Model Checking via Reachability Testing for Timed Automata

In this paper we develop an approach to model-checking for timed automata via reachability testing. As our specification formalism, we consider a dense-time logic with clocks. This logic may be used to express safety and bounded liveness properties of real-time systems. We show how to automatically synthesize, for every logical formula phi, a so-called test automaton T_phi in such a way that checking whether a system S satisfies the property phi can be reduced to a reachability question over the system obtained by making T_phi interact with S. The testable logic we consider is both of practical and theoretical interest. On the practical side, we have used the logic, and the associated approach to model-checking via reachability testing it supports, in the specification and verification in Uppaal of a collision avoidance protocol. On the theoretical side, we show that the logic is powerful enough to permit the definition of characteristic properties, with respect to a timed version ofthe ready simulation preorder, for nodes of deterministic, tau-free timed automata. This allows one to compute behavioural relations via our model-checking technique, therefore effectively reducing the problem of checking the existence of a behavioural relation among states of a timed automaton to a reachability problem

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important

Functional selectivity of adenosine receptor ligands

Adenosine receptors are plasma membrane proteins that transduce an extracellular signal into the interior of the cell. Basically every mammalian cell expresses at least one of the four adenosine receptor subtypes. Recent insight in signal transduction cascades teaches us that the current classification of receptor ligands into agonists, antagonists, and inverse agonists relies very much on the experimental setup that was used. Upon activation of the receptors by the ubiquitous endogenous ligand adenosine they engage classical G protein-mediated pathways, resulting in production of second messengers and activation of kinases. Besides this well-described G protein-mediated signaling pathway, adenosine receptors activate scaffold proteins such as β-arrestins. Using innovative and sensitive experimental tools, it has been possible to detect ligands that preferentially stimulate the β-arrestin pathway over the G protein-mediated signal transduction route, or vice versa. This phenomenon is referred to as functional selectivity or biased signaling and implies that an antagonist for one pathway may be a full agonist for the other signaling route. Functional selectivity makes it necessary to redefine the functional properties of currently used adenosine receptor ligands and opens possibilities for new and more selective ligands. This review focuses on the current knowledge of functionally selective adenosine receptor ligands and on G protein-independent signaling of adenosine receptors through scaffold proteins

Analog data transmission via fiber optics

In the SLAC Linear Collider Detector (SLD), as in most high-energy particle detectors, the electromagnetic noise environment is the limiting factor in electronic readout performance. Front-end electronics are particulary susceptible to electromagnetic interference (EMI), and great care has been taken to minimize its effects. The transfer of preprocessed analog signals from the detector environs, to the remote digital processing electronics, by conventional means (via metal conductors), may ultimately limit the performance of the system. Because it is highly impervious to EMI and ground loops, a fiber-optic medium has been chosen for the transmission of these signals. This paper describes several fiber-optic transmission schemes which satisfy the requirements of the SLD analog data transmission

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines\u27 performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha -1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario

Presynaptic control of striatal glutamatergic neurotransmission by adenosine A(1)-A(2A) receptor heteromers

The functional role of heteromers of G-protein-coupled receptors is a matter of debate. In the present study, we demonstrate that heteromerization of adenosine A1 receptors (A1Rs) and A2A receptors (A2ARs) allows adenosine to exert a fine-tuning modulation of glutamatergic neurotransmission. By means of coimmunoprecipitation, bioluminescence and time-resolved fluorescence resonance energy transfer techniques, we showed the existence of A1R-A2AR heteromers in the cell surface of cotransfected cells. Immunogold detection and coimmunoprecipitation experiments indicated that A1R and A2AR are colocalized in the same striatal glutamatergic nerve terminals. Radioligand-binding experiments in cotransfected cells and rat striatum showed that a main biochemical characteristic of the A1R-A2AR heteromer is the ability of A2AR activation to reduce the affinity of the A1R for agonists. This provides a switch mechanism by which low and high concentrations of adenosine inhibit and stimulate, respectively, glutamate release. Furthermore, it is also shown that A1R-A2AR heteromers constitute a unique target for caffeine and that chronic caffeine treatment leads to modifications in the function of the A1R-A2AR heteromer that could underlie the strong tolerance to the psychomotor effects of caffeine