scholarly journals TAPIOCA: Topological Attention and Predictive Inference of Chromatin Arrangement Using Epigenetic Features

2021 ◽  
Author(s):  
Max R Highsmith ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Predictive Inference ◽  
Chromatin Conformation ◽  
Biological Processes ◽  
Sequencing Data ◽  
Prior Art ◽  
Topologically Associated Domains ◽  
A Cell ◽  
Chromatin Arrangement

Chromatin conformation is an important characteristic of the genome which has been repeatedly demonstrated to play vital roles in many biological processes. Chromatin can be characterized by the presence or absence of structural motifs called topologically associated domains. The de facto strategy for determination of topologically associated domains within a cell line is the use of Hi-C sequencing data. However Hi-C sequencing data can be expensive or otherwise unavailable. Various epigenetic features have been hypothesized to contribute to the determination of chromatin conformation. Here we present TAPIOCA, a self-attention based deep learning transformer algorithm for the prediction of chromatin topology which circumvents the need for labeled Hi-C data and makes effective predictions of chromatin conformation organization using only epigenetic features. TAPIOCA outperforms prior art in established metrics of TAD prediction, while generalizing across cell lines beyond those used in training.

Radiology “forensics”: determination of age and sex from chest radiographs using deep learning

2021 ◽  
Author(s):  
Paul H. Yi ◽  
Jinchi Wei ◽  
Tae Kyung Kim ◽  
Jiwon Shin ◽  
Haris I. Sair ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Age And Sex

scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

scholarly journals Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 786
Author(s):  
Daniel M. Lang ◽  
Jan C. Peeken ◽  
Stephanie E. Combs ◽  
Jan J. Wilkens ◽  
Stefan Bartzsch
Keyword(s):  
Deep Learning ◽  
Cancer Patients ◽  
Oropharyngeal Cancer ◽  
Characteristic Curve ◽  
Learning Models ◽  
Convolutional Network ◽  
Hpv Status ◽  
3D Information ◽  
External Test

Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

scholarly journals Experimental study of the interaction range and association rate of surface-attached cadherin 11

1998 ◽  
Vol 95 (16) ◽  
pp. 9256-9261 ◽  
Author(s):  
Anne Pierres ◽  
Hélène Feracci ◽  
Véronique Delmas ◽  
Anne-Marie Benoliel ◽  
Jean-Paul Thiery ◽  
...  
Keyword(s):  
Adhesion Molecules ◽  
Bond Formation ◽  
Slow Motion ◽  
Classical Type ◽  
Association Rate ◽  
Interaction Range ◽  
A Cell ◽  
Coated Surfaces ◽  
The Relationship

We describe a method allowing quantitative determination of the interaction range and association rate of individual surface-attached molecules. Spherical beads (1.4 μm radius) were coated with recombinant outer domains of the newly described classical type II cadherin 11, a cell adhesion molecule. Beads were driven along cadherin-coated surfaces with a hydrodynamic force of ≈1 pN, i.e., much less than the mechanical strength of many ligand-receptor bonds. Spheres displayed periods of slow motion interspersed with arrests of various duration. Particle position was monitored with 50 Hz frequency and 0.025 μm accuracy. Nearly 1 million positions were recorded and processed. Comparison between experimental and computer-simulated trajectories suggested that velocity fluctuations might be related quantitatively to Brownian motion perpendicular to the surface. The expected amplitude of this motion was of order of 100 nm. Theoretical analysis of the relationship between sphere acceleration and velocity allowed simultaneous determination of the wall shear rate and van der Waals attraction between spheres and surface. The Hamaker constant was estimated at 2.9 × 10−23 J. The frequency of bond formation was then determined as a function of sphere velocity. Experimental data were consistent with the view that the rate of association between a pair of adhesion molecules was ≈1.2 × 10−3 s−1 and the interaction range was ≈10 nm. It is concluded that the presented methodology allows sensitive measurement of sphere-to-surface interactions (with ≈10 fN sensitivity) as well as the effective range and rate of bond formation between individual adhesion molecules.

Non-contact-based determination of membrane permeability to water and dimethyl sulfoxide of cells virtually trapped in a self-induced micro-vortex

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Hsiu-Yang Tseng ◽  
Chiu-Jen Chen ◽  
Zong-Lin Wu ◽  
Yong-Ming Ye ◽  
Guo-Zhen Huang
Keyword(s):  
Cell Membrane ◽  
Dimethyl Sulfoxide ◽  
Membrane Permeability ◽  
Cell Membrane Permeability ◽  
Cell Type ◽  
Cryoprotective Agents ◽  
A Cell

Cell-membrane permeability to water (Lp) and cryoprotective agents (Ps) of a cell type is a crucial cellular information for achieving optimal cryopreservation in the biobanking industry. In this work, a...

scholarly journals DISMIR: a deep learning-based cancer-detection method by integrating DNA sequence and methylation information of individual cell-free DNA reads

2021 ◽  
Author(s):  
Jiaqi Li ◽  
Lei Wei ◽  
Xianglin Zhang ◽  
Wei Zhang ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Cancer Detection ◽  
High Throughput Sequencing ◽  
Detection Method ◽  
Low Cost ◽  
Sequencing Data ◽  
Cell Free Dna ◽  
Free Dna ◽  
Novel Approach

ABSTRACTDetecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel non-invasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise prediction with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as “switching region” to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state, and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultra-low sequencing depths. Analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites’ methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

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