No abstract

Quality of microarray gene expression data has emerged as a new research topic. As in other areas, microarray quality is assessed by comparing suitable numerical summaries across microarrays, so that outliers and trends can be visualized and poor-quality arrays or variable-quality sets of arrays can be identified. Because each single array comprises tens or hundreds of thousands of measurements, the challenge is to find numerical summaries that can be used to make accurate quality calls. Toward this end, several new quality measures are introduced based on probe-level and probeset-level information, all obtained as a byproduct of the low-level analysis algorithms RMA/fitPLM for Affymetrix GeneChips. Quality landscapes spatially localize chip or hybridization problems. Numerical chip quality measures are derived from the distributions of normalized unscaled standard errors and relative log expressions. Quality of chip batches is assessed by residual scale factors. These quality assessment measures are demonstrated on a variety of data sets, including spike-in experiments, small lab experiments, and multisite studies. They are compared with Affymetrix's individual chip quality report.

Serial block face scanning electron microscopy (SBF-SEM) is a powerful method to analyze cells in 3D. Here, working at the resolution limit of the method, we describe a correlative light-SBF-SEM workflow to resolve microtubules of the mitotic spindle in human cells. We present four examples of uses for this workflow that are not practical by light microscopy and/or transmission electron microscopy. First, distinguishing closely associated microtubules within K-fibers; second, resolving bridging fibers in the mitotic spindle; third, visualizing membranes in mitotic cells, relative to the spindle apparatus; and fourth, volumetric analysis of kinetochores. Our workflow also includes new computational tools for exploring the spatial arrangement of microtubules within the mitotic spindle. We use these tools to show that microtubule order in mitotic spindles is sensitive to the level of TACC3 on the spindle.

The dichotomy index (I < O), a quantitative estimate of the circadian regulation of daytime activity and sleep, predicted overall cancer survival and emergency hospitalization, supporting its integration in a mHealth platform. Modifiable causes of I < O deterioration below 97.5%—(I < O)low—were sought in 25 gastrointestinal cancer patients and 33 age- and sex-stratified controls. Rest-activity and temperature were tele-monitored with a wireless chest sensor, while daily activities, meals, and sleep were self-reported for one week. Salivary cortisol rhythm and dim light melatonin onset (DLMO) were determined. Circadian parameters were estimated using Hidden Markov modelling, and spectral analysis. Actionable predictors of (I < O)low were identified through correlation and regression analyses. Median compliance with protocol exceeded 95%. Circadian disruption—(I < O)low—was identified in 13 (52%) patients and four (12%) controls (p = 0.002). Cancer patients with (I < O)low had lower median activity counts, worse fragmented sleep, and an abnormal or no circadian temperature rhythm compared to patients with I < O exceeding 97.5%—(I < O)high—(p < 0.012). Six (I < O)low patients had newly-diagnosed sleep conditions. Altered circadian coordination of rest-activity and chest surface temperature, physical inactivity, and irregular sleep were identified as modifiable determinants of (I < O)low. Circadian rhythm and sleep tele-monitoring results support the design of specific interventions to improve outcomes within a patient-centered systems approach to health care.