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For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.

There is controversy as to whether children with developmental language disorder (DLD) and those with high-functioning autism plus language impairment (HFA-LI) share similar language profiles. This study investigated the similarities and differences in the production of Chinese negative sentences by children with DLD and children with HFA-LI to provide evidence relevant to this controversy. The results reflect a general resemblance between the two groups in their lower-than-TDA (typically developing age-matched) performance. Both groups encountered difficulties in using negative markers, which suggests that they might be impaired in feature agreement. Slight differences were detected between the two groups. Specifically, children with DLD experienced difficulties with the agreement on the feature [+telic] and that on the feature [+dynamic], while children with HFA-LI had difficulties with the agreement on the feature [+dynamic] and that on the feature [−dynamic]. This study supports the idea of a common symptomatology for the two disorders. More importantly, it suggests that these two disorders, DLD and HFA-LI, are not altogether the same in terms of language impairment. This paper concludes that general labels should not be simply attached to any children with language disorders. Instead, atypical language is very worthy of further analysis in the categorization of language disorders. [ABSTRACT FROM AUTHOR]

Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences. Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores. Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro- F 1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein. Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales. [ABSTRACT FROM AUTHOR]

For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a "harmonized" alongside a "standard" dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models. [ABSTRACT FROM AUTHOR]

Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences.Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores.Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro-F1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein.Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales.