Heavy fermions and two loop electroweak corrections to b→s+γb\rightarrow s+\gamma

Applying effective Lagrangian method and on-shell scheme, we analyze the electroweak corrections to the rare decay $b\rightarrow s+\gamma$ from some special two loop diagrams in which a closed heavy fermion loop is attached to the virtual charged gauge bosons or Higgs. At the decoupling limit where the virtual fermions in inner loop are much heavier than the electroweak scale, we verify the final results satisfying the decoupling theorem explicitly when the interactions among Higgs and heavy fermions do not contain the nondecoupling couplings. Adopting the universal assumptions on the relevant couplings and mass spectrum of new physics, we find that the relative corrections from those two loop diagrams to the SM theoretical prediction on the branching ratio of $B\rightarrow X_{_s}\gamma$ can reach 5% as the energy scale of new physics $\Lambda_{_{\rm NP}}=200$ GeV.Comment: 30 pages,4 figure

Intelligent management of on-street parking provision for the autonomous vehicles era

The increasing degree of connectivity between vehicles and infrastructure, and the impending deployment of autonomous vehicles (AV) in urban streets, presents unique opportunities and challenges regarding the on-street parking provision for AVs. This study develops a novel simulation-optimisation approach for intelligent curbside management, based on a metaheuristic technique. The hybrid method balances curb lanes for driving or parking, aiming to minimise the average traffic delay. The model is tested using an idealised grid layout with a range of flow rates and parking policies. Results demonstrate delay decreased by 9%-27% from the benchmark case. Additionally, the traffic delay distribution shows the trade-offs between expanding road capacity and minimising traffic demand through curb management, indicating the interplay between curb parking and traffic management in the AV era

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters

Background\ud Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. \ud \ud Methods\ud We systematically outline sample size formulae (including required number of randomisation units, detectable difference and power) for CRCTs with a fixed number of clusters, to provide a concise summary for both binary and continuous outcomes. Extensions to the case of unequal cluster sizes are provided. \ud \ud Results\ud For trials with a fixed number of equal sized clusters (k), the trial will be feasible provided the number of clusters is greater than the product of the number of individuals required under individual randomisation ($n_i$) and the estimated intra-cluster correlation ($\rho$). So, a simple rule is that the number of clusters ($\kappa$) will be sufficient provided: \ud \ud $\kappa$ > $n_i$ x $\rho$\ud \ud Where this is not the case, investigators can determine the maximum available power to detect the pre-specified difference, or the minimum detectable difference under the pre-specified value for power. \ud \ud Conclusions\ud Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. \ud \u

Emotion-corpus guided lexicons for sentiment analysis on Twitter.

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks

The economic burden of influenza-associated outpatient visits and hospitalizations in China: a retrospective survey

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The Maximal U(1)LU(1)_L Inverse Seesaw from d=5d=5 Operator and Oscillating Asymmetric Sneutrino Dark Matter

The maximal $U(1)_L$ supersymmetric inverse seesaw mechanism (M$L$SIS) provides a natural way to relate asymmetric dark matter (ADM) with neutrino physics. In this paper we point out that, M$L$SIS is a natural outcome if one dynamically realizes the inverse seesaw mechanism in the next-to minimal supersymmetric standard model (NMSSM) via the dimension-five operator $(N)^2S^2/M_*$, with $S$ the NMSSM singlet developing TeV scale VEV; it slightly violates lepton number due to the suppression by the fundamental scale $M_*$, thus preserving $U(1)_L$ maximally. The resulting sneutrino is a distinguishable ADM candidate, oscillating and favored to have weak scale mass. A fairly large annihilating cross section of such a heavy ADM is available due to the presence of singlet.Comment: journal versio

Meteor-ablated Aluminum in the Mesosphere-Lower Thermosphere

The first global atmospheric model (WACCM-Al) of meteor-ablated aluminum was constructed from three components: The Whole Atmospheric Community Climate Model (WACCM6); a meteoric input function for Al derived by coupling an astronomical model of dust sources in the solar system with a chemical meteoric ablation model; and a comprehensive set of neutral, ion-molecule and photochemical reactions relevant to the chemistry of Al in the upper atmosphere. The reaction kinetics of two important reactions that control the rate at which Al+ ions are neutralized were first studied using a fast flow tube with pulsed laser ablation of an Al target, yielding k(AlO+ + CO) = (3.7 ± 1.1) × 10−10 and k(AlO+ + O) = (1.7 ± 0.7) × 10−10 cm3 molecule−1 s−1 at 294 K. The first attempt to observe AlO by lidar was made by probing the bandhead of the B2Σ+(v′ = 0) ← X2Σ+(v″ = 0) transition at λair = 484.23 nm. An upper limit for AlO of 60 cm−3 was determined, which is consistent with a night-time concentration of ∼5 cm−3 estimated from the decay of AlO following rocket-borne grenade releases. WACCM-Al predicts the following: AlO, AlOH and Al+ are the three major species above 80 km; the AlO layer at mid-latitudes peaks at 89 km with a half-width of ∼5 km, and a peak density which increases from a night-time minimum of ∼10 cm−3 to a daytime maximum of ∼60 cm−3; and that the best opportunity for observing AlO is at high latitudes during equinoctial twilight

High-Affinity Naloxone Binding to Filamin A Prevents Mu Opioid Receptor–Gs Coupling Underlying Opioid Tolerance and Dependence

Ultra-low-dose opioid antagonists enhance opioid analgesia and reduce analgesic tolerance and dependence by preventing a G protein coupling switch (Gi/o to Gs) by the mu opioid receptor (MOR), although the binding site of such ultra-low-dose opioid antagonists was previously unknown. Here we show that with approximately 200-fold higher affinity than for the mu opioid receptor, naloxone binds a pentapeptide segment of the scaffolding protein filamin A, known to interact with the mu opioid receptor, to disrupt its chronic opioid-induced Gs coupling. Naloxone binding to filamin A is demonstrated by the absence of [3H]-and FITC-naloxone binding in the melanoma M2 cell line that does not contain filamin or MOR, contrasting with strong [3H]naloxone binding to its filamin A-transfected subclone A7 or to immunopurified filamin A. Naloxone binding to A7 cells was displaced by naltrexone but not by morphine, indicating a target distinct from opioid receptors and perhaps unique to naloxone and its analogs. The intracellular location of this binding site was confirmed by FITC-NLX binding in intact A7 cells. Overlapping peptide fragments from c-terminal filamin A revealed filamin A2561-2565 as the binding site, and an alanine scan of this pentapeptide revealed an essential mid-point lysine. Finally, in organotypic striatal slice cultures, peptide fragments containing filamin A2561-2565 abolished the prevention by 10 pM naloxone of both the chronic morphine-induced mu opioid receptor–Gs coupling and the downstream cAMP excitatory signal. These results establish filamin A as the target for ultra-low-dose opioid antagonists previously shown to enhance opioid analgesia and to prevent opioid tolerance and dependence

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem