Idealized Slab Plasma approach for the study of Warm Dense Matter

Recently, warm dense matter (WDM) has emerged as an interdisciplinary field that draws increasing interest in plasma physics, condensed matter physics, high pressure science, astrophysics, inertial confinement fusion, as well as materials science under extreme conditions. To allow the study of well-defined WDM states, we have introduced the concept of idealized-slab plasmas that can be realized in the laboratory via (i) the isochoric heating of a solid and (ii) the propagation of a shock wave in a solid. The application of this concept provides new means for probing the dynamic conductivity, equation of state, ionization and opacity. These approaches are presented here using results derived from first-principles (density-functional type) theory, Thomas-Fermi type theory, and numerical simulations.Comment: 37 pages, 21 figures, available, pdf file only. To appear in: Laser and Particle beams. To appear more or less in this form in Laser and Particle beam

Trace amine receptor (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Trace amine-associated receptors were discovered from a search for novel 5-HT receptors [9], where 15 mammalian orthologues were identified and divided into two families. The TA1 receptor (nomenclature as agreed by the NC-IUPHAR Subcommittee for the Trace amine receptor [53]) has affinity for the endogenous trace amines tyramine, β-phenylethylamine and octopamine in addition to the classical amine dopamine [9]. Emerging evidence suggests that TA1 is a modulator of monoaminergic activity in the brain [90] with TA1 and dopamine D2 receptors shown to form constitutive heterodimers when co-expressed [28]. In addition to trace amines, receptors can be activated by amphetamine-like psychostimulants, and endogenous thyronamines

The G protein-coupled receptor subset of the dog genome is more similar to that in humans than rodents

<p>Abstract</p> <p>Background</p> <p>The dog is an important model organism and it is considered to be closer to humans than rodents regarding metabolism and responses to drugs. The close relationship between humans and dogs over many centuries has lead to the diversity of the canine species, important genetic discoveries and an appreciation of the effects of old age in another species. The superfamily of G protein-coupled receptors (GPCRs) is one of the largest gene families in most mammals and the most exploited in terms of drug discovery. An accurate comparison of the GPCR repertoires in dog and human is valuable for the prediction of functional similarities and differences between the species.</p> <p>Results</p> <p>We searched the dog genome for non-olfactory GPCRs and obtained 353 full-length GPCR gene sequences, 18 incomplete sequences and 13 pseudogenes. We established relationships between human, dog, rat and mouse GPCRs resolving orthologous pairs and species-specific duplicates. We found that 12 dog GPCR genes are missing in humans while 24 human GPCR genes are not part of the dog GPCR repertoire. There is a higher number of orthologous pairs between dog and human that are conserved as compared with either mouse or rat. In almost all cases the differences observed between the dog and human genomes coincide with other variations in the rodent species. Several GPCR gene expansions characteristic for rodents are not found in dog.</p> <p>Conclusion</p> <p>The repertoire of dog non-olfactory GPCRs is more similar to the repertoire in humans as compared with the one in rodents. The comparison of the dog, human and rodent repertoires revealed several examples of species-specific gene duplications and deletions. This information is useful in the selection of model organisms for pharmacological experiments.</p

Photoionized plasma calculations using laboratory and astrophysical models

We present numerical simulations from the code GALAXY, frequently employed to model the distribution of excitation and ionization, and the spectral emission from laboratory plasma experiments. In particular, preliminary calculations relevant to the Lawrence Livermore National Laboratory photoionization collaboration are presented, along with results which compare GALAXY with results from the astrophysical code CLOUDY

Trace amine receptor in GtoPdb v.2023.1

Trace amine-associated receptors were discovered from a search for novel 5-HT receptors [9], where 15 mammalian orthologues were identified and divided into two families. The TA1 receptor (nomenclature as agreed by the NC-IUPHAR Subcommittee for the Trace amine receptor [58]) has affinity for the endogenous trace amines tyramine, &#946;-phenylethylamine and octopamine in addition to the classical amine dopamine [9]. Emerging evidence suggests that TA1 is a modulator of monoaminergic activity in the brain [94] with TA1 and dopamine D2 receptors shown to form constitutive heterodimers when co-expressed [30]. In addition to trace amines, receptors can be activated by amphetamine-like psychostimulants, and endogenous thyronamines

On the hierarchical classification of G Protein-Coupled Receptors

Motivation: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. Results: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases