scholarly journals An interactive deep learning-based approach reveals mitochondrial cristae topologies

2021 ◽  
Author(s):  
Shogo Suga ◽  
Koki Nakamura ◽  
Bruno M Humbel ◽  
Hiroki Kawai ◽  
Yusuke Hirabayashi
Keyword(s):  
Deep Learning ◽  
Electron Tomography ◽  
Prediction Method ◽  
Regulatory Function ◽  
Tubular Structures ◽  
Mitochondrial Membranes ◽  
Accurate Analysis ◽  
Human In The Loop ◽  
Serial Scanning ◽  
Analysis Platform

Outer and inner mitochondrial membranes are highly specialized structures with distinct functional properties. Reconstructing complex 3D ultrastructural features of mitochondrial membranes at the nanoscale requires analysis of large volumes of serial scanning electron tomography data. While deep-learning-based methods improved in sophistication recently, time-consuming human intervention processes remain major roadblocks for efficient and accurate analysis of organelle ultrastructure. In order to overcome this limitation, we developed a deep-learning image analysis platform called Python-based Human-In-the-LOop Workflows (PHILOW). Our implementation of an iterative segmentation algorithm and Three-Axis-Prediction method not only improved segmentation speed, but also provided unprecedented ultrastructural detail of whole mitochondria and cristae. Using PHILOW, we found that 42% of cristae surface exhibits tubular structures that are not recognizable in light microscopy and 2D electron microscopy. Furthermore, we unraveled a fundamental new regulatory function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in controlling the balance between lamellar versus tubular cristae subdomains.

scholarly journals Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles

Nanoscale ◽  
2021 ◽  
Author(s):  
Alexander Skorikov ◽  
Wouter Heyvaert ◽  
Wiebke Albrecht ◽  
Daan Pelt ◽  
Sara Bals
Keyword(s):  
Deep Learning ◽  
Electron Tomography ◽  
Energy Dispersive ◽  
Elemental Distribution ◽  
X Ray ◽  
Powerful Approach ◽  
Dose Efficiency

The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex,...

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

scholarly journals Nanoscale chromatin imaging and analysis platform bridges 4D chromatin organization with molecular function

2021 ◽  
Vol 7 (1) ◽  
pp. eabe4310
Author(s):  
Yue Li ◽  
Adam Eshein ◽  
Ranya K.A. Virk ◽  
Aya Eid ◽  
Wenli Wu ◽  
...  
Keyword(s):  
Electron Tomography ◽  
Super Resolution ◽  
Population Level ◽  
Average Diameter ◽  
Live Cells ◽  
Molecular Function ◽  
Label Free ◽  
Multiple Length Scales ◽  
Chromatin Packing ◽  
Analysis Platform

Extending across multiple length scales, dynamic chromatin structure is linked to transcription through the regulation of genome organization. However, no individual technique can fully elucidate this structure and its relation to molecular function at all length and time scales at both a single-cell level and a population level. Here, we present a multitechnique nanoscale chromatin imaging and analysis (nano-ChIA) platform that consolidates electron tomography of the primary chromatin fiber, optical super-resolution imaging of transcription processes, and label-free nano-sensing of chromatin packing and its dynamics in live cells. Using nano-ChIA, we observed that chromatin is localized into spatially separable packing domains, with an average diameter of around 200 nanometers, sub-megabase genomic size, and an internal fractal structure. The chromatin packing behavior of these domains exhibits a complex bidirectional relationship with active gene transcription. Furthermore, we found that properties of PDs are correlated among progenitor and progeny cells across cell division.

scholarly journals A stock price prediction method based on deep learning technology

2021 ◽  
Vol 5 (1) ◽  
pp. 55-72
Author(s):  
Xuan Ji ◽  
Jiachen Wang ◽  
Zhijun Yan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Stock Price ◽  
Prediction Method ◽  
Series Data ◽  
Learning Technology ◽  
Content Type ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Text Feature

Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

scholarly journals Research on prediction method of S-wave velocity based on deep learning

2021 ◽  
Vol 2083 (4) ◽  
pp. 042065
Author(s):  
Guojie Yang ◽  
Shuhua Wang
Keyword(s):  
Deep Learning ◽  
Wave Velocity ◽  
Prediction Method ◽  
P Wave ◽  
Learning Technology ◽  
S Wave ◽  
Advantages And Disadvantages ◽  
Wave Prediction ◽  
Wide Range ◽  
Velocity Prediction

Abstract Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.

scholarly journals Propagation Characteristics of Debris Flows Considering Entrainment Effect

2018 ◽  
Vol 7 (3.34) ◽  
pp. 122
Author(s):  
Seokil Jeong ◽  
Junseon Lee ◽  
Chang Geun Song ◽  
Seung Oh Lee
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Flow Behavior ◽  
Prediction Method ◽  
Heavy Rain ◽  
Accurate Analysis ◽  
Propagation Length ◽  
2D Simulation ◽  
Entrainment Effect ◽  
The Relationship

Background/Objectives: Due to the extreme climate and the localized heavy rain, the frequency of debris flow has been increasing. Therefore, there is a growing expectation for accurate numerical analysis.Methods/Statistical analysis: We present a prediction method that can calculate the propagation length of the debris flow. This analysis indicates the relationship between the potential energy and the propagation length of the debris flow. To study the behavior of the debris flow accurately, the change in the momentum force must be considered; otherwise the calculation accuracy of the debris flow behavior is inevitably low.Findings: Entrainment is a common behavior in a debris flow that leads to changes in the momentum force. Here, we analyzed the change in the momentum force using a 2D simulation model that included entrainment. The results show how the debris flow behaves with changes in the momentum force. When entrainment is considered, the propagation length tends to be underestimated. With detailed information, the uncertainty in the prediction accuracy can be reduced.Improvements/Applications: If studies on the material properties of debris flow would be added, it will be possible to carry out various and accurate analysis of the debris flow  

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