1,394,668 Works

Genetic adaptations of an island pit-viper to aunique sedentarylife with extreme seasonal food availability

Bin Lu, Yin Qi, XiaoPing Wang, Jingsong Shi, Jinzhong Fu & Yayong Wu
Figure S1 contains estimates of divergence time. Table S1 contains genomic data, assembly version, and data sources used in this study. Table S2 contains calibration points used in divergence date estimation. Table S3 contains list of genes that bear signals of positive selection in the Shedao pit-viper. Table S4 contains list of genes that possess sites of convergent substitutions. Table S5 contains list of genes that possess sites of parallel substitutions. File S1 contains codon...

Genetic adaptations of an island pit-viper to aunique sedentarylife with extreme seasonal food availability

Bin Lu, Yin Qi, XiaoPing Wang, Jingsong Shi, Jinzhong Fu & Yayong Wu
Figure S1 contains estimates of divergence time. File S1 contains codon sequences with evidences of positive selection in FASTA format. File S2 contains protein sequences containing convergent/parallel substitutionsin FASTA format. File S3 contains protein sequences containing multiple amino acid substitutions specific to Shedao pit-viper in FASTA format. File S4 contains codon sequence alignmentsof all single-copy genes used in this study in FASTA format.Table S1 contains genomic data, assembly version, and data sources used in this...

MOESM4 of Contrasting bacterial and archaeal distributions reflecting different geochemical processes in a sediment core from the Pearl River Estuary

Wenxiu Wang, Jianchang Tao, Haodong Liu, Penghui Li, Songze Chen, Peng Wang & Chuanlun Zhang
Additional file 4: Table S3. Annotations of top 100 OTUs belonging to archaeal and bacterial groups, respectively.

MOESM3 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 3: Table S3. Identified lipid species from two ovarian cell lines derived exosomes.

MOESM4 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 4: Table S4. Identified lipid classes from two ovarian cell lines derived exosomes.

MOESM4 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 4: Table S4. Identified lipid classes from two ovarian cell lines derived exosomes.

MOESM3 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 3: Table S3. Identified lipid species from two ovarian cell lines derived exosomes.

MOESM2 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 2: Table S2. consistent presence / absence expression protein profile.

MOESM1 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 1: Table S1. Identified proteins from two ovarian cell lines derived exosomes.

MOESM2 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 2: Table S2. consistent presence / absence expression protein profile.

MOESM1 of Proteomic and lipidomic analysis of exosomes derived from ovarian cancer cells and ovarian surface epithelial cells

Lin Cheng, Kun Zhang, Yunan Qing, Dong Li, Manhua Cui, Peng Jin & Tianmin Xu
Additional file 1: Table S1. Identified proteins from two ovarian cell lines derived exosomes.

MOESM9 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 9: Fig. S5. Comparison of methylation frequencies between nine prostanoid receptor genes and other epigenetic factors. (A) 5hmC levels, (B) DNMT3A mRNA levels, (C) DNMT3B mRNA levels. *P

MOESM2 of Competencies and training of radiographers and technologists for PET/MR imaging - a study from the UK MR-PET network

Marius Mada, Paula Hindmarch, James Stirling, James Davies, David Brian, Anna Barnes, Alexander Hammers, Nick Gulliver, Karl Herholz, John O’Brien & John-Paul Taylor
Additional file 2. Expert panel membership list

MOESM6 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 6: Fig. S3. Hypermethylation patterns in 36 matched pairs of head and neck tumors and adjacent normal mucosal tissues. The NMVs for the PTGDR1 (A), PTGDR2 (B), PTGER1 (C), PTGER2 (D), PTGER3 (E), PTGER4 (F), PTGFR (G), PTGIR (H) and TBXA2R (I) promoters were significantly higher in head and neck tumor tissues (T) than in paired adjacent normal mucosal tissue (N). The differences were significant as determined by the Student’s t‑test. *P

MOESM5 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 5: Fig. S2. Receiver operating characteristic (ROC) curves for the methylation markers in cancer tissue versus adjacent normal mucosal tissue. Based on the ROC curve analysis, Area Under Curves (AUCs) are 0.6767 for PTGDR1 (A), 0.6265 for PTGDR2 (B), 0.6574 for PTGER1 (C), 0.6154 for PTGER2 (D), 0.4784 for PTGER3 (E), 0.5405 for PTGER4 (F), 0.6289 for PTGFR (G), 0.6736 for PTGIR (H) and 0.6605 for TBXA2R (I).

MOESM7 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 7: Table S4. Results of log-rank tests for effect of number of methylated genes on disease free survival in 274 HNSCC.

MOESM3 of Competencies and training of radiographers and technologists for PET/MR imaging - a study from the UK MR-PET network

Marius Mada, Paula Hindmarch, James Stirling, James Davies, David Brian, Anna Barnes, Alexander Hammers, Nick Gulliver, Karl Herholz, John O’Brien & John-Paul Taylor
Additional file 3. PET/MR courses

MOESM5 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 5: Fig. S2. Receiver operating characteristic (ROC) curves for the methylation markers in cancer tissue versus adjacent normal mucosal tissue. Based on the ROC curve analysis, Area Under Curves (AUCs) are 0.6767 for PTGDR1 (A), 0.6265 for PTGDR2 (B), 0.6574 for PTGER1 (C), 0.6154 for PTGER2 (D), 0.4784 for PTGER3 (E), 0.5405 for PTGER4 (F), 0.6289 for PTGFR (G), 0.6736 for PTGIR (H) and 0.6605 for TBXA2R (I).

MOESM8 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 8: Fig. S4. Kaplan–Meier survival curves. Kaplan–Meier survival curves for PTGDR1 in (A) patients with hypopharyngeal cancer (n = 69), for PTGER4 in (B) patients with laryngeal cancer (n = 51), and for PTGIR and TBXA2R in (C and D) patients with oropharyngeal cancer (n = 79). The log-rank test was used to compare the survival times between patients with methylated (red lines) and unmethylated (blue lines) genes. *P

MOESM3 of Competencies and training of radiographers and technologists for PET/MR imaging - a study from the UK MR-PET network

Marius Mada, Paula Hindmarch, James Stirling, James Davies, David Brian, Anna Barnes, Alexander Hammers, Nick Gulliver, Karl Herholz, John O’Brien & John-Paul Taylor
Additional file 3. PET/MR courses

MOESM4 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 4: Table S3. Results of the ROC curve analysis, the sensitivity, specificity, and cutoff value.

MOESM6 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 6: Fig. S3. Hypermethylation patterns in 36 matched pairs of head and neck tumors and adjacent normal mucosal tissues. The NMVs for the PTGDR1 (A), PTGDR2 (B), PTGER1 (C), PTGER2 (D), PTGER3 (E), PTGER4 (F), PTGFR (G), PTGIR (H) and TBXA2R (I) promoters were significantly higher in head and neck tumor tissues (T) than in paired adjacent normal mucosal tissue (N). The differences were significant as determined by the Student’s t‑test. *P

MOESM7 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 7: Table S4. Results of log-rank tests for effect of number of methylated genes on disease free survival in 274 HNSCC.

MOESM8 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 8: Fig. S4. Kaplan–Meier survival curves. Kaplan–Meier survival curves for PTGDR1 in (A) patients with hypopharyngeal cancer (n = 69), for PTGER4 in (B) patients with laryngeal cancer (n = 51), and for PTGIR and TBXA2R in (C and D) patients with oropharyngeal cancer (n = 79). The log-rank test was used to compare the survival times between patients with methylated (red lines) and unmethylated (blue lines) genes. *P

MOESM9 of Prostanoid receptor genes confer poor prognosis in head and neck squamous cell carcinoma via epigenetic inactivation

Kiyoshi Misawa, Masato Mima, Yamada Satoshi, Atsushi Imai, Daiki Mochizuki, Ryuji Ishikawa, Junya Kita, Yuki Yamaguchi, Shiori Endo, Yuki Misawa & Hiroyuki Mineta
Additional file 9: Fig. S5. Comparison of methylation frequencies between nine prostanoid receptor genes and other epigenetic factors. (A) 5hmC levels, (B) DNMT3A mRNA levels, (C) DNMT3B mRNA levels. *P

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