RESUMEN
This study analyzed the effects of Ca2+ metal ions among culture medium components on the Chlorella sorokiniana strain DSCG150 strain cell growth. The C. sorokiniana strain DSCG150 grew based on a multiple fission cell cycle and growth became stagnant in the absence of metal ions in the medium, particularly Ca2+. Flow cytometry and confocal microscopic image analysis results showed that in the absence of Ca2+, cell growth became stagnant as the cells accumulated into four autospores and could not transform into daughter cells. Genetic analysis showed that the absence of Ca2+ caused upregulation of calmodulin (calA) and cell division control protein 2 (CDC2_1) genes, and downregulation of origin of replication complex subunit 6 (ORC6) and dual specificity protein phosphatase CDC14A (CDC14A) genes. Analysis of gene expression patterns by qRT-PCR showed that the absence of Ca2+ did not affect cell cycle progression up to 4n autospore, but it inhibited Chlorella cell fission (liberation of autospores). The addition of Ca2+ to cells cultivated in the absence of Ca2+ resulted in an increase in n cell population, leading to the resumption of C. sorokiniana growth. These findings suggest that Ca2+ plays a crucial role in the fission process in Chlorella.
Asunto(s)
Calcio , Ciclo Celular , Chlorella , Chlorella/metabolismo , Chlorella/genética , Chlorella/crecimiento & desarrollo , Calcio/metabolismo , Medios de Cultivo/química , Calmodulina/metabolismo , Calmodulina/genética , Proliferación CelularRESUMEN
The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000-1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.