Augmented renal clearance of aminoglycosides using population-based pharmacokinetic modelling with Bayesian estimation in the paediatric ICU.

Objective: To evaluate augmented renal clearance (ARC) using aminoglycoside clearance (CLAMINO24h) derived from pharmacokinetic (PK) modelling.

Methods: A retrospective study at two paediatric hospitals of patients who received tobramycin or gentamicin from 1999 to 2016 was conducted. Compartmental PK models were constructed using the Pmetrics package, and Bayesian posteriors were used to estimate CLAMINO24h.

Semantic Provenance Graph for Reproducibility of Biomedical Research Studies: Generating and Analyzing Graph Structures from Published Literature.

Objective: To characterize the scientific reproducibility of biomedical research studies by query and analysis of semantic provenance graphs generated from provenance metadata terms extracted from PubMed articles.

Methods: We develop a new semantic provenance graph generation algorithm that uses a provenance ontology developed as part of the Provenance for Clinical and Health Research (ProvCaRe) project. The ProvCaRe project has processed and extracted provenance metadata from more than 1.

ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata.

Objective: Reproducibility of research studies is key to advancing biomedical science by building on sound results and reducing inconsistencies between published results and study data. We propose that the available data from research studies combined with provenance metadata provide a framework for evaluating scientific reproducibility. We developed the ProvCaRe platform to model, extract, and query semantic provenance information from 435, 248 published articles.

ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies.

Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of increasingly expensive drug trials, and allows funding agencies to make informed decisions. However, there is a growing "crisis" of reproducibility as evidenced by a recent Nature journal survey of more than 1500 researchers that found that 70% of researchers were not able to replicate results from other research groups and more than 50% of researchers were not able reproduce their own research results. In 2016, the National Institutes of Health (NIH) announced the "Rigor and Reproducibility" guidelines to support reproducibility in biomedical research.

An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper).

Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility.

Nuclear factor-erythroid-2 related transcription factor-1 (Nrf1) is regulated by O-GlcNAc transferase.

The Nrf1 (Nuclear factor E2-related factor 1) transcription factor performs a critical role in regulating cellular homeostasis. Using a proteomic approach, we identified Host Cell Factor-1 (HCF1), a co-regulator of transcription, and O-GlcNAc transferase (OGT), the enzyme that mediates protein O-GlcNAcylation, as cellular partners of Nrf1a, an isoform of Nrf1. Nrf1a directly interacts with HCF1 through the HCF1 binding motif (HBM), while interaction with OGT is mediated through HCF1.

Scientific Reproducibility in Biomedical Research: Provenance Metadata Ontology for Semantic Annotation of Study Description.

Scientific reproducibility is key to scientific progress as it allows the research community to build on validated results, protect patients from potentially harmful trial drugs derived from incorrect results, and reduce wastage of valuable resources. The National Institutes of Health (NIH) recently published a systematic guideline titled "Rigor and Reproducibility " for supporting reproducible research studies, which has also been accepted by several scientific journals. These journals will require published articles to conform to these new guidelines.

Correlates of quality of life among individuals with epilepsy enrolled in self-management research: From the US Centers for Disease Control and Prevention Managing Epilepsy Well Network.

Rationale: Epilepsy is a chronic neurological condition that causes substantial burden on patients and families. Quality of life may be reduced due to the stress of coping with epilepsy. For nearly a decade, the Centers for Disease Control (CDC) Prevention Research Center's Managing Epilepsy Well (MEW) Network has been conducting research on epilepsy self-management to address research and practice gaps.

Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification.

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This "neuroscience Big data" represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks.

Increased Energy Expenditure, Ucp1 Expression, and Resistance to Diet-induced Obesity in Mice Lacking Nuclear Factor-Erythroid-2-related Transcription Factor-2 (Nrf2).

The NRF2 (also known as NFE2L2) transcription factor is a critical regulator of genes involved in defense against oxidative stress. Previous studies suggest thatNrf2plays a role in adipogenesisin vitro, and deletion of theNrf2gene protects against diet-induced obesity in mice. Here, we demonstrate that resistance to diet-induced obesity inNrf2(-/-)mice is associated with a 20-30% increase in energy expenditure.