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15 results for “Nutrient balance”
Data from: Balanced replacement of fish meal with Hermetia illucens meal allows efficient hepatic nutrient metabolism and increased fillet lipid quality in gilthead sea bream (Sparus aurata) juveniles
<p>In the present study, gilthead sea bream (<em>Sparus aurata</em>) juveniles were reared using sustainable feeds containing insect meal from <em>Hermetia illucens</em> larvae and poultry by-products meal. Proteomics and Proton Nuclear Magnetic Resonance-based metabolomics analysis were used to assess the metabolic impact of tested dietary formulations in sea bream liver, whereas the composition of muscle fillet was characterized by means of metabolomics and gas chromatography of fatty acids methyl esters. Replacing fish meal with insect meal in a 5% fish meal diet did not substantially alter metabolism of dietary nutrients, leading to small but statistically detectable effects solely on lauric acid content of sea bream fillet, and few alterations in some markers of immune response, such as leukocyte elastase inhibitor-like, granzyme B (G,H)-like, and two associated ortholog groups namely serpin B, and chymase). Liver morphology confirmed the absence of structural damage or inflammation in the insect meal-fed group, which showed a lower amount of hepatic lipid deposition and accumulation, too.</p>
Balancing the nutrient needs: Optimizing growth in Malus sieversii seedlings through tailored nitrogen and phosphorus effects
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Long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, a modeling analysis.
A study investigating the mechanisms that control long-term response of tussock tundra to fire and to increases in air temperature, CO2, nitrogen deposition and phosphorus weathering. The MBL MEL was used to simulate the recovery of three types of tussock tundra, unburned, moderately burned, and severely burned in response to changes in climate and nutrient additions. The simulations indicate that the recovery of nutrients lost during wildfire is difficult under a warming climate because warming increases nutrient cycles and subsequently leaching within the ecosystem. The study was published in Ecological Applications (in press, 2016). This dataset is the long term archive of the results published in the paper. The full dataset has been broken into two parts because of the number and size of the files. Part 1 contains MBL MEL executable, a model description file in word, and the input files to run the simulations. Part 2 contains the output files for all simulations. Both Part 1 and Part 2 contain several different types of files. In Part 1 the comma separated ascii file included with the dataset is one of the many driver files used for the simulations. The variable descriptions below describe the variables in that file and all the driver files. In Part 2 the comma separated ascii file included with the dataset is one of the many output files from the simulations. The variable descriptions below describe the variables in that file and all the output files. To access all the files in the dataset be sure to download the two zip files described in the Methods section below. Note that the full download is large, over 700 MB for each part. Permanent Archive of the data published in Jiang, et al., in press, Modeling long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, Ecological Applications.
Nutrient balance and energy-acquisition effectiveness: Do birds adjust their fruit diet to achieve intake targets?
<p>1. According to diet-regulation hypotheses, animals select food to regulate the intake of macronutrients or maximise energy feeding efficiency. Specifically, the nutrient balance model proposes that foraging is primarily a process of balancing multiple nutrients to achieve a nutritional intake target, while the energy maximisation model proposes that foraging aims to maximise energy.</p> <p>2. Here, we evaluate the adjustment of fruit diets (the fruit-derived component of the diets) to nutritional and energy intake targets, characterizing the nutrient balance and energy maximisation strategies across fruit-eating bird species with different fruit-handling behaviours ("gulpers", which swallow whole fruits, and "mashers", which process the fruit in the beak) in subtropical Andean forests. Food-handling behaviour determines the food intake rate and, consequently, influences animal efficiency to obtain nutrients and energy.</p> <p>3. We used extensive field data from the diet of fruit-eating birds to test how species adjust their food intake. We used nutritional geometry to explore macronutrient balance and the effectiveness framework to explore energy-acquisition effectiveness.</p> <p>4. Observed diets showed a good fit with predictions of a diet balanced in macronutrient proportions. With few exceptions, diets clustered near an optimal macronutrient mixture and did not differ from each other in terms of maximising energy intake. Moreover, when comparing our results with a random diet based on local fruit availability, birds tended to fit better to the nutritional target, and less to the energy target, than expected from a random diet. Fruit-handling behaviour did not affect the ability of bird species to reach a nutritional target but it affected species energy acquisition, which was lower in mashers than in gulpers.</p> <p>5. This study explores for the first time different diet-regulation strategies in wild fruit-eating birds, and supports the argument that the diet reflects a specific regulation of macronutrients. Understanding why birds select fruits is a complex question requiring multiple considerations. The nutrient balance model explains the relevance of nutrient composition in the fruit selection by fruit-eating birds, although it is still necessary to determine its relative importance with respect to other dietary drivers.</p>
soil nutrient balance(NPK) dataset of Rwanda
<p>This soil nutrient balance dataset contains raw, processed and analyzed data of soil nutrient depletion workflow developed by Uwiragiye et al.[1]. Soil nutrient (NPK) balance were predicted only in covers cropland of Rwanda (Fig.1). This dataset was developed from freely available data from different sources (Table 1). The main folder which contain all data is called ’’Data”. This main folder has five sub folders (soil nutrient flows, soil erosion and sediments, environmental covariates, NPK balance modelling results and code used for soil nutrient balance modelling)</p> <p>This is a dataset developed from available data for soil nutrient modelling in humid areas : A published paper </p> <p>Y. Uwiragiye, M. Junior Yannick Ngaba, M. Zhao, A.S. Elrys, G.B.M. Heuvelink, J. Zhou, Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda, CATENA. 217 (2022) 106499. https://doi.org/https://doi.org/10.1016/j.catena.2022.106499.</p>
Nutrient balance and energy-acquisition effectiveness: Do birds adjust their fruit diet to achieve intake targets?
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Data from: Daily protein prioritization and longterm nutrient balancing in a dietary generalist, the blue monkey
<p>Animals must make dietary choices to achieve adequate nutrient intake, however it is challenging to study in the field such nutritional strategies in wild populations. We explored the nutritional strategy of a generalist social primate, the blue monkey (<i>Cercopithecus mitis)</i>. We hypothesized that females balance the intake of nutrients, specifically non-protein energy and available protein (hereafter, protein), both on a daily and long-term basis. When balancing was not possible, we expected subjects to prioritize constant protein intake, allowing non-protein energy to vary more. To understand the ecology of nutrient balancing, we examined how <span>habitat use,</span> food availability, diet composition, social dominance rank and reproductive demand influenced nutrient intake. Over 9 months, we conducted 371 all-day focal follows on 24 adult females in Kakamega Forest, Kenya. Subjects exhibited short- and long-term nutritional strategies. On a daily basis, they balanced non-protein energy to protein intake but when balancing was impossible, monkeys prioritized protein intake. Over the long-term, they balanced non-protein energy:protein intake in a 3.8:1 ratio. The ratio related positively to <span>fruit in the diet and negatively to time in near-natural forest, but we found no evidence that it related to</span> <span>food availability, reproductive demand, or dominance rank. Lower-ranked females had broader daily diets, however, which may reflect </span>behavioral feeding strategies to cope with social constraints. Overall, females prioritized daily protein, allowing less variation in protein intake than other aspects such as non-protein energy:protein ratio and non-protein energy intake. The emerging pattern of nutrient balancing in primates suggests that diverse dietary strategies evolved to allow adherence to a balance of non-protein energy:protein despite various social and environmental constraints. The data from this study also add to a small but growing number of studies that document nutritional strategies in wild animal populations.</p>
Rice yield and nutrient balance in southwest China
<p>The optimal application of nutrients, such as nitrogen and phosphorus, to the soil is crucial for achieving high crop yields with minimal environmental impact. However, the effect of spatio-temporal changes in soil nutrient supply on crop yield is poorly understood in China. Here, we present a framework that combines environmental data, fertilizer field experiments, and machine learning to estimate the rice yield responses to different nutrient conditions and overall farmland nutrient sustainability in southwest China from 2009 to 2019. This dataset contains data on the spatial distribution of rice yield forecasts and farmland nutrient balances in Southwest China in 2009 and 2019.</p>
Rice yield and nutrient balance in southwest China
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Data from: Daily protein prioritization and longterm nutrient balancing in a dietary generalist, the blue monkey
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Analysis of nutrients and data synthesis, mass balance: central Arizona-Phoenix metropolitan area
Geospatial analysis of the greater phoenix metropolitan area. Evaluation of nutrients and data synthesis, mass balance of: Phoenix citrus groves, Phoenix crops, Phoenix Dairy Farms, Phoenix Agricultural Landuse, Phoenix Stockyard Locations.
Transcriptional profiling of nutrient-balanced early trilineage differentiations derived from H9 hPSCs.
GEO Series GSE127270. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Data from: Functional implications of omnivory for dietary nutrient balance
Captive experiments have shown that many species regulate their macronutrient (i.e. protein, lipid and carbohydrate) intake by selecting complementary food types, but the relationships between foraging strategies in the wild and nutrient regulation remain poorly understood. Using the pine marten as a model species, we collated available data from the literature to investigate effects of seasonal and geographic variation in diet on dietary macronutrient balance. Our analysis showed that despite a high variety of foods comprising the diet, typical of a generalist predator, the macronutrient energy ratios of pine martens were limited to a range of 50–55% of protein, 38–42% of lipids and 5–10% of carbohydrates. This broad annual stabilisation of macronutrient ratios was achieved by using alternative animal foods to compensate for the high fluctuation of particular prey items, and sourcing non-protein energy (carbohydrates and fats) from plant-derived foods, particularly fruits. Macronutrient balance varied seasonally, with higher carbohydrate intake in summer–autumn, due to opportunistic fruit consumption, and higher protein intake in winter–spring. In terms of their proportional dietary carbohydrate intake the pine marten's nutritional strategy fell between that of true carnivores (e.g. the wolf) and more omnivorous feeders (e.g. the European badger). However, in terms of energy contributed by protein pine martens are equivalent to obligate carnivores such as the wolf and domesticated cat, and different to some omnivorous carnivores such as the domesticated dog and grizzly bears.
Data from: Functional implications of omnivory for dietary nutrient balance
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Dataset: Water and Nutrient Mass Balances of Upper Klamath Lake, WY 1992-2018.
<p>Monthly and annual water and nutrient (total phosphorus and total nitrogen) mass balances were developed for Upper Klamath Lake (UKL) over water year (WY) 1992-2018. UKL is a shallow, hyper-eutrophic lake located in south-central Oregon, USA. The water and nutrient balances were computed using available flow and water quality sampling data for various inflow sources and the lake outflow. Changes in water and nutrient mass storage were computed from measured lake surface elevations using elevation-area-volume curves based on the lake bathymetry, and biweekly water quality sampling. The net retention of nutrients were computed by difference from the other measured or estimated inflow, outflow, and storage terms. </p> <p>The data sources and methodologies used to generate this dataset are described in the following report:</p> <ul> <li><em>Walker, Jeffrey D, & Kann, Jacob. (2022). Water and Nutrient Balances of Upper Klamath Lake, Water Years 1992–2018. Zenodo. <a href="https://doi.org/10.5281/zenodo.6607800">https://doi.org/10.5281/zenodo.6607800</a></em></li> </ul> <p>This repository contains the following files:</p> <ul> <li><strong>ukl-mb-mon.csv</strong>: monthly flows, loads, and flow-weighted mean (FWM) concentrations of total phosphorus (TP) and total nitrogen (TN) for each mass balance term</li> <li><strong>ukl-mb-wyr.csv</strong>: annual flows, loads, and flow-weighted mean (FWM) concentrations of TP and TN for each mass balance term based on water years (WY = Oct 1 - Sep 30; e.g., WY 2018 = Oct 1, 2017 - Sep 30, 2018)</li> </ul> <p>Mass balance terms include:</p> <ul> <li><strong>tribs_7mile_dike</strong>: Sevenmile Canal @ Dike Road (outlet to Agency Lake)</li> <li><strong>tribs_wood_dike</strong>: Wood River @ Dike Road (outlet to Agency Lake)</li> <li><strong>tribs_wood_dike-weed</strong>: Wood River between Dike and Weed Roads</li> <li><strong>tribs_wood_weed</strong>: Wood River @ Weed Road</li> <li><strong>tribs_sprague</strong>: Sprague River</li> <li><strong>tribs_williamson-sprague</strong>: Williamson River excluding Sprague River basin</li> <li><strong>tribs_williamson</strong>: Williamson River (outlet to Upper Klamath Lake)</li> <li><strong>tribs_total</strong>: Total gauged tributaries (Sevenmile Canal + Wood River + Williamson River)</li> <li><strong>pumped_alr</strong>: Pumped inflows from Agency Lake Ranch</li> <li><strong>pumped_wrdp</strong>: Pumped inflows from Williamson River Delta Preserve (Tulana + Goose Bay)</li> <li><strong>pumped_ungauged</strong>: Pumped inflows from other ungauged agricultural areas</li> <li><strong>pumped_total</strong>: Total pumped inflows (ALR + WRDP + Ungauged Pumped Areas)</li> <li><strong>ungauged</strong>: Ungauged drainage basins</li> <li><strong>total_external</strong>: Total external inflows (Gauged Tributaries + Ungauged Basins + Pumped Areas)</li> <li><strong>precip</strong>: Precipitation (total atmospheric deposition for nutrient loads)</li> <li><strong>evap</strong>: Evaporation</li> <li><strong>net_inflow</strong>: Net inflow (Total External Inflows + Precipitation - Evaporation)</li> <li><strong>outflow</strong>: Lake outflow</li> <li><strong>storage</strong>: Lake mean storage (Upper Klamath and Agency Lakes)</li> <li><strong>dstorage</strong>: Change in lake storage</li> <li><strong>retention</strong>: Net retention</li> <li><strong>anthro</strong>: Anthropogenic inflows</li> <li><strong>background</strong>: Background inflows</li> </ul> <p>See Table D1 (Appendix D) of Walker and Kann (2022) for equations to compute flows and loads of terms derived from other directly measured or estimated terms.</p> <p>File columns:</p> <ul> <li><strong>wyear</strong>: Water year (Oct 1 - Sep 30; e.g., WY 2018 = Oct 1, 2017 - Sep 30, 2018)</li> <li><strong>date</strong>: Date on the first day of each month (monthly dataset only)</li> <li><strong>term</strong>: Mass balance term (see above)</li> <li><strong>param</strong>: Water quality parameter (tp or tn)</li> <li><strong>flow_hm3</strong>: Flow or storage volume (hm^3 = 1e6 m^3 = 0.81071 kacre-ft)</li> <li><strong>load_kg</strong>: Load or storage mass (kg = 1e-3 metric tonne or mton)</li> <li><strong>conc_ppb</strong>: Concentration (ppb = ug/L = 1e3 mg/L)</li> <li><strong>area_km2</strong>: Drainage/surface area associated with each term (km2)</li> <li><strong>runoff_m</strong>: Unit-area runoff (m) equal to flow_hm3 divided by area_km2</li> <li><strong>export_kg_km2</strong>: Nutrient export rate (kg/km2) equal to load_kg divided by area_km2</li> </ul>
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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.