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870 results for “Model development”
Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)
<p>Data regarding <strong>Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)</strong></p>
Wildlife response to disturbances in agent-based models: synthesis on current development and challenges (Systematic Review)
<p>This dataset was obtained from a systematic screening of 124 articles presenting agent-based models of wildlife response to disturbances. It is part of a manuscript for submission to the journal Methods in Ecology and Evolution. The manuscript is entitled: Wildlife response to disturbances in agent-based models: synthesis on current development and challenges.</p>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</p>
Development of a prognostic model for early breast cancer integrating neutrophil to lymphocyte ratio and clinical-pathological characteristics
<p><strong>Abstract</strong></p> <p>Breast cancer-related inflammation is critical in tumorigenesis, cancer progression, and patient prognosis. Several inflammatory markers derived from peripheral blood cells count, such as the neutrophil-lymphocyte ratio (NLR), derived neutrophil-lymphocyte ratio (dNLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) are considered as prognostic markers in several types of malignancy. Here, we investigate and validate a prognostic model in early breast cancer (eBC) patients to predict disease-free survival (DFS) based on readily available baseline clinicopathological prognostic factors and preoperative peripheral blood-derived indexes. We analyzed a training cohort of 710 BC patients and two external validation cohorts of 980 and 157 eBC patients, respectively, with different demographic origins. An elevated preoperative NLR is a better DFS predictor than PLR, MLR, and SII in patients with eBC. The prognostic model generated in this study was able to classify patients into three groups with different risks of relapse based on ECOG-PS, presence of comorbidities, T and N stage, PgR status, and NLR. Prognostic models derived from the combination of clinicopathological features and peripheral blood indices, such as NLR, represent attractive markers mainly because they are easily detectable and applicable in daily clinical practice. More comprehensive prospective studies are needed to unveil their actual effectiveness.</p>
Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients
<p>Supplementary materials for the article titled “Development and validation of metabolic models for predicting survival and immune status of hepatocellular carcinoma patients”</p>
Development of a Machine Learning-Based Model to Determine the Optimum and Safe Restriping Timing of Thermoplastic Pavement Markings in Hot and Humid Climates
<p>Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R<sup>2</sup> (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics and to propose cost-effective restriping strategies for thermoplastics used in hot and humid climate service conditions. To achieve this objective, National Transportation Product Evaluation Program (NTPEP) data were mined and analyzed. Results indicated that the service life (SL) of thermoplastics ranged between 0.4 and 12.1 years (according to the initial retroreflectivity, traffic, and surface type) with an average value of 3.4 ± 0.2 years. Four regression models with relatively high accuracy were developed to predict the SL of thermoplastics based on key variables. In addition, the genetic algorithm was used to develop a model that predicts the future retroreflectivty of these pavement markings. The predicted values were compared against actual retroreflectivity measurements collected from a field experiment at Louisiana State University. The results of this study could be used to make effective decisions related to restriping scheduling. Using the proposed models in restriping scheduling can result in considerable cost savings (up to $8,212 per lane-mile), as compared to the conventional restriping strategy.</p>
Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques
<p>Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Figure 1. ROC Curve of CAR, IL-6, IL-6/LY, and NLR</p> <p> </p> <p>Figure 2. Kaplan Meier curve of (a) CAR (b) IL-6 (c) IL-6/LY (d) NLR blue line represents group above cut off and green one represents group below cut-off</p> <p> </p>
Data used for modeling in Energy-water-land-CCUS nexus model: carbon dioxide opportunities based on optimized regional development
<p>In this dataset, the data used for modeling technologies in an energy-water-land-CCUS nexus model in Khark Island in Iran, and the main sources for gathering them are presented.</p>
Dataset underpinning the development of a PBK model for inhalation of TiO2 NPs in rats
<table> <tbody> <tr> <td> <p>This dataset contains concentration-time profiles (expressed as % of the initial dose) of 20 nm TiO2 nanoparticles following a 2-hour inhalation experiment in rats, with tissue and excreta samples taken over a period of 28 days post exposure. The data were extracted from the publication of Kreyling et al. (2019) (<a href="https://doi.org/10.1186/s12989-019-0303-7">https://doi.org/10.1186/s12989-019-0303-7</a>) </p> <p> </p> <p> </p> </td> </tr> </tbody> </table>
Data from: genetic resources of macroalgae: development of an efficient method using microsatellite markers in non-model organisms
<p><span>Red and brown seaweeds are species with high ecological and economic importance. Here we report the feasibility of cost-effective molecular marker development in 6 species from different clades. Microsatellites markers of two brown seaweed species <em>Alaria esculenta</em>, <em>Pylaiella littoralis</em>, and of four red seaweed species <em>Calliblepharis jubata</em>, <em>Gracilaria gracilis</em>, <em>Gracilaria dura </em>and <em>Palmaria palmata</em> were identified and characterized using genomic sequences of Double-Digest Restriction site Associated DNA (ddRAD). A total of 64,623,186 reads were generated from two runs of multiplexed Illumina Miseq sequencing for which 30,636 reads containing microsatellites and 15,443 microsatellite loci with primers pairs were found. Five hundred seventy-six primers pairs were selected for amplification trials and levels of polymorphism. From the 338 that gave a positive amplification, 142 primers pairs were polymorphic. For genetic analyses two or three populations per species from 13 different geographic locations were used. A total of 28 usable polymorphic markers for <em>A. esculenta</em>, 18 for <em>P. littoralis</em>, 11 for <em>C. jubata</em>, 14 for <em>G. gracilis</em>, 21 for <em>G. dura </em>and 13 for <em>P. palmata </em>were developed. The overall number of alleles per locus ranged from 2 to 22. These 105 new microsatellite markers will be useful for further studies of population genetics, breeding programs and conservation genetics of these species. Compared with traditional approaches, our study yielded thousands of microsatellite loci in a short tim</span><span>e with affordable costs in six different species. This study based on ddRAD-sequencing for the development of microsatellite markers provides preliminary data u</span><span>sing a few individuals from two distinct populations on the genetic structure and reproduction mode of a non-model species as shown </span>with the detection of clonality for the two red algae, <em>C. jubata </em>and <em>G. dura</em> and the detection of highly genetically divergent populations corresponding probably to different cryptic species under the name of<em> P. littoralis</em>.</p>
Developing small Cas9 hybrids using molecular modeling
<p>The RNA-guided CRISPR-Cas9 from <i>Streptococcus pyogenes</i> is the best characterized enzyme for gene editing. Its large size, however, precludes it from being packaged together with its single guide (sg)RNA into a single adeno-associated virus, limiting <i>in vivo</i> applications. Here, we developed smaller Cas9 hybrids, made of the PAM interacting domain (PID) of <i>S pyogenes</i> and the catalytic domains of the smaller Cas9 orthologues, as well as sgRNA cognate hybrids. Molecular modeling revealed that the presence of a sgRNA stabilizes Cas9. Making the D10A mutation to turn Cas9 into a nickase dramatically alters its binding energy to the sgRNA, showing that the approach can identify functionally relevant changes. However, we found that the four Cas9/sgRNA hybrid pairs tested in human cells failed to edit target sequences. We conclude that <i>in silico</i> approaches can identify functional changes caused by point mutations but they may not be well suited for designing Cas9/sgRNA hybrids. </p>
Development and Validation of the Client Centered Occupational Therapy Service Model
ClinicalTrials.gov study NCT04465422. IPD Sharing: NO. Countries: 1. Publications: 4.
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
ClinicalTrials.gov study NCT06199388. IPD Sharing: NO. Countries: 1. Publications: 2.
Real-PD Trial: Development of Clinical Prognostic Models for Parkinson's Disease
ClinicalTrials.gov study NCT02474329. IPD Sharing: Not stated. Countries: 1. Publications: 9.
The Development of a Human Model of Respiratory Syncytial Virus Infection
ClinicalTrials.gov study NCT01349543. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development of a Dietary Intervention Model Based on Genetic Data as an Implementation of a Healthy Lifestyle in the Management of Systemic Lupus Erythematosus Patients
ClinicalTrials.gov study NCT07183007. IPD Sharing: NO. Countries: 1. Publications: 1.
Development of Human Nasal Challenge Models With Microbial Constituents and Grass Pollen
ClinicalTrials.gov study NCT02090374. IPD Sharing: Not stated. Countries: 1. Publications: 7.
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.
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
ClinicalTrials.gov study NCT06831357. IPD Sharing: NO. Countries: 1. Publications: 8.
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.