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Fig. 3 in Hydrochemical Conditions Of Phytoplankton Development In Zaporizhian (Dnipro) Reservoir

Fig. 3. Changes in the biomass (%) of the phytoplankton community in different parts of the Zaporozhye reservoir: 1 – Сyanophyta; 2 – Chlorophyta; 3 – Bacillariophyta; 4 – Euglenophyta; 5 – Chrysophyta.

Fig. 2 in Hydrochemical Conditions Of Phytoplankton Development In Zaporizhian (Dnipro) Reservoir

Fig. 2. Distribution of phytoplankton abundance in different parts of the Zaporizhia reservoir: 1 – Samara bay; 2 – Festival Wharf; 3 – Monastyrskyi island; 4 – the entry of the Mokra Sura river; 5 – the lower part of the reservoir (Viyskove village).

Fig. 3 in Water level influences on body condition of Geophagus brasiliensis (Perciformes: Cichlidae) in a Brazilian oligotrophic reservoir

Fig. 3. Means and standard error (vertical lines) for gonadosomatic index (GSI) by sex and size classes for Geophagus brasiliensis in the Lajes Reservoir. Between-sex significant differences: * p <0.05; ** p <0.01.

Fig. 2 in Water level influences on body condition of Geophagus brasiliensis (Perciformes: Cichlidae) in a Brazilian oligotrophic reservoir

Fig. 2. Means and standard errors (vertical lines) for body condition (K) by sex and water level periods for Geophagus brasiliensis in the Lajes Reservoir. Distinct letters (a, b, c) indicate significant differences at 95% level of confidence.

FIGURE 3 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 3 Variation in HeV AUC from flying foxes. (A) The forest plot displays annual estimates and 95% confidence intervals ordered by latitude and year; points are scaled by the inverse sampling variance. The horizontal axis uses a modulus transformation to accommodate wide upper bounds of some confidence intervals. (B) Fitted values and 95% confidence intervals for the top GAM, with raw data (scaled by inverse sampling variance) and modelled means coloured by roost type. Transparency denotes AUC derived from truncated time series (≤20 weeks)

FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year

FIGURE 1 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 1 Spatiotemporal variation in HeV shedding for the nine Australian flying fox roosts sampled from 2012 through 2014. Curve height indicates the weekly proportion of HeV-positive urine pools, with roosts shown in order of latitude and coloured by roost type. Ticks show sampling time points. Dark grey shading indicates regional acute food shortage events, and dashed lines with light grey shading indicate the Austral winter (i.e. June through August)

FIGURE 4 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 4 Spatiotemporal variation in regional HeV spillover events during the flying fox surveillance period (2012–2014) and its relationship with HeV AUC. (a) Maps display the annual distributions of spillovers (coloured by year) in relation to the nine analysed roosts. (b) Modelled relationships between AUC and spillover counts are shown with fitted values and 95% confidence intervals from GAMs for 50, 100, 200, 300, 400 and 500 km buffers of each roost. Raw data are overlaid and scaled by the inverse of the sampling variance for AUC

opencc-by-4.0May 2020View details →

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Mechanical and hydraulic transport properties of transverse-isotropic Gneiss deformed under deep reservoir stress and pressure conditions.

"This is the ReadMe file corresponding to the study entitled: "Mechanical and hydraulic transport properties of transverse-isotropic Gneiss deformed under deep reservoir stress and pressure conditions"          "By M. Acosta, & M. Violay."          This study has been published in the International Journal of Rock Mechanics and Mining Sciences in June 2020.      https://doi.org/10.1016/j.ijrmms.2020.104235      This Read-Me file has been last edited on 2020-06-31          This readme file describes the data repository and supplementary files accompanying the above publication.       For any further queries please contact mateo.acosta@epfl.ch          The following files are included:          --- Regarding Figure 3.          "1)  ""Acosta_et_al_2020_Figure3Data.xlsx"" "     This is the processed data from the experiments described in Figure1 of the article.     "In this .xlsx File, each sheet corresponds to one figure panel as follows: "          Fig.3: One experiment example Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); --- Regarding Figure 4.          "1)  ""Acosta_et_al_2020_Figure4Data.xlsx"" "     This is the processed data from the experiments described in Figure1 of the article.     "In this .xlsx File, each sheet corresponds to one figure panel as follows: "          Fig.4a&g: Beta=0deg Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); Fig.4b&h: Beta=30deg Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); Fig.4c&i: Beta=45deg Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); Fig.4d&j: Beta=60deg Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); Fig.4e&k: Beta=90deg Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); Fig.4f&l: LPG Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %); --- Regarding all other Figures, the tables provided in the article allow reproduction of these.

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Fig. 1 in Hydrochemical Conditions Of Phytoplankton Development In Zaporizhian (Dnipro) Reservoir

Fig. 1. Scheme of the Zaporizhian (Dnipro) reservoir.

opencc-by-4.0Oct 2023View details →

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X-ray microcomputed tomography (μCT) datasets of hydrogen (H2) drainage and imbibition in a layered sandstone at reservoir pressure and temperature conditions.

This dataset contains raw (unfiltered), filtered, and segmented image stacks of the dry and wet scans (containing fluids) for two pore-scale X-ray H2 flow experiments in a layered Clashach sandstone sample at 50°C and 10 MPa. Please refer to the readme file '00_Readme.pdf' for details about the dataset.

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Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment, which have been hypothesized to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyze three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasizes the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.

opencc-zeroSep 2022View details →

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Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

Open the record for dataset details and reuse information.

publicSep 2022View details →

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Data from: Experimental study of the supercritical CO2 diffusion coefficient in porous media under reservoir conditions

Reliable measurement of CO2 diffusion coefficient in consolidated oil-saturated porous media is critical for design, performance of carbon capture and storage (CCS) and CO2 enhanced oil recovery (EOR) project. In this study, a thorough experimental investigation on supercritical CO2 diffusion in n-decane saturated Berea core (50 and 100 mD) was conducted at elevated pressure (10 to 25 MPa) and temperature (333.15 to 373.15 K), which simulated the real reservoir condition. Supercritical CO2 diffusion coefficients in Berea core were calculated by a model appropriate for diffusion in porous media based on Fick's law. The results show that with an increase in pressure, temperature and permeability, the supercritical CO2 diffusion coefficient increases correspondingly. The supercritical CO2 diffusion coefficient first increases slowly at 10 MPa and then grow significantly with the increase of pressure. The effect of temperature becomes weaker at elevated pressure condition. And the effect of permeability keeps steady despite of temperature change in experiments. The effect of gas state and porous media on supercritical CO2 diffusion coefficient is further discussed by comparing the results in this study with previous research. Based on the experimental results, an empirical correlation for supercritical CO2 diffusion coefficient in n-decane saturated porous media is developed. The experimental results contribute to the study of supercritical CO2 diffusion in compact porous media.

opencc-zeroDec 2018View details →

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Fig. 4 in Hydrochemical Conditions Of Phytoplankton Development In Zaporizhian (Dnipro) Reservoir

Fig. 4. Distribution of phytoplankton biomass in different parts of the Zaporizhian reservoir: 1 – Samara bay; 2 – Festival Wharf; 3 – Monastyrskyi island; 4 – the entry of the Mokra Sura river; 5 – the lower part of the reservoir (Viyskove village).

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Fig. 1 in Water level influences on body condition of Geophagus brasiliensis (Perciformes: Cichlidae) in a Brazilian oligotrophic reservoir

Fig. 1. Means and standard errors (vertical lines) for condition (K) by sex and size classes for Geophagus brasiliensis in the Lajes Reservoir. Between-sex significant differences: * p <0.05; ** p <0.01.

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Fig. 4 in Water level influences on body condition of Geophagus brasiliensis (Perciformes: Cichlidae) in a Brazilian oligotrophic reservoir

Fig. 4. Means and standard errors (vertical lines) for gonadosomatic index (GSI) by sex and water level periods for Geophagus brasiliensis in the Lajes Reservoir. Distinct letters (a, b, c) indicate significant differences at 95% level of confidence.