Abstract
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.
Highlights
Sci. 2021, 22, 6134 a wide range of application fields, including biomedicine, especially in relation to the progress achieved in the production of new types of ionomers
Fourier Transform Infrared spectroscopy (FTIR) analysis was performed in an Attenuated Total Reflection (ATR) by a Nicolet 6700 (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a Golden Gate ATR accessory, at a resolution of 2 cm−1 and co-adding 100 scans
The polyurethane disks were immersed in water and, at increasing times, disks were removed from water and weighed, after removal of the excess of solvent using filter Paper
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ionic groups can modulate PU’s ability to conjugate proteins, drugs or biologically active substances They can act as ligands for metal ions to obtain inorganic−organic hybrid coordination polymers with defined structures, which are gaining a growing attention in different application fields including biomedicine [16,17,18,19]. Within a framework of broadening this platform of polymers, several PU anionomers with different diisocyanates (aliphatic and aromatic) and two monomers’ molar ratios (2:1:1 and 3:2:1 diisocyanate:ionic monomer:polyol) were synthesized to investigate the effect of variable hard phase and ionic group content on PU hard/soft phase segregation and physical properties. The obtained metal coordinated polymers were characterized in terms of their ability to inhibit the growth of two relevant clinical Pathogens, Staphylococcus epidermidis and Pseudomonas aeruginosa
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
