Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data.

MOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test. The package is capable of combining positive- and negative-ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database. AVAILABILITY AND IMPLEMENTATION: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.

The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.

Effects of surface morphology and composition of titanium implants on osteogenesis and inflammatory responses: a review.

Titanium and its alloys have been widely used in bone tissue defect treatment owing to their excellent comprehensive properties. However, because of the biological inertness of the surface, it is difficult to achieve satisfactory osseointegration with the surrounding bone tissue when implanted into the body. Meanwhile, an inflammatory response is inevitable, which leads to implantation failure. Therefore, solving these two problems has become a new research hotspot. In current studies, various surface modification methods were proposed to meet the clinical needs. Yet, these methods have not been classified as a system to guide the follow-up research. These methods are demanded to be summarized, analyzed, and compared. In this manuscript, the effect of physical signal regulation (multi-scale composite structure) and chemical signal regulation (bioactive substance) generated by surface modification in promoting osteogenesis and reducing inflammatory responses was generalized and discussed. Finally, from the perspective of material preparation and biocompatibility experiments, the development trend of surface modification in promoting titanium implant surface osteogenesis and anti-inflammatory research was proposed.