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- Supplementary data for High magnification Smartphone-Dermatoscope Integration: A Portable and Accessible Alternative to Super High-Magnification VideodermatoscopySupplementary data for the manuscript entitled "High magnification Smartphone-Dermatoscope Integration: A Portable and Accessible Alternative to Super High-Magnification Videodermatoscopy"
- Dataset
- LINF_050012700tRNA 4-demethylwyosine synthase (AdoMet-dependent); Leishmania infantum (strain JPCM5)
- Dataset
- Supplementary material: visualization of experimental delamination in DCB test and tomographyThese videos present different stages of the DCB test and tomographic analysis, allowing a complete visualization of the delamination process in the material. Through this supplementary material, it is possible to obtain a global and dynamic understanding of the behavior of the material during the test, beyond what a single static image can offer.
- Dataset
- LINF_050014500E2 ubiquitin-conjugating enzyme; Leishmania infantum (strain JPCM5)
- Dataset
- Meteorological and rainfall chemical data in Guangyuan (2022-2023)Including Air pressure, Air temperature, Relative humidity, Wind speed, Rainfall amount during the rainfall sampling days, and the major ion concentrations, pH, EC
- Dataset
- LINF_050012000PGP motif-containing protein; Leishmania infantum (strain JPCM5)
- Dataset
- CD4+ anti-TGFβ CAR-T cells and CD8+ conventional CAR-T cells exhibit synergistic antitumor effectsThe Original western blot images
- Dataset
- Vehicle Non-exhaust Emissions Significantly Contribute to Urban PM pollution in New Energy Vehicles EraBased on the Tianjin Statistical Yearbook, we obtained the population of small passenger vehicles from 2002 to 2021. Moreover, we employed established methodologies from prior research(Sun et al., 2019) to adopt a trend extrapolation approach for predicting vehicle population from 2022 to 2035. We employed a logistic model to predict the population of new energy vehicles from 2022 to 2035 based on nationwide vehicle population data and Automotive Industry Association statistical data. The population of conventional fuel vehicles was determined by subtracting the population of new energy vehicles from the overall vehicle population. The VKT is a key foundational data for constructing emission inventory, reflecting the overall activity level of a vehicle fleet. The inspection station database records vehicle age and cumulative mileage, and with appropriate data processing to provide a highly reliable annual average mileage. Tianjin PM10 concentration and ADMS model simulation of brake and tire wear PM10 hourly concentration.
- Dataset
- AI dataset of 3D head models with different cranial deformitiesThis repository presents a dataset generate by the method described in the article "Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities", published in Journal of Biomedical Informatics (2022). The dataset comprises synthetic head models (STL files) with normal head shapes and with different cranial deformities, namely: plagiocephaly, brachycephaly, scaphocephaly, combined plagiocephaly/brachycephaly, and combined plagiocephaly/scaphocephaly. The main goal of this dataset is to promote the development of methods to assess head shape. The proposed framework software is available under request. To request for the software, the user must fill the "Software Access Request Form" and send it to htorres@ipca.pt.
- Dataset
- Evaluating the Performance of ChatGPT on Dermatology Board-Style Exams: A Meta-Analysis of Text-Based and Image-Based Question Accuracy1. Supplemental Figure I: A PRISMA diagram illustrating the study selection process. This diagram shows how articles were retrieved from PubMed and SCOPUS databases, with the search terms related to ChatGPT, dermatology, and exam-style questions. 2. Supplemental Figure II: This figure presents the total accuracy for each GPT model (ChatGPT-3, ChatGPT-3.5, and ChatGPT-4) across all the questions tested. It highlights the number of studies reporting performance for each model and provides 95% confidence intervals. 3. Supplemental Figure III: This figure breaks down the performance of each GPT model according to specific dermatology categories as per the American Board of Dermatology (ABD) criteria, including dermatopathology, general dermatology, pediatric dermatology, science research, and surgical dermatology. It also provides 95% confidence intervals for each category. 4. Supplemental Figure IV: This figure compares the performance of each GPT model on visual versus text-based questions. Since GPT-3 and GPT-3.5 lack visual recognition capabilities, this comparison primarily focuses on the performance of ChatGPT-4 in interpreting visual questions. The figure also provides 95% confidence intervals for each performance category
- Dataset
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