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Figs 12-19 in Notes on Rhithrodytes BAMEUL, 1989, with the description of R. minimus nov.sp. from Algeria (Coleoptera, Dytiscidae. Hydroporini, Siettitiina)
Figs 12-19: Habitus of: (12) Rhithrodytes minimus nov.sp.; (13) R. dorsoplagiatus; (14) R. numidicus; (15) R. argaensis; (16) R. agnus; (17) R. sexguttatus; (18) R. crux; (19) R. bimaculatus (scale bar 2.5 mm).
Figs 8-9 in Notes on Rhithrodytes BAMEUL, 1989, with the description of R. minimus nov.sp. from Algeria (Coleoptera, Dytiscidae. Hydroporini, Siettitiina)
Figs 8-9: Female syntype of Hydroporus sexguttatus AUBÉ, 1838: (8) habitus; (9) labels (scale bar 2 mm).
Figs 4-5 in Notes on Rhithrodytes BAMEUL, 1989, with the description of R. minimus nov.sp. from Algeria (Coleoptera, Dytiscidae. Hydroporini, Siettitiina)
Figs 4-5: Ventral surface of Rhithrodytes minimus nov.sp.: (4) head; (5) part of metathorax and abdomen (significance of arrows explained in text).
Figure 2. Specimen from R in The morphology, ontogeny, and inferred behaviour of the deep-sea echinoid Calymne relicta (Holasteroida)
Figure 2. Specimen from R/V 'Akademik Kurchatov' cruise 37, station 3787. A, aboral surface. B, oral surface. C, anterior surface. D, posterior surface. E, right side.
Figure 14. Antennae. A, Agalope trimacula. B, Rhodopsona rutila. C, D, Eterusia aedea formosana. E, Cyclosia midama. F, Heteropan scintillans. G, H, Callizygaena glacon. I, Adscita statices. J, Inouela formosensis. K, Zygaena filipendulae. L, Phauda mimica. M, N, Lactura dives. O, Anomoeotis levis. P, Himantopteus fuscinervis. Q, R in The phylogenetic relationships of Chalcosiinae (Lepidoptera, Zygaenoidea, Zygaenidae)
Figure 14. Antennae. A, Agalope trimacula. B, Rhodopsona rutila. C, D, Eterusia aedea formosana. E, Cyclosia midama. F, Heteropan scintillans. G, H, Callizygaena glacon. I, Adscita statices. J, Inouela formosensis. K, Zygaena filipendulae. L, Phauda mimica. M, N, Lactura dives. O, Anomoeotis levis. P, Himantopteus fuscinervis. Q, R, Chalcosiopsis variata.
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 in Neoselachians (Chondrichthyes, Elasmobranchii) from the Lower and lower Upper Cretaceous of north-eastern Spain
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 mm.
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
<p><strong>Description</strong></p> <p>This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman. </p> <p>The data consists of the following files:</p> <ul> <li>ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format</li> <li>design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format</li> <li>enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format</li> </ul> <p>Details on dataset construction can be found in our paper and dataloaders can be found in our <a href="https://github.com/awfderry/ml-structure-bias">Github repo</a>.</p> <p><strong>Reference</strong></p> <p>A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.</p> <p><strong>Dataset References</strong></p> <p>Datasets used were derived from the following works:</p> <p>Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In <em>Proteins: Structure, Function and Bioinformatics</em> (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823</p> <p>Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). <em>Generative Models for Graph-Based Protein Design</em>. https://openreview.net/pdf?id=SJgxrLLKOE</p> <p>Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. <em>Nucleic Acids Research</em>, <em>42 </em>(Database issue), D485–D489.</p>
R script to simulate the phenology of the pine processionary moth, Thaumetopoea pityocampa, from the egg to the last larval instar (L5).
<p>This folder contains :</p> <p>- ReadMe file with the following explanation</p> <p>- the R script to simulate the phenology of the pine processionary moth (PPM_phenological_model.R),</p> <p>- the flight curve of the pine processionary moth in Orleans (France) in 2019 (Flight_Orleans_2019.csv ; col 1 = date, col 2 = number of adult catches, col 3 = cumulated number of catches) </p> <p>- temperature dataset from June 2019 to December 2020 (Temperature.csv; col 1 = mean daily temperature, col 2 = Date, col 3 = Julian day continuously counted from one year to another). These meteorological data were recorded by the agroclimatic station at Orleans (number 45234, north latitude 47.827 °, east longitude 1.909 °), part of the INRAE national agroclimatic network managed by the service unit AgroClim (Avignon, France).</p> <p>The R script was used in R version 3.5.1 (2018-07-02)</p> <p>The R script will call automatically the flight curve and the temperature dataset given that you have placed them in your working directory, it requires the following R packages : lubridate, hydroGOF, ggplot2 (to be installed beforehand). The function coded to simulate the phenology of the pine processionary moth is called « param ». Input parameters are :<br> - Rm (Maximal development rate in days^(-1) ),<br> - Tm (Optimum temperature in °C)<br> - To (Spread of curve in °C)<br> for each life stage from the egg to L5.<br> This phenology model accumulates the daily development rate given by the Taylor equation and provides for each life stage (resegg, resL1,… resL5) : "min First day" (first day with an individual entering at this stage), "max First day" (last day with an individual entering at this stage), "min Last day" (first day with an individual achieving this stage development), "max Last day" (last day with an individual achieving this stage development), "mean duration" (mean duration of this life stage across all individuals). This is a summary of the phenology but “tabnb” provides the number of individuals present in each stage according to the date.</p> <p>To download the software R, please visit: <a href="https://www.r-project.org/">https://www.r-project.org/</a></p> <p>This work was supported by: the national project called PHENEC (grant from the French Research Agency – ANR – number19-CE32-0007; <a href="https://www6.inrae.fr/phenec/Project-description">https://www6.inrae.fr/phenec/Project-description</a>), the SOERE called TEMPO (<a href="https://tempo.pheno.fr/soere-tempo_eng/">https://tempo.pheno.fr/soere-tempo_eng/</a>), and the French region Val de Loire.</p> <p>Associated scientific article:<br> Poitou L, Laparie M, Pincebourde S, Rousselet J, Suppo C, Robinet C (2022) Warming causes atypical phenology in a univoltine moth with differentially sensitive larval stages. Frontiers in Ecology and Evolution, DOI: 10.3389/fevo.2022.825875.</p>
Data & R code for: Paquette & Hargreaves 'Biotic interactions are more often important at species' warm vs. cool range edges'
<p>Predicting which ecological factors constrain species distributions is a fundamental ecological question and critical to forecasting geographic responses to global change. Darwin hypothesized that abiotic factors generally impose species' high-latitude and high-elevation (typically cool) range limits, whereas biotic interactions more often impose species' low-latitude/low-elevation (typically warm) limits, but empirical support has been mixed. Here, we clarify three predictions arising from Darwin's hypothesis, and show that previously mixed support is partially due to researchers testing different predictions. Using a comprehensive literature review (885 range limits), we find that biotic interactions, including competition, predation, and parasitism, contributed to >60% of range limits, and influenced species' warm limits more often than cool limits. Abiotic factors contributed more often than biotic interactions to cool range limits, but temperature contributed frequently to both cool and warm limits. Our results suggest that most range limits will be sensitive to climate warming, but warm-limit responses will depend strongly on biotic interactions.</p>
The R package enerscape: A general energy landscape framework for terrestrial movement ecology
<ol> <li> <p class="western"><span>Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new R package <i>enerscape</i> to quantify and predict animal movement in real landscapes based on energy expenditure. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. <i>Enerscape</i> only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of <i>enerscape</i>, we analyze the energy landscape for the Marsican bear (<i>Ursus arctos marsicanus</i>) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel.</span></p> </li> <li> <p class="western"><span><i>Enerscape</i> allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human-wildlife conflicts in the park. A major strength of <i>enerscape</i> is that it requires only widely available topographic and body size data. As such, <i>enerscape</i> permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data is not readily available, such as for the example with the bear. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, <i>enerscape</i> is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions.</span></p> </li> </ol>
Figures 93–100. Cybocephalus kathrynae T. R. Smith. 93 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 93–100. Cybocephalus kathrynae T. R. Smith. 93) Antenna. 94) Proleg. 95) Mesoleg. 96) Metaleg. 97) Median lobe. 98) Median lobe, lateral view. 99) Basal plate. 100) Metanotum.
Figures 85–92. Cybocephalus iviei T. R. Smith. 85 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 85–92. Cybocephalus iviei T. R. Smith. 85) Antenna. 86) Proleg. 87) Mesoleg. 88) Metaleg. 89) Median lobe. 90) Median lobe, lateral view. 91) Basal plate. 92) Metanotum.
Figures 71–80. Cybocephalus geoffreysmithi T. R. Smith. 71 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 71–80. Cybocephalus geoffreysmithi T. R. Smith. 71) Antenna. 72) Proleg. 73) Mesoleg. 74) Metaleg. 75) Median lobe, variant. 76) Median lobe, variant. 77) Median lobe, lateral view. 78) Basal plate, variant. 79) Basal plate, variant. 80) Basal plate, variant.
Figures 120–130. Cybocephalus randalli T. R. Smith. 120 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 120–130. Cybocephalus randalli T. R. Smith. 120) Antenna, variant. 121) Antenna, variant. 122) Proleg. 123) Mesoleg. 124) Metaleg. 125) Median lobe. 126) Median lobe, lateral view. 127) Basal plate, variant. 128) Basal plate, variant. 129) Metanotum, wingless phenotype. 130) Metanotum, winged and reduced wing phenotypes.
Figures 33–36. Cybocephalus antilleus T. R. Smith. 33 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 33–36. Cybocephalus antilleus T. R. Smith. 33) Antenna. 34) Median lobe. 35) Median lobe, lateral view. 36) Basal plate.
Figures 22–28. Conglobatus fullertoni T. R. Smith. 22 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 22–28. Conglobatus fullertoni T. R. Smith. 22) Antenna. 23) Proleg. 24) Mesoleg. 25) Metaleg. 26) Median lobe. 27) Median lobe, lateral view. 28) Basal plate.
Figures 81–84. Cybocephalus huastecus T. R. Smith. 81 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 81–84. Cybocephalus huastecus T. R. Smith. 81) Antenna. 82) Median lobe. 83) Median lobe, lateral view. 84) Basal plate.
Figures 64–70. Cybocephalus edmondsoni T. R. Smith. 64 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 64–70. Cybocephalus edmondsoni T. R. Smith. 64) Antenna. 65) Proleg. 66) Mesoleg. 67) Metaleg. 68) Median lobe. 69) Median lobe, lateral view. 70) Basal plate.
Figures 54–63. Cybocephalus championi T. R. Smith. 54 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 54–63. Cybocephalus championi T. R. Smith. 54) Antenna. 55) Proleg. 56) Mesoleg. 57) Metaleg. 58) Median lobe, variant. 59) Median lobe, variant. 60) Median lobe, lateral view, variant. 61) Median lobe, lateral view, variant. 62) Basal plate, variant. 63) Basal plate, variant.
Figures 37–40. Cybocephalus beverlyae T. R. Smith. 37 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 37–40. Cybocephalus beverlyae T. R. Smith. 37) Antenna. 38) Median lobe. 39) Median lobe, lateral view. 40) Basal plate.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.