Strong Correspondence in Evapotranspiration and Carbon Dioxide Fluxes Between Different Eddy Covariance Systems Enables Quantification of Landscape Heterogeneity in Dryland Fluxes

This is the final version. Available on open access from Wiley via the DOI in this recordData Availability Statement: Post-processed half-hourly data from AmeriFlux systems are available at https://ameriflux.lbl.gov/login/?redirect_to=/data/download-data/ and the data from the low-frequency systems are archived with the NERC Environmental Information Data Centre (https://doi.org/10.5285/e96466c3-5b67-41b0-9252-8f8f393807d7). The code repository is archived on Zenodo (https://doi.org/10.5281/zenodo.4730586).The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature-based climate-change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower-cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower-cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low-frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower-cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.Natural Environment Research Council (NERC)National Science Foundation (NSF)Department of EnergyOppenheimer Programme in African Landscape System

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Engineered Toxins “Zymoxins” Are Activated by the HCV NS3 Protease by Removal of an Inhibitory Protein Domain

The synthesis of inactive enzyme precursors, also known as “zymogens,” serves as a mechanism for regulating the execution of selected catalytic activities in a desirable time and/or site. Zymogens are usually activated by proteolytic cleavage. Many viruses encode proteases that execute key proteolytic steps of the viral life cycle. Here, we describe a proof of concept for a therapeutic approach to fighting viral infections through eradication of virally infected cells exclusively, thus limiting virus production and spread. Using the hepatitis C virus (HCV) as a model, we designed two HCV NS3 protease-activated “zymogenized” chimeric toxins (which we denote “zymoxins”). In these recombinant constructs, the bacterial and plant toxins diphtheria toxin A (DTA) and Ricin A chain (RTA), respectively, were fused to rationally designed inhibitor peptides/domains via an HCV NS3 protease-cleavable linker. The above toxins were then fused to the binding and translocation domains of Pseudomonas exotoxin A in order to enable translocation into the mammalian cells cytoplasm. We show that these toxins exhibit NS3 cleavage dependent increase in enzymatic activity upon NS3 protease cleavage in vitro. Moreover, a higher level of cytotoxicity was observed when zymoxins were applied to NS3 expressing cells or to HCV infected cells, demonstrating a potential therapeutic window. The increase in toxin activity correlated with NS3 protease activity in the treated cells, thus the therapeutic window was larger in cells expressing recombinant NS3 than in HCV infected cells. This suggests that the “zymoxin” approach may be most appropriate for application to life-threatening acute infections where much higher levels of the activating protease would be expected

Search for Gravitational Waves Associated with Gamma-Ray Bursts during the First Advanced LIGO Observing Run and Implications for the Origin of GRB 150906B

We present the results of the search for gravitational waves (GWs) associated with γ-ray bursts detected during the first observing run of the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO). We find no evidence of a GW signal for any of the 41 γ-ray bursts for which LIGO data are available with sufficient duration. For all γ-ray bursts, we place lower bounds on the distance to the source using the optimistic assumption that GWs with an energy of ${10}^{-2}{M}_{\odot }{c}^{2}$ were emitted within the $16$–$500$ Hz band, and we find a median 90% confidence limit of 71 Mpc at 150 Hz. For the subset of 19 short/hard γ-ray bursts, we place lower bounds on distance with a median 90% confidence limit of 90 Mpc for binary neutron star (BNS) coalescences, and 150 and 139 Mpc for neutron star–black hole coalescences with spins aligned to the orbital angular momentum and in a generic configuration, respectively. These are the highest distance limits ever achieved by GW searches. We also discuss in detail the results of the search for GWs associated with GRB 150906B, an event that was localized by the InterPlanetary Network near the local galaxy NGC 3313, which is at a luminosity distance of $54$ Mpc (z = 0.0124). Assuming the γ-ray emission is beamed with a jet half-opening angle $\leqslant 30^\circ$, we exclude a BNS and a neutron star–black hole in NGC 3313 as the progenitor of this event with confidence >99%. Further, we exclude such progenitors up to a distance of 102 Mpc and 170 Mpc, respectively

Environmental and developmental controls over the seasonal pattern of isoprene emission from aspen leaves.

Isoprene emission from plants represents one of the principal biospheric controls over the oxidative capacity of the continental troposphere. In the study reported here, the seasonal pattern of isoprene emission, and its underlying determinants, were studied for aspen trees growing in the Rocky Mountains of Colorado. The springtime onset of isoprene emission was delayed for up to 4 weeks following leaf emergence, despite the presence of positive net photosynthesis rates. Maximum isoprene emission rates were reached approximately 6 weeks following leaf emergence. During this initial developmental phase, isoprene emission rates were negatively correlated with leaf nitrogen concentrations. During the autumnal decline in isoprene emission, rates were positively correlated with leaf nitrogen concentration. Given past studies that demonstrate a correlation between leaf nitrogen concentration and isoprene emission rate, we conclude that factors other than the amount of leaf nitrogen determine the early-season initiation of isoprene emission. The late-season decline in isoprene emission rate is interpreted as due to the autumnal breakdown of metabolic machinery and loss of leaf nitrogen. In potted aspen trees, leaves that emerged in February and developed under cool, springtime temperatures did not emit isoprene until 23 days after leaf emergence. Leaves that emrged in July and developed in hot, midsummer temperatures emitted isoprene within 6 days. Leaves that had emerged during the cool spring, and had grown for several weeks without emitting isoprene, could be induced to emit isoprene within 2 h of exposure to 32°C. Continued exposure to warm temperatures resulted in a progressive increase in the isoprene emission rate. Thus, temperature appears to be an important determinant of the early season induction of isoprene emission. The seasonal pattern of isoprene emission was examined in trees growing along an elevational gradient in the Colorado Front Range (1829-2896 m). Trees at different elevations exhibited staggered patterns of bud-break and initiation of photosynthesis and isoprene emission in concert with the staggered onset of warm, springtime temperatures. The springtime induction of isoprene emission could be predicted at each of the three sites as the time after bud break required for cumulative temperatures above 0°C to reach approximately 400 degree days. Seasonal temperature acclimation of isoprene emission rate and photosynthesis rate was not observed. The temperature dependence of isoprene emission rate between 20 and 35°C could be accurately predicted during spring and summer using a single algorithm that describes the Arrhenius relationship of enzyme activity. From these results, it is concluded that the early season pattern of isoprene emission is controlled by prevailing temperature and its interaction with developmental processes. The late-season pattern is determined by controls over leaf nitrogen concentration, especially the depletion of leaf nitrogen during senescence. Following early-season induction, isoprene emission rates correlate with photosynthesis rates. During the season there is little acclimation to temperature, so that seasonal modeling simplifies to a single temperature-response algorithm