Bivariate random-effects meta-analysis and the estimation of between-study correlation

BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (ρ(B)). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates ρ(B )as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on [Formula: see text] (B). Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating ρ(B). CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Climate change effects on people’s livelihood

Generally climate is defined as the long-term average weather conditions of a particular place, region, or the world. Key climate variables include surface conditions such as temperature, precipitation, and wind. The Intergovernmental Panel on Climate Change (IPCC) broadly defined climate change as any change in the state of climate which persists for extended periods, usually for decades or longer (Allwood et al. 2014). Climate change may occur due to nature’s both internal and external processes. External process involves anthropogenic emission of greenhouse gases to the atmosphere, and volcanic eruptions. The United Nations Framework Convention on Climate Change (UNFCCC) made a distinction between climate change attributable to human contribution to atmospheric composition and natural climate variability. In its Article 1, the UNFCCC defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” (United Nations 1992, p. 7)

understanding and tackling poverty and vulnerability in mountain livelihoods in the hindu kush himalaya

This chapter critically reviews the existing knowledge on livelihoods, poverty, and vulnerability in the Hindu Kush Himalaya (HKH). Development in mountain areas and the practices of people in these areas are uniquely conditioned by distinct characteristics that we term "mountain specificities". Some of these specificities—such as inaccessibility, fragility, and marginality—constrain development. Others—such as abundant biological diversity, ecological niches, and adaptation mechanisms—present development opportunities for mountain people

Core barrier formation near integer q surfaces in DIII-D

Recent DIII-D experiments have significantly improved the understanding of internal transport barriers (ITBs) that are triggered close to the time when an integer value of the minimum in q is crossed. While this phenomenon has been observed on many tokamaks, the extensive transport and fluctuation diagnostics on DIII-D have permitted a detailed study of the generation mechanisms of q-triggered ITBs as pertaining to turbulence suppression dynamics, shear flows, and energetic particle modes. In these discharges, the evolution of the q profile is measured using motional Stark effect polarimetry and the integer q min crossings are further pinpointed in time by the observation of Alfvén cascades. High time resolution measurements of the ion and electron temperatures and the toroidal rotation show that the start of improved confinement is simultaneous in all three channels, and that this event precedes the traversal of integer q min by 5-20 ms. There is no significant low-frequency magnetohydrodynamic activity prior to or just after the crossing of the integer q min and hence magnetic reconnection is determined not to be the precipitant of the confinement change. Instead, results from the GYRO code point to the effects of zonal flows near low order rational q values as playing a role in ITB triggering. A reduction in local turbulent fluctuations is observed at the start of the temperature rise and, concurrently, an increase in turbulence poloidal flow velocity and flow shear is measured with the beam emission spectroscopy diagnostic. For the case of a transition to an enduring internal barrier the fluctuation level remains at a reduced amplitude. The timing and nature of the temperature, rotation, and fluctuation changes leading to internal barriers suggests transport improvement due to increased shear flow arising from the zonal flow structures. © 2005 American Institute of Physics