Acrylamide (AA) is a highly reactive organic compound capable of polymerization to form polyacrylamide, which is commonly used throughout a variety of industries. Given its toxic effect on humans and animals, the last 20 years have seen an increased interest in research devoted to the AA. One of the main sources of AA is food. AA appears in heated food following the reaction between amino acids and reduced sugars. Large concentrations of AA can be found in popular staples such as coffee, bread or potato products. An average daily consumption of AA is between 0.3-2.0 μg/kg b.w. Inhalation of acrylamide is related with occupational exposure. AA delivered with food is metabolized in the liver by cytochrome P450. AA biotransformation and elimination result in formation of toxic glycidamide (GA). Both, AA and GA can be involved in the coupling reaction with the reduced glutathione (GSH) forming glutathione conjugates which are excreted with urine. Biotransformation of AA leads to the disturbance in the redox balance. Numerous research proved that AA and GA have significant influence on physiological functions including signal propagation in peripheral nerves, enzymatic and hormonal regulation, functions of muscles, reproduction etc. In addition AA and GA show neurotoxic, genotoxic and cancerogenic properties. In 1994, International Agency for Research on Cancer (IARC) classified acrylamide as a potentially carcinogenic substance to human., M. Semla, Z. Goc, M. Martiniaková, R. Omelka, G. Formicki., and Obsahuje bibliografii
Abstract: By action model, we understand any logic-based representation of effects and executability preconditions of individual actions within a certain domain. In the context of artificial intelligence, such models are necessary for planning and goal-oriented automated behaviour. Currently, action models are commonly hand-written by domain experts in advance. However, since this process is often difficult, time-consuming, and error-prone, it makes sense to let agents learn the effects and conditions of actions from their own observations. Even though the research in the area of action learning, as a certain kind of inductive reasoning, is relatively young, there already exist several distinctive action learning methods. We will try to identify the collection of the most important properties of these methods, or challenges that they are trying to overcome, and briefly outline their impact on practical applications., Abstrakt: Podle akčního modelu chápeme logickou reprezentaci efektů a předpokladů vykonatelnosti jednotlivých akcí v rámci určité domény. V kontextu umělé inteligence jsou tyto modely nezbytné pro plánování a cílené automatizované chování. V současné době jsou akční modely běžně ručně psány odborníky domény předem. Vzhledem k tomu, že tento proces je často obtížný, časově náročný a náchylný k chybám, má smysl nechat agenty seznámit se s účinky a podmínkami akcí z vlastních pozorování. I když je výzkum v oblasti akčního učení, jako určitý druh indukčního uvažování, relativně mladý, existuje již několik výrazných metod učení. Pokusíme se identifikovat sbírku nejdůležitějších vlastností těchto metod., and Michal Čertický
Helicobacter pylori has been implicated in stimulation of immune system, development of autoimmune endocrinopathies as autoimmune thyroiditis (AT) and on other hand induction of immunosupresion activates gastric and extra-gastric diseases such as gastric ulcer or cancer. It causes persistent lifelong infection despite local and systemic immune response. Our results indicate that Helicobacter pylori might cause inhibition of the specific cellular immune response in Helicobacter pyloriinfected patients with or without autoimmune diseases such as AT. We cannot also declare the carcinogenic effect in oropharynx. However the association of any infection agents and cancerogenesis exists. The adherence of Helicobacter pylori expression and enlargement of benign lymphatic tissue and the high incidence of the DNA of Helicobacter pylori in laryngopharyngeal and oropharyngeal cancer is reality. LTT appears to be a good tool for detection of immune memory cellular response in patients with Helicobacter pylori infection and AT. All these complications of Helicobacter pylori infection can be abrogated by successful eradication of Helicobacter pylori., J. Astl, I. Šterzl., and Obsahuje bibliografii
Trusses are suitable load-bearing structural systems for heavy concentrated loads. In this paper, it is shown that it is possible to use active control mechanisms to enhance the load-bearing capacity of the trusses. Under heavy loading, some elernents of a truss might experience high stresses and show non-linear behavior, resulting in large deformations in the truss. Under such a condition, some elernents of the truss might damage which can lead to the collapse of the truss. Application of control forces on some of the degrees of freedom of the truss can render help the truss tolerate larger forces before its collapse. A neural network can then be trained to learn the relationship between the Information about the external loads on the truss, as input, and the required control forces, as output, and act as a neuro-controller for the truss. This method is explained and then tested on a smáli truss to show the capabilities of the method.