695-708, 2017 http://www.atmos-meas-tech.net/10/695/2017/ doi:10.5194/amt-10-695-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License.Characterisation of bioaerosols has important implications within environment and public health sectors. Recent developments in ultraviolet light-induced fluorescence (UV-LIF) detectors such as the Wideband Integrated Bioaerosol Spectrometer (WIBS) and the newly introduced Multiparameter Bioaerosol Spectrometer (MBS) have allowed for the real-time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents, bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we tested hierarchical agglomerative clustering with various different linkages. For supervised learning, 11 methods were tested, including decision trees, ensemble methods (random forests, gradient boosting and AdaBoost), two implementations for support vector machines (libsvm and liblinear) and Gaussian methods (Gaussian naïve Bayesian, quadratic and linear discriminant analysis, the k-nearest neighbours algorithm and artificial neural networks). The methods were applied to two different data sets produced using the new MBS, which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. The first data set contained mixed PSLs and the second contained a variety of laboratory-generated aerosol. Clustering in general performs slightly worse than the supervised learning methods, correctly classifying, at best, only 67. 6 and 91. 1 % for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 82. 8 and 98. 27 % of the testing data, respectively, across the two data sets. A possible alternative to gradient boosting is neural networks. We do however note that this method requires much more user input than the other methods, and we suggest that further research should be conducted using this method, especially using parallelised hardware such as the GPU, which would allow for larger networks to be trained, which could possibly yield better results. We also saw that some methods, such as clustering, failed to utilise the additional shape information provided by the instrument, whilst for others, such as the decision trees, ensemble methods and neural networks, improved performance could be attained with the inclusion of such information.Peer reviewe

Evaluation of Machine Learning Algorithms for Classification of Primary Biological Aerosol using a new UV-LIF spectrometer

© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License.Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non- biological fluorescent interferents bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and Ad-aBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. Clustering, in general performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets.Peer reviewe

Machine learning for improved data analysis of biological aerosol using the WIBS

Abstract. Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will provide different responses in the presence of ultraviolet light which potentially could be used to discriminate between different types of biological aerosol. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has made is possible to collect size, morphology and fluorescence measurements in real-time. However, it is unclear without studying responses from the instrument in the laboratory, the extent to which we can discriminate between different types of particles. Collection of laboratory data is vital to validate any approach used to analyse the data and to ensure that the data available is utilised as effectively as possible. In this manuscript we test a variety of methodologies on traditional reference particles and a range of laboratory generated aerosols. Hierarchical Agglomerative Clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting. Whilst HAC was able to effectively discriminate between the reference particles, yielding a classification error of only 1.8 %, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5 % and 24.2 %. Furthermore, there is a worryingly large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable attain consistent results across the different sets of laboratory generated aerosol tested. The best results were obtained using gradient boosting, where the misclassification rate was between 4.38 % and 5.42 %. The largest contribution to this error was the pollen samples where 28.5 % of the samples were misclassified as fungal spores. The technique was also robust to changes in data preparation provided a fluorescent threshold was applied to the data. Where laboratory training data is unavailable, DBSCAN was found to be an potential alternative to HAC. In the case of one of the data sets where 22.9 % of the data was left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42 % on the classified data. These results could not be replicated however for the other data set where 26.8 % of the data was not classified and a classification error of 13.8 % was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring different selection of parameters dependent on the preparation used. Further analysis will also be required to confirm our selection of parameters when using this method on ambient data. There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely improve on current discrimination between pollen, bacteria and fungal spores and even between their different types, however the need for extensive laboratory training data sets will grow as a result. </jats:p

Measurement of event shapes in deep inelastic scattering at HERA

Inclusive event-shape variables have been measured in the current region of the Breit frame for neutral current deep inelastic ep scattering using an integrated luminosity of 45.0 pb^-1 collected with the ZEUS detector at HERA. The variables studied included thrust, jet broadening and invariant jet mass. The kinematic range covered was 10 < Q^2 < 20,480 GeV^2 and 6.10^-4 < x < 0.6, where Q^2 is the virtuality of the exchanged boson and x is the Bjorken variable. The Q dependence of the shape variables has been used in conjunction with NLO perturbative calculations and the Dokshitzer-Webber non-perturbative corrections (`power corrections') to investigate the validity of this approach.Comment: 7+25 pages, 6 figure

Laryngeal embryonal rhabdomyosarcoma in an adult - A case presentation in the eyes of geneticists and clinicians

<p>1. Abstract</p> <p>Background</p> <p>Rhabdomyosarcoma is a solid tumor, resulting from dysregulation of the skeletal myogenesis program. For rhabdomyosarcomas (RMS) with a predilection for the head and neck, genitourinary tract, extremities, trunk, retroperitoneum, the larynx is still an unusual site. Till now only several cases of this laryngeal tumor have been described in world literature in the adult population. The entire spectrum of genetic factors underlying RMS development and progression is unclear until today. Multiple signaling pathways seem to be involved in ERMS development and progression.</p> <p>Case presentation</p> <p>In this paper we report an interesting RMS case in which the disease was located within the glottic region. We report an embryonal rhabdomyosarcoma of the larynx in 33 year-old man. After unsuccessful chemotherapy hemilaryngectomy was performed. In follow up CT no signs of recurrence were found. Recently patient is recurrence free for 62 months.</p> <p>Conclusions</p> <p>Considering the histological diagnosis and the highly aggressive nature of the lesion for optimal diagnosis positron electron tomography (PET) and computerized tomography (CT) of the neck and thorax should be performed. At this time surgical treatment with adjuvant radiotherapy seems to be the treatment of choice for this disease. Rhabdomyosarcoma of the larynx has a better prognosis than elsewhere in the body, probably because of its earlier recognition and accessibility to radical surgery.</p

Measurement of inclusive D*+- and associated dijet cross sections in photoproduction at HERA

Inclusive photoproduction of D*+- mesons has been measured for photon-proton centre-of-mass energies in the range 130 < W < 280 GeV and a photon virtuality Q^2 < 1 GeV^2. The data sample used corresponds to an integrated luminosity of 37 pb^-1. Total and differential cross sections as functions of the D* transverse momentum and pseudorapidity are presented in restricted kinematical regions and the data are compared with next-to-leading order (NLO) perturbative QCD calculations using the "massive charm" and "massless charm" schemes. The measured cross sections are generally above the NLO calculations, in particular in the forward (proton) direction. The large data sample also allows the study of dijet production associated with charm. A significant resolved as well as a direct photon component contribute to the cross section. Leading order QCD Monte Carlo calculations indicate that the resolved contribution arises from a significant charm component in the photon. A massive charm NLO parton level calculation yields lower cross sections compared to the measured results in a kinematic region where the resolved photon contribution is significant.Comment: 32 pages including 6 figure

Angular and Current-Target Correlations in Deep Inelastic Scattering at HERA

Correlations between charged particles in deep inelastic ep scattering have been studied in the Breit frame with the ZEUS detector at HERA using an integrated luminosity of 6.4 pb-1. Short-range correlations are analysed in terms of the angular separation between current-region particles within a cone centred around the virtual photon axis. Long-range correlations between the current and target regions have also been measured. The data support predictions for the scaling behaviour of the angular correlations at high Q2 and for anti-correlations between the current and target regions over a large range in Q2 and in the Bjorken scaling variable x. Analytic QCD calculations and Monte Carlo models correctly describe the trends of the data at high Q2, but show quantitative discrepancies. The data show differences between the correlations in deep inelastic scattering and e+e- annihilation.Comment: 26 pages including 10 figures (submitted to Eur. J. Phys. C

Plastisol Foaming Process. Decomposition of the Foaming Agent, Polymer Behavior in the Corresponding Temperature Range and Resulting Foam Properties

The decomposition of azodicarbonamide, used as foaming agent in PVC - plasticizer (1/1) plastisols was studied by DSC. Nineteen different plasticizers, all belonging to the ester family, two being polymeric (polyadipates), were compared. The temperature of maximum decomposition rate (in anisothermal regime at 5 K min-1 scanning rate), ranges between 434 and 452 K. The heat of decomposition ranges between 8.7 and 12.5 J g -1. Some trends of variation of these parameters appear significant and are discussed in terms of solvent (matrix) and viscosity effects on the decomposition reactions. The shear modulus at 1 Hz frequency was determined at the temperature of maximum rate of foaming agent decomposition, and differs significantly from a sample to another. The foam density was determined at ambient temperature and the volume fraction of bubbles was used as criterion to judge the efficiency of the foaming process. The results reveal the existence of an optimal shear modulus of the order of 2 kPa that corresponds roughly to plasticizer molar masses of the order of 450 ± 50 g mol-1. Heavier plasticizers, especially polymeric ones are too difficult to deform. Lighter plasticizers such as diethyl phthalate (DEP) deform too easily and presumably facilitate bubble collapse

Observation of Scaling Violations in Scaled Momentum Distributions at HERA

Charged particle production has been measured in deep inelastic scattering (DIS) events over a large range of $x$ and $Q^2$ using the ZEUS detector. The evolution of the scaled momentum, $x_p$, with $Q^2,$ in the range 10 to 1280 $GeV^2$, has been investigated in the current fragmentation region of the Breit frame. The results show clear evidence, in a single experiment, for scaling violations in scaled momenta as a function of $Q^2$.Comment: 21 pages including 4 figures, to be published in Physics Letters B. Two references adde

D* Production in Deep Inelastic Scattering at HERA

This paper presents measurements of D^{*\pm} production in deep inelastic scattering from collisions between 27.5 GeV positrons and 820 GeV protons. The data have been taken with the ZEUS detector at HERA. The decay channel $D^{*+}\to (D^0 \to K^- \pi^+) \pi^+$ (+ c.c.) has been used in the study. The $e^+p$ cross section for inclusive D^{*\pm} production with $5<Q^2<100 GeV^2$ and $y<0.7$ is 5.3 \pms 1.0 \pms 0.8 nb in the kinematic region {$1.3<p_T(D^{*\pm})<9.0$ GeV and $| \eta(D^{*\pm}) |<1.5$}. Differential cross sections as functions of p_T(D^{*\pm}), $\eta(D^{*\pm}), W$ and $Q^2$ are compared with next-to-leading order QCD calculations based on the photon-gluon fusion production mechanism. After an extrapolation of the cross section to the full kinematic region in p_T(D^{*\pm}) and $\eta$(D^{*\pm}), the charm contribution $F_2^{c\bar{c}}(x,Q^2)$ to the proton structure function is determined for Bjorken $x$ between 2 $\cdot$ 10$^{-4}$ and 5 $\cdot$ 10$^{-3}$.Comment: 17 pages including 4 figure