A high-throughput all-optical laser-scanning imaging flow cytometer with biomolecular specificity and subcellular resolution

2018 ◽  
Vol 11 (2) ◽  
pp. e201700178 ◽  
Author(s):  
Wenwei Yan ◽  
Jianglai Wu ◽  
Kenneth K. Y. Wong ◽  
Kevin K. Tsia
Keyword(s):  
High Throughput ◽  
Laser Scanning ◽  
Flow Cytometer ◽  
All Optical ◽  
Scanning Imaging ◽  
Subcellular Resolution ◽  
Optical Laser

scholarly journals Chemical sectioning fluorescence tomography: high-throughput, high-contrast, multicolor, whole-brain imaging at subcellular resolution

Cell Reports ◽  
2021 ◽  
Vol 34 (5) ◽  
pp. 108709
Author(s):  
Xiaojun Wang ◽  
Hanqing Xiong ◽  
Yurong Liu ◽  
Tao Yang ◽  
Anan Li ◽  
...  
Keyword(s):  
Brain Imaging ◽  
High Throughput ◽  
High Contrast ◽  
Whole Brain ◽  
Fluorescence Tomography ◽  
Subcellular Resolution

scholarly journals Machine learning methodology for high throughput personalized neutron dose reconstruction in mixed neutron + photon exposures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Igor Shuryak ◽  
Helen C. Turner ◽  
Monica Pujol-Canadell ◽  
Jay R. Perrier ◽  
Guy Garty ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Mean Squared Error ◽  
Ex Vivo ◽  
Probability Distributions ◽  
Micronucleus Assay ◽  
Neutron Dose ◽  
Cell Probability ◽  
Scanning Imaging ◽  
Automated Scanning

AbstractWe implemented machine learning in the radiation biodosimetry field to quantitatively reconstruct neutron doses in mixed neutron + photon exposures, which are expected in improvised nuclear device detonations. Such individualized reconstructions are crucial for triage and treatment because neutrons are more biologically damaging than photons. We used a high-throughput micronucleus assay with automated scanning/imaging on lymphocytes from human blood ex-vivo irradiated with 44 different combinations of 0–4 Gy neutrons and 0–15 Gy photons (542 blood samples), which include reanalysis of past experiments. We developed several metrics that describe micronuclei/cell probability distributions in binucleated cells, and used them as predictors in random forest (RF) and XGboost machine learning analyses to reconstruct the neutron dose in each sample. The probability of “overfitting” was minimized by training both algorithms with repeated cross-validation on a randomly-selected subset of the data, and measuring performance on the rest. RF achieved the best performance. Mean R2 for actual vs. reconstructed neutron doses over 300 random training/testing splits was 0.869 (range 0.761 to 0.919) and root mean squared error was 0.239 (0.195 to 0.351) Gy. These results demonstrate the promising potential of machine learning to reconstruct the neutron dose component in clinically-relevant complex radiation exposure scenarios.

On all-optical laser cleaning and inspection of contaminated concave metal surfaces

2000 ◽  
Vol 33 (4) ◽  
pp. 311-315 ◽  
Author(s):  
Ville Hyvärinen ◽  
Mika Sorjonen ◽  
Kai-Erik Peiponen ◽  
Raimo Silvennoinen ◽  
Tapani Niskanen ◽  
...  
Keyword(s):  
Metal Surfaces ◽  
Laser Cleaning ◽  
All Optical ◽  
Optical Laser

Bidirectional beam expanding field mirror for monostatic laser scanning imaging system

2021 ◽  
pp. 103973
Author(s):  
Yang Qu ◽  
Yijun Jiang ◽  
Xue Jia ◽  
Chang Chen ◽  
Mingzhi Duan ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Imaging System ◽  
Scanning Imaging
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