Establishment of a Bivector Genetic Transformation System in Recalcitrant Maize Inbred Lines

Maize is an important grain crop with high nutritional value. An effective transformation system is crucial for the genetic improvement of maize traits, but many important maize inbred lines remained recalcitrant to transformation. In this study, we developed a bivector transformation system that worked well in two recalcitrant maize inbred lines. This system included an induction vector (ZmBBM-ZmWUS) and an indicator vector (GFP), using microprojectile bombardment technology combined with Agrobacterium-mediated transformation. We found that the Zheng58 and Mo17 recalcitrant inbred lines could be transformed with this system. The whole transformation cycle lasted only 52 days, 38 days less than the traditional transformation cycle. Additionally, it was possible to eliminate inference of the induction vector and obtained progenies with only the target gene. Our results suggested that the bivector system was an optimization of the current maize transformation methods and could potentially be used in genetic improvement of maize inbred lines.

Endo-irreversible thermo-mechanical Carnot engine with new concept of entropy production action coefficient

Thermostatics of CARNOT engines has been extended by more recent research based on endo-reversible model. Our model assumes exo-reversibility but endo-irreversibility to determine new upper-bound to thermomechanical conversion. We propose a functional expression of entropy production related to transformation cycle durations. This approach analyses the energy, entropy and power consequences. We introduce a new concept of entropy production actions that results in three optimums : maximum energy related to transformation durations, maximum energy associated with equipartition of entropy actions, optimal power for given period cycle. Keywords : thermodynamics, efficiency, optimization, energy, power, Carnot engine

Category-Theoretic Formulation of Model-Based Systems Architecting: The Concept-Model-Graph-View-Concept Transformation Cycle

We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.

Long-Term Transformational Cycle of a Conjuncture of the Agrarian Food Market of Russia

The article reveals the features and structure of the Russian specific agricultural transformation cycle of 1990-2019, which is characterized by a special dynamics of the long-term situation of the agri-food market with its own phases (periods) of development. The necessity of applying special forms and methods of state regulation of demand and supply at the stages of recovery and recovery growth is justified. Scenarios of the post-transformation cycle of the long-term conjuncture in the agri-food market with long-term phases of rise and a new quality of economic growth are considered.