77 Works

Additional file 4 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 4: Supplementary Figure S4. Statistical analysis of the number of differential NRGs and prognostic NRGs of the four GCs. Red bars show up-regulated NRGs in cancer tissue. The green bars show NRGs that are downregulated in cancer tissue. The blue bars show the sum of the differential NRGs. Yellow bars indicate prognostic NRGs.

Additional file 6 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 6: Supplementary Figure S6. Expression values of NRGs in prognostic signature of OV. A-D Expression values of NRGs in TCGA for BACH2 (A), MLKL (B), MYC (C), MYCN (D), and SIRT2 (E). F-J Expression values of NRGs in our cohort for BACH2 (F), MLKL (I), MYC (G), MYCN (H), and SIRT2 (J).

Additional file 8 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 8: Supplementary Figure S8. The Nomogram model based on risk model and clinical features for GCs. A-B The Nomogram (A) and calibration curve (B) for CESC. C-D The Nomogram (C) and calibration curve (D) for OV. (E-F) The Nomo©m (E) and calibration curve (F) for UCEC. G-H The Nomogram (G) and calibration curve (H) for UCS.

Additional file 1 of CAFs-derived SCUBE1 promotes malignancy and stemness through the Shh/Gli1 pathway in hepatocellular carcinoma

Jungang Zhao, Rizhao Li, Jiacheng Li, Ziyan Chen, Zixia Lin, Baofu Zhang, Liming Deng, Gang Chen & Yi Wang
Supplementary Material 1

Additional file 1 of Comparison of intestinal flora between patients with chronic and advanced Schistosoma japonicum infection

Chen Zhou, Junhui Li, Chen Guo, Zhaoqin Zhou, Zhen Yang, Yu Zhang, Jie Jiang, Yu Cai, Jie Zhou & Yingzi Ming
Additional file 1: Fig. S1. Shannon curve showed that all samples were saturated.

Additional file 1 of Comparison of intestinal flora between patients with chronic and advanced Schistosoma japonicum infection

Chen Zhou, Junhui Li, Chen Guo, Zhaoqin Zhou, Zhen Yang, Yu Zhang, Jie Jiang, Yu Cai, Jie Zhou & Yingzi Ming
Additional file 1: Fig. S1. Shannon curve showed that all samples were saturated.

Additional file 9 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 9: Supplementary Figure S9. The waterfall plot of somatic mutation features established with risk scores. A-B The waterfall plot of somatic mutation in CESC for high-risk group (A) and low-risk group B. C-D The waterfall plot of somatic mutation in OV for high-risk group (C) and low-risk group D. E-F The waterfall plot of somatic mutation in UCEC for high-risk g©p (E) and low-risk group F. G-H The waterfall plot of somatic mutation...

Additional file 2 of CAFs-derived SCUBE1 promotes malignancy and stemness through the Shh/Gli1 pathway in hepatocellular carcinoma

Jungang Zhao, Rizhao Li, Jiacheng Li, Ziyan Chen, Zixia Lin, Baofu Zhang, Liming Deng, Gang Chen & Yi Wang
Supplementary Material 2

Additional file 11 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 11: Supplementary Figure S11. Expression of immune checkpoints in the low and high-risk groups for CESC (A), OV (B), UCEC (C), and UCS D. ∗P<0.05; ∗∗P<0.01; ∗∗∗P<0.001; ns: not significant. The blue bars represent the low-risk group and the red bars represent the high-risk group.

figure s1.pdf

Narges Alipanah-Lechner, Lucile Neyton, Eran Mick, Andrew Willmore, Aleksandra Leligdowicz, Kevin Contrepois, Alejandra Jauregui, Hanjing Zhuo, Carolyn Hendrickson, Antonio Gomez, Pratik Sinha, Kirsten N. Kangelaris, Kathleen D. Liu, Michael A. Matthay, Angela Rogers & Carolyn S. Calfee
Overrepresentation analysis of differentially abundant metabolites between the hyperinflammatory ARDS and hypoinflammatory ARDS phenotype groups. Panel A) Figure represents significant metabolites with associated Human Metabolome Database (HMDB) identifiers based on the unadjusted analysis (n=131). Panel B) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex, dexmedetomidine, propofol, kidney and liver injury (n=58). Panel C) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex,...

Figure S1

Narges Alipanah-Lechner, Lucile Neyton, Eran Mick, Andrew Willmore, Aleksandra Leligdowicz, Kevin Contrepois, Alejandra Jauregui, Hanjing Zhuo, Carolyn Hendrickson, Antonio Gomez, Pratik Sinha, Kirsten N. Kangelaris, Kathleen D. Liu, Michael A. Matthay, Angela Rogers & Carolyn S. Calfee
Overrepresentation analysis of differentially abundant metabolites between the hyperinflammatory ARDS and hypoinflammatory ARDS phenotype groups. Panel A) Figure represents significant metabolites with associated Human Metabolome Database (HMDB) identifiers based on the unadjusted analysis (n=131). Panel B) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex, dexmedetomidine, propofol, kidney and liver injury (n=58). Panel C) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex,...

Figure S2

Narges Alipanah-Lechner, Lucile Neyton, Eran Mick, Andrew Willmore, Aleksandra Leligdowicz, Kevin Contrepois, Alejandra Jauregui, Hanjing Zhuo, Carolyn Hendrickson, Antonio Gomez, Pratik Sinha, Kirsten N. Kangelaris, Kathleen D. Liu, Michael A. Matthay, Angela Rogers & Carolyn S. Calfee
Top 20 differentially abundant metabolites in hyperinflammatory ARDS compared to hypoinflammatory ARDS by FDR adjusted p-value. Panel A) No adjustment for Simplified Acute Physiology Score II (SAPSII). Panel B) Adjusted for SAPSII. ✱Denotes metabolites no longer significant when SAPSII is included as a covariate in the model.

Additional file 2 of Birth weight is associated with obesity and T2DM in adulthood among Chinese women

Pu Song, Hui Hui, Manqing Yang, Peng Lai, Yan Ye, Ying Liu & Xuekui Liu
Additional file 2. Supplement Figure.

Mec1-Independent Activation of the Rad53 Checkpoint Kinase Revealed by Quantitative Analysis of Protein Localization Dynamics

Grant Brown
The replication checkpoint is essential for accurate DNA replication and repair, and maintenance of genomic integrity when a cell is challenged with genotoxic stress. Several studies have defined the complement of proteins that change subcellular location in the budding yeast Saccharomyces cerevisiae following chemically-induced DNA replication stress using methyl methanesulfonate (MMS) or hydroxyurea (HU). How these protein movements are regulated remains largely unexplored. We find that the essential checkpoint kinases Mec1 and Rad53 are responsible...

A Reference Library for Characterizing Protein Subcellular Localizations by Image-Based Machine Learning

David Andrews
Libraries composed of 789,011 and 523,319 optically validated reference confocal micrographs of 17 and 20 EGFP fusion proteins localized at key cell organelles as landmarks in murine and human cells were generated for assignment of subcellular localization in mammalian cells. For each image of individual cells, 160 morphology and statistical features were used to train a random forests classifier to automatically assign the localization of proteins and dyes in both cell types and to analyze...

A Reference Library for Characterizing Protein Subcellular Localizations by Image-Based Machine Learning

David Andrews
Libraries composed of 789,011 and 523,319 optically validated reference confocal micrographs of 17 and 20 EGFP fusion proteins localized at key cell organelles as landmarks in murine and human cells were generated for assignment of subcellular localization in mammalian cells. For each image of individual cells, 160 morphology and statistical features were used to train a random forests classifier to automatically assign the localization of proteins and dyes in both cell types and to analyze...

Additional file 3 of Increased diaphragm echodensity correlates with postoperative pulmonary complications in patients after major abdominal surgery: a prospective observational study

Xin Fu, Zhen Wang, Luping Wang, Guangxuan Lv, Yisong Cheng, Bo Wang, Zhongwei Zhang, Xiaodong Jin, Yan Kang, Yongfang Zhou & Qin Wu
Supplementary Material 3

Additional file 3 of Increased diaphragm echodensity correlates with postoperative pulmonary complications in patients after major abdominal surgery: a prospective observational study

Xin Fu, Zhen Wang, Luping Wang, Guangxuan Lv, Yisong Cheng, Bo Wang, Zhongwei Zhang, Xiaodong Jin, Yan Kang, Yongfang Zhou & Qin Wu
Supplementary Material 3

Additional file 1 of Shrimp miR-965 transfers tumoricidal mitochondria

Hyueyun Kim, Ji Ha Choi, Chang Mo Moon, Jihee Lee Kang, Minna Woo & Minsuk Kim
Additional file 1: Supplementary Figure 1. Morphology and apoptosis in breast tumor cells. (A) Using tomographic microscopy, the number of filopodia was measured in primary epithelial breast tumor cells and MDA-MB-453. (B and C) Viability and morphology of MDA-MB-453 were observed for 48 h. (D) Efficiency of stained mitochondria uptake into MDA-MB-453. (E) Comparison of unsealed mitochondria derived from DMBA-induced mammary carcinoma. (F) Role of unsealed mitochondria on apoptosis in cardiomyocytes and breast epithelial cells....

Additional file 5 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 5: Supplementary Figure S5. The ROC curves for the risk model in the four GCs. A-C The ROC curves of CESC for training (A), validation (B), and total (C) sets. D-F The ROC curves of OV for training (D), valida©n (E), and total (F) sets. G-I The ROC curves of UCEC for training (G), validation (H), and total (I) sets. J-L The ROC curves of UCS for training (J), validation (K), and total...

Additional file 7 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 7: Supplementary Figure S7. Clinical value of risk score by independent prognostic analysis. A-H The Univariate Cox regression analysis and Multivariate Cox regression analysis for CESC (A-B), OV (C-D), UCEC (E-F), and UCS G-H.

Additional file 8 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 8: Supplementary Figure S8. The Nomogram model based on risk model and clinical features for GCs. A-B The Nomogram (A) and calibration curve (B) for CESC. C-D The Nomogram (C) and calibration curve (D) for OV. (E-F) The Nomo©m (E) and calibration curve (F) for UCEC. G-H The Nomogram (G) and calibration curve (H) for UCS.

Additional file 1 of A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Jianfeng Zheng, Xintong Cai, Yu Zhang, Huihui Wang, Li Liu, Fengling Tang, Linying Liu & Yang Sun
Additional file 1: Supplementary Figure S1. Mutation frequency and expression variation of the 76 necroptosis-related genes (NRGs). A-D Mutation frequency of NRGs in patients with CESC (A), OV (B), UCEC (C), and UCS D. The small figure above shows the TMB, the number on the right shows the mutation frequency of each NRG, and the figure on the right shows the proportion of each vari©. E Expression levels of NRGs in four gynecological tumors. The...

Additional file 4 of Prognostic value of creatinine-to-cystatin c ratio in patients with type 2 diabetes mellitus: a cohort study

Wen Wei, Shanggang Li, Jin Liu, Yong Liu, Kaihong Chen, Shiqun Chen, Mei Tu & Hong Chen
Additional file 4: Figure S2. Hazard ratios for long-term all-cause mortality in different subgroups.

Figure S1.pdf

Narges Alipanah-Lechner, Lucile Neyton, Eran Mick, Andrew Willmore, Aleksandra Leligdowicz, Kevin Contrepois, Alejandra Jauregui, Hanjing Zhuo, Carolyn Hendrickson, Antonio Gomez, Pratik Sinha, Kirsten N. Kangelaris, Kathleen D. Liu, Michael A. Matthay, Angela Rogers & Carolyn S. Calfee
Overrepresentation analysis of differentially abundant metabolites between the hyperinflammatory ARDS and hypoinflammatory ARDS phenotype groups. Panel A) Figure represents significant metabolites with associated Human Metabolome Database (HMDB) identifiers based on the unadjusted analysis (n=131). Panel B) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex, dexmedetomidine, propofol, kidney and liver injury (n=58). Panel C) Figure represents significant metabolites with HMDB identifiers based on the analysis adjusted for age, sex,...

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