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Figure 4 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 4. Comparisons of regression curves of TL growth during the first (●) and second half (○) of gestation (top graph) and the early (●), mid (○) and late (▲) pregnancy (bottom graph). The slopes of the regression lines for first half of gestation (F = 63.31, P <0.0001, df1 = 1, df2 = 33) and for early (F = 50.05, P <0.0001, df1 = 1, df2 = 37) and mid pregnancy (F = 135.04, P <0.0001, df1 = 1, df2 = 32) were different than those for the second half of gestation and late pregnancy, respectively. Note that the animals double in length during late pregnancy (315–473 d).
Figure 3 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 3. Individual growth rate data from three animals (Animal 1, 2, 3). Regression line slopes during the first two-thirds of pregnancy (top graph) were similar (F = 0.48, P = 0.62, df1 = 2, df2 =18), while regression slopes where different (F = 15.13, P = 0.03, df1 = 2, df2 =3) from the second half to term. Animal 1 (▲) did not have any TL data beyond the first half of gestation so TL length data were used from the farthest in gestation and then again at term. Note that while growth rates were similar during the first two-thirds of pregnancy, fetuses were already different in size when initially detected.
Figure 2 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 2. Fetal growth curve comparison illustrating different growth rates resulting in wide range in estimated gestation length as compared to known gestation length determined in this study. Data from Heide-Jørgensen and Teilmann (1994; dotted line) predicts a gestation length of 310 d for a 150 cm calf and similar to our study used a 2nd order polynomial regression to describe their data. Kleinenberg et al. ([1964] 1969: dashed line) developed a curve of the average monthly embryo/fetal growth. They did not provide the curve, only the predicted age at TL, which we then used to fit to a growth curve, which predicts 150 cm calf as 338 d.
Figure 1 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 1. Ultrasonographic images of beluga fetuses. All images have yellow caliper lines used to measure dimensions. Biparietal diameter (A, B) at two different stages of gestation show the ovoid shaped skull and echo produced from falx (arrows) located midline between the parietal bones (arrowheads). The thoracic diameter (C) as measured between the yellow caliper marks (arrowheads) on the lateral side of the fetal thorax (d1 = 6.66 cm) at the level of the heart (white arrow) and thoracic circumference (c = 24.04 cm) determined by using the elliptical measurement caliper function to include the dorsal to ventral diameter (d2 = 8.67 cm). The total length of a fetus (D) which is bent in utero, thus requiring the addition of two separate measurements (arrowheads), 1) 8.38 cm from the cranial most aspect of the skull to mid abdomen and 2) 6.91 cm from mid abdomen to distal most portion of the peduncle for a total length of 15.29 cm.
Fig. 4 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 4. Proportions of correctly predicted observations of microhabitat use of Gambelia sila using temperature-based activity estimation based on physical model temperatures. Lizard microhabitat use was predicted correctly most often when they were in the open, but overall microhabitat use was not accurately predicted with TBAE in this heliothermic lizard.
Fig. 3 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 3. Temperature-based activity estimation resulted in accurate prediction of above-ground activity by Gambelia sila more often than accurate prediction of below-ground (burrow) occupation. Using air temperature (T air) was superior to using physical model temperatures when predicting below-ground occupation. For both methods, ~93% of observations predicted to be above ground were correct, whereas 62% (using T air) and 51% (using physical models) were correct for below-ground predictions.
Fig. 2 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila
Fig. 2. Proportions of correct predictions using air temperature to predict surface activity versus below-ground refuge use of Gambelia sila. This method resulted in accurate predictions 64–76% of the time among the various temperature differentials shown on the x-axis. Predictions were maximized (76% correct) using the criterion that lizards are above ground when their body temperatures (T b) are at least 6 °C above the air temperature (T ).
Brazilian Cohort for Predicting Cardiovascular Events Using Machine Learning (PRE-CARE ML project)
<p>The project PRE-CARE ML addresses the development and internal and external validation of predictive models for the assessment of risks of major adverse cardiovascular events. </p> <p>Global and local interpretability analyses of predictions were conducted towards improving model reliability and tailoring preventive interventions. </p> <p>The models were trained and validated in a retrospective cohort with the use of data from Hospital das Clínicas da Faculdade de Medicina de Ribeirao Preto, Brazil</p> <p>The International Classification of Diseases (ICD-10) classified patients with MACE (case group). (see Table I for the ICD-10 codes that defined MACE).</p> <p>Table 1: ICD-10 Codees for MACE definition</p> <table> <tbody> <tr> <td><strong>ICD-10</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>I20</td> <td>Angina pectoris</td> </tr> <tr> <td>I21</td> <td>Acute myocardial infarction</td> </tr> <tr> <td>I24</td> <td>Other acute ischaemic heart diseases</td> </tr> <tr> <td>I46</td> <td>Cardiac arrest</td> </tr> <tr> <td>I63</td> <td>Cerebral infarction</td> </tr> <tr> <td>I64</td> <td>Stroke, not specified as hemorrhage or infarction</td> </tr> <tr> <td>I71</td> <td>Aortic aneurysm and dissection</td> </tr> <tr> <td>I74</td> <td>Arterial embolism and thrombosis</td> </tr> </tbody> </table> <p>Only a patient’s first MACE was considered, and all previous hospitalizations within a 5-year window were considered MACE. The control group (non-MACE) involved hospitalizations with no MACE and no death within a 5-year window (between 2017 and 2022). A sample of 6,000 MACE (labeled as 1) cases and 12,000 non-MACE (labeled as 0) ones was constructed with data from HCFMRP for training and internal validation purposes. Another balanced MIMIC IV sample of 8,000 MACE cases and 8,000 nonMACE cases was used for external validation.</p>
Figs. 1 and 2 in Studies of ambrosia beetles (Coleoptera: Curculionidae) in their native ranges help predict invasion impact
Figs. 1 and 2. Machilus (Lauraceae) trees in Huisun Forest, Taiwan, colonized by Xyleborus glabratus and Raffaelea lauricola. 1. Wood pieces excised from an injured but living tree. Staining is a reliable sign of R. lauricola establishment. 2. Injured trees showing symptoms of laurel wilt. Photographs by A. Black.
Fig. 2 in Environmental conditions predict helminth prevalence in red foxes in Western Australia
Fig. 2. Prevalence of Uncinaria stenocephala and Dipylidium caninum from red foxes at each sampling location.
Fig. 1 in Environmental conditions predict helminth prevalence in red foxes in Western Australia
Fig. 1. Prevalence of helminths in red foxes (n=147) from sampling locations throughout southwest Western Australia. Numbers in parentheses indicate sample size at each location.
Fig. 1 in Testing the robustness of transmission network models to predict ectoparasite loads. One lizard, two ticks and four years
Fig. 1. Transmission networks generated with (a) a short time window of infection; and (b) a long time window of infection, from the GPS location data of the lizards in the study population in 2010. Nodes represent individual lizards and edges between nodes are directed towards the lizard that is at risk of infection. The edges are weighted as described in the main text and the thicker the line the more weight is associated with that edge.
Fig. 3 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 3. Prey content species richness accumulation curves based on 1000 randomizations using Estimate 9.1.0. Data obtained from stomach flushing or necropsy of American alligators from (A) coastal or (B) inland habitats between 2008 and 2011. The black broken line (- -) represents the upper 95% confidence level, and the broken dotted broken line (- · -) represents the lower 95% confidence level of the species accumulation curve. The slow approach to the asymptote in coastal habitats suggests prey contents of sampled alligators did not capture all probable prey. The asymptote of inland alligators slowly begins to plateau, which may suggest our sampling efforts were close to capturing most of the probable prey of alligators.
Fig. 2 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 2. Nematode species richness accumulation curve (A) and Coleman rarefaction curve (B) based on 1000 randomizations using Estimate 9.1.0. The black broken line (- -) represents the upper 95% confidence level, and the broken dotted broken line (- · -) represents the lower 95% confidence level of the species accumulation curve (A). Upper and lower Coleman standard deviations are represented by solid black lines (B). Data obtained from stomach flushing or necropsy of American alligators from Florida and Georgia between 2008 and 2011. The rapid approach to the asymptote suggests we captured all possible species of alligator nematodes.
Fig. 1 in Predicting Baylisascaris procyonis roundworm prevalence, presence and abundance in raccoons (Procyon lotor) of southwestern Ohio using landscape features
Fig. 1. Map of the townships of Greene and Clark Counties Ohio. The data represent the proportion of raccoons from an individual township that had raccoon roundworms when necropsied. This map also demonstrates the mean patch size, proportion of landscape modified by urbanization and the proportion of landscape modified by agriculture for the nine townships.
Fig. 3 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 3. In marginal grazing systems in Europe sheep often occupy separate summer and winter grazing areas, analogous to the summer and winter ranges of migratory ruminants. In the uplands of Wales, UK, (shown here) sheep are often grazed on extensive areas of land at low stocking densities over the summer period, and sent to lowland dairy farms for winter grazing at higher stocking densities. (Photo: Rose, H.).
Fig. 2 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 2. The relative seasonal incidence of ovine parasitic gastroenteritis (PGE) in the Southwest of England, UK, based on monthly diagnoses of (a) Nematodosis (NOS = species not otherwise specified), (b) Haemonchosis and (c) Nematodirosis (van Dijk et al., 2008).
Fig. 1 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 1. Comparison of the instantaneous daily development rate of Ostertagia ostertagi (grey) and O. gruehneri (black) at a range of constant temperatures. Instantaneous daily development rates were estimated from the time to 50% development of L3, derived from data published in the literature (O. ostertagi: Rose, 1961; Pandey, 1972; Young et al., 1980) and original data (O. gruehneri: Hoar, 2012) as described by Azam et al. (2012).
Рис. 5. Варианты преΑсказанной Αоменной структуры скавенΑжер-рецепторов гемоцитов моΛΛюсков Planorbarius corneus. Сокращения (зΑесь и ΑаΛее): SR — богатый цистеином Αомен скавенΑжер-рецептора, Filament — Αомен промежуточного фиΛамента, TSP1 — повторы тромбоспонΑина типа 1, KR — крингΛ-Αомен, LDLa — Αомен рецептора Λипопротеинов низкой пΛотности кΛасса А Fig. 5. Variants of the predicted domain structure of scavenger receptors from hemocytes of Planorbarius corneus molluscs. Abbreviations (here and in what follows): SR — scavenger receptor Cys-rich domain, Filament — intermediate filament protein, TSP1 — thrombospondin type 1 repeats, KR — kringle domain, LDLa — low-density lipoprotein receptor domain class A in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 5. Варианты преΑсказанной Αоменной структуры скавенΑжер-рецепторов гемоцитов моΛΛюсков Planorbarius corneus. Сокращения (зΑесь и ΑаΛее): SR — богатый цистеином Αомен скавенΑжер-рецептора, Filament — Αомен промежуточного фиΛамента, TSP1 — повторы тромбоспонΑина типа 1, KR — крингΛ-Αомен, LDLa — Αомен рецептора Λипопротеинов низкой пΛотности кΛасса А Fig. 5. Variants of the predicted domain structure of scavenger receptors from hemocytes of Planorbarius corneus molluscs. Abbreviations (here and in what follows): SR — scavenger receptor Cys-rich domain, Filament — intermediate filament protein, TSP1 — thrombospondin type 1 repeats, KR — kringle domain, LDLa — low-density lipoprotein receptor domain class A
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
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.