Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates

Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions

A human alveolar macrophage genome-scale metabolic reconstruction was reconstructed from tailoring a global human metabolic network, Recon 1, by using computational algorithms and manual curation.A genome-scale host–pathogen network of the human alveolar macrophage and Mycobacterium tuberculosis is presented. This involved integrating two genome-scale network reconstructions.The reaction activity and gene essentiality predictions of the host–pathogen model represent a more accurate depiction of infection.Integration of high-throughput data into a host-pathogen model followed by systems analysis was performed in order to elucidate major metabolic differences under different types of M. tuberculosis infection

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

New measurements of thousand-seed weights of species in the Pannonian flora

For understanding local and regional seed dispersal and plant establishment processes and for considering the ecotypes and other forms of specific variability, hard data of locally or regionally measured traits are necessary. We provided newly measured seed weight data of 193 taxa, out of which 24 taxa had not been represented in the SID, LEDA or BiolFlor databases. Our new measurements and formerly published data of locally collected seed weight records together covers over 70% of the Pannonian flora. However, there is still a considerable lack in seed weight data of taxonomically problematic genera, even though they are represented in the Pannonian flora with a relatively high number of species and/or subspecies (e.g. Sorbus, Rosa, Rubus, Crataegus and Hieracium). Our regional database contains very sporadic data on aquatic plants (including also numerous invasive species reported from Hungary and neighbouring countries) and some rare weeds distributed in the southwestern part of the country. These facts indicate the necessity of further seed collection and measurements

Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology

Aerobic glycolysis is a seemingly wasteful mode of ATP production that is seen both in rapidly proliferating mammalian cells and highly active contracting muscles, but whether there is a common origin for its presence in these widely different systems is unknown. To study this issue, here we develop a model of human central metabolism that incorporates a solvent capacity constraint of metabolic enzymes and mitochondria, accounting for their occupied volume densities, while assuming glucose and/or fatty acid utilization. The model demonstrates that activation of aerobic glycolysis is favored above a threshold metabolic rate in both rapidly proliferating cells and heavily contracting muscles, because it provides higher ATP yield per volume density than mitochondrial oxidative phosphorylation. In the case of muscle physiology, the model also predicts that before the lactate switch, fatty acid oxidation increases, reaches a maximum, and then decreases to zero with concomitant increase in glucose utilization, in agreement with the empirical evidence. These results are further corroborated by a larger scale model, including biosynthesis of major cell biomass components. The larger scale model also predicts that in proliferating cells the lactate switch is accompanied by activation of glutaminolysis, another distinctive feature of the Warburg effect. In conclusion, intracellular molecular crowding is a fundamental constraint for cell metabolism in both rapidly proliferating- and non-proliferating cells with high metabolic demand. Addition of this constraint to metabolic flux balance models can explain several observations of mammalian cell metabolism under steady state conditions

Inferences from Surface Brightness Fluctuations of Zwicky 3146 via the Sunyaev-Zel’dovich Effect and X-Ray Observations

The galaxy cluster Zwicky 3146 is a sloshing cool-core cluster at z = 0.291 that in Sunyaev-Zel’dovich (SZ) imaging does not appear to exhibit significant pressure substructure in the intracluster medium. We perform a surface brightness fluctuation analysis via Fourier amplitude spectra on SZ (MUSTANG-2) and X-ray (XMM-Newton) images of this cluster. These surface brightness fluctuations can be deprojected to infer pressure and density fluctuations from the SZ and X-ray data, respectively. In the central region (Ring 1, r < 100′′ = 440 kpc, in our analysis), we find fluctuation spectra that suggest injection scales around 200 kpc (∼140 kpc from pressure fluctuations and ∼250 kpc from density fluctuations). When comparing the pressure and density fluctuations in the central region, we observe a change in the effective thermodynamic state from large to small scales, from isobaric (likely due to the slow sloshing) to adiabatic (due to more vigorous motions). By leveraging scalings from hydrodynamical simulations, we find an average 3D Mach number ≈0.5. We further compare our results to other studies of Zwicky 3146 and, more broadly, to other studies of fluctuations in other clusters

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p

Properties of the Line-of-Sight Velocity Field in the Hot and X-ray Emitting Circumgalactic Medium of Nearby Simulated Disk Galaxies

The hot, X-ray-emitting phase of the circumgalactic medium in galaxies is believed to be the reservoir of baryons from which gas flows onto the central galaxy and into which feedback from AGN and stars inject mass, momentum, energy, and metals. These effects shape the velocity fields of the hot gas, which can be observed by X-ray IFUs via the Doppler shifting and broadening of emission lines. In this work, we analyze the gas kinematics of the hot circumgalactic medium of Milky Way-mass disk galaxies from the TNG50 simulation with synthetic observations to determine how future instruments can probe this velocity structure. We find that the hot phase is often characterized by outflows outward from the disk driven by feedback processes, radial inflows near the galactic plane, and rotation, though in other cases the velocity field is more disorganized and turbulent. With a spectral resolution of $\sim$1 eV, fast and hot outflows ($\sim$200-500 km s$^{-1}$) can be measured, depending on the orientation of the galaxy on the sky. The rotation velocity of the hot phase ($\sim$100-200 km s$^{-1}$) can be measured using line shifts in edge-on galaxies, and is slower than that of colder gas phases but similar to stellar rotation velocities. By contrast, the slow inflows ($\sim$50-100 km s$^{-1}$) are difficult to measure in projection with these other components. We find that the velocity measured is sensitive to which emission lines are used. Measuring these flows will help constrain theories of how the gas in these galaxies forms and evolves.Comment: 41 pages, 29 figures, submitted to Ap