How does a β-hairpin fold/unfold? Competition between topology and heterogeneity in a solvable model

We study the competition between topological effects and sequence inhomogeneities in determining the thermodynamics and the un/folding kinetics of a β-hairpin. Our work utilizes a new exactly solvable model that allows for arbitrary configurations of native contacts. In general, the competition between heterogeneity and topology results in a crossover of the dominant transition state. Interestingly, near this crossover, the single reaction coordinate picture can be seriously misleading. Our results also suggest that inferring the folding pathway from unfolding simulations is not always justified.

A Model for Signal Transduction during Gamete Release in the Fucoid Alga Pelvetia compressa1

Fucoid algae release gametes into seawater following an inductive light period (potentiation), and gamete expulsion from potentiated receptacles of Pelvetia compressa began about 2 min after a light-to-dark transition. Agitation of the medium reversed potentiation, with an exponential time course completed in about 3 h. Light regulated two signaling pathways during potentiation and gamete expulsion: a photosynthetic pathway and a photosynthesis-independent pathway in which red light was active but blue light was not. Uptake of K+ appears to have an important role in potentiation, because a 50% inhibition of potentiation occurred in the presence of the tetraethylammonium ion, a K+-channel blocker. A central role of anion channels in the maintenance of potentiation is suggested by the premature release of gametes in the light when receptacles were incubated with inhibitors of slow-type anion channels. An inhibitor of tyrosine kinases, tyrphostin A63, also inhibited potentiation. A model for gamete release from P. compressa is presented that proposes that illumination results in the accumulation of ions (e.g. K+) throughout the cells of the receptacle during potentiation, which then move into the extracellular matrix during gamete expulsion to generate osmomechanical force, resulting in gamete release.

Kinetic Model for Micro Algae Cell Concentration and Size Distribution: Application to Nannochloropsis gaditana

The cell concentration and size distribution of the microalgae Nannochloropsis gaditana were studied over the whole growth process. Various samples were taken during the light and dark periods the algae were exposed to. The distributions obtained exhibited positive skew, and no change in the type of distribution was observed during the growth process. The size distribution shifted to lower diameters in dark periods while in light periods the opposite occurred. The overall trend during the growth process was one where the size distribution shifted to larger cell diameters, with differences between initial and final distributions of individual cycles becoming smaller. A model based on the Logistic model for cell concentration as a function of time in the dark period that also takes into account cell respiration and growth processes during dark and light periods, respectively, was proposed and successfully applied. This model provides a picture that is closer to the real growth and evolution of cultures, and reveals a clear effect of light and dark periods on the different ways in which cell concentration and diameter evolve with time.

The relationship between the mismatch negativity (MMN) and psycholinguistic models of spoken word processing

Background: The results from previous studies have indicated that a pre-attentive component of the event-related potential (ERP), the mismatch negativity (MMN), may be an objective measure of the automatic auditory processing of phonemes and words. Aims: This article reviews the relationship between the MMN data and psycholinguistic models of spoken word processing, in order to determine whether the MMN may be used to objectively pinpoint spoken word processing deficits in individuals with aphasia. Main Contribution: This article outlines the ways in which the MMN data support psycholinguistic models currently used in the clinical management of aphasic individuals. Furthermore, the cell assembly model of the neurophysiological mechanisms underlying spoken word processing is discussed in relation to the MMN and psycholinguistic models. Conclusions: The MMN data support current theoretical psycholinguistic and neurophysiological models of spoken word processing. Future MMN studies that include normal and aphasic populations will further elucidate the role that the MMN may play in the clinical management of aphasic individuals.

An illustration of variable precision rough sets model:an analysis of the findings of the UK Monopolies and Mergers Commission

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.

Word of mouth communication within online communities:conceptualizing the online social network

Word of mouth (WOM) communication is a major part of online consumer interactions, particularly within the environment of online communities. Nevertheless, existing (offline) theory may be inappropriate to describe online WOM and its influence on evaluation and purchase.The authors report the results of a two-stage study aimed at investigating online WOM: a set of in-depth qualitative interviews followed by a social network analysis of a single online community. Combined, the results provide strong evidence that individuals behave as if Web sites themselves are primary "actors" in online social networks and that online communities can act as a social proxy for individual identification. The authors offer a conceptualization of online social networks which takes the Web site into account as an actor, an initial exploration of the concept of a consumer-Web site relationship, and a conceptual model of the online interaction and information evaluation process. © 2007 Wiley Periodicals, Inc. and Direct Marketing Educational Foundation, Inc.

Attentional shifting and the role of the dorsal pathway in visual word recognition

A substantial amount of evidence has been collected to propose an exclusive role for the dorsal visual pathway in the control of guided visual search mechanisms, specifically in the preattentive direction of spatial selection [Vidyasagar, T. R. (1999). A neuronal model of attentional spotlight: Parietal guiding the temporal. Brain Research and Reviews, 30, 66-76; Vidyasagar, T. R. (2001). From attentional gating in macaque primary visual cortex to dyslexia in humans. Progress in Brain Research, 134, 297-312]. Moreover, it has been suggested recently that the dorsal visual pathway is specifically involved in the spatial selection and sequencing required for orthographic processing in visual word recognition. In this experiment we manipulate the demands for spatial processing in a word recognition, lexical decision task by presenting target words in a normal spatial configuration, or where the constituent letters of each word are spatially shifted relative to each other. Accurate word recognition in the Shifted-words condition should demand higher spatial encoding requirements, thereby making greater demands on the dorsal visual stream. Magnetoencephalographic (MEG) neuroimaging revealed a high frequency (35-40 Hz) right posterior parietal activation consistent with dorsal stream involvement occurring between 100 and 300 ms post-stimulus onset, and then again at 200-400 ms. Moreover, this signal was stronger in the shifted word condition, compared to the normal word condition. This result provides neurophysiological evidence that the dorsal visual stream may play an important role in visual word recognition and reading. These results further provide a plausible link between early stage theories of reading, and the magnocellular-deficit theory of dyslexia, which characterises many types of reading difficulty. © 2006 Elsevier Ltd. All rights reserved.

In search of a theory of list memory: an inquiry into the efects of long-term information on short-term recall

Over recent years, evidence has been accumulating in favour of the importance of long-term information as a variable which can affect the success of short-term recall. Lexicality, word frequency, imagery and meaning have all been shown to augment short term recall performance. Two competing theories as to the causes of this long-term memory influence are outlined and tested in this thesis. The first approach is the order-encoding account, which ascribes the effect to the usage of resources at encoding, hypothesising that word lists which require less effort to process will benefit from increased levels of order encoding, in turn enhancing recall success. The alternative view, trace redintegration theory, suggests that order is automatically encoded phonologically, and that long-term information can only influence the interpretation of the resultant memory trace. The free recall experiments reported here attempted to determine the importance of order encoding as a facilitatory framework and to determine the locus of the effects of long-term information in free recall. Experiments 1 and 2 examined the effects of word frequency and semantic categorisation over a filled delay, and experiments 3 and 4 did the same for immediate recall. Free recall was improved by both long-term factors tested. Order information was not used over a short filled delay, but was evident in immediate recall. Furthermore, it was found that both long-term factors increased the amount of order information retained. Experiment 5 induced an order encoding effect over a filled delay, leaving a picture of short-term processes which are closely associated with long-term processes, and which fit conceptions of short-term memory being part of language processes rather better than either the encoding or the retrieval-based models. Experiments 6 and 7 aimed to determine to what extent phonological processes were responsible for the pattern of results observed. Articulatory suppression affected the encoding of order information where speech rate had no direct influence, suggesting that it is ease of lexical access which is the most important factor in the influence of long-term memory on immediate recall tasks. The evidence presented in this thesis does not offer complete support for either the retrieval-based account or the order encoding account of long-term influence. Instead, the evidence sits best with models that are based upon language-processing. The path urged for future research is to find ways in which this diffuse model can be better specified, and which can take account of the versatility of the human brain.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

Effects of syllable structure in aphasic errors:implications for a new model of speech production

Current models of word production assume that words are stored as linear sequences of phonemes which are structured into syllables only at the moment of production. This is because syllable structure is always recoverable from the sequence of phonemes. In contrast, we present theoretical and empirical evidence that syllable structure is lexically represented. Storing syllable structure would have the advantage of making representations more stable and resistant to damage. On the other hand, re-syllabifications affect only a minimal part of phonological representations and occur only in some languages and depending on speech register. Evidence for these claims comes from analyses of aphasic errors which not only respect phonotactic constraints, but also avoid transformations which move the syllabic structure of the word further away from the original structure, even when equating for segmental complexity. This is true across tasks, types of errors, and, crucially, types of patients. The same syllabic effects are shown by apraxic patients and by phonological patients who have more central difficulties in retrieving phonological representations. If syllable structure was only computed after phoneme retrieval, it would have no way to influence the errors of phonological patients. Our results have implications for psycholinguistic and computational models of language as well as for clinical and educational practices.

A weakly-supervised Bayesian model for violence detection from social media

Social streams have proven to be the mostup-to-date and inclusive information on cur-rent events. In this paper we propose a novelprobabilistic modelling framework, called violence detection model (VDM), which enables the identification of text containing violent content and extraction of violence-related topics over social media data. The proposed VDM model does not require any labeled corpora for training, instead, it only needs the in-corporation of word prior knowledge which captures whether a word indicates violence or not. We propose a novel approach of deriving word prior knowledge using the relative entropy measurement of words based on the in-tuition that low entropy words are indicative of semantically coherent topics and therefore more informative, while high entropy words indicates words whose usage is more topical diverse and therefore less informative. Our proposed VDM model has been evaluated on the TREC Microblog 2011 dataset to identify topics related to violence. Experimental results show that deriving word priors using our proposed relative entropy method is more effective than the widely-used information gain method. Moreover, VDM gives higher violence classification results and produces more coherent violence-related topics compared toa few competitive baselines.

Dynamic joint sentiment-topic model

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) Sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and shorttimescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. © 2013 ACM 2157-6904/2013/12-ART5 $ 15.00.

Granular superconductors:from the nonlinear σ model to the Bose-Hubbard description

We modify a nonlinear σ model (NLσM) for the description of a granular disordered system in the presence of both the Coulomb repulsion and the Cooper pairing. We show that under certain controlled approximations the action of this model is reduced to the Ambegaokar-Eckern-Schön (AES) action, which is further reduced to the Bose-Hubbard (or “dirty-boson”) model with renormalized coupling constants. We obtain an effective action which is more general than the AES one but still simpler than the full NLσM action. This action can be applied in the region of parameters where the reduction to the AES or the Bose-Hubbard model is not justified. This action may lead to a different picture of the superconductor-insulator transition in two-dimensional systems.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.