scholarly journals Channel noise in nerve membranes and lipid bilayers

1975 ◽  
Vol 8 (4) ◽  
pp. 451-506 ◽  
Author(s):  
F Conti ◽  
E. Wanke

The basic principles underlying fluctuation phenomena in thermodynamics have long been understood (for reviews see Kubo, 1957; Kubo, Matsuo & Kazuhiro 1973 Lax, 1960). Classical examples of how fluctuation analysis can provide an insight into the corpuscular nature of matter are the determination of Avogadro's number according to Einstein's theory of Brownian motion (see, e.g. Uhlenbeck & Ornstein, 1930; Kac, 1947) and the evaluation of the electronic charge from the shot noise in vacuum tubes (see Van der Ziel, 1970).

2017 ◽  
Vol 19 (25) ◽  
pp. 16806-16818 ◽  
Author(s):  
M. Doktorova ◽  
D. Harries ◽  
G. Khelashvili

Computational methodology that allows to extract bending rigidity and tilt modulus for a wide range of single and multi-component lipid bilayers from real-space analysis of fluctuations in molecular dynamics simulations.


2011 ◽  
Vol 50 ◽  
pp. 43-61 ◽  
Author(s):  
Joseph K. Zolnerciks ◽  
Edward J. Andress ◽  
Michael Nicolaou ◽  
Kenneth J. Linton

ABC (ATP-binding cassette) transporters are primary active membrane proteins that translocate solutes (allocrites) across lipid bilayers. The prototypical ABC transporter consists of four domains: two cytoplasmic NBDs (nucleotide-binding domains) and two TMDs (transmembrane domains). The NBDs, whose primary sequence is highly conserved throughout the superfamily, bind and hydrolyse ATP to power the transport cycle. The TMDs, whose primary sequence and protein fold can be quite disparate, form the translocation pathway across the membrane and generally (but not always) determine allocrite specificity. Structure determination of ABC proteins initially took advantage of the relative ease of expression and crystallization of the hydrophilic bacterial NBDs in isolation from the transporter complex, and revealed detailed information on the structural fold of these domains, the amino acids involved in the binding and hydrolysis of nucleotide, and the head-to-tail arrangement of the NBD–NBD dimer interface. More recently, several intact transporters have been crystallized and three types have, so far, been characterized: type I and II ABC importers, and ABC exporters. All three are present in prokaryotes, but only the ABC exporters appear to be present in eukaryotes. Their structural determination has provided insight into the mechanisms of energy and signal transduction between the NBDs and TMDs (i.e. between the ATP- and allocrite-binding sites) and, for some, the nature of the allocrite-binding site(s) within the TMDs. In this chapter, we focus primarily on the ABC exporters and describe the structural, biochemical and biophysical evidence for and against the controversial bellows-like mechanism proposed for allocrite efflux.


Author(s):  
Isabel Abad-Álvaro ◽  
Diego Leite ◽  
Dorota Bartczak ◽  
Susana Cuello ◽  
Beatriz Gomez-Gomez ◽  
...  

Toxicological studies concerning nanomaterials in complex biological matrices usually require a carefully designed workflow that involves handling, transportation and preparation of a large number of samples without affecting the nanoparticle...


Author(s):  
Olga Wronikowska ◽  
Maria Zykubek ◽  
Agnieszka Michalak ◽  
Anna Pankowska ◽  
Paulina Kozioł ◽  
...  

AbstractMephedrone is a widely used drug of abuse, exerting its effects by interacting with monoamine transporters. Although this mechanism has been widely studied heretofore, little is known about the involvement of glutamatergic transmission in mephedrone effects. In this study, we comprehensively evaluated glutamatergic involvement in rewarding effects of mephedrone using an interdisciplinary approach including (1) behavioural study on effects of memantine (non-selective NMDA antagonist) on expression of mephedrone-induced conditioned place preference (CPP) in rats; (2) evaluation of glutamate concentrations in the hippocampus of rats following 6 days of mephedrone administration, using in vivo magnetic resonance spectroscopy (MRS); and (3) determination of glutamate levels in the hippocampus of rats treated with mephedrone and subjected to MRS, using ion-exchange chromatography. In the presented research, we confirmed priorly reported mephedrone-induced rewarding effects in the CPP paradigm and showed that memantine (5 mg/kg) was able to reverse the expression of this effect. MRS study showed that subchronic mephedrone administration increased glutamate level in the hippocampus when measured in vivo 24 h (5 mg/kg, 10 mg/kg and 20 mg/kg) and 2 weeks (5 mg/kg and 20 mg/kg) after last injection. Ex vivo chromatographic analysis did not show significant changes in hippocampal glutamate concentrations; however, it showed similar results as obtained in the MRS study proving its validity. Taken together, the presented study provides new insight into glutamatergic involvement in rewarding properties of mephedrone.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.


Author(s):  
Sean Keane ◽  
Karmun Cheng ◽  
Kaitlyn Korol

In-line inspection (ILI) tools play an important role within integrity management and substantial investment is made to continuously advance performance of the existing technologies and, where necessary, to develop new technologies. Performance measurement is typically focused for the purpose of understanding the measured performance in relation to the ILI vendor specification and for the determination of residual uncertainty regarding pipeline integrity. These performance measures may not provide the necessary insight into what type of investment into a technology is necessary to further reduce residual uncertainty regarding pipeline integrity, and beyond that, what investment, as an operator, results in an effective and efficient reduction in uncertainty. The paper proposes a reliability based approach for investigating uncertainty associated with ultrasonic crack ILI technology for the purpose of identifying efficient investment into the technology that results in an effective and measurable improvement. Typical performance measures and novel performance measurement methods are presented and reviewed with respect to what information they can provide to assist in investment decisions. Finally, general observations are made regarding Enbridge’s experience using ultrasonic crack ILI technology and areas currently being investigated.


Transport ◽  
2009 ◽  
Vol 24 (3) ◽  
pp. 192-199 ◽  
Author(s):  
Ilona Jaržemskienė

The measurement of terminal productivity is the issue of extreme importance to both terminal owners and management and customers. As the sector of transport is highly intensive in terms of investments into the infrastructure, the productivity of a terminal may play a crucial role in competing with other terminals. Productivity is defined in terms of inputs and output. The majority of the available studies, wherein this issue is addressed, are generally focused on the determination of functional dependence between inputs and output using the method of regressive analysis. The present article provides an insight into the Data Envelopment Analysis method as a tool for measuring productivity. This technique enables a rather accurate evaluation of terminal productivity by means of comparative analysis, which, in fact, appears to be the only feasible alternative in cases where statistic data required for performing regressive analysis is lacking.


Until quite recently no satisfactory equation had been obtained for the representation of the viscosity of dilute solutions of strong electrolytes. An empirical equation was recently proposed by Jones and Dole to fit the only accurate data then available. Their equation may be represented thus : η = 1 + A √ c + B c , η = relative viscosity of the solution c = concentration in moles per litre A and B are constants. Jones and Dole realized that the coefficient A is due to interionic forces and in a series of later publications Falkenhagen, Dole and Vernon have deduced a theoretical equation giving values of A in terms of well-known physical constants. Their complete equation may be written η = 1 + ε √N v 1 z 1 /30η 0 √1000D k T ( z 1 + z 2 ) 4 π × [¼ μ 1 z 2 + μ 2 z 1 / μ 1 μ 2 - z 1 z 2 (μ 1 - μ 2 ) 2 /μ 1 μ 2 (√μ 1 z 1 + μ 2 z 2 + √(μ 1 + μ 2 ) ( z 1 + z 2 ) ) 2 ]√ c , where N = Avogadro's number v 1 , v 2 = numbers of ions z 1 , z 2 = valencies of ions μ 1 , μ 2 = absolute mobilities of ions D = dielectric constant of solvent k = Boltzmann's constant ε = electronic charge η 0 = viscosity of solvent T = absolute temperature.


Sign in / Sign up

Export Citation Format

Share Document