Effects of arbuscular mycorrhizal fungi on growth and nitrogen uptake of <i>Chrysanthemum morifolium</i> under salt stress - Fig 4

<p>Effects of arbuscular mycorrhizal fungi on shoot N concentration (A), shoot P concentration (B), root N concentration (C) and root P concentration (D) of <i>C</i>. <i>morifolium</i> plants under 0, 50 and 200 mM NaCl. Symbols are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196408#pone.0196408.g001" target="_blank">Fig 1</a>.</p

Optimum water depth ranges of dominant submersed macrophytes in a natural freshwater lake

<div><p>Macrophytes show a zonal distribution along the lake littoral zone because of their specific preferred water depths while the optimum growth water depths of dominant submersed macrophytes in natural lakes are not well known. We studied the seasonal biomass and frequency patterns of dominant and companion submersed macrophytes along the water depth gradient in Lake Erhai in 2013. The results showed that the species richness and community biomass showed hump-back shaped patterns along the water depth gradient both in polydominant and monodominant communities. Biomass percentage of <i>Potamogenton maackianus</i> showed a hump-back pattern while biomass percentages of <i>Ceratophyllum demersum</i> and <i>Vallisneria natans</i> appeared U-shaped patterns across the water depth gradient in polydominant communities whereas biomass percentage of <i>V</i>. <i>natans</i> increased with the water depth in monodominant communities. Dominant species demonstrated a broader distribution range of water depth than companion species. Frequency and biomass of companion species declined drastically with the water depth whereas those of dominant species showed non-linear patterns across the water depth gradient. Namely, along the water depth gradient, biomass of <i>P</i>. <i>maackianus</i> and <i>V</i>. <i>natans</i> showed hump-back patterns and biomasses of <i>C</i>. <i>demersum</i> displayed a U-shaped pattern in the polydominant communities but biomass of <i>V</i>. <i>natans</i> demonstrated a hump-back pattern in the monodominant communities; frequency of <i>P</i>. <i>maackianus</i> showed a hump-back pattern and <i>C</i>. <i>demersum</i> and <i>V</i>. <i>natans</i> maintained high frequencies in the two types of communities. We can speculate that in Lake Erhai the optimum growth water depths of <i>P</i>. <i>maackianus</i> and <i>C</i>. <i>demersum</i> in the polydominant communities are 2.5–4.5 m and 1–2 m or 5–6 m, respectively and that of <i>V</i>. <i>natans</i> is 3–5 m in the polydominant communities and 2.5–5 m in the monodominant communities. This is the first report that the optimum water depth ranges in the horizontal direction of three dominant submersed macrophytes in a natural freshwater lake were determined.</p></div

DataSheet_1_sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.docx

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.</p

DataSheet_2_sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.xlsx

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.</p

Seasonal biomass patterns of submersed species across the water depth gradient in the polydominant and monodominant communities.

<p>Seasonal biomass patterns of submersed species across the water depth gradient in the polydominant and monodominant communities.</p

Colonization of <i>Chrysanthemum morifolium</i> by arbuscular mycorrhizal fungi (AMF) under salt stress.

<p>Colonization of <i>Chrysanthemum morifolium</i> by arbuscular mycorrhizal fungi (AMF) under salt stress.</p

Effects of salt, arbuscular mycorrhizal fungi (AMF) and their interactions on growth and nutrient parameters of <i>Chrysanthemum morifolium</i> plants.

<p>Effects of salt, arbuscular mycorrhizal fungi (AMF) and their interactions on growth and nutrient parameters of <i>Chrysanthemum morifolium</i> plants.</p

Effects of arbuscular mycorrhizal fungi on growth and nitrogen uptake of <i>Chrysanthemum morifolium</i> under salt stress - Fig 1

<p>Effects of arbuscular mycorrhizal fungi on leaf area (A) and root length (B) of <i>Chrysanthemum morifolium</i> plants under 0, 50 and 200 mM NaCl. NM, Fm, Dv and Fm+Dv represent inoculation with no mycorrhizal fungi, <i>Funneliformis mosseae</i>, <i>Diversispora versiformis</i> and the combined inoculums, respectively. Values are presented as the mean ± SE. Values followed by the same letter do not differ significantly at P <0.05 by the LSD multiple range test.</p

Relations between water depth and species richness, community biomass in the polydominant and monodominant communities.

<p>Relations between water depth and species richness, community biomass in the polydominant and monodominant communities.</p

Seasonal frequency patterns of submersed species across the water depth gradient in the polydominant and monodominant communities.

<p>Seasonal frequency patterns of submersed species across the water depth gradient in the polydominant and monodominant communities.</p