The distribution map of surface subsidence. by Desheng Xie (21439869)

Schematic diagram of the research area. by Desheng Xie (21439869)

The distribution map of flat spectral shape index. by Desheng Xie (21439869)

Disease burden trends of leukemia and lymphoma analyzed by joinpoint regression analysis in mainland China. by Yu Fu (16582)

Disease burden trends of multiple myeloma and other hematological neoplasms analyzed by joinpoint regression analysis in mainland China. by Yu Fu (16582)

Projections of incidence for hematological malignancies in mainland China. by Yu Fu (16582)

Boxplot mean cfPWV comparisons. by Johannes Nairz (18010746)

Age- and sex-specific distribution of prevalence, incidence, mortality, and DALY rates for leukemia in mainland China. by Yu Fu (16582)

Projections of mortality for hematological malignancies in mainland China. by Yu Fu (16582)

Age- and sex-specific distribution of prevalence, incidence, mortality, and DALY rates for lymphoma, multiple myeloma, and other hematological neoplasms in mainland China. by Yu Fu (16582)

Source data - by Desheng Xie (21439869)

“…<div><p>Coal fires are a significant environmental and geological threat, causing extensive ecological damage and loss of resources. …”

DataSheet1_Erlotinib regulates short-term memory, tau/Aβ pathology, and astrogliosis in mouse models of AD.doc by Hyun-ju Lee (7550741)

“…</p>Results and discussion<p>We found that erlotinib significantly enhanced short-term spatial memory and dendritic spine formation in 6-month-old P301S tau transgenic (PS19) mice. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCAECG_GAN. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Experimental hardware and software environment. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Classification model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”