Tone language experience benefits pitch processing in music and speech for typically developing individuals. No known studies have examined pitch processing in individuals with autism who speak a tone language. This study investigated discrimination and identification of melodic contour and speech intonation in a group of Mandarin-speaking individuals with high-functioning autism. Individuals with autism showed superior melodic contour identification but comparable contour discrimination relative to controls. In contrast, these individuals performed worse than controls on both discrimination and identification of speech intonation. These findings provide the first evidence for differential pitch processing in music and speech in tone language speakers with autism, suggesting that tone language experience may not compensate for speech intonation perception deficits in individuals with autism.
BackgroundPredicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.ResultsIn this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.ConclusionThe improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.
Background
Gallbladder cancer (GBC) is the most common cancer type of the biliary tract, and an association has been found between chronic calculous cholecystitis (CCC) and an increased incidence of GBC mortality. An understanding of the relationship between CCC and its carcinogenesis may enable us to prevent and cure GBC. In this study, we attempted to explore changes in the microbiome profile that take place during the transition from chronic cholecystitis mucosa to malignant lesions.
Results
Seven paired human GBC and CCC samples were provided by patients who had undergone laparoscopic cholecystectomy or radical cholecystectomy. Mucosal DNA extraction and metagenomic sequencing were performed to evaluate changes in the microbiota between the two groups. We found that GBC patients and CCC patients shared similar stable and permanent dominant species and showed apparent differences in their biliary microbial composition and gene function.
Peptostreptococcus stomatis
and
Enterococcus faecium
may potentially play a role in GBC progression. In addition, the metagenomic species profiles, co‐abundance and co‐exclusion correlations, and CAZyme prevalence showed significant differences between the CCC and GBC groups.
Conclusion
Our data suggested that changes in the microbiota between CCC and GBC may help deepen our understanding of the complex spectrum of different microbiotas involved in the development of GBC. Although the cohort size was small, this study has presented the first evidence of the existence of an altered biliary microbiota in GBC, which is clearly different from that in CCC patients.
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