The Lipopolysaccharide Core of Brucella abortus Acts as a Shield Against Innate Immunity Recognition

Innate immunity recognizes bacterial molecules bearing pathogen-associated molecular patterns to launch inflammatory responses leading to the activation of adaptive immunity. However, the lipopolysaccharide (LPS) of the gram-negative bacterium Brucella lacks a marked pathogen-associated molecular pattern, and it has been postulated that this delays the development of immunity, creating a gap that is critical for the bacterium to reach the intracellular replicative niche. We found that a B. abortus mutant in the wadC gene displayed a disrupted LPS core while keeping both the LPS O-polysaccharide and lipid A. In mice, the wadC mutant induced proinflammatory responses and was attenuated. In addition, it was sensitive to killing by non-immune serum and bactericidal peptides and did not multiply in dendritic cells being targeted to lysosomal compartments. In contrast to wild type B. abortus, the wadC mutant induced dendritic cell maturation and secretion of pro-inflammatory cytokines. All these properties were reproduced by the wadC mutant purified LPS in a TLR4-dependent manner. Moreover, the core-mutated LPS displayed an increased binding to MD-2, the TLR4 co-receptor leading to subsequent increase in intracellular signaling. Here we show that Brucella escapes recognition in early stages of infection by expressing a shield against recognition by innate immunity in its LPS core and identify a novel virulence mechanism in intracellular pathogenic gram-negative bacteria. These results also encourage for an improvement in the generation of novel bacterial vaccines

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Genome Degradation in Brucella ovis Corresponds with Narrowing of Its Host Range and Tissue Tropism

Brucella ovis is a veterinary pathogen associated with epididymitis in sheep. Despite its genetic similarity to the zoonotic pathogens B. abortus, B. melitensis and B. suis, B. ovis does not cause zoonotic disease. Genomic analysis of the type strain ATCC25840 revealed a high percentage of pseudogenes and increased numbers of transposable elements compared to the zoonotic Brucella species, suggesting that genome degradation has occurred concomitant with narrowing of the host range of B. ovis. The absence of genomic island 2, encoding functions required for lipopolysaccharide biosynthesis, as well as inactivation of genes encoding urease, nutrient uptake and utilization, and outer membrane proteins may be factors contributing to the avirulence of B. ovis for humans. A 26.5 kb region of B. ovis ATCC25840 Chromosome II was absent from all the sequenced human pathogenic Brucella genomes, but was present in all of 17 B. ovis isolates tested and in three B. ceti isolates, suggesting that this DNA region may be of use for differentiating B. ovis from other Brucella spp. This is the first genomic analysis of a non-zoonotic Brucella species. The results suggest that inactivation of genes involved in nutrient acquisition and utilization, cell envelope structure and urease may have played a role in narrowing of the tissue tropism and host range of B. ovis

Forest biodiversity, ecosystem functioning and the provision of ecosystem services

Forests are critical habitats for biodiversity and they are also essential for the provision of a wide range of ecosystem services that are important to human well-being. There is increasing evidence that biodiversity contributes to forest ecosystem functioning and the provision of ecosystem services. Here we provide a review of forest ecosystem services including biomass production, habitat provisioning services, pollination, seed dispersal, resistance to wind storms, fire regulation and mitigation, pest regulation of native and invading insects, carbon sequestration, and cultural ecosystem services, in relation to forest type, structure and diversity. We also consider relationships between forest biodiversity and multifunctionality, and trade-offs among ecosystem services. We compare the concepts of ecosystem processes, functions and services to clarify their definitions. Our review of published studies indicates a lack of empirical studies that establish quantitative and causal relationships between forest biodiversity and many important ecosystem services. The literature is highly skewed; studies on provisioning of nutrition and energy, and on cultural services, delivered by mixed-species forests are under-represented. Planted forests offer ample opportunity for optimising their composition and diversity because replanting after harvesting is a recurring process. Planting mixed-species forests should be given more consideration as they are likely to provide a wider range of ecosystem services within the forest and for adjacent land uses. This review also serves as the introduction to this special issue of Biodiversity and Conservation on various aspects of forest biodiversity and ecosystem services

Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton-proton collisions at root s=13TeV

A search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks is performed in proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC. The analyzed data sample corresponds to an integrated luminosity of 35.9 fb(-1). The signal is characterized by a large missing transverse momentum recoiling against a bottom quark-antiquark system that has a large Lorentz boost. The number of events observed in the data is consistent with the standard model background prediction. Results are interpreted in terms of limits both on parameters of the type-2 two-Higgs doublet model extended by an additional light pseudoscalar boson a (2HDM+a) and on parameters of a baryonic Z simplified model. The 2HDM+a model is tested experimentally for the first time. For the baryonic Z model, the presented results constitute the most stringent constraints to date.Peer reviewe

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯

Measurement of B_{s}^{0} meson production in pp and PbPb collisions at \sqrt{SNN}

The production cross sections of B_{s}^{0} mesons and charge conjugates are measured in proton-proton (pp) and PbPb collisions via the exclusive decay channel B_{s}^{0}→J/ψϕ→μ^{+}μ^{−}K^{+}K^{−} at a center-of-mass energy of 5.02 TeV per nucleon pair and within the rapidity range |y|<2.4 using the CMS detector at the LHC. The pp measurement is performed as a function of transverse momentum (p_{T}) of the B_{s}^{0} mesons in the range of 7 to 50 GeV/c and is compared to the predictions of perturbative QCD calculations. The B_{s}^{0} production yield in PbPb collisions is measured in two p_{T} intervals, 7 to 15 and 15 to 50 GeV/c, and compared to the yield in pp collisions in the same kinematic region. The nuclear modification factor (R_{AA}) is found to be 1.5±0.6(stat)±0.5(syst) for 7–15 GeV/c, and 0.87±0.30(stat)±0.17(syst) for 15–50 GeV/c, respectively. Within current uncertainties, the B_{s}^{0} results are consistent with models of strangeness enhancement, and suppression by parton energy loss, as observed for the B+ mesons