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Autophagy is an important stress response pathway responsible for the removal and recycling of damaged or redundant cytosolic constituents. Mitochondrial damage triggers selective mitochondrial autophagy (mitophagy), mediated by a variety of response factors including the Pink1/Parkin system. Using human retinal pigment epithelial cells stably expressing autophagy and mitophagy reporters, we have conducted parallel screens of regulators of endoplasmic reticulum (ER) and mitochondrial morphology and function contributing to starvation-induced autophagy and damage-induced mitophagy. These screens identified the ER chaperone and Ca2+ flux modulator, sigma non-opioid intracellular receptor 1 (SIGMAR1), as a regulator of autophagosome expansion during starvation. Screens also identified phosphatidyl ethanolamine methyl transferase (PEMT) and the IP3-receptors (IP3Rs) as mediators of Parkin-induced mitophagy. Further experiments suggested that IP3R-mediated transfer of Ca2+ from the ER lumen to the mitochondrial matrix via the mitochondrial Ca2+ uniporter (MCU) primes mitochondria for mitophagy. Importantly, recruitment of Parkin to damaged mitochondria did not require IP3R-mediated ER-to-mitochondrial Ca2+ transfer, but mitochondrial clustering downstream of Parkin recruitment was impaired, suggesting involvement of regulators of mitochondrial dynamics and/or transport. Our data suggest that Ca2+ flux between ER and mitochondria at presumed ER/mitochondrial contact sites is needed both for starvation-induced autophagy and for Parkin-mediated mitophagy, further highlighting the importance of inter-organellar communication for effective cellular homeostasis.

The systems approach to training has generally been applied to short-cycle, operative craft skills tasks. The system is more difficult to apply to complex, non-reptitive supervisory and managerial skills without considerable cost. This article reports the first part of a study to try to overcome this problem. (SE)

A representative of the Royal Air Force School of Education and a Field Training Advisor to the Civil Air Transport Industry Training Board continue the description of their simplified approach to job analysis. (LY)

This systems approach was developed for use in instructor training in the Royal Air Force's education program, but in general terms it is applicable to all branches of industry. (MF)

Summary Background & aims Measuring the nutritional status of trace elements in plasma is invalidated in the presence of a systemic inflammatory response. We examined the potential of erythrocytes to assess copper, zinc and selenium status in such situations. Methods Venous blood samples were withdrawn pre-operatively and at 12, 24, 48, 72 and 168 h post-operatively from 11 patients (6 males and 5 females) who were admitted for elective knee arthroplasty. C-reactive protein, albumin, copper, zinc, selenium and iron were measured in plasma and erythrocytes. Results Plasma zinc and selenium concentrations fell significantly: 95% confidence intervals (CI)=−32% to −44% and −22% to −36%, respectively. Copper concentrations fell transiently and then increased significantly: CI=12–43%. No significant changes were seen in trace element concentrations in erythrocytes expressed either as a ratio of haemoglobin or iron concentration. Erythrocyte iron levels correlated significantly with haemoglobin (r=0.93). Conclusions Plasma concentrations of copper, zinc and selenium are unreliable markers of status in patients with an acute inflammatory response. Erythrocyte concentrations of these trace elements may provide a more reliable measure in long-term studies of patients with a chronic systemic inflammatory response. Iron can be used instead of haemoglobin as the denominator when expressing erythrocyte concentrations of trace elements.

The Cold Dark Matter (CDM) model, wherein the dark matter is treated as a pressureless perfect fluid, provides a good fit to galactic and cosmological data. With the advent of precision cosmology, it should be asked whether this simplest model needs to be extended, and whether doing so could improve our understanding of the properties of dark matter. One established parameterisation for generalising the CDM fluid is the Generalised Dark Matter (GDM) model, in which dark matter is an imperfect fluid with pressure and shear viscosity that fulfill certain closure equations. We investigate these closure equations and the three new parametric functions they contain: the background equation of state w, the speed of sound c_s^2 and the viscosity c_{vis}^2. Taking these functions to be constant parameters, we analyse an exact solution of the perturbed Einstein equations in a GDM-dominated universe and discuss the main effects of the three parameters on the Cosmic Microwave Background (CMB). Our analysis suggests that the CMB alone is not able to distinguish between the GDM sound speed and viscosity parameters, but that other observables, such as the matter power spectrum, are required to break this degeneracy. We also consider other descriptions of imperfect fluids that have a non-perturbative definition and relate these to the GDM model. In particular, we consider scalar fields, an effective field theory (EFT) of fluids, an EFT of Large Scale Structure, non-equilibrium thermodynamics and tightly-coupled fluids. These descriptions could be used to extend the GDM model into the nonlinear regime of structure formation, which is necessary if the wealth of data available on those scales is to be employed in constraining the model. We also derive the initial conditions for adiabatic and isocurvature perturbations and provide the result in a form ready for implementation in Einstein-Boltzmann solvers., 37 pages, 10 figures, minor changes to match to match Phys. Rev. D 94, 043512 (2016)

We examine how the properties of dark matter, parameterized by an equation-of-state parameter w and two perturbative generalized dark matter (GDM) parameters, c 2 s (the sound speed) and (the viscosity), are constrained by existing cosmological data, particularly the Planck 2015 data release. We find that the GDM parameters are consistent with zero, and are strongly constrained, showing no evidence for extending the model of dark matter beyond the cold dark matter (CDM) paradigm. The equation of state of dark matter is constrained to be within -0.000896 < w < 0.00238 at the 99.7% confidence level (CL), which is several times stronger than constraints found previously using WMAP data. The parameters c 2 s and are constrained to be less than 3.21 × 10-6 and 6.06 ×10-6 respectively at the 99.7% CL. The inclusion of the GDM parameters does significantly affect the error bars on several ΛCDM parameters, notably the dimensionless dark matter density ω g and the derived parameters σ 8 and H 0. This can be partially alleviated with the inclusion of data constraining the expansion history of the universe. © 2016. The American Astronomical Society. All rights reserved. 830 2 Cited By :4

SummaryBackgroundHalf the epidemiological studies with information about menopausal hormone therapy and ovarian cancer risk remain unpublished, and some retrospective studies could have been biased by selective participation or recall. We aimed to assess with minimal bias the effects of hormone therapy on ovarian cancer risk.MethodsIndividual participant datasets from 52 epidemiological studies were analysed centrally. The principal analyses involved the prospective studies (with last hormone therapy use extrapolated forwards for up to 4 years). Sensitivity analyses included the retrospective studies. Adjusted Poisson regressions yielded relative risks (RRs) versus never-use.FindingsDuring prospective follow-up, 12 110 postmenopausal women, 55% (6601) of whom had used hormone therapy, developed ovarian cancer. Among women last recorded as current users, risk was increased even with

Background Half the epidemiological studies with information about menopausal hormone therapy and ovarian cancer risk remain unpublished, and some retrospective studies could have been biased by selective participation or recall. We aimed to assess with minimal bias the effects of hormone therapy on ovarian cancer risk. Methods Individual participant datasets from 52 epidemiological studies were analysed centrally. The principal analyses involved the prospective studies (with last hormone therapy use extrapolated forwards for up to 4 years). Sensitivity analyses included the retrospective studies. Adjusted Poisson regressions yielded relative risks (RRs) versus never-use. Findings During prospective follow-up, 12 110 postmenopausal women, 55% (6601) of whom had used hormone therapy, developed ovarian cancer. Among women last recorded as current users, risk was increased even with