Is native speaker intuition reliable for high-frequency context creation?

This study determined whether native speaker intuition could be relied upon to producecontextual content that mostly fell into what is considered high-frequency vocabulary. Native speakers wrote over 160,000 tokens worth of example sentences for high-frequency multi-word units derived from a corpus. The resulting database was examined to determine whether the content added by the native speakers mostly stayed within the high-frequency realm.Results showed that not only did the vast majority of native speakers\u27 tokens fall into the high-frequency realm, the percentage that fell into the high-frequency realm only dropped by 0.84 percent in comparison to the multi-word units alone despite the large amount of data beingadded. This study highlighted how the intuition of experienced ESL practitioners can be relied upon to produce high-frequency contextual content

Predictive Models of Assistance Dog Training Outcomes Using the Canine Behavioral Assessment and Research Questionnaire and a Standardized Temperament Evaluation

Assistance dogs can greatly improve the lives of people with disabilities. However, a large proportion of dogs bred and trained for this purpose are deemed unable to successfully fulfill the behavioral demands of this role. Often, this determination is not finalized until weeks or even months into training, when the dog is close to 2 years old. Thus, there is an urgent need to develop objective selection protocols that can identify dogs most and least likely to succeed, from early in the training process. We assessed the predictive validity of two candidate measures employed by Canine Companions for Independence (CCI), a national assistance dog organization headquartered in Santa Rosa, CA. For more than a decade, CCI has collected data on their population using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ) and a standardized temperament assessment known internally as the In-For-Training (IFT) test, which is conducted at the beginning of professional training. Data from both measures were divided into independent training and test datasets, with the training data used for variable selection and cross-validation. We developed three predictive models in which we predicted success or release from the training program using C-BARQ scores (N = 3,569), IFT scores (N = 5,967), and a combination of scores from both instruments (N = 2,990). All three final models performed significantly better than the null expectation when applied to the test data, with overall accuracies ranging from 64 to 68%. Model predictions were most accurate for dogs predicted to have the lowest probability of success (ranging from 85 to 92% accurate for dogs in the lowest 10% of predicted probabilities), and moderately accurate for identifying the dogs most likely to succeed (ranging from 62 to 72% for dogs in the top 10% of predicted probabilities). Combining C-BARQ and IFT predictors into a single model did not improve overall accuracy, although it did improve accuracy for dogs in the lowest 20% of predicted probabilities. Our results suggest that both types of assessments have the potential to be used as powerful screening tools, thereby allowing more efficient allocation of resources in assistance dog selection and training.Ministry of Food, Agriculture and Fisheries of Denmark (DFFE) [3304-FVFP-09-F-011]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

hapbin: An Efficient Program for performing Haplotype-Based Scans for Positive Selection in Large Genomic Datasets

Understanding how the genome is shaped by selective processes forms an integral part of modern biology. However, as genomic datasets continue to grow larger it is becoming increasingly difficult to apply traditional statistics for detecting signatures of selection to these cohorts. There is therefore a pressing need for the development of the next generation of computational and analytical tools for detecting signatures of selection in large genomic datasets. Here, we present hapbin, an efficient multithreaded implementation of extended haplotype homzygosity-based statistics for detecting selection, which is up to 3,400 times faster than the current fastest implementations of these algorithms

The Orthologue of Sjögren's Syndrome Nuclear Autoantigen 1 (SSNA1) in Trypanosoma brucei Is an Immunogenic Self-Assembling Molecule

Primary Sjögren's Syndrome (PSS) is a highly prevalent autoimmune disease, typically manifesting as lymphocytic infiltration of the exocrine glands leading to chronically impaired lacrimal and salivary secretion. Sjögren's Syndrome nuclear autoantigen 1 (SSNA1 or NA14) is a major specific target for autoantibodies in PSS but the precise function and clinical relevance of this protein are largely unknown. Orthologues of the gene are absent from many of the commonly used model organisms but are present in Chlamyodomonas reinhardtii (in which it has been termed DIP13) and most protozoa. We report the functional characterisation of the orthologue of SSNA1 in the kinetoplastid parasite, Trypanosoma brucei. Both TbDIP13 and human SSNA1 are small coiled-coil proteins which are predicted to be remote homologues of the actin-binding protein tropomyosin. We use comparative proteomic methods to identify potential interacting partners of TbDIP13. We also show evidence that TbDIP13 is able to self-assemble into fibril-like structures both in vitro and in vivo, a property which may contribute to its immunogenicity. Endogenous TbDIP13 partially co-localises with acetylated α-tubulin in the insect procyclic stage of the parasite. However, deletion of the DIP13 gene in cultured bloodstream and procyclic stages of T. brucei has little effect on parasite growth or morphology, indicating either a degree of functional redundancy or a function in an alternative stage of the parasite life cycle

Resistant starch and exercise independently attenuate weight regain on a high fat diet in a rat model of obesity

<p>Abstract</p> <p>Background</p> <p>Long-term weight reduction remains elusive for many obese individuals. Resistant starch (RS) and exercise may be useful for weight maintenance. The effects of RS, with or without exercise, on weight regain was examined during relapse to obesity on a high carbohydrate, high fat (HC/HF) diet.</p> <p>Methods</p> <p>Obesity-prone rats were fed <it>ad libitum </it>for 16 weeks then weight reduced on a low fat diet to induce a 17% body weight loss (weight reduced rats). Weight reduced rats were maintained on an energy-restricted low fat diet for 18 weeks, with or without a daily bout of treadmill exercise. Rats were then allowed free access to HC/HF diet containing low (0.3%) or high (5.9%) levels of RS. Weight regain, energy balance, body composition, adipocyte cellularity, and fuel utilization were monitored as rats relapsed to obesity and surpassed their original, obese weight.</p> <p>Results</p> <p>Both RS and exercise independently attenuated weight regain by reducing the energy gap between the drive to eat and suppressed energy requirements. Exercise attenuated the deposition of lean mass during relapse, whereas its combination with RS sustained lean mass accrual as body weight returned. Early in relapse, RS lowered insulin levels and reduced the deposition of fat in subcutaneous adipose tissue. Exercise cessation at five weeks of relapse led to increased weight gain, body fat, subcutaneous adipocytes, and decreased lean mass; all detrimental consequences to overall metabolic health.</p> <p>Conclusions</p> <p>These data are the first to show the complimentary effects of dietary RS and regular exercise in countering the metabolic drive to regain weight following weight loss and suggest that exercise cessation, in the context of relapse on a HC/HF diet, may have dire metabolic consequences.</p