Effect of iodothyronine hormone status on doxorubicin related cardiotoxicity

The anthracycline anticancer agent doxorubicin has been recognised to induce a dose-dependent cardiotoxicity. The chronic form of such complication is characterized by an irreversible cardiac damage and congestive heart failure. Although the pathogenesis of anthracycline cardiotoxicity seems to be multifactorial, the pivotal role has been attributed to reactive oxygen species formation. Because redox equilibrium in cardiomyocytes may be regulated via iodothyronine hormones, the aim of the study was to appraise the effect of hypothyroidism on heart damages induced by doxorubicin. The rats received methimazole in drinking water (0.001 and 0.025%) after doxorubicin administration (2.0, 5.0 and 15 mg/kg). The cardiac morphology and blood biochemical markers of heart damage were assessed. Decreased levels of iodothyronine hormones had not significant impact on cardiac morphological changes and no effect on the level of B-type natriuretic peptide in rats receiving doxorubicin. Lower hormonal levels had sporadic, diverse effect on blood transaminases, lactate dehydrogenase and creatine kinase levels, but any relation to time, doxorubicin doses and hypothyroid status was found. Hypothyreosis leads to increase in fatty acid binding protein in rats receiving higher dose of doxorubicin. Hypothyreosis had no effect on heart stretching and on necrosis at morphological level, but caused biochemical symptoms of cardiomyocyte necrosis in rats receiving doxorubicin

Automatic Induction of Classification Rules from Examples Using N-Prism

www.dis.port.ac.uk/~bramerma One of the key technologies of data mining is the automatic induction of rules from examples, particularly the induction of classification rules. Most work in this field has concentrated on the generation of such rules in the intermediate form of decision trees. An alternative approach is to generate modular classification rules directly from the examples. This paper seeks to establish a revised form of the rule generation algorithm Prism as a credible candidate for use in the automatic induction of classification rules from examples in practical domains where noise may be present and where predicting the classification for previously unseen instances is the primary focus of attention

A Window into the Early–Middle Stone Age Transition in Northeastern Africa—A Marine Isotope Stage 7a/6 Late Acheulean Horizon from the EDAR 135 Site, Eastern Sahara (Sudan)

This paper presents the results of the analysis of a late Acheulean horizon from the EDAR 135 site, which was discovered in the Eastern Desert, Sudan, in an area heavily transformed by modern mining activity. A lithic assemblage was discovered there, within a layer of gravel sediments formed by a paleostream in a humid period of the Middle Pleistocene. This layer is OSL dated between 220 ± 12 and 145 ± 20 ka (MIS 7a/6). These dates indicate that the assemblage could be the youngest trace of the Acheulean in northeastern Africa. Technological analysis of the lithics reveals different core reduction strategies, including not only ad hoc ones based on multiplatform cores, but also discoidal and prepared cores. The use of prepared core reduction methods has already been confirmed at other Late Acheulean sites in Africa and the Middle East. Microwear traces observed on lithic artifacts could relate to on-site butchering activities

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system

Predicting phishing websites based on self-structuring neural network

Internet has become an essential component of our everyday social and financial activities. Nevertheless, internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer’s confidence in e-commerce and online banking. Phishing is considered as a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain confidential information such as usernames, passwords and social security number. So far, there is no single solution that can capture every phishing attack. In this article, we proposed an intelligent model for predicting phishing attacks based on artificial neural network particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of web pages are constantly changing. Thus, we need to constantly improve the network structure in order to cope with these changes. Our model solves this problem by automating the process of structuring the network and shows high acceptance for noisy data, fault tolerance and high prediction accuracy. Several experiments were conducted in our research, and the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability