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58 results for “residual analysis”
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Supplementary Simulation Data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"
<p>Additional simulation data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"</p> <p><strong>Content:</strong></p> <p>'clusters_foldswitch': Contains representative structures in PDB format of the refolding landscape of RfaH using all-atom structure-based models. The QA and QB values indicated in each filename correspond to the fraction of native contacts contain in the representative structure in comparison to the total number of contacts in the structure of the autoinhibited ⍺-folded (A) and active β-folded (B) states of the C-terminal domain of RfaH.</p> <p>'input_one_fs': Contains a trajectory of RfaH refolding from the ⍺-folded to the β-folded state, with each frame contained into a separate PDB file, totalling 400 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'input_many_fs': Contains several trajectory of RfaH reversible refolding between the ⍺-folded and β-folded states, with each frame contained into a separate PDB file, totalling 11,999 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'output_one_fs': Contains output results from the analysis of local energetic frustration dynamics of the 400 frames contained in 'input_one_fs' using the windowing method available in the Colab notebook at https://github.com/pb3lab/RfaH-frustration.</p> <p>'output_many_fs': Contains output results from the analysis of local energetic frustration dynamics of the 11,999 frames contained in 'input_many_fs' using the windowing method available in the Python script at https://github.com/pb3lab/RfaH-frustration.</p>
Experimental data related to the publication: "In-situ analysis of the effect of residual fcc phase and special grain boundaries on the deformation dynamics in pure cobalt"
<p>The article figures were produced solely from these data sets employing data processing methods described therein. For experimental conditions and naming conventions please refer to the paper.</p> <p><br>1. Deformation data files contained within "deformation_data.zip":</p> <p>The .zip archive contains five files related to five samples of thermally treated cobalt:<br>def_co600.csv<br>def_co800.csv<br>def_co1100.csv<br>def_co1100-10c.csv<br>def_co1100-20c.csv</p> <p>The data were recorded during compression of the above-listed samples at room temperature. </p> <p><br>2. Acoustic emission (AE) data files contained within "AE_data.zip":</p> <p>The .zip archive contains four files related to four samples of thermally treated cobalt:<br>AE_co600.wav<br>AE_co800.wav<br>AE_co1100.wav<br>AE_co1100-20c.wav</p> <p>The AE data were recorded in continuous mode ("data streaming" at 2 MHz) during compression of the above-listed samples at room temperature. </p> <p> </p> <p>3. Electron back-scatter diffraction (EBSD) data files contained within "EBSD_data.zip":</p> <p>The .zip archive contains fifty-three .osc files related to samples of as-drawn and thermally treated cobalt within four folders:<br>0c - as-drawn and annealed samples (i.e. without thermal cycling)<br>10c - annealed samples after thermal cycling of 10 cycles<br>20c - annealed samples after thermal cycling of 20 cycles<br>ex-situ_def - ex-situ EBSD during deformation of selected samples</p> <p>The .osc data files represent EBSD data after clean-up procedures described in detail in the manuscript.</p> <p> </p> <p> </p> <p> </p>
Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique
<p><a>Exploring the effect of climate change and human activities on vegetation</a> is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long-term vegetation GIMMS NDVI data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detail human activities with some innovations. The results showed that (1) NDVI showed an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PⅠ: 1982–2000) than after it (PⅡ: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate-driven promotion had the strongest effect, followed by human-driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis showed that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PⅠ and economization during PII.</p>
Supplementary Material for "Model-Free Analysis of Experimental Residual Diploar Couplings in Small Organic Compounds"
<p>NMR Spectra (CLIP-HSQC, perfectCLIP-HSQC, TSE-PSYCHEDELIC) of isopinocampheol in six alignment conditions.</p> <p>Simulation input (experimental RDC data in six alignment media, input geometries, keywords) and output files (simulation / geometry trajectories, alignment data, SECONDA analysis) for isopinocampheol runs with the TITANIA software.</p>
Proximate analysis parameters for residual biomass samples from Southeast USA.
<p>Proximate analysis parameters for residual biomass samples for 142 samples collected from different logging mills in the Southeast USA for a USDA-AFRI research study (Grant.gov grant#: 12688125).</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
Analysis files for MSC Marc finite element software, Article: The analysis of shrink-fit connection – the methods of heating and the factors influencing the distribution of residual stresses
<p>This archive contains model files for Finite Element Analysis of the shrink-fit connection in crankshaft and the files for charts in GNUPlot.</p>
Cuisine in transition? Organic residue analysis of domestic containers from 9th-14th century Sicily
From the 9th to 14th centuries AD, Sicily experienced a series of rapid and quite radical changes in political regime, but the impact of these regime changes on the lives of the people that experienced them remains largely elusive within the historical narrative. We use a multi-faceted lipid residue approach to give direct chemical evidence of the use of 248 everyday domestic ceramic containers from Islamic and post-Islamic contexts in western Sicily to aid our understanding of daily habits throughout this period of political change. A range of commodities was successfully identified, including animal fats, vegetable products, fruit products, (potentially including wine), and plant resins. The study highlights the complexity of residues in Early Medieval Mediterranean society, as in many cases mixtures of commodities were observed, reflecting sequential cooking events and/or the complex mixtures reflective of medieval recipes. However, overall there were no clear changes in the composition of the residues following the imposition of Norman control over the island and through subsequent periods, despite some differences between urban centres and rural sites. Thus, lending to the idea that post-Islamic populations largely flourished and benefited from the agricultural systems, resources, and recipes left by their predecessors.
Beyond the surface: Untangling molecular complexity in organic residue analysis of coated archaeological ceramics
Open the record for dataset details and reuse information.
Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique
Open the record for dataset details and reuse information.
Cuisine in transition? Organic residue analysis of domestic containers from 9th-14th century Sicily
Open the record for dataset details and reuse information.
Measuring resilience and resistance in aging and Alzheimer disease using residual methods: A systematic review and meta-analysis
<p>Objective: There is currently a lack of consensus on how to optimally define and measure resistance and resilience in brain and cognitive aging. Residual methods use residuals from regression analysis to quantify the capacity to avoid (resistance) or cope (resilience) "better or worse than expected" given a certain level of risk or cerebral damage. We reviewed the rapidly-growing literature on residual methods in the context of aging and Alzheimer's disease (AD), and performed meta-analyses to investigate associations of residual-method based resilience and resistance measures with longitudinal cognitive and clinical outcomes.</p> <p>Methods: A systematic literature search of PubMed and Web-of-Science databases (consulted until March 2020) and subsequent screening led to 54 studies fulfilling eligibility criteria, including 10 studies suitable for the meta-analyses.</p> <p>Results: We identified articles using residual methods aimed at quantifying resistance (n=33), cognitive resilience (n=23) and brain resilience (n=2). Critical examination of the literature revealed that there is considerable methodological variability in how the residual measures were derived and validated. Despite methodological differences across studies, meta-analytic assessments showed significant associations of levels of resistance (HR[95%CI]=1.12[1.07-1.17], p<0.0001) and levels of resilience (HR[95%CI]=0.46[0.32-0.68], p<0.001) with risk of progression to dementia/AD. Resilience was also associated with rate of cognitive decline (β[95%CI]=0.05[0.01-0.08], p<0.01).</p> <p>Conclusion: This review and meta-analysis supports the usefulness of residual methods as appropriate measures of resilience and resistance, as they capture clinically meaningful information in aging and AD. More rigorous methodological standardization is needed, however, to increase comparability across studies and, ultimately, application in clinical practice.</p>
Fig. 6. Western blot analysis with a p in Mutagenesis of a Lotus japonicus GSK3β/Shaggy-like kinase reveals functionally conserved regulatory residues
Fig. 6. Western blot analysis with a p-GSK-3α/β (6D3) (Santa Cruz Biotechnology, Inc.) monoclonal antibody raised against a phosphopeptide corresponding to amino acids residues surrounding Tyr279 of human GSK3α and Tyr216 of human GSK3β. Lane 1: recombinant LjSK1 and lane 2: LjSK1 Y298A variant. Both proteins have a GST-tag.
Development and Validation of a Driving Simulator for Evaluating the Residual Effects of Drugs on Driving Performance - Sensitivity Analysis Using Zopiclone As a Positive Control
ClinicalTrials.gov study NCT04108351. IPD Sharing: NO. Countries: 1. Publications: 1.
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