A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0>3, and overall a specificity exceeding 85% and sensitivity better than 70%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

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Predicting Chromosome Flexibility from the Genomic Sequence Based on Deep Learning Neural Networks

Current Bioinformatics ◽

10.2174/1574893616666210827095829 ◽

2021 ◽

Vol 16 ◽

Author(s):

Jinghao Peng ◽

Jiajie Peng ◽

Haiyin Piao ◽

Zhang Luo ◽

Kelin Xia ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Genomic Sequence ◽

Sequence Data ◽

Function Analysis ◽

Double Helix ◽

Gm12878 Cell ◽

Genomic Sequence Analysis ◽

And Function ◽

Nuclear Processes

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the flexibility-rigidity index (FRI) and the Gaussian network model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Method: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.

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A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images

Computational Intelligence and Neuroscience ◽

10.1155/2020/8811630 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Mehdi Khoshboresh-Masouleh ◽

Reza Shah-Hosseini

Keyword(s):

Deep Learning ◽

Real Time ◽

Computational Cost ◽

High Definition ◽

Satellite Mission ◽

Real Time Processing ◽

National Space ◽

Spatial Features ◽

Efficiency And Effectiveness ◽

Earth Observation Satellite

In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.

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Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data

Viruses ◽

10.3390/v12050560 ◽

2020 ◽

Vol 12 (5) ◽

pp. 560

Author(s):

Margaret C. Steiner ◽

Keylie M. Gibson ◽

Keith A. Crandall

Keyword(s):

Neural Network ◽

Drug Resistance ◽

Deep Learning ◽

Sequence Data ◽

Classification Performance ◽

Resistance Mutations ◽

Evolutionary Mechanisms ◽

Drug Resistance Prediction ◽

Resistance Prediction ◽

Hiv 1

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

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Smart Pothole Detection Using Deep Learning Based on Dilated Convolution

Sensors ◽

10.3390/s21248406 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8406

Author(s):

Khaled R. Ahmed

Keyword(s):

Deep Learning ◽

Real Time ◽

Computational Cost ◽

Detection Accuracy ◽

Setup Cost ◽

Detection Algorithms ◽

Dilated Convolution ◽

Wide Range ◽

Adequate Accuracy ◽

Pothole Detection

Roads make a huge contribution to the economy and act as a platform for transportation. Potholes in roads are one of the major concerns in transportation infrastructure. A lot of research has proposed using computer vision techniques to automate pothole detection that include a wide range of image processing and object detection algorithms. There is a need to automate the pothole detection process with adequate accuracy and speed and implement the process easily and with low setup cost. In this paper, we have developed efficient deep learning convolution neural networks (CNNs) to detect potholes in real-time with adequate accuracy. To reduce the computational cost and improve the training results, this paper proposes a modified VGG16 (MVGG16) network by removing some convolution layers and using different dilation rates. Moreover, this paper uses the MVGG16 as a backbone network for the Faster R-CNN. In addition, this work compares the performance of YOLOv5 (Large (Yl), Medium (Ym), and Small (Ys)) models with ResNet101 backbone and Faster R-CNN with ResNet50(FPN), VGG16, MobileNetV2, InceptionV3, and MVGG16 backbones. The experimental results show that the Ys model is more applicable for real-time pothole detection because of its speed. In addition, using the MVGG16 network as the backbone of the Faster R-CNN provides better mean precision and shorter inference time than using VGG16, InceptionV3, or MobilNetV2 backbones. The proposed MVGG16 succeeds in balancing the pothole detection accuracy and speed.

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Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project

PsycEXTRA Dataset ◽

10.1037/e307182005-005 ◽

2004 ◽

Cited By ~ 3

Author(s):

Michael M. Wagner ◽

J. Espino ◽

F-C. Tsui ◽

P. Gesteland ◽

W. Chapman ◽

...

Keyword(s):

Real Time ◽

Disease Surveillance ◽

Chief Complaint ◽

Outbreak Detection ◽

The Real

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-378 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 378-1-378-7

Author(s):

Tyler Nuanes ◽

Matt Elsey ◽

Radek Grzeszczuk ◽

John Paul Shen

Keyword(s):

Real Time ◽

Mobile Device ◽

Computational Cost ◽

False Positives ◽

Compact Model ◽

High Quality ◽

False Negatives ◽

Trade Off ◽

Depth Reconstruction ◽

Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

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