Three microarray platforms: an analysis of their concordance in profiling gene expression

BACKGROUND: Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25–30 base), long oligonucleotide (50–80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard. RESULTS: The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation), scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values) between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of <0.05 and at expression threshold levels of 1.5 and 2-fold, the agreement among the platforms was very high, ranging from 93% to 100%. CONCLUSION: Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

The impact of home energy efficiency interventions and winter fuel payments on winter- and cold-related mortality and morbidity in England: a natural equipment mixed-methods study

Background England, and the UK more generally, has a large burden of winter- and cold-related mortality/morbidity in comparison with nearby countries in continental Europe. Improving the energy efficiency of the housing stock may help to reduce this, as well as being important for climate change and energy security objectives. Objectives To evaluate the impact of home energy efficiency (HEE) interventions on winter- and cold-related mortality/morbidity, including assessing the impact of winter fuel payments (WFPs) and fuel costs. Design A mixed-methods study – an epidemiological time-series analysis, an analysis of data on HEE interventions, the development and application of modelling methods including a multicriteria decision analysis and an in-depth interview study of householders. Setting England, UK. Participants The population of England. In-depth interviews were conducted with 12 households (2–4 participants each) and 41 individuals in three geographical regions. Interventions HEE interventions. Main outcome measures Mortality, morbidity and intervention-related changes to the home indoor environment. Data sources The Homes Energy Efficiency Database, mortality and hospital admissions data and weather (temperature) data. Results There has been a progressive decline in cold-related deaths since the mid-1970s. Since the introduction of WFPs, the gradient of association between winter cold and mortality [2.00%, 95% confidence interval (CI) 1.74% to 2.28%] per degree Celsius fall in temperature is somewhat weaker (i.e. that the population is less vulnerable to cold) than in earlier years (2.37%, 95% CI 0.22% to 2.53%). There is also evidence that years with above-average fuel costs were associated with higher vulnerability to outdoor cold. HEE measures installed in England in 2002–10 have had a relatively modest impact in improving the indoor environment. The gains in winter temperatures (around +0.09 °C on a day with maximum outdoor temperature of 5 °C) are associated with an estimated annual reduction of ≈280 cold-related deaths in England (an eventual maximum annual impact of 4000 life-years gained), but these impacts may be appreciably smaller than those of changes in indoor air quality. Modelling studies indicate the potential importance of the medium- and longer-term impacts that HEE measures have on health, which are not observable in short-term studies. They also suggest that HEE improvements of similar annualised cost to current WFPs would achieve greater improvements in health while reducing (rather than increasing) carbon dioxide emissions. In-depth interviews suggest four distinct householder framings of HEE measures (as home improvement, home maintenance, subsidised public goods and contributions to sustainability), which do not dovetail with current ‘consumerist’ national policy and may have implications for the uptake of HEE measures. Limitations The quantification of intervention impacts in this national study is reliant on various indirect/model-based assessments. Conclusions Larger-scale changes are required to the housing stock in England if the full potential benefits for improving health and for reaching increasingly important climate change mitigation targets are to be realised. Future work Studies based on data linkage at individual dwelling level to examine health impacts. There is a need for empirical assessment of HEE interventions on indoor air quality. Funding This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 6, No. 11. See the NIHR Journals Library website for further project information. </jats:sec

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year

The dialect dictionary

In this chapter, the making of dialect dictionaries is discussed. We dwell on the user-oriented metalexicographical considerations, and on the ensuing macrostructural and microstructural options. Special attention is devoted to fieldwork procedures for unwritten language varieties. We try and answer the basic questions of field work: What?, Where?, Who?, How? And How much

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe