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12,346 results for “derivatives”
Generation of Random laser from Dye-Derived Red-Emitting Carbon Dots
<p>Carbon dots are carbon-based nanoparticles renowned for their intense light-emitting capabilities covering the whole visible light range. Here, we overcome these problems by solvothermally synthesizing carbon dots starting from Neutral Red, a common red-emitting dye, as a molecular precursor. The obtained nanoparticles are highly luminescent in the red region, with a quantum yield comparable to that of the starting dye. Most importantly, the nanoparticle carbogenic matrix protects the Neutral Red molecules from photobleaching under ultraviolet excitation while preventing aggregation-induced quenching, thus allowing solid-state emission inside PVA. Finally, the dye-based carbon dots demonstrate stable and efficient random lasing emission in the red region.</p>
CONUS-wide HUC4 Watershed Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for HUC4 watersheds within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. Note that in some instances multiple HUC4 watersheds have been merged together into a HUC4 group, and that HUC4 watersheds without hydropower facilities are excluded. A hydropower facility's physical location, rather than the source of water, was used in assigning facilities to HUC4 watersheds.</p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>HUC4_group</td> <td>N/A</td> <td>HUC4 group; HUC4 numeric ID or multiple IDs</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with HUC4 group data by tab</p> <p>3) csv by HUC4 group:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa
<p>This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.</p> <p>The CSV file `intercomparison-results.csv` gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development. </p> <p>The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).</p> <p>Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? <em>Under review</em>. </p>
Cross-reactive CD8+ T cell responses to tumor-associated antigens (TAAs) and homologous microbiota-derived antigens (MoAs)
<p><strong><span>Background: </span></strong><span>We have recently shown extensive sequence and conformational homology between tumor-associated antigens (TAAs) and antigens derived from microorganisms (MoAs). The present study aimed to assess the breadth of T-cell recognition specific to MoAs and the corresponding TAAs in healthy subjects (HS) and patients with cancer (CP).</span></p> <p><strong><span>Method: </span></strong><span>A library of >100 peptide-MHC (pMHC) combinations was used to generate DNA-barcode labelled multimers. Homologous peptides were selected from the Cancer Antigenic Peptide Database, as well as Bacteroidetes/Firmicutes-derived peptides. They were incubated with CD8+ T cells from the peripheral blood of HLA-A*02:01 healthy individuals (n=10) and cancer patients (n=16). T cell recognition was identified using tetramer-staining analysis. Cytotoxicity assay was performed using as target cells TAP-deficient T2 cells loaded with MoA or the paired TuA.</span></p> <p><strong><span>Results: </span></strong><span>A total of 66 unique pMHC recognized by CD8+ T cells across all groups were identified. Of these, 21 epitopes from microbiota were identified as novel immunological targets. Reactivity against selected TAAs was observed for both HS and CP. pMHC tetramer staining confirmed CD8+ T cell populations cross-reacting with CTA SSX2 and paired microbiota epitopes. Moreover, PBMCs activated with the MoA where shown to release IFNγ as well as to exert cytotoxic activity against cells presenting the paired TuA.</span></p> <p><strong><span>Conclusions: </span></strong><span>Several predicted microbiota-derived MoAs are recognized by T cells in HS and CP. Reactivity against TAAs was observed also in HS, primed by the homologous bacterial antigens. CD8+ T cells cross-reacting with MAGE-A1 and paired microbiota epitopes were identified in three subjects. Therefore, the microbiota can elicit an extensive repertoire of natural memory T cells to TAAs, possibly able to control tumor growth (“natural anti-cancer vaccination”). In addition, non-self MoAs can be included in preventive/therapeutic off-the-shelf cancer vaccines with more potent anti-tumor efficacy than those based on TAAs.</span></p>
Dataset of management and growth curve derivatives to predict first lactation milk yield
<p>This dataset presents management and modeled variables of 78 Holstein-Friesian heifers. <span>Spreadsheets consist of seven sets of training and testing data randomly split with replacement.</span></p> <p></p> <p><span>In each spreadsheet, the included variables are:</span></p> <table> <tbody> <tr> <td><strong>Variable name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>ID</td> <td>Heifer ID tag</td> </tr> <tr> <td>Mes</td> <td>Month of birth of the heifer</td> </tr> <tr> <td>Estacion</td> <td>Birth season, according to Mes</td> </tr> <tr> <td>L_305_F_2x2</td> <td>First lactation milk yield is calculated as the integral from day 1 to 305 of a Fourier model representing daily milk yield (kg)</td> </tr> <tr> <td>Edad_IA</td> <td>Heifer's age at effective artificial insemination</td> </tr> <tr> <td>servicios</td> <td>Number of inseminations</td> </tr> <tr> <td>Edad_1P</td> <td>Heifer's age at first calving</td> </tr> <tr> <td>P_parto</td> <td>Heifer's weight at first calving (kg)</td> </tr> <tr> <td>B_parto</td> <td>Heifer's condition score at first calving</td> </tr> <tr> <td>Sexo_C</td> <td>Calf sex</td> </tr> <tr> <td>Peso_C</td> <td>Calf weight at birth (kg)</td> </tr> <tr> <td>wt_3._F, wt_6._F, wt_9._F, wt_12._F, wt_15._F, wt_18._F, wt_21._F</td> <td>Heifer's weight at various months after birth, as per a Fourier model of Heifer's growth curve (kg)</td> </tr> <tr> <td>X1STd_3._F, X1STd_6._F, X1STd_9._F, X1STd_12._F, X1STd_15._F, X1STd_18._F, X1STd_21._F</td> <td>First derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td>X2STd_3._F, X2STd_6._F, X2STd_9._F, X2STd_12._F, X2STd_15._F, X2STd_18._F, X2STd_21._F</td> <td>Second derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td> <p>wt_3._L to wt_21._L, X1STd_3._L to X1STd_21._L, </p> <p>X2STd_3._P to X2STd_21._P</p> </td> <td>Variables generated from a Power model</td> </tr> </tbody> </table>
A 1505-year sunshine duration reconstruction derived from stable carbon and oxygen isotopes
<p>Related to publication: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.172042" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.172042</a></p> <p>Olga V. Churakova (Sidorova), Rolf T.W. Siegwolf, Mikhail S. Zharkov, Matthias Saurer,<br>Dual carbon and oxygen isotopes in Siberian tree rings as indicator of millennia sunshine duration changes,<br>Science of The Total Environment,<br>2024,<br>172042,<br>ISSN 0048-9697,<br>https://doi.org/10.1016/j.scitotenv.2024.172042.<br>(https://www.sciencedirect.com/science/article/pii/S0048969724021855)<br>Abstract: The current lack of information on past summer sunshine duration variability from annually resolved palaeoclimatological archives is hindering progress in the understanding and modelling of the earth climate system. We show that a combination of tree-ring carbon and oxygen isotopes from Siberia provides robust information on summer sunshine duration, which we use for an annual 1505-year reconstruction of July sunshine duration variability (1,5K-SIB-JSDR). We found that the Medieval maximum is 56 % higher than the average over 1505 years. Rapid and drastic decreases in sunshine duration up to 60 % correspond to major stratospheric volcanic eruptions. Grand Solar Minima and total sunspot numbers are also well preserved in the 1,5K-SIB-JSDR. Coherency with a global air temperature composite and spring Arctic Oscillation indicate that a large-scale climate signal is retained in our sunshine reconstruction.</p> <p>Keywords: Stable isotopes; Sunshine duration; Reconstruction; Volcanic eruptions; Sunspot numbers; Atmospheric circulation patterns; Permafrost; Siberia</p>
Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs
<p>These are netcdfs of S1 raw data, intermediate products, derived snow depths, ancillary data (IMS snow coverage, tree percentage) and lidar snow depths used in an analysis of the Lievens et al. (2021) algorithm.</p> <p> </p> <p>9 sites - Banner 2020, Banner 2021, Cameron 2021, Dry Creek 2020, Fraser 2020, Fraser 2021, Little Cottonwood Canyon 2021, Mores 2020, Mores 2021</p> <p> </p> <p>Data Variables:</p> <p>s1 - sentinel 1 backscatter data. contains 3 bands - VV, VH, and incidence angle</p> <p>ims - IMS snow coverage data (4 = snow covered, 2 = None) []</p> <p>fcf - Forest coverage fraction [%]</p> <p>deltaCR - change in the S1 cross ratio through time [dB]</p> <p>deltaVV - change in S1 VV backscatter through time [dB]</p> <p>deltaGamma - change in combined gamma variable [dB]</p> <p>snow_index - snow index in dB that is converted to derived snow depth by C parameter [dB]</p> <p>snow_depth - derived snow depth from S1 [m]</p> <p>wet_flag - flagged for snow with -2dB of change in CR. 1 = wet, 0 = dry</p> <p>alt_wet_flag - snow flagged by negative snow_index. 1 = wet 0 = dry</p> <p>freeze_flag - snow flagged as refreezing by increase of 1 dB in CR</p> <p>wet_snow - combined wet flag, alt wet flag, freeze flag, and previous time step's wet snow to get current wet snow flags</p> <p>perma_wet - snow that is flagged as wet more than 50% of last four acquisitions after Feb 1</p> <p>lidar-sd - Lidar derived snow depths [m]</p> <p>lidar-vh - lidar derived vegetation heights [m]</p> <p>lidar-dem - lidar derived snow free dems [m]</p> <p>aspect - aspect in degrees from lidar-dem [°]</p> <p>easting - degrees of easting from aspect[°]</p> <p>north - degrees of northing from aspect[°]</p> <p>confidence - unused metric of confidence</p>
FIGURE 5 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 5 Mean (+SD) relative expression of gnrh2, fshb, lhb, fshr and lhr messenger (m)RNA in (a) the breeding season () 2n, and () 4n and (b) the non-breeding season in Carassius auratus red var. () 2n, and () 4n. (RCC,) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR,). T, gene detected in the testis; O, gene detected in the ovary. *, significant difference between RCC and 4nRR (P <0.05)
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)
FIGURE 2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 2 (a) The mature eggs (scale bar = 100 μm) and (b) mature sperm (scale bar = 10 μm) of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR)
FIGURE 3 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 3 Reverse-transcription (RT)-PCR analysis of the expression of (a) gnrh2, (b) fshb, (c) lhb, (d) fshr and (e) lhr messenger (m)RNA in various tissues of autotetraploid Carrasius auratus red var. ♀ x Megalobrama amblycephala ♂ (4nRR). The upper strip of each panel (a)–(e) shows the positive control of actin gene while the lower strip of each panel shows the RT-PCR amplification of the target gene
FIGURE 1 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 1 The gonadal structure of Carassius auratus red var. [RCC; (a)–(c)] and autotetraploids C. auratus red var. ♀ × Megalobrama amblycephala ♂ [4nRR; (d)–(f)]: (a) ovary of 7 month-old RCC containing many phase II and a few phase III oocytes; (b) ovary of 12 month-old RCC showing many mature phase IV ova; (c) testis of 12 month-old RCC with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes; (d) ovary of 7 month-old 4nRR containing phase II and a few phase III oocytes; (e) ovary of 12 month-old 4nRR with numerous mature phase IV ova; (f) testis of 12 month-old 4nRR with numerous mature sperms () and a small amount of spermatocytes () in the lobules of testes, the scale bars: (a), (b), (d), and (e) = 100 μm; (c) and (f) = 10 μm
F I G U R E 8 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 8 Allometric growth: regression curves for total length and body mass for the hybrid grouper in 5 months old (a), 6 months old (b) and 8 months old (c)
F I G U R E 7 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 7 Correlation coefficients among morphological traits of the hybrid grouper in 5 months old (a), 6 months old (b) and 8 months old (c)
F I G U R E 5 Phylogenetic study using the 5S in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 5 Phylogenetic study using the 5S rDNA sequences from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper
F I G U R E 6 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 6 Proportion of the total length and head length of the hybrid grouper, shown as percentages of body length
F I G U R E 3 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 3 Representative sequences of 5S rDNA. (a) Complete 5S coding regions from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. Internal control regions of the coding region are shaded. (b) Comparison of the nontranscribed spacer (NTS) sequences from Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. The NTS upstream TATA elements are shaded and asterisks mark variable sites in the NTS
F I G U R E 1 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 1 DNA content of (a) Epinephelus fuscoguttatus, (b) Epinephelus polyphekadion and (c) the hybrid grouper
F I G U R E 2 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 2 Chromosome spreads at metaphase in Epinephelus fuscoguttatus, Epinephelus polyphekadion and the hybrid grouper. (a) The 48 chromosomes of Epinephelus fuscoguttatus. (b) The 48 chromosomes of Epinephelus polyphekadion. (c) The 48 chromosomes of the hybrid grouper
Investigations on single and multi-grain optically stimulated luminescence (OSL) sensitivity and electron spin resonance (ESR) signals in quartz derived from sandstones: Insights on provenance of quartz in ancient depositional systems
<p><span>Trapped charge techniques of luminescence and electron spin resonance (ESR) are classic tools for dating Quaternary deposits. Over the past decade, these techniques have been routinely applied to investigate provenance and /or the sedimentary history of grains based on the different luminescence and ESR characteristics of quartz. Of these, optically stimulated luminescence (OSL) sensitivity is one of the most widely investigated parameter for luminescence-based provenance approach. A majority of studies on this parameter are based on evaluation of multi-grain OSL sensitivity of the samples. This is particularly concerning because single-grain quartz luminescence studies have shown that the luminescence signal of a multi-grain aliquot is contributed by less than ~1-10% of the total grains. Since the sole criteria for discrimination of sources based on luminescence sensitivity relies on its intensity, therefore the results based on multi-grain analysis will most likely be skewed depending on the proportion and ‘brightness’ of a few grains. This demands a need to evaluate the potential of single-grain quartz OSL sensitivity in provenance studies. In this study, we investigate single and multi-grain quartz OSL sensitivities from compositionally different sandstones with well-characterised sources based on U-Pb zircon ages. We further complement this analysis with characterisation of ESR centres commonly used in quartz provenance, namely E’<sub>1</sub> and [AlO<sub>4</sub>]<sup>0</sup> centres. Our study shows that single-grain quartz OSL sensitivity can help distinguish between sediments that have a predominant input from a single source as compared to those with contribution from multiple sources, which otherwise cannot be inferred from multi-grain studies. Moreover, our results on characterisation of quartz-based ESR intensity of E’<sub>1</sub> and saturated [AlO<sub>4</sub>]<sup>0</sup> centres successfully differentiates between sandstones and further complements the luminescence-based characterisation. </span></p>
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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
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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.