presentation1_ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.pdf by Paola Turina (10431428)

“…The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. …”

Table4_Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.XLSX by Xin Liu (43569)

“…<p>Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. …”

Table1_Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.XLSX by Xin Liu (43569)

“…<p>Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. …”

Table2_Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.XLSX by Xin Liu (43569)

“…<p>Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. …”

Table3_Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.XLSX by Xin Liu (43569)

“…<p>Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. …”

DataSheet1_Approach for machine learning based design of experiments for occupant simulation.docx by Bernd Schneider (88767)

“…Simultaneously biofidelic simulation models are resulting in very high computational costs and therefore the number of simulations should be limited to a feasible operational range. …”

Table_8_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_5_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_4_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_1_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_7_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_3_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_9_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_2_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Presentation_1_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.PPTX by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Table_6_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.docx by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Presentation_2_Quantitative pupillometry and radiographic markers of intracranial midline shift: A pilot study.PPTX by Ivy So Yeon Kim (14222975)

“…Radiographic midline shift is associated with worse functional outcomes and life-saving interventions. Better understanding of quantitative pupil characteristics would be a non–invasive, safe, and cost-effective way to improve identification of life-threatening mass effect and resource utilization of emergent radiographic imaging. …”

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”