708,304 Works

Additional file 1: of Canary: an atomic pipeline for clinical amplicon assays

Kenneth Doig, Jason Ellul, Andrew Fellowes, Ella Thompson, Georgina Ryland, Piers Blombery, Anthony Papenfuss & Stephen Fox
canary usage.docx: Description of the Canary command line options. (DOCX 136 kb)

Additional file 5: of Discovery of the fourth mobile sulfonamide resistance gene

Mohammad Razavi, Nachiket Marathe, Michael Gillings, Carl-Fredrik Flach, Erik Kristiansson & D. Joakim Larsson
Relative abundance of mobile sulfonamide resistance genes (sul1â 4) in metagenomic samples containing sul4. (XLSX 28 kb)

Additional file 4 of DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content

Thomas Lopdell, Kathryn Tiplady, Maksim Struchalin, Thomas Johnson, Michael Keehan, Ric Sherlock, Christine Couldrey, Stephen Davis, Russell Snell, Richard Spelman & Mathew Littlejohn
Table S4. Associations between tag variants and milk phenotypes. Tag variants represent the 26 validated QTL detected for the LC and LY phenotypes. Phenotypes are milk yield (litres/day), milk fat and milk protein yield (kg/day) and milk fat and protein concentrations (percentage). (XLSX 42 kb)

Additional file 8: of Training the salmon’s genes: influence of aerobic exercise, swimming performance and selection on gene expression in Atlantic salmon

Nicholas Robinson, Gerrit Timmerhaus, Matthew Baranski, Øivind Andersen, Harald Takle & Aleksei Krasnov
Differential gene expression detected for exercise regime among the heart from Lærdal inferior swimming parr. (ZIP 4464 kb)

Additional file 9: of Training the salmon’s genes: influence of aerobic exercise, swimming performance and selection on gene expression in Atlantic salmon

Nicholas Robinson, Gerrit Timmerhaus, Matthew Baranski, Øivind Andersen, Harald Takle & Aleksei Krasnov
Differential gene expression detected for exercise regime among the heart from Lærdal superior swimming parr. (ZIP 3092 kb)

Additional file 4: of Discovery of the fourth mobile sulfonamide resistance gene

Mohammad Razavi, Nachiket Marathe, Michael Gillings, Carl-Fredrik Flach, Erik Kristiansson & D. Joakim Larsson
List of previously not reported gene cassettes. (XLSX 325 kb)

Additional file 3 of DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content

Thomas Lopdell, Kathryn Tiplady, Maksim Struchalin, Thomas Johnson, Michael Keehan, Ric Sherlock, Christine Couldrey, Stephen Davis, Russell Snell, Richard Spelman & Mathew Littlejohn
Table S3. Tag-variant results for LC and LY QTL peaks in the validation data set. (XLSX 7 kb)

Additional file 3: of Discovery of the fourth mobile sulfonamide resistance gene

Mohammad Razavi, Nachiket Marathe, Michael Gillings, Carl-Fredrik Flach, Erik Kristiansson & D. Joakim Larsson
List of putative novel ARGs. (XLSX 110 kb)

Additional file 7: of Training the salmon’s genes: influence of aerobic exercise, swimming performance and selection on gene expression in Atlantic salmon

Nicholas Robinson, Gerrit Timmerhaus, Matthew Baranski, Øivind Andersen, Harald Takle & Aleksei Krasnov
Enrichment analysis for genes differentially expressed for swimming performance in parr. (XLSX 29 kb)

Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)

Weilin Pu, Chenji Wang, Sidi Chen, Dunmei Zhao, Yinghui Zhou, Yanyun Ma, Ying Wang, Caihua Li, Zebin Huang, Li Jin, Shicheng Guo, Jiucun Wang & Minghua Wang
Abstract Background DNA methylation has been implicated as a promising biomarker for precise cancer diagnosis. However, limited DNA methylation-based biomarkers have been described in esophageal squamous cell carcinoma (ESCC). Methods A high-throughput DNA methylation dataset (100 samples) of ESCC from The Cancer Genome Atlas (TCGA) project was analyzed and validated along with another independent dataset (12 samples) from the Gene Expression Omnibus (GEO) database. The methylation status of peripheral blood mononuclear cells and peripheral blood...

Perceived Medical School stress of undergraduate medical students predicts academic performance: an observational study

Thomas Kötter, Josefin Wagner, Linda Brüheim & Edgar Voltmer
Abstract Background Medical students are exposed to high amounts of stress. Stress and poor academic performance can become part of a vicious circle. In order to counteract this circularity, it seems important to better understand the relationship between stress and performance during medical education. The most widespread stress questionnaire designed for use in Medical School is the “Perceived Medical School Stress Instrument” (PMSS). It addresses a wide range of stressors, including workload, competition, social isolation...

Perceived Medical School stress of undergraduate medical students predicts academic performance: an observational study

Thomas Kötter, Josefin Wagner, Linda Brüheim & Edgar Voltmer
Abstract Background Medical students are exposed to high amounts of stress. Stress and poor academic performance can become part of a vicious circle. In order to counteract this circularity, it seems important to better understand the relationship between stress and performance during medical education. The most widespread stress questionnaire designed for use in Medical School is the “Perceived Medical School Stress Instrument” (PMSS). It addresses a wide range of stressors, including workload, competition, social isolation...

Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)

Weilin Pu, Chenji Wang, Sidi Chen, Dunmei Zhao, Yinghui Zhou, Yanyun Ma, Ying Wang, Caihua Li, Zebin Huang, Li Jin, Shicheng Guo, Jiucun Wang & Minghua Wang
Abstract Background DNA methylation has been implicated as a promising biomarker for precise cancer diagnosis. However, limited DNA methylation-based biomarkers have been described in esophageal squamous cell carcinoma (ESCC). Methods A high-throughput DNA methylation dataset (100 samples) of ESCC from The Cancer Genome Atlas (TCGA) project was analyzed and validated along with another independent dataset (12 samples) from the Gene Expression Omnibus (GEO) database. The methylation status of peripheral blood mononuclear cells and peripheral blood...

Additional file 2: of Discovery of the fourth mobile sulfonamide resistance gene

Mohammad Razavi, Nachiket Marathe, Michael Gillings, Carl-Fredrik Flach, Erik Kristiansson & D. Joakim Larsson
List of known ARGs, categorized by different families of antibiotics, identified as gene cassettes in both samples. (XLSX 20 kb)

Additional file 6: Figure S5. of Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)

Weilin Pu, Chenji Wang, Sidi Chen, Dunmei Zhao, Yinghui Zhou, Yanyun Ma, Ying Wang, Caihua Li, Zebin Huang, Li Jin, Shicheng Guo, Jiucun Wang & Minghua Wang
The detailed description of biomarker selection pipeline. (PDF 97 kb)

Additional file 2 of DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content

Thomas Lopdell, Kathryn Tiplady, Maksim Struchalin, Thomas Johnson, Michael Keehan, Ric Sherlock, Christine Couldrey, Stephen Davis, Russell Snell, Richard Spelman & Mathew Littlejohn
Figure S2. WGS resolution for 1Mbp windows centred on QTL peaks for lactose phenotypes. (PDF 1010 kb)

Additional file 1: Table S1. of Perceived Medical School stress of undergraduate medical students predicts academic performance: an observational study

Thomas Kötter, Josefin Wagner, Linda Brüheim & Edgar Voltmer
PMSS items (english wording); Table S2. Longitudinal results from our sample. Description of data / legend: The (english language) wording of the PMSS items [8, 16] is presented in Table S1. Table S2 shows the mean scores for the single PMSS items, as well as the sum scores for T1 and T2. Results of the dependent t-tests for paired samples are also shown in Table S2. (DOCX 18 kb)

Additional file 6: of Training the salmon’s genes: influence of aerobic exercise, swimming performance and selection on gene expression in Atlantic salmon

Nicholas Robinson, Gerrit Timmerhaus, Matthew Baranski, Øivind Andersen, Harald Takle & Aleksei Krasnov
Differential gene expression detected for swimming performance among the heart from Bolaks trained parr. (ZIP 613 kb)

Additional file 5: Figure S4. of Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)

Weilin Pu, Chenji Wang, Sidi Chen, Dunmei Zhao, Yinghui Zhou, Yanyun Ma, Ying Wang, Caihua Li, Zebin Huang, Li Jin, Shicheng Guo, Jiucun Wang & Minghua Wang
The expression profiles for the three genes using RNA-seq data from TCGA. (TIFF 3006 kb)

Additional file 1 of DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content

Thomas Lopdell, Kathryn Tiplady, Maksim Struchalin, Thomas Johnson, Michael Keehan, Ric Sherlock, Christine Couldrey, Stephen Davis, Russell Snell, Richard Spelman & Mathew Littlejohn
Figure S1. Stratification in the 30,000 discovery and validation animals, illustrated using PCA on the GRM matrix. Animals are coloured by the percentages of ancestry recorded in the LIC animal recording database. Breeds are Jersey and Holstein-Friesian. PCA was performed using GCTA [46]. (PDF 1770 kb)

Additional file 5: of Training the salmon’s genes: influence of aerobic exercise, swimming performance and selection on gene expression in Atlantic salmon

Nicholas Robinson, Gerrit Timmerhaus, Matthew Baranski, Øivind Andersen, Harald Takle & Aleksei Krasnov
Differential gene expression detected for swimming performance among the heart from Bolaks control parr. (ZIP 840 kb)

Additional file 1: of Discovery of the fourth mobile sulfonamide resistance gene

Mohammad Razavi, Nachiket Marathe, Michael Gillings, Carl-Fredrik Flach, Erik Kristiansson & D. Joakim Larsson
Figure S1. Predicted functions of open reading frames recovered by the chromosomal integron primer pair MRG284-MRG285 separated by samples. The results are based on known homologues in the CARD database. Figure S2. Predicted functions of open reading frames of the “clinical” and “environmental” integrons from the HS464-GCP2 amplicons separated by samples. The results are based on known homologues in the CARD database. Figure S3. Functional annotation of the open reading frames not previously reported in integrons....

Mechanically tuned 3 dimensional hydrogels support human mammary fibroblast growth and viability

Kathryn Woods, Catlyn Thigpen, Jennifer Wang, Hana Park & Abigail Hielscher
Abstract Background Carcinoma associated fibroblasts (CAFs or myofibroblasts) are activated fibroblasts which participate in breast tumor growth, angiogenesis, invasion, metastasis and therapy resistance. As such, recent efforts have been directed toward understanding the factors responsible for activation of the phenotype. In this study, we have investigated how changes in the mechanical stiffness of a 3D hydrogel alter the behavior and myofibroblast-like properties of human mammary fibroblasts (HMFs). Results Here, we utilized microbial transglutaminase (mTG) to...

Mechanically tuned 3 dimensional hydrogels support human mammary fibroblast growth and viability

Kathryn Woods, Catlyn Thigpen, Jennifer Wang, Hana Park & Abigail Hielscher
Abstract Background Carcinoma associated fibroblasts (CAFs or myofibroblasts) are activated fibroblasts which participate in breast tumor growth, angiogenesis, invasion, metastasis and therapy resistance. As such, recent efforts have been directed toward understanding the factors responsible for activation of the phenotype. In this study, we have investigated how changes in the mechanical stiffness of a 3D hydrogel alter the behavior and myofibroblast-like properties of human mammary fibroblasts (HMFs). Results Here, we utilized microbial transglutaminase (mTG) to...

Additional file 4: Figure S3. of Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)

Weilin Pu, Chenji Wang, Sidi Chen, Dunmei Zhao, Yinghui Zhou, Yanyun Ma, Ying Wang, Caihua Li, Zebin Huang, Li Jin, Shicheng Guo, Jiucun Wang & Minghua Wang
The ROC (Receiver Operating characteristics) curve for the subgroup analyzes. A-H represent the ROC curve for the young, old, male, female, smoked, non-smoked, alcohol, and non-alcohol subgroups, respectively. A-H each represent the overall ROC curve for the subgroup, which was calculated through a logistic regression model, incorporating the mean methylation percentage of the five genomic regions as the variables and without the adjustment for gender, age, and smoking status and alcohol status. (PDF 446 kb)

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