Cosmological parameter inference from galaxy clustering: the effect of the posterior distribution of the power spectrum

Citation: Kalus, B., Percival, W. J., & Samushia, L. (2016). YCosmological parameter inference from galaxy clustering: the effect of the posterior distribution of the power spectrum. Monthly Notices of the Royal Astronomical Society, 455(3), 2573-2581. doi:10.1093/mnras/stv2307We consider the shape of the posterior distribution to be used when fitting cosmological models to power spectra measured from galaxy surveys. At very large scales, Gaussian posterior distributions in the power do not approximate the posterior distribution P-R we expect for a Gaussian density field delta(k), even if we vary the covariance matrix according to the model to be tested. We compare alternative posterior distributions with P-R, both mode-by-mode and in terms of expected measurements of primordial non-Gaussianity parametrized by f(NL). Marginalising over a Gaussian posterior distribution P-f with fixed covariance matrix yields a posterior mean value of f(NL) which, for a data set with the characteristics of Euclid, will be underestimated by Delta f(NL) = 0.4, while for the data release 9 of the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey (BOSS DR9; Ahn et al.) it will be underestimated by Delta f(NL) = 19.1. Adopting a different form of the posterior function means that we do not necessarily require a different covariance matrix for each model to be tested: this dependence is absorbed into the functional form of the posterior. Thus, the computational burden of analysis is significantly reduced

Unbiased contaminant removal for 3D galaxy power spectrum measurements

Citation: Kalus, B., Percival, W. J., Bacon, D. J., & Samushia, L. (2016). Unbiased contaminant removal for 3D galaxy power spectrum measurements. Monthly Notices of the Royal Astronomical Society, 463(1), 467-476. doi:10.1093/mnras/stw2008We assess and develop techniques to remove contaminants when calculating the 3D galaxy power spectrum. We separate the process into three separate stages: (i) removing the contaminant signal, (ii) estimating the uncontaminated cosmological power spectrum and (iii) debiasing the resulting estimates. For (i), we show that removing the best-fitting contaminant (mode subtraction) and setting the contaminated components of the covariance to be infinite (mode deprojection) are mathematically equivalent. For (ii), performing a quadratic maximum likelihood (QML) estimate after mode deprojection gives an optimal unbiased solution, although it requires the manipulation of large matrices (N-mode being the total number of modes), which is unfeasible for recent 3D galaxy surveys. Measuring a binned average of the modes for (ii) as proposed by Feldman, Kaiser & Peacock (FKP) is faster and simpler, but is sub-optimal and gives rise to a biased solution. We present a method to debias the resulting FKP measurements that does not require any large matrix calculations. We argue that the sub-optimality of the FKP estimator compared with the QML estimator, caused by contaminants, is less severe than that commonly ignored due to the survey window

A map-based method for eliminating systematic modes from galaxy clustering power spectra with application to BOSS

We develop a practical methodology to remove modes from a galaxy survey power spectrum that are associated with systematic errors. We apply this to the BOSS CMASS sample, to see if it removes the excess power previously observed beyond the best-fit $\Lambda$CDM model on very large scales. We consider several possible sources of data contamination, and check whether they affect the number of targets that can be observed and the power spectrum measurements. We describe a general framework for how such knowledge can be transformed into template fields. Mode subtraction can then be used to remove these systematic contaminants at least as well as applying corrective weighting to the observed galaxies, but benefits from giving an unbiased power. Even after applying templates for all known systematics, we find a large-scale power excess, but this is reduced compared with that observed using the weights provided by the BOSS team. This excess is at much larger scales than the BAO scale and does not affect the main results of BOSS. However, it will be important for the measurement of a scale-dependent bias due to primordial non-Gaussianity. The excess is beyond that allowed by any simple model of non-Gaussianity matching Planck data, and is not matched in other surveys. We show that this power excess can further be reduced but is still present using "phenomenological" templates, designed to consider further potentially unknown sources of systematic contamination. As all discrepant angular modes can be removed using "phenomenological" templates, the potentially remaining contaminant acts radially.Comment: 19 pages, accepted by MNRA

Effects of phlebotomy-induced reduction of body iron stores on metabolic syndrome: results from a randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Metabolic syndrome (METS) is an increasingly prevalent but poorly understood clinical condition characterized by insulin resistance, glucose intolerance, dyslipidemia, hypertension, and obesity. Increased oxidative stress catalyzed by accumulation of iron in excess of physiologic requirements has been implicated in the pathogenesis of METS, but the relationships between cause and effect remain uncertain. We tested the hypothesis that phlebotomy-induced reduction of body iron stores would alter the clinical presentation of METS, using a randomized trial.</p> <p>Methods</p> <p>In a randomized, controlled, single-blind clinical trial, 64 patients with METS were randomly assigned to iron reduction by phlebotomy (n = 33) or to a control group (n = 31), which was offered phlebotomy at the end of the study (waiting-list design). The iron-reduction patients had 300 ml of blood removed at entry and between 250 and 500 ml removed after 4 weeks, depending on ferritin levels at study entry. Primary outcomes were change in systolic blood pressure (SBP) and insulin sensitivity as measured by Homeostatic Model Assessment (HOMA) index after 6 weeks. Secondary outcomes included HbA1c, plasma glucose, blood lipids, and heart rate (HR).</p> <p>Results</p> <p>SBP decreased from 148.5 ± 12.3 mmHg to 130.5 ± 11.8 mmHg in the phlebotomy group, and from 144.7 ± 14.4 mmHg to 143.8 ± 11.9 mmHg in the control group (difference -16.6 mmHg; 95% CI -20.7 to -12.5; <it>P </it>< 0.001). No significant effect on HOMA index was seen. With regard to secondary outcomes, blood glucose, HbA1c, low-density lipoprotein/high-density lipoprotein ratio, and HR were significantly decreased by phlebotomy. Changes in BP and HOMA index correlated with ferritin reduction.</p> <p>Conclusions</p> <p>In patients with METS, phlebotomy, with consecutive reduction of body iron stores, lowered BP and resulted in improvements in markers of cardiovascular risk and glycemic control. Blood donation may have beneficial effects for blood donors with METS.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01328210">NCT01328210</a></p> <p>Please see related article: <url>http://www.biomedcentral.com/1741-7015/10/53</url></p

Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development

SummaryThe impact of epigenetics on the differentiation of memory T (Tmem) cells is poorly defined. We generated deep epigenomes comprising genome-wide profiles of DNA methylation, histone modifications, DNA accessibility, and coding and non-coding RNA expression in naive, central-, effector-, and terminally differentiated CD45RA+ CD4+ Tmem cells from blood and CD69+ Tmem cells from bone marrow (BM-Tmem). We observed a progressive and proliferation-associated global loss of DNA methylation in heterochromatic parts of the genome during Tmem cell differentiation. Furthermore, distinct gradually changing signatures in the epigenome and the transcriptome supported a linear model of memory development in circulating T cells, while tissue-resident BM-Tmem branched off with a unique epigenetic profile. Integrative analyses identified candidate master regulators of Tmem cell differentiation, including the transcription factor FOXP1. This study highlights the importance of epigenomic changes for Tmem cell biology and demonstrates the value of epigenetic data for the identification of lineage regulators

Proteins on the catwalk: modelling the structural domains of the CCN family of proteins

The CCN family of proteins (CCN1, CCN2, CCN3, CCN4, CCN5 and CCN6) are multifunctional mosaic proteins that play keys roles in crucial areas of physiology such as angiogenesis, skeletal development tumourigenesis, cell proliferation, adhesion and survival. This expansive repertoire of functions comes through a modular structure of 4 discrete domains that act both independently and in concert. How these interactions with ligands and with neighbouring domains lead to the biological effects is still to be explored but the molecular structure of the domains is likely to play an important role in this. In this review we have highlighted some of the key features of the individual domains of CCN family of proteins based on their biological effects using a homology modelling approach

Identification and Validation of Novel Cerebrospinal Fluid Biomarkers for Staging Early Alzheimer's Disease

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the 'preclinical' stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85-0.94 95% confidence interval [CI]) and 0.88 (0.81-0.94 CI), respectively.Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions