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467 results for “risk to development”
CFMDG: a Coastal Flood Modelling Dataset in Gâvres (France) to support risk prevention and metamodels development
<p>Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, <strong>coastal flooding</strong> events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (<a href="http://perso.math.univ-toulouse.fr/riscope">https://perso.math.univ-toulouse.fr/riscope</a>/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a <strong>simulated flood database</strong> has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.</p> <p>The <strong>CFMDG dataset </strong>compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.</p> <p>The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site. </p> <p>Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast. </p> <p>Part of this dataset has already been used in (<a href="http://www.mdpi.com/2077-1312/9/11/1191">Idier et al., 2021</a>; <a href="http://www.sciencedirect.com/science/article/pii/S0951832021006293?via%3Dihub">López-Lopera et al., 2021</a>; <a href="https://hal.science/hal-02536624">Betancourt et al., 2022</a>), to develop metamodels and set up a coastal flood forecast and early warning prototype.</p> <p>We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.</p> <p>The table below summarizes the variables contained in the dataset, for each scenario.</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Description and unit </strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p>Scenario n°</p> </td> <td> <p>Number of the scenario.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>INPUTS (X)</strong></p> </td> </tr> <tr> <td> <p>NM</p> </td> <td> <p>Relative mean sea level, referenced to the French vertical datum (m, IGN69)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>T</p> </td> <td> <p>Tidal water level (m), referenced to the relative mean sea level</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>S</p> </td> <td> <p>Atmospheric storm surge (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Hs</p> </td> <td> <p>Significant wave height (m)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Tp</p> </td> <td> <p>Wave peak period (s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>Dp</p> </td> <td> <p>Wave peak direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Wind speed (m/s)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>DU</p> </td> <td> <p>Wind direction (° in nautical convention)</p> </td> <td> <p>Time series over 6h</p> </td> </tr> <tr> <td> <p>t</p> </td> <td> <p>Relative time centered on the high tide of each event (min)</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p>High Tide date</p> </td> <td> <p>UTC date for scenarios corresponding to past real events</p> </td> <td> <p>Not Concerned</p> </td> </tr> <tr> <td> <p><strong>OUTPUTS (Y)</strong></p> </td> </tr> <tr> <td> <p>Smax</p> </td> <td> <p>Maximum flooded area during the event (m²)</p> </td> <td> <p>Post-processed scalar output</p> </td> </tr> <tr> <td> <p>Hmax</p> </td> <td> <p>Maximum water depth reached during the event (m), provided for each inland location</p> </td> <td> <p>Post-processed functional (map) output</p> </td> </tr> <tr> <td> <p>longitude</p> </td> <td> <p>Longitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>latitude</p> </td> <td> <p>Latitude (°, WGS84)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>XL93</p> </td> <td> <p>Longitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> <tr> <td> <p>YL93</p> </td> <td> <p>Latitude (m, Lambert 93)</p> </td> <td> <p>For each inland location point</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><br> </p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
Figure 1 in Developing biosecurity plans for non-native species in marine dependent areas: the role of legislation, risk management and stakeholder engagement
Figure 1. Five-stage approach for risk assessment management of NNS in Shetland, adapted from the ecosystem-based risk management framework (Cormier et al. 2013).
An Evaluation of a Multi-target Stool DNA (Mt-sDNA) Test, Cologuard, for CRC Screening in Individuals Aged 45-49 and at Average Risk for Development of Colorectal Cancer: Act Now
ClinicalTrials.gov study NCT03728348. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Emerging risks of non-native species escapes from aquaculture: call for policy improvements in China and other developing countries
<p><span>1. Global aquaculture relies heavily on the farming of non-native aquatic species (hereafter, NAS). NAS escapes from aquaculture facilities can result in serious aquatic bio-invasions, which</span> has been <span>an important issue in the FAO <i>Blue Growth Initiative</i>. A r</span>egulatory quagmire regarding NAS farming and escapes, however, exists in most developing countries.</p> <p><span>2. We discuss aquaculture expansion and NAS escapes, illustrate emerging risks, and propose recommendations for improved aquaculture management</span> across developing countries and particularly for<span> China. </span></p> <p>3. <span>In </span>China<span>,</span> 68 NAS are known to have successfully established feral populations in natural habitats due to recurrent leakages or escapes; among the 68 NAS, 52<span> represent risks to native aquatic ecosystems. In addition to affecting a country's own biodiversity and ecosystem functions, NAS escapees can also threaten the </span>biosecurity<span> of shared waters in neighboring countries.</span></p> <p>4. <i>Policy implications</i>. <span>Non-native aquatic species (NAS) </span>escapes have already had adverse ecological effects in China and other developing countries. The importance of this problem, however, is not adequately recognized by current conservation policies in developing countries. To conserve biodiversity and to support the<span> goal of FAO's</span> sustainable aquaculture, developing countries <span>should now take responsible actions</span> to address NAS escapes <span>through policy and management improvements. Specifically, these</span> countries should pass comprehensive legislation, establish effective agencies and national standards and planning, and enhance integrated research and education to deal with risk assessment, prevention, monitoring, and control of <span>NAS</span> escapes. Given that China is the world's largest aquacultural producer, China can create a model for other developing countries that will increase the biosecurity and sustainability of global aquaculture.</p>
Data reported in development and cross-validation of a veterans mental health risk factor screen
<p>Background. VA primary care patients are routinely screened for current symptoms of PTSD, depression, and alcohol disorders, but many who screen positive do not engage in care. In addition to stigma about mental disorders and a high value on autonomy, some veterans may not seek care because of uncertainty about whether they need treatment to recover. A screen for mental health risk could provide an alternative motivation for patients to engage in care.</p> <p>Results. Twelve items assessing dissociation, emotional lability, life stress, and moral injury correctly classified 86% of those who later had elevated PTSD and/or depression symptoms (sensitivity) and 75% of those whose later symptoms were not elevated (specificity). Performance was also very good for 110 veterans who identified as members of ethnic/racial minorities.</p> <p>Conclusions. Mental health status was prospectively predicted in VA primary care patients with high accuracy using a screen that is brief, easy to administer, score, and interpret, and fits well into VA's integrated primary care. When care is readily accessible, appealing to veterans, and not perceived as stigmatizing, information about mental health risk may result in higher rates of engagement than information about current mental disorder status.</p>
HLA epitopic mismatch and risk of developing Donor Specific Antibodies in pediatric kidney transplantation
<p>The aim of this study was to evaluate the correlation between epitopic HLA mismatches and the risk of developing DSA in pediatric kidney recipients. There are very few publications concerning epitope HLA mismatch in pediatric kidney transplantation although this topic has been growing in scientific articles in recent years and is promising in adult recipients. The majority of children with chronic renal failure will require multiple kidney transplants during their lifetime and it is therefore important that we can limit the risk of antibody development in order to give them the best chance of survival for their future kidney transplants.</p>
Protocol for Women at Increased Risk of Developing Breast Cancer
ClinicalTrials.gov study NCT00291135. IPD Sharing: NO. Countries: 1. Publications: 3.
Study of Obeldesivir in Participants With COVID-19 Who Have a High Risk of Developing Serious or Severe Illness
ClinicalTrials.gov study NCT05603143. IPD Sharing: NO. Countries: 18. Publications: 2.
Developing and Evaluating a Machine-Learning Opioid Overdose Prediction & Risk-Stratification Tool in Primary Care
ClinicalTrials.gov study NCT06810076. IPD Sharing: NO. Countries: 1. Publications: 5.
Erlotinib Hydrochloride in Reducing Duodenal Polyp Burden in Patients With Familial Adenomatous Polyposis at Risk of Developing Colon Cancer
ClinicalTrials.gov study NCT02961374. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Budesonide in Treating Patients With Lung Nodules at High Risk of Developing Lung Cancer
ClinicalTrials.gov study NCT00321893. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Fish Oil and Green Tea Extract in Preventing Prostate Cancer in Patients Who Are at Risk for Developing Prostate Cancer
ClinicalTrials.gov study NCT00253643. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development and Testing of a Pediatric Cervical Spine Injury Risk Assessment Tool
ClinicalTrials.gov study NCT05049330. IPD Sharing: NO. Countries: 1. Publications: 2.
An Efficacy and Safety Study of Atabecestat in Participants Who Are Asymptomatic at Risk for Developing Alzheimer's Dementia
ClinicalTrials.gov study NCT02569398. IPD Sharing: Not stated. Countries: 13. Publications: 1.
Development and Evaluation of High Risk Group Prediction Model in T1 Stage Renal Cell Cancer Using Molecular Biomarkers
ClinicalTrials.gov study NCT03694912. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Exemestane in Preventing Cancer in Postmenopausal Women at Increased Risk of Developing Breast Cancer
ClinicalTrials.gov study NCT00083174. IPD Sharing: NO. Countries: 4. Publications: 4.
Study to Evaluate the Long-Term Safety of PA32540 in Subjects Who Are at Risk for Developing Aspirin-Associated Gastric Ulcers
ClinicalTrials.gov study NCT00995410. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development of the DRIVE Curriculum to Address Childhood Obesity Risk Factors
ClinicalTrials.gov study NCT02160847. IPD Sharing: NO. Countries: 1. Publications: 6.
The Impact of a Mobile Application Designed for Adults at Risk of Developing Diabetes
ClinicalTrials.gov study NCT05592288. IPD Sharing: YES. Countries: 1. Publications: 6.
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OpenNeuro
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