Linguistic and cognitive motivations for the typological primacy model of third language (L3) transfer: timing of acquisition and proficiency considered

This article elucidates the Typological Primacy Model (TPM; Rothman, 2010, 2011, 2013) for the initial stages of adult third language (L3) morphosyntactic transfer, addressing questions that stem from the model and its application. The TPM maintains that structural proximity between the L3 and the L1 and/or the L2 determines L3 transfer. In addition to demonstrating empirical support for the TPM, this article articulates a proposal for how the mind unconsciously determines typological (structural) proximity based on linguistic cues from the L3 input stream used by the parser early on to determine holistic transfer of one previous (the L1 or the L2) system. This articulated version of the TPM is motivated by argumentation appealing to cognitive and linguistic factors. Finally, in line with the general tenets of the TPM, I ponder if and why L3 transfer might obtain differently depending on the type of bilingual (e.g. early vs. late) and proficiency level of bilingualism involved in the L3 process.

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds. (C) 2010 Elsevier Masson SAS. All rights reserved.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.

Pedagogical methods in web-based language teaching-mapping

The purpose of this presentation is to introduce the research project progress in “the mapping of pedagogical methods in web-based language teaching" by Högskolan Dalarna (Dalarna University). This project will identify the differences in pedagogical methods that are used for online language classes. The pedagogical method defined in this project is what the teachers do to ensure students attain the learning outcomes, for example, planning, designing courses, leading students, knowing students' abilities, implementing activities, etc. So far the members of this project have analyzed the course plans (in the language department at Dalarna University) and categorized the learning outcomes. A questionnaire was constructed based on the learning outcomes and then either sent out remotely to teachers or completed face to face through interviews. The answers provided to the questionnaires enabled the project to identify many differences in how language teachers interact with their students but also, the way of giving feedback, motivating and helping students, types of class activities and materials used. This presentation introduces the progress of the project and identifies the challenges at the language department at Dalarna University. Finally, the advantages and problems of online language proficiency courses will be discussed and suggestions made for future improvement.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

Training on synthetic ethylene-vinyl acetate bench model allows novice medical students to acquire suture skills

PURPOSE: To assess the acquisition of suture skills by training on ethylene-vinyl acetate bench model in novice medical students.METHODS: Sixteen medical students without previous surgery experience (novices) were randomly divided into two groups. During one hour group A trained sutures on ethylene-vinyl acetate (EVA) bench model with feedback of instructors, while group B (control) received a faculty-directed training based on books and instructional videos. All students underwent a both pre-and post-tests to perform two-and three-dimensional sutures on ox tongue. All recorded performances were evaluated by two blinded evaluators, using the Global Rating Scale.RESULTS: Although both groups have had a better performance (p<0.05) in the post-test when compared with the pre-test, the analysis of post-test showed that group A (EVA) had a better performance (p<0.05) when compared with group B (control).CONCLUSION: The ethylene vinyl acetate bench model allowed the novice students to acquire suture skills faster when compared to the traditional model of teaching.

Effects of isolated cycle ergometer training on patients with moderate-to-severe chronic obstructive pulmonary disease

Background: Pulmonary rehabilitation (PR) programs are beneficial to patients with chronic obstructive pulmonary disease (COPD), and lower-extremity training is considered a fundamental component of PR. Nevertheless, the isolated effects of each PR component are not well established. Objective: We aimed to evaluate the effects of a cycle ergometry exercise protocol as the only intervention in a group of COPD patients, and to compare these results with a control group. Methods: 25 moderate-to-severe COPD patients were evaluated regarding pulmonary function, respiratory muscle strength, exercise capacity, quality of life and body composition. Patients were allocated to one of two groups: (a) the trained group (TG; n=13; 6 men) was submitted to a protocol of 24 exercise sessions on a cycle ergometer, with training intensity initially set at a heart rate (HR) close to 80% of maximal HR achieved in a maximal test, and load increase based on dyspnea scores, and (b) the control group (CG; n=12; 6 men) with no intervention during the protocol period. Results: TG showed within-group significant improvements in endurance cycling time, 6-min walking distance test, maximal inspiratory pressure and in the domain 'dyspnea' related to quality of life. Despite the within-group changes, no between-group significant differences were observed. Conclusion: In COPD patients, the results of isolated low-to-moderate intensity cycle ergometer training are not comparable to effects of multimodality and high-intensity training programs. Copyright (C) 2004 S. Karger AG, Basel.

High-Intensity Resistance Training with Insufficient Recovery Time Between Bouts Induce Atrophy and Alterations in Myosin Heavy Chain Content in Rat Skeletal Muscle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Maximal lactate steady state in running mice: Effect of exercise training

1. Maximal lactate steady state (MLSS) corresponds to the highest blood lactate concentration (MLSSc) and workload (MLSSw) that can be maintained over time without continual blood lactate accumulation and is considered an important marker of endurance exercise capacity. The present study was undertaken to determine MLSSw and MLSSc in running mice. In addition, we provide an exercise training protocol for mice based on MLSSw.2. Maximal lactate steady state was determined by blood sampling during multiple sessions of constant-load exercise varying from 9 to 21 m/min in adult male C57BL/6J mice. The constant-load test lasted at least 21 min. The blood lactate concentration was analysed at rest and then at 7 min intervals during exercise.3. The MLSSw was found to be 15.1 +/- 0.7 m/min and corresponded to 60 +/- 2% of maximal speed achieved during the incremental exercise testing. Intra- and interobserver variability of MLSSc showed reproducible findings. Exercise training was performed at MLSSw over a period of 8 weeks for 1 h/day and 5 days/week. Exercise training led to resting bradycardia (21%) and increased running performance (28%). of interest, the MLSSw of trained mice was significantly higher than that in sedentary littermates (19.0 +/- 0.5 vs 14.2 +/- 0.5 m/min; P = 0.05), whereas MLSSc remained unchanged (3.0 mmol/L).4. Altogether, we provide a valid and reliable protocol to improve endurance exercise capacity in mice performed at highest workload with predominant aerobic metabolism based on MLSS assessment.

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

Training courses for teachers through the distance learning: practice reports used virtual environment TELEDUC

This paper describes the experiences of long-distance courses, it focused on the continuing education of basic education teachers in all Brazilian territory. Such courses were offered by CECEMCA (Center for Continuing Education in Mathematics Education, Science and Environment), linked to the Universidade Estadual Paulista Julio de Mesquita Filho - Campus de Rio Claro during 15/01/2009 to 30/11/2009. The subjects report to the theme of Education, Geography and Environment, it was organized in four courses: "Introduction to Cartography," Environment and climate change - thinking a new paradigm of sustainable green planet "," Remote Sensing in environmental studies Environment "and" Methodological Alternatives for Inclusive Classroom: Experimenting with visual and hearing impairments". So, we show here, the feasibility and importance of distance learning tools for education, specifically teacher training, based on the results obtained in these courses.