Transcriptomic characterization of Caecomyces churrovis: a novel, non-rhizoid-forming lignocellulolytic anaerobic fungus

Anaerobic gut fungi are the primary colonizers of plant material in the rumen microbiome, but are poorly studied due to a lack of characterized isolates. While most genera of gut fungi form extensive rhizoidal networks, which likely participate in mechanical disruption of plant cell walls, fungi within the Caecomyces genus do not possess these rhizoids. Here, we describe a novel fungal isolate, Caecomyces churrovis, which forms spherical sporangia with a limited rhizoidal network yet secretes a diverse set of carbohydrate active enzymes (CAZymes) for plant cell wall hydrolysis. Despite lacking an extensive rhizoidal system, C. churrovis is capable of growth on fibrous substrates like switchgrass, reed canary grass, and corn stover, although faster growth is observed on soluble sugars. Gut fungi have been shown to use enzyme complexes (fungal cellulosomes) in which CAZymes bind to non-catalytic scaffoldins to improve biomass degradation efficiency. However, transcriptomic analysis and enzyme activity assays reveal that C. churrovis relies more on free enzymes compared to other gut fungal isolates. Only 15% of CAZyme transcripts contain non-catalytic dockerin domains in C. churrovis, compared to 30% in rhizoid-forming fungi. Furthermore, C. churrovis is enriched in GH43 enzymes that provide complementary hemicellulose degrading activities, suggesting that a wider variety of these activities are required to degrade plant biomass in the absence of an extensive fungal rhizoid network. Overall, molecular characterization of a non-rhizoid-forming anaerobic fungus fills a gap in understanding the roles of CAZyme abundance and associated degradation mechanisms during lignocellulose breakdown within the rumen microbiome

Graphical models for interactive POMDPs: representations and solutions

We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive inf uence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic inf uence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that IPOMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents’ candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance

Fertility, Living Arrangements, Care and Mobility

There are four main interconnecting themes around which the contributions in this book are based. This introductory chapter aims to establish the broad context for the chapters that follow by discussing each of the themes. It does so by setting these themes within the overarching demographic challenge of the twenty-first century – demographic ageing. Each chapter is introduced in the context of the specific theme to which it primarily relates and there is a summary of the data sets used by the contributors to illustrate the wide range of cross-sectional and longitudinal data analysed

Myeloid SLC2a1-deficient murine model revealed macrophage activation and metabolic phenotype are fueled by GLUT1

Macrophages (MFs) are heterogeneous and metabolically flexible, with metabolism strongly affecting immune activation. A classic response to proinflammatory activation is increased flux through glycolysis with a downregulation of oxidative metabolism, whereas alternative activation is primarily oxidative, which begs the question of whether targeting glucose metabolism is a viable approach to control MF activation. We created a murine model of myeloid-specific glucose transporter GLUT1 (Slc2a1) deletion. Bone marrow-derived MFs (BMDM) from Slc2a1 M -/ - mice failed to uptake glucose and demonstrated reduced glycolysis and pentose phosphate pathway activity. Activated BMDMs displayed elevated metabolism of oleate and glutamine, yet maximal respiratory capacity was blunted in MF lacking GLUT1, demonstrating an incomplete metabolic reprogramming. Slc2a1 M -/ - BMDMs displayed a mixed inflammatory phenotype with reductions of the classically activated pro- and anti-inflammatory markers, yet less oxidative stress. Slc2a1 M -/ - BMDMs had reduced proinflammatory metabolites, whereas metabolites indicative of alternative activation-such as ornithine and polyamines-were greatly elevated in the absence of GLUT1. Adipose tissue MFs of lean Slc2a1 M -/ - mice had increased alternative M2-like activation marker mannose receptor CD206, yet lack of GLUT1 was not a critical mediator in the development of obesity-associated metabolic dysregulation. However, Ldlr-/ - mice lacking myeloid GLUT1 developed unstable atherosclerotic lesions. Defective phagocytic capacity in Slc2a1 M -/ - BMDMs may have contributed to unstable atheroma formation. Together, our findings suggest that although lack of GLUT1 blunted glycolysis and the pentose phosphate pathway, MF were metabolically flexible enough that inflammatory cytokine release was not dramatically regulated, yet phagocytic defects hindered MF function in chronic diseases

Helium identification with LHCb

The identification of helium nuclei at LHCb is achieved using a method based on measurements of ionisation losses in the silicon sensors and timing measurements in the Outer Tracker drift tubes. The background from photon conversions is reduced using the RICH detectors and an isolation requirement. The method is developed using pp collision data at √(s) = 13 TeV recorded by the LHCb experiment in the years 2016 to 2018, corresponding to an integrated luminosity of 5.5 fb-1. A total of around 105 helium and antihelium candidates are identified with negligible background contamination. The helium identification efficiency is estimated to be approximately 50% with a corresponding background rejection rate of up to O(10^12). These results demonstrate the feasibility of a rich programme of measurements of QCD and astrophysics interest involving light nuclei