Table 2_Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait... حسب Guoqing Liu (93712)

"…Hub genes were screened using the least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms. A cellular model of AMI was established using oxygen- and glucose-deprived AC16 cells. …"

Image 2_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.jpeg حسب Ruoya Wang (842048)

"…Four high-risk independent prognostic factors (OAZ1, SRM, SMOX, and SMS) were validated as being upregulated in breast cancer tissues. …"

Image 3_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.jpeg حسب Ruoya Wang (842048)

"…Four high-risk independent prognostic factors (OAZ1, SRM, SMOX, and SMS) were validated as being upregulated in breast cancer tissues. …"

Table 2_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.xlsx حسب Ruoya Wang (842048)

"…Four high-risk independent prognostic factors (OAZ1, SRM, SMOX, and SMS) were validated as being upregulated in breast cancer tissues. …"

Identification of potential circadian rhythm-related hub genes and immune infiltration in preeclampsia through bioinformatics analysis حسب Juan Tang (437969)

FCP dataset for forecasting temperature, PV, price, and load حسب Hanwen Zhang (18259666)

"…<p dir="ltr">Singapore aims to transform into a green and sustainable city by 2030. One of the key actions is to phase out Internal Combustion Engine (ICE) vehicles and significantly expand electric vehicle (EV) adoption. …"

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems حسب 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>…"