Cosmological constraints from galaxy clustering

In this manuscript I review the mathematics and physics that underpins recent work using the clustering of galaxies to derive cosmological model constraints. I start by describing the basic concepts, and gradually move on to some of the complexities involved in analysing galaxy redshift surveys, focusing on the 2dF Galaxy Redshift Survey (2dFGRS) and the Sloan Digital Sky survey (SDSS). Difficulties within such an analysis, particularly dealing with redshift space distortions and galaxy bias are highlighted. I then describe current observations of the CMB fluctuation power spectrum, and consider the importance of measurements of the clustering of galaxies in light of recent experiments. Finally, I provide an example joint analysis of the latest CMB and large-scale structure data, leading to a set of parameter constraints.Comment: 30 pages, 13 figures. Lecture given at Third Aegean Summer School, The invisible universe: Dark matter and Dark energ

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Mechanistic coupling of protease signaling and initiation of coagulation by tissue factor

The crucial role of cell signaling in hemostasis is clearly established by the action of the downstream coagulation protease thrombin that cleaves platelet-expressed G-protein-coupled protease activated receptors (PARs). Certain PARs are cleaved by the upstream coagulation proteases factor Xa (Xa) and the tissue factor (TF)–factor VIIa (VIIa) complex, but these enzymes are required at high nonphysiological concentrations and show limited recognition specificity for the scissile bond of target PARs. However, defining a physiological mechanism of PAR activation by upstream proteases is highly relevant because of the potent anti-inflammatory in vivo effects of inhibitors of the TF initiation complex. Activation of substrate factor X (X) by the TF–VIIa complex is here shown to produce enhanced cell signaling in comparison to the TF–VIIa complex alone, free Xa, or Xa that is generated in situ by the intrinsic activation complex. Macromolecular assembly of X into a ternary complex of TF–VIIa–X is required for proteolytic conversion to Xa, and product Xa remains transiently associated in a TF–VIIa–Xa complex. By trapping this complex with a unique inhibitor that preserves Xa activity, we directly show that Xa in this ternary complex efficiently activates PAR-1 and -2. These experiments support the concept that proinflammatory upstream coagulation protease signaling is mechanistically coupled and thus an integrated part of the TF–VIIa-initiated coagulation pathway, rather than a late event during excessive activation of coagulation and systemic generation of proteolytic activity

Protein S stimulates inhibition of the tissue factor pathway by tissue factor pathway inhibitor

Tissue factor (TF) plays an important role in hemostasis, inflammation, angiogenesis, and the pathophysiology of atherosclerosis and cancer. In this article we uncover a mechanism in which protein S, which is well known as the cofactor of activated protein C, specifically inhibits TF activity by promoting the interaction between full-length TF pathway inhibitor (TFPI) and factor Xa (FXa). The stimulatory effect of protein S on FXa inhibition by TFPI is caused by a 10-fold reduction of the K(i) of the FXa/TFPI complex, which decreased from 4.4 nM in the absence of protein S to 0.5 nM in the presence of protein S. This decrease in K(i) not only results in an acceleration of the feedback inhibition of the TF-mediated coagulation pathway, but it also brings the TFPI concentration necessary for effective FXa inhibition well within range of the concentration of TFPI in plasma. This mechanism changes the concept of regulation of TF-induced thrombin formation in plasma and demonstrates that protein S and TFPI act in concert in the inhibition of TF activity. Our data suggest that protein S deficiency not only increases the risk of thrombosis by impairing the protein C system but also by reducing the ability of TFPI to down-regulate the extrinsic coagulation pathway