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opencc-by-4.0Sep 2022View details →

Fig. 6. Pair correlation function g in Fig. 6. Pair correlation function g in Parascorpaena poseidon Chou and Liao 2022

Fig. 6. Pair correlation function g(r) of suitable habitats. Where the observed gRipley (black line) or gTranslate (red dashed line) is greater than gPois (green dashed line) we can expect more clustering than expected and where the observed gRipley or gTranslate is less than gPois we can expect more dispersion than expected.

Fig. 3 in Behavioral, physiological and morphological correlates of parasite intensity in the wild Cururu toad (Rhinella icterica)

Fig. 3. Association between locomotor performance and pulmonary parasite intensity in Rhinella icterica (N = 20; r = –0.49, P = 0.03). SVL = snout-vent length.

Fig. 2 in Behavioral, physiological and morphological correlates of parasite intensity in the wild Cururu toad (Rhinella icterica)

Fig. 2. Association between standard metabolic rate and total parasite intensity in Rhinella icterica (N = 22; r = –0.45, P = 0.03).

Fig. 1 in Behavioral, physiological and morphological correlates of parasite intensity in the wild Cururu toad (Rhinella icterica)

Fig. 1. Association between the score of the first component of a PCA on morphological variables (large heart, kidney and intestine masses) and parasite intensities in Rhinella icterica (N = 16). Full circles represent total parasite intensity (r = 0.66, P <0.01), open circles represent pulmonary parasite intensity (r = 0.71, P <0.01), and open triangles represent intestinal parasite intensity (r = 0.51, P = 0.04).

opencc-by-4.0Oct 2021View details →

Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 in Evaluating the correlation between area, environmental heterogeneity, and species richness using terrestrial isopods (Oniscidea) from the Pontine Islands (West Mediterranean)

Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 scaling is shown. A right-angled projection of a point representing a response variable (ecological categories of species) onto an arrow representing an explanatory variable (biotope type)

opencc-by-4.0Oct 2021View details →

Fig. 2 in Evaluating the correlation between area, environmental heterogeneity, and species richness using terrestrial isopods (Oniscidea) from the Pontine Islands (West Mediterranean)

Fig. 2 Path analysis model. In this model, species richness (S) is the dependent variable. Area (A) and environmental heterogeneity (H) can have a direct effect on S, whereas A can also have an effect on H. The indicators used for A and S are the log-transformed area in square kilometres (LogA) and the number of species (LogS). Different indicators were used for environmental heterogeneity (B, LogB, Shannon, and 1-D, see main text). The symbols bAS, bAH, and bHS indicate the partial standardised regression coefficients

mCherry fluorescence correlation with western blot band intensity

Overview The results present in this dataset were obtained from recombinant Chlamydomonas reinhardtii expressing the fluorescent protein mCherry in different constructs formats, aiming to recombinant protein secretion.  Image present in this dataset were obtained by following the steps described in the protocol dx.doi.org/10.17504/protocols.io.kfpctmn.  Fluorescence measurements were obtained by growing cc1690 in 50 mL TAP media on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m2s). Then, 100 μL of each strain was transferred to a clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning 1 mL sample at 15000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. Files Info 021115_20171219 Correlation_Fluore_vs_Densito_mCherry_50mL_7_days.xlsx   -> data points for fluorescence measurements and pixel intensity obtained with FiJI (Fiji Is Just ImageJ).  022615 WB SP mCHerry Lysate 3.JPG -> Raw western blot image for lysate samples 022615 WB SP mCHerry Supernatant 3 .JPG -> Raw western blot image for supernatant samples  20171219 Fluor_Densito_Correlation_Lys.png -> Correlation plot of fluorescence and pixel intensity for lysate samples WB_Lysa_7days_for_Densitom.png  -> Prepared image for densitometry analysis of lysate samples 20171219 WB_Sup_7days_for_Densitom.png -> Correlation plot of fluorescence and pixel intensity for supernatant samples WB_Sup_7days_for_Densitom.png  -> Prepared image for densitometry analysis of supernatant samples   Consider citing our work.  Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

opencc-by-4.0Mar 2018View details →

Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'

One raw data file and one R script that reproduces all analyses and figures reported in the paper 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study' by Nettle et al.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students

The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female students, through applying the compute (Ljung- Box Q) to check the model accommodation on the function level 0.05 so the Q value occurs of primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value   equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions)  of the residues for  females primary stage students, in which the residues  value is located within the confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is shown.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students

We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students

The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage

We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.

opencc-by-4.0Sep 2018View details →

Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"

Data related to the publication Murschhauser et al.: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details. The format of the data is described below. The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity. Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers. The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below. The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory. The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following: <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul>

opencc-by-4.0Apr 2019View details →

Supplementing data and code for: "Correlation of mRNA delivery timing and protein expression in lipid-based transfection"

Supplementary data and code for Reiser et al.: Correlation of mRNA delivery timing and protein expression in lipid-based transfection. 2019, <a href="https://doi.org/10.1093/intbio/zyz030">doi:10.1093/intbio/zyz030</a>. See `README.pdf` for further details.