Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach

BACKGROUND: Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease. METHODS: Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer. RESULTS: Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2. CONCLUSIONS: Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification

The ocean carbon sink – impacts, vulnerabilities and challenges

Carbon dioxide (CO2) is, next to water vapour, considered to be the most important natural greenhouse gas on Earth. Rapidly rising atmospheric CO2 concentrations caused by human actions such as fossil fuel burning, land-use change or cement production over the past 250 years have given cause for concern that changes in Earth’s climate system may progress at a much faster pace and larger extent than during the past 20 000 years. Investigating global carbon cycle pathways and finding suitable adaptation and mitigation strategies has, therefore, become of major concern in many research fields. The oceans have a key role in regulating atmospheric CO2 concentrations and currently take up about 25% of annual anthropogenic carbon emissions to the atmosphere. Questions that yet need to be answered are what the carbon uptake kinetics of the oceans will be in the future and how the increase in oceanic carbon inventory will affect its ecosystems and their services. This requires comprehensive investigations, including high-quality ocean carbon measurements on different spatial and temporal scales, the management of data in sophisticated databases, the application of Earth system models to provide future projections for given emission scenarios as well as a global synthesis and outreach to policy makers. In this paper, the current understanding of the ocean as an important carbon sink is reviewed with respect to these topics. Emphasis is placed on the complex interplay of different physical, chemical and biological processes that yield both positive and negative air–sea flux values for natural and anthropogenic CO2 as well as on increased CO2 (uptake) as the regulating force of the radiative warming of the atmosphere and the gradual acidification of the oceans. Major future ocean carbon challenges in the fields of ocean observations, modelling and process research as well as the relevance of other biogeochemical cycles and greenhouse gases are discussed

Alpha Net: Adaptation with Composition in Classifier Space

Deep learning classification models typically train poorly on classes with small numbers of examples. Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples. Critically, the majority of these models transfer knowledge within model feature space. In this work, we demonstrate that transferring knowledge within classified space is more effective and efficient. Specifically, by linearly combining strong nearest neighbor classifiers along with a weak classifier, we are able to compose a stronger classifier. Uniquely, our model can be implemented on top of any existing classification model that includes a classifier layer. We showcase the success of our approach in the task of long-tailed recognition, whereby the classes with few examples, otherwise known as the "tail" classes, suffer the most in performance and are the most challenging classes to learn. Using classifier-level knowledge transfer, we are able to drastically improve - by a margin as high as 12.6% - the state-of-the-art performance on the "tail" categories.Comment: Under revie

Representation of genetic association via attributable familial relative risks in order to identify polymorphisms functionally relevant to rheumatoid arthritis

The results from association studies are usually summarized by a measure of evidence of association (frequentist or Bayesian probability values) that does not directly reflect the impact of the detected signals on familial aggregation. This article investigates the possible advantage of a two-dimensional representation of genetic association in order to identify polymorphisms relevant to disease: a measure of evidence of association (the Bayes factor, BF) combined with the estimated contribution to familiality (the attributable sibling relative risk, λs). Simulation and data from the North American Rheumatoid Consortium (NARAC) were used to assess the possible benefit under several scenarios. Simulation indicated that the allele frequencies to reach the maximum BF and the maximum attributable λs diverged as the size of the genetic effect increased. The representation of BF versus attributable λs for selected regions of NARAC data revealed that SNPs involved in replicated associations clearly departed from the bulk of SNPs in these regions. In the 12 investigated regions, and particularly in the low-recombination major histocompatibility region, the ranking of SNPs according to BF differed from the ranking of SNPs according to attributable λs. The present results should be generalized using more extensive simulations and additional real data, but they suggest that a characterization of genetic association by both BF and attributable λs may result in an improved ranking of variants for further biological analyses

Ontogeny of Human IgE-expressing B Cells and Plasma Cells

BACKGROUND: IgE‐expressing (IgE(+)) plasma cells (PCs) provide a continuous source of allergen‐specific IgE that is central to allergic responses. The extreme sparsity of IgE(+) cells in vivo has confined their study almost entirely to mouse models. OBJECTIVE: To characterize the development pathway of human IgE(+) PCs and to determine the ontogeny of human IgE(+) PCs. METHODS: To generate human IgE(+) cells, we cultured tonsil B cells with IL‐4 and anti‐CD40. Using FACS and RT‐PCR, we examined the phenotype of generated IgE(+) cells, the capacity of tonsil B‐cell subsets to generate IgE(+) PCs and the class switching pathways involved. RESULTS: We have identified three phenotypic stages of IgE(+) PC development pathway, namely (i) IgE(+)germinal centre (GC)‐like B cells, (ii) IgE(+) PC‐like ‘plasmablasts’ and (iii) IgE(+) PCs. The same phenotypic stages were also observed for IgG1(+) cells. Total tonsil B cells give rise to IgE(+) PCs by direct and sequential switching, whereas the isolated GC B‐cell fraction, the main source of IgE(+) PCs, generates IgE(+) PCs by sequential switching. PC differentiation of IgE(+) cells is accompanied by the down‐regulation of surface expression of the short form of membrane IgE (mIgE(S)), which is homologous to mouse mIgE, and the up‐regulation of the long form of mIgE (mIgE(L)), which is associated with an enhanced B‐cell survival and expressed in humans, but not in mice. CONCLUSION: Generation of IgE(+) PCs from tonsil GC B cells occurs mainly via sequential switching from IgG. The mIgE(L)/mIgE(S) ratio may be implicated in survival of IgE(+) B cells during PC differentiation and allergic disease

Impaired bidirectional communication between interneurons and oligodendrocyte precursor cells affects social cognitive behavior

Cortical neural circuits are complex but very precise networks of balanced excitation and inhibition. Yet, the molecular and cellular mechanisms that form the balance are just beginning to emerge. Here, using conditional γ-aminobutyric acid receptor B1- deficient mice we identify a γ-aminobutyric acid/tumor necrosis factor superfamily member 12-mediated bidirectional communication pathway between parvalbumin-positive fast spiking interneurons and oligodendrocyte precursor cells that determines the density and function of interneurons in the developing medial prefrontal cortex. Interruption of the GABAergic signaling to oligodendrocyte precursor cells results in reduced myelination and hypoactivity of interneurons, strong changes of cortical network activities and impaired social cognitive behavior. In conclusion, glial transmitter receptors are pivotal elements in finetuning distinct brain functions

MEndoB, a chimeric lysin featuring a novel domain architecture and superior activity for the treatment of staphylococcal infections

One of the most pressing challenges of our era is the rising occurrence of bacteria that are resistant to antibiotics. Staphylococci are prominent pathogens in humans, which have developed multiple strategies to evade the effects of antibiotics. Infections caused by these bacteria have resulted in a high burden on the health care system and a significant loss of lives. In this study, we have successfully engineered lytic enzymes that exhibit an extraordinary ability to eradicate staphylococci. Our findings substantiate the importance of meticulous lead candidate selection to identify therapeutically promising peptidoglycan hydrolases with unprecedented activity. Hence, they offer a promising new avenue for treating staphylococcal infections