DataSheet1_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.docx by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image4_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.pdf by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

DataSheet2_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.DOCX by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image5_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.PDF by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

DataSheet2_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.DOCX by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image4_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.pdf by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image2_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image2_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image3_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image5_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.PDF by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

DataSheet1_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.docx by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image1_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image3_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Image1_Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.JPEG by Zhengjun Lin (11050797)

“…</p><p>Methods: CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database. …”

Prediction of Two-Dimensional Group IV Nitrides A_<i>x</i>N_<i>y</i> (A = Sn, Ge, or Si): Diverse Stoichiometric Ratios, Ferromagnetism, and Auxetic Mechanical... by Heng Zhang (320479)

“…Using HSE06 functional calculations, a wide range of band gaps from metal to semiconductor (0.405–5.050 eV) and ultrahigh carrier mobilities (1–24 × 10³ cm² V^–1 s^–1) were evidenced in these 2D structures. …”

DataSheet_1_CXCL17 Is a Specific Diagnostic Biomarker for Severe Pandemic Influenza A(H1N1) That Predicts Poor Clinical Outcome.docx by Jose Alberto Choreño-Parra (10203338)

“…CXCL17 not only differentiated pandemic influenza A(H1N1) from other respiratory infections but showed prognostic value for influenza-associated mortality and renal failure in machine-learning algorithms and regression analyses. Using cell culture assays, we also identified that human alveolar A549 cells and peripheral blood monocyte-derived macrophages increase their CXCL17 production capacity after influenza A(H1N1) pdm09 virus infection. …”

DataSheet_1_CXCL17 Is a Specific Diagnostic Biomarker for Severe Pandemic Influenza A(H1N1) That Predicts Poor Clinical Outcome.docx by Jose Alberto Choreño-Parra (10203338)

“…CXCL17 not only differentiated pandemic influenza A(H1N1) from other respiratory infections but showed prognostic value for influenza-associated mortality and renal failure in machine-learning algorithms and regression analyses. Using cell culture assays, we also identified that human alveolar A549 cells and peripheral blood monocyte-derived macrophages increase their CXCL17 production capacity after influenza A(H1N1) pdm09 virus infection. …”

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>…”