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Dataset results
538 results for “Vector data”
Gene expression data from 4T1 cells stably transduced with lentiviral vectors encoding a control shRNA (shscramble) or two shRNAs targeting TM4SF1 (shTM4SF1-1 and shTM4SF1-5) and from 4TO7 cells stabl
GEO Series GSE62730. Mus musculus. 15 samples. Type: Expression profiling by array.
Expression data from cord blood CD34+ cells transduced with UBTF wildtype expression vectors, UBTF-TD expression vectors, or empty control vector.
GEO Series GSE189901. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Gene expression data from lineage negative c-kit positive sca-1 positive hematopoietic stem cells (LSK, Lin-Sca-1+c-kit+) isolated from Jak2VF/+ vector and Jak2VF/+ Hmga2 mice
GEO Series GSE99485. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Expression data of NCI-H441 cells stably expressing hsa-mir-365-2 vs empty vector
GEO Series GSE33672. Homo sapiens. 4 samples. Type: Expression profiling by array.
Intranasal delivery of NS1-deleted influenza virus vectored COVID-19 vaccine restrains the SARS-CoV-2 inflammatory response (Hamster Lung RNA-seq Data)
GEO Series GSE228112. Mesocricetus auratus. 29 samples. Type: Expression profiling by high throughput sequencing.
Validation data: Multi-objective support vector regression reduces systematic error in moderate resolution maps of tree species abundance
<p>Validation data used in the analysis presented by Legaard et al. (in review). Validation data are sufficient to replicate model comparisons presented in this paper. Note that model training data were provided by the USDA Forest Service, Forest Inventory and Analysis Program through a collaborative agreement, are maintained by the USDA Forest Service as confidential, and cannot be shared.</p> <p>Legaard, K., Simons-Legaard, E., Weiskittel, A., Multi-objective support vector regression reduces systematic error in moderate resolution maps of tree species abundance, Remote Sensing, in review.</p>
Data from: Trends in malaria in Odisha, India—an analysis of the 2003-2013 time-series data from the National Vector Borne Disease Control Program
Background: Although Odisha is the largest contributor to the malaria burden in India, no systematic study has examined its malaria trends. Hence, the spatio-temporal trends in malaria in Odisha were assessed against the backdrop of the various anti-malaria strategies implemented in the state. Methods: Using the district-wise malaria incidence and blood examination data (2003-2013) from the National Vector Borne Disease Control Program, blood examination-adjusted time-trends in malaria incidence were estimated and predicted for 2003-2013 and 2014-2016, respectively. An interrupted time series analysis using segmented regression was conducted to compare the disease trends between the pre (2003-2007) and post-intensification (2009-2013) periods. Key-informant interviews of state stakeholders were used to collect the information on the various anti-malaria strategies adopted in the state. Results: The state annual malaria incidence declined from 10.82/1000 to 5.28/1000 during 2003-2013 (adjusted annual decline: -0.54/1000, 95% CI: -0.78 to -0.30). However, the annual blood examination rate remained almost unchanged from 11.25% to 11.77%. The key-informants revealed that intensification of anti-malaria activities in 2008 led to a more rapid decline in malaria incidence during 2009-2013 as compared to that in 2003-2007 [adjusted decline: -0.83 (-1.30 to -0.37) and -0.27 (-0.41 to -0.13), respectively]. There was a significant difference in the two temporal slopes, i.e., -0.054 (-0.10 to -0.002, p=0.04) per 1000 population per month, between these two periods, indicating almost a 200% greater decline in the post-intensification period. Although, the seven southern high-burden districts registered the highest decline, they continued to remain in that zone, thereby, making the achievement of malaria elimination (incidence <1/1000) unlikely by 2017. Conclusion: The anti-malaria strategies in Odisha, especially their intensification since 2008, have helped improve its malaria situation in recent years. These successful measures need to be sustained and perhaps intensified further for eliminating malaria from Odisha.
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
Background: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly, and thus, they cannot enhance the capability of the predictive model's learning algorithm. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings: We identified the study areas in three provinces (Nakhon Pathom, Ratchaburi, and Samut Sakhon) of central Thailand that were reported to have a high incidence of dengue outbreaks. Prior to being added to the model, the infection data of the dengue vector, Aedes aegypti, the climate parameters, and the population density were collected from various sources and standardized. This process ensured that the data were not overwhelmed by each other in terms of the distance measures and to enhance the model effectiveness. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Aedes aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. The support vector machine (SVM), a machine learning technique, has been receiving attention in many research areas due to its remarkable generalization performance. In this research, we applied the SVM with the radial basis function (RBF) kernel, referred to as the SVM-R, to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions: The infection rates of the Aedes aegypti female mosq uitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. This approach is more reliable and practical for monitoring dengue outbreaks, particularly in locations with similar climates because it does not rely on only climate factors. In addition, we demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).
Data from: Knock-on community impacts of a novel vector: spillover of emerging DWV-B from Varroa-infested honeybees to wild bumblebees
Novel transmission routes can directly impact the evolutionary ecology of infectious diseases, with potentially dramatic effect on host populations and knock-on effects on the wider host community. The invasion of Varroa destructor, an ectoparasitic viral vector in Western honeybees, provides a unique opportunity to examine how a novel vector affects disease epidemiology in a host community. This specialist honeybee mite vectors deformed wing virus (DWV), an important re-emerging honeybee pathogen that also infects wild bumblebees. Comparing island honeybee and wild bumblebee populations with and without V. destructor, we show that V. destructor drives DWV prevalence and titre in honeybees and sympatric bumblebees. Viral genotypes are shared across hosts, with the potentially more virulent DWV-B overtaking DWV-A in prevalence in a current epidemic. This demonstrates disease emergence across a host community driven by the acquisition of a specialist novel transmission route in one host, with dramatic community level knock-on effects.
Data from: NGS library preparation may generate artifactual integration sites of AAV vectors
[No abstract entered]
Expression data from leaves of rice plants overexpressing the OsCPK4 gene and control plants(empty vector) in normal conditions
GEO Series GSE52353. Oryza sativa Japonica Group; Oryza sativa. 24 samples. Type: Expression profiling by array.
Data from: Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas
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Data from: Trends in malaria in Odisha, India—an analysis of the 2003-2013 time-series data from the National Vector Borne Disease Control Program
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Data from: Machine learning for characterization of insect vector feeding
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Data from: Knock-on community impacts of a novel vector: spillover of emerging DWV-B from Varroa-infested honeybees to wild bumblebees
Open the record for dataset details and reuse information.
Data from: NGS library preparation may generate artifactual integration sites of AAV vectors
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Data from: Risk of exposure to potential vector mosquitoes for rural workers in Northern Lao PDR
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SWOT Level 2 Lake Single-Pass Vector Prior Data Product, Version 2.0
The SWOT Level 2 Lake Single-Pass Vector Prior Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. <br> Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. <br> This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of lakes identified in the PLD.
SWOT Level 2 River Single-Pass Vector Data Product, Version 2.0
The SWOT Level 2 River Single-Pass Vector Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. <br> Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. <br> This dataset is the parent collection to the following sub-collections: <br> https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_node_2.0 <br> https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_reach_2.0 <br>
VIIRS (S-NPP) I Band 375 m Active Fire Product NRT (Vector data)
Near real-time (NRT) Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on that instrument's 375 m nominal resolution data. Compared to other coarser resolution (≥1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The 375 m product complements the baseline Suomi NPP/VIIRS 750 m active fire detection and characterization data, which was originally designed to provide continuity to the existing 1 km Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) active fire data record. Due to frequent data saturation issues, the current 375 m fire product provides detection information only with no sub-pixel fire characterization.VNP14IMGTDL_NRT are available through NASA FIRMS in the following formats: TXT, SHP, KML, WMS. These data are also provided through the LANCE FIRMS Fire Email Alerts. Please note only the TXT and SHP files contain all the attributes.
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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)
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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.