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4,157 results for “Survival.”
Data from: No effect of blood sampling or phytohaemagglutinin injection on post-fledging survival in a wild songbird
The injection of phytohaemagglutinin (PHA) and sampling of blood are widely used in studies of wild vertebrates to assess components of immune and endocrine function and health state and to obtain genetic material. Despite the pervasive use of these techniques in the life sciences, their potential effects on survival are rarely considered. For example, whether injection of the immunogen PHA into body parts critical for locomotion (e.g., the prepatagium, or wing web, in birds) affects survival has not been tested. Here, we test whether injection of PHA into the wing web and blood sampling from nestling house wrens affects their subsequent recruitment and survival as breeding adults. Capture-mark-recapture analysis on a large sample of young (N = 20,152 fledglings from 3959 broods) treated over 10 years revealed that neither PHA injection nor blood sampling affected individual survival and detection probability. Recruitment as a breeder varied among years, but this variation was not attributable to sampling effort, or the percent of all adults identified at the nest during a given year. Variation in the percent of adults identified was primarily attributable to the effect of nest depredation on our ability to capture nesting pairs. Our results indicating lack of an effect of blood sampling and immune stimulation on survival are encouraging, but we recommend further work to assess the potential negative effects of all commonly used techniques on the survival of study subjects in the wild, including the potential costs associated with mounting various immunological responses.
Data from: Sporadic late-onset nemaline myopathy: clinical spectrum, survival and treatment outcomes
OBJECTIVES: To describe the clinical phenotype, long-term treatment outcome and overall survival of sporadic late onset nemaline myopathy (SLONM) with or without a monoclonal protein (MP). METHODS: we conducted a retrospective chart review of patients seen between September 2000 and June 2017 and collected clinical, laboratory and survival data. Treatment response was classified as mild, moderate, or marked as adjudged by predefined criteria. RESULTS: We identified 28 patients with SLONM, 17 (61%) had an associated MP. Median age at symptom onset was 62 years. Diagnosis was often delayed by a median of 35 months from symptom onset. There was no difference in clinical or laboratory features between patients with or without MP. Although the majority of patients had proximal or axial weakness at onset, about 18% of patients had atypical presentations. 7/9 (78%) patients receiving intravenous immunoglobulin (IVIG), 6/8 (75%) receiving hematologic therapy as either autologous stem cell transplant (ASCT), or chemotherapy and 1/8 (13%) receiving immunosuppressive therapies responded to treatment (p=0.001). All 3 patients with marked response were treated with IVIG, 2 of them had a MP. The 5-year and 10-year overall survivals from symptom onset were 92% and 68% respectively, with no difference between patients with or without MP. CONCLUSIONS: SLONM has a wide spectrum of clinical presentations. In this contemporary case series, overall survival of patients did not seem to be affected by the presence of a MP. Initial treatment with IVIG is reasonable in all patients, followed by ASCT or chemotherapy as second-line therapy in patients with an associated MP.
Small survival axe
A small axe I did for a vr demo im trying to do. Made in blender, textured in substance painter. Source: Objaverse 1.0 / Sketchfab
Analysis Titanic Survival through Graph-Based Node with Neo4j: Unraveling Social Dynamics (Dataset & Cypher)
Open the record for dataset details and reuse information.
Data from: Survival and phenology of ducklings of Mallard, Domestic Duck and Gadwall in The Netherlands
<p>Previous analysis of demographic parameters of the Mallard (<em>Anas platyrhynchos</em>) population hinted at low duckling survival driving the decline, but no empirical data on duckling survival in the Netherlands were available. To address this knowledge gap, we initiated a citizen science project in 2016 to collect basic information about Mallard breeding biology and duckling survival. In the slipstream of this project, we also collected data for Domestic Duck (<em>A. platyrhynchos forma domestica</em>) and Gadwall (<em>Mareca strepera</em>) for comparison. </p> <p>Here we archive the research data underlying the analyses and graphs of the this publication in Limosa (2024, 97:49-61): "Survival and phenology of dablings of Mallard <em>Anas platyrhynchos</em>, Domestic Duck <em>A. platyrhynchos forma domestica</em> and Gadwall <em>Mareca strepera</em> in The Netherlands" (Dutch title: "Overleving en fenologie van kuikens van Wilde Eend, Soepeend en Krakeend in Nederland") by Erik Kleyheeg, Loes van den Bremer, Chris van Turnhout, Ties Maris, Eelke Jongejans & Niek van Rabenswaaij.</p> <p> </p> <p>The data file <strong><span>“waarnemingen 2016-2020 Limosa.csv”</span></strong><span> contains the following variables:</span></p> <p><span>year: Year of the study (2016 up to 2020)</span></p> <p><span>date: Date of observation</span></p> <p><span>time: Time of observation</span></p> <p><span>ID: Identifier of observation</span></p> <p><span>ID_family: Identifier of mallard family/brood (useful in case of successive observations)</span></p> <p><span>species: Species (dom = domestic duck/feral mallard, gad = gadwall, mal = mallard)</span></p> <p><span>N_chicks: Reported number of chicks</span></p> <p><span>age: Estimated age in weeks (1 = 0 to 1 week old, etc.)</span></p> <p><span>age_days: Estimated age in days</span></p> <p><span>observer: Anonymized observer</span></p> <p><span>remark: Remark</span></p> <p><span>latitude: Latitude of observation</span></p> <p><span>longitude: Longitude of observation</span></p> <p><span>surface: Whether ducklings were on land or water</span></p> <p><span>shore: Type of shoreline nearest to ducklings (emb = timbering/revetment, nhv = high vegetation, nlv = low </span><span>vegetation, nno = no vegetation, oth = other)</span></p> <p><span>water: Water quality (cle = clear, tur = turbid, wee = covered with duckweed, oth = other) </span></p> <p><span>N_deaths: Number of ducklings observed perishing during the observation</span></p> <p><span>cause_deaths: Observed cause of death (agg = aggression, bre = grey heron, bui = buzzard, hon = dog, kat = cat, nijl = Egyptian goose, pre = predator unknown, sno = pike, zkr = carrion crown, zon = mammal unknown, oth = other)</span></p> <p><span>breadfeeding: Whether or not ducks are fed with bread regularly at the site of the observation</span></p>
Figure 7 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey
Figure 7. Total number of dead birds and predation by predatory mammals and birds per week.
Cancer type and survival prediction based on transcriptomic feature map
<p>Upload all source data of our paper "Cancer type and survival prediction based on transcriptomic feature map"</p> <p># Zendo Project</p> <p> </p> <p>This project encompasses a series of files related to the integration of cancer survival data, feature extraction, and the augmentation of data using Generative Adversarial Networks (GANs). The project aims to improve the accuracy of survival predictions and support cancer research through in-depth analysis and processing of cancer-related data.</p> <p> </p> <p>## Project Overview</p> <p> </p> <p>This repository includes HDF5 files of cancer survival data, intermediate files for feature extraction, and data files augmented using GAN networks. Among them, `Data Integration.ipynb` is a Jupyter notebook used for slicing and merging `feat_part1.h5` to `feat_part6.h5`, while files starting with four letters are data files for specific cancer types, and files starting with `GAN` are data that have been augmented by GAN networks.</p> <p> </p> <p>## File Descriptions</p> <p> </p> <p>### Data Files</p> <p> </p> <p>- BLCA.h5: Survival data for bladder cancer.</p> <p>- BRCA.h5: Survival data for breast cancer.</p> <p>- HNSC.h5: Survival data for head and neck cancer.</p> <p>- KIRC.h5: Survival data for kidney clear cell carcinoma.</p> <p>- LIHC.h5: Survival data for liver hepatocellular carcinoma.</p> <p>- LUAD.h5: Survival data for lung adenocarcinoma.</p> <p>- LUSC.h5: Survival data for lung squamous cell carcinoma.</p> <p>- OVxx.h5: Survival data for ovarian cancer.</p> <p>- SKCM.h5: Survival data for skin cutaneous melanoma.</p> <p>- STAD.h5: Survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Feature Extraction Files</p> <p> </p> <p>- feat_part1.h5 to feat_part6.h5: Intermediate data files for feature extraction, used to store partial feature data during the processing.</p> <p> </p> <p>### Feature Map Files</p> <p> </p> <p>- fmap_BLCA.h5: Feature map data for bladder cancer.</p> <p>- fmap_BRCA.h5: Feature map data for breast cancer.</p> <p>- fmap_HNSC.h5: Feature map data for head and neck cancer.</p> <p>- fmap_KIRC.h5: Feature map data for kidney clear cell carcinoma.</p> <p>- fmap_LIHC.h5: Feature map data for liver hepatocellular carcinoma.</p> <p>- fmap_LUAD.h5: Feature map data for lung adenocarcinoma.</p> <p>- fmap_LUSC.h5: Feature map data for lung squamous cell carcinoma.</p> <p>- fmap_OVxx.h5: Feature map data for ovarian cancer.</p> <p>- fmap_SKCM.h5: Feature map data for skin cutaneous melanoma.</p> <p>- fmap_STAD.h5: Feature map data for stomach adenocarcinoma.</p> <p> </p> <p>### GAN Augmented Data Files</p> <p> </p> <p>- GAN500sur_BLCA.h5: Augmented survival data for bladder cancer.</p> <p>- GAN500sur_BRCA.h5: Augmented survival data for breast cancer.</p> <p>- GAN500sur_HNSC.h5: Augmented survival data for head and neck cancer.</p> <p>- GAN500sur_KIRC.h5: Augmented survival data for kidney clear cell carcinoma.</p> <p>- GAN500sur_LIHC.h5: Augmented survival data for liver hepatocellular carcinoma.</p> <p>- GAN500sur_LUAD.h5: Augmented survival data for lung adenocarcinoma.</p> <p>- GAN500sur_LUSC.h5: Augmented survival data for lung squamous cell carcinoma.</p> <p>- GAN500sur_OVxx.h5: Augmented survival data for ovarian cancer.</p> <p>- GAN500sur_SKCM.h5: Augmented survival data for skin cutaneous melanoma.</p> <p>- GAN500sur_STAD.h5: Augmented survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Jupyter Notebook</p> <p> </p> <p>- Data Integration.ipynb: The code for slicing and merging `feat_part1.h5` to `feat_part6.h5`, a core tool in the feature integration process.</p> <p> </p> <p>## Usage Guide</p> <p> </p> <p>1. Data Preparation: Place the original data files in the project directory.</p> <p>2. Feature Extraction: Run the `Data Integration.ipynb` notebook to merge the intermediate feature extraction files.</p> <p>3. Data Augmentation: Use GAN networks to augment survival data, generating new data files.</p> <p>4. Analysis Results: Use the augmented data for survival prediction analysis.</p> <p> </p> <p>## Contributing and Collaboration</p> <p> </p> <p>We welcome contributions and collaboration in any form. If you have any questions or suggestions about the project, please contact us through GitHub Issues.</p> <p> </p> <p> </p>
Metastatic colorectal cancer patients treated with regorafenib before TAS-102 or viceversa: examples of radiologic restagings and survival follow-up.
<p>Metastatic colorectal cancer patients treated with regorafenib before TAS-102 or viceversa: examples of radiologic restagings and survival follow-up.</p>
Pancancer survival prediction using a deep learning architecture with multimodal representation and integration
<p>The clinical data, gene expression (mRNA) data, and microRNA expression (miRNA) data were downloaded from the PanCanAtlas TCGA project (https://gdc.cancer.gov/about-data/publications/pancanatlas). The gene copy number variation (CNV) data was downloaded from UCSC Xena (http://xena.ucsc.edu/public/).</p> <p>Files prefixed with 'Pre' represent the preprocessed datasets. The preprocessing details can be found in 'Datasets and data preprocessing' subsection.</p>
Dataset supporting "Blebs Promote Cell Survival by Assembling Oncogenic Signaling Hubs" Weems et al. Nature
<p>All primary data supporting Weems et al. "Blebs Promote Cell Survival by Assembling Oncogenic Signaling Hubs" Nature <span>2023 Mar;615(7952):517-525. </span><span>doi: 10.1038/s41586-023-05758-6</span></p>
Effects of diesel exhaust particles on the health and survival of the buff-tailed bumblebee Bombus terrestris after acute and chronic oral exposure.
<p>Contents the data sets belonging to the publication <strong>Effects of diesel exhaust particles on the health and survival of the buff-tailed bumblebee <em>Bombus terrestris </em>after acute and chronic oral exposure. </strong>in the Journal of Hazardous materials. Included are the data sets to the experiments exploring the effects of acute and chronic oral toxicity of DEP respectively, and a data set including the PAH content of the analysed DEP samples</p>
Survival Axe
This is an Axe that i modelled in Maya and Textured in Substance Painter. This model took around 3 hours to complete and another 1 hour to Unwrap. Textures: Blinn 1 : 4k Blinn 2 : 4k Check out my Website: http://charlieandcolin3.wixsite.com/charlietinley Source: Objaverse 1.0 / Sketchfab
A Novel Online Calculator to Predict Long-term Survival and Recurrence Risk of Patients With Gastric Adenocarcinoma
ClinicalTrials.gov study NCT06337266. IPD Sharing: NO. Countries: 1. Publications: 0.
The Prognosis Effect of Indocyanine Green (ICG) Fluorescence Guided Surgery on Disease-free Survival (DFS) of Stage III Colorectal Cancer
ClinicalTrials.gov study NCT06583564. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing Refractoriness and Infectious Survival Events in AML
ClinicalTrials.gov study NCT06709235. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Prognostic Biomarkers of Survival at 6 Months for Patients Treated With Bevacizumab Glioblastomas in First Relapse After Failure of Radiochemotherapy
ClinicalTrials.gov study NCT03144167. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Time to Surgery and Survival Outcomes for Patients With Resected Colorectal Carcinoma: Multicenter Study (BIG RENAPE)
ClinicalTrials.gov study NCT04916054. IPD Sharing: NO. Countries: 1. Publications: 0.
Strategies to Improve Islet Survival
ClinicalTrials.gov study NCT00464555. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Mini-Screw as Orthodontic Anchoring: Survival, Clinical Efficiency and Experience of the Patient
ClinicalTrials.gov study NCT00137137. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Symptom Management on Inflammation and Survival in Metastatic Lung Cancer
ClinicalTrials.gov study NCT02271581. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.