Development of Iron Speciation Reference Materials for Palaeoredox Analysis

The development and application of geochemical techniques to identify redox conditions in modern and ancient aquatic environments has intensified over recent years. Iron (Fe) speciation has emerged as one of the most widely used procedures to distinguish different redox regimes in both the water column and sediments, and is the main technique used to identify oxic, ferruginous (anoxic, Fe(II) containing) and euxinic (anoxic, sulfidic) water column conditions. However, an international sediment reference material has never been developed. This has led to concern over the consistency of results published by the many laboratories that now utilise the technique. Here, we report an interlaboratory comparison of four Fe speciation reference materials for palaeoredox analysis, which span a range of compositions and reflect deposition under different redox conditions. We provide an update of extraction techniques used in Fe speciation, and assess the effects of both test portion mass, and the use of different analytical procedures, on the quantification of different Fe fractions in sedimentary rocks. While atomic adsorption spectroscopy and inductively coupled plasma‐optical emission spectrometry produced comparable Fe measurements for all extraction stages, the use of ferrozine consistently underestimated Fe in the extraction step targeting mixed ferrous‐ferric minerals such as magnetite. We therefore suggest that the use of ferrozine is discontinued for this Fe pool. Finally, we report the combined data of four independent Fe speciation laboratories to characterise the Fe speciation composition of the reference materials. These reference materials are available to the community to provide an essential validation of in‐house Fe speciation measurements

Model based Paleozoic atmospheric oxygen estimates: a revisit to GEOCARBSULF

Geological redox proxies increasingly point towards low atmospheric oxygen concentrations during the early Paleozoic Era, with a subsequent protracted rise towards present-day levels. However, these proxies currently only provide qualitative estimates of atmospheric O₂ levels. Global biogeochemical models, in contrast, are commonly employed to generate quantitative estimates for atmospheric O₂ levels through Earth’s history. Estimates for Paleozoic pO₂ generated by GEOCARBSULF, one of the most widely implemented carbon and sulfur cycle models, have historically suggested high atmospheric O₂ levels throughout the Paleozoic, in direct contradiction to competing models. In this study, we evaluate whether GEOCARBSULF can predict relatively low Paleozoic O₂ levels. We first update GEOCARBSULF by adopting the recent compilation of the δ¹³C value of marine buried carbonate and replacing the old formulation of the sulfur isotope fractionation factor with empirical sulfur isotope records. Following this we construct various O₂ evolution scenarios (with low O₂ levels in the early Paleozoic) and examine whether GEOCARBSULF can reproduce these scenarios by varying the weathering/degassing fluxes of carbon and sulfur, or carbonate δ¹³C. We show that GEOCARBSULF can, in fact, maintain low-O₂ (even 1–5% atm) levels through the early Paleozoic by only varying the carbonate δ¹³C within 2 standard deviation (SD) bounds permitted by the geological record. In addition, it can generate a middle–late Paleozoic rise in O₂ concentration, coincident with the diversification of land plants. However, we also argue that tracking atmospheric O₂ levels with GEOCARBSULF is highly dependent on carbonate carbon isotope evolution, and more accurate predictions will come from an improved C isotope record

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their ‘collective cognition’, or (ii) interaction with conspecifics can allow individuals to follow specific ‘leaders’, those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions – where individuals are able to consider the success of previous decision outcomes – the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated

Modelling the long-term carbon cycle, atmospheric CO2, and Earth surface temperature from late Neoproterozoic to present day

Over geological timescales, CO2 levels are determined by the operation of the long term carbon cycle, and it is generally thought that changes in atmospheric CO2 concentration have controlled variations in Earth's surface temperature over the Phanerozoic Eon. Here we compile independent estimates for global average surface temperature and atmospheric CO2 concentration, and compare these to the predictions of box models of the long term carbon cycle COPSE and GEOCARBSULF. We find a strong relationship between CO2 forcing and temperature from the proxy data, for times where data is available, and we find that current published models reproduce many aspects of CO2 change, but compare poorly to temperature estimates. Models are then modified in line with recent advances in understanding the tectonic controls on carbon cycle source and sink processes, with these changes constrained by modelling 87Sr/86Sr ratios. We estimate CO2 degassing rates from the lengths of subduction zones and rifts, add differential effects of erosion rates on the weathering of silicates and carbonates, and revise the relationship between global average temperature changes and the temperature change in key weathering zones. Under these modifications, models produce combined records of CO2 and temperature change that are reasonably in line with geological and geochemical proxies (e.g. central model predictions are within the proxy windows for >~75% of the time covered by data). However, whilst broad long-term changes are reconstructed, the models still do not adequately predict the timing of glacial periods. We show that the 87Sr/86Sr record is largely influenced by the weathering contributions of different lithologies, and is strongly controlled by erosion rates, rather than being a good indicator of overall silicate chemical weathering rates. We also confirm that a combination of increasing erosion rates and decreasing degassing rates over the Neogene can cause the observed cooling and Sr isotope changes without requiring an overall increase in silicate weathering rates. On the question of a source or sink dominated carbon cycle, we find that neither alone can adequately reconstruct the combination of CO2, temperature and strontium isotope dynamics over Phanerozoic time, necessitating a combination of changes to sources and sinks. Further progress in this field relies on >108 year dynamic spatial reconstructions of ancient tectonics, paleogeography and hydrology. Whilst this is a significant challenge, the latest reconstruction techniques, proxy records and modelling advances make this an achievable target

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

The impact of emotional well-being on long-term recovery and survival in physical illness: a meta-analysis

This meta-analysis synthesized studies on emotional well-being as predictor of the prognosis of physical illness, while in addition evaluating the impact of putative moderators, namely constructs of well-being, health-related outcome, year of publication, follow-up time and methodological quality of the included studies. The search in reference lists and electronic databases (Medline and PsycInfo) identified 17 eligible studies examining the impact of general well-being, positive affect and life satisfaction on recovery and survival in physically ill patients. Meta-analytically combining these studies revealed a Likelihood Ratio of 1.14, indicating a small but significant effect. Higher levels of emotional well-being are beneficial for recovery and survival in physically ill patients. The findings show that emotional well-being predicts long-term prognosis of physical illness. This suggests that enhancement of emotional well-being may improve the prognosis of physical illness, which should be investigated by future research

Ortho2ExpressMatrix—a web server that interprets cross-species gene expression data by gene family information

<p>Abstract</p> <p>Background</p> <p>The study of gene families is pivotal for the understanding of gene evolution across different organisms and such phylogenetic background is often used to infer biochemical functions of genes. Modern high-throughput experiments offer the possibility to analyze the entire transcriptome of an organism; however, it is often difficult to deduct functional information from that data.</p> <p>Results</p> <p>To improve functional interpretation of gene expression we introduce Ortho2ExpressMatrix, a novel tool that integrates complex gene family information, computed from sequence similarity, with comparative gene expression profiles of two pre-selected biological objects: gene families are displayed with two-dimensional matrices. Parameters of the tool are object type (two organisms, two individuals, two tissues, etc.), type of computational gene family inference, experimental meta-data, microarray platform, gene annotation level and genome build. Family information in Ortho2ExpressMatrix bases on computationally different protein family approaches such as EnsemblCompara, InParanoid, SYSTERS and Ensembl Family. Currently, respective all-against-all associations are available for five species: human, mouse, worm, fruit fly and yeast. Additionally, microRNA expression can be examined with respect to miRBase or TargetScan families. The visualization, which is typical for Ortho2ExpressMatrix, is performed as matrix view that displays functional traits of genes (differential expression) as well as sequence similarity of protein family members (BLAST e-values) in colour codes. Such translations are intended to facilitate the user's perception of the research object.</p> <p>Conclusions</p> <p>Ortho2ExpressMatrix integrates gene family information with genome-wide expression data in order to enhance functional interpretation of high-throughput analyses on diseases, environmental factors, or genetic modification or compound treatment experiments. The tool explores differential gene expression in the light of orthology, paralogy and structure of gene families up to the point of ambiguity analyses. Results can be used for filtering and prioritization in functional genomic, biomedical and systems biology applications. The web server is freely accessible at <url>http://bioinf-data.charite.de/o2em/cgi-bin/o2em.pl</url>.</p