In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this ...
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In this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cell’s p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events.
As a result of the ongoing Syrian civil war, almost 3 million refugees moved to Turkey since 2011 because of security reasons. However, the government operated refugee camps have been largely inadequate to accommodate...
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作者：
Kiyoshi KanazawaDidier SornetteFaculty of Engineering
Information and Systems University of Tsukuba Tennodai Tsukuba Ibaraki 305-8573 Japan and JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Management
Technology and Economics ETH Zurich Zurich 8092 Switzerland and Institute of Risk Analysis Prediction and Management (Risks-X) Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen 518055 China
The origin(s) of the ubiquity of probability distribution functions with power law tails is still a matter of fascination and investigation in many scientific fields from linguistic, social, economic, computer science...
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The origin(s) of the ubiquity of probability distribution functions with power law tails is still a matter of fascination and investigation in many scientific fields from linguistic, social, economic, computer sciences to essentially all natural sciences. In parallel, self-excited dynamics is a prevalent characteristic of many systems, from the physics of shot noise and intermittent processes, to seismicity, financial and social systems. Motivated by activation processes of the Arrhenius form, we bring the two threads together by introducing a general class of nonlinear self-excited point processes with fast-accelerating intensities as a function of “tension.” Solving the corresponding master equations, we find that a wide class of such nonlinear Hawkes processes have the probability distribution functions of their intensities described by a power law on the condition that (i) the intensity is a fast-accelerating function of tension, (ii) the distribution of marks is two sided with nonpositive mean, and (iii) it has fast-decaying tails. In particular, Zipf’s scaling is obtained in the limit where the average mark is vanishing. This unearths a novel mechanism for power laws including Zipf’s law, providing a new understanding of their ubiquity.
Industry 4.0 is a transformative initiative that integrates various technologies and reshapes industrial processes, production methods, and business models. However, forecasting future events within this paradigm shif...
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Industry 4.0 is a transformative initiative that integrates various technologies and reshapes industrial processes, production methods, and business models. However, forecasting future events within this paradigm shift presents significant challenges. Predicting the cycle time for new product development (NPD) in a dynamic and competitive environment, especially in a highly globalized market driven by innovation, is crucial. Previous research has shown that prototype manufacturing lead times are key parameters for predicting NPD times-to-market. This study investigates the predictive capabilities of artificial intelligence algorithms in estimating manufacturing lead times under varying part geometries and materials at an aerospace and medical equipment company. By leveraging predictive analysis and machine learning techniques, specifically Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) algorithms, the proposed methodology demonstrates its efficacy and variations. The results show that top-performing models achieve an accuracy rate exceeding 87 % and an average absolute error of less than one day, which have significant practical benefits for project production planners. They can utilize the most popular AI frameworks on easier-to-use programming platforms to estimate the time required to manufacture their prototypes, predict their new product development (NPD) cycle times, and negotiate lead times for in-house and third-party manufacturing more effectively, thereby improving project planning and delivery.
The repertoire of neurons and their progenitors depends on their location along the antero-posterior and dorso-ventral axes of the neural tube. To model these axes, we designed the Dual Orthogonal-Morphogen Assisted P...
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Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the ...
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Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to continuous spaces, we focus on policy sear...
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With the increasing demand for heterogeneous Unmanned Aerial Vehicle (UAV) swarms to perform complex tasks in urban environments, system design now faces major challenges, including efficient semantic understanding, f...
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The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the "cloud" will be substituted by the "crowd" where model training is brought to the ...
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