Scaling and balancing carbon dioxide fluxes in a heterogeneous tundra ecosystem of the Lena River Delta

The current assessments of the carbon turnover in the Arctic tundra are subject to large uncertainties. This problem can (inter alia) be ascribed to both the general shortage of flux data from the vast and sparsely inhabited Arctic region, as well as the typically high spatiotemporal variability of carbon fluxes in tundra ecosystems. Addressing these challenges, carbon dioxide fluxes on an active flood plain situated in the Siberian Lena River Delta were studied during two growing seasons with the eddy covariance method. The footprint exhibited a heterogeneous surface, which generated mixed flux signals that could be partitioned in such a way that both respiratory loss and photosynthetic gain were obtained for each of two vegetation classes. This downscaling of the observed fluxes revealed a differing seasonality in the net uptake of bushes (−0.89 µmol m−2 s−1) and sedges (−0.38 µmol mm−2 s−1) in 2014. That discrepancy, which was concealed in the net signal, resulted from a comparatively warm spring in conjunction with an early snowmelt and a varying canopy structure. Thus, the representativeness of footprints may adversely be affected in response to prolonged unusual weather conditions. In 2015, when air temperatures on average corresponded to climatological means, both vegetation-class-specific flux rates were of similar magnitude (−0.69 µmol m−2 s−1). A comprehensive set of measures (e.g. phenocam) corroborated the reliability of the partitioned fluxes and hence confirmed the utility of flux decomposition for enhanced flux data analysis. This scrutiny encompassed insights into both the phenological dynamic of individual vegetation classes and their respective functional flux to flux driver relationships with the aid of ecophysiologically interpretable parameters. For comparison with other sites, the decomposed fluxes were employed in a vegetation class area-weighted upscaling that was based on a classified high-resolution orthomosaic of the flood plain. In this way, robust budgets that take the heterogeneous surface characteristics into account were estimated. In relation to the average sink strength of various Arctic flux sites, the flood plain constitutes a distinctly stronger carbon dioxide sink. Roughly 42 % of this net uptake, however, was on average offset by methane emissions lowering the sink strength for greenhouse gases. With growing concern about rising greenhouse gas emissions in high-latitude regions, providing robust carbon budgets from tundra ecosystems is critical in view of accelerating permafrost thaw, which can impact the global climate for centuries

On the computation of a certain class of Hill determinants

AbstractHill's equation is studied for a particular class of periodic functions, which covers a broad range of practical applications. The essential quantity, which determines the stability criteria, is an infinite (or Hill) determinant. A set of recurrence relations for the computation of this determinant is derived. It is shown how these recurrence formulae also yield analytical results in two extremes

Semiquantitative activity-based detection of JWH-018, a synthetic cannabinoid receptor agonist, in oral fluid after vaping

The rapid proliferation of new synthetic cannabinoid receptor agonists (SCRAs) has initiated considerable interest in the development of so-called “untargeted” screening strategies. One of these new screening technologies involves the activity-based detection of SCRAs. In this study, we evaluated whether (synthetic) cannabinoid activity can be detected in oral fluid (OF) and, if so, whether it correlates with SCRA concentrations. OF was collected at several time points in a placebo-controlled JWH-018 administration study. The outcome of the cell-based cannabinoid reporter system, which monitored the cannabinoid receptor activation, was compared to the quantitative data for JWH-018, obtained via a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. A total of 175 OF samples were collected and analyzed via both methods. The cannabinoid reporter assay correctly classified the vast majority of the samples as either negative (<0.25 ng/mL; 74/75 = 99%) or having low (0.25−1.5 ng/mL; 16/16 = 100% and 1.5−10 ng/mL; 37/41 = 90%), mid (10−100 ng/mL; 23/25 = 92%) or high (>100 ng/mL; 16/18 = 89%) JWH-018 concentrations. Passing−Bablok regression analysis yielded a good linear correlation, with no proportional difference between both methods (slope 0.97; 95% confidence interval 0.86−1.14) and only a small systematic difference. This is the first study to demonstrate the applicability of an untargeted, activity-based approach for SCRA detection in OF. Additionally, the outcome of the cannabinoid reporter assay was compared to the gold standard (LC-MS/MS), showing a good correlation between both methods, indicating that the cannabinoid reporter assay can be used for an estimation of drug concentrations

Just Like the Real Thing: Fast Weak Simulation of Quantum Computation

Quantum computers promise significant speedups in solving problems intractable for conventional computers but, despite recent progress, remain limited in scaling and availability. Therefore, quantum software and hardware development heavily rely on simulation that runs on conventional computers. Most such approaches perform strong simulation in that they explicitly compute amplitudes of quantum states. However, such information is not directly observable from a physical quantum computer because quantum measurements produce random samples from probability distributions defined by those amplitudes. In this work, we focus on weak simulation that aims to produce outputs which are statistically indistinguishable from those of error-free quantum computers. We develop algorithms for weak simulation based on quantum state representation in terms of decision diagrams. We compare them to using state-vector arrays and binary search on prefix sums to perform sampling. Empirical validation shows, for the first time, that this enables mimicking of physical quantum computers of significant scale.Comment: 6 pages, 4 figure

The effect of dietary n-3 HUFA levels and DHA/EPA ratios on growth, survival and osmotic stress tolerance of Chinese mitten crab <i>Eriocheir sinensis</i> larvae

The effect of varying levels of dietary n-3 highly unsaturated fatty acid (HUFA) and docosahexaenoic acid/eicosapentaenoic acid (DHA/EPA) ratios on growth, survival and osmotic stress tolerance of Eriocheir sinensis zoea larvae was studied in two separate experiments. In experiment I, larvae were fed rotifers and Artemia enriched with ICES emulsions with 0, 30 and 50% total n-3 HUFA levels but with the same DHA/EPA ratio of 0.6. In experiment II, larvae were fed different combinations of enriched rotifers and Artemia, in which, rotifers were enriched with emulsions containing 30% total n-3 HUFA, but different DHA/EPA ratio of 0.6, 2 and 4; while Artemia were enriched with the same emulsions, but DHA/EPA ratio of 0.6 and 4. In both experiments, un-enriched rotifers cultured on baker's yeast and newly-hatched Artemia nauplii were used as control diets. Larvae were fed rotifers at zoea 1 and zoea 2 stages; upon reaching zoea 3 stage, Artemia was introduced.Experiment I revealed no significant effect of prey enrichment on the survival of megalopa among treatments, but higher total n-3 HUFA levels significantly enhanced larval development (larval stage index, LSI) and resulted in higher individual dry body weight of megalopa. Furthermore higher dietary n-3 HUFA levels also resulted in better tolerance to salinity stress. Experiment II indicated that at the same total n-3 HUFA level, larvae continuously receiving a low dietary DHA/EPA ratio had significantly lower survival at the megalopa stage and inferior individual body weight at the megalopa stage, but no negative effect was observed on larval development (LSI). The ability to endure salinity stress of zoea 3, zoea 5 and megalopa fed diets with higher DHA/EPA ratio was also improved

Collaborating Across State Lines to Leverage Cultural Competency Expertise

A statewide need for Latino cultural competency training for Utah State University (USU) Extension personnel was identified. The solution involved the collaborative efforts of our team of two USU Extension faculty members and one Washington State University (WSU) Extension faculty member on adaption and customization of a needs assessment tool and a training program originated at WSU. Our collaboration leveraged important limited resources such as subject-matter expertise, training materials, time, and funding while providing a venue for feedback and ideas to improve, update, and enhance an existing program. Garnering administrative support from the start is key to successful cross-state collaborative work and implementation of specialized training to expand Latino outreach capacity in Extension

Approximation of Quantum States Using Decision Diagrams

The computational power of quantum computers poses major challenges to new design tools since representing pure quantum states typically requires exponentially large memory. As shown previously, decision diagrams can reduce these memory requirements by exploiting redundancies. In this work, we demonstrate further reductions by allowing for small inaccuracies in the quantum state representation. Such inaccuracies are legitimate since quantum computers themselves experience gate and measurement errors and since quantum algorithms are somewhat resistant to errors (even without error correction). We develop four dedicated schemes that exploit these observations and effectively approximate quantum states represented by decision diagrams. We empirically show that the proposed schemes reduce the size of decision diagrams by up to several orders of magnitude while controlling the fidelity of approximate quantum state representations

As Accurate as Needed, as Efficient as Possible: Approximations in DD-based Quantum Circuit Simulation

Quantum computers promise to solve important problems faster than conventional computers. However, unleashing this power has been challenging. In particular, design automation runs into (1) the probabilistic nature of quantum computation and (2) exponential requirements for computational resources on non-quantum hardware. In quantum circuit simulation, Decision Diagrams (DDs) have previously shown to reduce the required memory in many important cases by exploiting redundancies in the quantum state. In this paper, we show that this reduction can be amplified by exploiting the probabilistic nature of quantum computers to achieve even more compact representations. Specifically, we propose two new DD-based simulation strategies that approximate the quantum states to attain more compact representations, while, at the same time, allowing the user to control the resulting degradation in accuracy. We also analytically prove the effect of multiple approximations on the attained accuracy and empirically show that the resulting simulation scheme enables speed-ups up to several orders of magnitudes.Comment: 6 pages, 2 figures, to be published at Design, Automation, and Test in Europe 202

NP-hardness of the cluster minimization problem revisited

The computational complexity of the "cluster minimization problem" is revisited [L. T. Wille and J. Vennik, J. Phys. A 18, L419 (1985)]. It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analog of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.Comment: 8 pages, 2 figures, accepted to J. Phys. A: Math. and Ge