Implementation of a shared data repository and common data dictionary for fetal alcohol spectrum disorders research

Alcohol exposure during gestation can lead to fetal alcohol spectrum disorders (FASD), an array of cognitive and physical developmental impairments. Over the past two and a half decades, Mammalian Target of Rapamycin (mTOR) has emerged at the nexus of many fields of study, and has recently been implicated in FASD etiology. mTOR plays an integral role in modulating anabolic and catabolic activities, including protein synthesis and autophagy. These processes are vital for proper development and can have long lasting effects following alcohol exposure, such as impaired hippocampal and synapse formation, reduced brain size, as well as cognitive, behavioral, and memory impairments. We highlight recent advances in the field of FASD, primarily with regard to animal model discoveries and discuss the interaction between alcohol and mTOR in the context of various tissues, including brain, placenta, bone, and muscle, with respect to developmental alcohol exposure paradigms. The current review focuses on novel FASD research within the context of the mTOR signaling and sheds light on mechanistic etiologies at various biological levels including molecular, cellular, and functional, across multiple stages of development and illuminates the dichotomy between the different mTOR complexes and their unique signaling roles.

Considering Prenatal Alcohol Exposure in a Developmental Origins of Health and Disease Framework.

A potential molecular target for morphological defects of fetal alcohol syndrome: Kir2.1

Two-year cortical trajectories are abnormal in children and adolescents with prenatal alcohol exposure

Ontario Health Teams (OHTs) are a newer model of intersectoral health care in Ontario, Canada designed to provide integrated care across a variety of sectors and organizations to a large attributed population. With the first cohort of OHTs being approved in 2019 and 4 cohorts being approved thereafter, each existing OHT is in a varying degree of maturity. Under the OHT model, community organizations coordinate with OHTs as a unified team to provide integrated and cohesive care with a focus on population health. This type of coordination invites the challenge of organizing and sharing data across organizational partners in a stream-lined way to deliver timely and evidence-based care. Two OHTs addressed this challenge by developing a data dictionary and repository to better integrate data flow and identify gaps in services to better serve all members of the communities. Based on the collective insights drawn from two OHTs, we present the results of a population health-based data development initiative. This process began with extensive engagement with partner organizations within each OHT and the collective governance teams, including patient and family advisory members. We then developed frameworks to begin asset mapping for data dictionary indicators to develop an understanding of what the current state was in the OHTs. After the indicator mapping was completed, key indicators that were relevant for the current priorities of the OHT as determined in a collective stakeholder process were identified to develop a dynamic and timely data repository within the OHTs. Significant learning has occurred with the development of the data dictionary and repository with the process encountering unexpected needs for education around data literacy and strategies to engage patients and collective governance structures. Because these projects were being engaged simultaneously in two different OHTs that varied by cohort and attributed population demographics, a comparison to identify common and differing enablers and challenges will be undertaken. Next steps once the data dictionary and repositories have been completed will be to evaluate the process and identify educational modules and frameworks that can be developed to promote data development and capacity within the OHTs in order to help them provide evidence-based integrated care to their communities. These resources will enable future collaboration, inform planning and decision-making, and identify existing and potential data gaps within the OHTs. Beyond the OHTs, understanding this process with the resulting frameworks and modules will be applicable across integrated care models of care that desire to build data literacy and capacity and engage in population health.

Editorial on the Research Topic Perspectives and recent advances in Fetal Alcohol Spectrum Disorders research 1. Effects of prenatal ethanol exposure on behavior and brain function. 2. Molecular and cellular mechanisms of ethanol-induced damage. 3. Neuroanatomical and brain structure changes due to prenatal ethanol exposure. Collectively, the included articles highlight that FASD is a highly complex neurodevelopmental disorder with life-long consequences that calls for concerted efforts of investigation on all fronts, at all levels, and across lifespan, to advance and improve treatment and management of FASD. Although research in this field has indeed come a long way, knowledge gaps remain and must be addressed, potentially utilizing some of the advances described in this Research Topic.

BackgroundFetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs among individuals with prenatal alcohol exposure (PAE). With improved prediction models, FAS can be diagnosed or treated early, if not completely prevented.ObjectiveIn this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (eg, timing of exposure to alcohol during pregnancy and type of alcohol consumed) were most influential in generating an accurate model.MethodsData from the collaborative initiative on fetal alcohol spectrum disorders from 2007 to 2017 were used to gather information about 595 women who consumed alcohol during pregnancy at 5 hospital sites around the United States. To obtain information about PAE, questionnaires or in-person interviews, as well as reviews of medical, legal, or social service records were used to gather information about alcohol consumption. Four different machine learning algorithms (logistic regression, XGBoost, light gradient-boosting machine, and CatBoost) were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Of the total cases, 80% were randomly selected for training, while 20% remained as test data sets for predicting FAS. Feature importance was also analyzed using Shapley values for the best-performing algorithm.ResultsOverall, there were 20 cases of FAS within a total population of 595 individuals with PAE. Most of the drinking occurred in the first trimester only (n=491) or throughout all 3 trimesters (n=95); however, there were also reports of drinking in the first and second trimesters only (n=8), and 1 case of drinking in the third trimester only (n=1). The CatBoost method delivered the best performance in terms of AUROC (0.92) and area under the precision-recall curve (AUPRC 0.51), followed by the logistic regression method (AUROC 0.90; AUPRC 0.59), the light gradient-boosting machine (AUROC 0.89; AUPRC 0.52), and XGBoost (AUROC 0.86; AURPC 0.45). Shapley values in the CatBoost model revealed that 12 variables were considered important in FAS prediction, with drinking throughout all 3 trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (eg, beer, wine, or liquor) scoring highly in overall feature importance. For most predictive measures, the best performance was obtained by the CatBoost algorithm, with an AUROC of 0.92, precision of 0.50, specificity of 0.29, F1 score of 0.29, and accuracy of 0.96.ConclusionsMachine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models among pregnant drinkers. For small training sets, which are common with FAS, boosting mechanisms like CatBoost may help alleviate certain problems associated with data imbalances and difficulties in optimization or generalization.

Sponsoring Organization: National Institute on Alcohol Abuse and Alcoholism, the Collaborative Initiative on Fetal Alcohol Spectrum Disorders, and the

Sex differences in associations between white matter microstructure and gonadal hormones in children and adolescents with prenatal alcohol exposure

Globally, the open science movement has as its main principle the openness of publicly funded data and research outputs. It is essential that actors in this movement include the development and implementation of legal, technical and technological infrastructure. The premise of this research was that if the purpose of open science is free access to research products, then technological infrastructure is fundamental to crystallise availability, visibility, reproducibility and unrestricted access. The research aimed to identify actions of the open science movement in Latin America and study technological infrastructure for data repositories developed and implemented there. The methodology comprised a bibliographic review and quantitative methods. For the exploratory analysis, the sources for retrieval of information were official Web portals of library entities, the Registry of Research Data Repositories, and the Dataverse Project. The search was based on defined variables, namely actions, data repository, infrastructure, library collaboration, and repositories in the Registry of Research Data Repositories and Dataverse. Findings were studied through a descriptive content analysis based on defined variables. We found that nine Latin American countries carry out open science actions; eight develop research data repositories; eight implement technological infrastructure to develop repositories; seven collaborate with libraries in open science actions; and seven have their repositories recorded in the Registry of Research Data Repositories and Dataverse, resulting in 62 research data repositories in Latin America. We concluded that these libraries are actors and laboratories with methodological and pragmatic elements that contribute to the organisation, reproducibility, transparency, management and open access that support the paradigm shift which the scholarly communication system requires.

Purpose In the present era, research data is a concern for researchers, as they are trying to find new ways to communicate their research findings and conclusions to other researchers in order to increase visibility and credibility. BRICS nations are fast emerging economies and contribute significantly in research output. This study makes an effort to analyze and explore the role of BRICS nations towards open access research data repository registered with Registry of Research Data Repositories. Design/methodology/approach The data were gathered from re3data repository, and the search was limited to BRICS nations. The data were further analyzed and tabulated as per set parameters, namely, country-wise distribution, types of contents, subject coverage and language diversity. Findings The findings depict that in terms of strength, India has the highest number of data repositories, thereby achieved the first rank among BRICS nations, and South Africa has the least number of data repositories, whereas in terms of content type and subject coverage, India again is leading among BRICS nations. The English language is used by repositories as the main language of the interface. Practical implications The study helps to understand the development of research data repositories by BRICS nations. The study is further beneficial to researchers, as Registry of Research Data Repository provides a single platform to access repositories from various disciplines. Readily available data saves time, money and efforts of researchers and helps the researcher in completing their research activity in a very short span of time. Originality/value The paper has investigated open access data repositories of BRICS nation that has not been attempted earlier. This gives readers comprehensive overview of research data repositories developed in fast emerging economies of the global. The paper can be very helpful for information managers, OA promoters and education and research policy makers to devise plans and policy bearing in mind the evolving research channels in emerging economies.

The NIEHS-supported Human Health and Exposure Analysis Resource (HHEAR) Data Center maintains a public-use data repository to promote reuse of environmental health data generated by the HHEAR program. The creation and maintenance of this repository requires the integration of information from a wide variety of epidemiologic studies. We have developed the Human Aware Data Acquisition Framework to enable this complex integration, supporting harmonization across multiple studies, and enabling meaningful search and access of the data deposited in the HHEAR Data Repository. To integrate data from a new study, investigators engage in an initial, time-consuming effort to link study data to the HHEAR ontology, a controlled vocabulary of environmental and public health terms. This is accomplished by generating a semantic data dictionary (SDD) from the data dictionaries and codebooks provided by HHEAR study investigators. Originally, this had been done manually by an expert in both epidemiological terminology and ontological modeling. To increase the accessibility of these tools for environmental health scientists who lack formal ontologic training, we have developed an SDD-Editor that simplifies the ontology modeling process. The SDD-Editor reuses elements common to epidemiologic data dictionaries and spreadsheet software, while integrating features needed to form semantic links between public health concepts and existing ontologies. The SDD-Editor suggests potential concept matches for study variables within the SDD using natural language processing to capture the semantic similarity between data dictionary and ontology class descriptions. If no suitable suggestion exists, investigators can search for ontology terms using a search engine powered by Bioportal. Once finished, a validator is run to check that the SDD has the correct format and all classes are valid. By automating parts of the ontology modeling process, the SDD-Editor greatly facilitates the dynamic integration of HHEAR environmental health studies into a single repository, benefiting the scientific community.

Identifying a common data dictionary across colorectal cancer outcome registries: A mapping exercise to identify opportunities for data dictionary harmonisation

HIV Exposure Without Infection Impacts Early Language Development: Outcomes from a South African Birth Cohort Study

The National Database for Autism Research (NDAR) is a US National Institutes of Health (NIH)-funded research data repository created by integrating heterogeneous datasets through data sharing agreements between autism researchers and the NIH. To date, NDAR is considered the largest neuroscience and genomic data repository for autism research. In addition to biomedical data, NDAR contains a large collection of clinical and behavioral assessments and health outcomes from novel interventions. Importantly, NDAR has a global unique patient identifier that can be linked to aggregated individual-level data for hypothesis generation and testing, and for replicating research findings. As such, NDAR promotes collaboration and maximizes public investment in the original data collection. As screening and diagnostic technologies as well as interventions for children with autism are expensive, health services research (HSR) and health technology assessment (HTA) are needed to generate more evidence to facilitate implementation when warranted. This article describes NDAR and explains its value to health services researchers and decision scientists interested in autism and other mental health conditions. We provide a description of the scope and structure of NDAR and illustrate how data are likely to grow over time and become available for HSR and HTA.