Transfer Mechanism, Uptake Kinetic Process, and Bioavailability of P, Cu, Cd, Pb, and Zn in Macrophyte Rhizosphere Using Diffusive Gradients in Thin Films

The transfer-uptake-bioavailability of phosphorus (P), Cu, Cd, Zn, and Pb in rhizosphere of <i>Zizania latifolia</i> (ZL) and <i>Myriophyllum verticiilaturn</i> (MV) cultivated in rhizoboxes in Lake Erhai (China) is evaluated by DGT (diffusive gradients in thin films) technique. DGT induced fluxes in sediments (DIFS) model reveals that resupply ability (r), liable pool size in sediment solid (<i>k</i>_d), kinetic parameter (<i>k</i>_–1), or response time (<i>T</i>_c) control the diffusion-resupply characters of P and Cu (standing for four metals) in rhizosphere interface. The linear fitting curves of element content in ZL or MV roots (<i>C</i>_root) against DGT (<i>C</i>_DGT), porewater (<i>C</i>₀), or sediment concentration demonstrate that <i>C</i>_root for five elements can be predicted by <i>C</i>_DGT more effectively than the other methods. (I) DOC (dissolved organic carbon) in porewater controlled by OM (organic matter) in solid plus pH for Cu and Cd or (II) DOP/DTP ratio in porewater (between dissolved organic P and dissolved total P) for P controlled by Fe-bound P and OM in solid, can affect phytoavailability in rhizosphere. They lead to (I) the larger slope (<i>s</i>) and the linear regression coefficient (<i>R</i>²) in the first part than those for the complete fitting curve (ZL or MV root against <i>C</i>_DGT(Cu) or <i>C</i>₀(Cu) and MV root against <i>C</i>_DGT(Cd)) or (II) the outliers above or below the fitting curve (ZL root (P) against <i>C</i>₀(P) or <i>C</i>_DGT(P)) and the larger <i>R</i>² without outliers. DGT–rhizobox–DIFS should be a reliable tool to research phytoremediation mechanism of macrophytes

CLUSS: Clustering of protein sequences based on a new similarity measure-1

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p>clustering results obtained on six randomly generated subsets: SS1, red; SS2, blue; SS3, green; SS4, yellow; SS5, gray; SS6, amber

CLUSS: Clustering of protein sequences based on a new similarity measure-5

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p>enBank: "", GenBank: "", GenBank: "" and "", which were recently analyzed by Côté . [41] and Fukamizo . [44] and characterized by their ability to recognize a substrate not yet associated with GH2 members

CLUSS: Clustering of protein sequences based on a new similarity measure-0

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p>he 1000 randomly generated subsets from the COG database. As shown, the obtained results are in good concordance with the functional reference characterization of COG. The average of the quality measure of the 1000 clusterings is equal to with a standard deviation equal to . More than of the 1000 clusterings obtained a quality measure superior to , and more than of the clusterings obtained a quality measure superior to . The minimum value of the quality measure is and the maximum value is

CLUSS: Clustering of protein sequences based on a new similarity measure-4

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p> (DDBJ: , DDBJ: ) "" and (GenBank: , DDBJ: ) "" activities. Subfamily SF_8 also includes closely related plant enzymes and bacterial enzymes produced by members of the genus Xanthomonas, including several plant pathogens

CLUSS: Clustering of protein sequences based on a new similarity measure-6

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p>ank: ), GaA(GenBank: ), GaK(GenBank: ), GaC(GenBank: ), GaEcl(DDBJ: ), GaL(GenBank: ), GIC(GenBank: ), GIE(GenBank: ), GIH(GenBank: ), GIL(GenBank: ), GIM(GenBank: ), GIF(GenBank: ), GIS(GenBank: ), MaA(EMBL: ), MaB(GenBank: ), MaC(GenBank: ), MaH(GenBank: ), MaM(GenBank: ), MaT(EMBL: ), CsAo(GenBank: ), CsS(DDBJ: ), CsG(NCBI: XM_382490), CsM(NCBI: XP_369600), CsN(NCBI: XP_331434), CsAn(GenBank: ), CsH(DDBJ: ), CsE(NCBI: XP_746417), UnA(GenBank: ), UnBv(GenBank: ), UnBc(NCBI: ZP_00425692), UnBm(GenBank: ), UnBp(NCBI: YP_107240), UnR(GenBank: )

CLUSS: Clustering of protein sequences based on a new similarity measure-2

<p><b>Copyright information:</b></p><p>Taken from "CLUSS: Clustering of protein sequences based on a new similarity measure"</p><p>http://www.biomedcentral.com/1471-2105/8/286</p><p>BMC Bioinformatics 2007;8():286-286.</p><p>Published online 4 Aug 2007</p><p>PMCID:PMC1976428.</p><p></p>clustering results obtained on the members of the G-protein family. CLUSS obtained the highest quality measure of all the clustering results for this family, which shows that the CLUSS grouping is nearest to the functional reference classification for the G-protein family

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

Adsorption of Lysine on Na-Montmorillonite and Competition with Ca²⁺: A Combined XRD and ATR-FTIR Study

Lysine adsorption at clay/aqueous interfaces plays an important role in the mobility, bioavailability, and degradation of amino acids in the environment. Knowledge of these interfacial interactions facilitates our full understanding of the fate and transport of amino acids. Here, X-ray diffraction (XRD) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) measurements were used to explore the dynamic process of lysine adsorption on montmorillonite and the competition with Ca²⁺ at the molecular level. Density functional theory (DFT) calculations were employed to determine the peak assignments of dissolved lysine in the solution phase. Three surface complexes, including dicationic, cationic, and zwitterionic structures, were observed to attach to the clay edge sites and penetrate the interlayer space. The increased surface coverage and Ca²⁺ competition did not affect the interfacial lysine structures at a certain pH, whereas an elevated lysine concentration contributed to zwitterionic-type coordination at pH 10. Moreover, clay dissolution at pH 4 could be inhibited at a higher surface coverage with 5 and 10 mM lysine, whereas the inhibition effect was inconspicuous or undetected at pH 7 and 10. The presence of Ca²⁺ not only could remove a part of the adsorbed lysine but also could facilitate the readsorption of dissolved Si⁴⁺ and Al³⁺ and surface protonation. Our results provide new insights into the process of lysine adsorption and its effects on montmorillonite surface sites

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