scholarly journals Simulation of Nanopore Sequencing Signals Based on BiGRU

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7244
Author(s):  
Weigang Chen ◽  
Peng Zhang ◽  
Lifu Song ◽  
Jinsheng Yang ◽  
Changcai Han
Keyword(s):  
Nucleotide Sequence ◽  
Real World ◽  
Ground Truth ◽  
Simulation Method ◽  
Learning Ability ◽  
Nanopore Sequencing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Simulated Signal ◽  
Signal Simulation

Oxford Nanopore sequencing is an important sequencing technology, which reads the nucleotide sequence by detecting the electrical current signal changes when DNA molecule is forced to pass through a biological nanopore. The research on signal simulation of nanopore sequencing is highly desirable for method developments of nanopore sequencing applications. To improve the simulation accuracy, we propose a novel signal simulation method based on Bi-directional Gated Recurrent Units (BiGRU). In this method, the signal processing model based on BiGRU is built to replace the traditional low-pass filter to post-process the ground-truth signal calculated by the input nucleotide sequence and nanopore sequencing pore model. Gaussian noise is then added to the filtered signal to generate the final simulated signal. This method can accurately model the relation between ground-truth signal and real-world sequencing signal through experimental sequencing data. The simulation results reveal that the proposed method utilizing the powerful learning ability of the neural network can generate the simulated signal that is closer to the real-world sequencing signal in the time and frequency domains than the existing simulation method.

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Mehta ◽  
S Niklitschek ◽  
F Fernandez ◽  
C Villagran ◽  
J Avila ◽  
...  
Keyword(s):  
Human Resources ◽  
Ground Truth ◽  
Cost Effective ◽  
Clinical Settings ◽  
Dense Layer ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Original Dataset ◽  
Low Pass ◽  
Normal Branch

Abstract Background EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings. Purpose To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources. Methods Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI. Results See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%. Conclusions Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage. Funding Acknowledgement Type of funding source: None

On: S. Hammer’s replies to N. C. Steenland, A. T. Herring, and W. C. Pearson’s discussions of “Airborne gravity is here” (GEOPHYSICS, 49, 310–311, March 1984; and 49, 470–477, April 1984).

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 170-170
Author(s):  
M. J. Hall
Keyword(s):  
Ground Truth ◽  
New Method ◽  
Airborne Gravity ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Hammer’s replies to Steenland’s, Herring’s and Pearson’s discussions of his paper, “Airborne gravity is here!,” are nothing short of incredulous. Both his paper and his replies would suggest that he did not expect those with experience in dynamic gravity to read them. Hammer accuses his critics of ignoring “…the low‐pass filter which was applied for realistic comparison with the airborne data.” I shall call this “Hammer’s Rule:” you filter the very standard against which you will compare any new method without concern for the truth. Hammer’s Rule frees us from annoyingly difficult rigor. If the airborne filter eliminates the anomaly, then so must the ground truth anomaly be eliminated. Fair is fair, and Hammer’s Rule gives a “realistic comparison” between something which is wrong and something which is wrong.

scholarly journals DeepSimulator1.5: a more powerful, quicker and lighter simulator for Nanopore sequencing

2020 ◽  
Vol 36 (8) ◽  
pp. 2578-2580 ◽  
Author(s):  
Yu Li ◽  
Sheng Wang ◽  
Chongwei Bi ◽  
Zhaowen Qiu ◽  
Mo Li ◽  
...  
Keyword(s):  
Length Distribution ◽  
Read Length ◽  
Supplementary Information ◽  
Nanopore Sequencing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Pore Model ◽  
The Real ◽  
Sequencing Technologies ◽  
Post Process

Abstract Motivation Nanopore sequencing is one of the leading third-generation sequencing technologies. A number of computational tools have been developed to facilitate the processing and analysis of the Nanopore data. Previously, we have developed DeepSimulator1.0 (DS1.0), which is the first simulator for Nanopore sequencing to produce both the raw electrical signals and the reads. However, although DS1.0 can produce high-quality reads, for some sequences, the divergence between the simulated raw signals and the real signals can be large. Furthermore, the Nanopore sequencing technology has evolved greatly since DS1.0 was released. It is thus necessary to update DS1.0 to accommodate those changes. Results We propose DeepSimulator1.5 (DS1.5), all three modules of which have been updated substantially from DS1.0. As for the sequence generator, we updated the sample read length distribution to reflect the newest real reads’ features. In terms of the signal generator, which is the core of DeepSimulator, we added one more pore model, the context-independent pore model, which is much faster than the previous context-dependent one. Furthermore, to make the generated signals more similar to the real ones, we added a low-pass filter to post-process the pore model signals. Regarding the basecaller, we added the support for the newest official basecaller, Guppy, which can support both GPU and CPU. In addition, multiple optimizations, related to multiprocessing control, memory and storage management, have been implemented to make DS1.5 a much more amenable and lighter simulator than DS1.0. Availability and implementation The main program and the data are available at https://github.com/lykaust15/DeepSimulator. Supplementary information Supplementary data are available at Bioinformatics online.

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

ANALYSIS AND IMPROVEMENT TECHNIQUES FOR THE TRANSFER FUNCTION OF A PLANAR LOW - PASS FILTER

2016 ◽  
Vol 15 (12) ◽  
pp. 2579-2586
Author(s):  
Adina Racasan ◽  
Calin Munteanu ◽  
Vasile Topa ◽  
Claudia Pacurar ◽  
Claudia Hebedean
Keyword(s):  
Transfer Function ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

scholarly journals Simulating Elliptic Low-pass Filter based on MOCCCII

2020 ◽  
Vol 965 ◽  
pp. 012037
Author(s):  
Nanan Chomnak ◽  
Siradanai Srisamranrungrueang ◽  
Natapong Wongprommoon
Keyword(s):  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

scholarly journals CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 900
Author(s):  
Hanseob Kim ◽  
Taehyung Kim ◽  
Myungho Lee ◽  
Gerard Jounghyun Kim ◽  
Jae-In Hwang
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Human Perception ◽  
Ground Truth ◽  
Prior Work ◽  
Comparative Experiment ◽  
User Perception ◽  
Depth Distortion ◽  
Real Objects ◽  
Visual Artifacts

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.

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