Structurally Diverse Histone Deacetylase Photoreactive Probes: Design,Synthesis, and Photolabeling Studies in Live Cells and Tissue

Histone deacetylase (HDAC) activity is modulated in vivo by post-translational modifications and formation of multiprotein complexes. Novel chemical tools to study how these factors affect engagement of HDAC isoforms by HDAC inhibitors (HDACi) in cells and tissues are needed. In this study, a synthetic strategy to access chemically diverse photoreactive probes (PRPs) was developed and used to prepare seven novel HDAC PRPs 9 – 15 . The class I HDAC isoform engagement by PRPs was determined in biochemical assays and photolabeling experiments in live SET-2, HepG2, HuH7, and HEK293T cell lines and in mouse liver tissue. Unlike the HDAC protein abundance and biochemical activity against recombinant HDACs, the chemotype of the PRPs and the type of cells were key in defining the engagement of HDAC isoforms in live cells. Our findings suggest that engagement of HDAC isoforms by HDACi in vivo may be substantially modulated in a cell- and tissue-type-dependent manner.     

Biotechnical measurement and software system controlled features for determining the level of psycho-emotional tension on man-machine systems by fuzzy measures

A new software technique for determining emotional tension on humans is developed by means of psychological tests connected with the psycho-emotional sphere and on indicators characterizing the state of a subject’s visual attention. Each of these two areas determines several factors for psycho-emotional tension. The level of tension is determined as an aggregate of these two components with the rules of fuzzy logic. The membership functions and solving rules for the psychological tests, and for the state of visual attention, are constructed using fuzzy logic theory. It is shown that the resulting method can be used as an informative feature for prediction of many properties, such as the situational awareness (training and experience) of an operator and the operator’s individual capacity to function (mental-physiological limits). The second group of indicators characterize properties such as switchable attention (the ability to switch attention) (SWA), concentration (ability to concentrate) (CNA), stability of visual attention (STA) and parameters determining the state of memory. One of the most important issues for man-machine systems is to evaluate the performance of operators under abnormal conditions such as stress or tension.     

Optimization of parallel firewalls filtering rules

As filtering policies are getting larger and more complex, packet filtering at firewalls needs to keep low delays. New firewall architectures are needed to enforce security and meet the increasing demand for high-speed networks. Two main architectures exist for parallelization, data-parallel and function-parallel firewalls. In the first, packets are distributed across a set of identical firewalls that implement the entire policy. In the second, each firewall implements a subset of the policy with a fewer number of rules, but the packets have to be duplicated and processed by all the firewalls. This paper proposes a new architecture function-parallel with pre-processing that combines the advantages of both architectures. The proposed architecture has the advantage of not duplicating the data, so that the processing time can be significantly reduced. Moreover, our architecture enables stateful inspection of packets, which is necessary to prevent multiple types of attacks. The performances of this architecture have been proven to be scalable for large security policies.

     

Development of a rapid prototyping design advice system

This paper describes the initial development of a computer based Rapid Prototyping Design Advice System. The system is intended to assist the designer or project manager, particularly those in small and medium sized companies, in planning the prototyping stage of product development. It provides the user with an efficient and user friendly development aid which uses information obtained from the user and Computer Aided Design data to recommend suitable rapid prototyping solutions.     

Realtime multi-aircraft tracking in aerial scene with deep orientation network

Tracking the aircrafts from an aerial view is very challenging due to large appearance, perspective angle, and orientation variations. The deep-patch orientation network (DON) method was proposed for the multi-ground target tracking system, which is general and can learn the target’s orientation based on the structure information in the training samples. Such approach leverages the performance of tracking-by-detection framework into two aspects: one is to improve the detectability of the targets by using the patch-based model for the target localization in the detection component and the other is to enhance motion characteristics of the individual tracks by incorporating the orientation information as an association metric in the tracking component. Based on the DON structure, you only look once (YOLO) and faster region convolutional neural network (FrRCNN) detection frameworks with simple online and realtime tracking (SORT) tracker are utilized as a case study. The Comparative experiments demonstrate that the overall detection accuracy is improved at the same processing speed of both detection frameworks. Furthermore, the number of Identity switches (IDsw) has reduced about 67% without affecting the computational complexity of the tracking component. Consequently, the presented method is efficient for realtime ground target-tracking scenarios.     

Software features for the estimation of an operators’ group activity in man-machine system

Methods for the selection of candidates for operators of man-machine systems are analyzed. Vector mean estimates of group intelligence and estimates defining the group’s collective decision-making ability to obtain the unified solution to the selection problem and to retain the correct original solution to the problem, and estimates of solution quality and interoperability in correct decision-making are proposed. The properties of the suggested estimates are studied with a test example of five candidates. Amongst these properties are estimates of the group’s psychological traits, such as average estimates of professional competence, static components of the intelligence vector for tested candidates, components of the trainability vector for tested candidates, average intelligence estimates for tested candidates, average estimates for the basis of groups, average different-mindedness estimates of groups, variations in the trainability of operators, and arithmetic estimates of group intelligence variability such as levels of awareness, knowledge level, mean speed of knowledge variation, comprehension and motivation in operators’ reasoning.     

Pole assignment of multivariable systems using proportional-derivative state feedback

In this article, the parametric solution to the pole assignment problem for multivariable linear time-invariant systems controlled by proportional-derivative (PD) state feedback is developed. The new expressions for the PD gain controllers are derived which describe the available degrees of freedom offered by PD state feedback. The freedom provided by PD state gain matrices is utilised to obtain closed-loop systems with robust and small gain matrices. Two computational algorithms are introduced, and their effectiveness is demonstrated by two simulation examples.     

Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing

This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.