NAIP/NLRC4 inflammasome activation in MRP8+ cells is sufficient to cause systemic inflammatory disease.

Inflammasomes are cytosolic multiprotein complexes that initiate protective immunity in response to infection, and can also drive auto-inflammatory diseases, but the cell types and signalling pathways that cause these diseases remain poorly understood. Inflammasomes are broadly expressed in haematopoietic and non-haematopoietic cells and can trigger numerous downstream responses including production of IL-1β, IL-18, eicosanoids and pyroptotic cell death. Here we show a mouse model with endogenous NLRC4 inflammasome activation in Lysozyme2 + cells (monocytes, macrophages and neutrophils) in vivo exhibits a severe systemic inflammatory disease, reminiscent of human patients that carry mutant auto-active NLRC4 alleles. Interestingly, specific NLRC4 activation in Mrp8 + cells (primarily neutrophil lineage) is sufficient to cause severe inflammatory disease. Disease is ameliorated on an Asc -/- background, and can be suppressed by injections of anti-IL-1 receptor antibody. Our results provide insight into the mechanisms by which NLRC4 inflammasome activation mediates auto-inflammatory disease in vivo

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections

Calculation of fragmentation functions in two-hadron semi-inclusive processes

We investigate the properties of interference fragmentation functions arising from the emission of two leading hadrons inside the same jet for inclusive lepton-nucleon deep-inelastic scattering. Using an extended spectator model for the mechanism of the hadronization, we give a complete calculation and numerical estimates for the examples of a proton-pion pair produced with invariant mass on the Roper resonance, and of two pions produced with invariant mass close to the $\rho$ mass. We discuss azimuthal angular dependence of the leading order cross section to point up favourable conditions for extracting transversity from experimental data.Comment: 5 pages, 3 figures in .eps format, AIP and epsfig styles included, to appear in proceedings of "Second Workshop on Physics with an Electron Polarized Light Ion Collider", MIT, Sept. 14-16, 200

Life and work on small-scale farms in Norway: an outlook based on survey results linked to financial data

This paper studies what causes (small-scale) farmers to leave their farms and typically move to urban areas. A data set is constructed by linking survey results with financial data, and the data set is analyzed by multivariate statistical techniques. Our results indicate that, while existence and size of future farm production is important, there is also a difference between farmers who primarily have financial objectives for their farming, and those who have more lifestyle oriented objectives. The latter group is, everything else being equal, more likely to stay on the farm. This could imply that, if preventing migration from rural to urban areas is a policy objective, production support schemes will be effective for some groups, but will be less effective for the group with lifestyle objectives. If this group is to be encouraged to stay on the countryside, policies directed at improving the general living conditions in the local community are likely to be more effective than specific support schemes related to agricultural production.migration, farmer objectives, agricultural policy, structural equation modelling, Agricultural and Food Policy, Research Methods/ Statistical Methods,

Sasakian quiver gauge theories and instantons on cones over round and squashed seven-spheres

We study quiver gauge theories on the round and squashed seven-spheres, and orbifolds thereof. They arise by imposing $G$-equivariance on the homogeneous space $G/H=\mathrm{SU}(4)/\mathrm{SU}(3)$ endowed with its Sasaki-Einstein structure, and $G/H=\mathrm{Sp}(2)/\mathrm{Sp}(1)$ as a 3-Sasakian manifold. In both cases we describe the equivariance conditions and the resulting quivers. We further study the moduli spaces of instantons on the metric cones over these spaces by using the known description for Hermitian Yang-Mills instantons on Calabi-Yau cones. It is shown that the moduli space of instantons on the hyper-Kahler cone can be described as the intersection of three Hermitian Yang-Mills moduli spaces. We also study moduli spaces of translationally invariant instantons on the metric cone $\mathbb{R}^8/\mathbb{Z}_k$ over $S^7/\mathbb{Z}_k$.Comment: 44 pages; v2: minor changes, reference added; Final version to appear in Nuclear Physics

Non-centro-symmetric superconductors Li2Pd3B and Li2(Pd0.8Pt0.2)3B: amplitude and phase fluctuations analysis of the experimental magnetization data

We report on magnetization data obtained as a function of temperature and magnetic field in Li2 (Pd0.8Pt0.2)3B and Li2Pd3B non-centro-symmetric superconductors. Reversible magnetization curves were plotted as M1/2 vs. T. This allows study of the asymptotic behavior of the averaged order parameter amplitude (gap) near the superconducting transition. Results of the analysis show, as expected, a mean field superconducting transition for Li2Pd3B. On contrary, a large deviation from the mean field behavior is revealed for Li2(Pd0.8Pt0.2)3B. This is interpreted as due to the strength of the non s-wave spin-triplet pairing in this Pt-containing compound which produces nodes in the order parameter and consequently, phase fluctuations. The diamagnetic signal above Tc(H) in Li2Pd3B is well explained by superconducting Gaussian fluctuations, which agrees with the observed mean field transition. For Li2(Pd0.8Pt0.2)3B the diamagnetic signal above Tc(H) is much higher than the expected Gaussian values and appears to be well explained by three dimensional critical fluctuations of the lowest-Landau-level type, which somehow agrees with the scenario of a phase mediated transition.Comment: 7 pages (1 column) 3 figure

Geometric Modular Action, Wedge Duality and Lorentz Covariance are Equivalent for Generalized Free Fields

The Tomita-Takesaki modular groups and conjugations for the observable algebras of space-like wedges and the vacuum state are computed for translationally covariant, but possibly not Lorentz covariant, generalized free quantum fields in arbitrary space-time dimension d. It is shown that for $d\geq 4$ the condition of geometric modular action (CGMA) of Buchholz, Dreyer, Florig and Summers \cite{BDFS}, Lorentz covariance and wedge duality are all equivalent in these models. The same holds for d=3 if there is a mass gap. For massless fields in d=3, and for d=2 and arbitrary mass, CGMA does not imply Lorentz covariance of the field itself, but only of the maximal local net generated by the field

An engineering analysis of a closed cycle plant growth module

The SOLGEM model is a numerical engineering model which solves the flow and energy balance equations for the air flowing through a growing environment, assuming quasi-steady state conditions within the system. SOLGEM provides a dynamic simulation of the controlled environment system in that the temperature and flow conditions of the growing environment are estimated on an hourly basis in response to the weather data and the plant growth parameters. The flow energy balance considers the incident solar flux; incoming air temperature, humidity, and flow rate; heat exchange with the roof and floor; and heat and moisture exchange with the plants. A plant transpiration subroutine was developed based plant growth research facility, intended for the study of bioregenerative life support theories. The results of a performance analysis of the plant growth module are given. The estimated energy requirements of the module components and the total energy are given