The Experts below are selected from a list of 6009 Experts worldwide ranked by ideXlab platform
I.-jeng Wang - One of the best experts on this subject based on the ideXlab platform.
-
2005 Special Issue: Bayesian model selection for mining mass spectrometry data
Neural Networks, 2005Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.
-
Bayesian model selection for mining mass spectrometry data
2005Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.
-
Using domain knowledge to constrain structure learning in a Bayesian bioagent detector
Proceedings. 2005 IEEE International Joint Conference on Neural Networks 2005., 1Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A novel procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.
Oliver Hayden - One of the best experts on this subject based on the ideXlab platform.
-
Sensor strategies for Microorganism Detection—from physical principles to imprinting procedures
Analytical and Bioanalytical Chemistry, 2003Co-Authors: Franz L. Dickert, Peter Lieberzeit, Oliver HaydenAbstract:Detecting cells and Microorganisms in different matrices is becoming an increasingly important task in a variety of fields including bioprocess control, food technology, health care, and environmental analysis. In this review, fast on-line Detection methods for this purpose are presented including different recognition and transducer strategies.
-
Sensor strategies for Microorganism Detection--from physical principles to imprinting procedures.
Analytical and bioanalytical chemistry, 2003Co-Authors: Franz L. Dickert, Peter Lieberzeit, Oliver HaydenAbstract:Detecting cells and Microorganisms in different matrices is becoming an increasingly important task in a variety of fields including bioprocess control, food technology, health care, and environmental analysis. In this review, fast on-line Detection methods for this purpose are presented including different recognition and transducer strategies.
Byoung Chan Kim - One of the best experts on this subject based on the ideXlab platform.
-
Fast and continuous Microorganism Detection using aptamer-conjugated fluorescent nanoparticles on an optofluidic platform
Biosensors & bioelectronics, 2014Co-Authors: Jinyang Chung, Jae Hee Jung, Joon Sang Kang, Jongsoo Jurng, Byoung Chan KimAbstract:Fast and accurate pathogen Detection in aquatic environments is challenging in many biomedical studies and microbial diagnostic applications. In this study, we developed a real-time, continuous, and non-destructive single cell Detection method using target specific aptamer-conjugated fluorescent nanoparticles (A-FNPs) and an optofluidic particle-sensor platform. A-FNPs selectively bound to the surfaces of target bacteria (Escherichia coli) and labeled them with high affinity and selectivity so that target bacteria can be countable particles in an optofluidic particle-sensor. A-FNP-labeled target bacterial complexes were detected by the optofluidic particle-sensing system, which provides rapid and continuous single-cell Detection. A-FNPs selectively bound to E. coli with a dissociation constant of 0.83 nM, but did not bind Enterobacter aerogenes or Citrobacter freundii strains, which lacked affinity for the aptamer used. We demonstrated that our optofluidic device achieves a Detection throughput of ~100 particles per second with high accuracy (~85%) in detecting single bacterial cells conjugated with A-FNPs. This approach can be immediately extended to the real-time, high-throughput Detection of other Microorganisms such as viruses that are selectively conjugated with A-FNPs. Collectively, these data suggest that optofluidic systems are widely applicable for the fast and continuous Detection of microbial cells.
-
A sensitive method to detect Escherichia coli based on immunomagnetic separation and real-time PCR amplification of aptamers.
Biosensors & bioelectronics, 2009Co-Authors: Hye-jin Lee, Byoung Chan Kim, Kyung Woo Kim, Young Keun Kim, Jungbae KimAbstract:Aptamers, single-stranded nucleic acids, provide a unique opportunity as amplifiable molecules using polymerase chain reaction (PCR) as well as recognition molecules like antibodies. We report a highly sensitive Detection of Escherichia coli by taking advantage of the aptamer amplification as well as the specific binding of aptamers onto E. coli. This unique approach consists of three steps. First, the target E. coli was captured by antibody-conjugated magnetic beads. Second, the RNA aptamers were bound onto the surface of captured E. coli in a sandwich way. Finally, the heat-released aptamers were amplified by using real-time reverse-transcriptase-PCR (RT-PCR). The aptamer amplification in this approach has enabled a sensitive Detection of Microorganisms, such as the Detection of 10 E. coli in 1 ml sample. When compared to the amplification of nucleic acids extracted from the target Microorganisms, this approach can not only prevent the loss of target nucleic acids during the sample preparation by obviating the necessity of cell lysis, but also provide an additional mechanism of signal amplification due to the binding of many aptamers to the surface of each E. coli. Detection of E. coli in this approach showed a wide dynamic range from 10(1) to 10(7)E. coli per ml, which can be explained by the exponential amplification of aptamers. This report has demonstrated, for the first time, the effective use of aptamer amplification in the development of sensitive Microorganism Detection. It is anticipated that the present approach will be easily expanded and employed in various types of Microorganism Detection.
Da-wen Sun - One of the best experts on this subject based on the ideXlab platform.
-
Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview.
Comprehensive reviews in food science and food safety, 2018Co-Authors: Kaiqiang Wang, Da-wen SunAbstract:Microorganism contamination and foodborne disease outbreaks are of public concern worldwide. As such, the food industry requires rapid and nondestructive methods to detect Microorganisms and to control food quality. However, conventional methods such as culture and colony counting, polymerase chain reaction, and immunoassay approaches are laborious, time-consuming and require trained personnel. Therefore, the emergence of rapid analytical methods is essential. This review introduces 6 spectroscopic and spectral imaging techniques that apply infrared spectroscopy, surface-enhanced Raman spectroscopy, terahertz time-domain spectroscopy, laser-induced breakdown spectroscopy, hyperspectral imaging, and multispectral imaging for Microorganism Detection. Recent advances of these technologies from 2011 to 2017 are outlined. Challenges in the application of these technologies for Microorganism Detection in food matrices are addressed. These emerging spectroscopic and spectral imaging techniques have the potential to provide rapid and nondestructive Detection of Microorganisms. They should also provide complementary information to enhance the performance of conventional methods to prevent disease outbreaks and food safety problems.
Anshu Saksena - One of the best experts on this subject based on the ideXlab platform.
-
2005 Special Issue: Bayesian model selection for mining mass spectrometry data
Neural Networks, 2005Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.
-
Bayesian model selection for mining mass spectrometry data
2005Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.
-
Using domain knowledge to constrain structure learning in a Bayesian bioagent detector
Proceedings. 2005 IEEE International Joint Conference on Neural Networks 2005., 1Co-Authors: Anshu Saksena, Dennis Lucarelli, I.-jeng WangAbstract:A novel procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to Microorganism Detection from mass spectrometry data.