The Dirac Sea Contribution To The Energy Of An Electroweak String

We present a systematic determination of the order hbar fermionic energy shift when an electroweak string is perturbed. We show that the combined effect of zero modes, bound states and continuum states is to lower the total fermionic ground state energy of the string when the Higgs instability of the string is excited. The effect of the Dirac sea is thus to destabilise the string. However, this effect can be offset by populating positive energy states. Fermions enhance the stability of an electroweak string with sufficiently populated fermionic bound states.Comment: 57 pages, 11 figure

Multiple Phosphorylation of Rhodopsin and the In Vivo Chemistry Underlying Rod Photoreceptor Dark Adaptation

AbstractDark adaptation requires timely deactivation of phototransduction and efficient regeneration of visual pigment. No previous study has directly compared the kinetics of dark adaptation with rates of the various chemical reactions that influence it. To accomplish this, we developed a novel rapid-quench/mass spectrometry-based method to establish the initial kinetics and site specificity of light-stimulated rhodopsin phosphorylation in mouse retinas. We also measured phosphorylation and dephosphorylation, regeneration of rhodopsin, and reduction of all-trans retinal all under identical in vivo conditions. Dark adaptation was monitored by electroretinography. We found that rhodopsin is multiply phosphorylated and then dephosphorylated in an ordered fashion following exposure to light. Initially during dark adaptation, transduction activity wanes as multiple phosphates accumulate. Thereafter, full recovery of photosensitivity coincides with regeneration and dephosphorylation of rhodopsin

Quark-Gluon Plasma at RHIC and the LHC: Perfect Fluid too Perfect?

Relativistic heavy ion collisions have reached energies that enable the creation of a novel state of matter termed the quark-gluon plasma. Many observables point to a picture of the medium as rapidly equilibrating and expanding as a nearly inviscid fluid. In this article, we explore the evolution of experimental flow observables as a function of collision energy and attempt to reconcile the observed similarities across a broad energy regime in terms of the initial conditions and viscous hydrodynamics. If the initial spatial anisotropies are very similar for all collision energies from 39 GeV to 2.76 TeV, we find that viscous hydrodynamics might be consistent with the level of agreement for v2 of unidentified hadrons as a function of pT . However, we predict a strong collision energy dependence for the proton v2(pT). The results presented in this paper highlight the need for more systematic studies and a re-evaluation of previously stated sensitivities to the early time dynamics and properties of the medium.Comment: 11 pages, 9 figures, submitted to the New Journal of Physics focus issue "Strongly Correlated Quantum Fluids: From Ultracold Quantum Gases to QCD Plasmas

Critical Statistical Charge for Anyonic Superconductivity

We examine a criterion for the anyonic superconductivity at zero temperature in Abelian matter-coupled Chern-Simons gauge field theories in three dimensions. By solving the Dyson-Schwinger equations, we obtain a critical value of the statistical charge for the superconducting phase in a massless fermion-Chern-Simons model.Comment: 11 pages; to appear in Phys Rev

A model of professional self-identity formation in student doctors and dentists: a mixed method study.

BACKGROUND: Professional self-identity [PSI] can be defined as the degree to which an individual identifies with his or her professional group. Several authors have called for a better understanding of the processes by which healthcare students develop their professional identities, and suggested helpful theoretical frameworks borrowed from the social science and psychology literature. However to our knowledge, there has been little empirical work examining these processes in actual healthcare students, and we are aware of no data driven description of PSI development in healthcare students. Here, we report a data driven model of PSI formation in healthcare students. METHODS: We interviewed 17 student doctors and dentists who had indicated, on a tracking questionnaire, the most substantial changes in their PSI. We analysed their perceptions of the experiences that had influenced their PSI, to develop a descriptive model. Both the primary coder and the secondary coder considered the data without reference to the existing literature; i.e. we used a bottom up approach rather than a top down approach. RESULTS: The results indicate that two overlapping frames of reference affect PSI formation: the students' self-perception and their perception of the professional role. They are 'learning' both; neither is static. Underpinning those two learning processes, the following key mechanisms operated: [1] When students are allowed to participate in the professional role they learn by trying out their knowledge and skill in the real world and finding out to what extent they work, and by trying to visualise themselves in the role. [2] When others acknowledge students as quasi-professionals they experience transference and may respond with counter-transference by changing to meet expectations or fulfil a prototype. [3] Students may also dry-run their professional role (i.e., independent practice of professional activities) in a safe setting when invited. CONCLUSIONS: Students' experiences, and their perceptions of those experiences, can be evaluated through a simple model that describes and organises the influences and mechanisms affecting PSI. This empirical model is discussed in the light of prevalent frameworks from the social science and psychology literature

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Flavopiridol Pharmacogenetics: Clinical and Functional Evidence for the Role of SLCO1B1/OATP1B1 in Flavopiridol Disposition

Flavopiridol is a cyclin-dependent kinase inhibitor in phase II clinical development for treatment of various forms of cancer. When administered with a pharmacokinetically (PK)-directed dosing schedule, flavopiridol exhibited striking activity in patients with refractory chronic lymphocytic leukemia. This study aimed to evaluate pharmacogenetic factors associated with inter-individual variability in pharmacokinetics and outcomes associated with flavopiridol therapy.Thirty-five patients who received single-agent flavopiridol via the PK-directed schedule were genotyped for 189 polymorphisms in genes encoding 56 drug metabolizing enzymes and transporters. Genotypes were evaluated in univariate and multivariate analyses as covariates in a population PK model. Transport of flavopiridol and its glucuronide metabolite was evaluated in uptake assays in HEK-293 and MDCK-II cells transiently transfected with SLCO1B1. Polymorphisms in ABCC2, ABCG2, UGT1A1, UGT1A9, and SLCO1B1 were found to significantly correlate with flavopiridol PK in univariate analysis. Transport assay results indicated both flavopiridol and flavopiridol-glucuronide are substrates of the SLCO1B1/OATP1B1 transporter. Covariates incorporated into the final population PK model included bilirubin, SLCO1B1 rs11045819 and ABCC2 rs8187710. Associations were also observed between genotype and response. To validate these findings, a second set of data with 51 patients was evaluated, and overall trends for associations between PK and PGx were found to be consistent.Polymorphisms in transport genes were found to be associated with flavopiridol disposition and outcomes. Observed clinical associations with SLCO1B1 were functionally validated indicating for the first time its relevance as a transporter of flavopiridol and its glucuronide metabolite. A second 51-patient dataset indicated similar trends between genotype in the SLCO1B1 and other candidate genes, thus providing support for these findings. Further study in larger patient populations will be necessary to fully characterize and validate the clinical impact of polymorphisms in SLCO1B1 and other transporter and metabolizing enzyme genes on outcomes from flavopiridol therapy

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks