Anti-proliferative effects of physiological concentrations of enterolactone in models of prostate tumourigenesis

SCOPE: There is evidence that a mammalian lignan, enterolactone (ENL), decreases the proliferation rate of prostate cancer cells, although previous studies have used concentrations difficult to achieve through dietary modification. We have therefore investigated the anti-proliferative effects of ENL in an in vitro model of prostate tumourigenesis at concentrations reported to occur in a range of male populations. METHODS AND RESULTS: The effects of 0.1 and 1 μM ENL on three markers of viability and proliferation (metabolic activity, growth kinetics, and cell cycle progression) were assessed in the RWPE-1, WPE1-NA22, WPE1-NB14, WPE1-NB11, WPE1-NB26, LNCaP, and PC-3 cell lines over 72 h. Based on these data, we quantified the expression levels of 12 genes involved in the control of DNA replication initiation using TaqMan real-time PCR in the WPE1-NA22, WPE1-NB14, WPE1-NB11, and WPE1-NB26 cell lines. ENL significantly inhibited the abnormal proliferation of the WPE1-NB14 and WPE1-NB11 cell lines and appears to be a consequence of decreased expression of abnormal chromatin licensing and DNA replication factor 1. CONCLUSION: In contrast to previous studies, concentrations of ENL that are reported after dietary intervention restrict the proliferation of early-stage tumourigenic prostate cell lines by inhibiting the abnormal formation of complexes that initiate DNA replication.

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.

Safety and quality and stage of distribution

Let 0 denote the level of quality inherent in a food product that is delivered to some terminal market. In this paper, I characterize allocations over 0 and provide an economic rationale for regulating safety and quality standards in the food system. Zusman and Bockstael investigate the theoretical foundations for imposing standards and stress the importance of providing a tractable conceptual foundation. Despite a wealth of contributions that are mainly empirical (for reviews of these works see, respectively, Caswell and Antle), there have been relatively few attempts to model formally the linkages between farm and food markets when food quality and consumer safety are at issue. Here, I attempt to provide such a framework, building on key contributions in the theoretical literature and linking them in a simple model of quality determination in a vertically related marketing channel. The food-marketing model is due to Gardner. Spence provides a foundation for Pareto-improving intervention in a deterministic model of quality provision, and Leland, building on the classic paper by Akerlof, investigates licensing and minimum standards when the information structure is incomplete. Linking these ideas in a satisfactory model of the food markets is the main objective of the paper.

Researching task difficulty: towards understanding L2 proficiency

The prime purpose of this article is to put teachers’, learners’ and research perspectives of task difficulty (TD) together and to investigate whether teachers’ and learners’ perceptions of and criteria for TD are in line with the available research on TD. A summary of three interrelated empirical studies on learner and teacher perceptions of TD is presented before the findings are discussed in light of the current models of TD. The chapter concludes by arguing that cognitive demands of a task are a signifcant factor that contributes to TD and should be considered more critically by L2 educators and SLA researchers.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Gradient competence at the syntax-discourse interface

In this article, we present additional support of Duffield's (2003, 2005) distinction between Underlying Competence and Surface Competence. Duffield argues that a more fine-grained distinction between levels of competence and performance is warranted and necessary. While underlying competence is categorical, surface competence is more probabilistic and gradient, being sensitive to lexical and constructional contingencies, including the contextual appropriateness of a given construction. We examine a subset of results from a study comparing native and learner competence of properties at the syntax-discourse interface. Specifically, we look at the acceptability of Clitic Right Dislocation in native and L2 Spanish, in discourse-appropriate context. We argue that Duffield's distinction is a possible explanation of our results.

Higher Education: Understanding the impact of distance learning mode on user information assimilation and satisfaction

There is increasing pressure to capture of video within Higher Education. Although much research has looked at how communication technologies enhance information transfer during playback of video, consideration of technical issues seems incongruous if we do not consider how presentation mode affects information assimilated by, and satisfaction of, learners with a range of individual differences, and from a range of different backgrounds. This paper considers whether a relationship exists between the media and presentation mode used in recorded content, and the level of information assimilation and satisfaction perceived by learners with a range of individual differences. Results aim to inform learning practitioners whether generic delivery is justified, or whether tailoring content delivery enhances the experience of specific learner groups.

Catch-up strategies in the Indian auto components industry: domestic firms’ responses to market liberalization

Market liberalization in emerging-market economies and the entry of multinational firms spur significant changes to the industry/institutional environment faced by domestic firms. Prior studies have described how such changes tend to be disruptive to the relatively backward domestic firms, and negatively affect their performance and survival prospects. In this paper, we study how domestic supplier firms may adapt and continue to perform, as market liberalization progresses, through catch-up strategies aimed at integrating with the industry's global value chain. Drawing on internalization theory and the literatures on upgrading and catch-up processes, learning and relational networks, we hypothesize that, for continued performance, domestic supplier firms need to adapt their strategies from catching up initially through technology licensing/collaborations and joint ventures with multinational enterprises (MNEs) to also developing strong customer relationships with downstream firms (especially MNEs). Further, we propose that successful catch-up through these two strategies lays the foundation for a strategy of knowledge creation during the integration of domestic industry with the global value chain. Our analysis of data from the auto components industry in India during the period 1992–2002, that is, the decade since liberalization began in 1991, offers support for our hypotheses.

The role of context in forming young learners' attitudes and motivation to learning French

Despite the wealth of valuable information that has been generated by motivation studies to date, there are certain limitations in the common approaches. Quantitative and psychometric approaches to motivation research that have dominated in recent decades provided epiphenomenal descriptions of learner motivation within different contexts. However, these approaches assume homogeneity within a given group and often mask the variation between learners within the same, and different, contexts. Although these studies have provided empirical data to form and validate theoretical constructs, they have failed to recognise learners as individual ‘people’ that interact with their context. Learning context has become increasingly explicit in motivation studies, (see Coleman et al. 2007 and Housen et al. 2011), however it is generally considered as a background variable which is pre-existing and external to the individual. Stemming from the recent ‘social turn’ (Block 2003) in SLA research from a more cognitive-linguistic perspective to a more context-specific view of language learning, there has been an upsurge in demand for a greater focus on the ‘person in context’ in motivation research (Ushioda 2011). This paper reports on the findings of a longitudinal study of young English learners of French as they transition from primary to secondary school. Over 12 months, the study employed a mixed-method approach in order to gain an in-depth understanding of how the learners’ context influenced attitudes to language learning. The questionnaire results show that whilst the learners displayed some consistent and stable motivational traits over the 12 months, there were significant differences for learners within different contexts in terms of their attitudes to the language classroom and their levels of self-confidence. A subsequent examination of the qualitative focus group data provided an insight into how and why these attitudes were formed and emphasised the dynamic and complex interplay between learners and their context.