Time Changes with the Embodiment of Another’s Body Posture

The aim of the present study was to investigate whether the perception of presentation durations of pictures of different body postures was distorted as function of the embodied movement that originally produced these postures. Participants were presented with two pictures, one with a low-arousal body posture judged to require no movement and the other with a high-arousal body posture judged to require considerable movement. In a temporal bisection task with two ranges of standard durations (0.4/1.6 s and 2/8 s), the participants had to judge whether the presentation duration of each of the pictures was more similar to the short or to the long standard duration. The results showed that the duration was judged longer for the posture requiring more movement than for the posture requiring less movement. However the magnitude of this overestimation was relatively greater for the range of short durations than for that of longer durations. Further analyses suggest that this lengthening effect was mediated by an arousal effect of limited duration on the speed of the internal clock system

When Regions Collide: In What Sense a New ‘Regional Problem’?

The definitive, peer-reviewed and edited version of this article is published in Environment and Planning A, vol 46, part 10, pp. 2332-2352, 2014, DOI: 10.1068/a130341pGoing beyond the territorial/relational divide in regional studies requires researchers to do more than examine the extent to which territoriality and relationality are complementary alternatives. The variety of networked regional spaces means it is intellectually unsustainable to simply relate a single networked regional space to territory– scale without first considering how networked regional spaces interact. Illustrated through the experience of Germany, our paper demonstrates that interaction between different networked regional spaces (eg, city-regions and cross-border regions) is resulting in new networked regional imaginaries (eg, cross-border metropolitan regions). Its overall aim is to show that the production of entirely new networked spaces can assist in overcoming the contradictions present in one configuration of regions, but this only serves to create a new ‘regional problem’ requiring ever more complex configurations of regions

CRY1‐CBS binding regulates circadian clock function and metabolism

Circadian rhythms are generated by interlocked transcription-translation feedback loops that establish cell-autonomous biological timing of ∼24 h. Mutations in core clock genes that alter their stability or affinity for one another lead to changes in circadian period. The human CRY1Δ11 mutant lengthens circadian period to cause delayed sleep phase disorder (DSPD), characterized by a very late onset of sleep. CRY1 is a repressor that binds to the transcription factor CLOCK:BMAL1 to inhibit its activity and close the core feedback loop. We previously showed how the PHR (photolyase homology region) domain of CRY1 interacts with distinct sites on CLOCK and BMAL1 to sequester the transactivation domain from coactivators. However, the Δ11 variant alters an intrinsically disordered tail in CRY1 downstream of the PHR. We show here that the CRY1 tail, and in particular the region encoded by exon 11, modulates the affinity of the PHR domain for CLOCK:BMAL1. The PHR-binding epitope in exon 11 is necessary and sufficient to disrupt the interaction between CRY1 and the subunit CLOCK. Moreover, PHR-tail interactions are conserved in the paralog CRY2 and reduced when either CRY is bound to the circadian corepressor PERIOD2. Discovery of this autoregulatory role for the mammalian CRY1 tail and conservation of PHR-tail interactions in both mammalian cryptochromes highlights functional conservation with plant and insect cryptochromes, which also utilize PHR-tail interactions to reversibly control their activity

Machine Learning Helps Identify CHRONO as a Circadian Clock Component

<div><p>Over the last decades, researchers have characterized a set of “clock genes” that drive daily rhythms in physiology and behavior. This arduous work has yielded results with far-reaching consequences in metabolic, psychiatric, and neoplastic disorders. Recent attempts to expand our understanding of circadian regulation have moved beyond the mutagenesis screens that identified the first clock components, employing higher throughput genomic and proteomic techniques. In order to further accelerate clock gene discovery, we utilized a computer-assisted approach to identify and prioritize candidate clock components. We used a simple form of probabilistic machine learning to integrate biologically relevant, genome-scale data and ranked genes on their similarity to known clock components. We then used a secondary experimental screen to characterize the top candidates. We found that several physically interact with known clock components in a mammalian two-hybrid screen and modulate <i>in vitro</i> cellular rhythms in an immortalized mouse fibroblast line (NIH 3T3). One candidate, Gene Model 129, interacts with BMAL1 and functionally represses the key driver of molecular rhythms, the BMAL1/CLOCK transcriptional complex. Given these results, we have renamed the gene CHRONO (computationally highlighted repressor of the network oscillator). Bi-molecular fluorescence complementation and co-immunoprecipitation demonstrate that CHRONO represses by abrogating the binding of BMAL1 to its transcriptional co-activator CBP. Most importantly, CHRONO knockout mice display a prolonged free-running circadian period similar to, or more drastic than, six other clock components. We conclude that CHRONO is a functional clock component providing a new layer of control on circadian molecular dynamics.</p></div

Integration of core clock features.

<p>(A) List of exemplar core clock genes used as example models of core clock components. (B–E) Metric functions describing core clock features were generated from published data. Distributions of these metrics among nonclock genes (left panel) and exemplar clock genes (center panel) were used to construct evidence factors (right panel). (B) Cycling was evaluated using time-course microarray data from liver, pituitary, and NIH 3T3 cells. (C) Circadian disturbance metric quantifies the influence of RNAi-mediated gene knockdown on circadian dynamics in the U2OS model system. (D) The interaction metric counts the number of interactions inferred between each gene and the exemplar set of core clock genes. (E) The tissue ubiquity scores were taken from an EST database. (F) List of 20 genes most likely to have a core circadian function as determined by evidence factor integration. Genes highlighted in blue were included in the exemplar training set. Genes highlighted in purple were not in the training set but have been identified as having a role in the circadian clock. <i>Gm129</i> was selected for further characterization.</p