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.
Charge density waves are emergent quantum states that spontaneously reduce crystal symmetry, drive metal-insulator transitions, and precede superconductivity. In low-dimensions, distinct quantum states arise, however,...
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Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, resea...
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This study investigates and compares the impact of different etching techniques on the fabrication of GaN high electron mobility transistors (HEMTs) between the inductively coupled plasma reactive ion etching (ICP-RIE...
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Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with ...
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Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge: assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N−1)/2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge: What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census track and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the +1 state and a node which remained safe is in the −1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-st
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered seque...
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Ultrafast (femtosecond pulse) laser irradiation provides unique laser-material interactions associated with high instantaneous electric fields and short timescales that lead to a nonequilibrium state between excited e...
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Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap >4.0 eV), such as boron nitride (BN) and diamond, hold significant importance for the development of high-power electronics in the next gen...
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We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.
ISBN:
(纸本)9781957171258
We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.
This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal compo...
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ISBN:
(数字)9781728110806
ISBN:
(纸本)9781728110813
This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.
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