Discounting of reward sequences: a test of competing formal models of hyperbolic discounting

Humans are known to discount future rewards hyperbolically in time. Nevertheless, a formal recursive model of hyperbolic discounting has been elusive until recently, with the introduction of the hyperbolically discounted temporal difference (HDTD) model. Prior to that, models of learning (especially reinforcement learning) have relied on exponential discounting, which generally provides poorer fits to behavioral data. Recently, it has been shown that hyperbolic discounting can also be approximated by a summed distribution of exponentially discounted values, instantiated in the μAgents model. The HDTD model and the μAgents model differ in one key respect, namely how they treat sequences of rewards. The μAgents model is a particular implementation of a Parallel discounting model, which values sequences based on the summed value of the individual rewards whereas the HDTD model contains a non-linear interaction. To discriminate among these models, we observed how subjects discounted a sequence of three rewards, and then we tested how well each candidate model fit the subject data. The results show that the Parallel model generally provides a better fit to the human data

Mg II line profiles of the Mira S Carinae

High-dispersion IUE observations obtained to investigate the evolution of the shock structure of the Mira S Carinae (S Car) produced, despite very limited phase coverage, a set of five spectra of the Mg II h and k lines. There is significant emission from both the h and k lines at velocities of -150 km/sec relative to the stellar photosphere. The h-to-k ratio of the Mg II doublet remains below the theoretically predicated values of 2:1 to 1:1, and shows a smooth dependence on the optical phase. Archival studies of other Miras (e.g., R Car) indicate that S Car is not unique in possessing unusual and highly variable Mg II h and k line profiles

Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis

BackgroundAtopic dermatitis (AD; eczema) is characterized by a widespread abnormality in cutaneous barrier function and propensity to inflammation. Filaggrin is a multifunctional protein and plays a key role in skin barrier formation. Loss-of-function mutations in the gene encoding filaggrin (FLG) are a highly significant risk factor for atopic disease, but the molecular mechanisms leading to dermatitis remain unclear.ObjectiveWe sought to interrogate tissue-specific variations in the expressed genome in the skin of children with AD and to investigate underlying pathomechanisms in atopic skin.MethodsWe applied single-molecule direct RNA sequencing to analyze the whole transcriptome using minimal tissue samples. Uninvolved skin biopsy specimens from 26 pediatric patients with AD were compared with site-matched samples from 10 nonatopic teenage control subjects. Cases and control subjects were screened for FLG genotype to stratify the data set.ResultsTwo thousand four hundred thirty differentially expressed genes (false discovery rate, P < .05) were identified, of which 211 were significantly upregulated and 490 downregulated by greater than 2-fold. Gene ontology terms for “extracellular space” and “defense response” were enriched, whereas “lipid metabolic processes” were downregulated. The subset of FLG wild-type cases showed dysregulation of genes involved with lipid metabolism, whereas filaggrin haploinsufficiency affected global gene expression and was characterized by a type 1 interferon–mediated stress response.ConclusionThese analyses demonstrate the importance of extracellular space and lipid metabolism in atopic skin pathology independent of FLG genotype, whereas an aberrant defense response is seen in subjects with FLG mutations. Genotype stratification of the large data set has facilitated functional interpretation and might guide future therapy development

Comparative quasi-static mechanical characterization of fresh and stored porcine trachea specimens

Abstract: Tissues of the upper airways of critically ill patients are particularly vulnerable to mechanical damage associated with the use of ventilators. Ventilation is known to disrupt the structural integrity of respiratory tissues and their function. This damage contributes to the vulnerability of these tissues to infection. We are currently developing tissue models of damage and infection to the upper airways. As part of our studies, we have compared how tissue storage conditions affect mechanical properties of excised respiratory tissues using a quasi-static platform. Data presented here show considerable differences in mechanical responses of stored specimens compared to freshly excised specimens. These data indicate that implementation of storage and maintenance procedures that minimize rapid degradation of tissue structure are essential for retaining the material properties in our tissue trauma models