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Dataset results
184 results for “Pandas”
Signal detection theory applied to giant pandas: Do pandas go out of their way to make sure their scent marks are found?
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Data from: Roles of soil microbes in shaping the nutrient accumulation of dietary bamboo of giant pandas
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Data from: TAS2R20 variants confer dietary adaptation to high-quercitrin bamboo leaves in Qinling giant pandas
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Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
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Data from: Altitude difference might contribute to the genetic divergence of giant panda' staple food Bamboo (Fargesia spathacea complex) based on 14 SSR markers
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PANDA challenge trained models
<p>This dataset includes pre-trained efficientNet-B0 network as well as models manually trained using aforementioned backbone.</p>
Gut microbiota in reintroduction of Giant Panda
<p>Reintroduction is a key approach in the conservation of endangered species. In recent decades many reintroduction projects have been conducted for conservation purposes, but the rate of success has been low. Given the important role of gut microbiota in health and diseases, we questioned whether gut microbiota <a name="_Hlk522007838">would play a crucial role in giant panda's wild training process</a>. The wild procedure is when -captive-born babies live with their mothers in a wilderness enclosure and learn wilderness survival skills from their mothers. During the wild training process, the baby pandas undergo wilderness survival tests and regular physical examinations. Based on their performance through these tests, the top subjects (age 2-3 years old) are released into the wild while the others are translocated to captivity. <a name="_Hlk17030785">After release, we tracked one released panda (Zhangxiang) and collected its fecal samples for 5 months (Jan 16<sup>th</sup> 2013 to Mar 29<sup>th</sup> 2014). </a>Here, we analyzed the Illumina HiSeq sequencing data (V4 region of 16S rRNA gene) from captive pandas (n=24) , wild-training baby pandas (n=8) of which 6 were released and 2 were unreleased, wild-training mother pandas (n=8), one released panda (Zhangxiang), and wild giant pandas (n=18). Our results showed that <a name="_Hlk10634744">the gut microbiota of wild-training pandas is significantly different from that of wild pandas but similar to that of captive ones. </a><a name="_Hlk522008735">The gut microbiota of the released panda Zhangxiang gradually changed to become similar to those of wild pandas after release. In addition, </a><a name="_Hlk16625538">we identified several bacteria that were enriched in the released baby pandas before release</a>, compared with the unreleased baby pandas. These bacteria include several known gut-health related beneficial taxa such as <i>Roseburia</i>, <i>Coprococcus</i>, <i>Sutterella, Dorea,</i> and <i>Ruminococcus</i>.<i> </i><a name="_Hlk10634845">Therefore, our results suggest that certain members of the gut microbiota may be important in panda reintroduction. </a></p>
Data from: Walking in a heterogeneous landscape: dispersal, gene-flow and conservation implications for the giant panda in the Qinling Mountains
Understanding the interaction between life history, demography and population genetics in threatened species is critical for the conservations of viable populations. In the context of habitat loss and fragmentation, identifying the factors that underpin the structuring of genetic variation within populations can allow conservationists to evaluate habitat quality and connectivity and help to design dispersal corridors effectively. In this study, we carried out a detailed, fine-scale landscape genetic investigation of a giant panda population for the first time, using a large microsatellite data set and examined the role of isolation-by-barriers (IBB), isolation-by-distance (IBD) and isolation-by-resistance (IBR) in shaping the genetic variation pattern of giant pandas in the Qinling Mountains. We found that the Qinling population comprises one continuous genetic cluster, and among the landscape hypotheses tested, gene flow was found to be correlated with resistance gradients for two topographic factors, rather than geographical distance or barriers. Gene-flow was inferred to be facilitated by easterly slope aspect and to be constrained by land surface with high topographic complexity. These factors are related to benign micro-climatic conditions for both the pandas and the food resources they rely on and more accessible topographic conditions for movement, respectively. We identified optimal corridors based on these results, aiming to promote gene flow between human-induced habitat fragments. These findings provide insight into the permeability and affinities of the giant panda habitat and offer important reference for the conservation of the giant panda and its habitat.
Data from: Atmospheric deposition exposes Qinling pandas to toxic pollutants
The giant panda (Ailuropoda melanoleuca) is one of the most endangered animals in the world, and it is recognized worldwide as a symbol for conservation. A previous study showed that wild and captive pandas, especially those of the Qinling subspecies, were exposed to toxicants in their diet of bamboo; the ultimate origin of these toxicants is unknown. Here we show that atmospheric deposition is the most likely origin of heavy metals and persistent organic pollutants (POPs) in the diets of captive and wild Qinling pandas. Average atmospheric deposition was 199, 115 and 49 g∙m−2∙yr−1 in the center of Xi'an city, at China's Shaanxi Wild Animal Research Center (SWARC), and at Foping National Nature Reserve (FNNR), respectively. Atmospheric deposition of heavy metals (As, Cd, Cr, Pb, Hg, Co, Cu, Zn, Mn and Ni) and POPs was highest at Xi'an city, intermediate at SWARC, and lowest at FNNR. Soil concentrations of the aforementioned heavy metals other than As and Zn also were significantly higher at SWARC than at FNNR. Efforts to conserve Qinling pandas may be compromised by air pollution attendant to China's economic development. Improvement of air quality and reductions of toxic emissions are urgently required to protect China's iconic species.
Data from: Patterns of genetic differentiation at MHC class I genes and microsatellites identify conservation units in the giant panda
Background: Evaluating patterns of genetic variation is important to identify conservation units (i.e., evolutionarily significant units [ESUs], management units [MUs], and adaptive units [AUs]) in endangered species. While neutral markers could be used to infer population history, their application in the estimation of adaptive variation is limited. The capacity to adapt to various environments is vital for the long-term survival of endangered species. Hence, analysis of adaptive loci, such as the major histocompatibility complex (MHC) genes, is critical for conservation genetics studies. Here, we investigated 4 classical MHC class I genes (Aime-C, Aime-F, Aime-I, and Aime-L) and 8 microsatellites to infer patterns of genetic variation in the giant panda (Ailuropoda melanoleuca) and to further define conservation units. Results: Overall, we identified 24 haplotypes (9 for Aime-C, 1 for Aime-F, 7 for Aime-I, and 7 for Aime-L) from 218 individuals obtained from 6 populations of giant panda. We found that the Xiaoxiangling population had the highest genetic variation at microsatellites among the 6 giant panda populations and higher genetic variation at Aime-MHC class I genes than other larger populations (Qinling, Qionglai, and Minshan populations). Differentiation index (FST)-based phylogenetic and Bayesian clustering analyses for Aime-MHC-I and microsatellite loci both supported that most populations were highly differentiated. The Qinling population was the most genetically differentiated. Conclusions: The giant panda showed a relatively higher level of genetic diversity at MHC class I genes compared with endangered felids. Using all of the loci, we found that the 6 giant panda populations fell into 2 ESUs: Qinling and non-Qinling populations. We defined 3 MUs based on microsatellites: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. We also recommended 3 possible AUs based on MHC loci: Qinling, Minshan-Qionglai, and Daxiangling-Xiaoxiangling-Liangshan. Furthermore, we recommend that a captive breeding program be considered for the Qinling panda population.
Data from: Transcriptome-derived tetranucleotide microsatellites and their associated genes from the giant panda (Ailuropoda melanoleuca)
Recently, an increasing number of microsatellites or Simple Sequence Repeats (SSRs) have been found and characterized from transcriptome. Such SSRs can be employed as putative functional markers to easily tag corresponding genes, which play an important role in biomedical studies and genetic analysis. However, the transcriptome-derived SSRs for giant panda (Ailuropoda melanoleuca) are not yet available. In the present work, we identified and characterized 20 tetranucleotide microsatellite loci from a transcript database generated from the blood of giant panda. Furthermore, we assigned their predicted transcriptome locations: 16 loci were assigned to untranslated regions (UTRs) and 4 loci were assigned to coding regions (CDSs). Gene identities of 14 transcripts contained corresponding microsatellites were determined, which provide useful information to study the potential contribution of SSRs to gene regulation in giant panda. The polymorphic information content (PIC) values ranged from 0.293 to 0.789 with an average of 0.603 for the 16 UTRs-derived SSRs. Interestingly, four CDS-derived microsatellites developed in our study were also polymorphic, and the instability of these four CDS-derived SSRs was further validated by re-genotyping and sequencing. The genes contained these four CDS-derived SSRs were embedded with various types of repeat motifs. The interaction of all the length-changing SSRs might provide a way against coding region frameshift caused by microsatellite instability. We hope these newly gene-associated biomarkers would pave the way for genetic and biomedical studies for giant panda in the future. In sum, this set of transcriptome-derived markers complements the genetic resources available for giant panda.
The ceramics panda 陶制熊猫 铜官窑陶瓷研究所
The ceramics panda 陶制熊猫 铜官窑陶瓷研究所 Source: Objaverse 1.0 / Sketchfab
Dataset for PANDA-examples
<p>These datasets are used to implement the PANDA tutorial of exploring intra-tumor heterogeneity within melanoma (https://github.com/Zhangxf-ccnu/PANDA-examples). </p>
FIGURES 9–13. Cyclocarya paliurus and Juglans sigllata. 9 in A new subspecies of Araragi panda Hsu & Chou (Lepidoptera, Lycaenidae, Theclini) from Sichuan, western China
FIGURES 9–13. Cyclocarya paliurus and Juglans sigllata. 9. leaf of C. paliurus, 10. fruits of C. paliurus, 11. bud of C. paliurus, 12. leaf and fruits of J. sigllata, 13. bud of J. sigllata.
FIGURES 5–8. Immatures Araragi panda sichuanensis Hsu & Li, subsp. nov. 5 in A new subspecies of Araragi panda Hsu & Chou (Lepidoptera, Lycaenidae, Theclini) from Sichuan, western China
FIGURES 5–8. Immatures Araragi panda sichuanensis Hsu & Li, subsp. nov. 5, lateral view of ovum, 6, dorsal view of ovum, 7, final instar larva, 8, pupa (all photos taken from the type locality).
Reproduction of PANDA: analysis for simulations and applications
<p>These data are derived from analyses based on PANDA results and are consistent with those presented in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Analysis/simulations/paired_scenario.zip"</p> <p>"Analysis/simulations/unpaired_scenario.zip"</p> <p>"Analysis/simulations/merfish.zip"</p> <p>"Analysis/simulations/reference_choice.zip"</p> <p>"Analysis/simulations/parameter_sensitivity.zip"</p> <p>"Analysis/simulations/time_memory.zip"</p> <p>"Analysis/applications/melanoma.zip"</p> <p>"Analysis/applications/mouse_brain.zip"</p> <p>"Analysis/applications/human_heart.zip"</p>
Reproduction of PANDA: processed data for simulations and applications
<p>These data are processed data for simulations and applications used to reproduce the results in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Data/processed_data/simulations/paired_scenario.zip"</p> <p>"Data/processed_data/simulations/unpaired_scenario.zip"</p> <p>"Data/processed_data/simulations/merfish.zip"</p> <p>"Data/processed_data/simulations/reference_choice.zip"</p> <p>"Data/processed_data/applications/melanoma.zip"</p> <p>"Data/processed_data/applications/mouse_brain.zip"</p> <p>"Data/processed_data/applications/human_heart.zip"</p>
Reproduction of PANDA: results for applications
<p>These data are the results obtained from applying PANDA to applications and can be used to reproduce the analysis in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Results/applications/melanoma.zip"</p> <p>"Results/applications/mouse_brain.zip"</p> <p>"Results/applications/human_heart.zip"</p>
Reproduction of PANDA: results for simulations (parameter sensitivity)
<p>These data are the results obtained from applying PANDA to parameter sensitivity analysis and can be used to reproduce the analysis in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Results/simulations/parameter_sensitivity/paired/PANDA_results/parameter_sensitivity_paired_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/parameter_sensitivity/paired/PANDA_results/st_results/parameter_sensitivity_paired_PANDA_results_st_results_*.zip"</p> <p>"Results/simulations/parameter_sensitivity/unpaired/PANDA_results/parameter_sensitivity_unpaired_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/parameter_sensitivity/unpaired/PANDA_results/st_results/parameter_sensitivity_unpaired_PANDA_results_st_results_*.zip"</p>
Reproduction of PANDA: results for simulations
<p>These data are the results obtained from applying PANDA to simulations and can be used to reproduce the analysis in the paper "Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA".</p> <p>To ensure that the file paths match those used in the code, please place the files in the following directories within your working directory before extracting them:</p> <p>"Results/simulations/paired_scenario/paired_scenario_comparison_methods.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/paired_scenario_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_uniform_ST.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_boundary_ST.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_uniform_Visium.zip"</p> <p>"Results/simulations/paired_scenario/PANDA_results/st_results/paired_scenario_PANDA_results_st_results_boundary_Visium.zip"</p> <p>"Results/simulations/unpaired_scenario/unpaired_scenario_comparison_methods.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/unpaired_scenario_PANDA_results_sc_results.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_uniform_ST.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_boundary_ST.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_uniform_Visium.zip"</p> <p>"Results/simulations/unpaired_scenario/PANDA_results/st_results/unpaired_scenario_PANDA_results_st_results_boundary_Visium.zip"</p> <p>"Results/simulations/merfish.zip"</p> <p>"Results/simulations/reference_choice.zip"</p> <p>"Results/simulations/time_memory.zip"</p>
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Allen Brain Atlas
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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.