scholarly journals RHYTHMICITY IN THE PROTOPLASMIC STREAMING OF A SLIME MOLD, PHYSARUM POLYCEPHALUM

1958 ◽  
Vol 41 (6) ◽  
pp. 1205-1222 ◽  
Author(s):  
Uichiro Kishimoto
Keyword(s):  
Gaussian Distribution ◽  
Electric Potential ◽  
Distribution Curve ◽  
Slime Mold ◽  
Protoplasmic Streaming ◽  
Arbitrary Phase ◽  
Wave Forms ◽  
The Mean ◽  
Phase Angles ◽  
Constant Period

The electric potential difference (1 to 15 mv.) between two loci of the slime mold connected with a strand of protoplasm changes rhythmically with the same period (60 to 180 seconds) as that of the back and forth protoplasmic streaming along the strand. Generally some phase difference is observed between them. Periods of the electric potential rhythm show a Gaussian distribution. Amplitudes give a somewhat different distribution curve. Wave forms are not always simple harmonic ones, but are distorted more or less. However, auto-correlation analysis proves that there is a dominant rhythm of a nearly constant period which coincides with the mean period of the Gaussian distribution curve. Calculations made on an assumption that the electric potential rhythm is the result of many elementary rhythms (i.e., same periodicity, arbitrary phase angles) distributed throughout the plasmodium, give a satisfactory coincidence with the observed distribution for the amplitude. The predominance of a rhythm of a nearly constant periodicity suggests the existence of well organized interactions among components of a contractile protein network, the rhythmic deformation of which is supposed to be responsible for the protoplasmic streaming and for the electric potential rhythm.

scholarly journals Efficient magic state factories with a catalyzed|CCZ⟩to2|T⟩transformation

Quantum ◽  
2019 ◽  
Vol 3 ◽  
pp. 135 ◽  
Author(s):  
Craig Gidney ◽  
Austin G. Fowler
Keyword(s):  
Fault Tolerant ◽  
Code Distance ◽  
Construction Techniques ◽  
Arbitrary Phase ◽  
Shor's Algorithm ◽  
The Mean ◽  
Toffoli Gates ◽  
Surface Code ◽  
The Cost ◽  
Phase Angles

We present magic state factory constructions for producing|CCZ⟩states and|T⟩states. For the|CCZ⟩factory we apply the surface code lattice surgery construction techniques described in \cite{fowler2018} to the fault-tolerant Toffoli \cite{jones2013, eastin2013distilling}. The resulting factory has a footprint of12d×6d(wheredis the code distance) and produces one|CCZ⟩every5.5dsurface code cycles. Our|T⟩state factory uses the|CCZ⟩factory's output and a catalyst|T⟩state to exactly transform one|CCZ⟩state into two|T⟩states. It has a footprint25%smaller than the factory in \cite{fowler2018} but outputs|T⟩states twice as quickly. We show how to generalize the catalyzed transformation to arbitrary phase angles, and note that the caseθ=22.5∘produces a particularly efficient circuit for producing|T⟩states. Compared to using the12d×8d×6.5d|T⟩factory of \cite{fowler2018}, our|CCZ⟩factory can quintuple the speed of algorithms that are dominated by the cost of applying Toffoli gates, including Shor's algorithm \cite{shor1994} and the chemistry algorithm of Babbush et al. \cite{babbush2018}. Assuming a physical gate error rate of10−3, our CCZ factory can produce∼1010states on average before an error occurs. This is sufficient for classically intractable instantiations of the chemistry algorithm, but for more demanding algorithms such as Shor's algorithm the mean number of states until failure can be increased to∼1012by increasing the factory footprint∼20%.

scholarly journals RHYTHMICITY IN THE PROTOPLASMIC STREAMING OF A SLIME MOLD, PHYSARUM POLYCEPHALUM

1958 ◽  
Vol 41 (6) ◽  
pp. 1223-1244 ◽  
Author(s):  
Uichiro Kishimoto
Keyword(s):  
Electric Potential ◽  
Atmospheric Pressure ◽  
Applied Pressure ◽  
Pressure Change ◽  
External Pressure ◽  
Slime Mold ◽  
Protoplasmic Streaming ◽  
Protoplasmic Movement ◽  
Made In

The electric potential difference (1 to 15 mv.) between two loci of the slime mold connected with a strand of protoplasm changes rhythmically with the same period (60 to 180 seconds) as that of back and forth protoplasmic streaming along the strand. When atmospheric pressure at a part of the plasmodium is increased (about 10 cm. H2O), the electric potential at this part becomes positive (0 to 20 mv.) to another part with a time constant of 2 to 15 minutes. If the atmospheric pressure at a part of the plasmodium is changed (about 10 cm. H2O) periodically, the electric potential rhythm also changes with the same period as that of the applied pressure change, and the amplitude of the former grows to a new level (i.e., forced oscillation). The electric potential rhythm, in this case, is generally delayed about 90° in phase angle from the external pressure change. The period of the electric potential rhythm which coincided with that of the pressure change is maintained for a while after stopping the application of the pressure change, if the period is not much different from the native flow rhythm. Such a pressure effect is brought about by the forced transport of protoplasm and is reversible as a rule. In the statistical analysis made by Kishimoto (1958) and in the rheological treatment made in the report, the rhythmic deformation of the contractile protein networks is supposed to be the cause of the protoplasmic flow along the strand and of the electric potential rhythm. The role of such submicroscopic networks in the protoplasm in various kinds of protoplasmic movement is emphasized.

Ca-Histochemistry in slime mold plasmodia

1978 ◽  
Vol 36 (2) ◽  
pp. 130-131
Author(s):  
Ulrich Dierkes
Keyword(s):  
Physarum Polycephalum ◽  
Carbon Coating ◽  
Freeze Drying ◽  
Freeze Fracture ◽  
Freeze Substitution ◽  
Uranyl Acetate ◽  
Slime Mold ◽  
Staining Procedure ◽  
Protoplasmic Streaming ◽  
Intracellular Ca

Calcium is supposed to play an important role in the control of protoplasmic streaming in slime mold plasmodia. The motive force for protoplasmic streaming is generated by the interaction of actin and myosin. This contraction is supposed to be controlled by intracellular Ca-fluxes similar to the triggering system in skeleton muscle. The histochemical localisation of calcium however is problematic because of the possible diffusion artifacts especially in aquous media.To evaluate this problem calcium localisation was studied in small pieces of shock frozen (liquid propane at -189°C) plasmodial strands of Physarum polycephalum, which were further processed with 3 different methods: 1) freeze substitution in ethanol at -75°C, staining in 100% ethanol with 1% uranyl acetate, and embedding in styrene-methacrylate. For comparison the staining procedure was omitted in some preparations. 2)Freeze drying at about -95°C, followed by immersion with 100% ethanol containing 1% uranyl acetate, and embedding. 3) Freeze fracture, carbon coating and SEM investigation at temperatures below -100° C.

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

scholarly journals The colorimetric properties of the spectrum

1931 ◽  
Vol 108 (759) ◽  
pp. 576-576 ◽  
Keyword(s):  
Distribution Curve ◽  
National Physical Laboratory ◽  
Group Of Seven ◽  
Physical Laboratory ◽  
Luminosity Functions ◽  
Direct Measurements ◽  
Energy Distribution Curve ◽  
The Mean ◽  
General Method

The paper describes an investigation carried out at the National Physical Laboratory to determine the colorimetric properties of a group of seven subjects as obtained from direct measurements of the trichromatic coefficients of the spectrum on a trichromatic colorimeter. The “spectral distribution curves of the primaries,” by means of which the colorimetric quality of a heterochromatic stimulus may be computed from its energy distribution curve, are obtained by combining the experimentally determined trichromatic coefficients with the International Standard visibility curve. This procedure is a simplification, applicable to the mean results of a normal group, of a general method by which the chromatic and luminosity functions of any subject or group of subjects can be determined from one set of observations. The general method is described in an Appendix.

A model for the effect of bias for cholesterol on the population at risk.

1988 ◽  
Vol 34 (11) ◽  
pp. 2256-2259 ◽  
Author(s):  
M H Kroll ◽  
M Ruddel ◽  
R J Elin
Keyword(s):  
At Risk ◽  
Standard Deviation ◽  
Reference Values ◽  
Gaussian Distribution ◽  
Population Distribution ◽  
Reference Value ◽  
Mean Value ◽  
The Body ◽  
Methodological Bias ◽  
The Mean

Abstract The location of the Reference Value for an analyte within the population distribution affects the magnitude of error due to methodological bias. Using the gaussian distribution, we evaluated the effects of systematic and proportional biases of the method (positive and negative), mean value, and standard deviation on the magnitude of error. We chose four Reference Values for cholesterol as a model. For a population with a mean of 2.0 and SD of 0.36 g of cholesterol per liter, a 3% positive proportional bias causes sixfold more error at the 50th percentile than at the 97.5th. In general, the error for a given bias (proportional or systematic) is greater for a Reference Value within the body than at the tails of the distribution. Further, the magnitude of the error varies as a function of the mean and standard deviation of the population.

scholarly journals On the influence of the spatial distribution of fine content in the hydraulic conductivity of sand-clay mixtures

2018 ◽  
Vol 22 (4) ◽  
pp. 239-249 ◽  
Author(s):  
William Mario Fuentes ◽  
Carolina Hurtado ◽  
Carlos Lascarro
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Numerical Model ◽  
Soil Sample ◽  
Gaussian Distribution ◽  
Geotechnical Engineering ◽  
Empirical Relation ◽  
Fine Content ◽  
The Mean

Sand-clay mixtures are one of the most usual types of soils in geotechnical engineering. These soils present a hydraulic conductivity which highly depends on the fine content. In this work, it will be shown, that not only the mean fine content of a soil sample affects its hydraulic conductivity, but also its spatial distribution within the sample. For this purpose, a set of hydraulic conductivity tests with sand-clay mixtures have been conducted to propose an empirical relation of the hydraulic conductivity depending on the fine content. Then, a numerical model of a large scaled hydraulic conductivity test is constructed. In this model, the heterogeneity of the fine content is simulated following a Gaussian distribution. The equivalent hydraulic conductivity resulting of the whole model is then computed and the influence of the spatial distribution of the fine content is evaluated. The results indicate that the hydraulic conductivity is not only related to the mean fine content, but also on its heterogeneity.

A New Microcontact Model Developed for Variable Fractal Dimension, Topothesy, Density of Asperity, and Probability Density Function of Asperity Heights

2006 ◽  
Vol 74 (4) ◽  
pp. 603-613 ◽  
Author(s):  
Jeng Luen Liou ◽  
Jen Fin Lin
Keyword(s):  
Fractal Dimension ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Distribution ◽  
Fractal Theory ◽  
Radius Of Curvature ◽  
The Mean ◽  
Contact Surfaces ◽  
Non Gaussian

In the present study, the fractal theory is applied to modify the conventional model (the Greenwood and Williamson model) established in the statistical form for the microcontacts of two contact surfaces. The mean radius of curvature (R) and the density of asperities (η) are no longer taken as constants, but taken as variables as functions of the related parameters including the fractal dimension (D), the topothesy (G), and the mean separation of two contact surfaces. The fractal dimension and the topothesy varied by differing the mean separation of two contact surfaces are completely obtained from the theoretical model. Then the mean radius of curvature and the density of asperities are also varied by differing the mean separation. A numerical scheme is thus developed to determine the convergent values of the fractal dimension and topothesy corresponding to a given mean separation. The topographies of a surface obtained from the theoretical prediction of different separations show the probability density function of asperity heights to be no longer the Gaussian distribution. Both the fractal dimension and the topothesy are elevated by increasing the mean separation. The density of asperities is reduced by decreasing the mean separation. The contact load and the total contact area results predicted by variable D, G*, and η as well as non-Gaussian distribution are always higher than those forecast with constant D, G*, η, and Gaussian distribution.

Sign in / Sign up

Export Citation Format

Share Document