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1,384 results for “DR”
Distribution. Guinea, Liberia, Ivory Coast, Ghana, Cameroon, DR Congo, and W Uganda. in Vespertilionidae
Distribution. Guinea, Liberia, Ivory Coast, Ghana, Cameroon, DR Congo, and W Uganda.
Distribution. Confirmed records from DR Congo, Uganda, and Tanzania; it might occur in Kenya. in Vespertilionidae
Distribution. Confirmed records from DR Congo, Uganda, and Tanzania; it might occur in Kenya.
Distribution. Known only from two localities in Angola and one in DR Congo. in Vespertilionidae
Distribution. Known only from two localities in Angola and one in DR Congo.
Distribution. Zambia, including the Luangwa Valley; probably also SE DR Congo (Katanga) and Malawi. in Bovidae
Distribution. Zambia, including the Luangwa Valley; probably also SE DR Congo (Katanga) and Malawi.
Distribution. DR Congo and S Central African Republic. in Bovidae
Distribution. DR Congo and S Central African Republic.
Distribution. Itombwe Mountains in DR Congo, W of Lake Tanganyika. in Bovidae
Distribution. Itombwe Mountains in DR Congo, W of Lake Tanganyika.
Distribution. Endemic to the Upemba Swamps in Katanga, S DR Congo. in Bovidae
Distribution. Endemic to the Upemba Swamps in Katanga, S DR Congo.
Distribution. Sudan, NE DR Congo, N Uganda, and far W Ethiopia. in Bovidae
Distribution. Sudan, NE DR Congo, N Uganda, and far W Ethiopia.
Distribution. Known only from type locality in extreme NE DR Congo. in Molossidae
Distribution. Known only from type locality in extreme NE DR Congo.
Distribution. Patchily in C Africa from S Cameroon E into N DR Congo and W Uganda. in Molossidae
Distribution. Patchily in C Africa from S Cameroon E into N DR Congo and W Uganda.
Distribution. Known only from type locality in EC DR Congo. in Molossidae
Distribution. Known only from type locality in EC DR Congo.
Distribution. Only known from Mt Kahuz1, E DR Congo. in Nesomyidae
Distribution. Only known from Mt Kahuz1, E DR Congo.
Distribution. SW DR Congo and Angola. in Nesomyidae
Distribution. SW DR Congo and Angola.
Distribution. Restricted to the Albertine Rift in E DR Congo, SW Uganda, W Rwanda, and N Burundi. in Nesomyidae
Distribution. Restricted to the Albertine Rift in E DR Congo, SW Uganda, W Rwanda, and N Burundi.
Supplementary material 1 from: Hanslowe EB, Yackel Adams AA, Nafus MG, Page DA, Bradke DR, Erickson FT, Bailey LL (2022) Chew-cards can accurately index invasive rat densities in Mariana Island forests. NeoBiota 74: 29-56. https://doi.org/10.3897/neobiota.74.80242
Appendix 1, Tables S1–S4
Supplementary material 2 from: Hanslowe EB, Yackel Adams AA, Nafus MG, Page DA, Bradke DR, Erickson FT, Bailey LL (2022) Chew-cards can accurately index invasive rat densities in Mariana Island forests. NeoBiota 74: 29-56. https://doi.org/10.3897/neobiota.74.80242
Figure S1
Distribution. NE Central African Republic, S South Sudan, C & W Uganda, and NE DR Congo. in Leporidae
Distribution. NE Central African Republic, S South Sudan, C & W Uganda, and NE DR Congo.
Dr (Colon Cancer)
<p>Colon adenocarcinoma (COAD) is the commonest colon cancer exhibiting high mortality. Due to the association with cancers progression, long noncoding RNAs (lncRNAs) become prognostic biomarkers. This study, using relevant clinic information and expression profiles of lncRNA originating in The Cancer Genome Atlas database, aims to construct a prognostic lncRNA signature to estimate the prognosis for patients. In the training cohort, prognosis related lncRNAs were selected from differently expressed lncRNAs by univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regress and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by those lncRNAs. Prognostic model was able to calculate each COAD patient's risk score and split the patients to groups of low and high risk. Compared to the low-risk group, the high-risk group had significant poor prognosis. Then, the prognostic signature was validated in validation and all cohorts. The receiver operating characteristic (ROC) curve and c-index were performed in all cohort. Moreover, those prognostic lncRNAs signature were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in clinic. Finally, 7 lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with 7 prognostic lncRNAs suggested 4 significantly up-regulated pathways, which are AGE-RAGE signaling pathway, focal adhesion, ECM-receptor interaction and PI3K/Akt signaling pathway. To sum up, our study verified that the mentioned 7 lncRNAs can be biomarkers to predict the prognosis of COAD patients and design personalized treatment.</p>
Supplementary material 3 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S2
Supplementary material 2 from: Kendig AE, Canavan S, Anderson PJ, Flory SL, Gettys LA, Gordon DR, Iannone III BV, Kunzer JM, Petri T, Pfingsten IA, Lieurance D (2022) Scanning the horizon for invasive plant threats using a data-driven approach. NeoBiota 74: 129-154. https://doi.org/10.3897/neobiota.74.83312
Table S1
ScienceDex guides
Understand access before you commit
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.