A chemical sensor based on a photonic-crystal L3 nanocavity defined in a silicon-nitride membrane

The application of a silicon-nitride based L3 optical nanocavity as a chemical sensor is explored. It is shown that by adjusting the thickness of an ultra-thin Lumogen Red film deposited onto the nanocavity surface, the fundamental optical mode undergoes a progressive red-shift as the layer-thickness increases, with the cavity being able to detect the presence of a single molecular monolayer. The optical properties of a nanocavity whose surface is coated with a thin layer of a porphyrin-based polymer are also explored. On exposure of the cavity to an acidic-vapour, it is shown that changes in the optical properties of the porphyrin-film (thickness and refractive index) can be detected through a reversible shift in the cavity mode wavelength. Such effects are described using a finite difference time-domain model

Bayesian modeling of differential gene expression.

We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of genes for further investigation. We give empirical evidence that expression-level dependent array effects are needed, and explore different nonlinear functions as part of our model-based approach to normalization. The model includes gene-specific variances but imposes some necessary shrinkage through a hierarchical structure. Model criticism via posterior predictive checks is discussed. Modeling the array effects (normalization) simultaneously with differential expression gives fewer false positive results. To choose a list of genes, we propose to combine various criteria (for instance, fold change and overall expression) into a single indicator variable for each gene. The posterior distribution of these variables is used to pick the list of genes, thereby taking into account uncertainty in parameter estimates. In an application to mouse knockout data, Gene Ontology annotations over- and underrepresented among the genes on the chosen list are consistent with biological expectations

Bayesian modeling of differential gene expression.

We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of genes for further investigation. We give empirical evidence that expression-level dependent array effects are needed, and explore different nonlinear functions as part of our model-based approach to normalization. The model includes gene-specific variances but imposes some necessary shrinkage through a hierarchical structure. Model criticism via posterior predictive checks is discussed. Modeling the array effects (normalization) simultaneously with differential expression gives fewer false positive results. To choose a list of genes, we propose to combine various criteria (for instance, fold change and overall expression) into a single indicator variable for each gene. The posterior distribution of these variables is used to pick the list of genes, thereby taking into account uncertainty in parameter estimates. In an application to mouse knockout data, Gene Ontology annotations over- and underrepresented among the genes on the chosen list are consistent with biological expectations

Industrial Brush Coiler Attachment

Problem Statement: Company initially desired a new machine that would be able to produce external coiled brushes that would help in increasing revenue and project opportunities. The design has shifted to making an attachment to the existing equipment that would help in this effort instead of developing a completely new unit. Rationale: Sealeze sees this as an opportunity of increasing revenue and also taking on the effort of making externally coiled brushes more efficiently. If successful, more clients can be taken in and it would make Sealeze a more versatile company. Approach: The main approach revolved around weekly meetings with Sealeze. E-mail was utilized daily in order to make sure that the desire of the company were met. There were at least three different iterations to the design until one was settled upon. Main design tool used was SolidWorks and the design model was shown to the company frequently. Interim Results and Conclusions: The main problems that were of concern revolved around the amount of force needed to bend the brush and in a circular fashion. Calculations were done to insure that the brush would be bent with the right amount of force when also taking into consideration the motor driven components that were guiding the brush. Anticipated Results and Conclusions: According to the calculations, it is expected that the brush will not buckle while the machine is running and will be able to formed into the desired spiral.https://scholarscompass.vcu.edu/capstone/1059/thumbnail.jp

A transparent TMPyP/TiO2 composite thin film as an HCl sensitive optochemical gas sensor

Tetracationic porphyrin (TMPyP) molecules were incorporated into an optically transparent TiO2 thin film, prepared by Glancing Angle Physical Vapour Deposition (GAPVD), by simple infiltration (at pH 6.4). The preparation of optically transparent TMPyP/TiO2 composite thin films provides a method for the integration of the porphyrin molecules into photonic devices for direct monitoring of gases. Previously, UV-visible and fluorescence spectral techniques have been used to study the reversible protonation of TMPyP in aqueous solution. The optical spectrum of TMPyP shows an intense Soret band at 423 nm with a 22 nm red shift upon protonation by HCl. The experimental conditions for monitoring the concentration of HCl gas by absorption spectroscopy have been optimized. The maximum absorbance change was observed at the Soret band wavelength. A selected temperature of 80 °C and a 300 s recovery period were found to be the optimum operating parameters (response time t50 = 16.8 ± 0.7 s). The composite with smaller surface concentration of TMPyP (¿ = 0.3 × 10-9 mol cm -2) presented the best detection limit (0.1 ppm). The response of the composite sensor was highly stable for several months.Ministerio de Educación y Ciencia PET2007 0363 01/ 02, TEC201021830C0201, CSD20070000

Bell's local causality is a d-separation criterion

This paper aims to motivate Bell's notion of local causality by means of Bayesian networks. In a locally causal theory any superluminal correlation should be screened off by atomic events localized in any so-called \textit{shielder-off region} in the past of one of the correlating events. In a Bayesian network any correlation between non-descendant random variables are screened off by any so-called \textit{d-separating set} of variables. We will argue that the shielder-off regions in the definition of local causality conform in a well defined sense to the d-separating sets in Bayesian networks.Comment: 13 pages, 8 figure

Evidence of a large seasonal coastal upwelling system along the southern shelf of Australia

2000 FLORIDA AVE NW, WASHINGTON, USA, DC, 2000

Neighborkeepers: Buddies for Better HealthAn Intervention to Improve Physical Activity Habits

Introduction: associated health complications have increased steadily across the United States. As a result, physical activity considerations have become a more significant focus of healthcare providers and government agencies. Recent studies suggest that social support network approaches, such as the buddy-system, improve participant adherence to physical activity regimens. To improve physical activity frequency and adherence, we implemented a buddy system approach with participants involved in a community outreach organization.https://scholarworks.uvm.edu/comphp_gallery/1014/thumbnail.jp

Wildfire impact : natural experiment reveals differential short-term changes in soil microbial communities

A wildfire which overran a sensor network site provided an opportunity (a natural experiment) to monitor short-term post-fire impacts (immediate and up to three months post-fire) in remnant eucalypt woodland and managed pasture plots. The magnitude of fire-induced changes in soil properties and soil microbial communities was determined by comparing (1) variation in fire-adapted eucalypt woodland vs. pasture grassland at the burnt site; (2) variation at the burnt woodland-pasture sites with variation at two unburnt woodland-pasture sites in the same locality; and (3) temporal variation pre- and post-fire. In the eucalypt woodland, soil ammonium, pH and ROC content increased post-fire, while in the pasture soil, soil nitrate increased post-fire and became the dominant soluble N pool. However, apart from distinct changes in N pools, the magnitude of change in most soil properties was small when compared to the unburnt sites. At the burnt site, bacterial and fungal community structure showed significant temporal shifts between pre- and post-fire periods which were associated with changes in soil nutrients, especially N pools. In contrast, microbial communities at the unburnt sites showed little temporal change over the same period. Bacterial community composition at the burnt site also changed dramatically post-fire in terms of abundance and diversity, with positive impacts on abundance of phyla such as Actinobacteria, Proteobacteria and Firmicutes. Large and rapid changes in soil bacterial community composition occurred in the fire-adapted woodland plot compared to the pasture soil, which may be a reflection of differences in vegetation composition and fuel loading. Given the rapid yet differential response in contrasting land uses, identification of key soil bacterial groups may be useful in assessing recovery of fire-adapted ecosystems, especially as wildfire frequency is predicted to increase with global climate change

Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them