Model-based high-throughput design of ion exchange protein chromatography

2016 ◽  
Vol 1459 ◽  
pp. 67-77 ◽  
Author(s):  
Rushd Khalaf ◽  
Julia Heymann ◽  
Xavier LeSaout ◽  
Florence Monard ◽  
Matteo Costioli ◽  
...  
Keyword(s):  
Ion Exchange ◽  
High Throughput ◽  
Protein Chromatography ◽  
Model Based ◽  
Exchange Protein

High-throughput ion exchange purification of positively charged recombinant protein in the presence of negatively charged dextran sulfate

2014 ◽  
Vol 30 (2) ◽  
pp. 516-520 ◽  
Author(s):  
Lam Raga A. Markely ◽  
Lutfiye Kurt ◽  
Janet Lau ◽  
Sarthak Mane ◽  
Bing Guan ◽  
...  
Keyword(s):  
Ion Exchange ◽  
Recombinant Protein ◽  
High Throughput ◽  
Dextran Sulfate ◽  
Positively Charged

Model-based prediction of monoclonal antibody retention in ion-exchange chromatography

2013 ◽  
Vol 1298 ◽  
pp. 17-25 ◽  
Author(s):  
Bertrand Guélat ◽  
Lydia Delegrange ◽  
Pascal Valax ◽  
Massimo Morbidelli
Keyword(s):  
Monoclonal Antibody ◽  
Ion Exchange ◽  
Ion Exchange Chromatography ◽  
Exchange Chromatography ◽  
Model Based

scholarly journals A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 175 ◽  
Author(s):  
Orly Enrique Apolo-Apolo ◽  
Manuel Pérez-Ruiz ◽  
Jorge Martínez-Guanter ◽  
Gregorio Egea
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
High Throughput ◽  
Model Performance ◽  
Wheat Breeding ◽  
Plant Phenotyping ◽  
Indirect Methods ◽  
Area Index ◽  
Model Based ◽  
Recent Emergence

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

Model-Based Process Challenge of an Industrial Ion-Exchange Chromatography Step

2011 ◽  
Vol 35 (1) ◽  
pp. 183-190 ◽  
Author(s):  
K. Westerberg ◽  
E. Broberg Hansen ◽  
M. Degerman ◽  
T. Budde Hansen ◽  
B. Nilsson
Keyword(s):  
Ion Exchange ◽  
Ion Exchange Chromatography ◽  
Exchange Chromatography ◽  
Model Based ◽  
Chromatography Step

Micro-Plate Based Monolithic Ion-Exchange Chromatography for High Throughput Protein Purification Process Design

2008 ◽  
Vol 41 (3) ◽  
pp. 200-205 ◽  
Author(s):  
Noriko Yoshimoto ◽  
Yuko Nishijima ◽  
Parvin Akbarzadehlaleh ◽  
Sachie Fujii ◽  
Mitsuyo Abe ◽  
...  
Keyword(s):  
Ion Exchange ◽  
Protein Purification ◽  
High Throughput ◽  
Process Design ◽  
Ion Exchange Chromatography ◽  
Exchange Chromatography ◽  
Purification Process
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