Synthesis of Novel Phosphonic‐Type Activity‐Based Probes for Neutrophil Serine Proteases and Their Application in Spleen Lysates of Different Organisms

Neutrophils are a type of granulocyte important in the “first line of defense” of the innate immune system. Upon activation, they facilitate the destruction of invading microorganisms by the production of superoxide radicals, as well as the release of the enzymatic contents of their lysozymes. These enzymes include specific serine proteases: cathepsin G, neutrophil elastase, proteinase 3, as well as the recently discovered neutrophil serine protease 4 (NSP4). Under normal conditions, the proteolytic activity of neutrophil proteases is tightly regulated by endogenous serpins; however, this mechanism can be subverted during tissue stress, thereby resulting in the uncontrolled activity of serine proteases, which induce chronic inflammation and subsequent pathology. Herein, we describe the development of low‐molecular‐weight activity‐based probes that specifically target the active sites of neutrophil proteases.     

Parameter identification and slip estimation of induction machine

This paper presents a newly developed algorithm for induction machine rotor speed estimation and parameter detection. The proposed algorithm is based on spectrum analysis of the stator current. The main idea is to find the best fit of motor parameters and rotor slip with the group of characteristic frequencies which are always present in the current spectrum. Rotor speed and parameters such as pole pairs or number of rotor slots are the results of the presented algorithm. Numerical calculations show that the method yields very accurate results and can be an important part of machine monitoring systems.     

A study on using data clustering for feature extraction to improve the quality of classification

There is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions, so a question arises, in which scenarios exactly does it help? Since we were unable to find a rigorous study addressing this question, in this paper, we try to shed some light on the concept of using clustering for classification. To do so, we first put forward a framework for incorporating clustering as a method of feature extraction for classification. The framework is generic w.r.t. similarity measures, clustering algorithms, classifiers, and datasets and serves as a platform to answer ten essential questions regarding the studied subject. Each answer is formulated based on a separate experiment on 16 publicly available datasets, followed by an appropriate statistical analysis. After performing the experiments and analyzing the results separately, we discuss them from a global perspective and form general conclusions regarding using clustering as feature extraction for classification.

     

Methods of eliminating pollutant emissions from the work environment in welding and related processes

Abstract

The welding and joining of dissimilar metals which have very different properties, such as aluminium and carbon steel, is considered to be a subject for research and development in the welding/joining sector continuing into the 21st century. There are also huge requirements and expectations for this sector.¹ Due to the aforementioned, the research and development of welding and joining of dissimilar materials have been carried out over many years; for instance, eutectic bonding of copper pipe and aluminium pipe was developed 30 years ago and this process is still applied for the heat pipes of refrigerators. Recently it has even progressed for applications in joining of wide plate materials of aluminium alloy and stainless steel by means of the vacuum rolling process² and also for weldments of aluminium alloy and carbon steel joined by means of friction welding and employed as automobile components.³ However, there are problems from aspects of cost and restrictions concerning the configurations for which joining is feasible using conventional welding and joining processes and these techniques have not yet reached the stage where they can be applied in a number of industrial sectors. Accordingly, an extensive programme of research and development has been deployed in recent years using fusion welding processes, such as electron beam and laser welding and brazing, diffusion bonding and also friction stir welding (FSW).⁴     

Influence of nanoparticles of platinum on chicken embryo development and brain morphology

Platinum nanoparticles (NP-Pt) are noble metal nanoparticles with unique physiochemical properties that have recently elicited much interest in medical research. However, we still know little about their toxicity and influence on general health. We investigated effects of NP-Pt on the growth and development of the chicken embryo model with emphasis on brain tissue micro- and ultrastructure. The embryos were administered solutions of NP-Pt injected in ovo at concentrations from 1 to 20 μg/ml. The results demonstrate that NP-Pt did not affect the growth and development of the embryos; however, they induced apoptosis and decreased the number of proliferating cells in the brain tissue. These preliminary results indicate that properties of NP-Pt might be utilized in brain cancer therapy, but potential toxic side effects must be elucidated in extensive follow-up research.