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678 results for “sugars”
Fig. 1 in The Impact Of Sowing Time On Sugar Content And Snow Mould Development In Winter Wheat
Fig. 1. The average temperature of ten-day periods during overwintering.
Fig. 2 in Host plant preference of Melanotus communis (Coleoptera: Elateridae) among weeds and sugar cane varieties found in Florida sugar cane fields
Fig. 2. Diagram of the larval host plant tests.
Fig. 1 in Host plant preference of Melanotus communis (Coleoptera: Elateridae) among weeds and sugar cane varieties found in Florida sugar cane fields
Fig. 1. Diagram of the adult host plant tests.
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CLM-Crop sugar cane
<p>This is model output from CLM-Crop for sugar cane as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS sugar beet
<p>This is model output from LPJ-GUESS for sugar beet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL sugar cane
<p>This is model output from LPJmL for sugar cane as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL sugar beet
<p>This is model output from LPJmL for sugar beet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Whole-Genome Resequencing identifies SNPs in Sucrose Synthase and Sugar Transporter Genes Associated with Sweetness in Coconut
<p><span>This vcf file constitute underlying raw data material for the manuscript</span> "<span>Whole-Genome Resequencing identifies SNPs in Sucrose Synthase and Sugar Transporter Genes Associated with Sweetness in Coconut"</span>. <span>The SNP genotype data came from a whole-genome resequencing and were called using an unpublished coconut reference genome. SNPs with a non-missing and minor allele frequency (MAF) less than 5% were removed. Finally, 19,149,289 SNPs were selected and used in the population study and gene mining.</span></p>
Data from: Anopheles gambiae: metabolomic profiles in sugar-fed, blood-fed and Plasmodium falciparum-infected midgut
The mosquito midgut is a physiological organ essential for the nutrient acquisition as well as an interface that encounters various mosquito borne pathogens. Metabolomic characterization would reveal biochemical fingerprints that are generated by various cellular processes. The metabolite profiles of the mosquito midgut will provide an overview of the biochemical events in both physiological states and the dynamic responses to pathogen infections. In this study, the midgut metabolic profiles of Anopheles gambiae mosquitoes following feeding with sugar, human blood, mouse blood, and Plasmodium falciparum-infected human blood were examined. A mass spectrometry system coupled to liquid and gas chromatography produced a time series of metabolites in the midgut at discrete conditions (sugar feeding, 24hr and 48hr post normal blood and P. falciparum-infected blood feeding). Triplicates were included to ensure system validity. A total of 512 individual compounds were identified, 511 were assigned to 8 super-pathways and 75 sub-pathways. The dataset can be used for further inquiry into the metabolic dynamics of sugar and blood digestion and of malaria parasite infection.
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
<p>Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills.</p> <p>Using a dataset of 25000 labelled tags of fieldwork-realistic quality, a Convolutional Neural Network (Faster R-CNN) was trained to detect the nectar-producing floral units of 25 taxa in surveyors' quadrat images of native, weed-rich grassland in the UK.</p> <p>Floral unit detection on a test set of 50 model-unseen images of comparable vegetation returned a precision of 90%, recall of 86% and F1 score (the harmonic mean of precision and recall) of 88%. Model performance was consistent across the range of floral abundance in this habitat. </p> <p>Comparison of the nectar sugar mass estimates made by the CNN and three human surveyors returned similar means and standard deviations. Over half of the nectar sugar mass estimates made by the model fell within the absolute range of those of the human surveyors.</p> <p>The optimal number of quadrat image samples was determined to be the same for the CNN as for the average human surveyor. For a standard quadrat sampling protocol of 10–15 replicates, this application of deep learning could cut pollinator-plant survey time per stand of vegetation from hours to minutes.</p> <p>The CNN is restricted to a single view of a quadrat, with no scope for manual examination or specimen collection, though in contrast to human surveyors its object detection is deterministic and floral unit definition is standardised.</p> <p>As agri-environment schemes move from prescriptive to results-based, this approach provides an independent barometer for grassland management which is usable by both landowner and scheme administrator. The model can be adapted to visual estimations of other ecological resources such as winter bird food, floral pollen volume, insect infestation and tree flowering/fruiting, and by adjustment of classification threshold may show acceptable taxonomic differentiation for presence-absence surveys.</p>
Sugar and nitrogen digestive processing does not explain the specialized relationship between euphonias and low quality fruits
<p>In the Neotropical region, euphonias (Euphonia spp., Fringillidae) are the quintessential example of specialized bird frugivores, making the bulk of feeding visits to certain mistletoes (Phoradendron spp., Santalaceae) and epiphytes in the genus Rhipsalis (Cactaceae), whose fruits have high water and low sugar and protein concentrations. Surprisingly, a mechanistic explanation for such specialized, otherwise rare, relationships is lacking. Using captive birds and artificial diets, we contrasted euphonias with frugivorous tanagers in the genus Thraupis (Thraupidae), which rarely eats Rhipsalis fruits, to test the hypothesis that the digestive capacity of euphonias entails them to exploit such low-energy fruits. We expected that compensatory feeding in response to decreasing energy density would occur only in euphonias, whose higher reliance on fruits would entail a lower nitrogen requirement than the tanagers. Euphonias and tanagers were both able to compensate energy intake as sugar density decreased, and both species had the same mass-corrected energy intake at any given sugar concentration. Similarly, euphonias and tanagers did not differ in mass-corrected maintenance nitrogen requirement. Therefore, the physiological traits we investigated do not explain euphonia´s specialization on Rhipsalis fruits. The fast rates of fruit passage typical of specialized avian frugivores as euphonias that entail the processing of a large volume of fruits, and the putative better abilities of such birds to deal with secondary compounds likely present in Rhipsalis fruits are other possible mechanisms that should be considered in future studies to unveil the mechanisms underlying the intriguing specialized relationships between euphonias and certain fruits.</p>
Supporting Information - Fabbri et al. 2022 - Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment
<p>The supporting information of the journal article "Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment" from Fabbri et al. (2022) includes one file with the following content:</p> <p>S1 Details of consequential modelling: feedstock<br> S1.1 Identification type of changes (demand or supply)<br> S1.2 Identification of constrains in the market<br> S1.3 Identification of product substitutions<br> S1.4 Identification of affected production technology<br> S1.5 Identification of marginal crop and marginal supplier<br> S2 Details of consequential modelling: by-products<br> S3 Model parameters and unit processes<br> S3.1 Sugar beet<br> S3.2 Sugar cane<br> S3.3 Wheat<br> S3.4 Maize<br> S3.5 Wood<br> S3.6 Residual woodchips and sawdust<br> S4 Review of land use change accounting methods<br> S4.1 Direct land use change (dLUC)<br> S4.2. Indirect land use change (iLUC)<br> S5 Additional results<br> S5.1 Influence of spatial differentiation in LCIA<br> S5.2 Influence of indirect land use change (iLUC)<br> S6 References</p>
Bumblebees' food preferences are jointly shaped by rapid valuation of nectar sugar concentration and viscosity
<p>Animals are often assumed to follow a strategy of energy maximisation, and therefore should evaluate feeding options based on energy intake rates. Contrastingly, rhesus macaque's learned food preferences are based on sensory properties, e.g. sweetness and resistance, regardless of energy differences. Here, we show that nectar sugar concentration (sweetness) and nectar viscosity (resistance) drive preferences of bumblebees, classical models for economic and foraging decision-making. Using a tasteless/odourless biopolymer (tylose), we created feeding options that differed in sweetness and resistance, properties that affect energy intake rate and can be immediately sensed. When energy intake rates were similar, bumblebees developed preferences based on sweetness and resistance. When energy intake rates were different but sweetness and resistance were balanced against each other, bees developed no preferences. Decision dynamics during training indicated that bumblebees simultaneously evaluated sweetness and resistance to make decisions quickly (in seconds). These results indicate that bumblebees' food preferences are jointly affected by the immediate sensation of nectar sweetness and resistance as positively and negatively reinforcing properties, respectively, irrespective of energy intake rate. From these findings we propose that subjective valuations of sensory food properties should be considered as constraints in models of foraging behaviour.</p>
Data for: Integrated multi-trophic aquaculture with sugar kelp and oysters in a shallow coastal salt pond and open estuary site
<p>The data set includes environmental data as well as kelp and oyster data from an integrated multi-trophic aquaculture study where sugar kelp was planted on four established oyster farms in Rhode Island, USA over 2 growing seasons (Year 1 = 2017-2018; Year 2 = 2018-2019). At each site, we planted to 60 m kelp lines (denoted as Line 1 and Line 2) approximately three weeks apart to determine optimal planting time. Kelp blade length and width was recorded at periodic intervals, and tissues were collected for carbon, nitrogen, δ15N, and δ13C analyses. Oyster growth data was collected from the same sites across the same timespans. Environmental data includes data collected on monthly farm visits with a YSI Sonde, as well as dissolved nutrients in the seawater. In addition, temperatures were logged on kelp lines every 15 minutes during each growing season.</p>
A dataset of nectar sugar production for flowering plants found in urban greenspaces
<ol> <li>Nectar and pollen are floral resources that provide food for insect pollinators, so quantifying their supplies can help us to understand and mitigate pollinator declines. However, most existing datasets of floral resource measurements focus on native plants found in rural landscapes, so cannot be used effectively for estimating supplies in urban green spaces, where non-native ornamental plants often predominate.</li> <li>We sampled floral nectar sugar in 225 plant taxa found in UK residential gardens and other urban green spaces, focussing on the most common species. The vast majority (94%) of our sampled taxa are non-native, filling an important research gap and ensuring these data are also relevant outside of the UK.</li> <li>Our dataset includes values of daily nectar sugar production for all 225 taxa and nectar sugar concentration for around half (102) of those sampled. Nectar extraction was conducted according to published methods, ensuring our values can be combined with other datasets.</li> <li>We anticipate that the two main uses of these data are (1) to estimate the nectar production of habitats and landscapes, and (2) to identify high-nectar plants of conservation importance. To increase the utility of our data we provide guidance for scaling nectar values up from single flowers to floral units, as is commonly done in field studies.</li> </ol>
Source data: Negative Membrane potential accelerates sugar uptake by stabilizing the outward-facing conformation of the Na+/glucose symporter vSGLT
<p><strong>Galactose uptake source data.</strong></p><p>Excel file (annotated and color coded).</p><p> </p><p><strong>Double electron-electron resonance (DEER) source data.</strong></p><p>File name indicates Figure number, construct and conditions.<br>First column: Time in microseconds.<br>Second Column: Magnitude dipolar evolution data (phase corrected and normalized).</p><p> </p><p>Information for associated MD simulations under <a href="http://dx.doi.org/10.5281/zenodo.10000256">10.5281/zenodo.10000256.</a></p><p> </p>
Sugar-replacement Sweeteners, and Blood Sugar Control
ClinicalTrials.gov study NCT01128829. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study to Assess Safety of Canagliflozin and Metformin Hydrochloride Combination Given as a Supplement to Diet and Exercise to Improve Blood Sugar Level in Indian Adult Participants With Diabetes
ClinicalTrials.gov study NCT04288778. IPD Sharing: YES. Countries: 1. Publications: 1.
A Research Study to See How a New Weekly Insulin, Insulin Icodec When Given Along With Semaglutide Helps in Reducing the Blood Sugar Level in Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT05813912. IPD Sharing: YES. Countries: 5. Publications: 0.
d13C Added Sugar Intake Biomarker: Determining Validity in Children
ClinicalTrials.gov study NCT02455388. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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OpenNeuro
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