SAGE and quantitative PCR (qRT-PCR) analysis of select genes: (A) Genes found to have reversible expression upon smoking cessation

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Box plots of SAGE data and histograms for qRT-PCR for and . Distribution of ratios between both current vs. former and current vs. former and never (Additional file IV) were found to be statistically different. (B) Genes found to be either partially or fully irreversible. Box plots of SAGE data and histograms for qRT-PCR for and . Distribution of ratios between current vs. former and former vs. never were statistically different for and in addition, was statistically significant for the combination of current and former vs. never. Box plot analysis was done using the Statistics toolbox from the program. Red lines in the boxes represent the median expression value in terms of tags per million (TPM), and red "plus" signs represent outliers (values which are greater than 1.5 times the maximum value). The bottom and top part of the boxes represent the 2and 3quartiles of the data respectively. The error bars represent the 5and 95percentiles of the data. Quantitative RT-PCR validation was performed on a second cohort of nine current smokers, seven former smokers and six never smokers. Plotted is the average expression ratio relative to the average expression in never smokers of current (red), former (blue) and never (green) smokers. Statistical significance was determined using a one-tailed p-value from the Mann Whitney U Test (Supplemental Table IX)

(A) SAGE library statistics: Summary statistics of the 24 SAGE libraries analyzed in this study

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Mapping information was based on the May 10th, 2006 version of [45]. In total, over 3,000,000 SAGE tags were sequenced, with over 110,000 unique tags represented upon the exclusion of super singleton tags. (Super singleton tags are tags which have a count of 1 in a single library only). Approximately 75 % of these 110,000 unique tags, (potentially representing as many unique transcripts), mapped to an annotated cluster. As multiple SAGE tags frequently map to the same cluster, we have identified at a total of 25,653 distinct clusters within our dataset, approximately 68% of which represent previously characterized genes. Notably, 25% of the unique tags had no mapping, suggesting much information is currently unknown. (B) Transcriptome Venn diagram: Venn diagram of the transcriptomes of current, former and never smokers. Reported is the number of tags which are expressed in every library group at a raw tag count greater than or equal to 2, representing the tags which are constitutively expressed in each set. Nearly 2000 SAGE tags, mapping to over 1700 genes are common to all 24 SAGE libraries. A lower number of never smokers may have contributed to a higher number of preferentially expressed transcripts in this group

(A) Cluster analysis of current, former and never smokers: Single link hierarchical clustering using the 609 SAGE tags comprised in Additional file representing tags differentially expressed between current and never smokers

<p><b>Copyright information:</b></p><p>Taken from "Effect of active smoking on the human bronchial epithelium transcriptome"</p><p>http://www.biomedcentral.com/1471-2164/8/297</p><p>BMC Genomics 2007;8():297-297.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2001199.</p><p></p> Distance measure used was a Euclidean distance. The visualization package [23] was used for clustering. Green rectangles represent samples with lower expression for the particular gene amongst the samples, and red rectangles represent samples where the gene is highly expressed relative to other samples. (B) Principal component analysis of current, former and never smokers. Expression values used were scaled to tags per million (TPM). Each tag was then normalized by dividing its value by the maximum value for that tag seen in all the libraries. Subsequently, this value was then multiplied by 6 and then subtracted by 3 to put the values ratios in the range of -3 to 3. A co-variance based approach was used and the statistics toolbox in (Mathworks) was used. Current smokers are represented in red, former smokers are represented in blue and never smokers are represented in green

Baseline Characteristics of Study Participants.

<p>FEV₁, forced expiratory volume in 1 second. FVC, forced vital capacity.</p><p>P value denotes comparison between COPD and control subjects.</p

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol–1 K–1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures

The relationship between baseline BAL pro-SFTPB and lung function.

<p>Significant relationship is noted between baseline BAL pro-SFTPB and FEV₁% predicted, as well as between baseline pro-SFTPB and FEV₁/FVC ratio.</p

BAL and plasma biomarkers at baseline and 4 weeks after budesonide/formoterol treatment in COPD patients.

<p>Budesonide/formoterol significantly increased the BAL concentrations of pro-SFTPB. Data are presented as median with interquartile range and outliers are shown.</p

Biomarker Levels in BAL and Plasma at Baseline and at 4 Weeks of Treatment with Inhaled Budesonide/Formoterol Combination.

<p>BAL, bronchoalveolar lavage. FEV₁, forced expiratory volume in 1 second. FVC, forced vital capacity. Pro-SFTPB, pro-surfactant protein-B. SP-D, surfactant protein-D. CCSP-16, club cell secretory protein-16. PARC, pulmonary and activation regulated chemokine.</p><p>Data are expressed as median and interquartile range.</p><p>P denotes the comparison between COPD and Control subjects.</p

Additional file 1 of Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review

Additional file 1: Table S1. PRISMA-P 2015 Checklist. Table S2. Primary literature search. Database: Ovid MEDLINE(R) and Epub Ahead of Print, InProcess, In-Data-Review & Other Non-Indexed Citations, Daily and Versions

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Models trained through this workflow achieved an error (mean absolute error of 0.43 J mol–1 K–1) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures