Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss

The association between hypoalbuminemia and poor prognosis in patients with cancer is well recognized. However, the factors that contribute to the fall in albumin concentrations are not well understood. In the present study, we examined the relationship between circulating albumin concentrations, weight loss, the body cell mass (measured using total body potassium), and the presence of an inflammatory response (measured using C- reactive protein) in male patients (n=40) with advanced lung or gastrointestinal cancer. Albumin concentrations were significantly correlated with the percent ideal body weight (r=0.390, p lt 0.05), extent of reported weight loss (r=-0.492, p lt 0.01), percent predicted total body potassium (adjusted for age, height, and weight, r=0.686, p lt 0.001), and logo C-reactive protein concentrations (r=-0.545, p lt 0.001). On multiple regression analysis, the percent predicted total body potassium and log(10) C-reactive protein concentrations accounted for 63% of the variation in albumin concentrations (r(2) = 0.626, p lt 0.001). The interrelationship between albumin, body cell mass, and the inflammatory response is consistent with the concept that the presence of an ongoing inflammatory response contributes to the progressive loss of these vital protein components of the body and the subsequent death of patients with advanced cancer

Quantum computation with realistic magic-state factories

Leading approaches to fault-tolerant quantum computation dedicate a significant portion of the hardware to computational factories that churn out high-fidelity ancillas called magic states. Consequently, efficient and realistic factory design is of paramount importance. Here we present the most detailed resource assessment to date of magic-state factories within a surface code quantum computer, along the way introducing a number of techniques. We show that the block codes of Bravyi and Haah [Phys. Rev. A 86, 052329 (2012)] have been systematically undervalued; we track correlated errors both numerically and analytically, providing fidelity estimates without appeal to the union bound. We also introduce a subsystem code realization of these protocols with constant time and low ancilla cost. Additionally, we confirm that magic-state factories have space-time costs that scale as a constant factor of surface code costs. We find that the magic-state factory required for postclassical factoring can be as small as 6.3 million data qubits, ignoring ancilla qubits, assuming 10^−4 error gates and the availability of long-range interactions

IL-1b and TNF-a induce increased expression of CCL28 by airway epithelial cells via an NFjB-dependent pathway

CCL28 is a mucosal chemokine that attracts eosinophils and T cells via the receptors CCR3 and CCR10. Consequently, it is a candidate mediator of the pathology associated with asthma. This study examined constitutive and induced expression of CCL28 by A549 human airway epithelial-like cells. Real-time RT-PCR and ELISA of cultured cells and supernatants revealed constitutive levels of CCL28 expression to be low, whereas IL-1b and TNF-a, induced signiï¬cantly increased expression. Observations from induced sputum and human airway biopsies supported this. Signal transduction studies revealed that IL-1b and TNF-a stimulation induced NFjB phosphorylation in A549 cells, but antagonist inhibition of NFjB p50âp65 phosphorylation correlated with marked reduction of IL-1b or TNF-a induced CCL28 expression. Together these studies imply a role for CCL28 in the orchestration of airway inï¬ammation, and suggest that CCL28 is one link between microbial insult and the exacerbation of pathologies such as asthma, through an NFjB-dependent mechanism

Study of the inner dust envelope and stellar photosphere of the AGB star R Doradus using SPHERE/ZIMPOL

We use high-angular-resolution images obtained with SPHERE/ZIMPOL to study the photosphere, the warm molecular layer, and the inner wind of the close-by oxygen-rich AGB star R Doradus. We present observations in filters V, cntH$\alpha$, and cnt820 and investigate the surface brightness distribution of the star and of the polarised light produced in the inner envelope. Thanks to second-epoch observations in cntH$\alpha$, we are able to see variability on the stellar photosphere. We find that in the first epoch the surface brightness of R Dor is asymmetric in V and cntH$\alpha$, the filters where molecular opacity is stronger, while in cnt820 the surface brightness is closer to being axisymmetric. The second-epoch observations in cntH$\alpha$ show that the morphology of R Dor changes completely in a timespan of 48 days to a more axisymmetric and compact configuration. The polarised intensity is asymmetric in all epochs and varies by between a factor of 2.3 and 3.7 with azimuth for the different images. We fit the radial profile of the polarised intensity using a spherically symmetric model and a parametric description of the dust density profile, $\rho(r)=\rho_\circ r^{-n}$. On average, we find exponents of $- 4.5 \pm 0.5$ that correspond to a much steeper density profile than that of a wind expanding at constant velocity. The dust densities we derive imply an upper limit for the dust-to-gas ratio of $\sim 2\times10^{-4}$ at 5.0 $R_\star$. Given the uncertainties in observations and models, this value is consistent with the minimum values required by wind-driving models for the onset of a wind, of $\sim 3.3\times10^{-4}$. However, if the steep density profile we find extends to larger distances from the star, the dust-to-gas ratio will quickly become too small for the wind of R Dor to be driven by the grains that produce the scattered light.Comment: 10 pages, 8 figures, 4 table

Personal tutoring: a recognition of ‘levelness’ in the support for undergraduates

The changing terrain of higher education in the UK, and particularly the greater diversity of the student body, has undoubtedly led to the need for universities to provide greater support, both from frontline teaching staff and in the provision of extra institutional services. Added to the mix are sectoral concerns for the wellbeing and welfare of the student. It is therefore unsurprising that we are seeing a renewed focus on, and interest in, personal tutoring. Taking a qualitative approach, we set out to explore the needs of undergraduate students, on an event management programme, in relation to personal tutoring. Outlined in this paper are the different senses of personal tutoring as student transition through their course

Impact of resonant magnetic perturbations on the L-H transition on MAST

The impact of resonant magnetic perturbations (RMPs) on the power required to access H-mode is examined experimentally on MAST. Applying RMP in n = 2, 3, 4 and 6 configurations delays the L-H transition at low applied fields and prevents the transition at high fields. The experiment was primarily performed at RMP fields sufficient to cause moderate increases in ELM frequency, fmitigated/ fnatural ∼ 3. To obtain H-mode with RMPs at this field, an increase of injected beam power is required of at least 50% for n = 3 and n = 4 RMP and 100% for n = 6 RMP. In terms of power threshold, this corresponds to increases of at least 20% for n = 3 and n = 4 RMPs and 60% for n = 6 RMPs. This 'RMP affected' power threshold is found to increase with RMP magnitude above a certain minimum perturbed field, below which there is no impact on the power threshold. Extrapolations from these results indicate large increases in the L-H power threshold may be required for discharges requiring large mitigated ELM frequency

Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System