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184 results for “Pandas”
Magnetic Resonance Imaging Scan of the Brain of a Giant panda (Ailuropoda melanoleuca)
<p>Magnetic Resonance Imaging Scan of the Brain of a Giant panda (<i>Ailuropoda melanoleuca</i>) from http://braincatalogue.org/Giant_panda</p>
Fig. 1. Map showing a in Graveyards of Giant Pandas at the Bottom of the Sea? A Strange-Looking New Species of Colonial Ascidians in the Genus Clavelina (Tunicata: Ascidiacea)
Fig. 1. Map showing a sampling locality, Tonbara, off Kumejima Island, Japan.
FIGURE 4 in A late Turolian giant panda from Bulgaria and the early evolution and dispersal of the panda lineage
FIGURE 4. Dental ratio of P4 of Ailuropodini. Abbreviations: L, length; W, width, in mm.
FIGURE 1 in A late Turolian giant panda from Bulgaria and the early evolution and dispersal of the panda lineage
FIGURE 1. Location of Ognyanovo in Bulgaria.
Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
<p><span><strong>Aim</strong>:</span><span> Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (<em>Ailuropoda melanoleuca</em>). </span></p> <p><span><strong>Location</strong>:</span><span> Sichuan Province, China.</span></p> <p><span><strong>Methods</strong>: </span><span>We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also</span><span> compared the differences in </span><span>distributional changes of giant pandas among the five mountains in the study area. </span></p> <p><span><strong>Results</strong>: </span><span>Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. </span></p> <p><span><strong>Main conclusions</strong>: </span><span>Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies. </span></p>
Supplementary Data to: "A single nucleotide mutation in DUOX2 gene causes some of the panda's unique metabolic phenotypes"
<p>This file contains the data set associated with the manuscript entitled: "A single nucleotide mutation in the dual-oxidase 2 (<em>DUOX2</em>) gene causes some of the panda’s unique metabolic phenotypes", National Science Review, DOI: <a href="http://dx.doi.org/10.1093/nsr/nwab125">10.1093/nsr/nwab125</a></p>
Image data set based on the age of giant pandas
<p>The conservation of the giant panda (<em>Ailuropoda melanoleuca</em>), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information which is mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.</p>
Giant panda distribution ranges in the Liangshan Mountains
<p><span>Comprehending the population trend and understanding the distribution range dynamics of species</span><span> is necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species' environmental preferences and formulating protection policies. Here, we studied </span><span>the rear-edge population of the flagship species, giant pandas (</span><span><em>Ailuropoda</em> <em>melanoleuca</em></span><span>), </span><span>to 1) assess their population trend using their distribution patterns, 2) evaluate their distribution dynamics change from the 2nd (1988) to the 3rd (2001) surveys (2–3 Interval) and 3rd to the 4th (2013) survey (3–4 Interval) using a machine learning algorithm (The Extremely Gradient Boosting), and 3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the 2<sup>nd</sup> survey (k = 1.050), improved by the 3<sup>rd</sup> survey (k = 0.97), but got worse by the 4<sup>th</sup> survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that more study is required to understand the micro-environment and animal distribution dynamics. We provide a fresh perspective on the dynamics of Giant Panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction. </span></p>
FIG. 17 in New Fossil Giant Panda Relatives (Ailuropodinae, Ursidae): A Basal Lineage of Gigantic Mio-Pliocene Cursorial Carnivores
FIG. 17. Holotype skull of Huracan qiui, sp. nov., HMV 2005, from Wangjiashan, China.
FIG. 22 in New Fossil Giant Panda Relatives (Ailuropodinae, Ursidae): A Basal Lineage of Gigantic Mio-Pliocene Cursorial Carnivores
FIG. 22. Chronology of Agriotheriini (Ailuropodinae, Ursidae).
FIG. 4 in New Fossil Giant Panda Relatives (Ailuropodinae, Ursidae): A Basal Lineage of Gigantic Mio-Pliocene Cursorial Carnivores
FIG. 4. Dental terminology of bear dentitions used in this study (from Jiangzuo et al., 2019).
PANDA cell-type-specific networks for S858R and WT mouse cerebral cortex
<p>The below files are from the data/results directory of this associated project and include the following:</p> <ul> <li><strong>PANDA networks </strong>: cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT mouse cerebral cortex tissues (n = 16). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (cerebral cortex) followed by _PANDA.Rdata. (example: astrocytes_heterozygouscortexexpression_PANDA.Rdata)</li> </ul>
PANDA cell-type-specific networks for S858R and WT mouse kidney
<p>The below files are from the data/results directory of this associated project and include the following:</p> <ul> <li><strong>PANDA networks</strong> : cell-type-specific TF-gene regulatory networks constructed using multi-omic inputs (snRNA-seq, TF-motif, and PPI) for all cell types in S858R and WT kidney tissues (n = 34). All the files included for PANDA networks indicate the condition (heterozygous or control), tissue (kidney) followed by _PANDA.Rdata. (example: pericytes_heterozygouskidneyexpression_PANDA.Rdata)</li> </ul>
Signal detection theory applied to giant pandas: Do pandas go out of their way to make sure their scent marks are found?
<p><span>Inter-animal communication allows signals released by an animal to be perceived by others. Scent-marking is the primary mode of such communication in giant pandas </span><span>(<em>Ailuropoda melanoleuca</em>)</span><span>. Signal detection theory propounds that animals choose the substrate and location of their scent marks so that the signals released are transmitted more widely and last longer. We believe that pandas trade off scent-marking because they are an energetically marginal species and it is costly to generate and mark chemical signals. Existing studies only indicate where pandas mark more frequently, but their selection preferences remain unknown. This study investigates whether the marking behavior of pandas is consistent with signal detection theory. Feces count, reflecting habitat use intensity, was combined with mark count to determine the selection preference for marking. The results showed that pandas preferred to mark ridges with animal trails and that most marked tree species were locally dominant. In addition, marked plots and species were selected for lower energy consumption and a higher chance of being detected. Over 90% of the marks used were the longest-surviving anogenital gland secretion marks, and over 80% of the marks were oriented toward animal trails. Our research demonstrates that pandas go out of their way to make sure their marks are found. This study not only sheds light on the mechanisms of scent-marking by pandas but also guides us toward more precise conservation of the panda habitat.</span></p>
Intravenous Immunoglobulin for PANDAS
ClinicalTrials.gov study NCT01281969. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Ecological and anthropogenic drivers of local extinction and colonization of giant pandas over the past 30 years
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Data from: Red pandas on the move: Weather and disturbance effects on habitat specialists
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Integrating temporal and spatial dimensions of alpine adaptation: Camera-trap insights on bharal (Pseudois nayaur) in Giant Panda National Park
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Image data set based on the age of giant pandas
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Giant panda distribution ranges in the Liangshan Mountains
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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.