Spatial Analysis Reveals Targetable Macrophage-Mediated Mechanisms of Immune Evasion in Hepatocellular Carcinoma Minimal Residual Disease حسب Renumathy Dhanasekaran (14962092)

"…In this procedure, DNA-barcoded antibodies targeted tissue antigens and were visualized through iterative hybridization with complementary fluorescent DNA oligonucleotides. Our algorithmic pipeline processed the raw imaging data to segment and identify single cells, accurately localize them within tissues, and quantify their marker expressions. …"

In-situ wearable-based dataset of continuous heart rate variability monitoring accompanied by sleep diaries حسب Aitolkyn Baigutanova (20798113)

"…The recordings were sampled every 100 ms (10 Hz), allowing for short-term HRV computation for each 5-minute segment of raw data. We validated the collected signals by examining collection frequency, analyzing the correlation between smartwatch sensor data and computed HRV, and comparing HRV and sleep-related feature distributions with existing literature. …"

Figures and Tables حسب Divya C D (22799186)

"…Robots Comput. Vision XXXI: Algorithms and Techniques, Burlingame, CA, USA, Jan. 23–24, 2012.…"

dataset حسب taku toyama (19858248)

"…</p><p dir="ltr">Data Details</p><p dir="ltr">Images</p><p dir="ltr">Total of 1,146 OCT B-scan images.…"

Table 1_The different member equivalent circulating density prediction model and drilling parameter optimization under narrow density window.xlsx حسب Wanchun Zhao (18285081)

"…An ECD prediction model based on drilling parameters and segmented reservoir layers is also proposed. The model uses nonlinear regression algorithms to predict ECD values for different members. …"

Image 2_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg حسب Dibash Basukala (20772110)

"…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …"

Image 4_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg حسب Dibash Basukala (20772110)

"…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …"

Image 3_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg حسب Dibash Basukala (20772110)

"…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …"

Image 1_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg حسب Dibash Basukala (20772110)

"…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …"

Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg حسب Dibash Basukala (20772110)

"…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …"

Annotated dataset of simulated voiding sound for urine flow estimation حسب 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. …"