The role of myeloid receptors on murine plasmacytoid dendritic cells in induction of type I interferon

This study tested the hypothesis that a set of predominantly myeloid restricted receptors (F4/80, CD36, Dectin-1, CD200 receptor and mannan binding lectins) and the broadly expressed CD200 played a role in a key function of plasmacytoid DC (pDC), virally induced type I interferon (IFN) production. The Dectin-1 ligands zymosan, glucan phosphate and the anti-Dectin-1 monoclonal antibody (mAb) 2A11 had no effect on influenza virus induced IFNα/β production by murine splenic pDC. However, mannan, a broad blocking reagent against mannose specific receptors, inhibited IFNα/β production by pDC in response to inactivated influenza virus. Moreover, viral glycoproteins (influenza virus haemagglutinin and HIV-1 gp120) stimulated IFNα/β production by splenocytes in a mannan-inhibitable manner, implicating the function of a lectin in glycoprotein induced IFN production. Lastly, the effect of CD200 on IFN induction was investigated. CD200 knock-out macrophages produced more IFNα than wild-type macrophages in response to polyI:C, a MyD88-independent stimulus, consistent with CD200's known inhibitory effect on myeloid cells. In contrast, blocking CD200 with an anti-CD200 mAb resulted in reduced IFNα production by pDC-containing splenocytes in response to CpG and influenza virus (MyD88-dependent stimuli). This suggests there could be a differential effect of CD200 on MyD88 dependent and independent IFN induction pathways in pDC and macrophages. This study supports the hypothesis that a mannan-inhibitable lectin and CD200 are involved in virally induced type I IFN induction.

Rensch's rule in large herbivorous mammals derived from metabolic scaling

Rensch’s rule, which states that the magnitude of sexual size dimorphism tends to increase with increasing body size, has evolved independently in three lineages of large herbivorous mammals: bovids (antelopes), cervids (deer), and macropodids (kangaroos). This pattern can be explained by a model that combines allometry,life-history theory, and energetics. The key features are thatfemale group size increases with increasing body size and that males have evolved under sexual selection to grow large enough to control these groups of females. The model predicts relationships among body size and female group size, male and female age at first breeding,death and growth rates, and energy allocation of males to produce body mass and weapons. Model predictions are well supported by data for these megaherbivores. The model suggests hypotheses for why some other sexually dimorphic taxa, such as primates and pinnipeds(seals and sea lions), do or do not conform to Rensh’s rule.

Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

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.