5-Arylidenerhodanines as P-gp Modulators: An Interesting Effect of the Carboxyl Group on ABCB1 Function in Multidrug-Resistant Cancer Cells

Multidrug resistance (MDR) is considered one of the major mechanisms responsible for the failure of numerous anticancer and antiviral chemotherapies. Various strategies to overcome the MDR phenomenon have been developed, and one of the most attractive research directions is focused on the inhibition of MDR transporters, membrane proteins that extrude cytotoxic drugs from living cells. Here, we report the results of our studies on a series newly synthesized of 5-arylidenerhodanines and their ability to inhibit the ABCB1 efflux pump in mouse T-lymphoma cancer cells. In the series, compounds possessing a triphenylamine moiety and the carboxyl group in their structure were of particular interest. These amphiphilic compounds showed over 17-fold stronger efflux pump inhibitory effects than verapamil. The cytotoxic and antiproliferative effects of target rhodanines on T-lymphoma cells were also investigated. A putative binding mode for 11, one of the most potent P-gp inhibitors tested here, was predicted by molecular docking studies and discussed with regard to the binding mode of verapamil

Generalization of ALMM Based Learning Method for Planning and Scheduling

This paper refers to a machine learning method for solving NP-hard discrete optimization problems, especially planning and scheduling. The method utilizes a special multistage decision process modeling paradigm referred to as the Algebraic Logical Metamodel based learning methods of Multistage Decision Processes (ALMM). Hence, the name of the presented method is the ALMM Based Learning method. This learning method utilizes a specifically built local multicriterion optimization problem that is solved by means of scalarization. This paper describes both the development of such local optimization problems and the concept of the learning process with the fractional derivative mechanism itself. It includes proofs of theorems showing that the ALMM Based Learning method can be defined for a much broader problem class than initially assumed. This significantly extends the range of the prime learning method applications. New generalizations for the prime ALMM Based Learning method, as well as some essential comments on a comparison of Reinforcement Learning with the ALMM Based Learning, are also presented

Synchronization of distributed robotics control system

The paper presents the novel project that has been proposed and realized together with the implementation of the process of heating thermosetting components based on advanced industrial equipment and software tools. In the fundamental task of optimization, the phenomenon of process synchronization based on a centralized algorithm adapted to the specific requirements of production was taken into account. The proposed research station was optimized and located in the laboratory of the Interdisciplinary Computer Modelling of the University of Rzeszow, that specializes in raising the efficiency of industrial processe

Intelligent ALMM System - implementation assumptions for its Knowledge Base

The paper introduces the concept of implementation assumptions about the Knowledge Base (KB) system cooperating with intelligent information system for the discrete optimization of problem solving, named Intelligent ALMM System. This system utilizes a modeling paradigm named Algebraic Logical Meta Model of Multistage Decision Processes (ALMM) and its theory, both developed by Dudek-Dyduch E. The system solves combinatorial and discrete optimization problems including NP-hard problems with possible user assistance. The models of problems are stored in a Problem Model Library. In this paper the idea of KB for the storage of the properties of problems is presented. The concept of the KB on problems presented in previous works has been extended by introducing an additional module pertaining to the properties of a problems library. A discussion was presented in the context of the selection of tools that enable the construction of such a library as well as its architecture. In the adopted strategy of storing the properties of problems, the interface for exchanging information is compatible with the library of problems using polymorphic and component properties of object-oriented programming. Considerations are explained by means of a sample UML diagram and interface prototypes

State Analysis of the Water Quality in Rivers in Consideration of Diffusion Phenomenon

The waters of rivers are not only used for consumption, industry and agriculture but have also found their way into the transport and energy generation sectors. Many disturbances introduced into the aquatic environment are of the natural variety, which are the result of “admixtures” contained in water, e.g., through contact with soil, and of man-made types, which are directly related to humanities destructive influences. In the presented examinations, the most important processes affecting the spread and transport of these pollutants are taken into account, i.e., advection and diffusion. The authors present observations on the influence of the diffusion phenomenon on river flow modelling processes. Such an approach allows for the separation of the dynamics of water flow and the dynamics of transport of the dissolved substance mass. Specifically, phenomena occurring in relation to spatial coordinates, time and variable parameter values in the proposed mathematical model were analysed. Ultimately, this research will contribute to the correct design and implementation of a complementary diffusion module as an extension to an intelligent water quality control and monitoring system. The Intelligent Analytical Computing Control System architecture under development already includes other modules such as the Intelligent Filtration and Prediction Module and, complemented by the Intelligent Diffusion Module, provides a complementary tool for monitoring river hydromorphology. Implementation of the above solution will help to improve water quality, thus preventing and eliminating the appearance of undesirable pollutants in rivers, and increase the standard of living in the current threatened environmental world

Intelligent ALMM System - implementation assumptions for its Knowledge Base

The paper introduces the concept of implementation assumptions about the Knowledge Base (KB) system cooperating with intelligent information system for the discrete optimization of problem solving, named Intelligent ALMM System. This system utilizes a modeling paradigm named Algebraic Logical Meta Model of Multistage Decision Processes (ALMM) and its theory, both developed by Dudek-Dyduch E. The system solves combinatorial and discrete optimization problems including NP-hard problems with possible user assistance. The models of problems are stored in a Problem Model Library. In this paper the idea of KB for the storage of the properties of problems is presented. The concept of the KB on problems presented in previous works has been extended by introducing an additional module pertaining to the properties of a problems library. A discussion was presented in the context of the selection of tools that enable the construction of such a library as well as its architecture. In the adopted strategy of storing the properties of problems, the interface for exchanging information is compatible with the library of problems using polymorphic and component properties of object-oriented programming. Considerations are explained by means of a sample UML diagram and interface prototypes

Use of a DNN in Recording and Analysis of Operator Attention in Advanced HMI Systems

The main objective of this research was to propose a smart technology to record and analyse the attention of operators of transportation devices where human–machine interaction occurs. Four simulators were used in this study: General Aviation (GA), Remotely Piloted Aircraft System (RPAS), AS 1600, and Czajka, in which a spatio-temporal trajectory of system operator attention describing the histogram distribution of cockpit instrument observations was sought. Detection of the position of individual instruments in the video stream recorded by the eyetracker was accomplished using a pre-trained Fast R-CNN deep neural network. The training set for the network was constructed using a modified Kanade–Lucas–Tomasi (KLT) algorithm, which was applied to optimise the labelling of the cockpit instruments of each simulator. A deep neural network allows for sustained instrument tracking in situations where classical algorithms stop their work due to introduced noise. A mechanism for the flexible selection of Area Of Interest (AOI) objects that can be tracked in the recorded video stream was used to analyse the recorded attention using a mobile eyetracker. The obtained data allow for further analysis of key skills in the education of operators of such systems. The use of deep neural networks as a detector for selected instrument types has made it possible to universalise the use of this technology for observer attention analysis when applied to a different objects-sets of monitoring and control instruments

Exocyclic Sulfur and Selenoorganic Compounds Towards Their Anticancer Effects: Crystallographic and Biological Studies

BACKGROUND/AIM: Multidrug resistance leads to therapeutic difficulties. There is great interest in experimental chemotherapy regarding multidrug resistance inhibitors and new anticancer agents. The aim of this study was to evaluate the anticancer activity of exocyclic sulfur and selenoorganic compounds on mouse T-lymphoma cell lines. MATERIALS AND METHODS: A series of eighteen sulfur and selenium analogues of 2[1H]-pyrimidinone and hydantoin derivatives were evaluated towards their efflux modulating, cytotoxic and antiproliferative effects in mouse T-lymphoma cells. The combination assay with doxorubicin on multidrug resistant mouse T-lymphoma cells was performed in order to see the nature of drug interactions. Crystal structures were determined for two selected compounds with the highest efflux-modulating activity. RESULTS: The sulfur analogues with aromatic rings almost perpendicular to pyrimidinethione ring at positions 1 and 6 showed the highest efflux inhibitory action, while all selenium analogues showed good antiproliferative and cytotoxic activities. CONCLUSION: The sulfur analogues can be modified towards improving their efflux inhibitory activity, whereas the selenium towards antiproliferative and cytotoxic activities

Using Artificial Neural Networks to Solve the Problem Represented by BOD and DO Indicators

The paper presents a new approach to solving the problem of water quality control in rivers. We proposed an intelligent system that monitors and controls the quality of water in a river. The distributed measuring system works with a central control system that uses the intelligent analytical computing system. The Biochemical Oxygen Demand (BOD) and Dissolved Oxygens (DO) index was used to assess the state of water quality. Because the results for the DO measurement are immediate, while the measurement of the BOD parameter is performed in a laboratory environment over a period of several days, we used Artificial Neural Networks (ANN) for immediate estimation BOD to overcome the problem of controlling river water quality in real time. Mathematical models of varying complexity that represent indicators of water quality in the form of BOD and DO were presented and described with ordinary and distributed-parameters differential equations. The two-layered feed-forward neural network learned with supervised strategy has been tasked with estimating the BOD state coordinate. Using classic ANN properties, the difficult-to-measure river ecological state parameters interpolation effect was achieved. The quality of the estimation obtained in this way was compared to the quality of the estimation obtained using the Kalman–Bucy filter. Based on the results of simulation studies obtained, it was proved that it is possible to control river aeration based on the measurements of particular state coordinates and the use of an intelligent module that completes the “knowledge” concerning unmeasured data. The presented models can be further applied to describe other cascade objects