Proteomic signals in modular transcriptional cascades: A discrete time and cellular automaton approach

2010 ◽  
Vol 239 (12) ◽  
pp. 967-971 ◽  
Author(s):  
A.S. Carstea ◽  
A.T. Grecu ◽  
D. Grecu
Keyword(s):  
Cellular Automaton ◽  
Discrete Time ◽  
Transcriptional Cascades

MEMRISTOR CELLULAR AUTOMATA AND MEMRISTOR DISCRETE-TIME CELLULAR NEURAL NETWORKS

2009 ◽  
Vol 19 (11) ◽  
pp. 3605-3656 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cellular Automaton ◽  
Discrete Time ◽  
Cellular Neural Network ◽  
Brain Functions ◽  
Rsa Algorithm ◽  
Logical Operations ◽  
Almost All ◽  
Higher Brain Functions

In this paper, we design a cellular automaton and a discrete-time cellular neural network (DTCNN) using nonlinear passive memristors. They can perform a number of applications, such as logical operations, image processing operations, complex behaviors, higher brain functions, RSA algorithm, etc. By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of memristor cellular automaton. Furthermore, it can perform more than one function at the same time, that is, it allows multitasking.

Epidemic dynamics: discrete-time and cellular automaton models

2003 ◽  
Vol 328 (1-2) ◽  
pp. 13-22 ◽  
Author(s):  
R. Willox ◽  
B. Grammaticos ◽  
A.S. Carstea ◽  
A. Ramani
Keyword(s):  
Cellular Automaton ◽  
Discrete Time ◽  
Epidemic Dynamics

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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