eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Carbon monoxide induces cardiac arrhythmia via induction of the late Na+ current

Our data indicate that the proarrhythmic effects of CO arise from activation of NO synthase, leading to NO-mediated nitrosylation of Na(V)1.5 and to induction of the late Na(+) current. We also show that the antianginal drug ranolazine can abolish CO-induced early after-depolarizations, highlighting a novel approach to the treatment of CO-induced arrhythmias.

The induction of mental and contact contamination

Background: Extreme fear of contamination within Obsessive Compulsive Disorder is traditionally conceptualised as a physical phenomenon. More recent research has supported the notion of ‘mental’ contamination, in which people feel contaminated in the absence of physical contact. The current research sought to determine whether feelings of contact and mental contamination could be induced within a non-clinical sample, whether the impact of mental and contact contamination was comparable in terms of associated feelings and behaviour and whether related psychopathology related to the impact of the tasks. Methods: Undergraduate students (n=60) completed OCD relevant measures and were randomly assigned to either a contact contamination condition (CC: moving a bucket of fake vomit) or a mental contamination condition (MC: thinking about a bucket of vomit). Results: Both manipulations induced feelings of contamination. Participants in the contact condition had significantly greater urges to wash than those in the mental condition. Neutralising behaviour did not differ across conditions. Conclusions: Feelings of contamination can be induced in the absence of physical contact and for those in the MC group, some aspects of OCD-relevant psychopathology were related to the impact of the manipulation. These findings have implications for the understanding and treatment of contamination-related fears in OCD.

Repressor element 1 silencing transcription factor couples loss of pluripotency with neural induction and neural differentiation

Neural differentiation of embryonic stem cells (ESCs) requires coordinated repression of the pluripotency regulatory program and reciprocal activation of the neurogenic regulatory program. Upon neural induction, ESCs rapidly repress expression of pluripotency genes followed by staged activation of neural progenitor and differentiated neuronal and glial genes. The transcriptional factors that underlie maintenance of pluripotency are partially characterized whereas those underlying neural induction are much less explored, and the factors that coordinate these two developmental programs are completely unknown. One transcription factor, REST (repressor element 1 silencing transcription factor), has been linked with terminal differentiation of neural progenitors and more recently, and controversially, with control of pluripotency. Here, we show that in the absence of REST, coordination of pluripotency and neural induction is lost and there is a resultant delay in repression of pluripotency genes and a precocious activation of both neural progenitor and differentiated neuronal and glial genes. Furthermore, we show that REST is not required for production of radial glia-like progenitors but is required for their subsequent maintenance and differentiation into neurons, oligodendrocytes, and astrocytes. We propose that REST acts as a regulatory hub that coordinates timely repression of pluripotency with neural induction and neural differentiation.

The effect of proteolysis on the induction of cell death by monomeric alpha-lactalbumin.

α-Lactalbumin (α-la) is a major whey protein found in milk. Previous data suggested that α-la has antiproliferative effects in human adenocarcinoma cell lines such as Caco-2 and HT-29. However, the cell death inducing α-la was not a naturally occurring monomer but either a multimeric variant or an α-la:oleic acid complex (HAMLET/BAMLET). Proteolysis showed that both human and bovine α-la are susceptible to digestion. ELISA assays assessing cell death with the native undigested α-la fractions showed that undigested protein fractions did have a significant cell death effect on CaCo-2 cells. Bovine α-la was also more effective than human α-la. A reduction in activity corresponded with lower concentrations of the protein and partial digestion and fragmentation of the protein using trypsin and pepsin. This suggests that the tertiary structure is vital for the apoptotic effect.

Rule Type Identification using TRCM for trend analysis in Twitter

This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Categorization and construction of rule based systems

Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then‘ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

Rule-based morphological processing in a second language: a behavioural investigation

According to dual-system accounts of English past-tense processing, regular forms are decomposed into their stem and affix (played=play+ed) based on an implicit linguistic rule, whereas irregular forms (kept) are retrieved directly from the mental lexicon. In second language (L2) processing research, it has been suggested that L2 learners do not have rule-based decomposing abilities, so they process regular past-tense forms similarly to irregular ones (Silva & Clahsen 2008), without applying the morphological rule. The present study investigates morphological processing of regular and irregular verbs in Greek-English L2 learners and native English speakers. In a masked-priming experiment with regular and irregular prime-target verb pairs (playedplay/kept-keep), native speakers showed priming effects for regular pairs, compared to unrelated pairs, indicating decomposition; conversely, L2 learners showed inhibitory effects. At the same time, both groups revealed priming effects for irregular pairs. We discuss these findings in the light of available theories on L2 morphological processing.

Civilian casualties and nuclear weapons: the application of the rule of distinction

This chapter considers the possible use in armed conflict of low-yield (also known as tactical) nuclear weapons. The Legality of the Threat or Use of Nuclear Weapons Advisory Opinion maintained that it is a cardinal principle that a State must never make civilians an object of attack and must consequently never use weapons that are incapable of distinguishing between civilian and military targets. As international humanitarian law applies equally to any use of nuclear weapons, it is argued that there is no use of nuclear weapons that could spare civilian casualties particularly if you view the long-term health and environmental effects of the use of such weaponry.

Depth to mate and the 50-move rule

The most popular endgame tables (EGTs) documenting ‘DTM’ Depth to Mate in chess endgames are those of Eugene Nalimov but these do not recognise the FIDE 50-move rule ‘50mr’. This paper marks the creation by the first author of EGTs for sub-6-man (s6m) chess and beyond which give DTM as affected by the ply count pc. The results are put into the context of previous work recognising the 50mr and are compared with the original unmoderated DTM results. The work is also notable for being the first EGT generation work to use the functional programming language HASKELL.

Quantitative single-molecule localization microscopy combined with rule-based modeling reveals ligand-induced TNF-R1 reorganization toward higher-order oligomers

We report on the assembly of tumor necrosis factor receptor 1 (TNF-R1) prior to ligand activation and its ligand-induced reorganization at the cell membrane. We apply single-molecule localization microscopy to obtain quantitative information on receptor cluster sizes and copy numbers. Our data suggest a dimeric pre-assembly of TNF-R1, as well as receptor reorganization toward higher oligomeric states with stable populations comprising three to six TNF-R1. Our experimental results directly serve as input parameters for computational modeling of the ligand-receptor interaction. Simulations corroborate the experimental finding of higher-order oligomeric states. This work is a first demonstration how quantitative, super-resolution and advanced microscopy can be used for systems biology approaches at the single-molecule and single-cell level.