Spectral Linewidth and Time Interval Distribution of Photoelectric Pulses

1975 ◽  
Vol 14 (S1) ◽  
pp. 97 ◽  
Author(s):  
Atsushi Kikkawa ◽  
Kunihiko Ohkubo ◽  
Norihito Suzuki
Keyword(s):  
Time Interval ◽  
Spectral Linewidth ◽  
Interval Distribution

Single-channel analysis of a K channel at basolateral membrane of rabbit proximal convoluted tubule

1988 ◽  
Vol 254 (1) ◽  
pp. F105-F113 ◽  
Author(s):  
L. Parent ◽  
J. Cardinal ◽  
R. Sauve
Keyword(s):  
Single Channel ◽  
Basolateral Membrane ◽  
Proximal Convoluted Tubule ◽  
Time Interval ◽  
Distribution Analysis ◽  
Patch Clamp Technique ◽  
Bathing Medium ◽  
Patch Pipette ◽  
K Concentration ◽  
Interval Distribution

The basolateral membrane of the rabbit proximal convoluted tubule (PCT) is known to be largely permeable to K ions. The patch-clamp technique was used to investigate the molecular basis of this K permeability. At room temperature and with a high-K solution (127 mM) in both the bathing medium and the patch pipette, current jumps associated with an inward-rectifying channel could be detected in every active cell-attached experiment. When the K concentration in the pipette was changed from 200 to 5 mM KCl (NaCl replacement), the single-channel conductance for inward currents changed from 54 to 10 pS. The observed shift in the zero current potential measured as a function of the patch pipette K concentration could be fitted using the Goldman-Hodgkin-Katz equation with a permeability ratio PNa/PK = 0.06. The channel was found to be moderately voltage dependent (e-fold per 56 mV depolarization). For instance, the open-channel probability (Po) increased from 0.06 to 0.16 following a membrane depolarization from -50 to +50 mV. A time interval distribution analysis showed for the open state a dominant single time constant of 14 and 10 ms at 50 and -50 mV, respectively. Two time constants equal to 1 (flickering) and 26 ms at +50 mV and to 0.6 and 300 ms at -50 mV were obtained for the closed-state interval distribution. Based on this analysis, it was concluded that the decrease of Po at negative potentials was due more to the appearance of prolonged silent periods than from a change in the channel mean open time.

Evaluation of fluctuating dead times by time interval distribution measurement

1994 ◽  
Vol 43 (2) ◽  
pp. 164-169 ◽  
Author(s):  
R. Igarashi ◽  
S. Fukushima ◽  
Y. Yamada ◽  
T. Uemura ◽  
A. Saitoh ◽  
...  
Keyword(s):  
Time Interval ◽  
Distribution Measurement ◽  
Interval Distribution

Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

2018 ◽  
Vol 72 (10) ◽  
pp. 1133-1138 ◽  
Author(s):  
Nur Syamsi Syam ◽  
Seongjin Maeng ◽  
Myo Gwang Kim ◽  
Soo Yeon Lim ◽  
Sang Hoon Lee
Keyword(s):  
Dead Time ◽  
Statistical Parameter ◽  
Parameter Study ◽  
Time Interval ◽  
Interval Distribution

Flaring time interval distribution and spatial correlation of major X-ray solar flares

2001 ◽  
Vol 106 (A12) ◽  
pp. 29951-29961 ◽  
Author(s):  
Y.-J. Moon ◽  
G. S. Choe ◽  
H. S. Yun ◽  
Y. D. Park
Keyword(s):  
Spatial Correlation ◽  
Solar Flares ◽  
Time Interval ◽  
X Ray ◽  
Interval Distribution

Two-fold time interval distribution of photoelectric pulses of gaussian-lorentzian light

1977 ◽  
Vol 20 (3) ◽  
pp. 325-329 ◽  
Author(s):  
H. Sonoda ◽  
H. Sato ◽  
A. Kikkawa ◽  
N. Suzuki
Keyword(s):  
Time Interval ◽  
Interval Distribution

scholarly journals On the relationship between serial interval, infectiousness profile and generation time

2020 ◽  
Author(s):  
Sonja Lehtinen ◽  
Peter Ashcroft ◽  
Sebastian Bonhoeffer
Keyword(s):  
Symptom Onset ◽  
Generation Time ◽  
Time Distribution ◽  
Contact Tracing ◽  
Time Interval ◽  
Single Individual ◽  
Serial Interval ◽  
The Impact ◽  
The Relationship ◽  
Interval Distribution

The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing the distribution of generation times (time interval between the points of infection of an infector and infectee in a transmission pair) requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; the generation time distribution is therefore often estimated based on the serial interval distribution (distribution of time intervals between symptom onset of an infector and an infectee). This estimation follows one of two approaches: i) approximating the generation time distribution by the serial interval distribution; or ii) deriving the generation time distribution from the serial interval and incubation period (time interval between infection and symptom onset in a single individual) distributions. These two approaches make different -- and not always explicitly stated -- assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.

Problem on a digitization of a time interval distribution

1984 ◽  
Vol 55 (11) ◽  
pp. 1876-1877
Author(s):  
Ryuji Igarashi ◽  
Yuuki Yamada ◽  
Yuichi Narita
Keyword(s):  
Time Interval ◽  
Interval Distribution
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