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Colombia. Economia.2005 2016. Transporte Terrestre. Banca Comercial. Data

Colombia. Economia.2005 2016. Transporte Terrestre. Banca Comercial.  Data

Colombia. Economia. 2005 2016. Transporte Terrestre. Peajes y Viajes

Colombia. Economia. 2005 2016. Transporte Terrestre. Peajes y Viajes

Fig. 8 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 8. Intermediate cardenolide profiles in inflorescence-grafted plants. Plants were grafted at the inflorescence stage, with the graft junction above the leaves of the stock and below the developing inflorescence of the scion. Cardenolide content was measured in (A) flowers, (B) green siliques, and (C) dry seeds of the grafted plants. The principal component analysis (PCA) is of eight detected cardenolides. Ellipses signify 95% confidence intervals. Bar graphs of the data are in Supplemental Fig. S4.

Fig. 7 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 7. Cardenolides transport in aboveground tissue. The stalks of three to four-week-old wildtype and 454 cardenolide mutant plants were grafted. Two weeks later, cardenolides were measured in (A) leaves above the graft junction, (B) leaves below the graft junction, (C) stems immediately above the graft junction, and (D) stems immediately below the graft junction. The principal component analysis (PCA) is of eight detected cardenolides. Bar graphs of the data are in Supplemental Fig. S3. Ellipses signify 95% confidence intervals.

Fig. 5 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 5. Cotyledons retain the maternal plant cardenolide phenotype until true leaf emergence. Cardenolides were measured in F2 progeny of wt x 454 F1 plants. (A) Cardenolide content of single F2 seeds, (B) cardenolide content of F2 cotyledons from individual plants before true leaf emergence, (C) principal component analysis (PCA) of cardenolide content of true leaves of F2 plants, and (D) cardenolide content of F2 true leaves. Different letters indicate P <0.05 differences for each cardenolide, ANOVA followed by Tukey's HSD test. Bars are mean ± s.d. of N = 4–6 homozygotes and 20–24 F2s. wt = wildtype E. cheiranthoides var. Elbtalaue, 454 = 454 cardenolide mutant line. Ellipses in the PCA plot represent 95% confidence intervals. Peak areas were normalized to an ouabain internal standard.

Fig. 6 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 6. Grafting experiments show that shoot genotype determines root cardenolide phenotype. Seedlings were grafted at the cotyledon stage and cardenolides were measured in leaves and roots after three weeks. (A) Scion (leaf) cardenolides in grafted plants. (B) Stock (root) cardenolides in grafted plants. Different letters indicate P <0.05 differences for each cardenolide, ANOVA followed by Tukey's HSD test. Bars are mean ±s.d. of N =5–13 for shoot samples and 4–10 for root samples. wt = wildtype E. cheiranthoides var Elbtalaue; 454 = 454 cardenolide mutant line. Peak areas were normalized to an ouabain internal standard.

Fig. 3 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 3. Maternal genotype determines seed cardenolide phenotype. Seed cardenolide content was measured in seeds from naturally self-pollinated and manually crossed plants. Different letters indicate P <0.05 differences for each cardenolide, ANOVA followed by Tukey's HSD test. Bars are mean ± s.d. of N = 4–5. wt = wildtype E. cheiranthoides var. Elbtalaue, 454 = 454 cardenolide mutant line. Peak areas were normalized to an ouabain internal standard.

Fig. 4 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 4. Cotyledons retain the maternal plant cardenolide phenotype until true leaf emergence. (A) Cardenolide content of cotyledons before true leaf emergence, (B) cardenolide content of cotyledons after true leaf emergence, (C) cardenolide content of true leaves. Different letters indicate P <0.05 differences for each cardenolide, ANOVA followed by Tukey's HSD test. Bars are mean ± s.d. of N = 6–10. wt = wildtype E. cheiranthoides var. Elbtalaue, 454 = 454 cardenolide mutant line. Peak areas were normalized to an ouabain internal standard.

Fig. 1 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 1. As described by Züst et al. (2020), abundant cardenolides in E. cheiranthoides have digitoxigenin, cannogenol, cannogenin, or strophanthidin as the steroid core. Sugar side chains added to these steroid cores provide additional structural variation. The side chains of Dig-10, Dig-19, Dig-20, and Can-32 have not been fully characterized but are predicted based on MS fragmentation.

Fig. 2 in Acropetal and basipetal cardenolide transport in Erysimum cheiranthoides (wormseed wallflower)

Fig. 2. Principal component analysis (PCA) biplot of genin abundance as a percentage of total cardenolide abundance. Variable loadings for the first two principal components are displayed as vectors.

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Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part II: Main runs)

This is Part II of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files for every simulations used in the paper. Each zip file corresponds to a model run.  TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100. TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200. TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY. Main scenarios: - 01: Spartina (Figures 1-5, S3-S10) - 02: Salicornia (Figures 1-5, S3-S10) - 83: No vegetation (Figures 1-5, S3, S8-S10) Additional scenarios: - 146: Spartina, low bulk drag coefficient (Figure S3) - 147: Spartina, very low bulk drag coefficient (Figure S3) - 148: Salicornia, low bulk drag coefficient (Figure S3) - 149: Salicornia, very low bulk drag coefficient (Figure S3) - 122: Spartina, low settling velocity (Figure S8) - 123: Spartina, high settling velocity (Figure S8) - 124: Salicornia, low settling velocity (Figure S8) - 125: Salicornia, high settling velocity (Figure S8) - 126: No vegetation, low settling velocity (Figure S8) - 127: No vegetation, high settling velocity (Figure S8) - 128: Spartina, low critical bed erosion shear stress (Figure S8) - 129: Spartina, high critical bed erosion shear stress (Figure S8) - 130: Salicornia, low critical bed erosion shear stress (Figure S8) - 131: Salicornia, high critical bed erosion shear stress (Figure S8) - 132: No vegetation, low critical bed erosion shear stress (Figure S8) - 133: No vegetation, high critical bed erosion shear stress (Figure S8) - 134: Spartina, low Partheniades constant (Figure S8) - 143: Spartina, high Partheniades constant (Figure S8) - 136: Salicornia, low Partheniades constant (Figure S8) - 144: Salicornia, high Partheniades constant (Figure S8) - 138: No vegetation, low Partheniades constant (Figure S8) - 145: No vegetation, high Partheniades constant (Figure S8) - 150: Spartina, low sediment dry bulk density (Figure S8) - 151: Spartina, high sediment dry bulk density (Figure S8) - 152: Salicornia, low sediment dry bulk density (Figure S8) - 153: Salicornia, high sediment dry bulk density (Figure S8) - 154: No vegetation, low sediment dry bulk density (Figure S8) - 155: No vegetation, high sediment dry bulk density (Figure S8) - 76: Spartina, replicate #1 (Figures S9-S10) - 77: Spartina, replicate #2 (Figures S9-S10) - 78: Spartina, replicate #3 (Figures S9-S10) - 88: Spartina, replicate #4 (Figures S9-S10) - 80: Salicornia, replicate #1 (Figures S9-S10) - 81: Salicornia, replicate #2 (Figures S9-S10) - 82: Salicornia, replicate #3 (Figures S9-S10) - 89: Salicornia, replicate #4 (Figures S9-S10) - 85: No vegetation, replicate #1 (Figures S9-S10) - 86: No vegetation, replicate #2 (Figures S9-S10) - 87: No vegetation, replicate #3 (Figures S9-S10) - 90: No vegetation, replicate #4 (Figures S9-S10)

Spin sensitive transport in spin liquid candidate RuCl3: revealing a robust spin anisotropy

Source data of manuscript's figures.

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Effects of submerged flexible vegetation on scalar transport in an open-channel flow

The data were used in the paper "Effects of submerged flexible vegetation on scalar transport in an open-channel flow" which was submitted to "Water Resources Research".In this paper, a 3D numerical model simulating scalar transfer in water flow with flexible submerged vegetation is established. The effects of vegetation on enhancing the scalar transfer and the selection of turbulent Schmidt number are investigated by numerical simulations.

Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB.

The RpoS/σS sigma subunit of RNA polymerase is the master regulator of the general stress response in many Gram-negative bacteria.We have shown that in Salmonella enterica serovar Typhimurium, RpoS activates transcription of the sitABCD and mntH  genes, involved in iron and manganese transport, and that of corA encoding the main magnesium transporter (Levi-Meyrueis et al. 2014, Metaane et al. 2022, Metaane et al. 2023). In addition, RpoS represses expression of the CbiO protein produced from the cbiMNQO operon encoding a high affinity cobalt uptake system (Lago et al. 2017, Metaane et al. 2022, Metaane et al. 2023).  Moreover, Inductively coupled plasma mass spectrometry analyses have revealed that the ΔrpoS mutation reduces the cellular concentration of manganese and magnesium and increases the concentration of cobalt of stationary phase Salmonella (Metaane et al. 2022). These findings suggested that a tight control of uptake and availability of manganese, magnesium and cobalt might be critical for quiescent bacteria. Consistent with this hypothesis, our recent findings unraveled the importance of RpoS and magnesium in the regrowth potential of quiescent Salmonella cells (Metaane et al. 2022).Unexpectedly, our recent work revealed that, under magnesium proficient environmental conditions, the absence of the housekeeping Mg2+ transporter CorA is sensed by the cell which induces compensatory mechanisms to minimize the impact of a ΔcorA mutation on protein content, magnesium homeostasis, growth, and motility of Salmonella (Metaane et al. 2023). In this study, we used a mass spectrometry-based proteomics approach to address the physiological impact of the SitABCD, MntH and CbiMNQO transporters on quiescent  Salmonella. A comprehensive quantitative proteomic analysis was performed using wild-type and ΔrpoS strains of Salmonella ATCC14028 carrying deletions of these genes and grown to late stationary phase in nutrient-rich LB medium, i.e. the growth conditions previously used to characterize the RpoS- transcriptome, proteome and ionome (Levi-Meyrueis et al. 2014, Lago et al. 2017, Metaane et al. 2022). Since CorA can also import cobalt, a ΔcorA mutation was included and combined with the ΔcbiMNQO mutation.     Accession number.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD043760.This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).ReferencesLevi-Meyrueis C, Monteil V, Sismeiro O, Dillies MA, Monot M, Jagla B, Coppée J-Y, Dupuy B, Norel F. Expanding the RpoS/sigmaS-network by RNA sequencing and identification of sigmaS-controlled small RNAs in Salmonella. PloS one. 2014;9(5):e96918.Lago M, Monteil V, Douche T, Guglielmini J, Criscuolo A, Maufrais C, Matondo M, Norel F. Proteome remodelling by the stress sigma factor RpoS/sigma(S) in Salmonella: identification of small proteins and evidence for post-transcriptional regulation. Scientific reports. 2017;7(1):2127.Metaane S, Monteil V, Ayrault S, Bordier L, Levi-Meyreuis C, Norel F. The stress sigma factor sigmaS/RpoS counteracts Fur repression of genes involved in iron and manganese metabolism and modulates the ionome of Salmonella enterica serovar Typhimurium. PLoS one. 2022;17(3):e0265511.Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of Salmonella, is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one 2023;18(9):e0291736.NOREL, & MONTEIL. (2023). Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS) (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8085835

Dataset in support of the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e

This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e. The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file. The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651

Dataset supporting the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e

This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e. The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file in the CloudJ sub-folders. The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651

Fig. 4. 109 in ABCG transporter proteins with beneficial activity on plants

Fig. 4. 109Cd radioactivity in shoots and roots of Arabidopsis plants measured using liquid scintillation counter (Kim et al., 2007).

Fig. 1 in ABCG transporter proteins with beneficial activity on plants

Fig. 1. Kanamycin resistance in T1 transgenic seeds with pABC. (a) Segregation of T1 seeds when plated on 200 mg/l of kanamycin containing medium. (b) Northern blot analysis shows expression of AtABCG19 gene (Mentewab and Stewart 2005).

Fig. 3 in ABCG transporter proteins with beneficial activity on plants

Fig. 3. Arabidopsis plants grown in medium containing different concentrations of Cd and Pb. (a) Decrease in growth of plants with AtPDR8-RNAi (8i-2) and AtPDR8 knockout plants(ko-1), whereas normal growth occurs in wild-type plants(WT) in media containing different concentrations of CdCl2. (b) Decreased resistance to Pb in Arabidopsis plants with 8i-2 and knockout of AtPDR8 (ko-2) when grown in Pb(NO3)2 containing medium (Kim et al., 2007).

Fig. 6 in ABCG transporter proteins with beneficial activity on plants

Fig. 6. Mutants of AtABCG26 with loss of function (abcg26-1) show less amount of self-pollination due to impaired sporopollenin formation whereas wild type plants with normal AtABCG26 perform usual self-pollination (Choi et al., 2011).