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108 results for “model foods”
Supplementary material 2 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Parameter settings
Supplementary material 1 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
QMRA_Salmonella_egg_Virginie.fskx
Figure 2 from: Desvignes V, Buschhardt T, Guillier L, Sanaa M (2019) Quantitative microbial risk assessment for Salmonella in eggs. Food Modelling Journal 1: e39643. https://doi.org/10.3897/fmj.1.39643
Figure 2 Predicted number of salmonellosis per million servings of eggs, according to the cooking method.
Data from: Species' ecological functionality alters the outcome of fish stocking success predicted by a food-web model
Fish stocking is used worldwide in conservation and management but its effects on food-web dynamics and ecosystem stability are poorly known. To better understand these effects and predict the outcomes of stocking, we used an empirically validated network model of a well-studied lake ecosystem. We simulate two stocking scenarios with two native fish species valuable for fishing. In the first scenario, we stock planktivorous fish (whitefish) larvae in the ecosystem. This leads to 1% increase in adult whitefish biomasses and decreases the biomasses of the top predator (perch). In the second scenario, we also stock perch larvae in the ecosystem. This decreases the planktivorous whitefish and the oldest top predator age class biomasses, and destabilizes the ecosystem. Our results demonstrate that the effects of stocking depend on the species' position in the food web and thus cannot be assessed without considering interacting species. We further show that stocking can lead to undesired outcomes from both management and conservation perspectives. The gains of stocking can remain minor and have adverse effects on the entire ecosystem.
Data from: Predictive power of food web models based on body size decreases with trophic complexity
Food web models parameterized using body size show promise to predict trophic Interaction Strengths (IS) and abundance dynamics. However, this remains to be rigorously tested in food webs beyond simple trophic modules, where indirect and intraguild interactions could be important and driven by traits other than body size. We systematically varied predator body size, guild composition and richness in microcosm insect webs and compared experimental outcomes with predictions of IS from models with allometrically scaled parameters. Body size was a strong predictor of IS in simple modules (r2=0.92), but with increasing complexity the predictive power decreased, with model IS being consistently overestimated. We quantify the strength of observed trophic interaction modifications, partition this into density-mediated vs. behaviour-mediated indirect effects and show that model shortcomings in predicting IS is related to the size of behaviour-mediated effects. Our findings encourage development of dynamical food web models explicitly including and exploring indirect mechanisms.
Data from: Diurnal variation in the production of vocal information about food supports a model of social adjustment in wild songbirds
Wintering songbirds have been widely shown to make economic foraging decisions to manage the changing balance of risks from predation and starvation over the course of the day. In this study, we ask whether the communication and use of information about food availability differ throughout the day. First, we assessed temporal variation in food-related vocal information produced in foraging flocks of tits (Paridae) using audio recordings at RFID-equipped feeding stations. Vocal activity was highest in the morning and decreased into the afternoon. This pattern was not explained by there being fewer birds present, as we found that group sizes increased over the course of the day. Next, we experimentally tested the underlying causes for this diurnal calling pattern. We set up bird feeders with or without playback of calls from tits, either in the morning or in the afternoon, and compared latency to feeder discovery, accumulation of flock members, and total number of birds visiting the feeder. Irrespective of time of day, playbacks had a strong effect on all three response measures when compared to silent control trials, demonstrating that tits will readily use vocal information to improve food detection throughout the day. Thus, the diurnal pattern of foraging behaviour did not appear to affect use and production of food-related vocalizations. Instead, we suggest that, as the day progresses and foraging group sizes increase, the costs of producing calls at the food source (e.g. competition and attraction of predators) outweigh the benefits of recruiting group members (i.e. adding individuals to large groups only marginally increases safety in numbers), causing the observed decrease in vocal activity into the afternoon. Our findings imply that individuals make economic social adjustments based on conditions of their social environment when deciding to vocally recruit group members.
Figure 4 from: Sundermann EM, Nauta M, Swart A (2021) A ready-to-use dose-response model of Campylobacter jejuni implemented in the FSKX-standard. Food Modelling Journal 2: e63309. https://doi.org/10.3897/fmj.2.63309
Figure 4 The probability of illness and infection for the human population after consumption of Campylobacter jejuni-contaminated food. The probabilities are calculated based on the outbreak dataset with 1000 various mean doses (the so-called OutbreakVarMeanDoses simulation).
Supplementary material 1 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
QRA simulator
Supplementary material 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
The code documentation for the ALMaSS Population_Manager class
Figure 7 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).
Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.
Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.
Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.
Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.
Figure 6 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 6 The relative batch risk (with respect to a baseline risk value) is plotted as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 5 Batch rejection probability as a function of the initial STEC (main pathogenic serotypes MPS-STEC) concentration (CFU/ml).
Figure 4 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 4 Evolution of STEC colony size during draining, salting and ripening of cheese fabrication. The decline rate for the MPS O157:H7 strain and non-MPS strains are equal (orange line) and significantly higher than the decline rate of MPS non-O157:H7 strain (red line). The three phases, namely, draining, salting and ripening are separated by vertical blue dotted lines.
Figure 3 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 3 Evolution of STEC (main pathogenic serotypes MPS-STEC) in log10 CFU/ml during the storage and moulding step. The blue vertical line shows the end of the storage phase.
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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)
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.