Cannabidiol modulates excitatory-inhibitory ratio to counter hippocampal hyperactivity

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Transitions between irregular and rhythmic firing patterns in excitatory-inhibitory neuronal networks

Changes in firing patterns are an important hallmark of the functional status of neuronal networks. We apply dynamical systems methods to understand transitions between irregular and rhythmic firing in an excitatory-inhibitory neuronal network model. Using the geometric theory of singular perturbations, we systematically reduce the full model to a simpler set of equations, one that can be studied analytically. The analytic tools are used to understand how an excitatory-inhibitory network with a fixed architecture can generate both activity patterns for possibly different values of the intrinsic and synaptic parameters. These results are applied to a recently developed model for the subthalamopallidal network of the basal ganglia. The results suggest that an increase in correlated activity, corresponding to a pathological state, may be due to an increased level of inhibition from the striatum to the inhibitory GPe cells along with an increased ability of the excitatory STN neurons to generate rebound bursts. Action Editor: Carson Chow     

Object selection by an oscillatory neural network

We describe a new solution to the problem of consecutive selection of objects in a visual scene by an oscillatory neural network with the global interaction realised through a central executive element (central oscillator). The frequency coding is used to represent greyscale images in the network. The functioning of the network is based on three main principles: (1) the synchronisation of oscillators via phase-locking, (2) adaptation of the natural frequency of the central oscillator, and (3) resonant increase of the amplitudes of the oscillators which work in-phase with the central oscillator. Examples of network simulations are presented to show the reliability of the results of consecutive selection of objects under conditions of constant and varying brightness of the objects.     

Assembling an intermediate filament network by dynamic cotranslation

We have been able to observe the dynamic interactions between a specific messenger RNA (mRNA) and its protein product in vivo by studying the synthesis and assembly of peripherin intermediate filaments (IFs). The results show that peripherin mRNA-containing particles (messenger ribonucleoproteins [mRNPs]) move mainly along microtubules (MT). These mRNPs are translationally silent, initiating translation when they cease moving. Many peripherin mRNPs contain multiple mRNAs, possibly amplifying the total amount of protein synthesized within these "translation factories." This mRNA clustering is dependent on MT, regulatory sequences within the RNA and the nascent protein. Peripherin is cotranslationally assembled into insoluble, nonfilamentous particles that are precursors to the long IF that form extensive cytoskeletal networks. The results show that the motility and targeting of peripherin mRNPs, their translational control, and the assembly of an IF cytoskeletal system are linked together in a process we have termed dynamic cotranslation.     

Purification of acetohydroxy acid synthase by separation in an aqueous two-phase system

Extraction in a polyethylene glycol (PEG)–phosphate aqueous two-phase system was considered as a primary step in purification of the acetohydroxy acid synthase III large catalytic subunit from an E. coli extract. Extraction optimization was achieved by varying the system parameters. Two systems with the following weight compositions were chosen for purification: PEG-2000 (16%)–phosphate (6%) and PEG-4000 (14%)–phosphate (5.5%)–KCl (8%), both at pH 7.0 and 1 mg total protein per 1 g system. Significant purification was achieved by a single extraction step with 70% recovery of the enzyme. After an additional ion-exchange chromatography step, pure enzyme was obtained in a 50% overall yield.     

Preparative-scale amino acid separation by thermal parametric pumping on an ion-exchange resin

Thermal parametric pumping was experimentally investigated for the concentration and separation of amino acids. Previous theories of parametric pumping were improved by taking into account dissociation equilibria in the liquid phase. Experiments were carried out with a mixture of glutamic acid, aspartic acid, serine and threonine in a highly acidic solution (HCl). A multi-component equilibrium model was mainy used to simulate the experimental results and to investigate the effect of chloride concentration over a wide range. It is shown that it is always possible to concentrate the amino acids and to separate some of them under certain conditions.     

Pattern-recognition by an artificial network derived from biologic neuronal systems

A novel artificial neural network, derived from neurobiological observations, is described and examples of its performance are presented. This DYnamically STable Associative Learning (DYSTAL) network associatively learns both correlations and anticorrelations, and can be configured to classify or restore patterns with only a change in the number of output units. DYSTAL exhibits some particularly desirable properties: computational effort scales linearly with the number of connections, i.e., it is0(N) in complexity; performance of the network is stable with respect to network parameters over wide ranges of their values and over the size of the input field; storage of a very large number of patterns is possible; patterns need not be orthogonal; network connections are not restricted to multi-layer feed-forward or any other specific structure; and, for a known set of deterministic input patterns, the network weights can be computed, a priori, in closed form. The network has been associatively trained to perform the XOR function as well as other classification tasks. The network has also been trained to restore patterns obscured by binary or analog noise. Neither global nor local feedback connections are required during learning; hence the network is particularly suitable for hardware (VLSI) implementation.     

Technology of streptomycin sulfate separation by two-stage foam separation

Industrial discharges from manufacturing streptomycin sulfate (SS) are inhibitory to biological wastewater treatment and need to be stripped of residual SS. For effective SS recovery from the wastewater, a two-stage foam separation technology was investigated using a column with a vertical ellipsoid-shaped channel (VEC) and a conventional one, and sodium dodecyl sulfate (SDS) served as the collector. The mechanism of enhancing foam drainage by VEC was theoretically analyzed. In the first stage, the column with VEC was used and under the optimal conditions of the liquid-loading volume 300 mL, volumetric airflow rate 100 mL/min, the initial pH 7.0 and the molar ratio of SDS to SS 8.0, an improved SS enrichment ratio of 16.7 was obtained. In the second stage, a conventional column was used and with a volumetric airflow rate of 450 mL/min, the foamate had a SS concentration of about 0.5 g/L, so it was used as the feed solution of the first stage. By the two-stage technology, the total SS recovery percentage reached as high as 99.7%. Thus, it was significantly effective for the two-stage foam separation technology to recover SS from the simulative wastewater.     

Texturising by phase separation

The most common food processing operations, thermal and mechanical treatments, both affect phase state and texture of foods. The phase separation threshold for food biopolymer mixtures is usually below their concentrations characteristically found in food. Phase-separation underpins texturisation processes during food processing and digestion. The distribution of water between the phases, the adsorption of lipids between the phases, the deformation of dispersed particles in flowing water-in-water emulsions and the gelation of the phases result in the specific morphology and texture of food. A historical experience in texturisation includes the development of cooked food and texturized vegetable analogues of animal foods.     

Rapid separation of solasodine glycosides by an immunoaffinity column using anti-solamargine monoclonal antibody

Immunoaffinity column using anti-solamargine monoclonal antibody for separation of solasodine glycosides was established. This method was specific for solasodine glycosides which was detected by thin layer chromatography and the western blotting. Total solasodine glycosides have been separated directly from the crude extract of Solanum khasianum fruit by the newly established immunoaffinity column. This revised version was published online in August 2006 with corrections to the Cover Date.     

Phase separation in an aqueous quaternary system

(1) We have measured the incompatible phase separation that occurs in a polyethylene glycol-sodium dextran sulphate-sodium chloride-water system and have determined a critical point. (2) We have measured the activity coefficients of sodium chloride in critical-point concentrations of polyethylene glycol and sodium dextran sulphate respectively, and the osmotic coefficient of sodium dextran sulphate at the critical-point concentration. (3) With use of the relevant thermodynamic equations for a quaternary ionic system, we have determined the interaction coefficients between polyethylene glycol and dextran sulphate and between polyethylene glycol and sodium chloride. The former could be due mainly to volume exclusion, but the latter is too large to be explained on that basis.     

A model of an autocatalytic network formed by error-prone self-replicative species

A generalized model ofn catalytically-coupled self-replicative molecules witherror-prone replication is presented. A generalized mathematical formulation of this model and the outline of its asymptotic behaviour have been developed. Due to the complexity of the model, only in simple situations is it possible to draw general conclusions from the standard analysis. Some complex situations are illustrated by means of numerical integration of particular examples.     

Improvements in protein secondary structure prediction by an enhanced neural network

Computational neural networks have recently been used to predict the mapping between protein sequence and secondary structure. They have proven adequate for determining the first-order dependence between these two sets, but have, until now, been unable to garner higher-order information that helps determine secondary structure. By adding neural network units that detect periodicities in the input sequence, we have modestly increased the secondary structure prediction accuracy. The use of tertiary structural class causes a marked increase in accuracy. The best case prediction was 79% for the class of all-alpha proteins. A scheme for employing neural networks to validate and refine structural hypotheses is proposed. The operational difficulties of applying a learning algorithm to a dataset where sequence heterogeneity is under-represented and where local and global effects are inadequately partitioned are discussed.     

Prediction of protein secondary structure by an enhanced neural network.

Computational model of neural network is used for prediction of secondary structure of globular proteins of known sequence. In contrast to earlier works some information about expected tertiary interactions were built in into the neural network. As a result the prediction accuracy was improved by 3% to 5%. Possible applications of this new approach are briefly discussed.     

Synthesis of PHBV block copolymers driven by an oscillatory genetic network

Artificial genetic networks constitute a powerful tool to achieve various biotechnological objectives. In this work, we propose the modification of an oscillatory genetic network, known as the repressilator, to drive synthesis of poly(3hydroxybutyrate-co-3hydroxyvalerate) (PHBV) block copolymer chains in recombinant Escherichia coli cells. To study the feasibility of this idea, we developed a detailed mathematical model describing the dynamics of the genetic network, which drive the formation of monomer units that are subsequently incorporated into actively growing block copolymer chains. Extensive simulation studies have shown that appropriate choice of the molecular characteristics of the network and manipulation of extracelllular conditions lead to tight control of both the micro- and macro-structures of the resulting block copolymer chains. Thus, the model can guide network design aiming at producing block copolymer structures with desirable characteristics.