DataSheet1_Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.docx by Binbin Zou (11358315)

“…However, the biological function of glycosylation-related genes in LGG remains largely unexplored. …”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

Table 1_Recognition and treatment of attention deficit-hyperactivity disorder in patients with treatment-resistant burning mouth syndrome: a retrospective case study.docx by Kaori Takahashi (187346)

“…However, the role of ADHD diagnosis and treatment on BMS and associated brain function abnormalities remains unexplored. …”

ISAURO Cognitive Framework v0.1.1 – Public Research Summary (Under 4-Year Embargo) by Megan DeHerrera (21361439)

“…<p>{</p><p dir="ltr"> "title": "ISAURO Cognitive Framework v0.1.1 – Public Research Summary (Under 4-Year Embargo)",</p><p dir="ltr"> "authors": [</p><p> {</p><p dir="ltr"> "name": "Megan Irene DeHerrera",</p><p dir="ltr"> "affiliation": "Revelación Cognitive Research",</p><p dir="ltr"> "orcid_id": "https://orcid.org/0009-0000-2408-9132"</p><p> }</p><p> ],</p><p dir="ltr"> "description": "The ISAURO Cognitive Framework, developed by Megan Irene DeHerrera under Revelación Cognitive Research, is a modular, culturally-aware AI architecture inspired by the adaptive functions of the human brain. It integrates recursive cognition, logic-based memory routing, and trust-calibrated reasoning across a neuromodular system.…”

EXASCALE COMPUTING AND ITS IMPACT ON HIGH-PERFORMANCE COMPUTING by Jace Marden (22025762)

“…Possibilities are still not completely explored, exascale may result in a greater understanding of medical and materials science, more powerful algorithms for artificial intelligence (AI) and Machine Learning (ML), or the ability to create functional mimics of the human brain for neurological and possible cybernetic developments. …”

Presentation 1_Nonlinear kernel-based fMRI activation detection.pdf by Chendi Han (22221919)

“…<p>Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. …”

Presentation 2_Nonlinear kernel-based fMRI activation detection.zip by Chendi Han (22221919)

“…<p>Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. …”

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…These are separately stored in the folders “description” and “posts”.</p><h2>Algorithms for Evaluating Release Amount (RA)</h2><p dir="ltr">The algorithms are split into the following three notebooks based on their functions:</p><ol><li><b>"1_RA_extraction.ipynb"</b>:</li><li><ul><li>Identifies oil spill-related incidents from raw incident data.…”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18859198)

“…<p dir="ltr">The hippocampus, a region of critical interest within clinical neuroscience, is recognized as a complex structure comprising distinct subfields with unique functional attributes, connectivity patterns, and susceptibilities to disease. …”

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> by ahmed hossam (21420446)

“…</p><p dir="ltr">1.NanoNeuroBot is an AI-guided, ingestible nanobot pill engineered to cross the blood-brain barrier and deliver site-specific neuronal regeneration. …”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

Parameter estimates of mixed generalized Gaussian distribution for modelling the increments of electroencephalogram data by Zoe Salinger (19090613)

“…<p>Electroencephalogram (EEG) is used to monitor child's brain during coma by recording data on electrical neural activity of the brain. …”

High-Dimensional Covariance Regression with Application to Co-Expression QTL Detection by Rakheon Kim (21656910)

“…In this article, we present a new sparse covariance regression framework that models the covariance matrix as a function of subject-level covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can be used to determine if and how gene co-expressions vary with genetic variations. …”

Table 1_Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.docx by Begüm Özkaynak (20445146)

“…This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. …”

Data Sheet 1_COL8A1 as a pro-inflammatory mediator bridges immune evasion and therapy resistance in glioma.docx by Jiachong Wang (22818095)

“…Background<p>Glioma remains the most aggressive and therapy-resistant brain tumor, with a highly immunosuppressive tumor microenvironment. …”

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). …”

Image 13_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 4_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”