Uncovering the Genetic Landscape for Multiple Sleep-Wake Traits

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior

The short- and long-run inconsistency of the expansionary austerity theory: a post-Keynesian/Evolutionist critique

This work provides a critical analysis of the expansionary austerity theory (EAT). The focus is on the theoretical weaknesses of the EAT—the extreme circumstances and fragile assumptions under which expansionary consolidations might take place. The paper presents a simple theoretical model based on both the post-Keynesian and the evolutionary/institutionalist schools. First, it shows that well-designed austerity measures hardly trigger short-run economic expansions in the context of expected long-lasting consolidation plans dealing with remarkably high debt-to-GDP ratios, when the so-called “financial channel” is not operative (i.e. in the context of monetarily sovereign economies), or when the degree of export responsiveness to internal devaluation is low. Even in the context of non–monetarily sovereign countries (e.g. members of the eurozone), austerity’s effectiveness crucially depends on its highly disputable capacity to immediately stabilize fiscal variables. The paper then analyses some possible long-run economic dynamics. Path dependency and cumulativeness make the short-run effects of fiscal consolidation elements of paramount importance to (hopefully) obtaining any medium-to-long-run benefit. Should these effects be even slightly contractionary, short-run costs can breed an endless spiral of recession and ballooning debt in the long run. If so, in the case of non–monetarily sovereign countries debt forgiveness may emerge as the ultimate solution to restore economic soundness. Alternatively, institutional innovations like those adopted since mid-2012 by the European Central Bank are required to stabilize the economy, even though they are unlikely to restore rapid growth in the absence of more active fiscal stimuli

CHANNEL AND SPECTRUM ESTIMATION FOR SOFTWARE DEFINED RADIO

Software defined radios are rapidly increasing in both research and commercial usage for many different applications. As the number of deployed systems increase, a difficult problem that remains is efficient usage of the Radio-Frequency (RF) spectrum to be shared among all these devices. Two key tasks for the radio to perform here include spectral estimation of the RF environment and channel estimation of the communication channel for which the data will be transmitted. These two steps are linked as the communication channel can change over different portions of the RF-spectrum. In this work, an algorithmic approach is presented for passive and active channel estimation procedures for wideband software-defined radios. The algorithm is comprised of first channel quality estimation followed by communication channel planning to optimize the overall performance.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection