On the existence of stimulus-event relations for sensory neurons: A statistical method

1980 ◽  
Vol 38 (4) ◽  
pp. 201-212 ◽  
Author(s):  
J. G. Pedersen
Keyword(s):  
Statistical Method ◽  
Sensory Neurons ◽  
Stimulus Event

Neural representation of the acoustic biotope: On the existence of stimulus-event relations for sensory neurons

1979 ◽  
Vol 32 (3) ◽  
pp. 175-185 ◽  
Author(s):  
A. M. H. J. Aertsen ◽  
J. W. T. Smolders ◽  
P. I. M. Johannesma
Keyword(s):  
Sensory Neurons ◽  
Neural Representation ◽  
Stimulus Event

Glucuronyl 3-sulfate occurs exclusively on distal unmyelinated terminals of selected sensory neurons in the peripheral nervous system

1995 ◽  
Vol 53 ◽  
pp. 958-959
Author(s):  
S.S. Spicer ◽  
B.A. Schulte
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Cerebral Cortex ◽  
Peripheral Nervous System ◽  
Sensory Neurons ◽  
Sensory Nerves ◽  
Tissue Antigens ◽  
The Central Nervous System ◽  
The Brain ◽  
Leu 7

Generation of monoclonal antibodies (MAbs) against tissue antigens has yielded several (VC1.1, HNK- 1, L2, 4F4 and anti-leu 7) which recognize the unique sugar epitope, glucuronyl 3-sulfate (Glc A3- SO4). In the central nervous system, these MAbs have demonstrated Glc A3-SO4 at the surface of neurons in the cerebral cortex, the cerebellum, the retina and other widespread regions of the brain.Here we describe the distribution of Glc A3-SO4 in the peripheral nervous system as determined by immunostaining with a MAb (VC 1.1) developed against antigen in the cat visual cortex. Outside the central nervous system, immunoreactivity was observed only in peripheral terminals of selected sensory nerves conducting transduction signals for touch, hearing, balance and taste. On the glassy membrane of the sinus hair in murine nasal skin, just deep to the ringwurt, VC 1.1 delineated an intensely stained, plaque-like area (Fig. 1). This previously unrecognized structure of the nasal vibrissae presumably serves as a tactile end organ and to our knowledge is not demonstrable by means other than its selective immunopositivity with VC1.1 and its appearance as a densely fibrillar area in H&E stained sections.

The elements of statistical method.

1916 ◽  
Author(s):  
Willford I. King
Keyword(s):  
Statistical Method

Considerations when choosing a statistical method for data analysis

2020 ◽  
Vol 22 (3) ◽  
pp. 20-29
Author(s):  
Duncan McKechnie ◽  
Murray Fisher
Keyword(s):  
Data Analysis ◽  
Statistical Method

Income and Life Quality of Farmer Households Suffering Social Exclusion

2012 ◽  
pp. 63-87
Author(s):  
Anh Mai Ngoc ◽  
Ha Do Thi Hai ◽  
Huyen Nguyen Thi Ngoc
Keyword(s):  
Labor Market ◽  
Statistical Method ◽  
Social Exclusion ◽  
Central Government ◽  
Life Quality ◽  
Economic Aspect ◽  
Local Authorities ◽  
The Impact

This study uses descriptive statistical method to analyze the income and life qual- ity of 397 farmer households who are suffering social exclusion in an economic aspect out of a total of 725 households surveyed in five Northern provinces of Vietnam in 2010. The farmers’ opinions of the impact of the policies currently prac- ticed by the central government and local authorities to give them access to the labor market are also analyzed in this study to help management officers see how the poli- cies affect the beneficiaries so that they can later make appropriate adjustments.

PROPENSITY SCORE MATCHING AS A MODERN STATISTICAL METHOD FOR BIAS CONTROL IN OBSERVATIONAL STUDIES WITH BINARY OUTCOME

Human Ecology ◽  
2016 ◽  
pp. 50-64 ◽  
Author(s):  
A. M. Grjibovski ◽  
S. V. Ivanov ◽  
М. A. Gorbatova ◽  
A. A. Dyussupov
Keyword(s):  
Propensity Score ◽  
Statistical Method ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Binary Outcome

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

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