Table1_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

Table2_A real-world pharmacovigilance analysis of eslicarbazepine acetate using the FDA adverse events reporting system (FAERS) database from 2013 (Q4) to 2024 (Q1).XLSX by Huafei Tang (19710232)

“…</p>Methods<p>By extracting all available data since the FDA approval of ESL (2013Q4-2024Q1), disproportionality analysis was performed using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms. …”

<b>The remaining categories of the DsDPM 66 dataset, including coal_miner, compressed_oxygen_self_rescuer, and mining_helmet</b> by Pengzhen Zhao (18421255)

“…The experimental results show that the proposed dataset can effectively improve the accuracy of various object detection and pose estimation models in coal mines, filling the data gap in the coal mining field and providing valuable resources for the development of coal mine safety monitoring and automation technologies.…”

Polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…An example of how to extract data and plot the physical properties is provided in https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/read_data.py and https://github.com/dtu-energy/cathode-generation-workflow/tree/main/extract_data/utils.py contains two functions, one used to attached Bader charges to an ASE atom object an another to combine multiple XYZ data files.…”

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. …”

Image 4_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. …”

Image 2_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. …”

Image 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. …”

Image 3_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. …”

Image 1_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Image2_From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.tif by Lei Cao (134409)

“…A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. …”

Table1_From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.docx by Lei Cao (134409)

“…A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. …”

Image 3_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Image1_From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.tif by Lei Cao (134409)

“…A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. …”

Image 2_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

CANDID-II Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”