Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.

Cancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification.

Biochar synthesized via pyrolysis of Broussonetia papyrifera leaves: mechanisms and potential applications for phosphate removal.

In this study, Broussonetia papyrifera leaves collected from land near a restored manganese mine in the Hunan Province of China were converted into biochar under high-temperature anaerobic conditions, regeneration and utilization of agricultural and forest waste, and applied to the prevention of eutrophication. The physicochemical properties of the B. papyrifera biochar were characterized using Micromeritics 3Flex analyzer, scanning electron microscope (SEM), Fourier transform infrared spectrometer (FT-IR), thermogravimetric analyzer (TGA), X-ray photoelectron spectrometer (XPS), zeta potential meter (zeta), and X-ray diffraction (XRD).

Identification of Key Candidate Genes and Pathways in Endometrial Cancer by Integrated Bioinformatical Analysis.

Endometrial Cancer is the most common female genital tract malignancy, its pathogenesis is complex, not yet fully described. To identify key genes of Endometrial Cancer we downloaded the gene chip GSE17025 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified through the GEO2R analysis tool.