Stereochemistry of the N-glycosylation sites in glycoproteins

The stereochemical features displayed by the N-glycosidic linkagein crystalline N-linked glycoproteins are analyzed. From thestatistical analysis of 44 different glycosylation sites belongingto 26 glycoproteins of the Brookhaven Protein Data Bank, a meanstandard geometry for the GlcNAc moiety, along with a rationalizationof its confornia-tional behavior, can be proposed. As for theglycopeptide linkage, the distribution of observed conformationshas been analyzed on the basis of molecular mechanics calculations.The rotamer distribution of the Asn side chains conforms tothat observed on non-glycosylated structures, and it agreeswith the pattern of flexible conformations gathered from NMRmeasurements. In characterizing the protein-glycan interactions,some hydrogen bonds occur. Stacking between the amphiphilicmoiety of the glycan and some surrounding aromatic, or at leasthydrophobic, amino acid residues is also found. When lookingat the secondary structure of the glycosylated peptide, only25% of the glycosylation sites correspond to situations whereAsn is located at the top of a ß-turn. Other typesof secondary structure exist which fulfil the spatial requirementof having the glycan exposed at the surface of the protein.These data can be compared with the most recent studies on thepeptide conformation which would be required for glycosylation.     

A Pragmatic Approach to Reuse in Tactical Theorem Proving

In interactive theorem proving, tactics and tacticals have been introduced to automate proof search. In this scenario, user interaction traditionally is restricted to the mode in which the user decides which tactic to apply on the top-level, without being able to interact with the tactic once it has begun running.We propose a technique to allow the implementation of derivational analogy in tactical theorem proving. Instead of replaying tactics including backtracked dead ends our framework makes choice points in tactics explicit and thus avoids dead ends when reusing tactics. Additionally users can override choices a tactic has made or add additional steps to a derivation without terminating the tactic. The technique depends on an efficient replay of tactic executions without repeating search that the original computation may have involved.     

Stability Properties of Cerebellar Neural Networks: The Purkinje Cell — Climbing Fiber Dynamic Module

In the last few decades it has been proven, that the cerebellum takes part in learning the bulk of motor control. The mechanisms which provide such properties are still largely unknown, but an involvement of parallel fibers and climbing fibers in this process, as have been proposed decades ago in cerebellar learning theories, is now clear. Among difficulties of the learning theories is an evident necessity for spontaneous activity of the cerebellar climbing fibers [5]. Recently, the group of M. Mauk proposed an elegant explanation of this inconsistency [11, 12]. We present here a stochastic model of a cerebellar module, based on this new approach. Theoretical treatment yields some consequences for experimental verification. Besides an explanation of real cerebellar functions, the analyzed control system presents a new paradigm for neural network memorizing systems.     

Pyrene-adamantane-β-cyclodextrin: An efficient host–guest system for the biofunctionalization of SWCNT electrodes

The design of nanostructured biological architectures based on host–guest interactions between β-cyclodextrin and adamantane was investigated on SWCNT coatings using glucose oxidase (GOX) as biomolecule model. β-Cyclodextrin tagged GOX was immobilized on adamantane functionalized carbon nanotubes, deposited on platinum electrodes. Different functionalization techniques to attach “pyrene adamantane” on nanotubes were studied and compared in terms of the performances of the subsequently constructed glucose biosensors. The best results were obtained by dipping the nanotube deposit into a pyrene-adamantane solution followed by electropolymerization of the adsorbed pyrene monolayer. The constructed biosensor exhibited a good linear response toward glucose concentrations between 2 × 10⁻⁷ M and 1.6 × 10⁻³ M. The maximum current density and glucose sensitivity were 154.9 μA cm⁻² and 14.4 mA M⁻¹ cm⁻², respectively.     

Bayesian uncertainty analysis with applications to turbulence modeling

In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the Spalart-Allmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the Spalart-Allmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.     

The Role of Pericytes in Neurovascular Unit Remodeling in Brain Disorders

Neurons are extremely vulnerable cells that tightly rely on the brain’s highly dynamic and complex vascular network that assures an accurate and adequate distribution of nutrients and oxygen. The neurovascular unit (NVU) couples neuronal activity to vascular function, controls brain homeostasis, and maintains an optimal brain microenvironment adequate for neuronal survival by adjusting blood-brain barrier (BBB) parameters based on brain needs. The NVU is a heterogeneous structure constituted by different cell types that includes pericytes. Pericytes are localized at the abluminal side of brain microvessels and contribute to NVU function. Pericytes play essential roles in the development and maturation of the neurovascular system during embryogenesis and stability during adulthood. Initially, pericytes were described as contractile cells involved in controlling neurovascular tone. However, recent reports have shown that pericytes dynamically respond to stress induced by injury upon brain diseases, by chemically and physically communicating with neighboring cells, by their immune properties and by their potential pluripotent nature within the neurovascular niche. As such, in this paper, we would like to review the role of pericytes in NVU remodeling, and their potential as targets for NVU repair strategies and consequently neuroprotection in two pathophysiologically distinct brain disorders: ischemic stroke and Alzheimer’s disease (AD).     

Low‐Temperature Chemical Synthesis of High‐Purity Diacylglycerols (DAG) from Monoacylglycerols (MAG)

A chemical method was developed for low‐temperature synthesis of DAG from MAG followed by an easy purification procedure in order to obtain high‐purity DAG. Solvent‐assisted and solvent‐free reaction conditions were used, combined with different catalysts (sodium methoxide, p‐toluenesulfonic acid, methanesulfonic acid, and sulfuric acid). All reactions were performed at 35 and 70 °C. By increasing both acidity and polarity of the catalyst the equilibrium shifts towards the formation of DAG. When using sulfuric acid in solvent‐assisted condition at 70 °C, 88 % conversion was obtained after 20 min of reaction (77 % w/w DAG in the reaction mixture after evaporation of the solvent). After purifying by means of column chromatography, 96 % pure DAG were obtained. The overall yield of DAG was 81 %.     

Factors responsible for rapid dissipation of acidic herbicides in the coastal lagoons of the Camargue (Rhône River Delta, France)

This study was aimed at investigating which processes cause acidic herbicides (e.g., bentazone, MCPA and dichlorprop) to rapidly disappear in the lagoons of the Rhône delta, which are peculiar brackish and shallow aquatic environments. The use of the model MASAS (Modeling of Anthropogenic Substances in Aquatic Systems) revealed that sorption, sedimentation, volatilization, flushing and abiotic hydrolysis had a minor role in the attenuation of the investigated herbicides. Laboratory scale biodegradation and photodegradation studies were conducted to better assess the significance of these two processes in the natural attenuation of herbicides in brackish (lagoons) waters with respect to fresh waters (canals draining paddy fields). Herbicide biodegradation rates were significantly lower in lagoon water than in canal water. Consequently, photodegradation was the main dissipation route of all investigated herbicides. The contribution of indirect photolysis was relevant for MCPA and dichlorprop while direct photolysis dominated for bentazone removal. There is a need to further investigate the identity of phototransformation products of herbicides in lagoons.     

Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro

The present paper aims to demonstrate the interest of fuzzy inference systems in system modeling when human interaction is important. It discusses the originality of FIS and their capability to integrate expertise and rule learning from data into a single framework, analyzing their place relatively to concurrent approaches. An open source software implementation is presented, with a focus on the useful features for modeling. Two real world case studies are presented to illustrate the approach and the software utility.