Thermally operated valve Patent

Gas valve operated by thermally expanding and contracting devic

Reduced empathic concern leads to utilitarian moral judgments in trait alexithymia

Recent research with moral dilemmas supports dual-process model of moral decision making. This model posits two different paths via which people can endorse utilitarian solution that requires personally harming someone in order to achieve the greater good (e.g., killing one to save five people): (i) weakened emotional aversion to the prospect of harming someone due to reduced empathic concern for the victim; (ii) enhanced cognition which supports cost-benefit analysis and countervails the prepotent emotional aversion to harm. Direct prediction of this model would be that personality traits associated with reduced empathy would show higher propensity to endorse utilitarian solutions. As per this prediction, we found that trait alexithymia, which is well-known to have deficits in empathy, was indeed associated with increased utilitarian tendencies on emotionally aversive personal moral dilemmas and this was due to reduced empathic concern for the victim. Results underscore the importance of empathy for moral judgments in harm/care domain of morality

Orbiting quarantine facility. The Antaeus report

A mission plan for the Orbiting Quarantine Facility (OQF) is presented. Coverage includes system overview, quarantine and protocol, the laboratory, support systems, cost analysis and possible additional uses of the OQF

Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

<p>Abstract</p> <p>Background</p> <p>Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip^®.</p> <p>Results</p> <p>We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip^®processed data. Datasets with unbalanced numbers of up and down regulated genes seem to be particularly susceptible to this type of intensity-dependent technical variation. Unbalanced gene regulation is common when studying cancer samples or genetically manipulated animal models and preservation of this biologically relevant information, while removing technical variation has not been well addressed in the literature. We propose a method based on using rank-invariant, endogenous transcripts as reference points for normalization (GRSN). While the use of rank-invariant transcripts has been described previously, we have added to this concept by the creation of a global rank-invariant set of transcripts used to generate a robust average reference that is used to normalize all samples within a dataset. The global rank-invariant set is selected in an iterative manner so as to preserve unbalanced gene expression. Moreover, our method works well as an overlay that can be applied to data already processed with other probe set summary methods. We demonstrate that this additional normalization step at the "probe set level" effectively corrects a specific type of technical variation that often distorts samples in datasets.</p> <p>Conclusion</p> <p>We have developed a simple post-processing tool to help detect and correct non-linear technical variation in microarray data and demonstrate how it can reduce technical variation and improve the results of downstream statistical gene selection and pathway identification methods.</p