346 research outputs found
Development of a Pan Listeria DNA Aptamer
No full textListeria monocytogenes is a pathogenic bacteria that can be found in a variety of foods and causes foodborne illness. While L. monocytogenes is the only pathogenic species of Listeria, regulatory agencies require routine monitoring of the six major species of Listeria in high-risk food products, as they are indicators of potential L. monocytogenes contamination. Antibodies, which are plagued by major limitations including cost of production and storage are currently being used to detect Listeria. DNA aptamers, which are short single-stranded DNA molecules that can serve as detection molecules in place of antibodies, are potential alternatives as they are not plagued by these same limitations. Currently, there are no aptamers capable of detecting these six Listeria species simultaneously. Listeria's p60 protein, also known as invasion-associated protein, is an ideal target for the detection of all six Listeria species as it is secreted in large amounts by all of them. In the early stages of this project, bioinformatics techniques were employed to identify a region of the p60 protein that is highly conserved among the six species of interest. After cloning, expressing and purifying this conserved p60 fragment, it was used as the target of detection in a cost-effective and robust aptamer-based method that was developed and optimized as part of the project. To date, two aptamers capable of detecting all six Listeria species of interest have been generated and validated. Lastly, these aptamers have been implemented into a simple-to-use detection device prototype which is currently being investigated to determine sensitivity and specificity
Recommended from our members
Assessing the validity of SafeGraph data for visitor monitoring in Yellowstone National Park
No full textYun Liang is a Ph.D. student in the Department of Recreation, Park and Tourism Management at the Pennsylvania State University. Her academic interest is to combine social media data analysis with conservation in national parks or protected areas.
Junjun Yin, Ph.D., is an assistant research professor at the Social Science Research Institute and Population Research Institute, Pennsylvania State University. His research interests center on computational GIScience with a focus on developing geospatial Big Data analytics for studying urban and population dynamics concerning urban mobility, accessibility, and sustainability.
Bing Pan, Ph.D., is an Associate Professor in the Department of Recreation, Park, and Tourism Management at Pennsylvania State University, University Park. His research interests include data analytics, tourism big data, destination marketing, and benefits of travel.
Michael Lin is a Ph.D. student in the School of Hospitality Management at the Pennsylvania State University. His academic interest is strategy and finance.
Guangqing Chi, Ph.D., is an associate professor of rural sociology and demography and director of the Computational and Spatial Analysis Core of the Pennsylvania State University. His research is focused on socio-environmental systems by developing spatial and Big Data analytic methods. He leads generalizing Twitter data for social science research.Monitoring visitor demographics and temporal visitation patterns will help park managers allocate resources, develop infrastructure, and predict the demands of visitors. Previous studies have only validated temporal visitation patterns with big data and traditional survey data, while research on validation national park visitor demographics using mobile data is scant. This study compares SafeGraph data (a type of mobile Location-based Service data) and survey/count data, assessing visitor demographics and temporal visitation patterns in Yellowstone National Park. The comparison between two data sources suggests that SafeGraph data can serve as an additional and complementary source of information to traditional visitor use study/count data. However, biases of SafeGraph data, such as data at an aggregation level and data only in the United States, resulting in differences compared to traditional survey/count data. This study contributes to understanding visitors in national parks by validating a new data source
Hierarchical Alternating Least Squares Methods for Quaternion Nonnegative Matrix Factorizations
No full textIn this report, we discuss a simple model for RGB color and polarization images under a unified framework of quaternion nonnegative matrix factorization (QNMF) and present a hierarchical nonnegative least squares method to solve the factor matrices. The convergence analysis of the algorithm is discussed as well. We test the proposed method in the polarization image and color facial image representation. Compared to the state-of-the-art methods, the experimental results demonstrate the effectiveness of the hierarchical nonnegative least squares method for the QNMF model
Sketch-based skeleton-driven 2D animation and motion capture.
No full textThis research is concerned with the development of a set of novel sketch-based skeleton-driven 2D animation techniques, which allow the user to produce realistic 2D character animation efficiently. The technique consists of three parts: sketch-based skeleton-driven 2D animation production, 2D motion capture and a cartoon animation filter. For 2D animation production, the traditional way is drawing the key-frames by experienced animators manually. It is a laborious and time-consuming process. With the proposed techniques, the user only inputs one image ofa character and sketches a skeleton for each subsequent key-frame. The system then deforms the character according to the sketches and produces animation automatically. To perform 2D shape deformation, a variable-length needle model is developed, which divides the deformation into two stages: skeleton driven deformation and nonlinear deformation in joint areas. This approach preserves the local geometric features and global area during animation. Compared with existing 2D shape deformation algorithms, it reduces the computation complexity while still yielding plausible deformation results. To capture the motion of a character from exiting 2D image sequences, a 2D motion capture technique is presented. Since this technique is skeleton-driven, the motion of a 2D character is captured by tracking the joint positions. Using both geometric and visual features, this problem can be solved by ptimization, which prevents self-occlusion and feature disappearance. After tracking, the motion data are retargeted to a new character using the deformation algorithm proposed in the first part. This facilitates the reuse of the characteristics of motion contained in existing moving images, making the process of cartoon generation easy for artists and novices alike. Subsequent to the 2D animation production and motion capture,"Cartoon Animation Filter" is implemented and applied. Following the animation principles, this filter processes two types of
cartoon input: a single frame of a cartoon character and motion capture data from an image sequence. It adds anticipation and follow-through to the motion with related squash and stretch effect
In-line inspection of unpiggable buried live gas pipes using circumferential EMAT guided waves
No full textScale-up of B-doped diamond anode system for electrochemical oxidation of phenol simulated wastewater in batch mode
No full textScale-up of boron-doped diamond (BDD) anode system is critical to the practical application of electrochemical oxidation in bio-refractory organic wastewater treatment. In this study, the scale-up of BDD anode system was investigated on batch-mode electrochemical oxidation of phenol simulated wastewater. It was demonstrated that BDD anode system was successfully scaled up by 121 times without performance deterioration based on the COD and specific energy consumption (E(sp)) models in bath mode. The COD removal rate and E(sp) for the scaled-up BDD anode system through enlarging the total anode area while keeping similar configuration, remained at the similar level as those before being scaled up, under the same area/volume value, current density, retention time and wastewater characteristics. The COD and E(sp) models used to describe the smaller BDD anode system satisfactorily predicted the performance of the scaled-up BDD anode system. Under the suitable operating conditions, the COD of phenol simulated wastewater was reduced from 540 mgl(-1) to 130 mgl(-1) within 3 h with an Esp of only 34.76 kWh m(-3) in the scaled-up BDD anode system. These results demonstrate that BDD anode system is very promising in practical bio-refractory organic wastewater treatment. (C) 2011 Elsevier Ltd. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000295997000061&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701ElectrochemistrySCI(E)EI10ARTICLE259439-94475
Symmetric orthogonal approximation to symmetric tensors with applications to image reconstruction
No full textCopyright © 2018 John Wiley & Sons, Ltd. The main objective of this paper is to study an approximation of symmetric tensors by symmetric orthogonal decomposition. We propose and study an iterative algorithm to determine a symmetric orthogonal approximation and analyze the convergence of the proposed algorithm. Numerical examples are reported to demonstrate the effectiveness of the proposed algorithm. We also apply the proposed algorithm to represent correlated face images. We demonstrate better face image reconstruction results by combining principal components and symmetric orthogonal approximation instead of combining principal components and higher-order SVD results.link_to_subscribed_fulltex
Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization
No full text© 2018 Society for Industrial and Applied Mathematics. In this paper, we study orthogonal nonnegative matrix factorization. We demonstrate the coefficient matrix can be sparse and low-rank in the orthogonal nonnegative matrix factorization. By using these properties, we propose to use a sparsity and nuclear norm minimization for the factorization and develop a convex optimization model for finding the coefficient matrix in the factorization. Numerical examples including synthetic and real-world data sets are presented to illustrate the effectiveness of the proposed algorithm and demonstrate that its performance is better than other testing methods.link_to_subscribed_fulltex
Assessment of photoplethysmogram signal quality based on frequency domain and time series parameters
No full text- …
