Image 1_Decoding monocyte heterogeneity in sepsis: a single-cell apoptotic signature for immune stratification and guiding precision therapy.tif by Wenjuan Duan (11875262)

“…</p>Methods<p>We integrated single-cell and bulk transcriptomic data from four independent cohorts. A machine learning pipeline incorporating SVM, RF, XGB, and GLM algorithms was used to identify hub genes associated with monocyte apoptosis. …”

Table 1_Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer.docx by Rui Wang (52434)

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.…”

Table 2_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…</p>Methods<p>Transcriptomic data related to MDD were obtained from public databases. …”

Table 1_A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy.xlsx by Yang Li (7082)

“…Subsequently, machine learning algorithms were used to predict the classifications and prognoses. …”

Table 1_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…</p>Methods<p>Transcriptomic data related to MDD were obtained from public databases. …”

Table 3_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…</p>Methods<p>Transcriptomic data related to MDD were obtained from public databases. …”

Table 7_Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder.xlsx by Shengjie Xiong (21216725)

“…</p>Methods<p>Transcriptomic data related to MDD were obtained from public databases. …”

VIF values for logistic prediction models. by Yang Xu (178421)

“…</p><p>Methods</p><p>Retrospective data from 1,304 cardiac surgery patients (1,028 AKI cases and 276 non-AKI controls) were extracted from the MIMIC-IV database. …”

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. …”

KS statistic plot in predicting LAT/SEC risk. by Chaoqun Huang (529718)

“…A retrospective study of 1,222 NVAF patients was conducted. Nine ML algorithms combined with demographic, clinical, and laboratory data were applied to develop prediction models for LAT/SEC in NVAF patients. …”

Multivariate logistic regression analysis. by Chaoqun Huang (529718)

“…A retrospective study of 1,222 NVAF patients was conducted. Nine ML algorithms combined with demographic, clinical, and laboratory data were applied to develop prediction models for LAT/SEC in NVAF patients. …”

Fig 2 - by Elizabeth Ajalo (19234898)

“…<div><p>Background</p><p>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”

Characteristics of the participants. by Elizabeth Ajalo (19234898)

“…<div><p>Background</p><p>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”

Fig 1 - by Elizabeth Ajalo (19234898)

“…<div><p>Background</p><p>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”

Fig 3 - by Elizabeth Ajalo (19234898)

“…<div><p>Background</p><p>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”

Figures and Tables by Divya C D (22799186)

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

Table 1_A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.docx by Panying Rong (2697181)

“…To evaluate the utility of the muscle network as a bulbar measurement tool, a heterogenous ALS cohort, consisting of eight individuals with overt clinical bulbar symptoms and seven without, along with 10 neurologically healthy controls, was employed to train and validate statistical and machine learning algorithms to assess the disease effects on the network features and the relation of the network performance to the current clinical diagnostic standard and behavioral patterns of bulbar involvement.…”

Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca (20435036)

“…Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km² of seagrass habitat extent. …”

Table 3_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 6_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”