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678 results for “sugars”
Figure 2 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 2: Crop sequence in rotation 1: standard sugar beets variety "Mixer"; Cereals; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 3 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 3: Crop sequence in rotation 2: standard sugar beets variety "Mixer"; Cereals; WOSR; Cereals; "Mixer." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 1 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 1: A screenshot of the user interface showing a selected crop rotation and the estimated final SBN population (Pf) values, sugar yield (tonnes/ha), income (SEK/ha) and the reproduction factor (Rf) values.
Figure 9 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 9: (A) relationship between initial SBN population (Pi eggs g−1 soil) and reproduction factors (Rf) of three sugar beets varieties estimated by SBNWatch; (B) relationship between initial SBN population Pi (eggs g−1 soil) and reproduction factors (Rf) of four sugar beets varieties sown in microplots in 2013–2014 (n = 4). The bars represent means of Rf ± Sd.
Figure 7 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 7: Crop sequence in rotation 6: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; Oil radish; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 5 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 5: Crop sequence in rotation 4: tolerant sugar beets variety "Julietta"; Cereals; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Figure 6 in Development of a decision support system for managing Heterodera schahtii in sugar beet production
Figure 6: Crop sequence in rotation 5: tolerant sugar beets variety "Julietta"; Cereals; WOSR; Cereals; "Julietta." The SBN initial population (Pi eggs g−1 soil) = 2.
Fig. 3 in The Impact Of Sowing Time On Sugar Content And Snow Mould Development In Winter Wheat
Fig. 3. The content of carbohydrates depending Fig. 4. The average content of residual carbohyon the sowing time. drates in comparison with the carbohydratecon- tent in autumn (2005-2007).
Fig. 2 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 2. Population dynamics of the natural enemies Harmonia axyridis, Doru lineare, and Billaea claripalpis in sugar cane from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil. Arrows indicate the harvesting and budding periods.
Fig. 1 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 1. Population dynamics of sugar cane pests from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil: (A) Means of internodes, attacked internodes, and number of Diatraea saccharalis larvae per culm; (B) Insects of Mahanarva fimbriolata per square m, and infested culms (%) by Saccharicoccus sacchari, and Melanaphis sacchari. Arrows indicate the harvesting and budding periods.
Linked collectors and determiners for: Solanum hydroides (Solanaceae): a prickly novelty from the land of the sugar loaves, central Brazilian Atlantic Forest.
Natural history specimen data linked to collectors and determiners held within, "Solanum hydroides (Solanaceae): a prickly novelty from the land of the sugar loaves, central Brazilian Atlantic Forest". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5">https://bionomia.net/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5">https://gbif.org/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5</a>. Formatted as a Frictionless Data package.
Dataset of the Techno-economic assessment (TEA) of the sugar beet pulp biorefinery
<p>This dataset contains information of the CAPEX, OPEX, raw material costs, and labor costs of a sugar beet pulp. These data can be used to perform techno-economic analysis. More information is provided in the file.</p>
Dataset of life cycle assessment (LCA) model for sugar beet pulp biorefinery
<p>This dataset contains information of the foreground and background systems used to model a sugar beet pulp biorefinery. More information can be found in the file.</p>
Fig. 3 in Diel movement patterns of Pacific sugar limpet, Patelloida saccharina (Gastropoda: Patellogastropoda: Lottiidae) in response to semi-diurnal tides of Samal Island, Philippines
Fig. 3. Movement patterns of Patelloida saccharina limpets in Catagman, Samal Island, during various lunar phases. A, New Moon, 27–28 March 2017; B, First Quarter, 4–5 April 2017; C, Full Moon, 9–10 April 2017; D, Last Quarter, 19–20 April 2017. The left and right y-axes correspond to the percentage of moving limpets and tidal height measurements in metres above chart datum (C.D.), respectively. Grey vertical bars represent the percentage of actively moving limpets during each lunar phase. The bar at the bottom corresponds to the day-night cycle: (from left to right) white = daytime, grey = transitioning to sunset, black = night-time, grey = sunrise. Note that the activity of P. saccharina limpets was observed during dark periods only, from sunset to night-time until before sunrise, as long as they were covered by the tide.
Fig. 2 in Diel movement patterns of Pacific sugar limpet, Patelloida saccharina (Gastropoda: Patellogastropoda: Lottiidae) in response to semi-diurnal tides of Samal Island, Philippines
Fig. 2. Patelloida saccharina clamped on: A, bare rock face; B, rock covered with sand and turf algae. C, portion of the 5 × 5 cm grid quadrat laid on a reef patch with three P. saccharina individuals. Apices of limpet shells were painted with nail polish. Photographs by EY Zapanta and MA Fortaleza.
Changes in sugar-sweetened beverage consumption in the first two years (2018 – 2020) of San Francisco's tax: A prospective longitudinal study
<p><strong>Background:</strong> Sugar-sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018.</p> <p><strong>Methods & findings</strong>: A longitudinal panel of adults (n = 1,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline (11/17–1/18), one (11/18–1/19), and two years (11/19-1/20) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. Weighted generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by 34.1% (-3.68 oz, p = 0.004) from baseline to 2 years post-tax, versus San José which declined 16.5% by 2 years post-tax (-1.29 oz, p = 0.157), a non-significant difference-in-differences (-17.6%, adjusted AMR = 0.79, p = 0.224). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, p = 0.031). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, p = 0.010) among those living below 200% of the federal poverty level 2-years post-tax.</p> <p><strong> Conclusions:</strong> Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents.</p>
Characterization of mAMCase sugar-binding subsites
<p>This directory contains all files required to characterize sugar-binding subsites across mouse AMCase structure 8FR9, 8FRA, and 8GCA as presented in <strong>Figure 2</strong> of <a href="https://www.biorxiv.org/content/10.1101/2023.06.03.542675">Díaz et al.<em> </em>(2023)</a>.</p> <p>Structure models were analyzed in PyMOL. Figures were compiled using Adobe Illustrator.</p> <p> </p> <p>Files included in this directory:</p> <p><strong>Figures</strong></p> <p>- contains PNGs of all sugar-binding subsites, canonical subsites, and noncanonical subsites with and without 2fo-fc electron density displayed.</p> <p> </p> <p><strong>PyMOL</strong></p> <p>- contains all structural models, 2mFo-DFc maps, mFo-DFc maps, PyMOL script, and PyMOL session used to generate <strong>Figure 2</strong>.</p> <p> </p> <p>Contact:<br> Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu</p> <p>James Fraser, jfraser@fraserlab.com</p>
Amino sugars and neutral sugars in the biomass of different taxa
<p>Amino sugar and neutra sugar necromass biomarkers in the biomass of archaea, bacteria, fungi and plant species. </p>
Sugar signal manipulation by T6P for yield on wheat grain - whole grain RNA-seq
<p>Trehalose 6-phosphate (T6P) is a powerful internal sugar signal in plants yet cannot be directly added nor<br>fully genetically controlled. A timed microdose of a plant-permeable T6P signalling precursor, DMNB-T6P, causes substantial yield improvements. This repository contains normalised counts table for a RNA-seq of wheat (Triticum aestivum) whole grains after spray with mock/DMNB-T6P.</p> <p>DESeq2 normalised count table for raw reads deposited in ncbi SRA under Bioproject PRJNA1007614. Related manuscript in press in Nature Biotechnology.</p> <p> </p> <p>Methods extract below:</p> <p>(...) Following RNA integrity analysis and quantitation (Bioanalyser; Agilent, USA), poly-A enriched cDNA libraries were generated and sequenced on an Illumina Novaseq 6000 sequencing platform generating 30–50 million 150 bp paired-end reads per sample. Low-quality reads and adaptor sequences were removed with Trimmomatic (trimmomatic-0.39.jar PE ILLUMINACLIP:TruSeq3-PE.fa:2:30:10:2:True TRAILING:30 MINLEN:40). The reads were aligned to the wheat reference genome (<em>Triticum aestivum</em> iwgsc_refseqv2.1 using HISAT2/2.2.1-foss-2019b with default parameters and converted to Bam format with SAMtools. Gene abundance was quantified using featureCounts with the High Confidence iwgsc_refseqv2.1 annotation (counting only primary alignments of read pairs with a quality cut-off of 10). Raw counts were normalized using the trimmed mean of M-values method by DESeq2.</p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</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.