scholarly journals Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

2016 ◽  
Author(s):  
Natalie Sauerwald ◽  
She Zhang ◽  
Carl Kingsford ◽  
Ivet Bahar
Keyword(s):  
Long Range ◽  
Network Model ◽  
Structural Dynamics ◽  
Large Scale ◽  
Multiple Scales ◽  
Structural Basis ◽  
Unified Framework ◽  
Modeling Framework ◽  
Chromatin Dynamics ◽  
Genome Wide

AbstractUnderstanding the three-dimensional (3D) architecture of the chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to have a glimpse into the 3D genome organization and identify topologically associated domains (TADs), but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (> 50 megabases) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to the task of modeling chromatin dynamics using Hi-C contact data. We show that the GNM can identify structural dynamics at multiple scales: it can quantify the fluctuations in the positions of gene loci, find large genomic compartments and smaller TADs that undergo en-bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with DNase-seq and ATAC-seq measurements on accessibility, the previously identified A and B compartments of chromatin structure, and pairs of interacting loci identified by ChIA-PET. We describe a method to use the GNM to identify novel cross-correlated distal domains (CCDDs) representing regions of long-range dynamic coupling and show that CCDDs are often associated with increased gene coexpression using a large-scale analysis of 212 expression experiments. Together, these results show that GNM provides a mathematically well-founded unified framework for assessing chromatin dynamics and the structural basis of genome-wide observations.

scholarly journals NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics

2017 ◽  
Author(s):  
P. Sanz-Leon ◽  
P. A. Robinson ◽  
S. A. Knock ◽  
P. M. Drysdale ◽  
R. G. Abeysuriya ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Brain Activity ◽  
Field Potential ◽  
Neural Field ◽  
Neural Systems ◽  
Unified Framework ◽  
Plain Text ◽  
Wide Range ◽  
Key Aspects

AbstractA user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

scholarly journals Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

2017 ◽  
Vol 45 (7) ◽  
pp. 3663-3673 ◽  
Author(s):  
Natalie Sauerwald ◽  
She Zhang ◽  
Carl Kingsford ◽  
Ivet Bahar
Keyword(s):  
Long Range ◽  
Network Model ◽  
Elastic Network Model ◽  
Elastic Network ◽  
Genome Wide

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Finite cell method for functionally graded materials based on V-models and homogenized microstructures

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Wassermann ◽  
Nina Korshunova ◽  
Stefan Kollmannsberger ◽  
Ernst Rank ◽  
Gershon Elber
Keyword(s):  
Functionally Graded Materials ◽  
Large Scale ◽  
Functionally Graded ◽  
Material Behavior ◽  
Modeling Framework ◽  
Finite Cell Method ◽  
Geometric Framework ◽  
Graded Materials ◽  
Cell Method ◽  
Finite Cell

AbstractThis paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach.

scholarly journals Structural basis of long-range to short-range synaptic transition in NHEJ

Nature ◽  
2021 ◽  
Author(s):  
Siyu Chen ◽  
Linda Lee ◽  
Tasmin Naila ◽  
Susan Fishbain ◽  
Annie Wang ◽  
...  
Keyword(s):  
Long Range ◽  
Short Range ◽  
Structural Basis
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