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1.
Guilherme Giannini Artioli Craig Sale Rebecca Louise Jones 《European Journal of Sport Science》2019,19(1):30-39
AbstractCarnosine was originally discovered in skeletal muscle, where it exists in larger amounts than in other tissues. The majority of research into the physiological roles of carnosine have been conducted on skeletal muscle. Given this and the potential for muscle carnosine content to be increased with supplementation, there is now a large body of research examining the ergogenic effects (or otherwise) of carnosine. More recent research, however, points towards a potential for carnosine to exert a wider range of physiological effects in other tissues, including the brain, heart, pancreas, kidney and cancer cells. Taken together, this is suggestive of a potential for carnosine to have therapeutic benefits in health and disease, although this is by no means without complication. Herein, we will provide a review of the current literature relating to the potential therapeutic effects of carnosine in health and disease. 相似文献
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Barbara L. Craig 《Archival Science》2005,5(2-4):391-402
The author, a professor at the University of Toronto, touches briefly on the extensive and rich archival literature that supports
the teaching of macroappraisal, but notes that this is not the only educational material she offers her students when teaching
appraisal theory. She discusses the usefulness to archivists of literature from the fields of ethnography, organizational
knowing, records in history, personal documentary behaviour, memory, and communications, noting that the use of texts from
these fields can encourage students to reflect on their own presumptions and to develop a taste for the wide reading and research
that must support appraisal. 相似文献
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Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models). 相似文献
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