The Hurdle model identifies genes with cell cycle phase-dependent expression.

<p>(A) Hurdle model strength of evidence of cell cycle phase dependent expression for genes within our panel versus Cyclebase rank. P-values (-log10(p)) are shown on the y-axis. <i>Ranked</i> genes (red) are ordered on the x-axis according to Cyclebase rank. Unranked genes (blue) appear in alphabetical order. Genes significant after Bonferroni adjustment are annotated with their names (B) Hurdle model strength of evidence of cell cycle phase dependent expression in <i>ranked</i> genes versus phase of peak expression estimated from bulk data in Cyclebase. Experimentally observed peak times broadly match the times estimated from bulk data. Concordance in observed peak times is greater for genes with stronger evidence of differential expression. (C) Cumulative number of significant (red) or all (blue) <i>ranked</i> genes versus Cyclebase rank. Genes with lower Cyclebase rank, and hence stronger evidence of cycle regulation in bulk expression, are detected more often than genes with weaker evidence as shown by the minimal gap between significant (red) and all (blue) <i>ranked</i> gene lines at Cyclebase rank <150.</p

Box and whiskers plot of cell cycle deviance ratio in ranked and unranked genes.

<p>The proportion of stochastic variability in the Hurdle Model explained by cell cycle is shown on the primary y-axis (left) for <i>ranked</i> and <i>unranked</i> genes, with box giving the 25^th, 50^th and 75^th percentiles, and whiskers showing 1.5 times the inter-quartile range. The deflated scale on the secondary y-axis (right) shows the deviance as a percentage of the most completely explained gene (TOP2A, 27%) and is intended as an upper bound for the amount of remaining biological deviance in non-cell-cycle genes. Under this conservative rescaling, cell cycle explains only 25% of the deviance in 75% of unranked genes.</p

Individual cells were flow sorted by DNA content, and gene expression profiled.

<p>(A) H9, MB231, and PC3 cells were cultured and sorted into lysis buffer. The resulting lysate was amplified via multiplexed target enrichment (MTE) and digital counts of expression were optically read via nCounter. (B) Individual cells were sorted into three populations based on retention of Hoechst dye (G0/G1, S and G2/M). (C) The density distribution of log counts for each gene was generally bimodal with some genes showing clear changes in distribution between cell cycle phase.</p

Coexpression networks estimated using the Hurdle model.

<p>Data from three cell lines and three cycles are combined and adjusted for additive effects of cell line and pre-amplification efficiency. Networks of the top 60 edges (ranked by partial correlation) using logistic regressions on discretized expression (A,D), linear regressions on positive, continuous expression values (B,E), and combining the top 30 edges from discrete and continuous components are shown (C,F). Panels A–C adjust for additive cell cycle effects, while panels D–F are unadjusted. The shape of the node corresponds to the cycle with peak expression from cyclebase, while the saturation of the node corresponds to the ranking. Blue and green edges are partial correlations detected from discrete expression and continuous expression, respectively. Red edges are detected in both discrete and continuous expression.</p

Impact of Building Height and Volume on Cardiac Arrest Response Time

<p>Emergency medical services (EMS) care may be delayed when out-of-hospital cardiac arrest (OHCA) occurs in tall or large buildings. We hypothesized that larger building height and volume were related to a longer <i>curb-to-defibrillator activation</i> interval. We retrospectively evaluated 3,065 EMS responses to OHCA in a large city between 2003–13 that occurred indoors, prior to EMS arrival, and without prior deployment of a defibrillator. The two-tiered EMS system uses automated external defibrillator-equipped basic life support firefighters followed by paramedics dispatched from a single call center. We calculated three time intervals obtained from the computerized dispatch report and time-synchronized defibrillators: initial 911 call to address curb arrival by first unit (<i>call-to-curb</i>), curb arrival to defibrillator power on (<i>curb-to-defib on</i>), and the combined <i>call-to-defib on</i> interval. Building height and surface area were measured with a validated program based on aerial photography. Buildings were categorized by height as short (<25 ft), medium (26–64 ft) and tall (>64 ft). Volume was categorized as small (<60,000 ft³), midsize (60,000–1,202,600 ft³) and large (>1,202,600 ft³). Intervals were compared using the two-tailed Mann-Whitney test. EMS responded to 1,673 OHCA events in short, 1,134 in medium, and 258 in tall buildings. There was a 1.14 minute increase in median <i>curb-to-defib on</i> interval from 1.97 in short to 3.11 minutes in tall buildings (<i>p</i> < 0.01). Taller buildings, however, had a shorter <i>call-to-curb</i> interval (4.73 for short vs 3.96 minutes for tall, <i>p</i> < 0.01), such that the difference in <i>call-to-defib on</i> interval was only 0.27 minutes: 6.87 for short and 7.14 for tall buildings. A similar relationship was observed for small-volume compared to large-volume building: longer <i>curb-to-AED</i> (1.90 vs. 3.01 minutes, <i>p</i> < 0.01), but shorter <i>call-to-curb</i> (4.87 vs. 4.05, <i>p</i> < 0.01); the difference in <i>call-to-defib on</i> was 0.18 minutes. Both taller and larger-volume buildings had longer <i>curb-to-AED</i> intervals but shorter 911 <i>call-to-curb</i> arrival intervals. As a consequence, building height and volume had a modest overall relationship with interval from call to defibrillator application. These results do not support the hypothesis that either taller or larger-volume buildings need cause poorer outcomes in urban environments.</p

Additional file 1: of MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Supplemental methods, extended derivations, and supplementary figures and tables. (PDF 3940 kb

Confirmation of the Reported Association of Clonal Chromosomal Mosaicism with an Increased Risk of Incident Hematologic Cancer

<div><p>Chromosomal abnormalities provide clinical utility in the diagnosis and treatment of hematologic malignancies, and may be predictive of malignant transformation in individuals without apparent clinical presentation of a hematologic cancer. In an effort to confirm previous reports of an association between clonal mosaicism and incident hematologic cancer, we applied the anomDetectBAF algorithm to call chromosomal anomalies in genotype data from previously conducted Genome Wide Association Studies (GWAS). The genotypes were initially collected from DNA derived from peripheral blood of 12,176 participants in the Group Health electronic Medical Records and Genomics study (eMERGE) and the Women’s Health Initiative (WHI). We detected clonal mosaicism in 169 individuals (1.4%) and large clonal mosaic events (>2 mb) in 117 (1.0%) individuals. Though only 9.5% of clonal mosaic carriers had an incident diagnosis of hematologic cancer (multiple myeloma, myelodysplastic syndrome, lymphoma, or leukemia), the carriers had a 5.5-fold increased risk (95% CI: 3.3–9.3; p-value = 7.5×10⁻¹¹) of developing these cancers subsequently. Carriers of large mosaic anomalies showed particularly pronounced risk of subsequent leukemia (HR = 19.2, 95% CI: 8.9–41.6; p-value = 7.3×10⁻¹⁴). Thus we independently confirm the association between detectable clonal mosaicism and hematologic cancer found previously in two recent publications.</p> </div

Summary of characteristics of studies included in the analysis.

1<p>Number of individuals included and analyzed in study;</p>2<p>Predominant ancestral group;</p>3<p>Age at baseline and/or sample collection;</p>4<p>Only phase 1 data included;</p>5<p>Only phase 2 data included.</p

Counts of detected mosaic anomalies by chromosomal location and event type.

<p>Counts of detected mosaic anomalies by chromosomal location and event type.</p

Kaplan Meier plots of the proportion of individuals remaining without diagnosed A) Hematologic cancer stratified by presence (blue) or absence (red) of a mosaic anomaly or B) Leukemia stratified by presence (blue) or absence (red) of a large mosaic anomaly (>2 mb).

<p>Kaplan Meier plots of the proportion of individuals remaining without diagnosed A) Hematologic cancer stratified by presence (blue) or absence (red) of a mosaic anomaly or B) Leukemia stratified by presence (blue) or absence (red) of a large mosaic anomaly (>2 mb).</p