Pharmacological activity of new imidazole-4,5-dicarboxylic acid derivatives in dopaminergic transmission suppression ttests in mice and rats

Objective: To study the antiparkinsonian activity of new 1,2-substituted imidazole-4,5-dicarboxylic acids in dopaminergic transmission suppression tests in mice and rats. Materials and methods: On a model of reserpine extrapyramidal disorders, the derivatives of imidazole-dicarboxylic acids (IEM2258, IEM2248, IEM2247) were injected into the lateral brain ventricles of the mice 30 minutes after injecting reserpine at the doses of 0.1–0.5 mmol. Locomotor activity was analyzed in the Open-field test 2 hours later. In the catalepsy model, the studied agents were injected, using a pre-implanted cannula, with a simultaneous intraperitoneal injection of haloperidol. The severity of catalepsy was assessed with the Morpurgo method. Amantadine was used as a comparator drug in all the tests. Results: It was shown that IEM2258 significantly increased the main indicators of locomotor activity in the Open-field test at all the studied doses. The value of the antiparkinsonian effect of IEM2258 at doses of 0.4–0.5 mmol significantly exceeded that of amantadine. The antiparkinsonian effect of IEM2247 was maximally expressed and was significantly different from those in the control and comparator group at doses of 0.2 and 0.3 mmol. For all the experimental groups, a significant decrease in the manifestations of catalepsy in comparison with control indexes was determined. Discussion: The results made it possible to suggest the involvement of imidazole-4,5-dicarboxylic acids derivatives in the process of experimental improvement of dopaminergic neuromodulation and efficiency in animals. Conclusion: The data showed a significant dose-dependent antiparkinsonian activity of new imidazole-4,5-dicarboxylic acid derivatives, which makes it promising to develop these agents and to further search for effective and safe antiparkinsonian drugs in this pharmacological class. Graphical abstract

Practice of Experimental Improvement and Development of Space Robotic Systems

The paper discusses issues of applying space robotic systems (RSs), shows the role and place of humanoid RSs as high-tech service systems used to support activity of cosmonauts when implementing future space programs. It also considers the principles for the creation of a versatile computer-assisted humanoid RS to conduct psycho-physiological and ergonomic studies, to mature the skills of controlling various RSs using virtual reality technologies and means to monitor and control the motion and behavioral activity of an operator.

Develop machine learning-based regression predictive models for engineering protein solubility

Motivation Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning (ML) models for improving protein solubility in vivo. Results We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combining it with various ML algorithms, we achieved a R2 of 0.4115 when support vector machine algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value—there are only two possible values so many proteins have the same one. Availability and implementation We present the ML workflow as a series of IPython notebooks hosted on GitHub (https://github.com/xiaomizhou616/protein_solubility). The workflow can be used as a template for analysis of other expression and solubility datasets. Supplementary information Supplementary data are available at Bioinformatics online.

براہوئی شاعری ٹی لوزی آزموندہ

This research article shows the experimental improvement in Bráhuí poetry although Bráhuí poetry experiences prosperous itself regarding style, rhyme and genre since 1990. It further proves that senior most lyrists have initiated lexical experiment in Bráhuí poetry. When some ones thoughts, opinions and feelings would dresses up with stylistic experience, then it shows the hardship, sorrows' and trial of life, it is known as self defined poetry too, Which deals with your inner life activity. Such feelings generate lexical experiment and improvement in Bráhuí. This was the period when Bráhuí "ĢAZAL" reached its Destination.