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2,281 results for “laboratories”
Mitigating Rainfall Induced Soil Erosion through Bio-approach: From Laboratory Test to Field Trail
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Figure 1 from: Kadiyska T, Cherneva R, Cherneva Z, Marchev S, Madzharova D, Tourtourikov I, Mitev V (2024) Laboratory and genetic predictors for severe COVID-19 infection. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e120638
Figure 1 The OAS–RNase L pathway and the NLRP3 inflammasome activation. DsRNAs and IFN signaling activate the OAS gene. OAS1 induces the synthesis of the second messenger 2–5A from ATP. 2–5A then binds and transform the RNase L monomer to its active dimer formation. The active RNase L degrades viral RNA, thus inhibiting virus replication. Cleaved RNA, through DHX33 (DEAH-box helicase 33), which binds to cytosolic RNAs and interacts with MAVS (Mitochondrial Antiviral Signaling protein), can activate NLRP3 inflammasome. In most cases, this stimulates the normal anti-viral response, but theoretically, further stimulation of the already hyperactivated NLRP3 inflammasome would intensify the CS.
Data for "• Can we achieve atmospheric chemical environments in the laboratory? An integrated model-measurement approach to chamber SOA studies"
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Figure 1 from: Neumann M, Ehnert F (2024) Knowledge re-integration in real-world laboratories to transform cities and communities: report on workshop designs. Research Ideas and Outcomes 10: e124018. https://doi.org/10.3897/rio.10.e124018
Figure 1 Zooming into phase C: the process of knowledge re-integration into societal practice in a TDR process.
CLINICAL AND LABORATORY FEATURES OF CHRONIC VIRAL HEPATITIS B, EFFECTIVENESS OF ETIOTROPIC THERAPY
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Smart Sprayer Working in Laboratory and Vineyard - Videos
<p>Smart Sprayer Working in Laboratory and Vineyard. Videos of the performed tests.</p>
Supplementary material 1 from: Hall RM, Urban B, Skalova H, Moravcová L, Sölter U, Starfinger U, Kazinczi G, van Valkenburg J, Fenesi A, Konstantinovic B, Uludag A, Lommen S, Karrer G (2021) Seed viability of common ragweed (Ambrosia artemisiifolia L.) is affected by seed origin and age, but also by testing method and laboratory. NeoBiota 70: 193-221. https://doi.org/10.3897/neobiota.70.66915
Supporting tables and figures
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes
<p> </p> <p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p>
Combining metabolomics and experimental evolution reveals key mechanisms underlying longevity differences in laboratory evolved Drosophila melanogaster populations
<p>Experimental evolution with Drosophila melanogaster has been used extensively for decades to study aging and longevity. In recent years, the addition of DNA and RNA sequencing to this framework has allowed researchers to leverage the statistical power inherent to experimental evolution to study the genetic basis of longevity itself. Here, we incorporated metabolomic data into to this framework to generate even deeper insights into the physiological and genetic mechanisms underlying longevity differences in three groups of experimentally evolved D. melanogaster populations with different aging and longevity patterns. Our metabolomic analysis found that aging alters mitochondrial metabolism through increased consumption of NAD<sup>+</sup> and increased usage of the TCA cycle. Combining our genomic and metabolomic data produced a list of biologically relevant candidate genes. Among these candidates, we found significant enrichment for genes and pathways associated with neurological development and function, and carbohydrate metabolism. While we do not explicitly find enrichment for aging canonical genes, neurological dysregulation and carbohydrate metabolism are both known to be associated with accelerated aging and reduced longevity. Taken together, our results provide plausible genetic mechanisms for what might be driving longevity differences in this experimental system. More broadly, our findings demonstrate the value of combining multiple types of omic data with experimental evolution when attempting to dissect mechanisms underlying complex and highly polygenic traits such as aging.</p>
Comparison of the oviposition and predation impact on Dendroctonus micans (Kug.) of wild and laboratory-reared Rhizophagus grandis Gyll. on oriental spruce in Turkey
<p>Laboratory and statistical results</p>
FIGURE 10 in Correspondence of larval and postlarval instars in two species of the subgenus Zaracarus (Acari: Erythraeidae: Erythraeus) established with laboratory rearing
FIGURE 10. Erythraeus (Zaracarus) rupestris (Linnaeus, 1758), adult: Leg setae.
FIGURE 8 in Correspondence of larval and postlarval instars in two species of the subgenus Zaracarus (Acari: Erythraeidae: Erythraeus) established with laboratory rearing
FIGURE 8. Erythraeus (Zaracarus) rupestris (Linnaeus, 1758), adult: Habitus, dorsal view.
FIGURE 3 in Correspondence of larval and postlarval instars in two species of the subgenus Zaracarus (Acari: Erythraeidae: Erythraeus) established with laboratory rearing
FIGURE 3. Erythraeus (Zaracarus) budapestensis Fain and Ripka, 1998, larva: General view.
Laboratory findings
<p>Laboratory findings</p>
Demographic data and laboratory results
<p>Demographic data and laboratory results</p>
Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry [DATA]
<p>Data and metadata for the publication "Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry", published in Geophysical Research Letters.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.5% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.5% contaminant spike-in. </p>
Demographic data and laboratory results
<p>The demographic information of the study participants and the laboratory outcomes</p>
Supplementary material 2 from: Heuer K, Ghosh S, Robinson Sterling A, Toro R (2016) Open Neuroimaging Laboratory. Research Ideas and Outcomes 2: e9113. https://doi.org/10.3897/rio.2.e9113
Cartoon showing the idea of distributed collaboration on Magnetic Resonance Imaging data.
Data set - Laboratory experiments of extreme wave transformation on a coral reefs with a steep slope
<p><strong>Data set - Laboratory experiments of extreme wave transformation on a coral reef with a steep slope</strong></p> <p>This data set includes measurements of wave transformation and wave runup on a physical model (scale 1:60) of a coral reef with a very steep slope (1/2.26). The fore reef morphology is based on the reef at Teahupo'o, Tahiti, French Polynesia according to Harris et al. (2019). The bathymetry is extended with a reef lagoon and planar slope (1/9.64) inland. Wave conditions from the study area are employed to estimate extreme wave conditions.</p> <p>Laboratory experiments were carried out at the Coastal Processes and Engineering Laboratory, IINGEN UNAM, in Sisal, Yucatan. The data set is aimed to assess the performance of numerical models to predict hihgly nonlinear wave transformation over steep slopes.</p> <p>The dataset consists of:</p> <p><strong>-Waves: </strong>free surface elevation time series were aquired at 50 Hz using resistive wave gauges. Moreover, a 2-m long sensor was installed parallel to the beach in order to measure wave runup. Irregular, regular, and solitary waves were tested. The information is recorded in .txt files. The files are in the folders Irregular_waves.zip, Regular waves.zip and Solitary waves.zip, corresponding to irregular, regular, and solitary waves, respectively.</p> <p><strong>-Horizontal velocity:</strong> An electromagnetic current meter was installed near the breakpoint to measure the horizontal current velocity at 16 Hz. The information corresponding to each test is recorded in .txt files. The files are in the folder Electromagnetic current meter.zip.</p> <p><strong>-Bathymetry:</strong> A .txt file is included with the x-z coordinates of the reef profile used (Profile_Teahupoo.txt).</p> <p><strong>-Simulated cases:</strong> An Excel file with the laboratory tests and the sensor locations for each test is included (Cases.xlsx).</p> <p><strong>-ERA-5 data: </strong>A .txt file is included with the ERA-5 wave reanalysis data used (-18° S, -149.5° W) (ERA5_data.txt).</p>
ScienceDex guides
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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.