Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table 1_Integrated analysis of N-glycosylation and Alzheimer’s disease: identifying key biomarkers and mechanisms.xls by Hao Zhang (15339)

“…Differential expression profiling identified 6,845 DEGs, including TMEM59, MLEC, and MAX. Machine learning algorithms consistently prioritized these three genes as core N-glycosylation-related biomarkers, alongside APP as a key associated molecule. …”

Image 1_Integrated analysis of N-glycosylation and Alzheimer’s disease: identifying key biomarkers and mechanisms.tif by Hao Zhang (15339)

“…Differential expression profiling identified 6,845 DEGs, including TMEM59, MLEC, and MAX. Machine learning algorithms consistently prioritized these three genes as core N-glycosylation-related biomarkers, alongside APP as a key associated molecule. …”

Annotated dataset of simulated voiding sound for urine flow estimation by Marcos Lazaro Alvarez (20107426)

“…Filename format: [device]_f_0_30s.wav ### Example: - `um_f_0s.wav` - `phone_f_0s.wav` - `oppo_f_0s.wav` ## Purpose The goal of this dataset is to provide a standardized audio repository for the development, training and validation of machine learning algorithms for voiding flow prediction. This enables researchers to: - Benchmark different approaches on a common dataset - Develop flow estimation models using synthetic audio before transferring them to real-world applications - Explore the spectral and temporal structure of urination-related audio signals ## Flow Generation - Pump Used: L600-1F precision peristaltic pump - Flow Range: 1–50 ml/s (based on ICS-reported ranges for male uroflowmetry) - Calibration: Pump flows were validated using a graduated cylinder - Noise Isolation: The pump was placed in a separate room (via 15m silicone tubing) to eliminate pump noise from recordings ## Recording Devices | Device | Sampling Rate | Frequency Range | Description | |---------|----------------|------------------|--------------------------------------| | UM | 192 kHz | 0–96 kHz | High-quality ultrasonic microphone | | Phone | 48 kHz | 0–24 kHz | Android smartphone (Mi A1) | | Watch | 44.1 kHz | 0–22.05 kHz | Oppo Smartwatch with built-in mic | Each recording was carried out using a custom mobile or desktop app with preset parameters. ## Recording Environment - Recordings were made in a bathroom with a standard ceramic toilet containing water at the bottom. - The nozzle height varied between 73–86 cm depending on flow rate to ensure consistent water impact. - Microphone heights: - UM: 84 cm - Phone: 95 cm - Watch: 86 cm (simulating wrist height) ## Data Collection Protocol 1. …”

Machine vision system for quantification of aortic and pulmonic valvuloplasty catheter compliance by Jiazhe Tang (17596080)

“…Upon ballon inflation, the defocused image is then refocused though passive focusing algorithms used to identify the best focal position. …”

Image 6_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf by Claire R. van Genugten (20626733)

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”

Presentation 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.pptx by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 4_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 3_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 7_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 2_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Table 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.docx by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Image 5_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”