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Fig. 1. Linear carapace variables used for A in Fig. 25 in Fig. 20. Sesarmops mora n in Paralbunea dayriti
Fig. 1. Linear carapace variables used for A, Eocyzicus argillaquus and B, Eulimnadia texana, following Defretin-Lefranc (1965) and Tasch (1987): A, most anterior point of the valve; B, most posterior point of the valve; C, most ventral point of the valve; D, anterior extremity of the dorsal margin; E, posterior extremity of the dorsal margin; U, midpoint of the larval valve (located on the umbo, but not necessarily the midpoint of the umbo). a, vertical distance of A to A'; b, vertical distance of B to B'; c, horizontal distance of C to A''; Arr, horizontal distance of E to B'; Av, horizontal distance of D to A'; Ch, length of the dorsal margin; Cr, horizontal distance of U' to A'; u, vertical distance of Ch to highest point of the umbo; L, valve length; H, valve height.
Figure 5.(a)Linear (y=0.45x + 57.74) dose-response relationship between plasma propranolol to % β- adrenergeric blockade derived from healthy study participants and translate into patients with angina pectoris. This image has been adapted from(Pine et al., 1975).-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Apharmacodynamic model,with parameters in the table below, may be used to visualize the<br> propranolol concentration-effect (β-blockade) relationship in patients suffering from angina pectoris.<br> These results have been adapted from the Pine et al article published in Circulation in 1975 which<br> identified a linear relationship plasma Propranolol (ng/mL) to an effect of % β-Adrenergic Blockade<br> in a single-oral dose of 40mg Propranolol in exercising individuals (Pine et al., 1975).</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1
<p>If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102). </p> <p>In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 2. The representation of the rule
<p>In order to model these derivations in the stratified graph G, each production of the grammar will be represented in the labeled graph G0 by a direct arc of the form given in Figure 2.</p> <p> </p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism
<p>A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).</p>
Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions
<p>The tarball contains Satisfiability Modulo Theories (SMT) and Verification Modulo Theories (VMT) benchmarks for the theories of Nonlinear Real Arithmetic (NRA) and NRA extended with Transcendental Functions (NTA). These benchmarks have been collected in the following works:</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Invariant Checking of NRA Transition Systems via Incremental Reduction to LRA with EUF". In proc. Tools and Algorithms for the Construction and Analysis of Systems, TACAS'17, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Satisfiability Modulo Transcendental Functions via Incremental Linearization". In proc. Int. Conference on Automated Deduction, CADE, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions". ACM Transactions on Computational Logics. 2018. To appear.</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
A linearized Navier-Stokes input output model
<p>Matrices for the experiments reported in DOI:10.1137/140980016</p>
Description of the HealthyMinorCereals oat (Avena sativa L.) diversity panel and best linear unbiased estimators (BLUEs) of agro-morphological traits
<p>Best linear unbiased estimators (BLUEs) of various agro-morphological traits of oat (Avena sativa) germplasm evaluated in Estonia and the Czech Republic between 2014 and 2019 (9 environments in total) within the FP7 HealthyMinorCereals project.</p>
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia). in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia).
Text-fig. 9. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83), as well as according to recent Howells's database (n = 2524, f = 1156, m = 1368) with variables M1 (GOL) and M45 (ZYB). in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 9. Sex estimation of Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using Henke's Late Upper Palaeolithic and Mesolithic database (n = 129, f = 46, m = 83), as well as according to recent Howells's database (n = 2524, f = 1156, m = 1368) with variables M1 (GOL) and M45 (ZYB).
Text-fig. 10. Regional affinity of the Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using the Henke's Late Upper Palaeolithic and Mesolithic database (Europe, n = 76) with variables M1 (GOL), M5 (BNL), M8 (XCB), M40 (BPL), W – West Europe, CE – Central-East Europe, S – South Europe. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 10. Regional affinity of the Moča skull (Komárno district, southern Slovakia), linear discriminant analysis using the Henke's Late Upper Palaeolithic and Mesolithic database (Europe, n = 76) with variables M1 (GOL), M5 (BNL), M8 (XCB), M40 (BPL), W – West Europe, CE – Central-East Europe, S – South Europe.
FIGURE 5 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 5. Principal components analysis of 3D fixed and semi landmarks. 1, PC 1 vs PC 2; point size represents relative centroid size of specimen. 2-4, Landmark configuration of shape represented by low PC 1 score, typical of largest specimens, in dorsal, anterior, and right lateral views, respectively. 5-7, Landmark configuration of shape represented by high PC 1 score, typical of smallest specimens, in dorsal, anterior, and right lateral views, respectively.
FIGURE 4 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 4. Slice through volume rendering of Cryptolithus tesselatus (AMNH FI-101479), shown in dorsal view in inset. Red line in inset shows the orientation of the slice across the specimen. Bright white area is sediment trapped within the bilaminar structure of the cephalon. Blue arrows point to suture between upper and lower lamellae; red arrows point to fringe-pits.
FIGURE 1 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 1. Cephalon of Cryptolithus tesselatus (AMNH FI-101479) showing morphological terms used in this paper, following Whittington (1968) and Hughes et al. (1975). Concentric arcs are labeled according to their placement relative to the girder (expressed on the ventral side): E = external; I = internal. "Fringe-pits" are circled in yellow; the "F-pits" represent a subset of these interior to the labeled concentric arcs. Specimen is 6.6 mm long.
FIGURE 8 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 8. Length vs width of Cryptolithus tesselatus cephala, coded for the number of concentric arcs of fringe-pits expressed in each specimen. The first three concentric arcs (E, I1, and I2) are complete when first expressed. Based on clustering, I3 is likely completed over three molts, first by only 1-3 fringe-pits, then 8-10 fringe-pits, then 13-15 fringe-pits with the anteriormost in line with the 10th radial rows of fringe-pits in arcs E-I. The dataset includes the 23 2 specimens, which were CT-scanned as well as 31 additional silicified specimens from the collection; specimens that were CT-scanned are outlined in red. Arrows indicate the specimens shown in Figures 1 and 7. Inset in upper right corner is a magnified view of the specimens in the dashed box. The scaling component describing the relationship between length and width is 1.153 (1 = isometric growth).
FIGURE 7 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 7. Additions of fringe-pits associated with early meraspid stages of ontogeny in Cryptolithus tesselatus. 1, meraspid stage 2 showing two concentric arcs of fringe-pits, AMNH FI-101498, x35. 2, meraspid stage 2 showing two concentric arcs of fringe-pits, AMNH FI-101499, x35. 3, merapid stage 3 showing three concentric arcs of fringe-pits and first few fringe-pits of I3, FI-101496, x20. 4, later meraspid stage showing complete set of fringe-pits, AMNH FI- 101494, x15. Scale bars are 1 mm.
FIGURE 3 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 3. Placement of points defining patch on glabella. Points in red are redundant to fixed landmarks as described in the text and Appendix 2. After the surface landmarks were extracted using Landmark Editor, the redundant landmarks were removed from the final data file. Specimen shown is AMNH FI-101482; specimen is 7.1 mm long.
FIGURE 10 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 10. Allometry in the cranidia/cephala of other trilobite species as described by 2D geometric morphometrics. 1, Marrolithus bureaui, data from figure 5 of Delabroye and Crônier (2008), breakpoint shown is at 2.8, which was the best supported threshold model (Table 2). 2, Aulacopleura koninckii, data from figure 3 of Hong et al. (2014), breakpoint set at 2.0. 3, Triarthrus becki, data from figure 6 of Kim et al. (2002); breakpoint at 0.6. 4, Zacanthopsis palmeri, data from figure 13 of Hopkins and Webster (2009), breakpoint set at 0.8. 5, Haniwa quadrata, data from figure 5 of Park and Choi (2011b), breakpoint set at 1.4. 6, Liostracina tangwangzhaiensis, data from figure 3 of Park et al. (2014), breakpoint set at 1.55. 7, Apatokephalus latilimbatus, data from figure 4 of Park and Kihm (2015), breakpoint set at 0.85. 8, Olenellus gilberti, data from figure 23B of Webster (2015), breakpoint set at 1.0. Breakpoints are all in units of natural log of centroid size. Red lines = linear regression models; blue lines = threshold models.
FIGURE 2 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 2. Different views of 3D surface model rendering of Cryptolithus tesselatus showing placement of fixed landmarks. All landmarks are indicated at least once, with the exception of 11 (paired with 12). Unpaired landmarks = 17– 20; paired landmarks = 1–16, 21–23, 42; semi-landmarks along first internal list shown by dashed line and represented by landmarks 24–41. See Appendix 2 for full description of all landmarks. Surface reconstruction is of AMNH FI-101479.
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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.