1,860 research outputs found
Financial Competitiveness of Macau in Comparison with Other Gaming Destinations
This paper analyzes the financial competitiveness of the Macau gaming industry visa- vis its counterparts in North America and Europe. The analysis covers casino product structure, revenue composition, assets productivity and financial returns of Macau versus those of gaming destinations in North America and Europe. The findings reveal that while Macau is advantageously positioned in terms of assets productivity and financial returns, its casino product structure and revenue composition seem at odds with today\u27s gaming trend. Macau is facing challenges from emerging competitors in Asia. To maintain a stable gaming revenue growth and retain its competitiveness, Macau must modify its casino product structure and revenue composition. Pursuing a more diversified market is a critical step towards the goal
SNARE based peptide linking as an efficient strategy to retarget botulinum neurotoxin’s enzymatic domain to specific neurons using diverse neuropeptides as targeting domains
Many disease states are caused by miss-regulated neurotransmission. A small fraction of these
diseases can currently be treated with botulinum neurotoxin type A (BoNT/A). BoNT/A is
composed of three functional domains – the light chain (Lc) is a zinc metalloprotease that
cleaves intracellular SNAP25 which inhibits exocytosis, the translocation domain (Td) that
enables the export of the light chain from the endosome to the cytosol, and the receptor binding
domain (Rbd) that binds to extracellular gangliosides and synaptic vesicle glycoproteins while
awaiting internalisation [1]. Current endeavours are directed towards retargeting Bont/A as well
as finding safer methods of preparation and administration. Recently, our laboratory has
developed a SNARE based linking strategy to recombine non-toxic BoNT/A fragments into a
functional protein by simple mixing [2]. This SNARE based linking strategy permits the stepwise
assembly of highly stable macromolecular complexes [2,3]. Onto these three SNARE
peptides, diverse functional groups can be attached to the N- or C- terminus by direct synthesis
and/or by genetic design. To enhance the therapeutic potential of BoNT/A, this method enables
the rapid assembly of a large array of neuropeptide-SNAREs to their cognate LcTd-SNARE. A
substitution of the Rbd with various neuropeptide sequences permits a large throughput
combinatorial assay of LcTd to target new cell types. In this study, we have fused LcTd to 3
different Synaptobrevin sequences; we also use a small protein staple, and 26 different
Syntaxin-neuropeptide fusions (permitting the assay of 78 new chimeric LcTd proteins with
modified targeting domains). These neuropeptides such as, but not exclusively, somatostatin
(SS), vasoactive intestinal peptide,
substance P, opioid peptide analogues,
Gonadotropin releasing hormone,
and Arginine Vasopressin,
which natively function through G
protein coupled receptors (GPCR)
can undergo agonist induced
internalisation upon activation.
The ability of our new constructs,
once endocytosed, to inhibit
neurotransmitter release was tested
on different neuronal cell lines
with immunoblotting of endogenous
SNAP25. This cleavage by
Lc reflects the ultimate readout of
the enzyme’s efficacy, which
incorporates the cell surface
binding, internalisation kinetics, translocation of the Lc to the cytosol, and finally the enzymatic
cleavage of SNAP25. Internalisation of the toxins can also be monitored with confocal
microscopy and FACS by the substitution of the staple peptide for a fluorescent homologue.
Figure 1 shows that whole boNT/A (upper left) can have its Rbd replaced with SNARE
peptides, which will fuse together to form highly stable chimeric proteins with an altered
targeting domain (right). Figure 1 also shows 4 different neuropeptide synthaxins in complex,
resolved on SDS-PAGE gel (bottom left lanes 1-4, boiled 1’-4’).
Fig. 1. SNARE-linked botulinum neurotoxins used for the
retargeting of Bont/A.
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Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach
In this paper, a short-term load forecasting approach based network
reconfiguration is proposed in a parallel manner. Specifically, a support
vector regression (SVR) based short-term load forecasting approach is designed
to provide an accurate load prediction and benefit the network reconfiguration.
Because of the nonconvexity of the three-phase balanced optimal power flow, a
second-order cone program (SOCP) based approach is used to relax the optimal
power flow problem. Then, the alternating direction method of multipliers
(ADMM) is used to compute the optimal power flow in distributed manner.
Considering the limited number of the switches and the increasing computation
capability, the proposed network reconfiguration is solved in a parallel way.
The numerical results demonstrate the feasible and effectiveness of the
proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and
Computers 201
Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation
This paper aims to propose a two-step approach for day-ahead hourly
scheduling in a distribution system operation, which contains two operation
costs, the operation cost at substation level and feeder level. In the first
step, the objective is to minimize the electric power purchase from the
day-ahead market with the stochastic optimization. The historical data of
day-ahead hourly electric power consumption is used to provide the forecast
results with the forecasting error, which is presented by a chance constraint
and formulated into a deterministic form by Gaussian mixture model (GMM). In
the second step, the objective is to minimize the system loss. Considering the
nonconvexity of the three-phase balanced AC optimal power flow problem in
distribution systems, the second-order cone program (SOCP) is used to relax the
problem. Then, a distributed optimization approach is built based on the
alternating direction method of multiplier (ADMM). The results shows that the
validity and effectiveness method.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and
Computers 201
Deep Learning Training with Simulated Approximate Multipliers
This paper presents by simulation how approximate multipliers can be utilized
to enhance the training performance of convolutional neural networks (CNNs).
Approximate multipliers have significantly better performance in terms of
speed, power, and area compared to exact multipliers. However, approximate
multipliers have an inaccuracy which is defined in terms of the Mean Relative
Error (MRE). To assess the applicability of approximate multipliers in
enhancing CNN training performance, a simulation for the impact of approximate
multipliers error on CNN training is presented. The paper demonstrates that
using approximate multipliers for CNN training can significantly enhance the
performance in terms of speed, power, and area at the cost of a small negative
impact on the achieved accuracy. Additionally, the paper proposes a hybrid
training method which mitigates this negative impact on the accuracy. Using the
proposed hybrid method, the training can start using approximate multipliers
then switches to exact multipliers for the last few epochs. Using this method,
the performance benefits of approximate multipliers in terms of speed, power,
and area can be attained for a large portion of the training stage. On the
other hand, the negative impact on the accuracy is diminished by using the
exact multipliers for the last epochs of training.Comment: Presented at: IEEE International Conference on Robotics and
Biomimetics (ROBIO) 2019, Dali, China, December 2019. WINNER OF THE MOZI BEST
PAPER IN AI AWAR
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