Interspecific competition between entomopathogenic nematodes (Steinernema) is modified by their bacterial symbionts (Xenorhabdus)

Background  

Symbioses between invertebrates and prokaryotes are biological systems of particular interest in order to study the evolution of mutualism. The symbioses between the entomopathogenic nematodes Steinernema and their bacterial symbiont Xenorhabdus are very tractable model systems. Previous studies demonstrated (i) a highly specialized relationship between each strain of nematodes and its naturally associated bacterial strain and (ii) that mutualism plays a role in several important life history traits of each partner such as access to insect host resources, dispersal and protection against various biotic and abiotic factors. The goal of the present study was to address the question of the impact of Xenorhabdus symbionts on the progression and outcome of interspecific competition between individuals belonging to different Steinernema species. For this, we monitored experimental interspecific competition between (i) two nematode species: S. carpocapsae and S. scapterisci and (ii) their respective symbionts: X. nematophila and X. innexi within an experimental insect-host (Galleria mellonella). Three conditions of competition between nematodes were tested: (i) infection of insects with aposymbiotic IJs (i.e. without symbiont) of both species (ii) infection of insects with aposymbiotic IJs of both species in presence of variable proportion of their two Xenorhabdus symbionts and (iii) infection of insects with symbiotic IJs (i.e. naturally associated with their symbionts) of both species.     

8-Aryl xanthines potent inhibitors of phosphodiesterase 5

In clinical studies, several inhibitors of phosphodiesterase 5 (PDE5) have demonstrated utility in the treatment of erectile dysfunction. We describe herein a series of 8-aryl xanthine derivatives which function as potent PDE5 inhibitors with, in many cases, high levels of selectivity versus other PDE isoforms.     

Drought-induced modifications of photosynthetic electron transport in intact leaves: Analysis and use of neural networks as a tool for a rapid non-invasive estimation

Water deficit is one of the most important environmental factors limiting sustainable crop yields and it requires a reliable tool for fast and precise quantification. In this work we use simultaneously recorded signals of photoinduced prompt fluorescence (PF) and delayed fluorescence (DF) as well as modulated reflection (MR) of light at 820nm for analysis of the changes in the photosynthetic activity in detached bean leaves during drying. Depending on the severity of the water deficit we identify different changes in the primary photosynthetic processes. When the relative water content (RWC) is decreased to 60% there is a parallel decrease in the ratio between the rate of excitation trapping in the Photosystem (PS) II reaction center and the rate of reoxidation of reduced PSII acceptors. A further decrease of RWC to 20% suppresses the electron transfer from the reduced plastoquinone pool to the PSI reaction center. At RWC below values 15%, the reoxidation of the photoreduced primary quinone acceptor of PSII, Q(A)(-), is inhibited and at less than 5%, the primary photochemical reactions in PSI and II are inactivated. Using the collected sets of PF, DF and MR signals, we construct and train an artificial neural network, capable of recognizing the RWC in a series of "unknown" samples with a correlation between calculated and gravimetrically determined RWC values of about R(2)≈0.98. Our results demonstrate that this is a reliable method for determination of RWC in detached leaves and after further development it could be used for quantifying of drought stress of crop plants in situ. This article is part of a Special Issue entitled: Photosynthesis Research for Sustainability: from Natural to Artificial.     

Using iRT, a normalized retention time for more targeted measurement of peptides

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.     

Triangular Relationship between Sleep Spindle Activity,General Cognitive Ability and the Efficiency of Declarative Learning

EEG sleep spindle activity (SpA) during non-rapid eye movement (NREM) sleep has been reported to be associated with measures of intelligence and overnight performance improvements. The reticular nucleus of the thalamus is generating sleep spindles in interaction with thalamocortical connections. The same system enables efficient encoding and processing during wakefulness. Thus, we examined if the triangular relationship between SpA, measures of intelligence and declarative learning reflect the efficiency of the thalamocortical system. As expected, SpA was associated with general cognitive ability, e.g. information processing speed. SpA was also associated with learning efficiency, however, not with overnight performance improvement in a declarative memory task. SpA might therefore reflect the efficiency of the thalamocortical network and can be seen as a marker for learning during encoding in wakefulness, i.e. learning efficiency.     

Targeting the extracellular membrane-proximal domain of membrane-bound IgE by passive immunization blocks IgE synthesis in vivo

The classical allergic reaction starts seconds or minutes after Ag contact and is committed by Abs produced by a special subset of B lymphocytes. These Abs belong to the IgE subclass and are responsible for Type I hyperreactivity reactions. Treatment of allergic diseases with humanized anti-IgE Abs leads primarily to a decrease of serum IgE levels. As a consequence, the number of high-affinity IgE receptors on mast cells and basophils decreases, leading to a lower excitability of the effector cells. The biological mechanism behind anti-IgE therapy remains partly speculative; however, it is likely that these Abs also interact with membrane IgE (mIgE) on B cells and possibly interfere with IgE production. In the present work, we raised a mouse mAb directed exclusively against the extracellular membrane-proximal domain of mIgE. The interaction between the monoclonal anti-mIgE Ab and mIgE induces receptor-mediated apoptosis in vitro. Passive immunization experiments lead to a block of newly synthesized specific IgEs during a parallel application of recombinant Bet v1a, the major birch pollen allergen. The decrease of allergen-specific serum IgE might be related to tolerance-inducing mechanisms stopping mIgE-displaying B cells in their proliferation and differentiation.     

Which Is Better: Holdout or Full-Sample Classifier Design?

Is it better to design a classifier and estimate its error on the full sample or to design a classifier on a training subset and estimate its error on the holdout test subset? Full-sample design provides the better classifier; nevertheless, one might choose holdout with the hope of better error estimation. A conservative criterion to decide the best course is to aim at a classifier whose error is less than a given bound. Then the choice between full-sample and holdout designs depends on which possesses the smaller expected bound. Using this criterion, we examine the choice between holdout and several full-sample error estimators using covariance models and a patient-data model. Full-sample design consistently outperforms holdout design. The relation between the two designs is revealed via a decomposition of the expected bound into the sum of the expected true error and the expected conditional standard deviation of the true error.