Evidence for a Lowest Energy 3MLCT Excited State in [Fe(tpy)(CN)3]–

Transient absorption data of [FeII(tpy)(CN)3]- reveals spectroscopic signatures indicative of 3MLCT with a ~10 ps kinetic component. These data are supported by DFT and TD-DFT calculations, which show that excited...

MATHEMATICAL MODELING OF THE RATIONAL CHOICE OF THE SIMPLEST MECHANISMS OF ECONOMIC INTEGRATION

The article concerns a certain economic and mathematical approach to the rational choice problem analysis of economic systems integration mechanisms. It is believed that the essence of integration lies in rearrangement of derived net product between the systems for their development, and the means of mentioned rearrangement determine the possible integration mechanisms of relevant economic systems. The mathematical model of two economic systems integration is built to analyse the processes of economies confunction in view of different mechanisms of their integration. At the model’s core are the elements of the well-known model of Solow economic dynamics, describing its production constituent. The model made of two differential equations systems reflecting the evolution of fixed production assets of consolidated economic systems with various mechanisms of their integration. Within the framework of the developed model is proposed an approach for rational choice of economic integration mechanisms, which relies on constructing and analyzing charts of the optimal ways of the examined economic systems confunction. There the domains of selected options of economic systems development model are represented, for which the best is an appropriate mechanism of their integration. This article presents the examples of constructing and analyzing both separate charts of the optimal integration mechanisms of economic systems and their certain sequence. It reflects the transformation processes of the corresponding areas of charts under the influence of some internal and external factors. As such factors were considered the criterion of the integration mechanism choice and the intensity of the scientific and technological progress impact. The latter comprises a kinetic component which was introduced into the constructed model and is responsible for the influence of autonomous scientific and technological progress on the development of investigated economic systems.

COMPUTER INTERFACES OF THE BRAIN FOR NEUROREHABILITATION - ITS CURRENT STATE AS A STRATEGY FOR REHABILITATION AFTER A STROKE

The idea of using computer-integrated interfaces (BCIs) for rehabilitation comes relatively recently. In essence, BCI for neurological rehabilitation involves recording and decoding the patient's local brain signals when attempting to perform a task (even if incomplete), or during the creation of a mental image. The main goal is to attract individual parts of the brain and promote neuroplasticity. Recorded labeling can be used in various ways: (1) distinguishing and improving kinematic training by feedbacking the patient using a fictitious kinetic component, for example in a virtual environment; (2) achieving the desired physical activity through robotic orthotics or motivational rehabilitation. Functionality associated with the limbs of the patient - encourages and improves task performance, even "closing" a broken sensory ring, providing the patient with appropriate sensory responses; (3) understand brain reorganization after injury, influence or even measure plasticity changes in brain networks. For example, using brain stimulation to restore balance between the two hemispheres, as evidenced by a functional recording of brain activity during movement, may help recovery. Its potential benefit to patients has been demonstrated at various levels, and its versatility in frontal applications makes it adaptable to a large population. The condition and condition of many new rehabilitation systems must be assessed in relation to our current and somewhat tried-and-tested traditional methods, as well as the wide range of possible brain damage. The heterogeneity of expression after injury inevitably leads to the decoding of brain symptoms and thus its use in pathological conditions, requiring controlled clinical trials.

The Kinetic Component in Drug Discovery: Using the Most Basic Pharmacological Concepts to Advance in Selecting Drugs to Combat CNS Diseases

To reach the central nervous system (CNS), drugs must cross the brain-blood barrier and have appropriate pharmacokinetic/dynamic properties. However, in early drug discovery steps, the selection of lead compounds, for example, those targeting G-protein-coupled receptors (GPCRs), is made according to i) affinity, which is calculated in in vitro equilibrium conditions, and ii) potency, a signal transduction-related parameter, usually quantified at a fixed time-point in a heterologous expression system. This paper argues that kinetics must be considered in the early steps of lead compound selection. While affinity calculation requires the establishment of a ligand-receptor equilibrium, the signal transduction starts as soon as the receptor senses the agonist. Taking cAMP production as an example, the in vitro-measured cytoplasmic levels of this cyclic nucleotide do not depend on equilibrium dissociation constant, KD. Signaling occurs far from the equilibrium and correlates more with the binding rate (kon) than with KD. Furthermore, residence time, a parameter to consider in lead optimization, may significantly vary from in vitro to in vivo conditions. The results are discussed from the perspective of dopaminergic neurotransmission and dopaminereceptor- based drug discovery.

Single-residue physicochemical characteristics kinetically partition membrane protein self-assembly and aggregation

Ninety-five percent of all transmembrane proteins exist in kinetically trapped aggregation-prone states that have been directly linked to neurodegenerative diseases. Interestingly, the primary sequence almost invariably avoids off-pathway aggregate formation, by folding reliably into its native, thermodynamically stabilized structure. However, with the rising incidence of protein aggregation diseases, it is now important to understand the underlying mechanism(s) of membrane protein aggregation. Micromolecular physicochemical and biochemical alterations in the primary sequence that trigger the formation of macromolecular cross-β aggregates can be measured only through combinatorial spectroscopic experiments. Here, we developed spectroscopic thermal perturbation with 117 experimental variables to assess how subtle protein sequence variations drive the molecular transition of the folded protein to oligomeric aggregates. Using the Yersinia pestis outer transmembrane β-barrel Ail as a model, we delineated how a single-residue substitution that alters the membrane-anchoring ability of Ail significantly contributes to the kinetic component of Ail stability. We additionally observed a stabilizing role for interface aliphatics, and that interface aromatics physicochemically contribute to Ail self-assembly and aggregation. Moreover, our method identified the formation of structured oligomeric intermediates during Ail aggregation. We show that the self-aggregation tendency of Ail is offset by the evolution of a thermodynamically compromised primary sequence that balances folding, stability, and oligomerization. Our approach provides critical information on how subtle changes in protein primary sequence trigger cross-β fibril formation, with insights that have direct implications for deducing the molecular progression of neurodegeneration and amyloidogenesis in humans.

Fully Nonlocal Functionals Along the Adiabatic Connection of Density Functional Theory

Inspired by the exact form of the strongly interacting limit of density functional theory, Vuckovic and Gori Giorgi have recently proposed [J. Phys. Chem. Lett. 2017, 8, 2799] the multiple radii functional (MRF), a new framework for the construction of exchange-correlation (xc) energy approximations able to describe strong correlation electronic effects. To facilitate the construction of improved approximations based on the MRF functional, in the present work we use reverse engineering strategies to reveal the forms of the MRF functional which reproduce the exact xc functional for small atoms. We also develop a procedure that allows the MRF functional to be built on the top of exact exchange. Using the adiabatic connection representation of the xc functional, we also investigate routes for the construction of the correlation functional by combining information from the physical, weakly and strongly interacting regimes. We highlight the advantages of this approach over previous adiabatic connection-based approaches for the treatment of strong correlation and discuss how it can be used for recovering the presently missing kinetic component of the correlation energy in the MRF framework.<br> <pre><br></pre>

Fully Nonlocal Functionals Along the Adiabatic Connection of Density Functional Theory

Inspired by the exact form of the strongly interacting limit of density functional theory, Vuckovic and Gori Giorgi have recently proposed [J. Phys. Chem. Lett. 2017, 8, 2799] the multiple radii functional (MRF), a new framework for the construction of exchange-correlation (xc) energy approximations able to describe strong correlation electronic effects. To facilitate the construction of improved approximations based on the MRF functional, in the present work we use reverse engineering strategies to reveal the forms of the MRF functional which reproduce the exact xc functional for small atoms. We also develop a procedure that allows the MRF functional to be built on the top of exact exchange. Using the adiabatic connection representation of the xc functional, we also investigate routes for the construction of the correlation functional by combining information from the physical, weakly and strongly interacting regimes. We highlight the advantages of this approach over previous adiabatic connection-based approaches for the treatment of strong correlation and discuss how it can be used for recovering the presently missing kinetic component of the correlation energy in the MRF framework.<br> <pre><br></pre>