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Demand responsive programs - an emerging resource for competitive electricity markets?
The restructuring of regional electricity markets in the U.S. has been accompanied by numerous problems, including generation capacity shortages, transmission congestion, wholesale price volatility, and reduced system reliability. These problems have created significant new opportunities for technologies and business approaches that allow load serving entities and other aggregators, to control and manage the load patterns of their wholesale or retail end-users. These technologies and business approaches for manipulating end-user load shapes are known as Load Management or, more recently, Demand Responsive programs. Lawrence Berkeley National Laboratory (LBNL) is conducting case studies on innovative demand responsive programs and presents preliminary results for five case studies in this paper. These case studies illustrate the diversity of market participants and range of technologies and business approaches and focus on key program elements such as target markets, market segmentation and participation results; pricing scheme; dispatch and coordination; measurement, verification, and settlement; and operational results where available
Measurement error in a multi-level analysis of air pollution and health: a simulation study.
BACKGROUND: Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health. METHODS: Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO2 and PM10 concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally. RESULTS: In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1. CONCLUSION: While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models
Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses
In this paper, we systematically investigate both the synfire propagation and
firing rate propagation in feedforward neuronal network coupled in an
all-to-all fashion. In contrast to most earlier work, where only reliable
synaptic connections are considered, we mainly examine the effects of
unreliable synapses on both types of neural activity propagation in this work.
We first study networks composed of purely excitatory neurons. Our results show
that both the successful transmission probability and excitatory synaptic
strength largely influence the propagation of these two types of neural
activities, and better tuning of these synaptic parameters makes the considered
network support stable signal propagation. It is also found that noise has
significant but different impacts on these two types of propagation. The
additive Gaussian white noise has the tendency to reduce the precision of the
synfire activity, whereas noise with appropriate intensity can enhance the
performance of firing rate propagation. Further simulations indicate that the
propagation dynamics of the considered neuronal network is not simply
determined by the average amount of received neurotransmitter for each neuron
in a time instant, but also largely influenced by the stochastic effect of
neurotransmitter release. Second, we compare our results with those obtained in
corresponding feedforward neuronal networks connected with reliable synapses
but in a random coupling fashion. We confirm that some differences can be
observed in these two different feedforward neuronal network models. Finally,
we study the signal propagation in feedforward neuronal networks consisting of
both excitatory and inhibitory neurons, and demonstrate that inhibition also
plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience
(published
Observation of Bose-Einstein Condensation in a Strong Synthetic Magnetic Field
Extensions of Berry's phase and the quantum Hall effect have led to the
discovery of new states of matter with topological properties. Traditionally,
this has been achieved using gauge fields created by magnetic fields or spin
orbit interactions which couple only to charged particles. For neutral
ultracold atoms, synthetic magnetic fields have been created which are strong
enough to realize the Harper-Hofstadter model. Despite many proposals and major
experimental efforts, so far it has not been possible to prepare the ground
state of this system. Here we report the observation of Bose-Einstein
condensation for the Harper-Hofstadter Hamiltonian with one-half flux quantum
per lattice unit cell. The diffraction pattern of the superfluid state directly
shows the momentum distribution on the wavefuction, which is gauge-dependent.
It reveals both the reduced symmetry of the vector potential and the twofold
degeneracy of the ground state. We explore an adiabatic many-body state
preparation protocol via the Mott insulating phase and observe the superfluid
ground state in a three-dimensional lattice with strong interactions.Comment: 6 pages, 5 figures. Supplement: 6 pages, 4 figure
Exploring 4D Quantum Hall Physics with a 2D Topological Charge Pump
The discovery of topological states of matter has profoundly augmented our
understanding of phase transitions in physical systems. Instead of local order
parameters, topological phases are described by global topological invariants
and are therefore robust against perturbations. A prominent example thereof is
the two-dimensional integer quantum Hall effect. It is characterized by the
first Chern number which manifests in the quantized Hall response induced by an
external electric field. Generalizing the quantum Hall effect to
four-dimensional systems leads to the appearance of a novel non-linear Hall
response that is quantized as well, but described by a 4D topological invariant
- the second Chern number. Here, we report on the first observation of a bulk
response with intrinsic 4D topology and the measurement of the associated
second Chern number. By implementing a 2D topological charge pump with
ultracold bosonic atoms in an angled optical superlattice, we realize a
dynamical version of the 4D integer quantum Hall effect. Using a small atom
cloud as a local probe, we fully characterize the non-linear response of the
system by in-situ imaging and site-resolved band mapping. Our findings pave the
way to experimentally probe higher-dimensional quantum Hall systems, where new
topological phases with exotic excitations are predicted
Estimating the evidence of selection and the reliability of inference in unigenic evolution
<p>Abstract</p> <p>Background</p> <p>Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.</p> <p>Results</p> <p>We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease <monospace>I-Bmol</monospace>, comparing our predictions with the results of existing methods.</p> <p>Conclusions</p> <p>Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with <monospace>I-Bmol</monospace> proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.</p> <p>Availability</p> <p>Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at <url>http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution</url>.</p
Bioactivation of Trimethoprim to Protein-Reactive Metabolites in Human Liver Microsomes
ABSTRACT The formation of drug-protein adducts via metabolic activation and covalent binding may stimulate an immune response or may result in direct cell toxicity. Protein covalent binding is a potentially pivotal step in the development of idiosyncratic adverse drug reactions (IADRs). Trimethoprim (TMP)-sulfamethoxazole (SMX) is a combination antibiotic that commonly causes IADRs. Recent data suggest that the contribution of the TMP component of TMP-SMX to IADRs may be underappreciated. We previously demonstrated that TMP is bioactivated to chemically reactive intermediates that can be trapped in vitro by N-acetyl cysteine (NAC), and we have detected TMP-NAC adducts (i.e., mercapturic acids) in the urine of patients taking TMP-SMX. However, the occurrence and extent of TMP covalent binding to proteins was unknown
Spatio-temporal Models of Lymphangiogenesis in Wound Healing
Several studies suggest that one possible cause of impaired wound healing is
failed or insufficient lymphangiogenesis, that is the formation of new
lymphatic capillaries. Although many mathematical models have been developed to
describe the formation of blood capillaries (angiogenesis), very few have been
proposed for the regeneration of the lymphatic network. Lymphangiogenesis is a
markedly different process from angiogenesis, occurring at different times and
in response to different chemical stimuli. Two main hypotheses have been
proposed: 1) lymphatic capillaries sprout from existing interrupted ones at the
edge of the wound in analogy to the blood angiogenesis case; 2) lymphatic
endothelial cells first pool in the wound region following the lymph flow and
then, once sufficiently populated, start to form a network. Here we present two
PDE models describing lymphangiogenesis according to these two different
hypotheses. Further, we include the effect of advection due to interstitial
flow and lymph flow coming from open capillaries. The variables represent
different cell densities and growth factor concentrations, and where possible
the parameters are estimated from biological data. The models are then solved
numerically and the results are compared with the available biological
literature.Comment: 29 pages, 9 Figures, 6 Tables (39 figure files in total
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