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506 results for “assessment model”
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
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
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 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.
Figure 2 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 2 Histogram of STEC (main pathogenic serotypes MPS-STEC) concentration (log10 (CFU/ml)) in milk put into production.
Figure 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
Figure 1 Schematic diagram of the batch level simulator of the risk assessment model. Modules are denoted by pink coloured boxes with the blue boxes denoting the set of corresponding input parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} \theta = \{\theta^{\rm farm}, \theta^{\rm cheese}, \theta^{\rm con}, \theta^{\rm post}\} \end{equation*} \end{varwidth} \end{document} and the orange boxes denoting the outputs, namely, milk loss per batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M^{\rm batch} \end{equation*} \end{varwidth} \end{document} , probability of rejecting a particular batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} P^{\rm batch} \end{equation*} \end{varwidth} \end{document} and batch risk \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} R^{\rm batch} \end{equation*} \end{varwidth} \end{document} .
The TUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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The DUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment
<p>Abstract</p> <p>Purpose</p> <p>Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA.</p> <p>Methods</p> <p>Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts.</p> <p>Results and discussion</p> <p>According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N<sub>2</sub>O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner.</p> <p>Conclusions</p> <p>Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.</p>
Conservation status assessment of banana crop wild relatives using species distribution modelling
<p><strong>Aim: </strong>Crop wild relatives (CWR) are an essential source of genetic material for the improvement of certain traits in related crop species. Despite their importance, increasing public, scientific and political support, large gaps exist in the amount of genetic material collected and conserved of many CWR. Here, we construct a dataset on the distribution of wild banana species (Musa spp.) and assess their risk and conservation status. We deal with the questions (1) what areas are potentially suitable for wild banana species (2) how much of the wild banana diversity is currently at risk or insufficiently conserved ex- and in-situ.</p> <p><span><strong>Location: </strong>Native distribution area of wild banana species, ranging from the northeastern states of India to northeastern Australia.</span></p> <p><span><strong>Methods:</strong> We assessed the potential environmental range of wild species using a species distribution modelling approach with MaxEnt. Extinction risk was evaluated following IUCN criterion B and the <i>ex- </i>and <i>in-situ</i> conservation status was assessed using an indicator for biodiversity and sustainable development targets.</span></p> <p><span><strong>Results:</strong> We found that 11 out of 59 assessed species can be considered as vulnerable and nine as endangered. Highest species richness was found along the border of south China and northern Vietnam, in the northeastern states of India and on the Malayan peninsula. Our distribution modelling approach indicates that the northern Indo-Burmese region has the highest environmental suitability for most wild banana species and that lowland rainforests in general are highly suitable for bananas. Assessment of <i>in</i>- and <i>ex-situ</i> conservation status indicates that 56 out of 59 assessed species are currently insufficiently conserved <i>ex-situ</i> and that 49 are of high priority for further conservation. Additional <i>in-situ</i> conservation is of high priority for six species and of medium priority for 40 species.</span></p> <p><span><strong>Main Conclusions:</strong> To date, little of the banana CWR are sufficiently conserved both <i>in- </i>and <i>ex-situ</i>.</span></p>
Application of health belief model for the assessment of COVID-19 preventive behavior and its determinants among students: A structural equation modeling analysis
<p><strong>Background:</strong> COVID-19 is a new pandemic that poses a threat to people globally. In Ethiopia, where classrooms are limited, students are at higher risk for COVID-19 unless they take consistent preventative actions. However, there is a lack of evidence in the study area regarding student compliance with COVID-19 preventive behavior (CPB) and its predictors.</p> <p><strong>Objective:</strong> This study aimed to assess CPB and its predictors among students based on the perspective of the Health Belief Model (HBM).</p> <p><strong>Method and materials:</strong> A school-based cross-sectional survey was conducted from November to December 2020 to evaluate the determinants of CPB among high school students using a self-administered structured questionnaire. 370 participants were selected using stratified simple random sampling. Descriptive statistics were used to summarize data, and partial least squares structural equation modeling (PLS-SEM) analyses to evaluate the measurement and structural models proposed by the HBM and to identify associations between HBM variables. A T-value of > 1.96 with 95% CI and a P-value of < 0.05 were used to declare the statistical significance of path coefficients.</p> <p><strong> Result:</strong> A total of 370 students participated with a response rate of 92%. The median (interquartile range) age of the participants (51.9% females) was 18 (2) years. Only 97 (26.2%), 121 (32.7%), and 108 (29.2%) of the students had good practice in keeping physical distance, frequent hand washing, and facemask use respectively. The HBM explained 43% of the variance in CPB. Perceived barrier (β= - 0.15, p < 0.001) and self-efficacy (β= 0.51, p <0.001) were significant predictors of student compliance to CPB. Moreover, the measurement model demonstrated that the instrument had acceptable reliability and validity.</p> <p><strong>Conclusion and recommendations:</strong> COVID-19 prevention practice is quite low among students. HBM demonstrated adequate predictive utility in predicting CPBs among students, where perceived barriers and self-efficacy emerged as significant predictors of CPBs. According to the findings of this study, theory-based behavioral change interventions are urgently required for students to improve their prevention practice. Furthermore, these interventions will be effective if they are designed to remove barriers to CPBs and improve students' self-efficacy in taking preventive measures.</p>
Supplementary material 1 from: Ferreira EM, Valerio F, Medinas D, Fernandes N, Craveiro J, Costa P, Silva JP, Carrapato C, Mira A, Santos SM (2022) Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 155-175. https://doi.org/10.3897/natureconservation.47.72781
Figures S1–S3
Supplementary material 2 from: Keck F, Hürlemann S, Locher N, Stamm C, Deiner K, Altermatt F (2022) A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess richness of mayflies, stoneflies and caddisflies in rivers. Metabarcoding and Metagenomics 6: e79351. https://doi.org/10.3897/mbmg.6.79351
Tables S1–S4
CodeQual: A dataset for fine-tuning Large Language Models for code quality assessment task
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Fig. 3 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig. 3. Ecosystem service class: wild plants and their outputs, indicator: Potential blueberry yield.
Dataset for paper: Maturity assessment and maturity models in healthcare: a multivocal literature review
<p>Includes: 1) Pool of primary sources, 2) Index of subject-focuses of models</p>
Model outputs: Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
<p>This dataset contains the fire model outputs of various variables used in the initial FireMIP benchmarking paper.</p>
Dataset related to "Translational approach to assess brain injury after cardiac arrest in preclinical models: a narrative review"
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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.