Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory.

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)average=0.0107,Raverage2=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

Brazilian stingless bee propolis and geopropolis: promising sources of biologically active compounds

ABSTRACT Stingless bee products such as honey, pollen, propolis, and geopropolis have been used for centuries in traditional medicine for the treatment of several illnesses. Investigation of the biological activity of stingless bee products, especially propolis and geopropolis, has revealed promising therapeutic properties. About 20% of total Neotropical stingless bees can be found in Brazil. Despite the species diversity, studies on their biological activity are scarce. The present review focuses on the antioxidant and antimicrobial activities of propolis and geopropolis from Brazilian stingless bees. In addition, the toxicity of these natural products was addressed. In order to provide new evidences for the toxic potential of propolis and geopropolis components, an in silico analysis was performed using the ADMET PredictorTM software. We observed that most of studies evaluated only crude ethanol extracts of a limited number of stingless bees species. Propolis and geopropolis displayed antioxidant capacity and antimicrobial activity. Concerning the toxic potential, the extracts of stingless bees propolis and geopropolis were considered safe. Nonetheless, in vitro and in vivo toxicological studies are still necessary.

Consensus-Driven Development of a Terminology for Biobanking, the Duke Experience.

Biobanking at Duke University has existed for decades and has grown over time in silos and based on specialized needs, as is true with most biomedical research centers. These silos developed informatics systems to support their own individual requirements, with no regard for semantic or syntactic interoperability. Duke undertook an initiative to implement an enterprise-wide biobanking information system to serve its many diverse biobanking entities. A significant part of this initiative was the development of a common terminology for use in the commercial software platform. Common terminology provides the foundation for interoperability across biobanks for data and information sharing. We engaged experts in research, informatics, and biobanking through a consensus-driven process to agree on 361 terms and their definitions that encompass the lifecycle of a biospecimen. Existing standards, common terms, and data elements from published articles provided a foundation on which to build the biobanking terminology; a broader set of stakeholders then provided additional input and feedback in a secondary vetting process. The resulting standardized biobanking terminology is now available for sharing with the biobanking community to serve as a foundation for other institutions who are considering a similar initiative.

OBIB-a novel ontology for biobanking.

BACKGROUND: Biobanking necessitates extensive integration of data to allow data analysis and specimen sharing. Ontologies have been demonstrated to be a promising approach in fostering better semantic integration of biobank-related data. Hitherto no ontology provided the coverage needed to capture a broad spectrum of biobank user scenarios. METHODS: Based in the principles laid out by the Open Biological and Biomedical Ontologies Foundry two biobanking ontologies have been developed. These two ontologies were merged using a modular approach consistent with the initial development principles. The merging was facilitated by the fact that both ontologies use the same Upper Ontology and re-use classes from a similar set of pre-existing ontologies. RESULTS: Based on the two previous ontologies the Ontology for Biobanking (http://purl.obolibrary.org/obo/obib.owl) was created. Due to the fact that there was no overlap between the two source ontologies the coverage of the resulting ontology is significantly larger than of the two source ontologies. The ontology is successfully used in managing biobank information of the Penn Medicine BioBank. CONCLUSIONS: Sharing development principles and Upper Ontologies facilitates subsequent merging of ontologies to achieve a broader coverage.

Demonstration of a frozen sample aliquotter to prepare plasma and serum aliquots without thawing frozen parent samples.

Human biospecimens represent invaluable resources to advance molecular medicine, epidemiology, and biomarker discovery/validation, among other biomedical research. Biobanks typically cryopreserve biospecimens to safeguard their biochemical composition. However, exposing specimens repeatedly to freeze/thaw cycles can degrade their integrity in unforeseen ways. Those biobanks storing liquid samples, thus, regularly make a fundamental compromise at collection time between freezing samples in many small volumes (e.g., 0.5 mL or smaller) or in fewer, larger volumes (e.g., 1.8 mL). The former eliminates the need to expose samples to repeated freeze/thaw cycling, although increasing up-front labor costs, consumables used, and cold storage space requirements. The latter decreases up-front labor costs, consumables, and cold storage requirements, yet exposes samples repeatedly to damaging freeze/thaw cycles when smaller aliquots are needed for analysis. The Rhode Island BioBank at Brown University (RIBB) thoroughly evaluated the performance of an original technology that minimizes a sample's exposure to freeze/thaw cycling by enabling the automated extraction of frozen aliquots from one single frozen parent sample without thawing it. A technology that eliminates unnecessary sample exposures to freeze/thaw cycles could help protect sample integrity, extend its useful life, and effectively rectify and eliminate the aforementioned need to compromise. This report presents the results of the evaluation, and conclusively demonstrates the technology's ability to extract multiple uniform frozen aliquots from a single cryotube of never-thawed frozen human plasma, which faithfully represent the parent sample when analyzed for typical biochemical analytes, showing a coefficient of variability lower than 5.5%.