A population of ultraviolet-dim protoclusters detected in absorption.

Galaxy protoclusters, which will eventually grow into the massive clusters we see in the local Universe, are usually traced by locating overdensities of galaxies1. Large spectroscopic surveys of distant galaxies now exist, but their sensitivity depends mainly on a galaxy's star-formation activity and dust content rather than its mass. Tracers of massive protoclusters that do not rely on their galaxy constituents are therefore needed. Here we report observations of Lyman-α absorption in the spectra of a dense grid of background galaxies2,3, which we use to locate a substantial number of candidate protoclusters at redshifts 2.2 to 2.8 through their intergalactic gas. We find that the structures producing the most absorption, most of which were previously unknown, contain surprisingly few galaxies compared with the dark-matter content of their analogues in cosmological simulations4,5. Nearly all of the structures are expected to be protoclusters, and we infer that half of their expected galaxy members are missing from our survey because they are unusually dim at rest-frame ultraviolet wavelengths. We attribute this to an unexpectedly strong and early influence of the protocluster environment6,7 on the evolution of these galaxies that reduced their star formation or increased their dust content.

A proteomic study of microgravity cardiac effects: feature maps of label-free LC-MALDI data for differential expression analysis.

We present a computational analysis of Mass Spectrometry (MS) data based on a proteomic study of mice cardiac tissue samples whose measured changes in peptide and protein abundance were obtained under normal (control or CTRL) and simulated microgravity conditions (hind-limb unloading or HLU). A pipeline consisting of experimental and computational steps has been designed to achieve, respectively, pre-fractionation to simplify mouse heart protein extracts and data synthesis by feature consensus maps. Both acid and neutral protein fractions obtained from differential solubility have been digested, and peptide mixtures have been resolved by LC-MALDI. The computational tools employed to analyze the MS data and reduce their complex dimensionality have included spectra alignment, denoising and normalization to obtain good-quality peak detection performance. In turn, features could be identified and further refined by searching patterns across repeated measurements obtained under differential conditions (HLU-CTRL, acid-neutral protein extracts). The assessment of reproducibility aspects for evaluating the efficacy of label-free comparative proteomic analysis, combined with the goal of identifying from HLU-CTRL consensus maps candidate proteins with differential expression, led to a computationally efficient approach delivering proteins related to the basic heart functions, cardiac stress and energy supply.