Oxidation resistance of nano-reinforced PC-refractories modified with phenol formaldehyde resin. Part 4. Thermodynamic evaluation of phase formation within Mg–O–C–Al, Mg–O–C–Ni and МgO‒Al₂O₃‒NiO‒SiO₂ systems using SiC + Al + Ni (NiO) complex antioxidant

Results are given for the synthesis and co-existence of phases formed from components of complex organic- inorganic antioxidant formed during modification of phenol-formaldehyde resin (PFR) and graphite with silica alkoxide and inorganic or organic nickel precursors. Thermodynamic analysis is given for the Mg–Al–C and Mg–O–Ni–C systems. It is shown that the periclase and carbon can coexist with aluminum and nickel, and also that oxidized antioxidants Al₂O₃ and NiO can interact respectively with the periclase and with the synthesized SiC formed during modification of PFR with silica. In considering the NiO‒MgO‒Al₂O₃‒SiO₂ system it is established that during service noble spinel will be synthesized from the complex antioxidant components, facilitating an increase in PC-refractory durability in service

Анкилозирующий спондилоартрит в ревматологической практике Карелии

The paper describes the pathological aspects of an inflammatory process in ankylosing spondyloarthritis (AS), the role of muscle spasm in maintaining the intensity of pain syndrome and stiffness, the need for the early diagnosis of AS, and the significance of the early use of nonster-oidal anti-inflammatory drugs in these patients. The results of clinical trials and the authorsX data demonstrate the high efficacy and good tolerance of nimesulide (nise) in AS.Представлены патоморфологические аспекты воспалительного процесса при анкилозирующем спондилоартрите (АС), роль мышечного спазма в поддержании интенсивности болевого синдрома и скованности, необходимость ранней диагностики АС, значение раннего применения нестероидных противовоспалительных препаратов у таких пациентов. Результаты клинических исследований и собственные данные авторов демонстрируют высокую эффективность и хорошую переносимость нимесулида (найз) при АС

Features of fractal conformity and bioconsolidation in the early myogenesis gene expression and their relationship to the genetic diversity of chicken breeds

Simple Summary In the bodies of animals, including birds, gene expression leads to the synthesis of many proteins. To provide optimal cellular and organismal properties and functions, many genes should work in concert, reaching certain balanced relationships (or networks) between them and the intensities of their expression. Here, we studied the expression of several genes responsible for muscle formation and growth in chick embryos of diverse breeds belonging to various utility types. Using two mathematical (fractal) models and the respective indices, we showed that there are specific coordinated patterns of gene expression in the embryonic breast and thigh muscles. These patterns correlated with growth rate of chicks after hatching and depended on a utility type of the breeds studied. Overall, the proposed models contributed to an expanded understanding of the coordinated gene expression in early development and growth, providing additional characteristics of genetic diversity in chickens. Abstract Elements of fractal analysis are widely used in scientific research, including several biological disciplines. In this study, we hypothesized that chicken breed biodiversity manifests not only at the phenotypic level, but also at the genetic-system level in terms of different profiles of fractal conformity and bioconsolidation in the early myogenesis gene expression. To demonstrate this effect, we developed two mathematical models that describe the fractal nature of the expression of seven key genes in the embryonic breast and thigh muscles in eight breeds of meat, dual purpose, egg and game types. In the first model, we produced breed-specific coefficients of gene expression conformity in each muscle type using the slopes of regression dependencies, as well as an integral myogenesis gene expression index (MGEI). Additionally, breed fractal dimensions and integral myogenesis gene expression fractal dimension index (MGEFDI) were determined. The second gene expression model was based on plotting fractal portraits and calculating indices of fractal bioconsolidation. The bioconsolidation index of myogenesis gene expression correlated with the chick growth rate and nitric oxide (NO) oxidation rate. The proposed fractal models were instrumental in interpreting the genetic diversity of chickens at the level of gene expression for early myogenesis, NO metabolism and the postnatal growth of chicks

Unraveling signatures of chicken genetic diversity and divergent selection in breed-specific patterns of early myogenesis, nitric oxide metabolism and post-hatch growth

Due to long-term domestication, breeding and divergent selection, a vast genetic diversity in poultry currently exists, with various breeds being characterized by unique phenotypic and genetic features. Assuming that differences between chicken breeds divergently selected for economically and culturally important traits manifest as early as possible in development and growth stages, we aimed to explore breed-specific patterns and interrelations of embryo myogenesis, nitric oxide (NO) metabolism and post-hatch growth rate (GR). These characteristics were explored in eight breeds of different utility types (meat-type, dual purpose, egg-type, game, and fancy) by incubating 70 fertile eggs per breed. To screen the differential expression of seven key myogenesis associated genes (MSTN, GHR, MEF2C, MYOD1, MYOG, MYH1, and MYF5), quantitative real-time PCR was used. We found that myogenesis associated genes expressed in the breast and thigh muscles in a coordinated manner showing breed specificity as a genetic diversity signature among the breeds studied. Notably, coordinated (“accord”) expression patterns of MSTN, GHR, and MEFC2 were observed both in the breast and thigh muscles. Also, associated expression vectors were identified for MYOG and MYOD1 in the breast muscles and for MYOG and MYF5 genes in the thigh muscles. Indices of NO oxidation and post-hatch growth were generally concordant with utility types of breeds, with meat-types breeds demonstrating higher NO oxidation levels and greater GR values as compared to egg-type, dual purpose, game and fancy breeds. The results of this study suggest that differences in early myogenesis, NO metabolism and post-hatch growth are breed-specific; they appropriately reflect genetic diversity and accurately capture the evolutionary history of divergently selected chicken breeds

The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment

Breed-specific patterns of early myogenesis, nitric oxide metabolism, and post-hatch growth in relation to genetic diversity and divergent selection in chickens [Породоспецифичные модели раннего миогенеза, метаболизма оксида азота и постнатального роста в связи с генетическим разнообразием и разнонаправленной селекцией у кур]

Aims: There is currently a significant genetic diversity across poultry breeds as a result of long-term domestication, breeding, and divergent selection, with each breed having its own distinctive phenotypic and genetic characteristics [1,2]. We presumed and set out to investigate whether differences between chicken breeds divergently selected for economically and culturally significant traits [3] manifest as early as possible in development and growth stages. Methods: Breed-specific patterns and relationships of embryo myogenesis, nitric oxide (NO) metabolism, and post-hatch growth rate were studied and analyzed [4]. Results: Our research revealed that myogenesis genes were coordinatedly expressed in the thigh and breast muscles, demonstrating breed uniqueness. Indicators of NO oxidation and post-hatch growth were largely consistent with utility breed types, with meat breeds showing higher NO oxidation levels and better growth rate values in comparison to egg, dual purpose, game, and fancy breeds. Conclusions: The findings of this study indicate that breed-specific variations in early myogenesis, NO metabolism, and post-hatch growth adequately represent genetic variety and reliably depict the evolutionary history of diversely chosen chicken breeds

A Semantic Web Management Model for Integrative Biomedical Informatics

Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MD Anderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis

The multiple faces of self-assembled lipidic systems

Lipids, the building blocks of cells, common to every living organisms, have the propensity to self-assemble into well-defined structures over short and long-range spatial scales. The driving forces have their roots mainly in the hydrophobic effect and electrostatic interactions. Membranes in lamellar phase are ubiquitous in cellular compartments and can phase-separate upon mixing lipids in different liquid-crystalline states. Hexagonal phases and especially cubic phases can be synthesized and observed in vivo as well. Membrane often closes up into a vesicle whose shape is determined by the interplay of curvature, area difference elasticity and line tension energies, and can adopt the form of a sphere, a tube, a prolate, a starfish and many more. Complexes made of lipids and polyelectrolytes or inorganic materials exhibit a rich diversity of structural morphologies due to additional interactions which become increasingly hard to track without the aid of suitable computer models. From the plasma membrane of archaebacteria to gene delivery, self-assembled lipidic systems have left their mark in cell biology and nanobiotechnology; however, the underlying physics is yet to be fully unraveled

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards