Calcium signaling explained in new model

The Hitachi Model HS-8 Electron Microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100061203 ◽

1967 ◽

Vol 25 ◽

pp. 238-239

Author(s):

H. Akabori ◽

K. Nishiwaki ◽

K. Yoneta

Keyword(s):

Electron Microscope ◽

New Model ◽

Predecessor Model

By improving the predecessor Model HS- 7 electron microscope for the purpose of easier operation, we have recently completed new Model HS-8 electron microscope featuring higher performance and ease of operation.

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516: A Simplified Method to Estimate the Absolute Renal Uptake of 99MTC-DMSA: Evaluation with a New Model using the Radioactivity of Nephrectomy Specimens as Reference

The Journal of Urology ◽

10.1016/s0022-5347(18)34756-6 ◽

2005 ◽

Vol 173 (4S) ◽

pp. 140-141

Author(s):

Mariana Lima ◽

Celso D. Ramos ◽

Sérgio Q. Brunetto ◽

Marcelo Lopes de Lima ◽

Carla R.M. Sansana ◽

...

Keyword(s):

New Model ◽

Renal Uptake ◽

Simplified Method ◽

The Absolute

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476 Chronic left atrial volume and pressure overload in the goat: a new model of sustained atrial fibrillation

EP Europace ◽

10.1016/s1099-5129(05)80314-9 ◽

2005 ◽

Vol 7 ◽

pp. 107-107

Keyword(s):

Atrial Fibrillation ◽

Left Atrial ◽

Pressure Overload ◽

Left Atrial Volume ◽

Atrial Volume ◽

New Model

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Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000261 ◽

2014 ◽

Vol 61 (5) ◽

pp. 402-415 ◽

Cited By ~ 9

Author(s):

Thorsten Meiser

Keyword(s):

Cognitive Processes ◽

Model Complexity ◽

Multinomial Model ◽

Stochastic Dependence ◽

New Model ◽

Multinomial Processing Tree ◽

Cognitive States ◽

The Family ◽

Tree Models ◽

Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

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