The 186 aa truncated protein produced by the tomato spontaneous non-ripening (nor) mutant enters the nucleus and combines with the promoters of its target genes, resulting in a gain of function.
...Abstract
The tomato non-ripening (nor) mutant generates a truncated 186-amino-acid protein (NOR186) and has been demonstrated previously to be a gain-of-function mutant. Here, we provide more evidence to support this view and answer the open question of whether the NAC-NOR gene is important in fruit ripening. Overexpression of NAC-NOR in the nor mutant did not restore the full ripening phenotype. Further analysis showed that the truncated NOR186 protein is located in the nucleus and binds to but does not activate the promoters of 1-aminocyclopropane-1-carboxylic acid synthase2 (SlACS2), geranylgeranyl diphosphate synthase2 (SlGgpps2), and pectate lyase (SlPL), which are involved in ethylene biosynthesis, carotenoid accumulation, and fruit softening, respectively. The activation of the promoters by the wild-type NOR protein can be inhibited by the mutant NOR186 protein. On the other hand, ethylene synthesis, carotenoid accumulation, and fruit softening were significantly inhibited in CR-NOR (CRISPR/Cas9-edited NAC-NOR) fruit compared with the wild-type, but much less severely affected than in the nor mutant, while they were accelerated in OE-NOR (overexpressed NAC-NOR) fruit. These data further indicated that nor is a gain-of-function mutation and NAC-NOR plays a significant role in ripening of wild-type fruit.
This study attempts to clarify the effect of canopy position on the physico-chemical parameters of apples cv. Braeburn. The experiments were carried out on fruit from the inner and outer part of the ...canopy in two growing seasons and at two harvest dates. Light measurements revealed that the average value of photo active radiation (PAR) for the inside and outside canopy amounted to 30.3 μmol/m2/s and 133.7 μmol/m2/s, respectively. Production year and canopy position significantly influenced ground color parameters a*, b*, C*, and h°, while the harvest date influenced all color parameters studied. For additional (red blush) coloration, the production year significantly influenced only the L* parameter, harvest date influenced all color parameters, and canopy position influenced L, a*, and C*. Only the fruits of the second harvest date showed more intense additional (red blush) coloration. The production year significantly affected fruit mass, firmness, total soluble solids (SSC), titratable acidity (TA), SSC/TA ratio, DPPH radical scavenging assay (AOP), total phenolic content (TPC), and total flavonoid content (TFC). The harvest date significantly influenced fruit mass, SSC, TA, SSC/TA, AOP, TPC, and TFC. The canopy position significantly influenced SSC, TA, AOP, TPC, and TFC. Regarding mineral content, the production year significantly affected the content of Fe, Ni, Cu, and Ca and the K/Ca ratio. The harvest date significantly affected Fe, Cu, Sr, K and K/Ca. The canopy position affected Fe, Ni, Zn, Sr, Ca, and K/Ca ratio, with a clear significant trend regarding the effect of canopy position only for Ca content (first and second year of the second harvest date) and K/Ca ratio (first year of both harvest dates). PCA analyses identified distinguishing features between apples, with differences defined specifically by AOP, TPC, TFC, Rb, Sr, Ca, and K/Ca on the PC 1 and Mn, Fe, Ni, Cu, and Zn on PC 2.
Wild or dog rose (Rosa canina L.) is a successful colonizer of various habitats and different soil types and is widely distributed across the Republic of Croatia. In this research, in order to ...estimate pomological variability in native dog rose populations, four genotypes from four locations in different geographic areas of Croatia were selected and sampled. The genotypes selected were: genotype G1, originating from the continental part of Croatia (Pitomača); genotypes G2 and G3, originating from the Mediterranean part of Croatia (Kukurini and Posedarje, respectively); and genotype G4, originating from the upland part of Croatia (Gračac). Fruits were harvested at optimum harvest dates in 2010 and 2012. Genotype had a significant effect on each studied pomological trait (length, width, geometric mean diameter, sphericity, volume, surface, shape index, weight, flesh weight, flesh ratio and total dry matter content), while year significantly affected all parameters except sphericity and shape index. The highest values for most pomological traits in 2010 and 2012 were found in the G4 and G3 genotypes, respectively. This research highlighted the existence of high variability in pomological traits among dog rose populations in Croatia, which emphasizes the possibility of further breeding and cultivation.
Fruit softening that occurs during fruit ripening and postharvest storage determines the fruit quality, shelf life and commercial value and makes fruits more attractive for seed dispersal. In ...addition, over-softening results in fruit eventual decay, render fruit susceptible to invasion by opportunistic pathogens. Many studies have been conducted to reveal how fruit softens and how to control softening. However, softening is a complex and delicate life process, including physiological, biochemical and metabolic changes, which are closely related to each other and are affected by environmental conditions such as temperature, humidity and light. In this review, the current knowledge regarding fruit softening mechanisms is summarized from cell wall metabolism (cell wall structure changes and cell-wall-degrading enzymes), plant hormones (ETH, ABA, IAA and BR et al.), transcription factors (MADS-Box, AP2/ERF, NAC, MYB and BZR) and epigenetics (DNA methylation, histone demethylation and histone acetylation) and a diagram of the regulatory relationship between these factors is provided. It will provide reference for the cultivation of anti-softening fruits.
To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In ...this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.
The 26S proteasome is an ATP-dependent proteolytic complex in eukaryotes, which is mainly responsible for the degradation of damaged and misfolded proteins and some regulatory proteins in cells, and ...it is essential to maintain the balance of protein levels in the cell. The ubiquitin-26S proteasome pathway, which targets a wide range of protein substrates in plants, is an important post-translational regulatory mechanism involved in various stages of plant growth and development and in the maturation process of fleshy fruits. Fleshy fruit ripening is a complex biological process, which is the sum of a series of physiological and biochemical reactions, including the biosynthesis and signal transduction of ripening related hormones, pigment metabolism, fruit texture changes and the formation of nutritional quality. This paper reviews the structure of the 26S proteasome and the mechanism of the ubiquitin-26S proteasome pathway, and it summarizes the function of this pathway in the ripening process of fleshy fruits.
•Lecithin improved storage life and maintained quality of goji berries.•Postharvest decay and weight loss greatly reduced in lecithin treated fruits.•Lecithin enhanced the antioxidative potential of ...goji berries during storage.•All bioactive compounds positively effected in lecithin treated fruits.
To enhance storage life and post-storage quality of fresh goji berries, three treatments with lecithin (1, 5, 10g·L−1) and two storage times (8, 16days) were evaluated. The significant effects on the physiological and biochemical parameters were varied. 1g·L−1 lecithin showed its main effects after 8days of storage by reduction in total weight loss and decay, SSC/TA ratio (also at 16days), and chlorophyll content and with highest scores of sensory attributes (also at 16days). 5g·L−1 lecithin showed its main effects after 16days of storage: highest SSC, highest TA (also at 8days), highest TPC, only significant reduction in DPPH antioxidant activity, and highest total flavonoid content. 10g·L−1 lecithin showed its main effects after 8days of storage with highest SSC, chlorophyll content, total flavonoid, DPPH, and ABTS antioxidant activity (also at 16days), but with least scores of sensory attributes.
Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality ...indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.
The effect of hot water dip (48° C) duration (6 or 12 minutes) (HWD 48° C 6' and HWD 48° C 12') and length of storage at 0° C in normal atmosphere (two or four weeks) on chemical and sensory quality ...of nectarine (Prunus persica var. nectarina cv. 'Venus') was studied. After two weeks of storage, HWD-treated fruits had significantly lower weight loss and SSC compared to control. There was no significant difference between HWD 48 °C 6' and HWD 48 °C 12' – treated fruit. HWD 48 °C 12' - treated fruit maintained sensory quality after two weeks of storage. After four weeks of storage, control fruit received higher scores compared to HWD – treated fruit for all traits, except for aroma which was still higher for HWD 48 °C 12' - treated fruit. Duration of hot water dip is significant factor for maintaining postharvest quality of nectarine fruit.
Hazelnut postharvest technology: A review Vrtodušić, Rea; Ivić, Dario; Jemrić, Tomislav ...
Journal of Central European agriculture,
01/2022, Letnik:
23, Številka:
2
Journal Article
Recenzirano
Odprti dostop
In the Republic of Croatia, hazel (Corylus avellana L.) is the fruit species with the largest recorded increase in cultivation. Due to its high content of lipids and moisture (at the time of harvest) ...hazelnuts are susceptible to quality deterioration (rancidity development, mycotoxins contamination etc.) and therefore proper harvest and post-harvest handling is necessary to preserve their quality. Hazelnuts are most often collected from the ground by the appropriate mechanization. After harvesting, proper drying of is a crucial measure. Hazelnuts in the shell should be dried to about 7 - 10% moisture, and the kernel moisture content should be from about 4 - 6%. The maximum drying temperature should never exceed 50 °C. Regarding the storage conditions, the relative humidity is the most important factor and should never exceed 70%, while it is optimal to range from about 55 - 65%. Low temperatures (<10 °C) are effective means of prolonging hazelnut storage life because numerous negative processes (rancidity development, microbiological activity, etc.) are notably slowed down. The use of a modified atmosphere (low oxygen concentrations) has also shown to be an effective measure in prolonging storage life.
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