Responses of marine benthic microalgae to elevated CO<inf>2</inf>

Increasing anthropogenic CO2 emissions to the atmosphere are causing a rise in pCO2 concentrations in the ocean surface and lowering pH. To predict the effects of these changes, we need to improve our understanding of the responses of marine primary producers since these drive biogeochemical cycles and profoundly affect the structure and function of benthic habitats. The effects of increasing CO2 levels on the colonisation of artificial substrata by microalgal assemblages (periphyton) were examined across a CO2 gradient off the volcanic island of Vulcano (NE Sicily). We show that periphyton communities altered significantly as CO2 concentrations increased. CO2 enrichment caused significant increases in chlorophyll a concentrations and in diatom abundance although we did not detect any changes in cyanobacteria. SEM analysis revealed major shifts in diatom assemblage composition as CO2 levels increased. The responses of benthic microalgae to rising anthropogenic CO2 emissions are likely to have significant ecological ramifications for coastal systems. © 2011 Springer-Verlag

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

Observation of a New Excited Beauty Strange Baryon Decaying to Ξb- π+π-

The Ξb-π+π- invariant mass spectrum is investigated with an event sample of proton-proton collisions at s=13 TeV, collected by the CMS experiment at the LHC in 2016-2018 and corresponding to an integrated luminosity of 140 fb-1. The ground state Ξb- is reconstructed via its decays to J/ψΞ- and J/ψΛK-. A narrow resonance, labeled Ξb(6100)-, is observed at a Ξb-π+π- invariant mass of 6100.3±0.2(stat)±0.1(syst)±0.6(Ξb-) MeV, where the last uncertainty reflects the precision of the Ξb- baryon mass. The upper limit on the Ξb(6100)- natural width is determined to be 1.9 MeV at 95% confidence level. The low Ξb(6100)- signal yield observed in data does not allow a measurement of the quantum numbers of the new state. However, following analogies with the established excited Ξc baryon states, the new Ξb(6100)- resonance and its decay sequence are consistent with the orbitally excited Ξb- baryon, with spin and parity quantum numbers JP=3/2-