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169 results for “morphological models”
Modeling the effects of lake morphology on chloride retention and salt-driven stratification in two urban lakes in St. Paul, MN
Road salt inputs have caused widespread salinization of urban lakes in northern temperate regions. Watershed characteristics are known to be important drivers of lake chloride concentrations, but there has been less focus on how lake morphometry influences seasonal and interannual dynamics in lake chloride, and how these chloride levels may alter mixing in the water column. We analyzed chloride retention for two urban lakes (Como Lake and Lake McCarrons) in Saint Paul, Minnesota, that are in adjacent watersheds and have similar surface areas, but differ in depth and water residence time. Summer chloride concentrations were negatively related to total summer precipitation for Como Lake (maximum depth 2.2 m), but the relationship was less strong for Lake McCarrons (maximum depth 7.6 m). We used a zero-dimensional model to simulate chloride dynamics in both lakes and tracked the fate of chloride over time. In Como Lake, the mass of chloride in the lake turns over within three years, whereas chloride inputs are retained for >10 years in Lake McCarrons. We then used a one-dimensional hydrodynamic lake model (GLM-AED) to examine how lake depth affects how current chloride loading rates alter lake mixing. Salt inputs significantly extended the duration of summer stratification for simulated lakes with depths of 8 m or more, and salt inputs increased the number of days of hypoxia and anoxia across all depths. These results underscore the importance of considering lake morphometry in understanding the effects of salt inputs on lake ecosystems.
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model
<p>The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.</p>
Seed morphology in the Vitaceae based on geometric models
<p>Images of the seeds in the Vitaceae used in the experiments corresponding to the article entitled: Seed morphology in the Vitaceae based on geometric models, by José Javier Martín-Gómez et al. (Agronomy, 2020 submitted)</p>
Deep Learning based Urban Morphology for City-scale Environmental Modeling
<p>The WRF simulations were performed using the Weather Research and Forecasting (WRF) model, version 4.2.1. The three nested domains are centered over Chicago, USA, with a spatial resolution of 9, 3, and 1 km for the outermost, middle, and innermost domains. The model was implemented with 42 pressure levels, with the first model level located at 21.2 m and the first 1 km vertical height containing 11 model levels. The initial and boundary conditions are taken from the National Centers for Environmental Prediction (NCEP) Final Reanalysis dataset at 1 degree spatial and 6-hourly temporal resolution.</p><p>The physics components include the WRF single moment 6 class for microphysics, Dudhia for shortwave, the Rapid Radiative Transfer Model for longwave radiation parameterizations, Bougeault for the planetary boundary layer, Noah for the land surface model, Building Environment Parametrization (BEP) for the urban model, and Grell for the cumulus scheme (only for the outermost domain of 9 km spatial resolution). The LCZs of Chicago, USA, are generated using the crowd-sourcing method. The training dataset, created manually, is obtained from the WUDAPT portal, and random forest classification is applied to Landsat 8 imagery to derive the LCZs for the desired region. The simulations are performed from 1/Jul/2018 00:00 to 7/Jul/2018 06:00, where the first 6 hours are discarded as spin-up time.</p><p>The Digital Synthetic City (DSC) of Chicago, USA, uses satellite imagery and global-scale population and elevation data as input to the automatic method for producing a statistically similar and synthetic city-scale 3D urban model as output.</p><p>The Control simulations use National Land Cover Database land use/land cover with NUDAPT parameters, the three default WRF urban classes, and corresponding UCPs; the WUDAPT uses the MODIS classes with additional urban LCZs and UCPs from Brousse et al. (2016), and the DSC uses the WUDAPT classes with UCPs generated from DSC method.</p><p>The dataset contains:</p><p>1. Output from DSC in Shapefile.</p><p>2. WRF model output for the third domain (1 km) spatial resolution domain for (a) NUDAPT or Control (b) WUDAPT or LCZs (c) DSC</p>
Fig. 12 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 12. The same morphospaces as Fig. 11 showing the distribution of 115 species based on measurements.
Fig. 10 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 10. Measurements of the whorl expansion rate (Wc) and width of umbilicus (D). Note that c1 and c2 are defined as the distances from the coiling axis to the centers of the whorls. d1 and d2 are distances of umbilical seams from the coiling axis.
Fig. 8 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 8. Sketches of radial cross sections of ammonoids to illustrate various types of shell forms, such as planorbicone (A, B), serpenticone (C, D), oxycone (E), discocone (F), platycone (G, H), spherocone (I, J, K), and cadicone (L). A. Paraceltites elegans. B. Pseudoclymenia dillensis. C. Tropigastrites lahontanus. D. Pterolytoceras sp. E. Beloceras sp. F. Phylloceras consanguineum Gemmellaro. G. Craspedites sp. H. Tetragonites glabrus. I. Damesites sugata. J. Goniatites multiliratus Gordon. K. Latanarcestes sp. L. Cabrieroceras sp.
Fig. 9 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 9. Theoretical morphospace composed of ae and ar displaying several examples of computer−generated ammonoids that represent observed types illustrated in Fig. 8. A. e = 1.5 and r = 0.27. B. e = 1.75 and r = 0.34. C. e = 2.0 and r = 0.17. D. e = 2.3 and r = 0.05. E. e = 2.0 and r = 0.6. F. e = 1.7 and r = 0.27. G. e = 1.8 and r = 0.24. H. e = 2.3 and r = 0.21. I. e = 2.3 and r = 0.32. J. e = 1.95 and r = 0.28. K. e = 1.8 and r = 0.10. L. e = 1.6 and r = 0.8.
Fig. 7 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 7. Relationship between whorl perimeter (L) and square root of cross−sectional area of the whorl (A0.5) which varies with aand avalues. Dashed line i e r in each diagram represents the A0.5/Lcurve in the case of isometry of E and R (a= a= 1.0). A0.5/Lis generally small when ahas a large value. In each i e r i r model, e = 1.9 and r = 0.24.
Fig. 11 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 11. Results of computer simulations. A–D, theoretical morphospaces based on the piggyback whorls model composed of ae and ar showing values of shape parameters (S, A0.5/L, W, D) of a theoretical model corresponding to each combination of aand a. E, negative correlation between Wand S values i c e r c obtained from theoretical models. F, positive correlation between D and S. The values of S, A0.5/L, Wor D are exhibited by the size of the plots, and × indii c cates that a "forbidden" combinations of ae, ar, e, and r was employed in each simulation resulting in failure of defining form (A–D).
Fig. 6 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 6. Allometric growth of ammonoids. Computer models were generated with ae and ar values of 0.97, 1.0, and 1.3. Each diagram shows ontogenetic change in relationship between whorl height (H) and whorl breadth (B) that are standardized by the perimeter of the last whorl (Lmax). Dashed line in each diagram represents the B/H curve in the case of isometry of E and R (ae = ar = 1.0). Note that B/H decreases with growth in the case of ar = 0.97. In each model, e = 1.9 and r = 0.24.
Fig. 5 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 5. Spectrum of the computer−produced ammonoids with various values of E and R when each of them is fixed throughout growth.
Fig. 4 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 4. Schematic diagram of an ammonoid whorl section. The shape of the whorl (S) is represented by the closed curve illustrated here which is given by the parametric equations indicated above the diagram. Figures in the right show examples of hypothetical whorl shapes with systematically varying the S value.
Fig. 3 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 3. Different shapes of succeeding whorl (gray area) piling up on the hemispherical preceding whorl with same Li, l, and A values.
Fig. 2 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 2. Schematic figure of a cross section of the ammonoid shell showing how to define parameters of the piggyback whorls model. The enlarging rate of the whorl periphery (E) is defined as the ratio of the total perimeter of the newly added whorl section (Li) to that of preceding whorl (Li–1). The proportion of the dorsal whorl (R) is given by the ratio of the circumferential length along the dorsal wall (l) with respect to Li. The shape of the whorl section (S) is represented by the ratio of breadth (B) and height (H) of the whorl.
Fig. 1 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 1. Radial cross sections of ammonoids to illustrate allometric growth. A. Beloceras sp., Devonian; Erfoud, Morocco. B. Phylloceras consanguineum Gemmellaro, 1876, Jurassic; Sakaraha, Madagascar. C. Meekoceras gracilitatis White, 1879, Triassic; Crittenden Spring, Nevada. D. Girtyoceras meslerianum (Girty, 1909), Carboniferous; Jackforth Creek, Oklahoma.
Fig. 13 in Piggyback whorls: A new theoretical morphologic model reveals constructional linkages among morphological characters in ammonoids
Fig. 13. Results of same computer simulations as shown in Fig. 11 with an ornamented whorl shape. In order to produce an ornamented closed curve, the following parametric equations were used: x = 0.5Bcoso, y = 0.5H(sino + 0.3|sin4o|Q), where Q = H/B/5 if o Ṥ π, while Q = 5B/H if o> π. Note that simulated S values tend to be large as compared with Fig. 11F.
Fig. 2 in Relationships between Dimensionless Models of Ammonoid Shell Morphology
Fig. 2. Plot of a sample of 1222 observations from 201 species of Mesozoic planispiral ammonites in the morphospace (H2/H1, H1/D), modified from Parent et al. (2010). The closed curves are the contours shown in Raup (1967: figs. 4, 8) as explained in text.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.