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36 results for “growth curves”
Dataset of management and growth curve derivatives to predict first lactation milk yield
<p>This dataset presents management and modeled variables of 78 Holstein-Friesian heifers. <span>Spreadsheets consist of seven sets of training and testing data randomly split with replacement.</span></p> <p></p> <p><span>In each spreadsheet, the included variables are:</span></p> <table> <tbody> <tr> <td><strong>Variable name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>ID</td> <td>Heifer ID tag</td> </tr> <tr> <td>Mes</td> <td>Month of birth of the heifer</td> </tr> <tr> <td>Estacion</td> <td>Birth season, according to Mes</td> </tr> <tr> <td>L_305_F_2x2</td> <td>First lactation milk yield is calculated as the integral from day 1 to 305 of a Fourier model representing daily milk yield (kg)</td> </tr> <tr> <td>Edad_IA</td> <td>Heifer's age at effective artificial insemination</td> </tr> <tr> <td>servicios</td> <td>Number of inseminations</td> </tr> <tr> <td>Edad_1P</td> <td>Heifer's age at first calving</td> </tr> <tr> <td>P_parto</td> <td>Heifer's weight at first calving (kg)</td> </tr> <tr> <td>B_parto</td> <td>Heifer's condition score at first calving</td> </tr> <tr> <td>Sexo_C</td> <td>Calf sex</td> </tr> <tr> <td>Peso_C</td> <td>Calf weight at birth (kg)</td> </tr> <tr> <td>wt_3._F, wt_6._F, wt_9._F, wt_12._F, wt_15._F, wt_18._F, wt_21._F</td> <td>Heifer's weight at various months after birth, as per a Fourier model of Heifer's growth curve (kg)</td> </tr> <tr> <td>X1STd_3._F, X1STd_6._F, X1STd_9._F, X1STd_12._F, X1STd_15._F, X1STd_18._F, X1STd_21._F</td> <td>First derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td>X2STd_3._F, X2STd_6._F, X2STd_9._F, X2STd_12._F, X2STd_15._F, X2STd_18._F, X2STd_21._F</td> <td>Second derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td> <p>wt_3._L to wt_21._L, X1STd_3._L to X1STd_21._L, </p> <p>X2STd_3._P to X2STd_21._P</p> </td> <td>Variables generated from a Power model</td> </tr> </tbody> </table>
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50% of females reach functional sexual maturity (CL50).
Fig. S4. Growth curves for A.intermedia when started the experiment with a in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes
Fig. S4. Growth curves for A.intermedia when started the experiment with a single cell. Color points represents each one of the single-cell experiments, color legend is in the left corner of the figure. Black line correspond to the average growth between experiments.
Figure 4 in Growth curve of Nile tilapia from different families of the AquaAmérica variety
Figure 4. Growth curve for torso length (cm) as a function of age (days) in three contrasting families (Family AA1, Family AA9 and Family AA14) of Nile tilapia (Oreochromis niloticus) AquaAmérica variety.
Figure 2 in Growth curve of Nile tilapia from different families of the AquaAmérica variety
Figure 2. Growth curve for total length (cm) as a function of age (days) in three contrasting families (Family AA1, Family AA9 and Family AA14) of Nile tilapia (Oreochromis niloticus) AquaAmérica variety.
Figure 3 in Growth curve of Nile tilapia from different families of the AquaAmérica variety
Figure 3. Growth curve for standard length (cm) as a function of age (days) in three contrasting families (Family AA1, Family AA9 and Family AA14) of Nile tilapia (Oreochromis niloticus) AquaAmérica variety.
Figure 5 in Growth curve of Nile tilapia from different families of the AquaAmérica variety
Figure 5. Growth curve for body width (cm) as a function of age (days) in three contrasting families (Family AA1, Family AA9 and Family AA14) of Nile tilapia (Oreochromis niloticus) AquaAmérica variety.
Fig. 4. Survival curves with 95 in Effect of temperature on growth, reproductive activity, and survival of the invasive bromeliad-eating weevil Metamasius callizona (Coleoptera: Curculionidae)
Fig. 4. Survival curves with 95% confidence intervals for Metamasius callizona adults at 3 temperatures. Numbers of individuals at time zero were: 16 °C, n = 54; 25 °C, n = 47; and 35 °C, n = 74.
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Data from: Antibiotics shift the temperature response curve of Escherichia coli growth
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Supplemental material for: Growth curves for children with X-linked hypophosphatemia"
<p><span><i>Context: </i>We characterized linear growth in infants and children with X-linked hypophosphatemia (XLH).</span></p> <p><span><i>Objective</i>: Provide linear growth curves for children with XLH from birth to early adolescence.</span></p> <p><span><i>Design</i>: Data from four prior studies of XLH were pooled to construct growth curves. UX023-CL002 was an observational, retrospective chart review. Pre-treatment data were collected from three interventional trials: two phase 2 trials (UX023‑CL201, UX023‑CL205) and a phase 3 trial (UX023-CL301). </span></p> <p><span><i>Setting</i>: Medical centers with expertise in treating XLH. </span></p> <p><span><i>Patients</i>: Children with XLH, 1-14 years of age.</span></p> <p><span><i>Intervention</i>: None.</span></p> <p><span><i>Main Outcome Measure</i>: Height-for-age linear growth curves including values for the 5<sup>th</sup>, 10<sup>th</sup>, 25<sup>th</sup>, 50<sup>th</sup>, 75<sup>th</sup>, 90<sup>th</sup>, and 95<sup>th</sup> percentiles for children with XLH compared to population norms. </span></p> <p><i>Results</i>: 228 patients (132 girls, 96 boys) with 2,381 height measurements were included. Nearly all subjects (>99%) reported prior management with supplementation therapy. Compared to the CDC growth curves, boys at age 0.25, 0.50, 0.75, 1.0, and 2.0 years-old had median height percentiles of 46%, 37%, 26%, 18%, and 5%, respectively; for girls the median height percentiles were 52%, 37%, 25%, 18%, and 7%, respectively. Annual growth in children with XLH fell below that of healthy children near 1 year of age and progressively declined during early childhood, with all median height percentiles <8% between 2 and 12 years old.</p> <p><i>Conclusion</i>: Children with XLH show decreased height gain by 1 year of age and remain below population norms thereafter. These data will help evaluate therapeutic interventions on linear growth for pediatric XLH.<a name="IDX"></a></p>
Data from: Estimating polymorphic growth curve sets with non-chronological data
<p>1. When we collect the growth curves of many individuals, orderly variation in the curves is often observed rather than a completely random mixture of various curves. Small individuals may exhibit similar growth curves, but the curves differ from those of large individuals, whereby the curves gradually vary from small to large individuals. It has been recognized that after standardization with the asymptotes, if all the growth curves are the same (anamorphic growth curve set), the growth curve sets can be estimated using non-chronological data; otherwise, i.e., if the growth curves are not identical after standardization with the asymptotes (polymorphic growth curve set), this estimation is not feasible. However, because a given set of growth curves determines the variation in the observed data, it may be possible to estimate polymorphic growth curve sets using non-chronological data.</p> <p>2. In this study, we developed an estimation method by deriving the likelihood function for polymorphic growth curve sets. The method involves simple maximum likelihood estimation. The weighted nonlinear regression and least squares method after the log-transform of the anamorphic growth curve sets were included as special cases.</p> <p>3. The growth curve sets of the height of cypress (<i>Chamaecyparis obtusa</i>) and larch (<i>Larix kaempferi</i>) trees were estimated. With the model selection process using the AIC and likelihood ratio test, the growth curve set for cypress was found to be polymorphic, whereas that for larch was found to be anamorphic. Improved fitting using the polymorphic model for cypress is due to resolving underdispersion (less dispersion in real data than model prediction).</p> <p>4. The likelihood function for model estimation depends not only on the distribution type of asymptotes, but the definition of the growth curve set as well. Consideration of these factors may be necessary, even if environmental explanatory variables and random effects are introduced.</p>
New tree‐level temperature response curves document sensitivity of tree growth to high temperatures across a US‐wide climatic gradient
<p>Temperature is a key climate indicator, whose distribution is expected to shift right in a warming world. However, the high temperature tolerance of trees is less widely understood than their drought tolerance, especially when it comes to sub-lethal impacts of temperature on tree growth. I use a large data set of annual tree ring widths, combined with a flexible degree-day model, to estimate the relationship between temperature and tree radial growth. I find that tree radial growth responds non-linearly to temperature across many ecoregions of the US: across temperate and/or dry ecoregions, spring-summer temperature increases are beneficial or mostly neutral for tree growth up to around 25-30°C in humid climates and 10-15°C in dry climates, beyond which temperature increases suppress growth. Thirty additional degree-days above the optimal temperature breakpoint lead to an average decrease in tree ring width of around 1-5%, depending on ecoregions, seasons, and inclusion or exclusion of temperature-mediated drought impacts. High temperatures have legacy effects across a 5-year horizon in dry ecoregions, but none in the temperate-humid South-East or among temperature-sensitive trees. I find limited evidence that trees acclimatize to high temperatures within their lifetime: local variation in exposure to high temperatures, which stems from local variation in the timing of tree birth, does not significantly impact the response to high temperatures, although temperature-sensitive trees acquire some heightened sensitivity from early exposure. I also find some evidence that trees adapt to high temperatures in the long-run: across humid ecoregions of the US, high temperatures are 40% less harmful to tree growth, where their average incidence is one standard deviation above average. Overall, these results highlight the strength of a new methodology which, applied to representative tree ring data, could contribute to predicting forest carbon uptake potential and composition under global change.</p>
FIGURE 2. Growth curve obtained for the strains. A in Polyphasic characterization of Nostoc commune (Cyanobacteria, Nostocaceae) isolated from rice growing agro-ecosystems of Dima Hasao district of Assam, North-East India
FIGURE 2. Growth curve obtained for the strains. A. Nostoc commune AUS-JR/DB/NT-003. B. Nostoc commune AUS-JR/DB/NT- 004.
Data from: Species-area curve and distance-decay relationship indicate habitat thresholds of ectomycorrhizal fungi in an old-growth Pseudotsuga menziesii landscape
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Data from: Estimating polymorphic growth curve sets with non-chronological data
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Supplemental material for: Growth curves for children with X-linked hypophosphatemia"
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New tree‐level temperature response curves document sensitivity of tree growth to high temperatures across a US‐wide climatic gradient
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Bertalanffy's growth curves of A. anatina duck mussels in 18 Polish lakes with reference to living conditions
<p>1. Post-maturation growth leading to indeterminate growth patterns is widespread in nature. However, its adaptive value is unclear. Life history theory suggests this allocation strategy may be favoured by temporal pulses in the intensity of mortality and/or the capacity to produce new tissues.</p> <p>2. Addressing the origin of indeterminate growth and the variability of growth patterns, we studied the growth of duck mussels, <i>Anodonta anatina</i>, a pan-European unionid, in 18 Polish lakes. For each population the sex, size and age of collected mussels were measured to estimate Bertalanffy's growth curve parameters. We integrated information on <i>A. anatina</i> mortality rates, lake trophy, biofouling by zebra mussels, <i>Dreissena polymorpha</i>, and the prevalence of parasitic trematode larvae to identify selective conditions in lakes.</p> <p>3. We found two sources of mortality in <i>A. anatina</i> populations, pertaining to adverse effects of zebra mussel biofouling and trophy state on mussel survival. Additionally, populations with heavier biofouling presented a smaller abundance of parasites, indicative of a relationship between filtering intensity and contraction of water-borne trematode larvae by filtering <i>A. anatina</i>.</p> <p>4. Consistently for each sex, populations with a greater trophy-related mortality were characterized in <i>A. anatina</i> by a smaller asymptotic size <i>L<sub>max</sub></i>, indicative of a life history response to mortality risk involving early maturation at a smaller body size. In all populations, females featured higher mortality and larger asymptotic size <i>vs.</i> males.</p> <p>5. Our findings support a theoretical view that adaptive responses to selection involve adjustments in the lifetime resource allocation patterns. These adjustments should be considered drivers of the origin of indeterminate growth strategy in species taking parental care by offspring brooding in body cavities.</p>
Arginine + Citrulline as a Supplement for Weight Gain in Fetus With a Decrease in Their Growth Curve
ClinicalTrials.gov study NCT05029778. IPD Sharing: Not stated. Countries: 0. Publications: 11.
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