scholarly journals A single-step method for RNA isolation from tropical crops in the field

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
J.-C. Breitler ◽  
C. Campa ◽  
F. Georget ◽  
B. Bertrand ◽  
H. Etienne
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Tropical Crops

Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction

1987 ◽  
Vol 162 (1) ◽  
pp. 156-159 ◽  
Author(s):  
Piotr Chomczynski ◽  
Nicoletta Sacchi
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Chloroform Extraction ◽  
Guanidinium Thiocyanate

The single-step method of RNA isolation by acid guanidinium thiocyanate–phenol–chloroform extraction: twenty-something years on

2006 ◽  
Vol 1 (2) ◽  
pp. 581-585 ◽  
Author(s):  
Piotr Chomczynski ◽  
Nicoletta Sacchi
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Chloroform Extraction ◽  
Guanidinium Thiocyanate

Single-Step Method of Total RNA Isolation by Guanidine-Phenol Extraction

2013 ◽  
Author(s):  
Piotr Chomczynski ◽  
William Wilfinger ◽  
Karol Mackey
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Total Rna ◽  
Phenol Extraction ◽  
Total Rna Isolation

Substitution of Chloroform by Bromochloropropane in the Single-Step Method of RNA Isolation

1995 ◽  
Vol 225 (1) ◽  
pp. 163-164 ◽  
Author(s):  
P. Chomczynski ◽  
K. Mackey
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method

Single-Step Method of Total RNA Isolation by Sodium Dodecyl Sulfate/Phenol Extraction from Cultured Cells

1997 ◽  
Vol 251 (2) ◽  
pp. 291-292 ◽  
Author(s):  
Jubao Duan ◽  
Xin Cai ◽  
Limei Zhou ◽  
Jiaxi Wang
Keyword(s):  
Sodium Dodecyl Sulfate ◽  
Cultured Cells ◽  
Sodium Dodecyl ◽  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Total Rna ◽  
Phenol Extraction ◽  
Dodecyl Sulfate ◽  
Total Rna Isolation

Single-Step Method of Total RNA Isolation by Guanidine-Phenol Extraction

2017 ◽  
pp. 1-6
Author(s):  
Piotr Chomczynski ◽  
William Wilfinger ◽  
Karol Mackey
Keyword(s):  
Rna Isolation ◽  
Single Step ◽  
Step Method ◽  
Total Rna ◽  
Phenol Extraction ◽  
Total Rna Isolation

Purification of Urokinase from Complex Mixtures Using Immobilized Monoclonal Antibody Against Urokinase Light Chain

1983 ◽  
Vol 49 (01) ◽  
pp. 024-027 ◽  
Author(s):  
David Vetterlein ◽  
Gary J Calton
Keyword(s):  
Monoclonal Antibody ◽  
Light Chain ◽  
Culture Media ◽  
Biological Fluids ◽  
Single Step ◽  
High Yield ◽  
Step Method ◽  
Commercial Preparation ◽  
E Coli ◽  
Tissue Culture Media

SummaryThe preparation of a monoclonal antibody (MAB) against high molecular weight (HMW) urokinase light chain (20,000 Mr) is described. This MAB was immobilized and the resulting immunosorbent was used to isolate urokinase starting with an impure commercial preparation, fresh urine, spent tissue culture media, or E. coli broth without preliminary dialysis or concentration steps. Monospecific antibodies appear to provide a rapid single step method of purifying urokinase, in high yield, from a variety of biological fluids.

scholarly journals High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

2019 ◽  
Vol 116 (40) ◽  
pp. 19848-19856 ◽  
Author(s):  
Alexandre Goy ◽  
Girish Rughoobur ◽  
Shuai Li ◽  
Kwabena Arthur ◽  
Akintunde I. Akinwande ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Single Step ◽  
Training Set ◽  
Step Method ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
Optical Domain

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

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