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. …”

Data Sheet 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.zip 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. …”

Table 1_Remote sensing-based assessment of four decades of land use/land cover change in the Sigi River Watershed, East Usambara Mountains, Tanzania.docx by Simon Chidodo (21572894)

“…</p>Methods:<p>Multi-temporal Landsat satellite images were classified using the Random Forest algorithm to assess LULC transitions across elevation and slope gradients.…”

Climate anomalies due to Cerrado native vegetation loss by Argemiro Leite-Filho (10283801)

“…<p dir="ltr">Description of the data and file structure</p><p dir="ltr">The Cerrado, occupying over 2 million square kilometers, is often referred to as the world's most biodiverse savanna. …”

Figure 3 from Chromothripsis-Mediated Small Cell Lung Carcinoma by Natasha Rekhtman (15014156)

“…<b>B,</b> CN log ratio plots from the FACETS algorithm displaying the distinctive oscillating CN states on chromosomes with chromothripsis. …”

Table_1_High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study.csv by Yanxia Kang (9949844)

“…The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.…”

DataSheet1_Machine learning’s effectiveness in evaluating movement in one-legged standing test for predicting high autistic trait.docx by Yoshimasa Ohmoto (19862184)

“…The data collected in the experiment were time-series data pertaining to pressure distribution on the sole of the foot and full-body images. A model to identify the participants belonging to High autistic trait group and Low autistic trait group was developed using a support vector machine (SVM) algorithm with 16 explanatory variables. …”

Particulate and Gas Emissions from Wildfires in the Southern Amazon, from 2020 to 2022, from GOES-16 Fire Radiative Power Retrievals by Thiago Ferreira da Nobrega (20713696)

“…The Fire/Hotspot Characterization algorithm processes GOES-16’s Advanced Baseline Imager data, providing insights into fire dynamics with an unparalleled temporal resolution of 10 min. …”

Particulate and Gas Emissions from Wildfires in the Southern Amazon, from 2020 to 2022, from GOES-16 Fire Radiative Power Retrievals by Thiago Ferreira da Nobrega (20713696)

“…The Fire/Hotspot Characterization algorithm processes GOES-16’s Advanced Baseline Imager data, providing insights into fire dynamics with an unparalleled temporal resolution of 10 min. …”

Forest canopy height mapping considering the seasonal rhythm of different dominant tree species by Yali Zhang (206227)

“…Extrapolating FCH samples at the plot scale to the global or regional level using spatially contiguous remote sensing images is currently a common method for large-scale vegetation attribute mapping. …”

Supplementary Material for: Weakly supervised deep learning can analyze focal liver lesions in contrast-enhanced ultrasound by figshare admin karger (2628495)

“…The emergence of artificial intelligence has opened doors to algorithms that could potentially aid in the diagnostic process. …”

Data Sheet 1_Modeling epithelial-mesenchymal transition in patient-derived breast cancer organoids.zip by Neta Bar-Hai (13232547)

“…Upon induction with TGF-β, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. …”

Table 1_Modeling epithelial-mesenchymal transition in patient-derived breast cancer organoids.xlsx by Neta Bar-Hai (13232547)

“…Upon induction with TGF-β, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. …”