A trust-region method for stochastic variational inference with applications to streaming data

Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization. We address this problem by replacing the natural gradient step of stochastic varitional inference with a trust-region update. We show that this leads to generally better results and reduced sensitivity to hyperparameters. We also describe a new strategy for variational inference on streaming data and show that here our trust-region method is crucial for getting good performance.Comment: in Proceedings of the 32nd International Conference on Machine Learning, 201

Public Perception of Pesticide Exposure in Vermont

Introduction: A pesticide is any substance intended for preventing, destroying, repelling, or mitigating any pest.1 The public generally believes that insects, diseases, and other pests need to be controlled but is also becoming concerned about the impact of pesticides on their health and the local ecosystem. Pesticide exposure occurs with public and private use. Studies indicate consumers have diverse levels of awareness, knowledge, and attitudes regarding pesticide use and health risks.2 The goal of this project is to identify levels of awareness, knowledge, and attitudes toward pesticide usage in Vermont to help State agencies focus public awareness and education.https://scholarworks.uvm.edu/comphp_gallery/1217/thumbnail.jp

Evaluating Net Groundwater Use from Remotely Sensed Evapotranspiration and Water Delivery Information

A detailed, comprehensive, and accurate identification of groundwater aquifer properties will likely never be fully achieved because of the high degree of variability and costs that testing involves. Furthermore, accurate estimates of boundary conditions are essential for groundwater modeling so that investigations of improved management scenarios can be conducted. The lack of key input values at the ground surface boundary limits the ability to accurately assess aquifer dynamics. Of major importance is actual evapotranspiration (water consumption or the loss of water to the atmosphere through transpiration and evaporation). The Irrigation Training and Research Center (ITRC) modified remotely sensed satellite imagery for spatial computation of actual evapotranspiration at high resolution, and integrated it into groundwater models. This paper focuses on an additional tool to assist in the calibration of groundwater models, which results in the NET contribution to or extraction from groundwater (NTFGW). By comparing surface water deliveries, precipitation, runoff, and evapotranspiration, the NTFGW can be computed spatially throughout a region. This provides a critical set of known information, in addition to historic groundwater elevation data, that can be used in model calibration

Basement-cover relations and internal structure of the Cape Smith klippe: A 1.9 Ga greenstone belt in northern Quebec, Canada

The Cape Smith Belt is a 380x60 km tectonic klippe composed of greenschistto amphibolite-grade mafic and komatiitic lava flows and fine-grained quartzose sediment, intruded by minor syn- to post-tectonic granitoids. Previously studied transects in areas of relatively high structural level show that the belt is constructed of seven or more north-dipping thrust sheets which verge toward the Superior Province (Archean) foreland in the south and away from an Archean basement massif (Kovik Antiform) external to the Trans-Hudson Orogen (Early Proterozoic) in the north. A field project (mapping and structural-stratigraphic-metamorphic studies) directed by MRS was begun in 1985 aimed at the structurally deeper levels of the belt and underlying basement, which are superby exposed in oblique cross-section (12 km minimum structural relief) at the west-plunging eastern end of the belt. Mapping now complete of the eastern end of the belt confirms that all of the metavolcanic and most of the metasedimentary rocks are allochthonous with respect to the Archean basement, and that the thrusts must have been rooted north of Kovik Antiform. The main findings follow

Using bacterial biomarkers to identify early indicators of cystic fibrosis pulmonary exacerbation onset

Acute periods of pulmonary exacerbation are the single most important cause of morbidity in cystic fibrosis patients, and may be associated with a loss of lung function. Intervening prior to the onset of a substantially increased inflammatory response may limit the associated damage to the airways. While a number of biomarker assays based on inflammatory markers have been developed, providing useful and important measures of disease during these periods, such factors are typically only elevated once the process of exacerbation has been initiated. Identifying biomarkers that can predict the onset of pulmonary exacerbation at an early stage would provide an opportunity to intervene before the establishment of a substantial immune response, with major implications for the advancement of cystic fibrosis care. The precise triggers of pulmonary exacerbation remain to be determined; however, the majority of models relate to the activity of microbes present in the patient's lower airways of cystic fibrosis. Advances in diagnostic microbiology now allow for the examination of these complex systems at a level likely to identify factors on which biomarker assays can be based. In this article, we discuss key considerations in the design and testing of assays that could predict pulmonary exacerbations

deciphering the ecology of cystic fibrosis bacterial communities towards systems level integration

Despite over a decade of cystic fibrosis (CF) microbiome research, much remains to be learned about the overall composition, metabolic activities, and pathogenicity of the microbes in CF airways, limiting our understanding of the respiratory microbiome's relation to disease. Systems-level integration and modeling of host–microbiome interactions may allow us to better define the relationships between microbiological characteristics, disease status, and treatment response. In this way, modeling could pave the way for microbiome-based development of predictive models, individualized treatment plans, and novel therapeutic approaches, potentially serving as a paradigm for approaching other chronic infections. In this review, we describe the challenges facing this effort and propose research priorities for a systems biology approach to CF lung disease

Heterogeneity in Surface Sensing Suggests a Division of Labor in Pseudomonas aeruginosa Populations

The second messenger signaling molecule cyclic diguanylate monophosphate (c-di-GMP) drives the transition between planktonic and biofilm growth in many bacterial species. Pseudomonas aeruginosa has two surface sensing systems that produce c-di-GMP in response to surface adherence. Current thinking in the field is that once cells attach to a surface, they uniformly respond by producing c-di-GMP. Here, we describe how the Wsp system generates heterogeneity in surface sensing, resulting in two physiologically distinct subpopulations of cells. One subpopulation has elevated c-di-GMP and produces biofilm matrix, serving as the founders of initial microcolonies. The other subpopulation has low c-di-GMP and engages in surface motility, allowing for exploration of the surface. We also show that this heterogeneity strongly correlates to surface behavior for descendent cells. Together, our results suggest that after surface attachment, P. aeruginosa engages in a division of labor that persists across generations, accelerating early biofilm formation and surface exploration

A refined, controlled 16S rRNA gene sequencing approach reveals limited detection of cerebrospinal fluid microbiota in children with bacterial meningitis

Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. Additionally, these refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads, such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples with low bacterial loads by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. Several bench and computational approaches were taken to address potential sources of error for low bacterial load samples. We compared DNA yields and sequencing results after applying three different DNA extraction approaches to an artificially constructed mock-bacterial community. We also compared two postsequencing computational contaminant removal strategies, decontam R and full contaminant sequence removal. All three extraction techniques followed by decontam R yielded similar results for the mock community. We then applied these methods to 22 CSF samples from children diagnosed with meningitis, which has low bacterial loads relative to other clinical infection samples. The refined 16S HTS pipelines identified the cultured bacterial genus as the dominant organism for only 3 of these samples. We found that all three DNA extraction techniques followed by decontam R generated similar DNA yields for mock communities at the low bacterial loads representative of CSF samples. However, the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches. Although we did not find current DNA-based diagnostics to be useful for pediatric meningitis samples, the utility of these methods for CSF shunt infection remains undefined. Future advances in sample processing methods to minimize or eliminate contamination will be required to improve the sensitivity and specificity of these methods for pediatric meningitis