Chemotrophic Microbial Mats and Their Potential for Preservation in the Rock Record

Putative microbialites are commonly regarded to have formed in association with photosynthetic microorganisms, such as cyanobacteria. However, many modern microbial mat ecosystems are dominated by chemotrophic bacteria and archaea. Like phototrophs, filamentous sulfur-oxidizing bacteria form large mats at the sediment/water interface that can act to stabilize sediments, and their metabolic activities may mediate the formation of marine phosphorites. Similarly, bacteria and archaea associated with the anaerobic oxidation of methane (AOM) catalyze the precipitation of seafloor authigenic carbonates. When preserved, lipid biomarkers, isotopic signatures, body fossils, and lithological indicators of the local depositional environment may be used to identify chemotrophic mats in the rock record. The recognition of chemotrophic communities in the rock record has the potential to transform our understanding of ancient microbial ecologies, evolution, and geochemical conditions. Chemotrophic microbes on Earth occupy naturally occurring interfaces between oxidized and reduced chemical species and thus may provide a new set of search criteria to target life-detection efforts on other planets

The Bayesian Decision Tree Technique with a Sweeping Strategy

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy

High accuracy results for the energy levels of the molecular ions H2+, D2+ and HD+, up to J=2

We present a nonrelativistic calculation of the rotation-vibration levels of the molecular ions H2+, D2+ and HD+, relying on the diagonalization of the exact three-body Hamiltonian. The J=2 levels are obtained with a very high accuracy of 10^{-14} a.u. (for most levels) representing an improvement by five orders of magnitude over previous calculations. The accuracy is also improved for the J=1 levels of H2+ and D2+ with respect to earlier works. Moreover, we have computed the sensitivities of the energy levels with respect to the mass ratios, allowing these levels to be used for metrological purposes.Comment: 11 page

Monkey-based Research on Human Disease: The Implications of Genetic Differences

Assertions that the use of monkeys to investigate human diseases is valid scientifically are frequently based on a reported 90–93% genetic similarity between the species. Critical analyses of the relevance of monkey studies to human biology, however, indicate that this genetic similarity does not result in sufficient physiological similarity for monkeys to constitute good models for research, and that monkey data do not translate well to progress in clinical practice for humans. Salient examples include the failure of new drugs in clinical trials, the highly different infectivity and pathology of SIV/HIV, and poor extrapolation of research on Alzheimer’s disease, Parkinson’s disease and stroke. The major molecular differences underlying these inter-species phenotypic disparities have been revealed by comparative genomics and molecular biology — there are key differences in all aspects of gene expression and protein function, from chromosome and chromatin structure to post-translational modification. The collective effects of these differences are striking, extensive and widespread, and they show that the superficial similarity between human and monkey genetic sequences is of little benefit for biomedical research. The extrapolation of biomedical data from monkeys to humans is therefore highly unreliable, and the use of monkeys must be considered of questionable value, particularly given the breadth and potential of alternative methods of enquiry that are currently available to scientists