A novel octopamine receptor with preferential expression in <i>Drosophila</i> mushroom bodies

Octopamine is a neuromodulator that mediates diverse physiological processes in invertebrates. In some insects, such as honeybees and fruit flies, octopamine has been shown to be a major stimulator of adenylyl cyclase and to function in associative learning. To identify an octopamine receptor mediating this function in Drosophila, putative biogenic amine receptors were cloned by a novel procedure using PCR and single-strand conformation polymorphism. One new receptor, octopamine receptor in mushroom bodies (OAMB), was identified as an octopamine receptor because human and Drosophila cell lines expressing OAMB showed increased cAMP and intracellular Ca2+ levels after octopamine application. Immunohistochemical analysis using an antibody made to the receptor revealed highly enriched expression in the mushroom body neuropil and the ellipsoid body of central complex, brain areas known to be crucial for olfactory learning and motor control, respectively. The preferential expression of OAMB in mushroom bodies and its capacity to produce cAMP accumulation suggest an important role in synaptic modulation underlying behavioral plasticity

To steal or not to steal: Firm attributes, legal environment, and valuation

Newly released data on corporate governance and disclosure practices reveal wide within-country variation, with the variation increasing as legal environment gets less investor friendly. This paper examines why firms practice high-quality governance when law does not require it; firm attributes that are related to the quality of governance; how the attributes interact with legal environment; and the relation between firm valuation and corporate governance. A simple model, in which a controlling shareholder trades off private benefits of diversion against costs that vary across countries and time, identifies three relevant firm attributes: investment opportunities, external financing, and ownership structure. Using firm-level governance and transparency data on 859 firms in 27 countries, we find that firms with greater growth opportunities, greater needs for external financing, and more concentrated cash flow rights practice higher-quality governance and disclose more. Moreover, firms that score higher in governance and transparency rankings are valued higher in the stock market. Equally important, all these relations are stronger in countries that are less investor friendly, demonstrating that firms do adapt to poor legal environments to establish efficient governance practices.http://deepblue.lib.umich.edu/bitstream/2027.42/39939/3/wp554.pd

Scalable Compression of Deep Neural Networks

Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems. In this paper, we propose a scalable representation of the network parameters, so that different applications can select the most suitable bit rate of the network based on their own storage constraints. Moreover, when a device needs to upgrade to a high-rate network, the existing low-rate network can be reused, and only some incremental data are needed to be downloaded. We first hierarchically quantize the weights of a pre-trained deep neural network to enforce weight sharing. Next, we adaptively select the bits assigned to each layer given the total bit budget. After that, we retrain the network to fine-tune the quantized centroids. Experimental results show that our method can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201

Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment

We present advances in the software engineering design and implementation of the multi-tier run-time system for the General Intensional Programming System (GIPSY) by further unifying the distributed technologies used to implement the Demand Migration Framework (DMF) in order to streamline distributed execution of hybrid intensional-imperative programs using Java.Comment: 11 pages, 3 figure

High H2 Storage of Hexagonal Metal−Organic Frameworks from First-Principles-Based Grand Canonical Monte Carlo Simulations

Stimulated by the recent report by Yaghi and co-workers of hexagonal metal−organic frameworks (MOF) exhibiting reversible binding of up to 7.5 wt % at 77 K and 70 bar for MOF-177 (called here IRMOF-2-24), we have predicted additional trigonal organic linkers, including IRMOF-2-60, which we calculate to bind 9.7 wt % H2 storage at 77 K and 70 bar, the highest known value for 77 K. These calculations are based on grand canonical Monte Carlo (GCMC) simulations using force fields that match accurate quantum mechanical calculations on the binding of H2 to prototypical systems. These calculations were validated by comparison to the experimental loading curve for IRMOF-2-24 at 77K. We then used the theory to predict the effect of doping Li into the hexagonal MOFs, which leads to substantial H2 density even at ambient temperatures. For example, IRMOF-2-96-Li leads to 6.0 wt % H2 storage at 273 K and 100 bar, the first material to attain the 2010 DOE target