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They are very nutritious, and they contain many essential vitamins and minerals. They are a source of beta carotene, lutein, and zeaxanthin, which are potent antioxidants that help fight free radicals in your body.

Most of us buy fruit judging by what they taste and look like. We don’t tend to notice or focus on how many seeds fruit has. The majority of fruits have a lot of small seeds, while only a few have a single seed. Which fruit has a single seed?

Apricots are filled with soluble and insoluble fiber. The soluble fiber dissolves in water and includes pectin, gums, and long chains of sugar called polysaccharides, while the insoluble kind doesn’t dissolve in water and includes cellulose, hemicellulose, and lignin.

5. Apricot

Avocado can help you prevent osteoporosis, as half of an avocado provides about 25% of the daily recommended intake of vitamin K, which is essential for bone health. Eating a diet rich in vitamin K can support bone health by enhancing calcium absorption and reducing urinary excretion of calcium.

These fruits have only one seed for a reason, and they are very beneficial. Most of them help keep your hair, skin, eyes, and gut health. Some of them aid in the fight against heart disease and diabetes. They can make you feel and look better.

Apricots contain chlorogenic acids, catechins, and quercetin, and they work to neutralize free radicals, harmful compounds that damage your cells and cause oxidative stress. Oxidative stress is associated with obesity and many chronic diseases, including heart disease.

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For the leaf-based protocol, leaf tissue was sampled from F6 rice lines derived from a single panicle of an F5 plant. The resulting F6 plants from each line were grown under field conditions and sampled 60 days after transplanting. Twelve F6 plants from each line were grown in the field and identified using a barcoded identification label. Each line’s barcode was scanned and synchronized with the “Coordinate” application. A waxed-paper envelope with the same barcode was also scanned and matched with the field barcode. Leaf tissue was collected from the first plant of each line and stored in waxed-paper envelopes. This process was repeated until all lines were sampled. The envelopes were taken to the lab and dried for 2 days at 50 °C using a convection oven or a lyophilizer. Envelopes containing dried leaf tissue were grouped in 94-sample batches to have uniform leaf-punches excised from the sample. To do this, dried tissue stored in the waxed-paper envelopes was taken out and four 4-mm diameter discs from each leaf sample were punched directly into a 96-deep-well plate using the AK-EP100 bench-top leaf puncher (Applied King, http://www.appliedking.com/lab-automation/) in the pre-defined order determined by the ‘Coordinate’ app. This last step was repeated for all samples until the batch was completed, leaving the last two wells empty as negative/positive controls. The plate was then covered with a silicon cap mat or sticky paper and shipped immediately to Intertek-AgriTech for DNA extraction, marker assay and scoring.

99%. This allows alternative tissue sampling protocols to be adapted for plants grown under field or greenhouse conditions. Likewise, a concordance rate of

To estimate the potential impacts of seed developmental stage on genotype data quality, F6 seeds harvested at 7, 15, 25, and 30 DAPI were subjected to DNA extraction and genotyped alongside standard leaf tissue sampling using ten KASP assays (Additional file 1: Table S5).

Statistical analysis

An analysis of variance (ANOVA) was used to test for significant differences in the CT values of DNA extracted from leaf tissue and single seeds with contrasting physical or chemical grain properties. Custom R scripts (2018, R core development team) were used for calculating the call-rate and concordance between single seed and leaf-based genotype data. SNP call rates were calculated as the average proportion of successfully called genotypes for each SNP across all samples from different accessions and seed developmental stages. The SNP genotypic concordance rate was measured as the proportion of exact genotypic matches between identical SNPs genotyped on samples processed using the single seed and leaf-based tissue sampling protocols. ANOVA was calculated using the R function ‘anova’ [9]. Multiple comparisons were estimated using the Tukey’s HSD (honestly significant difference) [5] method using the R function ‘HSD.test’ from the R package ‘agricolae’ [25].

CT values distribution and comparisons between single seed samples with varying physical and chemical properties. a Distribution of CT values for DNA from 96 different single seed samples in rice. The average CT value on rice seeds (CT = 24.3) is illustrated with a solid line and the average CT value obtained using leaf tissue (CT = 23.9) is illustrated by a dashed line. Analysis of variance among seeds with different b pericarp color, c grain size, d grain width, e active amylose-content, and f alkali digestibility. The average CT value estimated in leaf tissue (CT = 23.9) is illustrated in each boxplot by a dashed line and the acronym ‘n.s’ indicates a non-significant difference between classifications

99%) and the high genotypic concordance results between single seed and leaf-based MAS strategies (

Genotyping results aggregated across nine KASP assays showed an average SNP call rate of 98.38%, higher than the minimum 95% SNP call rate required to consider a genotyping project successful. Cartesian bi-plots for each KASP assay were created by plotting the fluorochrome dye intensity values which are commonly used to determine the allelic discriminatory capacity of each SNP assay (Fig. 2a–d). High quality allelic discrimination was observed among all SNP markers utilized in this study (Fig. 2a–d).

According to the vertex formula model(Y = A×(D-Dm) 2 +Ym) of the parabola, yield is the derivative to density: (2)

Fig 3 shows the simulation images of field seedling emergence in different conditions. They are the seedling emergence status respectively under the condition of 150,000 individual plant / hm 2 and 54,000 individual plant /hm 2 with the field emergence rate of 85% and 54,000 individual plant / hm 2 with the field emergence of 50%.

Received: November 28, 2017; Accepted: February 17, 2018; Published: March 5, 2018

Mathematical depiction of the problem

It can be assumed that maize’s yield is the quadratic function of planting density: (1)

In order to test the feasibility of computer simulation in field maize planting, the selection of the method of single seed precise sowing in maize is studied based on the quadratic function model Y = A×(D-Dm) 2 +Ym, which depicts the relationship between maize yield and planting density. And the advantages and disadvantages of the two planting methods under the condition of single seed sowing are also compared: Method 1 is optimum density planting, while Method 2 is the ideal seedling emergence number planting. It is found that the yield reduction rate and yield fluctuation of Method 2 are all lower than those of Method 1. The yield of Method 2 increased by at least 0.043 t/hm 2 , and showed more advantages over Method 1 with higher yield level. Further study made on the influence of seedling emergence rate on the yield of maize finds that the yields of the two methods are both highly positively correlated with the seedling emergence rate and the standard deviations of their yields are both highly negatively correlated with the seedling emergence rate. For the study of the break-up problem of sparse caused by the method of single seed precise sowing, the definition of seedling missing spots is put forward. The study found that the relationship between number of hundred-dot spot and field seedling emergence rate is as the parabola function y = -189.32x 2 + 309.55x – 118.95 and the relationship between number of spot missing seedling and field seedling emergence rate is as the negative exponent function y = 395.69e -6.144x . The results may help to guide the maize seeds production and single seed precise sowing to some extent.

Method 1: Planting according to the optimum density, the planting number is D*, then the actual number of seedling emergence is D S = D * ρ and the nutrition area of each individual plant is the reciprocal of actual planting number D s , 1/D S .

Copyright: © 2018 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.