Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…PCR validation confirmed the differential expression of NOX4 and NEK6 in both SSc and SSc-associated sarcopenia, demonstrating high predictive accuracy. …”

Table1_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.xls by Peichuan Xu (19862055)

“…Cell–cell communication analysis uncovered alteration in ANNEXIN pathway and a reduction in CXCL signaling between macrophages and monocytes, suggesting immune response dysregulation. Furthermore, ten algorithms identified three hub genes. Both experiments conducted in vitro and in vivo have conclusively shown that IRF7 serves as a crucial target for the treatment of IDD, and its knockdown alleviates IDD. …”

Image2_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.jpeg by Peichuan Xu (19862055)

“…Cell–cell communication analysis uncovered alteration in ANNEXIN pathway and a reduction in CXCL signaling between macrophages and monocytes, suggesting immune response dysregulation. Furthermore, ten algorithms identified three hub genes. Both experiments conducted in vitro and in vivo have conclusively shown that IRF7 serves as a crucial target for the treatment of IDD, and its knockdown alleviates IDD. …”

Image1_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.jpeg by Peichuan Xu (19862055)

“…Cell–cell communication analysis uncovered alteration in ANNEXIN pathway and a reduction in CXCL signaling between macrophages and monocytes, suggesting immune response dysregulation. Furthermore, ten algorithms identified three hub genes. Both experiments conducted in vitro and in vivo have conclusively shown that IRF7 serves as a crucial target for the treatment of IDD, and its knockdown alleviates IDD. …”

Table 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.xlsx by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Presentation 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.zip by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Data Sheet 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.docx by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table 1_Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma.docx by Xiang Xiong (1810234)

“…The progression-free survival (PFS) and overall survival (OS) rates of the low-risk group were significantly higher than those of the high-risk group. The risk signature functions as a prognostic factor that is independent of other variables. …”

Supplementary file 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial ce... by Jun Shi (289433)

“…Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface by Francesco Paesani (5128004)

“…The primary objectives are to establish a robust community of materials modeling developers and to enhance computational training at both undergraduate and graduate levels. The project seeks to recruit and train future leaders in materials modeling, foster community engagement, and promote coding literacy across disciplines.…”

Table 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.xlsx by Jun Shi (289433)

“…Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Patentability of 3D bioprinting technologies by Phoebe Li (4463947)

“…</p><p dir="ltr">(5) <i>In vitro</i> bioprinted (functional or cosmetic) human organs may be patentable.…”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”