Conferences related to Pathogens

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2014 IEEE Sensors Applications Symposium (SAS)

SAS provides a forum for sensor users and developers to meet and exchange information about novel sensors and emergent sensor applications. The main purpose of SAS is to collaborate and network with scientists,engineers, researchers, developers, and end-users through formal technical presentations, workshops, and informal interactions.

  • 2013 IEEE Sensors Applications Symposium (SAS)

    SAS 2013 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS 2013 include: Biosensors /Arrays, MEMS and Nanosensors, Sensor Networking, Smart Sensors and Standards, Virtual Sensors, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote Sensing, Homeland security, and Commercial Development.

  • 2012 IEEE Sensors Applications Symposium (SAS)

    SAS 2012 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS 2012 include: Biosensors /Arrays, MEMS and Nanosensors, Sensor Networking, Smart Sensors and Standards, Virtual Sensors, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote Sensing, Homeland security, and Commercial Development.

  • 2011 IEEE Sensors Applications Symposium (SAS)

    SAS -2010 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS -2010 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, Integrated System Health Management (ISHM), Multisensor Data Fusion.

  • 2010 IEEE Sensors Applications Symposium (SAS)

    SAS-2010 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS-2010 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote

  • 2009 IEEE Sensors Applications Symposium (SAS)

    SAS-2009 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS-2009 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, X-ray detectors and imaging, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestr


2013 8th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

2013 IEEE NEMS is the 8th annual International Conference on Nano/Micro Engineered and Molecular Systems which started in 2006. It covers Nano science and technology, Micro/nanofluidics and Bio chip, Micro/nano fabrication & metrology, Micro/Nano sensors, actuators and systemd, Flexible MEMS and printed electronics, Carbon Nanotube and Graphene based devices, etc.


2012 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

Bioinformatics, Computational Biology, Biomedical Engineering


2006 Distributed Diagnosis & Home Healthcare (D2H2)


2006 Emerging Information Technology Conference (EITC)



Periodicals related to Pathogens

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Engineering Management, IEEE Transactions on

Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.


Sensors Journal, IEEE

The Field of Interest of the IEEE Sensors Journal is the science and applications of sensing phenomena, including theory, design, and application of devices for sensing and transducing physical, chemical, and biological phenomena. The emphasis is on the electronics, physics, biology, and intelligence aspects of sensors and integrated sensor-actuators. (IEEE Guide for Authors) (The fields of interest of the IEEE ...




Xplore Articles related to Pathogens

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A causal probabilistic network for optimal treatment of bacterial infections

L. Leibovici; M. Fishman; H. C. Schonheyder; C. Riekehr; B. Kristensen; I. Shraga; S. Andreassen IEEE Transactions on Knowledge and Data Engineering, 2000

The fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The ...


Study on Vibrio Disease of Pseudosciaena crocea and Recognition of the Disease Resistant Group Based on Automatic Detection System for Hemogram

Jiang Zheng; Renxie Wu; Jun Wang; Yongquan Su; Yubao Li 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Chemical medicines and antibiotics can kill pathogenic microorganisms in aquiculture animals, but they will lead to environmental pollution and serious food safety problems. Therefore, study on the selection for the disease resistant group of animals is necessary to solve the problem and promote the development of aquaculture. From the view of the relationship between fishes and pathogenic microorganisms, the present ...


Stochastic Immune Layer Optimizer: efficient tool for optimization of combustion process in a boiler

G. Jarmoszewicz; K. Swirski; K. Wojdan 2006 1st Bio-Inspired Models of Network, Information and Computing Systems, 2006

The article presents a method for optimization of combustion process in a boiler. This solution is based on the artificial immune system. A layered optimization system is used, to minimize CO and NOx emission. This solution is implemented in real power plant. The results from this implementation have been presented. They confirm that presented solution is effective and usable in ...


Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network

Jira Jindalertudomdee; Morihiro Hayashida; Jiangning Song; Tatsuya Akutsu 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), 2016

Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's ...


Modelling the growth domain of Clostridium botulinum via kernel survival analysis

R. J. Foxall; G. C. Cawley; M. W. Peck Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

Clostridium botulinum is a bacterium present in the raw ingredients of many foods. It produces a powerful neurotoxin as part of its growth process, that can prove fatal when doses as small as 30ng are consumed. It is therefore vital to be able to accurately determine the food processing and storage conditions where toxin production is possible, known as the ...


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Educational Resources on Pathogens

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eLearning

A causal probabilistic network for optimal treatment of bacterial infections

L. Leibovici; M. Fishman; H. C. Schonheyder; C. Riekehr; B. Kristensen; I. Shraga; S. Andreassen IEEE Transactions on Knowledge and Data Engineering, 2000

The fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The ...


Study on Vibrio Disease of Pseudosciaena crocea and Recognition of the Disease Resistant Group Based on Automatic Detection System for Hemogram

Jiang Zheng; Renxie Wu; Jun Wang; Yongquan Su; Yubao Li 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Chemical medicines and antibiotics can kill pathogenic microorganisms in aquiculture animals, but they will lead to environmental pollution and serious food safety problems. Therefore, study on the selection for the disease resistant group of animals is necessary to solve the problem and promote the development of aquaculture. From the view of the relationship between fishes and pathogenic microorganisms, the present ...


Stochastic Immune Layer Optimizer: efficient tool for optimization of combustion process in a boiler

G. Jarmoszewicz; K. Swirski; K. Wojdan 2006 1st Bio-Inspired Models of Network, Information and Computing Systems, 2006

The article presents a method for optimization of combustion process in a boiler. This solution is based on the artificial immune system. A layered optimization system is used, to minimize CO and NOx emission. This solution is implemented in real power plant. The results from this implementation have been presented. They confirm that presented solution is effective and usable in ...


Host-Pathogen Protein Interaction Prediction Based on Local Topology Structures of a Protein Interaction Network

Jira Jindalertudomdee; Morihiro Hayashida; Jiangning Song; Tatsuya Akutsu 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), 2016

Understanding how pathogen's proteins interact with its host's proteins is the key concept for understanding pathogen's infection mechanism, which can lead to the discovery of improved therapeutics for treating infectious diseases. Several studies suggest that proteins from various pathogens tend to interact with human proteins involved in the same biological pathway. This implies that pathogens are inclined to target host's ...


Modelling the growth domain of Clostridium botulinum via kernel survival analysis

R. J. Foxall; G. C. Cawley; M. W. Peck Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

Clostridium botulinum is a bacterium present in the raw ingredients of many foods. It produces a powerful neurotoxin as part of its growth process, that can prove fatal when doses as small as 30ng are consumed. It is therefore vital to be able to accurately determine the food processing and storage conditions where toxin production is possible, known as the ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • Comparative Gene Prediction using Conditional Random Fields

    Computational gene prediction using generative models has reached a plateau, with several groups converging to a generalized hidden Markov model (GHMM) incorporating phylogenetic models of nucleotide sequence evolution. Further improvements in gene calling accuracy are likely to come through new methods that incorporate additional data, both comparative and species specific. Conditional Random Fields (CRFs), which directly model the conditional probability P(y|x) of a vector of hidden states conditioned on a set of observations, provide a unified framework for combining probabilistic and non- probabilistic information and have been shown to outperform HMMs on sequence labeling tasks in natural language processing. We describe the use of CRFs for comparative gene prediction. We implement a model that encapsulates both a phylogenetic-GHMM (our baseline comparative model) and additional non- probabilistic features. We tested our model on the genome sequence of the fungal human pathogen Cryptococcus neoformans. Our baseline comparative model displays accuracy comparable to the the best available gene prediction tool for this organism. Moreover, we show that discriminative training and the incorporation of non-probabilistic evidence significantly improve performance. Our software implementation, Conrad, is freely available with an open source license at http://www.broad.mit.edu/annotation/conrad/.



Standards related to Pathogens

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Jobs related to Pathogens

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