ABSTRACT
Rubber is a natural product, the main car tire component. Due to the characteristics acquired by this material after its vulcanization process, its degradation under natural conditions requires very long times, causing several environmental problems. In the present work, the existence of a bacterial consortium isolated from a discarded tire found within the Socabaya River with the ability to degrade shredded tire rubber without any chemical pretreatment is explored. Taking into consideration the complex chemical composition of a rubber tire and the described benefits of the use of pretreatments, the study is developed as a preliminary analysis. The augmentative growth technique was used, and the level of degradation was quantified as a percentage through the analysis of microbial respiration. Schiff's test and the use of comparative photographs of scanning electron microscopy (SEM) were also used. The consortium using next generation genetic sequencing was analyzed. A 4.94% degradation point was obtained after 20 days of experimentation, and it was found that the consortium was mostly made up with Delftia tsuruhatensis with 69.12% of the total genetic readings of the consortium and the existence of 15% of unidentified microbial strains at the genre level. The role played by the organisms in the degradation process is unknown. However, the positive results in the tests carried out show that the consortium had action on the shredded tire, showing a mineralization process.
ABSTRACT
Crop growth analysis is used for the assessment of crop yield potential and stress tolerance. Capturing continuous plant growth has been a goal since the early 20th century; however, this requires a large number of replicates and multiple destructive measurements. The use of machine vision techniques holds promise as a fast, reliable, and non-destructive method to analyze crop growth based on surrogates for plant traits and growth parameters. We used machine vision to infer plant size along with destructive measurements at multiple time points to analyze growth parameters of spring wheat genotypes. We measured side-projected area by machine vision and RGB imaging. Three traits, i.e., biomass (BIO), leaf dry weight (LDW), and leaf area (LA), were measured using low-throughput techniques. However, RGB imaging was used to produce side projected area (SPA) as the high throughput trait. Significant effects of time point and genotype on BIO, LDW, LA, and SPA were observed. SPA was a robust predictor of leaf area, leaf dry weight, and biomass. Relative growth rate estimated using SPA was a robust predictor of the relative growth rate measured using biomass and leaf dry weight. Large numbers of entries can be assessed by this method for genetic mapping projects to produce a continuous growth curve with fewer replicates.
