High performance low-energy buildings

The era of legislation and creditable methods towards producing sustainable buildings is upon us. Yet, a major barrier to achieving environmental responsive design is in the lack of available information at the programming or pre-design phases of a project. The review and evaluation of climate as well as energy-efficient strategies could be difficult to consider at these preliminary stages. Until recently, introducing energy simulation tools at the design stage has been difficult and perhaps next to impossible at a pre-design or programming stage. However, analysis of this sort is essential to &lsquo;green building rating&rsquo; or performance assessment schemes such as LEED (Leadership in Energy and Environmental Design) and BREEAM (Building Research Establishment Environment Assessment Method). This paper discusses the implementation of a particular tool, ENERGY-10, where &lsquo;basecase&rsquo; building defaults are compared to a low-energy case which has applied multiple energy-efficient strategies automatically. An annual hour-by-hour simulation provides a daylighting calculation with a subsequent thermal evaluation. Calculation results provide energy consumption, peak load equipment sizing, a RANK feature of the energy-efficient strategies, reporting of CO2, SO2 and NOx reduction, optimum glazing type as well as excellent graphic output. Consideration is given as to the approach of how such information can be introduced into the building project brief enforcing a low-energyperformance target.<br /

Vocal learning promotes patterned inhibitory connectivity.

Skill learning is instantiated by changes to functional connectivity within premotor circuits, but whether the specificity of learning depends on structured changes to inhibitory circuitry remains unclear. We used slice electrophysiology to measure connectivity changes associated with song learning in the avian analog of primary motor cortex (robust nucleus of the arcopallium, RA) in Bengalese Finches. Before song learning, fast-spiking interneurons (FSIs) densely innervated glutamatergic projection neurons (PNs) with apparently random connectivity. After learning, there was a profound reduction in the overall strength and number of inhibitory connections, but this was accompanied by a more than two-fold enrichment in reciprocal FSI-PN connections. Moreover, in singing birds, we found that pharmacological manipulations of RA's inhibitory circuitry drove large shifts in learned vocal features, such as pitch and amplitude, without grossly disrupting the song. Our results indicate that skill learning establishes nonrandom inhibitory connectivity, and implicates this patterning in encoding specific features of learned movements

Using molecular mechanics to predict bulk material properties of fibronectin fibers

The structural proteins of the extracellular matrix (ECM) form fibers with finely tuned mechanical properties matched to the time scales of cell traction forces. Several proteins such as fibronectin (Fn) and fibrin undergo molecular conformational changes that extend the proteins and are believed to be a major contributor to the extensibility of bulk fibers. The dynamics of these conformational changes have been thoroughly explored since the advent of single molecule force spectroscopy and molecular dynamics simulations but remarkably, these data have not been rigorously applied to the understanding of the time dependent mechanics of bulk ECM fibers. Using measurements of protein density within fibers, we have examined the influence of dynamic molecular conformational changes and the intermolecular arrangement of Fn within fibers on the bulk mechanical properties of Fn fibers. Fibers were simulated as molecular strands with architectures that promote either equal or disparate molecular loading under conditions of constant extension rate. Measurements of protein concentration within micron scale fibers using deep ultraviolet transmission microscopy allowed the simulations to be scaled appropriately for comparison to in vitro measurements of fiber mechanics as well as providing estimates of fiber porosity and water content, suggesting Fn fibers are approximately 75% solute. Comparing the properties predicted by single molecule measurements to in vitro measurements of Fn fibers showed that domain unfolding is sufficient to predict the high extensibility and nonlinear stiffness of Fn fibers with surprising accuracy, with disparately loaded fibers providing the best fit to experiment. This work shows the promise of this microstructural modeling approach for understanding Fn fiber properties, which is generally applicable to other ECM fibers, and could be further expanded to tissue scale by incorporating these simulated fibers into three dimensional network models