Teaching English tense and aspect with the help of cognitive grammar: An empirical study

Form-focused instruction is usually based on traditional practical/pedagogical grammar descriptions of grammatical features. The comparison of such traditional accounts with cognitive grammar (CG) descriptions seems to favor CG as a basis of pedagogical rules. This is due to the insistence of CG on the meaningfulness of grammar and its detailed analyses of the meanings of particular grammatical features. The differences between traditional and CG rules/descriptions are exemplified by juxtaposing the two kinds of principles concerning the use of the present simple and present progressive to refer to situations happening or existing at speech time. The descriptions provided the bases for the instructional treatment in a quasi-experimental study exploring the effectiveness of using CG descriptions of the two tenses, and of their interplay with stative (imperfective) and dynamic (perfective) verbs, and comparing this effectiveness with the value of grammar teaching relying on traditional accounts found in standard pedagogical grammars. The study involved 50 participants divided into three groups, with one of them constituting the control group and the other two being experimental ones. One of the latter received treatment based on CG descriptions and the other on traditional accounts. CG-based instruction was found to be at least moderately effective in terms of fostering mostly explicit grammatical knowledge and its effectiveness turned out be comparable to that of teaching based on traditional descriptions.

Overlay Network Creation and Maintenance with Selfish Users

Overlay networks have been used for adding and enhancing functionality to the end-users without requiring modifications in the Internet core mechanisms. Overlay networks have been used for a variety of popular applications including routing, file sharing, content distribution, and server deployment. Previous work has focused on devising practical neighbor selection heuristics under the assumption that users conform to a specific wiring protocol. This is not a valid assumption in highly decentralized systems like overlay networks. Overlay users may act selfishly and deviate from the default wiring protocols by utilizing knowledge they have about the network when selecting neighbors to improve the performance they receive from the overlay. This thesis goes against the conventional thinking that overlay users conform to a specific protocol. The contributions of this thesis are threefold. It provides a systematic evaluation of the design space of selfish neighbor selection strategies in real overlays, evaluates the performance of overlay networks that consist of users that select their neighbors selfishly, and examines the implications of selfish neighbor and server selection to overlay protocol design and service provisioning respectively. This thesis develops a game-theoretic framework that provides a unified approach to modeling Selfish Neighbor Selection (SNS) wiring procedures on behalf of selfish users. The model is general, and takes into consideration costs reflecting network latency and user preference profiles, the inherent directionality in overlay maintenance protocols, and connectivity constraints imposed on the system designer. Within this framework the notion of user’s "best response" wiring strategy is formalized as a k-median problem on asymmetric distance and is used to obtain overlay structures in which no node can re-wire to improve the performance it receives from the overlay. Evaluation results presented in this thesis indicate that selfish users can reap substantial performance benefits when connecting to overlay networks composed of non-selfish users. In addition, in overlays that are dominated by selfish users, the resulting stable wirings are optimized to such great extent that even non-selfish newcomers can extract near-optimal performance through naïve wiring strategies. To capitalize on the performance advantages of optimal neighbor selection strategies and the emergent global wirings that result, this thesis presents EGOIST: an SNS-inspired overlay network creation and maintenance routing system. Through an extensive measurement study on the deployed prototype, results presented in this thesis show that EGOIST’s neighbor selection primitives outperform existing heuristics on a variety of performance metrics, including delay, available bandwidth, and node utilization. Moreover, these results demonstrate that EGOIST is competitive with an optimal but unscalable full-mesh approach, remains highly effective under significant churn, is robust to cheating, and incurs minimal overheads. This thesis also studies selfish neighbor selection strategies for swarming applications. The main focus is on n-way broadcast applications where each of n overlay user wants to push its own distinct file to all other destinations as well as download their respective data files. Results presented in this thesis demonstrate that the performance of our swarming protocol for n-way broadcast on top of overlays of selfish users is far superior than the performance on top of existing overlays. In the context of service provisioning, this thesis examines the use of distributed approaches that enable a provider to determine the number and location of servers for optimal delivery of content or services to its selfish end-users. To leverage recent advances in virtualization technologies, this thesis develops and evaluates a distributed protocol to migrate servers based on end-users demand and only on local topological knowledge. Results under a range of network topologies and workloads suggest that the performance of the distributed deployment is comparable to that of the optimal but unscalable centralized deployment.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

Simulating Effects of Learning and Lesions with a Model of Intrinsic and Synaptically Gated Responses of Striatal Cholinergic Interneurons

The giant cholinergic interneurons of the striatum are tonically active neurons (TANs) that respond with characteristic pauses to novel events and to appetitive and aversive conditioned stimuli. Fluctuations in acetylcholine release by TANs modulate performance- and learning-related dynamics in the striatum. Whereas tonic activity emerges from intrinsic properties of these neurons, glutamatergic inputs from thalamic centromedian-parafascicular nuclei, and dopaminergic inputs from midbrain, are required for the generation of pause responses. No prior computational models encompass both intrinsic and synaptically-gated dynamics. We present a mathematical model that robustly accounts for behavior-related electrophysiological properties of TANs in terms of their intrinsic physiological properties and known afferents. In the model, balanced intrinsic hyperpolarizing and depolarizing currents engender tonic firing, and glutamatergic inputs from thalamus (and cortex) both directly excite and indirectly inhibit TANs. If the latter inhibition, presumably mediated by GABAergic interneurons, exceeds a threshold, its effect is amplified by a KIR current to generate a prolonged pause. In the model, the intrinsic mechanisms and external inputs are both modulated by learning-dependent dopamine (DA) signals and our simulations revealed that many learning-dependent behaviors of TANs are explicable without recourse to learning-dependent changes in synapses onto TANs. The "teaching signal" that modulates reinforcement learning at cortico-striatal synapses may be a sequence composed of an adaptively scaled DA burst, a brief ACh burst, and a scaled ACh pause. Such an interpretation is consistent with recent data on cholinergic control of LTD of cortical synapses onto striatal spiny projection neurons.

Exploring the physicochemical basis of cheese texture, rheology and functionality, with emphasis on cation-casein interactions

The physicochemical properties of cheese and milk gels are greatly influenced by molecular interactions between the casein proteins involving calcium. Novel experiments were designed to investigate the relationship between insoluble caseinbound cations and rheological properties of Cheddar cheese and rennet-induced milk gels. Cheddar cheese and rennet-induced milk gels were supplemented with Mg2+ or Sr2+ to compare their effects on their rheological properties to those previously reported in literature for Ca2+ supplementation. Sr2+ displayed behaviour similar to Ca2+ as observed by its ability to increase the rigidity of cheese and rennet milk gels and also decrease cheese meltability. Mg+2 had no influence on cheese rheological properties and was greatly inferior to Ca2+ and Sr2+ in its ability to increase rennet milk gel elasticity. Cheddar cheese was supplemented with the calcium-chelating salts trisodium citrate, disodium hydrogen phosphate or disodium EDTA, in an attempt to reduce the CCP content of cheese and thereby modify its rheological and functional properties. TSC and EDTA were successful in decreasing cheese CCP, whereas DSP caused an initial increase in CCP content. Cheddar cheese was supplemented with chlorides of iron, copper and zinc at salting to investigate the effects of concentrations of these elements in excess of those found innately or commonly in fortification studies, with emphasis on mineral equilibria changes and resultant alteration of rheological properties. Zinc addition was the only added metal that significantly influenced cheese rheological properties, leading to an increase in cheese rigidity and decreased cheese melt at elevated temperatures. Gum tragacanth was used as a fat-replacer in the manufacture of reduced-fat Cheddar cheese, in an attempt to improve the rheological, functional and sensory properties of reduced-fat Cheddar. Overall, the experimental work reported in this thesis generated new knowledge and theories about how casein-mineral interactions influence rheological properties of casein systems.

A cultural study on communication and technology with specific reference to media in China

This dissertation examines the role of communications technology in social change. It examines secondary data on contemporary China arguing that many interpretations of events in China are unsuitable at best and at worst conceptually damages our understanding of social change in China. This is especially the case in media studies under the ‘democratic framework’. It proposes that there is an alternative framework in studying the media and social change. This alternative conceptual framework is termed a zone of interpretative development offering a means by which to discuss events that take place in a mediated environment. Taking a theoretical foundation using the philosophy of Mikhail Bakhtin this dissertation develops a platform with which to understand communication technology from an anthropological perspective. Three media events from contemporary China are examined. The first examines the Democracy Wall event and the implications of using a public sphere framework. The second case examines the phenomenon of the Grass Mud Horse, a symbol that has gained popular purchase as a humorous expression of political dissatisfaction and develops the problems seen in the first case but with some solutions. Using a modification of Lev Vygotskiĭ’s zone of proximal development this symbol is understood as an expression of the collective recognition of a shared experience. In the second example from the popular TV talent show contests in China further expressions of collective experience are introduced. With the evidence from these media events in contemporary China this dissertation proposes that we can understand certain modes of communication as occurring in a zone of interpretative development. This proposed anthropological feature of social change via communication and technology can fruitfully describe meaning-formation in society via the expression and recognition of shared experiences.

Intrastereomic proteins from the asteroid Asterias rubens. separation, partial characterization ,and interactions with growing calcite crystals

info:eu-repo/semantics/published

Satisfaction and acceptance with learning management systems

More and more often, universities make the decision to implement integrated learning management systems. Nevertheless, these technological developments are not realized without any trouble, and are achieved with more or less success and user satisfaction (Valenduc, 2000). It is why the presented study aims at identifying the factors influencing learning management system satisfaction and acceptance among students. The Technology Acceptance model created by Wixom and Todd (2005) studies information system acceptance through user satisfaction, and has the benefit of incorporating several ergonomic factors. More precisely, the survey, based on this model, investigates behavioral attitudes towards the system, perceived ease of use, perceived usefulness, as well as system satisfaction, information satisfaction and also incorporates two groups of factors affecting separately the two types of satisfaction. The study was conducted on a representative sample of 593 students from a Brussels university which had recently implemented an integrated learning management system. The results show on one hand, the impact of system reliability, accessibility, flexibility, lay-out and functionalities offered on system satisfaction. And on the other hand, the impact of information accuracy, intelligibility, relevance, exhaustiveness and actualization on information satisfaction. In conclusion, the results indicate the applicability of the theoretical model with learning management systems, and also highlight the importance of each aforementioned factor for a successful implantation of such a system in universities.

Impact of gene variants on sex-specific regulation of human Scavenger receptor class B type 1 (SR-BI) expression in liver and association with lipid levels in a population-based study.

BACKGROUND: Several studies have noted that genetic variants of SCARB1, a lipoprotein receptor involved in reverse cholesterol transport, are associated with serum lipid levels in a sex-dependent fashion. However, the mechanism underlying this gene by sex interaction has not been explored. METHODS: We utilized both epidemiological and molecular methods to study how estrogen and gene variants interact to influence SCARB1 expression and lipid levels. Interaction between 35 SCARB1 haplotype-tagged polymorphisms and endogenous estradiol levels was assessed in 498 postmenopausal Caucasian women from the population-based Rancho Bernardo Study. We further examined associated variants with overall and SCARB1 splice variant (SR-BI and SR-BII) expression in 91 human liver tissues using quantitative real-time PCR. RESULTS: Several variants on a haplotype block spanning intron 11 to intron 12 of SCARB1 showed significant gene by estradiol interaction affecting serum lipid levels, the strongest for rs838895 with HDL-cholesterol (p=9.2x10(-4)) and triglycerides (p=1.3x10(-3)) and the triglyceride:HDL cholesterol ratio (p=2.7x10(-4)). These same variants were associated with expression of the SR-BI isoform in a sex-specific fashion, with the strongest association found among liver tissue from 52 young women<45 years old (p=0.002). CONCLUSIONS: Estrogen and SCARB1 genotype may act synergistically to regulate expression of SCARB1 isoforms and impact serum levels of HDL cholesterol and triglycerides. This work highlights the importance of considering sex-dependent effects of gene variants on serum lipid levels.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.

Memory and coping with stress: the relationship between cognitive-emotional distinctiveness, memory valence, and distress.

Cognitive-emotional distinctiveness (CED), the extent to which an individual separates emotions from an event in the cognitive representation of the event, was explored in four studies. CED was measured using a modified multidimensional scaling procedure. The first study found that lower levels of CED in memories of the September 11 terrorist attacks predicted greater frequency of intrusive thoughts about the attacks. The second study revealed that CED levels are higher in negative events, in comparison to positive events and that low CED levels in emotionally intense negative events are associated with a pattern of greater event-related distress. The third study replicated the findings from the previous study when examining CED levels in participants' memories of the 2004 Presidential election. The fourth study revealed that low CED in emotionally intense negative events is associated with worse mental health. We argue that CED is an adaptive and healthy coping feature of stressful memories.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data.

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

The Golden Rule Ethic, its Measurement, and Relationships with Well-Being and Prosocial Values Across Four Religions in India

As a psychological principle, the golden rule represents an ethic of universal empathic concern. It is, surprisingly, present in the sacred texts of virtually all religions, and in philosophical works across eras and continents. Building on the literature demonstrating a positive impact of prosocial behavior on well-being, the present study investigates the psychological function of universal empathic concern in Indian Hindus, Christians, Muslims and Sikhs.

I develop a measure of the centrality of the golden rule-based ethic, within an individual’s understanding of his or her religion, that is applicable to all theistic religions. I then explore the consistency of its relationships with psychological well-being and other variables across religious groups.

Results indicate that this construct, named Moral Concern Religious Focus, can be reliably measured in disparate religious groups, and consistently predicts well-being across them. With measures of Intrinsic, Extrinsic and Quest religious orientations in the model, only Moral Concern and religiosity predict well-being. Moral Concern alone mediates the relationship between religiosity and well-being, and explains more variance in well-being than religiosity alone. The relationship between Moral Concern and well-being is mediated by increased preference for prosocial values, more satisfying interpersonal relationships, and greater meaning in life. In addition, across religious groups Moral Concern is associated with better self-reported physical and mental health, and more compassionate attitudes toward oneself and others.

Two additional types of religious focus are identified: Personal Gain, representing the motive to use religion to improve one’s life, and Relationship with God. Personal Gain is found to predict reduced preference for prosocial values, less meaning in life, and lower quality of relationships. It is associated with greater interference of pain and physical or mental health problems with daily activities, and lower self-compassion. Relationship with God is found to be associated primarily with religious variables and greater meaning in life.

I conclude that individual differences in the centrality of the golden rule and its associated ethic of universal empathic concern may play an important role in explaining the variability in associations between religion, prosocial behavior and well-being noted in the literature.

Potent functional antibody responses elicited by HIV-I DNA priming and boosting with heterologous HIV-1 recombinant MVA in healthy Tanzanian adults.

UNLABELLED: Vaccine-induced HIV antibodies were evaluated in serum samples collected from healthy Tanzanian volunteers participating in a phase I/II placebo-controlled double blind trial using multi-clade, multigene HIV-DNA priming and recombinant modified vaccinia Ankara (HIV-MVA) virus boosting (HIVIS03). The HIV-DNA vaccine contained plasmids expressing HIV-1 gp160 subtypes A, B, C, Rev B, Gag A, B and RTmut B, and the recombinant HIV-MVA boost expressed CRF01_AE HIV-1 Env subtype E and Gag-Pol subtype A. While no neutralizing antibodies were detected using pseudoviruses in the TZM-bl cell assay, this prime-boost vaccination induced neutralizing antibodies in 83% of HIVIS03 vaccinees when a peripheral blood mononuclear cell (PBMC) assay using luciferase reporter-infectious molecular clones (LucR-IMC) was employed. The serum neutralizing activity was significantly (but not completely) reduced upon depletion of natural killer (NK) cells from PBMC (p=0.006), indicating a role for antibody-mediated Fcγ-receptor function. High levels of antibody-dependent cellular cytotoxicity (ADCC)-mediating antibodies against CRF01_AE and/or subtype B were subsequently demonstrated in 97% of the sera of vaccinees. The magnitude of ADCC-mediating antibodies against CM235 CRF01_AE IMC-infected cells correlated with neutralizing antibodies against CM235 in the IMC/PBMC assay. In conclusion, HIV-DNA priming, followed by two HIV-MVA boosts elicited potent ADCC responses in a high proportion of Tanzanian vaccinees. Our findings highlight the potential of HIV-DNA prime HIV-MVA boost vaccines for induction of functional antibody responses and suggest this vaccine regimen and ADCC studies as potentially important new avenues in HIV vaccine development. TRIAL REGISTRATION: Controlled-Trials ISRCTN90053831 The Pan African Clinical Trials Registry ATMR2009040001075080 (currently PACTR2009040001075080).