I\u27ll Wed You in the Golden Summertime

https://digitalcommons.library.umaine.edu/mmb-vp/5183/thumbnail.jp

In The Village By The Sea

https://digitalcommons.library.umaine.edu/mmb-vp/3627/thumbnail.jp

Only a Dream of the Golden Past

https://digitalcommons.library.umaine.edu/mmb-vp/3816/thumbnail.jp

Show the white of yo\u27 eyes

drawing of a tribal village with two insets, a drawing of a black girl / Starmer ; photo inset of Lottie Gilson.https://scholarsjunction.msstate.edu/cht-sheet-music/4061/thumbnail.jp

A study of concepts in sex education and maturity levels of students grades kindergarten through twelve

Call number: LD2668 .R4 1968 C7

Electronic Fock spaces: Phase prefactors and hidden symmetry

Efficient technique of manipulation with phase prefactors in electronic Fock spaces is developed. Its power is demonstrated on example of both relatively simple classic configuration interaction matrix element evaluation and essentially more complicated coupled cluster case. Interpretation of coupled cluster theory in terms of a certain commutative algebra is given.Comment: LaTex, 31 pages, submitted to Int. J. Quantum Che

Using Intervention Mapping to Develop an Efficacious Multicomponent Systems-Based Intervention to Increase Human Papillomavirus (HPV) Vaccination in a Large Urban Pediatric Clinic Network

Background: The CDC recommends HPV vaccine for all adolescents to prevent cervical, anal, oropharyngeal, vaginal, vulvar, and penile cancers, and genital warts. HPV vaccine rates currently fall short of national vaccination goals. Despite evidence-based strategies with demonstrated efficacy to increase HPV vaccination rates, adoption and implementation of these strategies within clinics is lacking. The Adolescent Vaccination Program (AVP) is a multicomponent systems-based intervention designed to implement five evidence-based strategies within primary care pediatric practices. The AVP has demonstrated efficacy in increasing HPV vaccine initiation and completion among adolescents 10-17 years of age. The purpose of this paper is to describe the application of Intervention Mapping (IM) toward the development, implementation, and formative evaluation of the clinic-based AVP prototype. Methods: Intervention Mapping (IM) guided the development of the Adolescent Vaccination Program (AVP). Deliverables comprised: a logic model of the problem (IM Step 1); matrices of behavior change objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); functional AVP component prototypes (IM Step 4); and plans for implementation (IM Step 5) and evaluation (IM Step 6). Results: The AVP consists of six evidence-based strategies implemented in a successful sequenced roll-out that (1) established immunization champions in each clinic, (2) disseminated provider assessment and feedback reports with data-informed vaccination goals, (3) provided continued medical and nursing education (with ethics credit) on HPV, HPV vaccination, message bundling, and responding to parent hesitancy, (4) electronic health record cues to providers on patient eligibility, and (5) patient reminders for HPV vaccine initiation and completion. Conclusions: IM provided a logical and systematic approach to developing and evaluating a multicomponent systems-based intervention to increase HPV vaccination rates among adolescents in pediatric clinics

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

The ecology of methane in streams and rivers: patterns, controls, and global significance

Streams and rivers can substantially modify organic carbon (OC) inputs from terrestrial landscapes, and much of this processing is the result of microbial respiration. While carbon dioxide (CO₂) is the major end‐product of ecosystem respiration, methane (CH₄) is also present in many fluvial environments even though methanogenesis typically requires anoxic conditions that may be scarce in these systems. Given recent recognition of the pervasiveness of this greenhouse gas in streams and rivers, we synthesized existing research and data to identify patterns and drivers of CH₄, knowledge gaps, and research opportunities. This included examining the history of lotic CH4 research, creating a database of concentrations and fluxes (MethDB) to generate a global‐scale estimate of fluvial CH₄ efflux, and developing a conceptual framework and using this framework to consider how human activities may modify fluvial CH₄ dynamics. Current understanding of CH₄ in streams and rivers has been strongly influenced by goals of understanding OC processing and quantifying the contribution of CH₄ to ecosystem C fluxes. Less effort has been directed towards investigating processes that dictate in situ CH₄ production and loss. CH₄ makes a meager contribution to watershed or landscape C budgets, but streams and rivers are often significant CH₄ sources to the atmosphere across these same spatial extents. Most fluvial systems are supersaturated with CH₄ and we estimate an annual global emission of 26.8 Tg CH₄, equivalent to ~15‐40% of wetland and lake effluxes, respectively. Less clear is the role of CH₄ oxidation, methanogenesis, and total anaerobic respiration to whole ecosystem production and respiration. Controls on CH₄ generation and persistence can be viewed in terms of proximate controls that influence methanogenesis (organic matter, temperature, alternative electron acceptors, nutrients) and distal geomorphic and hydrologic drivers. Multiple controls combined with its extreme redox status and low solubility result in high spatial and temporal variance of CH₄ in fluvial environments, which presents a substantial challenge for understanding its larger‐scale dynamics. Further understanding of CH₄ production and consumption, anaerobic metabolism, and ecosystem energetics in streams and rivers can be achieved through more directed studies and comparison with knowledge from terrestrial, wetland, and aquatic disciplines."Support for this paper was provided by funding from the North Temperate Lakes LTER program, NSF DEB‐0822700."https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/15-102

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