Inlärningseffekten på Limits of Stability på Transtibialt Amputerade Protesbrukare

Syftet med studien är att undersöka om det finns någon inlärningseffekt på testet Limits of Stability (LoS) för transtibialt amputerade protesbrukare och en kontrollgrupp. Sju transtibialt amputerade protesbrukare och en kontrollgrupp bestående av sju friska vuxna män upprepade testet LoS  fem gånger under fyra testtillfällen. Två kraftplattor och 69 reflexmarkörer användes för att samla in data. Testpersonerna placerades med en fot på varje kraftplatta och blev instruerade att förflytta sin center of pressure genom att luta kroppen från anklarna mot åtta mål som visades på en skärm tillsammans med deras center of pressure. Ordningen på målen var slumpvist utvalda. Datan analyserades med Friedmans test, eftersom den inte var normalfördelad, för att se om det fanns någon skillnad i resultatet mellan upprepningarna av testet och resultatet mellan testtillfällena. Det fanns några signifikanta skillnader mellan upprepningarna och mellan testtillfällena som tyder på att det finns en inlärningseffekt efter första upprepningen och första testtillfället, men resultatet var inte tillräckligt entydigt för att kunna dra några konkreta slutsatser. Vidare studier rekommenderas.

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools.

Personality types and languages learning strategies: chameleons changing colours

This exploration of associations between the reported Language Learning Strategy (LLS) preferences of learners of English as a Second Language (ESL) and their personality types is positioned within the contention that the two are generally related. Our findings unequivocally support the existence of this relationship. Moreover, they also provide a platform from which to understand the contribution to learning a second language of two very commonly cited personality traits, introversion/extroversion and neuroticism. However, they also provide the basis for the important caution that the association between personality types and LLS is quite volatile. We have found that it is variation rather than unwavering stability that features in how personality traits apply as predictive of ESL learners' specific LLS preferences. Such prediction is specified even further by the particular contexts of ESL learning where the LLS are applied, for example for listening or speaking and whether this occurs inside or outside a classroom. The implications of these findings for ESL teaching and learning are discussed as is the explanatory power of the chameleon metaphor.

Learning stable weights for data of varying dimension

In this paper we develop a data-driven weight learning method for weighted quasi-arithmetic means where the observed data may vary in dimension.