DANCE: a deep learning library and benchmark platform for single-cell analysis.

DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Changes in salivary biomarkers associated with periodontitis and diabetic neuropathy in individuals with type 1 diabetes.

The objective of this pilot clinical study was to identify salivary biomarkers that are associated with periodontal disease and measures of diabetic autonomic dysfunction. Saliva samples from 32 participants were obtained from 3 groups: healthy (H), type 1 diabetes mellitus (DM), and type 1 diabetes mellitus with neuropathy (DMN). Based on the periodontal examination, individuals' mean Periodontal Screening and Recording scores were categorized into two groups (periodontally healthy and gingivitis), and correlated to specific salivary inflammatory biomarkers assessed by a customized protein array and enzyme assay. The mean salivary IgA level in DM was 9211.5 ± 4776.4 pg/ml, which was significantly lower than H (17,182.2 ± 8899.3 pg/ml). IgA in DMN with healthy periodontium was significantly lower (5905.5 ± 3124.8 pg/ml) compared to H, although IgA levels in DMN patients with gingivitis (16,894. 6 ± 7084.3) were not. According to the result of a logistic regression model, IgA and periodontal condition were the indicators of the binary response given by H versus DM, and H versus DMN, respectively. These data suggest that selected salivary biomarkers, such as IgA, combined with a periodontal examination prior to obtaining salivary samples can offer a non-invasive method to assess risk for developing diabetic neuropathy.

Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves.

PREMISE: Leaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. Here, we measured the leaf morphology of more than 200 grapevines (Vitis spp.) over four years and modeled changes in leaf shape along the shoot to determine whether a composite leaf shape comprising all the leaves from a single shoot can better capture the variation and predict species identity compared with individual leaves. METHODS: Using homologous universal landmarks found in grapevine leaves, we modeled various morphological features as polynomial functions of leaf nodes. The resulting functions were used to reconstruct modeled leaf shapes across the shoots, generating composite leaves that comprehensively capture the spectrum of leaf morphologies present. RESULTS: We found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict the species identity of previously unassigned grapevines, which were verified with genotyping. DISCUSSION: Observations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape-an assemblage of modeled leaf snapshots across the shoot-is a better representation of the dynamic and essential shapes of leaves, in addition to serving as a better predictor of species identity than individual leaves.