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676 results for “Manis”
Sensitivity analysis script for: Why so many polyploids?
<p>While polyploids are common in nature, existing models suggest that polyploid establishment should be difficult and rare. We explore this apparent paradox by focusing on the role of unreduced gametes, as their union is the main route for formation of neopolyploids. Production of such gametes is affected by genetic and environmental factors, resulting in variation in the formation rate of unreduced gametes (<em>u</em>). Once formed, neopolyploids face minority cytotype exclusion (MCE) due to a lack of viable mating opportunities. More than a dozen theoretical models have explored factors that could permit neopolyploids to overcome minority cytotype exclusion and become established. Until now, however, none have explored variability in <em>u</em> and its consequences for the rate of polyploid establishment. Here, we determine the distribution that best fits available empirical data on <em>u</em>. We perform a global sensitivity analysis exploring the consequences of using empirical distributions of <em>u</em> to investigate effects on polyploid establishment. We determined in many cases <em>u</em> is best fit by a log-normal distribution. We found environmental stochasticity in <em>u</em> dramatically impacts model predictions when compared to a static <em>u</em>. Our results help reconcile previous modeling results suggesting high barriers to polyploid establishment with the observation that polyploids are common in nature.</p>
Text-fig. 10. Flowers and fruits. a: Pentamerous flower from top view, UAPC-ALTA S 67696. b, d: Part and counterpart of a flower with many stamens, type 1 UAPC-ALTA S 6560AB. c: Flower with many stamens, type 2, UAPC-ALTA S 26359. e: Small radially symmetric flower with pinnate venation in each sepal. UAPC-ALTA S 25356. f: Pteroheterochrosperma horseflyensis, UAPC-ALTA S 59494B. g, k: Fruit showing arched longitudinal ribs and and punctate surface, UAPC-ALTA S 26355A. h: Small flower UAPCALTA S 26360. i: Lagokarpos lacustris, UAPC-ALTA S 59498B. j: Lagokarpos lacustris, RBCM.EH2009.023.0003. Scale bars: a–d, g, h = 0.5 cm; e, f, k =0.2 cm; i, j = 1 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 10. Flowers and fruits. a: Pentamerous flower from top view, UAPC-ALTA S 67696. b, d: Part and counterpart of a flower with many stamens, type 1 UAPC-ALTA S 6560AB. c: Flower with many stamens, type 2, UAPC-ALTA S 26359. e: Small radially symmetric flower with pinnate venation in each sepal. UAPC-ALTA S 25356. f: Pteroheterochrosperma horseflyensis, UAPC-ALTA S 59494B. g, k: Fruit showing arched longitudinal ribs and and punctate surface, UAPC-ALTA S 26355A. h: Small flower UAPCALTA S 26360. i: Lagokarpos lacustris, UAPC-ALTA S 59498B. j: Lagokarpos lacustris, RBCM.EH2009.023.0003. Scale bars: a–d, g, h = 0.5 cm; e, f, k =0.2 cm; i, j = 1 cm.
Data of publication "Simulating the dynamics of large many-body quantum systems with Schrödinger-Feynman techniques"
<p>The development of powerful numerical techniques has drastically improved our understanding of quantum matter out of equilibrium. Inspired by recent progress in the area of noisy intermediate-scale quantum devices, this paper highlights hybrid Schrödinger-Feynman techniques as an innovative approach to efficiently simulate certain aspects of many-body quantum dynamics on classical computers. To this end, we explore the nonequilibrium dynamics of two large subsystems, which interact sporadically in time, but otherwise evolve independently from each other. We consider subsystems with tunable disorder strength, relevant in the context of many-body localization, where one subsystem can act as a bath for the other. Importantly, studying the full interacting system, we observe that signatures of thermalization are enhanced compared to the reference case of having two independent subsystems. Notably, with the here proposed Schrödinger-Feynman method, we are able to simulate the pure-state survival probability in systems significantly larger than accessible by standard sparse-matrix techniques.</p> <div> </div> <div> </div>
Figure 1 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 1. Exemplars of the plant hosts analyzed in this study. (a) Taeniopteris from Mitchell Creek Flats, specimen USNM-612206. (b) Zeilleropteris from Mitchell Creek Flats, specimen USNM-612216. (c) Auritifolia waggoneri from Colwell Creek Pond, specimen USNM-559854. (d) Taeniopteris from Colwell Creek Pond, specimen USNM-559818. (e) Johniphyllum multinerve from South Ash Pasture, specimen USNM-520377. (f) Euparyphoselis gibsonii from South Ash Pasture, specimen USNM-520383.
Figure 4 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 4. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different specimen area restrictions, for the four primarily and secondarily dominant Permian plant hosts represented by fewer than 400 specimens. The dashed gray line represents the mean value calculated for the complete dataset, and the dotted gray lines represent the 95 % confidence intervals for the complete dataset. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Figure 7 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 7. Surface area of individual specimens ordered by area for the two forms of Johniphyllum multinerve at SAP.
Figure 3 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 3. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different specimen area restrictions, for the three primarily dominant Permian plant hosts represented by 400 or more specimens. The dashed gray line represents the mean value calculated for the complete dataset, and the dotted gray lines represent the 95 % confidence intervals for the complete dataset. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Figure 6 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 6. Surface area of individual specimens, ordered by area for each plant host, for Auritifolia waggoneri and Taeniopteris spp. of CCP and Johniphyllum multinerve at SAP.
Figure 8 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 8. The DT diversity and the herbivory index of each specimen, plotted against its surface area.
Figure 5 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 5. Sequential increases in sample size, starting with the largest specimens, for the three primarily dominant Permian plant hosts represented by 400 or more specimens.
Figure 2 in Sampling fossil floras for the study of insect herbivory: how many leaves is enough?
Figure 2. Damage type (DT) diversity, the herbivory index (percentage of leaf area removed), and the proportion of specimens excluded, calculated with different subsampling routines for the three primarily dominant Permian plant hosts represented by 400 or more specimens. The dashed gray line represents the mean value calculated from the complete datasets, and the dotted gray lines represent the 95 % confidence intervals calculated from the complete datasets. For the complete datasets, all specimens with a surface area above 0.5 cm2 were examined. The 95 % confidence interval for each subsampling routine is represented by a light gray rectangle bounded by black lines. The thick black lines represent the mean values for each subsampling routine.
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Fig. 19 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 19. Rivudiva coveloae (Traver, 1971) male imago, holotype (PERC). A. Dorsal view. B. Detail of forewing. C. Genitalia (v.v.). D. Hind wing. E. Detail of genitalia (d.v.). F. Detail of genitalia (v.v.). G. Detail of genitalia (v.v.). Scale bars: A–B = 2.5 mm; C = 0.3 mm; D = 0.45 mm; E–G = 0.08 mm.
Fig. 18 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 18. Rivudiva naia sp. nov., holotype (UFRR). A. Margin of tergum IV. B. Paraproct. C. Cercus. D. Paracercus. E. Dorsal habitus of female nymph. Scale bar: E = 2.0 mm; A–D not to scale.
Fig. 17 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 17. Rivudiva naia sp. nov., holotype (UFRR). A. Foreleg (femur on anterior surface). B. Detail of fore claw. C. Posterior surface of forefemur. D. Posterior surface of hind femur. E. Hind leg (femur in anterior surface). Not to scale.
Fig. 21 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 21. Map of Brazil showing the distribution of the distinct species of ʻtrichobasis group' and Brazilian biomes.
Fig. 16 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 16. Rivudiva naia sp. nov., holotype (UFRR). A. Labrum (left v.v., right d.v.). B. Left mandible. C. Right mandible. D. Maxilla. E. Hypopharynx. F. Labium (left v.v., right d.v.). G. Shape of distal rows of setae of glossa. Not to scale.
Fig. 15 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 15. Rivudiva uiara sp. nov. A. Dorsal habitus of female nymph, holotype (INPA). B. Dorsal habitus of male nymph, paratype (INPA). Scale bar = 1.65 mm.
Fig. 20 in It is a mess! How many species are in Rivudiva trichobasis Lugo- Ortiz & McCafferty, 1998 (Ephemeroptera: Baetidae)?
Fig. 20. Rivudiva venezuelensis (Traver, 1943) male imago, holotype (PERC). A. Head (l.v.). B. Head (d.v.). C. Body (d.v.). D. Body (l.v.). E. Detail of forewing. F. Detail of genitalia (v.v.). G. Detail of genitalia (d.v.). H. Detail of genitalia (v.v.). Scale bars: A = 0.44 mm; B = 0.23 mm; C–D = 1.3 mm; E = 1.25 mm; F–H = 0.13 mm.
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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)
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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.