Eliminating the Hadronic Uncertainty

The Standard Model Lagrangian requires the values of the fermion masses, the Higgs mass and three other experimentally well-measured quantities as input in order to become predictive. These are typically taken to be $\alpha$, $G_\mu$ and $M_Z$. Using the first of these, however, introduces a hadronic contribution that leads to a significant error. If a quantity could be found that was measured at high energy with sufficient precision then it could be used to replace $\alpha$ as input. The level of precision required for this to happen is given for a number of precisely-measured observables. The $W$ boson mass must be measured with an error of $\pm13$\,MeV, $\Gamma_Z$ to $0.7$\,MeV and polarization asymmetry, $A_{LR}$, to $\pm0.002$ that would seem to be the most promising candidate. The r\^ole of renormalized parameters in perturbative calculations is reviewed and the value for the electromagnetic coupling constant in the $\overline{\rm MS}$ renormalization scheme that is consistent with all experimental data is obtained to be $\alpha^{-1}_{\overline{\rm MS}}(M^2_Z)=128.17$.Comment: 8 pages LaTeX2

A first direct measurement of the intergalactic medium temperature around a quasar at z=6

The thermal state of the intergalactic medium (IGM) provides an indirect probe of both the HI and HeII reionisation epochs. Current constraints on the IGM temperature from the Lya forest are restricted to the redshift range 2<z<4.5, limiting the ability to probe the thermal memory of HI reionisation toward higher redshift. In this work, we present the first direct measurement of the IGM temperature around a z=6 quasar by analysing the Doppler widths of Lya absorption lines in the proximity zone of SDSS J0818+1722. We use a high resolution (R= 40000) Keck/HIRES spectrum in combination with detailed numerical modelling to obtain the temperature at mean density, T_0=23600\pm^5000_6900K (\pm^9200_9300K) at 68 (95) per cent confidence assuming a prior probability 13500K<T_0<38500 K following HI and HeII reionisation. This enables us to place an upper limit on the redshift of HI reionisation, z_H, within 33 comoving Mpc of SDSS J0818+1722. If the quasar reionises the HeII in its vicinity, then in the limit of instantaneous reionisation we infer z_H<9.0 (11.0) at 68 (95) per cent confidence assuming photoheating is the dominant heat source and that HI reionisation is driven by ionising sources with soft spectra, typical of population II stars. If the HI and HeII in the IGM around SDSS J0818+1722 are instead reionised simultaneously by a population of massive metal-free stars, characterised by very hard ionising spectra, we obtain a tighter upper limit of z_H<8.4 (9.4). Initiating reionisation at higher redshifts produces temperatures which are too low with respect to our constraint unless the HI ionising sources or the quasar itself have spectra significantly harder than typically assumed.Comment: 15 pages, 9 figures, accepted to MNRA

Seasonality, phytoplankton succession and the biogeochemical impacts of an autumn storm in the northeast Atlantic Ocean

Phytoplankton chemotaxonomic distributions are examined in conjunction with taxon specific particulate biomass concentrations and phytoplankton abundances to investigate the biogeochemical consequences of the passage of an autumn storm in the northeast Atlantic Ocean. Chemotaxonomy indicated that the phytoplankton community was dominated by nanoplankton (2-20 μ), which on average represented 75±8% of the community. Microplankton (20-200 μ) and picoplankton (<2 μ) represented 21±7% and 4±3% respectively with the microplankton group composed of almost equal proportions of diatoms (53±17%) and dinoflagellates (47±17%). Total chlorophyll-a (TCHLa = CHLa + Divinyl CHLa) concentrations ranged from 22 to 677 ng L-1, with DvCHLa making minor contributions of between <1% and 13% to TCHLa. Higher DvCHLa contributions were seen during the storm, which deepened the surface mixed layer, increased mixed layer nutrient concentrations and vertically mixed the phytoplankton community leading to a post-storm increase in surface chlorophyll concentrations. Picoplankton were rapid initial respondents to the changing conditions with pigment markers showing an abrupt 4-fold increase in proportion but this increase was not sustained post-storm. 19’-HEX, a chemotaxonomic marker for prymnesiophytes, was the dominant accessory pigment pre- and post-storm with concentrations of 48-435 ng L-1, and represented 44% of total carotenoid concentrations. Accompanying scanning electron microscopy results support the pigment-based analysis but also provide detailed insight into the nano- and microplankton communities, which proved to be highly variable between pre-storm and post-storm sampling periods. Nanoplankton remained the dominant size class pre- and post-storm but the microplankton proportion peaked during the period of maximum nutrient and chlorophyll concentrations. Classic descriptions of autumn blooms resulting from storm driven eutrophication events promoting phytoplankton growth in surface waters should be tempered with greater understanding of the role of storm driven vertical reorganization of the water column and of resident phytoplankton communities. Crucially, in this case we observed no change in integrated chlorophyll, particulate organic carbon or biogenic silica concentrations despite also observing a ∼50% increase in surface chlorophyll concentrations which indicated that the surface enhancement in chlorophyll concentrations was most likely fed from below rather than resulting from in situ growth. Though not measured directly there was no evidence of enhanced export fluxes associated with this storm. These observations have implications for the growing practice of using chlorophyll fluorescence from remote platforms to determine ocean productivity late in the annual productivity period and in response to storm mixing

Major Powers and Militarized Conflict

This article attempts to answer the question of why major powers engage in more active foreign policy behaviors than minor powers. It does so by comparing two explanations for the increased conflict propensity of major powers. The first explanation focuses on major powers’ observable capabilities, while the second stresses their different behavior. We incorporate both into an ultimatum model of conflict in which a state’s cost of conflict consists of both observable and behavioral components. Using data from the period from 1870 to 2001, we empirically illustrate the observable and behavioral differences between major and minor powers. We then utilize a decomposition model to assess the relative significance of the two explanations. The results suggest that most of the difference in conflict propensity between major and minor powers can be attributed to observable differences

Chaos and localization in the wavefunctions of complex atoms NdI, PmI and SmI

Wavefunctions of complex lanthanide atoms NdI, PmI and SmI, obtained via multi-configuration Dirac-Fock method, are analyzed for density of states in terms of partial densities, strength functions ($F_k(E)$), number of principal components ($\xi_2(E)$) and occupancies (\lan n_\alpha \ran^E) of single particle orbits using embedded Gaussian orthogonal ensemble of one plus two-body random matrix ensembles [EGOE(1+2)]. It is seen that density of states are in general multi-modal, $F_k(E)$'s exhibit variations as function of the basis states energy and $\xi_2(E)$'s show structures arising from localized states. The sources of these departures from EGOE(1+2) are investigated by examining the partial densities, correlations between $F_k(E)$, $\xi_2(E)$ and \lan n_\alpha \ran^E and also by studying the structure of the Hamiltonian matrices. These studies point out the operation of EGOE(1+2) but at the same time suggest that weak admixing between well separated configurations should be incorporated into EGOE(1+2) for more quantitative description of chaos and localization in NdI, PmI and SmI.Comment: There are 9 figure

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired

Onset of experimental severe cardiac fibrosis is mediated by overexpression of angiotensin-converting enzyme 2

Angiotensin-converting enzyme (ACE) 2 is a recently identified homologue of ACE. There is great interest in the therapeutic benefit for ACE2 overexpression in the heart. However, the role of ACE2 in the regulation of cardiac structure and function, as well as maintenance of systemic blood pressure, remains poorly understood. In cell culture, ACE2 overexpression led to markedly increased myocyte volume, assessed in primary rabbit myocytes. To assess ACE2 function in vivo, we used a recombinant adeno-associated virus 6 delivery system to provide 11-week overexpression of ACE2 in the myocardium of stroke-prone spontaneously hypertensive rats. ACE2, as well as the ACE inhibitor enalapril, significantly reduced systolic blood pressure. However, in the heart, ACE2 overexpression resulted in cardiac fibrosis, as assessed by histological analysis with concomitant deficits in ejection fraction and fractional shortening measured by echocardiography. Furthermore, global gene expression profiling demonstrated the activation of profibrotic pathways in the heart mediated by ACE2 gene delivery. This study demonstrates that sustained overexpression of ACE2 in the heart in vivo leads to the onset of severe fibrosis

Estimating oceanic primary production using vertical irradiance and chlorophyll profiles from ocean gliders in the North Atlantic

An autonomous underwater vehicle (Seaglider) has been used to estimate marine primary production (PP) using a combination of irradiance and fluorescence vertical profiles. This method provides estimates for depth-resolved and temporally evolving PP on fine spatial scales in the absence of ship-based calibrations. We describe techniques to correct for known issues associated with long autonomous deployments such as sensor calibration drift and fluorescence quenching. Comparisons were made between the Seaglider, stable isotope (13C), and satellite estimates of PP. The Seaglider-based PP estimates were comparable to both satellite estimates and stable isotope measurements

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival