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

Bernstein modes in a weakly relativistic electron-positron plasma

The kinetic theory of weakly relativistic electron-positron plasmas, producing dispersion relations for the electrostatic Bernstein modes was addressed. The treatment presented preserves the full momentum dependence of the cyclotron frequency, albeit with a relaxation on the true relativistic form of the distribution function. The implications of this new treatment were confined largely to astrophysical plasmas, where relativistic electronpositron plasmas occur naturally

Association of variants in the SPTLC1 gene with juvenile amyotrophic lateral sclerosis

IMPORTANCE Juvenile amyotrophic lateral sclerosis (ALS) is a rare form of ALS characterized by age of symptom onset less than 25 years and a variable presentation.OBJECTIVE To identify the genetic variants associated with juvenile ALS.DESIGN, SETTING, AND PARTICIPANTS In this multicenter family-based genetic study, trio whole-exome sequencing was performed to identify the disease-associated gene in a case series of unrelated patients diagnosed with juvenile ALS and severe growth retardation. The patients and their family members were enrolled at academic hospitals and a government research facility between March 1, 2016, and March 13, 2020, and were observed until October 1, 2020. Whole-exome sequencing was also performed in a series of patients with juvenile ALS. A total of 66 patients with juvenile ALS and 6258 adult patients with ALS participated in the study. Patients were selected for the study based on their diagnosis, and all eligible participants were enrolled in the study. None of the participants had a family history of neurological disorders, suggesting de novo variants as the underlying genetic mechanism.MAIN OUTCOMES AND MEASURES De novo variants present only in the index case and not in unaffected family members.RESULTS Trio whole-exome sequencing was performed in 3 patients diagnosed with juvenile ALS and their parents. An additional 63 patients with juvenile ALS and 6258 adult patients with ALS were subsequently screened for variants in the SPTLC1 gene. De novo variants in SPTLC1 (p. Ala20Ser in 2 patients and p.Ser331Tyr in 1 patient) were identified in 3 unrelated patients diagnosed with juvenile ALS and failure to thrive. A fourth variant (p.Leu39del) was identified in a patient with juvenile ALS where parental DNA was unavailable. Variants in this gene have been previously shown to be associated with autosomal-dominant hereditary sensory autonomic neuropathy, type 1A, by disrupting an essential enzyme complex in the sphingolipid synthesis pathway.CONCLUSIONS AND RELEVANCE These data broaden the phenotype associated with SPTLC1 and suggest that patients presenting with juvenile ALS should be screened for variants in this gene.Genetics of disease, diagnosis and treatmen

Characterisation of bioaerosol emissions from a Colorado pine forest : results from the BEACHON-RoMBAS experiment

© Author(s) 2014. This work is distributed under the Creative Commons Attribution 3.0 LicenseThe behaviour of Primary Biological Aerosols (PBA) at an elevated, un-polluted North American forest site was studied using an Ultra Violet-Light Induced Fluorescence (UV-LIF) measurement technique in conjunction with Hierarchical Agglomerative Cluster Analysis (HA-CA). Contemporaneous UV-LIF measurements were made with two wide-band integrated bioaerosol spectrometers, WIBS-3 and WIBS-4, which sampled close to the forest floor and via a continuous vertical profiling system, respectively. Additionally, meteorological parameters were recorded at various heights throughout the forest and used to estimate PBAP fluxes. HA-CA using data from the two, physically-separated WIBS instruments independently yielded very similar cluster solutions. All fluorescent clusters displayed a diurnal minimum at midday at the forest floor with maximum concentration occurring at night. Additionally, the number concentration of each fluorescent cluster was enhanced, to different degrees, during wet periods. A cluster that displayed the greatest enhancement and highest concentration during sustained wet periods appears consistent with behaviour reported for fungal spores. A cluster that appears to be behaviourally consistent with bacteria dominated during dry periods. Fluorescent particle concentrations were found to be greater within the forest canopy than at the forest floor, indicating that the canopy was the main source of these particles rather than the minimal surface vegetation, which appeared to contribute little to overall PBA concentrations at this site. Fluorescent particle concentration was positively correlated with relative humidity (RH), and parameterisations of the aerosol response during dry and wet periods are reported. The aforementioned fungal spore-like cluster displayed a strong positive response to increasing RH. The bacteria-like cluster responded more strongly to direct rain-fall events than other PBA types. Peak concentrations of this cluster are shown to be exponentially correlated to peak rainfall rates. Parallel studies by Huffman et al. (2013) and Prenni et al. (2013) showed that the fluorescent particle concentrations correlated linearly with ice nuclei (IN) concentrations at this site during rain events. We discuss this result in conjunction with our cluster analysis to appraise the candidate IN.Peer reviewe

Observations of fluorescent and biological aerosol at a high-altitude site in Central France

© Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 LicenseTotal bacteria, fungal spore and yeast counts were compared with UV Light-Induced Fluorescence (UV-LIF) measurements of ambient aerosol at the summit of the Puy de Dˆome (pdD) mountain in Central France (1465 ma.s.l), which represents a background elevated site. Bacteria, fungal spores and yeast were enumerated by epifluorescence microscopy (EFM) and found to number 2.2 to 23 L−1 and 0.8 to 2 L−1, respectively. Bacteria counts on two successive nights were an order of magnitude larger than in the intervening day. A Wide Issue Bioaerosol Spectrometer, version 3 (WIBS-3) was used to perform UV-LIF measurements on ambient aerosol sized 0.8 to 20 µm. Mean total number concentration was 270 L−1 (σ = 66 L−1) found predominantly in a size mode at 2 µm for most of the campaign. Total concentration (fluorescent + non-fluorescent aerosol) peaked at 500 L−1 with a size mode at 1 µm because of a change in air mass origin lasting around 48 h. The WIBS-3 features two excitation and fluorescence detection wavelengths corresponding to di ff erent biological molecules. The mean fluorescent particle concentration after short-wave (280 nm; Tryptophan) excitation was 12 L−1 (σ = 6 L−1), and did not vary much through the campaign. In contrast the mean concentration of particles fluorescent after long-wave (370 nm; NADH) excitation was 95 L−1(σ = 25 L−1), and a nightly rise and subsequent fall of up to 100 L−1 formed a strong diurnal cycle in the latter. The fluorescent populations exhibited size modes at 3 µm and 2 to 3 µm, respectively. A hierarchical agglomerative cluster analysis algorithm was applied to the data and used to extract di ff erent particle factors. A cluster concentration time series representative of bacteria was identified. This was found to exhibit a diurnal cycle with a maximum peak appearing during the day. Analysis of organic mass spectra recorded using an Aerosol Mass Spectrometer 25 (AMS; Aerodyne Inc.) suggests that aerosol reaching the site at night was more aged than that during the day, indicative of sampling the residual layer at night. Supplementary meteorological data and previous work also show that pdD lies in the residual layer/free troposphere at night, and this is thought to cause the observed diurnal cycles in organic-type and fluorescent aerosol particles. Based on the observed disparity between bacteria and fluorescent particle concentrations, fluorescent non-PBA is likely to be important in the WIBS-3 data and the surprisingly high fluorescent concentration in the residual layer/free troposphere raises questions about a ubiquitous background in continental air during the summer.Peer reviewe

Engaging with charities on social media: comparing interaction on Facebook and Twitter

Social media are commonly assumed to provide fruitful online communities for organisations, whereby the brand and supporter-base engage in productive, two-way conversations. For charities, this provides a unique opportunity to reach an audience for a relatively low cost, yet some remain hesitant to fully embrace these services without knowing exactly what they will receive in return. This paper reports on a study that seeks to determine the extent to which these conversations occur, and compares this phenomenon on Facebook and Twitter for a sample of UK-based charities. Focus was placed on analysing conversations as signs of developing relationships, which have previously been shown to be a key target for charities on social media. The results of this study find that while there is an expected proportion of the audience who prefer to listen rather than engage, there is strong evidence of a core group of supporters on each site who repeatedly engage. Interestingly, disparities between how this occurs on Facebook and Twitter emerge, with the results suggesting that Facebook receives more conversations in response to the charities’ own posts, whereas on Twitter there is a larger observable element of unsolicited messages of people talking about the charity, which in turn produces a differing opportunity for the charity to extract value from the network. It is also found that posts containing pictures receive the highest number of responses on each site. These were a lot less common on Twitter and could therefore offer an avenue for charities to increase the frequency of responses they achieve