An open-pit mine segmentation dataset for deep learning by Lin Gang (18600526)

“…Firstly, by conducting comprehensive literature research, the Point of Interest (POI) data of open-pit mines was summarized. Based on this, Google Level 17 images were obtained. …”

Supplementary file 1_Single-cell and bulk transcriptomic analyses reveal PANoptosis-associated immune dysregulation of fibroblasts in periodontitis.zip by Erli Wu (17785482)

“…By integrating bulk transcriptomic data with machine learning algorithms, we identified and validated key PANoptosis-related genes, highlighting their potential as novel therapeutic targets.…”

<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.…”

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.…”

Image 1_Unveiling purine metabolism dysregulation orchestrated immunosuppression in advanced pancreatic cancer and concentrating on the central role of NT5E.pdf by Junqian Zhang (9315639)

“…We quantitatively appraised the PM traits of diverse cell subsets via scoring algorithms such as AUCell and Ucell. Moreover, cell development and cell-cell interaction analysis elucidated the alterations in TME induced by PM dysregulation. …”

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.…”

TempODEGraphNet.zip by Minseop Lee (20579561)

“…Experimental results reveal that the proposed model achieves a higher F1 score compared with conventional algorithms and static graph models. …”

Labeled sensor dataset of beef cattle behavior grazing desert rangelands by Andres Perea (21095165)

“…Proprietary onboard processing algorithms summarize the motion data into a one-dimension motion index (MI) aggregated every 1 minute. …”

Dependent Random Partitions by Shrinking Toward an Anchor by David B. Dahl (11761055)

“…We prove intuitive theoretical properties for our distribution and compare it to related partition distributions. We show that our model provides better out-of-sample fit in a real data application. …”

Supplementary file 1_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.docx by Wentong Li (492392)

“…This study aims to elucidate the regulatory network and diagnostic potential of the molecular chaperone system in VaD through the integration of multi-omics data and machine learning algorithms.</p>Methods<p>Transcriptomic data from frontal and temporal cortex (GSE122063, n=15)and white matter (GSE282111, n=8) samples were obtained from the GEO database. …”

Table 5_Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application.xlsx by Rui Peng (472678)

“…Neutralizing epitope, amino acid selection pressure, and evolution dynamics analyses on VP7 and VP4 were performed using BioEdit v.7.0.9.0 and PyMOL v.2.5.2, four algorithms (MEME, SLAC, FEL, and FUBAR) in the Datamonkey online software, and the MCMC model in BEAST v. 1.10.4, respectively. …”