Effect of Relative Air Himidity on the Stomatal Functionality in Fully Developed Leaves

Several studies have shown that stomata developed under long-term high relative air humidity (RH =85%) are malfunctional, resulting in a poor control of water loss. Yet, little is known about the dynamics of stomatal adaptation to moderate RH, and the possibilities to improve or reverse the destabilized stomatal responsiveness. In this study, a reciprocal transfer experiment was conducted in climate chambers using Rosa hybrida ‘Prophyta’, grown at moderate RH (60%) or at high RH (90%). The adaptation of fully developed leaves to the new RH environment was assessed at day 0, 4, 8 and 12 after plant transfer by measuring the transpiration rate in detached leaves. Stomata fully developed at high RH had a lower closing capacity in response to a decrease in leaf Relative Water Content (RWC) (i.e. water loss was considerably high at RWC below 20%, whereas in moderate RH stomata the water loss almost ceased at 57% RWC). Furthermore, stomata developed at high RH did not become functional after 12 days of cultivation at moderate RH. Similarly, stomata developed at moderate RH and transferred to high RH for a 12 day period did not loose their ability to close in response to desiccation. This indicates that stomatal functionality is determined during leaf development, while after this period stomata have a limited capacity to adapt to new RH environment. It is concluded that stomata from fully developed rose leaves conserve their behaviour independently of the post-development humidity leve

Real space mapping of topological invariants using artificial neural networks

Topological invariants allow to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wavefunctions under twisted boundary conditions. However, those procedures do not allow to calculate a topological invariant by evaluating the system locally, and thus require information about the wavefunctions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a 1-D topological superconductor and a 2-D quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the Kernel Polynomial Method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.Comment: 9 pages, 6 figure

Cosmological constant constraints from observation-derived energy condition bounds and their application to bimetric massive gravity

Among the various possibilities to probe the theory behind the recent accelerated expansion of the universe, the energy conditions (ECs) are of particular interest, since it is possible to confront and constrain the many models, including different theories of gravity, with observational data. In this context, we use the ECs to probe any alternative theory whose extra term acts as a cosmological constant. For this purpose, we apply a model-independent approach to reconstruct the recent expansion of the universe. Using Type Ia supernova, baryon acoustic oscillations and cosmic-chronometer data, we perform a Markov Chain Monte Carlo analysis to put constraints on the effective cosmological constant $\Omega^0_{\rm eff}$. By imposing that the cosmological constant is the only component that possibly violates the ECs, we derive lower and upper bounds for its value. For instance, we obtain that $0.59 < \Omega^0_{\rm eff} < 0.91$ and $0.40 < \Omega^0_{\rm eff} < 0.93$ within, respectively, $1\sigma$ and $3\sigma$ confidence levels. In addition, about 30\% of the posterior distribution is incompatible with a cosmological constant, showing that this method can potentially rule it out as a mechanism for the accelerated expansion. We also study the consequence of these constraints for two particular formulations of the bimetric massive gravity. Namely, we consider the Visser's theory and the Hassan and Roses's massive gravity by choosing a background metric such that both theories mimic General Relativity with a cosmological constant. Using the $\Omega^0_{\rm eff}$ observational bounds along with the upper bounds on the graviton mass we obtain constraints on the parameter spaces of both theories.Comment: 11 pages, 4 figures, 1 tabl

Giant meningioma in paranasal sinuses: an atypical nasal occupation

info:eu-repo/semantics/publishedVersio

Metabolite profiling characterises chemotypes of Musa diploids and triploids at juvenile and preflowering growth stages

Open Access Journal; Published online: 15 March 2019Bananas (Musa spp.) are consumed worldwide as dessert and cooking types. Edible banana varieties are for the most part seedless and sterile and therefore vegetatively propagated. This confers difficulties for breeding approaches against pressing biotic and abiotic threats and for the nutritional enhancement of banana pulp. A panel of banana accessions, representative of the diversity of wild and cultivated bananas, was analysed to assess the range of chemotypes available globally. The focus of this assessment was banana leaves at two growth stages (juvenile and pre-flowering), to see when during the plant growth metabolic differences can be established. The metabolic data corresponded to genomic trends reported in previous studies and demonstrated a link between metabolites/pathways and the genomes of M. acuminata and M. balbisiana. Furthermore, the vigour and resistance traits of M. balbisiana was connected to the phenolic composition and showed differences with the number of B genes in the hybrid accessions. Differences in the juvenile and pre-flowering data led to low correlation between the growth stages for prediction purposes

Correlations around an interface

We compute one-loop correlation functions for the fluctuations of an interface using a field theory model. We obtain them from Feynman diagrams drawn with a propagator which is the inverse of the Hamiltonian of a Poschl-Teller problem. We derive an expression for the propagator in terms of elementary functions, show that it corresponds to the usual spectral sum, and use it to calculate quantities such as the surface tension and interface profile in two and three spatial dimensions. The three-dimensional quantities are rederived in a simple, unified manner, whereas those in two dimensions extend the existing literature, and are applicable to thin films. In addition, we compute the one-loop self-energy, which may be extracted from experiment, or from Monte Carlo simulations. Our results may be applied in various scenarios, which include fluctuations around topological defects in cosmology, supersymmetric domain walls, Z(N) bubbles in QCD, domain walls in magnetic systems, interfaces separating Bose-Einstein condensates, and interfaces in binary liquid mixtures.Comment: RevTeX, 13 pages, 6 figure