40 research outputs found

    Water-soluble carbohydrates of root components and activity rhythms at vegetative growth stage of <i>Artemisia scoparia</i> in northeastern grassland of China

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    <div><p>The root system of perennials is composed of the roots of different growth years. The nutrient storage capacities and activities of roots are an important basis for judging root components and plant senescence. In this research, changes in the contents of water-soluble carbohydrate (WSC) were used as indicators of the nutrient storage and activity of roots of different life years. From the early resprouting stage to the rapid growth stage, <i>Artemisia scoparia</i> L. plants of 1–3 age classes were sampled and measured once every 18 days. The nutrient storage capacities and activity rhythms of plant root components of the three age classes were analysed quantitatively. Among the <i>A</i>. <i>scoparia</i> population in northeast China, the nutrient storage capacities of 1a/2a plant root collars and 2-year old roots were generally large, whereas those of 3a plant root collars and 3-year old roots were significantly reduced. As for changes in the WSC content in the root system at the 18 day resprouting stage, the decline rates in the root collars of the 1a and 2a plants were 102 and 109 times those of the 3a plants, respectively. The decline rate in the 2-year old roots of the 1a plants was 1.8 times that of the 2a plants and 29.6 times that of the 3a plants. When nutrients were most active, all root components of the 1a plants entered into the resprouting stage, but the 2/3-year old roots of the 2a plants lagged behind. All the root components of the 3a plants generally lagged. At the vegetative growth stage, the WSC contents in all root components of the 1a plants declined logarithmically. For the 3a plants, the content in the root collars decreased linearly with that in the 3-year old roots. The older root components (3-year old roots) of the 2a plants and all root components of the 3a plants exhibited signs of aging.</p></div

    Observed values and the logarithmic fitting curves of seasonal changes in the contents of water-soluble carbohydrate (WSC) in the 3-year old roots of 2a and 3a plants of <i>Artemisia scoparia</i>, and the average of total root components.

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    <p>(C2, 2a plants; C3, 3a plants; Cm, average of 2a and 3a plants; Tm, average of total root components as root collars, 2-yaer old roots and 3-year old roots of the plants of three age classes).</p

    Comparison among the <i>Artemisia scoparia</i> plants of three age classes in the contents of water-soluble carbohydrate (WSC) in the root components at different times.

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    <p>(A, root collars; B, 2-year old root; C, 3-year old root; D, average of same root components of the plants of three age class; In icon from A to C, the letter “a” is age class of the plants which is 1a, 2a, 3a; above the data column(mean±SE), different small letters mean significant difference(p<0.05), NS is no significant difference(p>0.05) in from A to C among three age classes; Am, average of root collars, Bm, average of 2-year old roots, Cm, average of 3-year old roots in D icon; the small letters or NS above the data column(mean±SE) are the same as the meaning from A to C in D among the root components).</p

    Comparison of <i>comb</i> with the old conflict coefficients in Example 9.

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    <p>Comparison of <i>comb</i> with the old conflict coefficients in Example 9.</p

    The relationship between <i>k</i> and 1 − <i>gir</i> in Example 3.

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    <p>The relationship between <i>k</i> and 1 − <i>gir</i> in Example 3.</p

    GBPAs and their <i>gir</i> and <i>k</i> in Example 3.

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    <p>GBPAs and their <i>gir</i> and <i>k</i> in Example 3.</p

    <i>Bel</i>(<i>θ</i><sub><i>i</i></sub>) and <i>Pl</i>(<i>θ</i><sub><i>i</i></sub>) for <i>m</i><sub>1</sub> and <i>m</i><sub>2</sub> in Example 2.

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    <p><i>Bel</i>(<i>θ</i><sub><i>i</i></sub>) and <i>Pl</i>(<i>θ</i><sub><i>i</i></sub>) for <i>m</i><sub>1</sub> and <i>m</i><sub>2</sub> in Example 2.</p

    Image3_Prioritization of risk genes for Alzheimer’s disease: an analysis framework using spatial and temporal gene expression data in the human brain based on support vector machine.PDF

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    Background: Alzheimer’s disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed.Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD.Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690–0.790]) and full feature sets (0.730 [0.678–0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained.Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.</p
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