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Abstract Exopolysaccharides (EPS) are organic macromolecules naturally secreted by many microorganisms. EPS is increasingly used for agriculture and industrial purposes. This study focuses on isolate Klebsiella pneumonia SSN1, Klebsiella quasipeumonniae SGM81 isolated from rhizosphere to explore its water retention efficiency under drought conditions. Neutron Radiography was used to visualise water distribution in the sand under normal and drought conditions in the presence and absence of EPS producing bacteria. The EPS production was studied by applying Box Behnken design (BBD) under drought stress which was artificially induced by using polyethene glycol 6000 under osmotic stress condition 3.65% w/v of EPS dry weight was obtained. The relative water content (RWC) is used to calculate the amount of water present in the sand and was further studied by Neutron Radiography imaging with appropriate controls. FTIR and HPLC were also carried out for the characterisation of the extracted EPS. The sand experiments revealed that after 24 h of evaporation, the highest RWC was maintained by SSN1 at 29.7% compared to SGM81 (19.06%). SSN1 was found to release L-arabinose as the main sugar of its EPS under drought stress conditions by HPLC method. The FTIR data indicated the presence of β-glucans and polysaccharide α-pyranose between wavenumber 700 cm−1–1500 cm−1 and 1017 cm−1–1200 cm−1 respectively. The HPLC characterization of extracted EPS from osmotic stressed cells (run 3) displayed a peak designated to L-arabinose at 10.3 retention time (RT) for 132.4 mM concentration. While from run 5 with the controlled condition indicated the presence of L-rhamnose at 7.3 RT for 87 mM concentration. Neutron radiography enables the visualisation of water distribution in the sand as well as water transport in root-soil systems in situ. SSN1 has elicited EPS production in drought conditions with a low level of nitrogen and carbon.

Farming of barley and chickpea is nitrogen (N) fertilizer dependent. Using strategies that increase the nitrogen use efficiency (NUE) and its components, nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUtE) would reduce the N fertilizer application in the soil and its adverse environmental effects. We evaluated the effects of three different strains of diazotroph Klebsiella (K.p. SSN1, K.q. SGM81, and K.o. M5a1) to understand the role of biological nitrogen fixation (BNF) and bacterial indole-3-acetic acid (IAA) on NUE of the plants. A field study revealed that K.p. SSN1 results in profound increment of root surface area by eightfold and threefold compared to uninoculated (control) in barley and chickpea, respectively. We measured significant increase in the plant tissue nitrogen, chlorophyll content, protein content, nitrate reductase activity, and nitrate concentration in the inoculated plants (p ≤ 0.05). Treated barley and chickpea exhibited higher NUE and the components compared to the control plants (K.p. SSN1 ≥ K.q. SGM81> K.o. M5a1). Specifically, K.q. SGM81 treatment in barley increased NUpE by 72%, while in chickpea, K.p. SSN1 increased it by 187%. The substantial improvement in the NUpE and NUE by the auxin producers K.p. SSN1 and K.q. SGM81 compared with non-auxin producer K.o. M5a1 was accompanied by an augmented root architecture suggesting larger contribution of IAA over marginal contribution of BNF in nitrogen acquisition from the soil.

Abstract Turbulence parametrizations will remain a necessary building block in kilometer‐scale Earth system models. In convective boundary layers, where the mean vertical gradients of conserved properties such as potential temperature and moisture are approximately zero, the standard ansatz which relates turbulent fluxes to mean vertical gradients via an eddy diffusivity has to be extended by mass‐flux parametrizations for the typically asymmetric up‐ and downdrafts in the atmospheric boundary layer. We present a parametrization for a dry and transiently growing convective boundary layer based on a generative adversarial network. The training and test data are obtained from three‐dimensional high‐resolution direct numerical simulations. The model incorporates the physics of self‐similar layer growth following from the classical mixed layer theory of Deardorff by a renormalization. This enhances the training data base of the generative machine learning algorithm and thus significantly improves the predicted statistics of the synthetically generated turbulence fields at different heights inside the boundary layer, above the surface layer. Differently to stochastic parametrizations, our model is able to predict the highly non‐Gaussian and transient statistics of buoyancy fluctuations, vertical velocity, and buoyancy flux at different heights thus also capturing the fastest thermals penetrating into the stabilized top region. The results of our generative algorithm agree with standard two‐equation mass‐flux schemes. The present parametrization provides additionally the granule‐type horizontal organization of the turbulent convection which cannot be obtained in any of the other model closures. Our proof of concept‐study also paves the way to efficient data‐driven convective parametrizations in other natural flows.

Two hybrid quantum-classical reservoir computing models are presented to reproduce the low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra=10^{5} and Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent convective heat flux. The latter is composed of vertical velocity and temperature and measures the global turbulent heat transfer across the convection layer; it manifests locally in coherent hot and cold thermal plumes that rise from the bottom and fall from the top boundaries. Both quantum algorithms differ by the arrangement of the circuit layers of the quantum reservoir, in particular the entanglement layers. The second of the two quantum circuit architectures, denoted H2, enables a complete execution of the reservoir update inside the quantum circuit without the usage of external memory. Their performance is compared with that of a classical reservoir computing model. Therefore, all three models have to learn the nonlinear and chaotic dynamics of the turbulent flow at hand in a lower-dimensional latent data space which is spanned by the time-dependent expansion coefficients of the 16 most energetic proper orthogonal decomposition (POD) modes. These training data are generated by a POD snapshot analysis from direct numerical simulations of the original turbulent flow. All reservoir computing models are operated in the reconstruction or open-loop mode, i.e., they receive three POD modes as an input at each step and reconstruct the 13 missing modes. We analyze different measures of the reconstruction error in dependence on the hyperparameters which are specific for the quantum cases or shared with the classical counterpart, such as the reservoir size and the leaking rate. We show that both quantum algorithms are able to reconstruct the essential statistical properties of the turbulent convection flow successfully with similar performance compared with the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of four to eight smaller in comparison with the classical case.

ABSTRACT Phenotypic heterogeneity in clonal bacterial batch cultures has been shown for a range of bacterial systems; however, the molecular origins of such heterogeneity and its magnitude are not well understood. Under conditions of extreme low-nitrogen stress in the model diazotroph Klebsiella oxytoca, we found remarkably high heterogeneity of nifHDK gene expression, which codes for the structural genes of nitrogenase, one key enzyme of the global nitrogen cycle. This heterogeneity limited the bulk observed nitrogen-fixing capacity of the population. Using dual-probe, single-cell RNA fluorescent in situ hybridization, we correlated nifHDK expression with that of nifLA and glnK-amtB, which code for the main upstream regulatory components. Through stochastic transcription models and mutual information analysis, we revealed likely molecular origins for heterogeneity in nitrogenase expression. In the wild type and regulatory variants, we found that nifHDK transcription was inherently bursty, but we established that noise propagation through signaling was also significant. The regulatory gene glnK had the highest discernible effect on nifHDK variance, while noise from factors outside the regulatory pathway were negligible. Understanding the basis of inherent heterogeneity of nitrogenase expression and its origins can inform biotechnology strategies seeking to enhance biological nitrogen fixation. Finally, we speculate on potential benefits of diazotrophic heterogeneity in natural soil environments. IMPORTANCE Nitrogen is an essential micronutrient for both plant and animal life and naturally exists in both reactive and inert chemical forms. Modern agriculture is heavily reliant on nitrogen that has been “fixed” into a reactive form via the energetically expensive Haber-Bosch process, with significant environmental consequences. Nitrogen-fixing bacteria provide an alternative source of fixed nitrogen for use in both biotechnological and agricultural settings, but this relies on a firm understanding of how the fixation process is regulated within individual bacterial cells. We examined the cell-to-cell variability in the nitrogen-fixing behavior of Klebsiella oxytoca, a free-living bacterium. The significance of our research is in identifying not only the presence of marked variability but also the specific mechanisms that give rise to it. This understanding gives insight into both the evolutionary advantages of variable behavior as well as strategies for biotechnological applications.

The dynamics in a confined turbulent convection flow is dominated by multiple long-lived macroscopic circulation states that are visited subsequently by the system in a Markov-type hopping process. In the present work, we analyze the short transition paths between these subsequent macroscopic system states by a data-driven learning algorithm that extracts the low-dimensional transition manifold and the related new coordinates, which we term collective variables, in the state space of the complex turbulent flow. We therefore transfer and extend concepts for conformation transitions in stochastic microscopic systems, such as in the dynamics of macromolecules, to a deterministic macroscopic flow. Our analysis is based on long-term direct numerical simulation trajectories of turbulent convection in a closed cubic cell at a Prandtl number Pr=0.7 and Rayleigh numbers Ra=10^{6} and 10^{7} for a time lag of 10^{5} convective free-fall time units. The simulations resolve vortices and plumes of all physically relevant scales, resulting in a state space spanned by more than 3.5 million degrees of freedom. The transition dynamics between the large-scale circulation states can be captured by the transition manifold analysis with only two collective variables, which implies a reduction of the data dimension by a factor of more than a million. Our method demonstrates that cessations and subsequent reversals of the large-scale flow are unlikely in the present setup, and thus it paves the way for the development of efficient reduced-order models of the macroscopic complex nonlinear dynamical system.

Inverse cascades of kinetic energy and thermal variance in the subset of vertically homogeneous modes in spectral space are found to cause a slow aggregation to a pair of convective supergranules that eventually fill the whole horizontally extended, three-dimensional, turbulent Rayleigh-Bénard convection layer when a heat flux is prescribed at the top and bottom. An additional weak rotation of the layer around the vertical axis stops this aggregation at a scale that is smaller than the lateral domain extension and ceases the inverse cascade for the thermal variance. The inverse cascade for the kinetic energy remains intact, even for times at which the root-mean-square values of temperature and velocity have reached the statistically steady state. This kinetic energy inverse cascade sustains the horizontally extended convection patterns which are best visible in the temperature field. The resulting characteristic length of the aggregated convection patterns depends on the thermal driving and linearly on the strength of rotation. Our study demonstrates the importance of inverse energy cascades beyond the two-dimensional turbulence case in a three-dimensional convection flow that is subject to a multiscale energy injection by thermal plumes and driven by boundary heat fluxes as typically present in natural geo- and astrophysical systems, such as solar convection.

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-dimensional thermal convection flow with three and eight degrees of freedom by a hybrid quantum-classical reservoir computing model. The high-dimensional quantum reservoir dynamics are established by universal quantum gates that rotate and entangle the individual qubits of the tensor product quantum state. A comparison of the quantum reservoir computing model with its classical counterpart shows that the same prediction and reconstruction capabilities of classical reservoirs with thousands of perceptrons can be obtained by a few strongly entangled qubits. We demonstrate that the mean squared error between model output and ground truth in the test phase of the quantum reservoir computing algorithm increases when the reservoir is decomposed into separable subsets of qubits. Furthermore, the quantum reservoir computing model is implemented on a real noisy IBM quantum computer for up to seven qubits. Our work thus opens the door to model the dynamics of classical complex systems in a high-dimensional phase space effectively with an algorithm that requires a small number of qubits.

Free-living nitrogen-fixing bacteria can improve growth yields of some non-leguminous plants and, if enhanced through bioengineering approaches, have the potential to address major nutrient imbalances in global crop production by supplementing inorganic nitrogen fertilisers. However, nitrogen fixation is a highly resource-costly adaptation and is de-repressed only in environments in which sources of reduced nitrogen are scarce. Here we investigate nitrogen fixation (nif) gene expression and nitrogen starvation response signaling in the model diazotroph Klebsiella oxytoca (Ko) M5a1 during ammonium depletion and the transition to growth on atmospheric N2. Exploratory RNA-sequencing revealed that over 50% of genes were differentially expressed under diazotrophic conditions, among which the nif genes are among the most highly expressed and highly upregulated. Isotopically labelled QconCAT standards were designed for multiplexed, absolute quantification of Nif and nitrogen-stress proteins via multiple reaction monitoring mass spectrometry (MRM-MS). Time-resolved Nif protein concentrations were indicative of bifurcation in the accumulation rates of nitrogenase subunits (NifHDK) and accessory proteins. We estimate that the nitrogenase may account for more than 40% of cell protein during diazotrophic growth and occupy approximately half the active ribosome complement. The concentrations of free amino acids in nitrogen-starved cells were insufficient to support the observed rates of Nif protein expression. Total Nif protein accumulation was reduced 10-fold when the NifK protein was truncated and nitrogenase catalysis lost (nifK1–1203), implying that reinvestment of de novo fixed nitrogen is essential for further nif expression and a complete diazotrophy transition. Several amino acids accumulated in non-fixing ΔnifLA and nifK1–1203 mutants, while the rest remained highly stable despite prolonged N starvation. Monitoring post-translational uridylylation of the PII-type signaling proteins GlnB and GlnK revealed distinct nitrogen regulatory roles in Ko M5a1. GlnK uridylylation was persistent throughout the diazotrophy transition while a ΔglnK mutant exhibited significantly reduced Nif expression and nitrogen fixation activity. Altogether, these findings highlight quantitatively the scale of resource allocation required to enable the nitrogen fixation adaptation to take place once underlying signaling processes are fulfilled. Our work also provides an omics-level framework with which to model nitrogen fixation in free-living diazotrophs and inform rational engineering strategies.

High-amplitude events of the out-of-plane vorticity component ω_{z} are analyzed by stereoscopic particle image velocimetry (PIV) in the bulk region of turbulent Rayleigh-Bénard convection in air. The Rayleigh numbers Ra vary from 1.7×10^{4} to 5.1×10^{5}. The experimental investigation is connected with a comprehensive statistical analysis of long-term time series of ω_{z} and individual velocity derivatives ∂u_{i}/∂x_{j}. A statistical convergence for derivative moments up to an order of 6 is demonstrated. Our results are found to agree well with existing high-resolution direct numerical simulation data in the same range of parameters, including the extreme vorticity events that appear in the far exponential tails of the corresponding probability density functions. The transition from Gaussian to non-Gaussian velocity derivative statistics in the bulk of a convection flow is confirmed experimentally. The experimental data are used to train a reservoir computing model, one implementation of a recurrent neural network, to reproduce highly intermittent experimental time series of the vorticity and thus reconstruct extreme out-of-plane vorticity events. After training the model with high-resolution PIV data, the machine learning model is run with sparsely seeded, continually available, and unseen measurement data in the reconstruction phase. The dependence of the reconstruction quality on the sparsity of the partial observations is also documented. Our latter result paves the way to machine-learning-assisted experimental analyses of small-scale turbulence for which time series of missing velocity derivatives can be provided by generative algorithms.

Turbulent fluids in nature, counter-intuitively, can exhibit large-scale order that persists for long times. Pandey et al. numerically characterize the formation of these superstructures in turbulent convection by separating the fast motions at small-scales from those that gradually vary at large scales.

bEBPs (bacterial enhancer-binding proteins) are AAA+ (ATPase associated with various cellular activities) transcription activators that activate gene transcription through a specific bacterial σ factor, σ54. σ54–RNAP (RNA polymerase) binds to promoter DNA sites and forms a stable closed complex, unable to proceed to transcription. The closed complex must be remodelled using energy from ATP hydrolysis provided by bEBPs to melt DNA and initiate transcription. Recently, large amounts of structural and biochemical data have produced insights into how ATP hydrolysis within the active site of bEBPs is coupled to the re-modelling of the closed complex. In the present article, we review some of the key nucleotides, mutations and techniques used and how they have contributed towards our understanding of the function of bEBPs. [ABSTRACT FROM AUTHOR]

Many rhizobacteria isolated from plant rhizosphere produce various phytohormones in the form of secondary metabolites, the most common of which is Indole-3-acetic acid (IAA). Here, we detail analytical protocols of IAA detection and quantification, in vitro and in situ, as recently applied to Klebsiella SGM 81, a rhizobacterium isolated from the rhizosphere of Dianthus caryophyllus (a commercially important flower across the globe). Specifically, we describe a detailed protocol for a colorimetric assay using the Salkowski reagent method, which can be used to screen for the presence of Indole compounds. To further detect and quantify IAA, a highly accurate analytical approach of LC-MS/MS is used. To detect the presence of IAA around the root system of Dianthus caryophyllus, in situ staining of plant roots is done using Salkowski reagent.

Turbulent convection processes in nature are often found to be organized in a hierarchy of plume structures and flow patterns. The gradual aggregation of convection cells or granules to a supergranule which eventually fills the whole horizontal layer is reported and analyzed in spectral element direct numerical simulations of three-dimensional turbulent Rayleigh-Bénard convection at an aspect ratio of 60. The formation proceeds over a time span of more than 10^{4} convective time units for the largest accessible Rayleigh number and occurs only when the turbulence is driven by a constant heat flux which is imposed at the bottom and top planes enclosing the convection layer. The resulting gradual inverse cascade process is observed for both temperature variance and turbulent kinetic energy. An additional analysis of the leading Lyapunov vector field for the full turbulent flow trajectory in its high-dimensional phase space demonstrates that turbulent flow modes at a certain scale continue to give rise locally to modes with a longer wavelength in the turbulent case. As a consequence, successively larger convection patterns grow until the horizontal extension of the layer is reached. This instability mechanism, which is known to exist near the onset of constant heat flux-driven convection, is shown here to persist into the fully developed turbulent flow regime, thus connecting weakly nonlinear pattern formation with the one in fully developed turbulence. We discuss possible implications of our study for observed, but not yet consistently numerically reproducible, solar supergranulation which could lead to improved simulation models of surface convection in the Sun.

Microbes are responsible for over 10% of the global yield losses in staple crops such as wheat, rice, and maize. Understanding the decision-making strategies that enable bacterial plant pathogens to evade the host immune system and cause disease is essential for managing their ever growing threat to food security. Many utilise the needle-like type III secretion system (T3SS) to suppress plant immunity, by injecting effector proteins that inhibit eukaryotic signalling pathways into the host cell cytoplasm. Plants can in turn evolve resistance to specific pathogens via recognition and blocking of the T3SS effectors, so leading to an ongoing co-evolutionary ‘arms race’ between pathogen and host pairs. The extracytoplasmic function sigma factor HrpL co-ordinates the expression of the T3SS regulon in the leaf-dwelling Pseudomonas syringae and similar pathogens. Recently, we showed that association of HrpL with a target promoter directly adjacent to the hrpL gene imposes negative autogenous control on its own expression level due to overlapping regulatory elements. Our results suggest that by down-regulating T3SS function, this fine-tuning mechanism enables P. syringae to minimise effector-mediated elicitation of plant immunity.

Im April 2015 wurde in einem brandenburgischen Arboretum eine rötliche Bänderung an vorjährigen Nadeln von Pinus jeffreyi Balf. (Jeffrey-Kiefer) und Pinus ponderosa Douglas ex C. Lawson (Gelb-Kiefer) festgestellt. Später konnte diese Symptomatik bei systematischen Kon­trollen auch an Pinus attenuata Lemmon (Höcker-Kiefer) und Pinus thunbergii Parl. (Japanische Schwarz-Kiefer) beobachtet werden. Anhand von mikromorphologischen Untersuchungen und laborativen Analysen bestätigte sich der Verdacht auf eine Infektion durch den Quarantäneschadpilz Dothistroma septosporum (Dorogin) M. More­let (Erreger der Dothistroma-Nadelbräune). Erkrankt waren ausschließlich jüngere Bäume in einem Geländebereich mit anhaltend hoher Luftfeuchtigkeit. Der weltweit vorkommende Krankheitserreger befällt überwiegend Kiefern-Arten (Pinus spp.). Gravierende Schäden verursachte er bislang speziell auf der Südhalbkugel. In letzter Zeit konnte Dothistroma septosporum allerdings auch in einigen europäischen Ländern bemerkenswert oft nachgewiesen werden. Möglicherweise geht die verstärkte Präsenz des Pilzes nördlich des Äquators auf die sich seit einigen Jahrzehnten abzeichnende Klimaveränderung zurück. Da der Krankheitserreger imstande ist, auch an der in Europa heimischen Schwarz-Kiefer (Pinus nigra J.F. Arnold) umfangreiche Schäden hervorzurufen, resultieren aus dem Vorkommen forstwirtschaftliche Risi­ken. Zudem ist unklar, welche Intensität ein Befall von Reinbeständen der Gemeinen Kiefer (Pinus sylvestris L.) erreichen würde.

This study combines observations, large-eddy simulations (LES), and direct numerical simulations (DNS) in order to analyze entrainment and mixing in shallow cumulus clouds at all relevant spatial scales and, additionally, to verify the results by the multiple methods used. The observations are based on three flights of the CARRIBA campaign which are similar to the classical BOMEX case used for LES. Virtual flights in the LES data are used to validate the observational method of line measurements. It is shown that line measurements overrepresent the cloud core, and it is quantified how derived statistics depend on small perturbations of the flight track, which has to be taken in account for the interpretation of airborne observations. A linear relation between fluctuations of temperature and liquid water content has been found in both LES and observations in a good quantitative agreement. However, the constant of proportionality deviates from purely adiabatic estimates, which can be attributed to cloud edge mixing. The cloud edge is compared in detail in observations and LES, which agree qualitatively although the LES cloud edge is smoother due to the model's resolution. The resulting typical amplitudes of the turbulence fields from this comparison are compared with the large-scale forcing model which is used in a series of DNS which study the mixing below the meter scale, which show that LES does not resolve the intermittency of small-scale turbulence.

Using direct numerical simulations of isotropic turbulence in periodic cubes of several grid sizes, the largest being 8192 ^3 yielding a microscale Reynolds number of 1300, we study the properties of pressure Laplacian to understand differences in the inertial range scaling of enstrophy density and energy dissipation. Even though the pressure Laplacian is the difference between two highly intermittent quantities, it is non-intermittent and essentially follows Kolmogorov scaling, at least for low-order moments. Using this property, we show that the scaling exponents of local averages of dissipation and enstrophy remain unequal at all finite Reynolds numbers, though there appears to be a very weak tendency for the difference to decrease with increasing Reynolds number.

ABSTRACT Nitrogen regulation in Escherichia coli is a model system for gene regulation in bacteria. Growth on glutamine as a sole nitrogen source is assumed to be nitrogen limiting, inferred from slow growth and strong NtrB/NtrC-dependent gene activation. However, we show that under these conditions, the intracellular glutamine concentration is not limiting but 5.6-fold higher than in ammonium-replete conditions; in addition, α-ketoglutarate concentrations are elevated. We address this glutamine paradox from a systems perspective. We show that the dominant role of NtrC is to regulate glnA transcription and its own expression, indicating that the glutamine paradox is not due to NtrC-independent gene regulation. The absolute intracellular NtrC and GS concentrations reveal molecular control parameters, where NtrC-specific activities were highest in nitrogen-starved cells, while under glutamine growth, NtrC showed intermediate specific activity. We propose an in vivo model in which α-ketoglutarate can derepress nitrogen regulation despite nitrogen sufficiency. IMPORTANCE Nitrogen is the most important nutrient for cell growth after carbon, and its metabolism is coordinated at the metabolic, transcriptional, and protein levels. We show that growth on glutamine as a sole nitrogen source, commonly assumed to be nitrogen limiting and used as such as a model system for nitrogen limitation, is in fact nitrogen replete. Our integrative quantitative analysis of key molecules involved in nitrogen assimilation and regulation reveal that glutamine is not necessarily the dominant molecule signaling nitrogen sufficiency and that α-ketoglutarate may play a more important role in signaling nitrogen status. NtrB/NtrC integrates α-ketoglutarate and glutamine signaling—sensed by the UTase (glnD) and PII (glnB), respectively—and regulates the nitrogen response through self-regulated expression and phosphorylation-dependent activation of the nitrogen (ntr) regulon. Our findings support α-ketoglutarate acting as a global regulatory metabolite.

Das Eschentriebsterben nimmt in Deutschland unter den bedeutsamen Baumkrankheiten gegenwärtig eine zentrale Rolle ein. In den Baumschulen sind die wirtschaftlichen Schäden besonders groß, wenngleich auch Wälder in zunehmendem Maße betroffen sind. Die hier vorgestellten mykologischen und histologischen Untersuchungen wurden an dreijährigen Baumschulpflanzen durchgeführt. Dabei ist die durch Chalara fraxinea (Teleomorphe: Hymenoscyphus albidus) verursachte Infektionsrate in der Rinde, im äußeren und inneren Holzteil sowie im Mark für die Organe Spross und Wurzel ermittelt worden. Die Invasions- und Ausbreitungsstrategie des Pilzes wurde im verholzten Spross untersucht. Zudem wurde überprüft, ob bodenbürtige Oomyceten (Phytophthora- bzw. Pythium-Arten) als primäre oder maßgebliche Schädigungsursache in Frage kommen. Die Ergebnisse bestätigen die dominante Rolle von C. fraxinea als Verursacher des Eschentriebsterbens und schließen zugleich die Bedeutung von Oomyceten im Krankheitsprozess aus. Die Autoren kommen zu dem Schluss, dass der Pilz den verholzten Spross sehr effektiv aus zentralen Bereichen heraus dreidimensional zu erschließen vermag und dabei nicht vorrangig endophytisch lebt. Die zunächst den oberirdischen Teil der Pflanze schädigende Infektion kann sich nachfolgend auf alle Bereiche im Wirt ausdehnen.

The insufficient parameterization of low clouds which are caused by shallow convection remains one of the biggest sources of uncertainty in large-scale models of global atmospheric motion. One way to overcome this lack of understanding is to develop Boussinesq models of moist convection with simplified thermodynamics which allow for systematic studies of the cloud formation in different dynamical regimes and depend on a small set of system parameters only. This route makes the problem accessible to direct numerical simulations of turbulence without subgrid-scale modeling and provides an ideal testing bed for systematic and stepwise reductions of degrees of freedom. Such systematic reductions are studied here for a recently developed moist Rayleigh–Bénard convection model in the conditionally unstable regime. Our analysis is based on the proper orthogonal decomposition (POD) and determines the corresponding modes by a direct solution of the eigenvalue problem in form of an integral equation. The resulting reduced-order dynamical systems are obtained by a projection of the original equations of motion onto a finite set of POD modes. These modes are selected with respect to their energy as well as their ability to transport energy from large to small scales and to dissipate the energy at smaller scales efficiently such that an additional modal viscosity can be omitted for most cases. The reduced models reproduce important statistical quantities such as cloud cover, liquid water flux and global buoyancy transport to a very good degree. Furthermore we investigate different pathways to reduce the number of degrees of freedom in the low-dimensional models. The number of degrees of freedom can be compressed by more than two orders of magnitude until the models break down and cause significant deviations of essential mean transport quantities from the original fully resolved simulation data.

We present high-resolution direct numerical simulation studies of turbulent Rayleigh–Bénard convection in a closed cylindrical cell with an aspect ratio of one. The focus of our analysis is on the finest scales of convective turbulence, in particular the statistics of the kinetic energy and thermal dissipation rates in the bulk and the whole cell. The fluctuations of the energy dissipation field can directly be translated into a fluctuating local dissipation scale which is found to develop ever finer fluctuations with increasing Rayleigh number. The range of these scales as well as the probability of high-amplitude dissipation events decreases with increasing Prandtl number. In addition, we examine the joint statistics of the two dissipation fields and the consequences of high-amplitude events. We have also investigated the convergence properties of our spectral element method and have found that both dissipation fields are very sensitive to insufficient resolution. We demonstrate that global transport properties, such as the Nusselt number, and the energy balances are partly insensitive to insufficient resolution and yield correct results even when the dissipation fields are under-resolved. Our present numerical framework is also compared with high-resolution simulations which use a finite difference method. For most of the compared quantities the agreement is found to be satisfactory.

Extreme responses of a droplet ensemble during an entrainment and mixing process as present at the edge of a cloud are investigated by means of three-dimensional direct numerical simulations in the Euler–Lagrangian framework. We find that the Damköhler number Da , a dimensionless parameter which relates the fluid time scale to the typical evaporation time scale, can capture all aspects of the initial mixing process within the range of parameters accessible in this study. The mixing process is characterized by the limits of strongly homogeneous ( Da ≪ 1) and strongly inhomogeneous ( Da ≫ 1) regimes. We explore these two extreme regimes and study the response of the droplet size distribution to the corresponding parameter settings through an enhancement and reduction of the response constant K in the droplet growth equation. Thus, Da is varied while Reynolds and Schmidt numbers are held fixed, and initial microphysical properties are held constant. In the homogeneous limit minimal broadening of the size distribution is observed as the new steady state is reached, whereas in the inhomogeneous limit the size distribution develops strong negative skewness, with the appearance of a pronounced exponential tail. The analysis in the Lagrangian framework allows us to relate the pronounced negative tail of the supersaturation distribution to that of the size distribution.