Global regularity of three-dimensional Ricci limit spaces

In their recent work [ST17], Miles Simon and the second author established a local bi-Hölder correspondence between weakly noncollapsed Ricci limit spaces in three dimensions and smooth manifolds. In particular, any open ball of finite radius in such a limit space must be bi-Hölder homeomorphic to some open subset of a complete smooth Riemannian three-manifold. In this work we build on the technology from [ST16, ST17] to improve this local correspondence to a global-local correspondence. That is, we construct a smooth three-manifold M, and prove that the entire (weakly) noncollapsed three-dimensional Ricci limit space is homeomorphic to M via a globally-defined homeomorphism that is bi-Hölder once restricted to any compact subset. Here the bi-Hölder regularity is with respect to the distance dg on M, where g is any smooth complete metric on M. A key step in our proof is the construction of local pyramid Ricci flows, existing on uniform regions of spacetime, that are inspired by Hochard’s partial Ricci flows [Hoc16]. Suppose (M, g0, x0) is a complete smooth pointed Riemannian three-manifold that is (weakly) noncollapsed and satisfies a lower Ricci bound. Then, given any k ∈ N, we construct a smooth Ricci flow g(t) living on a subset of spacetime that contains, for each j ∈ {1, . . . , k}, a cylinder Bg0 (x0, j) × [0, Tj ], where Tj is dependent only on the Ricci lower bound, the (weakly) noncollapsed volume lower bound and the radius j (in particular independent of k) and with the property that g(0) = g0 throughout Bg0 (x0, k).</p

A Survey of Three-Dimensional Sound and its Applications

This research project seeks to develop a foundation of knowledge about three-dimensional sound (3-D sound) and its utilization across various media. Topics covered include the techniques by which 3-D sound is accomplished, its departures from the standard paradigm of stereophonic audio, the range of creative and engineering considerations within 3-D contexts and examinations of existing 3-D audio applications

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

Towards integration of environmental and health impact assessments for wild capture fishing and farmed fish with particular reference to public health and occupational health dimensions

The paper offers a review and commentary, with particular reference to the production of fish from wild capture fisheries and aquaculture, on neglected aspects of health impact assessments which are viewed by a range of international and national health bodies and development agencies as valuable and necessary project tools. Assessments sometimes include environmental health impact assessments but rarely include specific occupational health and safety impact assessments especially integrated into a wider public health assessment. This is in contrast to the extensive application of environmental impact assessments to fishing and the comparatively large body of research now generated on the public health effects of eating fish. The value of expanding and applying the broader assessments would be considerable because in 2004 the United Nations Food and Agriculture Organization reports there were 41,408,000 people in the total ‘fishing’ sector including 11,289,000 in aquaculture. The paper explores some of the complex interactions that occur with regard to fishing activities and proposes the wider adoption of health impact assessment tools in these neglected sectors through an integrated public health impact assessment tool

Phylogenetic relationship and virulence composition of Escherichia coli O26:H11 cattle and human strain collections in Scotland; 2002-2020

O26 is the commonest non-O157 Shiga toxin (stx)-producing Escherichia coli serogroup reported in human infections worldwide. Ruminants, particularly cattle, are the primary reservoir source for human infection. In this study, we compared the whole genomes and virulence profiles of O26:H11 strains (n = 99) isolated from Scottish cattle with strains from human infections (n = 96) held by the Scottish Escherichia coli O157/STEC Reference Laboratory, isolated between 2002 and 2020. Bovine strains were from two national cross-sectional cattle surveys conducted between 2002–2004 and 2014–2015. A maximum likelihood phylogeny was constructed from a core-genome alignment with the O26:H11 strain 11368 reference genome. Genomes were screened against a panel of 2,710 virulence genes using the Virulence Finder Database. All stx-positive bovine O26:H11 strains belonged to the ST21 lineage and were grouped into three main clades. Bovine and human source strains were interspersed, and the stx subtype was relatively clade-specific. Highly pathogenic stx2a-only ST21 strains were identified in two herds sampled in the second cattle survey and in human clinical infections from 2010 onwards. The closest pairwise distance was 9 single-nucleotide polymorphisms (SNPs) between Scottish bovine and human strains and 69 SNPs between the two cattle surveys. Bovine O26:H11 was compared to public EnteroBase ST29 complex genomes and found to have the greatest commonality with O26:H11 strains from the rest of the UK, followed by France, Italy, and Belgium. Virulence profiles of stx-positive bovine and human strains were similar but more conserved for the stx2a subtype. O26:H11 stx-negative ST29 (n = 17) and ST396 strains (n = 5) were isolated from 19 cattle herds; all were eae-positive, and 10 of these herds yielded strains positive for ehxA, espK, and Z2098, gene markers suggestive of enterohaemorrhagic potential. There was a significant association (p &lt; 0.001) between nucleotide sequence percent identity and stx status for the bacteriophage insertion site genes yecE for stx2 and yehV for stx1. Acquired antimicrobial resistance genes were identified in silico in 12.1% of bovine and 17.7% of human O26:H11 strains, with sul2, tet, aph(3″), and aph(6″) being most common. This study describes the diversity among Scottish bovine O26:H11 strains and investigates their relationship to human STEC infections

Exploring the Correlation between Hα\rm{H}\alpha-to-UV Ratio and Burstiness for Typical Star-forming Galaxies at z∼2z\sim2

The $\rm{H}\alpha$-to-UV luminosity ratio ($L(\rm H\alpha)/L(\rm UV)$) is often used to probe SFHs of star-forming galaxies and it is important to validate it against other proxies for burstiness. To address this issue, we present a statistical analysis of the resolved distribution of $\Sigma_{\rm{SFR}}$ as well as stellar age and their correlations with the globally measured $L(\rm H\alpha)/L(\rm UV)$ for a sample of 310 star-forming galaxies in two redshift bins of $1.37 < z < 1.70$ and $2.09 < z < 2.61$ observed by the MOSDEF survey. We use the multi-waveband CANDELS/3D-HST imaging of MOSDEF galaxies to construct $\Sigma_{\rm{SFR}}$ and stellar age maps. We analyze the composite rest-frame far-UV spectra of a subsample of MOSDEF targets obtained by the Keck/LRIS, which includes 124 star-forming galaxies (MOSDEF-LRIS) at redshifts $1.4 < z < 2.6$, to examine the average stellar population properties, and the strength of age-sensitive FUV spectral features in bins of $L(\rm H\alpha)/L(\rm UV)$. Our results show no significant evidence that individual galaxies with higher $L(\rm H\alpha)/L(\rm UV)$ are undergoing a burst of star formation based on the resolved distribution of $\Sigma_{\rm{SFR}}$ of individual star-forming galaxies. We segregate the sample into subsets with low and high $L(\rm H\alpha)/L(\rm UV)$. The high-$L(\rm H\alpha)/L(\rm UV)$ subset exhibits, on average, an age of $\log[\rm{Age/yr}]$ = 8.0, compared to $\log[\rm{Age/yr}]$ = 8.4 for the low-$L(\rm H\alpha)/L(\rm UV)$ galaxies, though the difference in age is significant at only the $2\sigma$ level. Furthermore, we find no variation in the strengths of Siiv$\lambda\lambda1393, 1402$ and Civ$\lambda\lambda1548, 1550$ P-Cygni features from massive stars between the two subsamples.Comment: 16 pages, 10 figures, published by the Monthly Notices of the Royal Astronomical Societ

Neoadjuvant Chemotherapy Modulates the Immune Microenvironment in Metastases of Tubo-Ovarian High-Grade Serous Carcinoma

This research was funded by Swiss Cancer League (BIL KLS-2883-02-2012), the European Research Council (ERC322566), Cancer Research UK (A16354), and Barts and The London Charity (467/1307)