Data Sheet 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.docx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively.…”

Table 5_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively.…”

Table 4_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively.…”

Table 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively.…”

Table 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. Microbial community phenotypes and potential functional pathways were predicted using BugBase and PICRUSt2, respectively.…”

Local Signal Detection on Irregular Domains with Generalized Varying Coefficient Models by Chengzhu Zhang (5432417)

“…We also establish the consistency of estimated nonparametric coefficient functions and the estimated null regions. The numerical performance of the proposed method is evaluated in both simulation cases and real data analysis. …”

CIAHS-Data.xls by Yingchang Li (22195585)

“…This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …”

Table 1_Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learning approach.docx by Aihemaitijiang Niyazi (22355542)

“…We proposed an improved metaheuristic algorithm (INPDOA) for AutoML optimization, validated against 12 CEC2022 benchmark functions. …”

<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.…”

Data Sheet 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.do... by Xuan Xu (781842)

“…</p>Results<p>Our analysis identified four significant cross-disease mitochondrial markers: EFHD1, SASH1, FAM110B, and SLC25A18. These markers showed both shared and unique expression profiles in AD and GBM, suggesting a common mitochondrial mechanism contributing to both diseases. …”

Table 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.xlsx by Xuan Xu (781842)

“…</p>Results<p>Our analysis identified four significant cross-disease mitochondrial markers: EFHD1, SASH1, FAM110B, and SLC25A18. These markers showed both shared and unique expression profiles in AD and GBM, suggesting a common mitochondrial mechanism contributing to both diseases. …”

Ricker seismic profile. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

Noise reduction on testing sets from STEAD. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

SNR comparison of real-field seismic profile. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

The flowchart of GWO-VMD method. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

The 147th single trace. by Zhenjing Yao (22189970)

“…Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. …”

Supplementary file 1_Unraveling the bacterial composition of a coral and bioeroding sponge competing in a marginal coral environment.docx by Sambhaji Mote (6311663)

“…This study focuses on the coral Turbinaria mesenterina and sponge C. thomasi, both known for their distinct symbiotic associations with Symbiodiniaceae. …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

Data Sheet 1_Unsupervised method for representation transfer from one brain to another.docx by Daiki Nakamura (20349885)

“…<p>Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. …”