Large-scale Data Repositories Research Articles

The present and future of the Cancer Dependency Map.

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

Content-based medical image retrieval with opponent class adaptive margin loss

The increasing utilization of medical imaging technology with digital storage capabilities has facilitated the compilation of large-scale data repositories. Fast access to image samples with similar appearance to suspected cases in these repositories can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on quantitative assessment of image similarity based on image features in a latent space. Since conventional methods based on hand-crafted features typically show poor generalization performance, learning-based CBIR methods have received attention recently. A common framework in this domain involves classifier-guided models that are trained to detect different image classes. Similarity assessments are then performed on the features captured by the intermediate stages of the trained models. While classifier-guided methods are powerful in inter-class discrimination, they are suboptimally sensitive to within-class differences in image features. An alternative framework instead performs task-agnostic training to learn an embedding space that enforces the representational discriminability of images. Within this representational-learning framework, a powerful method is triplet-wise learning that addresses the deficiencies of point-wise and pair-wise learning in characterizing the similarity relationships between image classes. However, the traditional triplet loss enforces separation between only a subset of image samples within the triplet via a manually-set constant margin value, so it can lead to suboptimal segregation of opponent classes and limited generalization performance. To address these limitations, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. To maintain optimally discriminative representations, OCAM considers relationships among all image pairs within the triplet and utilizes an adaptive margin value that is automatically selected per dataset and during the course of training iterations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). On average, OCAM shows an mAP performance of 86.30% in the KVASIR dataset, 70.30% in the ISIC 2019 dataset, and 85.57% in the X-RAY dataset. Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against competing triplet-wise methods at 1.52%, classifier-guided methods at 2.29%, and non-triplet representational-learning methods at 4.56%.

Pharmacogenomics: Driving Personalized Medicine.

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Identifying reduced hearing in children who have developmental disabilities: Insights for inclusive research practices with electronic health records.

Recent advancements in big data analytics and the formation of large-scale clinical data repositories provide a unique opportunity to determine the current state of pediatric hearing health care for children who have developmental disabilities. Before answering unresolved questions about diagnostic practice, it is paramount to determine a standard and reliable method for identifying children who have reduced hearing because clinical management is affected by hearing status. The purpose of this study was to compare 5 different methods for identifying cases of reduced hearing from pure-tone thresholds based on developmental disability status. Using retrospective clinical data from 100,960 children (0-18 years), hearing status was determined for a total of 226,580 encounters from three clinical sites. 9% of the children had a diagnosis of intellectual disability, autism spectrum disorder, Down syndrome, or cerebral palsy. Results revealed that encounters from children who have developmental disabilities were more likely to have insufficient data to allow hearing status to be determined. Moreover, methods with higher data demands (i.e., number of thresholds and ear-specific thresholds) resulted in fewer classifiable encounters. The average child age when hearing status was classified for the first time was older for children who have developmental disabilities than for children in the comparison group. Allowing thresholds to build up over multiple test sessions did result in more children who have developmental disabilities being classified than for single-encounter methods, but a meaningful decrease in child age at the time of classification was not seen for this strategy. Compared to the comparison group, children who have developmental disabilities were more likely to have reduced hearing that was stable over time, yet their hearing status was determined at older ages. Results provide key guidance to researchers for how to determine hearing status in children for big data applications using electronic health records. Furthermore, several assessment disparities are spotlighted for children who have developmental disabilities that warrant further investigation.

A training curriculum for retrieving, structuring, and aggregating information derived from the biomedical literature and large-scale data repositories.

Background: Biomedical research over the past two decades has become data and information rich. This trend has been in large part driven by the development of systems-scale molecular profiling capabilities and by the increasingly large volume of publications contributed by the biomedical research community. It has therefore become important for early career researchers to learn to leverage this wealth of information in their own research. Methods: Here we describe in detail a training curriculum focusing on the development of foundational skills necessary to retrieve, structure, and aggregate information available from vast stores of publicly available information. It is provided along with supporting material and an illustrative use case. The stepwise workflow encompasses; 1) Selecting a candidate gene; 2) Retrieving background information about the gene; 3) Profiling its literature; 4) Identifying in the literature instances where its transcript abundance changes in blood of patients; 5) Retrieving transcriptional profiling data from public blood transcriptome and reference datasets; and 6) Drafting a manuscript, submitting it for peer-review, and publication. Results: This resource may be leveraged by instructors who wish to organize hands-on workshops. It can also be used by independent trainees as a self-study toolkit. The workflow presented as proof-of-concept was designed to establish a resource for assessing a candidate gene’s potential utility as a blood transcriptional biomarker. Trainees will learn to retrieve literature and public transcriptional profiling data associated with a specific gene of interest. They will also learn to extract, structure, and aggregate this information to support downstream interpretation efforts as well as the preparation of a manuscript. Conclusions: This resource should support early career researchers in their efforts to acquire skills that will permit them to leverage the vast amounts of publicly available large-scale profiling data.

Separable Nonlinear Least Squares Search of Parameter Values in Photovoltaic Models

This article presents a new numerical approach for parameter extraction in five-parameter single-diode or double-diode photovoltaic models, through a separable nonlinear least squares treatment of the problem. The parameter separation technique realizes a reduction of the number of independent parameters as well as the computational complexity. Then, a penalty-assisted direct search algorithm is proposed to solve the optimization problem. The proposed method is applicable to current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–</i> voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> ) curves with at least 4 points or manufacturer datasheets with 3 key points, which breaks the 5-point limitation in previous numerical optimization methods. In two commonly-used case studies that are considered as the benchmark test for single-diode model, the proposed method shows a high accuracy close to the most accurate methods. After an additional curve-fitting refinement of the obtained parameters, the best accuracy documented until now is achieved in the case studies. Furthermore, the robustness of the proposed method is validated through a large-scale data repository containing more than one million <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I–V</i> curves. Finally, to facilitate the application of the proposed method, a graphical easy-to-use program has been developed and available to public users.

Cluster Analysis Identifies Phosphatidylcholine Species in the Liver and Heart as Prediction Hubs in Ossabaw Swine with Cardio‐Metabolic Heart Failure

We recently developed an Ossabaw swine model (Western Diet and aortic banding; WD-AB) that exhibits features consistent with cardio-metabolic heart failure (HF). This pre-clinical model has a pattern of findings possibly useful for enhancing our understanding of the interactions between metabolic syndrome and HF. The aim of this study is to use advanced quantitative methods to reveal novel relationships and candidate predictors we hypothesize may be important to cardio-metabolic HF. A large-scale data repository was created containing 142 hemodynamic, structural, and functional cardiovascular measures, 413 lipid species identified using shotgun lipidomics, and 28,386 mRNA-transcripts revealed by RNA-seq. Repository data was generated from heart, vascular, hepatic, and blood samples collected from 2 groups of intact female Ossabaw swine: 1) lean control (CON; N=5); and 2) WD-AB (N=4). Logistic regression models with AUC scores identified a subset of measurements predictive of HF status that included 1,237 measurements (4.3%). Log2 fold changes were then calculated and statistically significant changes (p<0.05) with absolute value log2 fold changes >1 were considered. Pearson correlations for these 153 measurements were analyzed to identify redundant information using k-medoids clustering analysis with an average silhouette approach. Twenty-eight left ventricle (LV) genes and 125 lipids in 2 clusters (Fig. 1 Circos plot) with center points were identified. Cluster 1 (7 mRNA-transcripts, 70 lipids) was composed of strong correlations with center point phosphatidylcholine (PC) alkyl ether (a) species 32 from the liver (LIVER_PC­_32.0a_754) that increased 10-fold in the WD-AB group (P<0.05; 25±4 CON vs. 277±56 WD-AB pmols/mg). Cluster 2 (21 mRNA-transcripts, 55 lipids) had center point PC species 38.4a from the LV (HEART_PC_38.4a_830) that increased ~3-fold in the WD-AB group (P<0.05; 37±3 CON vs. 102±3 WD-AB pmols/mg). Identified PCa species containing arachidonic acid are storage depots for bioactive oxylipids (eicosanoids) and a precursor for platelet activating factor production. Increased alkyl ether phospholipids may also indicate increased peroxisome proliferation (the site of alkyl ether biosynthesis) and the potential to increase unknown peroxisome proliferator-activated receptor (PPAR) endogenous ligands. In conclusion, PCa species identified from the heart and liver of WD-AB animals may be underappreciated and have predictive significance for cardio-metabolic HF. Ongoing work includes enrichment analysis to determine biological processes underlying clusters.

Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

Databases and warehouses are experiencing workload of different types such as Decision Support System (DSS), Online Transaction Processing (OLTP) and Mixed workloads. Handling variety of workload in autonomic systems is a critical task. After self-configuring the workload, the next challenge is workload performance tuning that motives towards self-predictive systems. Existing studies provide performance modeling solutions on small-scale data repositories of Database Management System (DBMS) and Data Warehouse (DWH) using either classical eager or lazy learning approaches. However, in real-world problems, we normally have to deal with large-scale data repositories. Therefore, there is a need to develop performance models that provide data augmentation to solve large-scale data repositories that are not publicly available. In this study, deep learning approaches have been investigated for performance tuning of large-scale data repositories. We propose a performance prediction model called Optimized GAN-based Deep Learning (OGDL) model. For data augmentation, Conditional Generative Adversarial Networks (CGAN) is applied. For autonomic perspective, we incorporated MAPE-K model to manage the workload autonomically. Different deep learning models are applied, and it was observed that Deep Belief Network (DBN) performed better as compared to other deep learning models such as Deep Neural Network (DNN). We performed a number of experiments and from results it is observed that deep learning models performed the best in comparison with classical machine learning and lazy learning and a $6-8\%$ increase in accuracy is recorded in our experiments using DBN. The proposed OGDL model performed the best in workload performance predictions in an optimized way.

Approaches to Optimize Medication Data Analysis in Clinical Cohort Studies.

Methods for pharmacoepidemiologic studies of large-scale data repositories are established. Although clinical cohorts of older adults often contain critical information to advance our understanding of medication risk and benefit, the methods best suited to manage medication data in these samples are sometimes unclear and their degree of validation unknown. We sought to provide researchers, in the context of a clinical cohort study of delirium in older adults, with guidance on the methodological tools to use data from clinical cohorts to better understand medication risk factors and outcomes. Prospective cohort study. The Successful Aging After Elective Surgery (SAGES) prospective cohort. A total of 560 older adults (aged ≥70 years) without dementia undergoing elective major surgery. Using the SAGES clinical cohort, methods used to characterize medications were identified, reviewed, analyzed, and distinguished by appropriateness and degree of validation for characterizing pharmacoepidemiologic data in smaller clinical data sets. Medication coding is essential; the American Hospital Formulary System, most often used in the United States, is not preferred over others. Use of equivalent dosing scales (e.g., morphine equivalents) for a single medication class (e.g., opioids) is preferred over multiclass analgesic equivalency scales. Medication aggregation from the same class (e.g., benzodiazepines) is well established; the optimal prevalence breakout for aggregation remains unclear. Validated scale(s) to combine structurally dissimilar medications (e.g., anticholinergics) should be used with caution; a lack of consensus exists regarding the optimal scale. Directed acyclic graph(s) are an accepted method to conceptualize causative frameworks when identifying potential confounders. Modeling-based strategies should be used with evidence-based, a priori variable-selection strategies. As highlighted in the SAGES cohort, the methods used to classify and analyze medication data in clinically rich cohort studies vary in the rigor by which they have been developed and validated.

GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets.

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient’s individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine’s main objective—ensuring the optimum diagnosis, treatment and prognosis for each individual—investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data—and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

FACEBOOK USER REACTIONS AND EMOTION: AN ANALYSIS OF THEIR RELATIONSHIPS AMONG THE ONLINE DIABETES COMMUNITY

With the advent of Web 2.0 technologies such as social media, online text sources provide large scale data repositories out of which valuable knowledge about human emotions can be derived. This paper aims to (i) detect and classify emotions of the Facebook diabetes community, (ii) examine the relationship of emotion and Facebook reactions, and (iii) identify user reaction predictors for each of the emotion. A total of 15K posts were randomly selected from several official Facebook diabetes support groups. Pre-processing was administered, resulting in 2475 Facebook posts for further analysis in this study. Emotion detection was first administered using Indico API, with results revealing anger, sadness and fear to be the top most emotions experienced, whilst love and wow emerged as the highest-ranking reactions. Precision and recall indicate the performance of the emotion detection mechanism ranged between 65 – 82% for all the emotions, compared to the human annotation. The average F-score recorded was 78%. Both love and wow were found to significantly predict joy and fear, whereas angry was found to predict anger. The findings indicate that human emotions can be effectively detected based on users’ textual communication, and significant relationships exists between several reactions and emotions.

Trust in Centralized Large-Scale Data Repository: A Qualitative Analysis.

Exponential increases in digital data and calls for participation in human research raise questions about when and why individuals voluntarily provide personal data. We conducted 36 in-depth interviews with ex-participants, participants, and nonparticipants in a biobank to identify key factors influencing trust in centralized large-scale data repository for human research. Our findings indicated that trust depends strongly on whether such data repository benefits the public, the interests of data collectors, the characteristics of the collected data, and application of informed consent for retaining control over personal data. Concerns about the aims and range of data repository appeared to influence withdrawal of participation. Our findings underscore ethical and practical issues relating to data collection and consent procedures in human research.

An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images.

Objective: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring’s health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. Method: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B) -mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. Results: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. Conclusion: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research.

Autonomic workload performance tuning in large-scale data repositories

The workload in large-scale data repositories involves concurrent users and contains homogenous and heterogeneous data. The large volume of data, dynamic behavior and versatility of large-scale data repositories is not easy to be managed by humans. This requires computational power for managing the load of current servers. Autonomic technology can support predicting the workload type; decision support system or online transaction processing can help servers to autonomously adapt to the workloads. The intelligent system could be designed by knowing the type of workload in advance and predict the performance of workload that could autonomically adapt the changing behavior of workload. Workload management involves effectively monitoring and controlling the workflow of queries in large-scale data repositories. This work presents a taxonomy through systematic analysis of workload management in large-scale data repositories with respect to autonomic computing (AC) including database management systems and data warehouses. The state-of-the-art practices in large-scale data repositories are reviewed with respect to AC for characterization, performance prediction and adaptation of workload. Current issues are highlighted at the end with future directions.

Detecting variation of emotions in online activities

Abstract Online text sources form evolving large scale data repositories out of which valuable knowledge about human emotions can be derived. Beyond the primary emotions which refer to the global emotional signals, deeper understanding of a wider spectrum of emotions is important to detect online public views and attitudes. The present work is motivated by the need to test and provide a system that categorizes emotion in online activities. Such a system can be beneficial for online services, companies recommendations, and social support communities. The main contributions of this work are to: (a) detect primary emotions, social ones, and those that characterize general affective states from online text sources, (b) compare and validate different emotional analysis processes to highlight those that are most efficient, and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. Study results demonstrate that the methodologies at hand succeed to detect a wider spectrum of emotions out of text sources.

Future priorities for Arctic freshwater science from the perspective of early career researchers

Freshwater systems are a major component of the terrestrial Arctic and are particularly sensitive to climatic and other environmental changes. Recent efforts have focussed on synthesizing and identifying gaps in the current understanding of Arctic freshwater systems. We aimed to identify research priorities for Arctic freshwater science from the perspective of early-career researchers, given their leading role as the next generation of scientists tasked with addressing these research areas. Using a discussion session and an online survey of early-career researchers, we identified five priority topics: (1) establishment of long-term monitoring sites across the Arctic, (2) improved understanding of the implications of permafrost thawing for biogeochemistry of Arctic rivers and lakes, (3) better model predictions of changes in freshwater systems and better integration with the wider modelling community, (4) improved estimates of environmental thresholds and tipping points within Arctic freshwater ecosystems, and (5) the need for community-based monitoring and assessment. These five topics underline the importance of interdisciplinary research and the necessity of developing large-scale environmental monitoring programs and data repositories. Such developments will facilitate long-term understanding of the impact of climate variability upon Arctic freshwater systems and will promote knowledge exchange between local and scientific communities.

The Virtuous Cycle of a Data Ecosystem.

Digital data of all types are being created at an ever-increasing rate, doubling approximately every two years. Annual data creation rates are estimated to reach 44 trillion gigabytes by 2020 [1]. Similarly, the rate at which primary scientific data are being collected is accelerating [2]. This astounding growth in scientific data creation has led to the contemporary discussion of scientific data sharing policies. Many of the criticisms levied against data sharing have focused on practical issues such as the economics and logistics of data storage, technical challenges for doing so, or appropriate attribution of credit [2–9]. In contrast, the arguments in favor of data sharing have focused largely on scientific replication, reproducibility [10], facilitation of collaborative research, and increased citations for publications that share data [11]. This is largely an ethical argument wherein there is an obligation to share data collected using public funds [3–6,12,13]. Rather than focusing on the much-discussed arguments against data sharing—cost, infrastructure, curation, privacy, and attribution/credit concerns—in this Perspective, I outline the overlooked benefits of data sharing: novel remixing and combining as well as bias minimization and meta-analysis. I argue that we must consider the weight of the costs against the true value of the possible benefits. If the decision for any individual researcher, university, or funding agency to implement data sharing policies comes down to a cost—benefit analysis based solely on replication versus storage, the cost—benefit analysis may be artificially tipped in favor of not sharing data caused by overlooking more subtle—but critical—benefits. These hidden benefits of data remixing cannot be appreciated when considering each individual dataset as an independent entity, and thus a richer consideration of those benefits is warranted. Although there is some evidence that, on the local scale, research groups may not make use of shared data [14], in this Perspective, I outline the ways in which research groups are beginning to take advantage of open data in novel, and sometimes surprising, ways. Rather than arguing for a centralized, large-scale data repository, I am advocating for a more organic development wherein we, institutionally, encourage the growth of a data ecosystem. This can be done via multiple venues, such as the general scientific data sharing sites figshare (https://figshare.com/) or the Dryad Digital Repository (http://datadryad.org/), each of which, in addition to Nature Publishing Group’s recently launched peer-reviewed data sharing journal, Scientific Data [15], provides citable Digital Object Identifiers for the data themselves. Such developments are addressing concerns regarding credit and help motivate data curation and contextualization. A data sharing ecosystem provides space for multiple diverse datasets to intermingle to encourage new, multidisciplinary discoveries for current and future scientists.

Emergence of collaboration networks around large scale data repositories: a study of the genomics community using GenBank

The advent of large data repositories and the necessity of distributed skillsets have led to a need to study the scientific collaboration network emerging around cyber-infrastructure-enabled repositories. To explore the impact of scientific collaboration and large-scale repositories in the field of genomics, we analyze coauthorship patterns in NCBIs big data repository GenBank using trace metadata from coauthorship of traditional publications and coauthorship of datasets. We demonstrate that using complex network analysis to explore both networks independently and jointly provides a much richer description of the community, and addresses some of the methodological concerns discussed in previous literature regarding the use of coauthorship data to study scientific collaboration.

PD28-07 ASSESSMENT OF THE RATE OF ADHERENCE TO INTERNATIONAL GUIDELINES OF ANDROGEN DEPRIVATION THERAPY WITH EXTERNAL BEAM RADIATION THERAPY- A POPULATION-BASED STUDY

You have accessJournal of UrologyProstate Cancer: Advanced (including Drug Therapy) I1 Apr 2016PD28-07 ASSESSMENT OF THE RATE OF ADHERENCE TO INTERNATIONAL GUIDELINES OF ANDROGEN DEPRIVATION THERAPY WITH EXTERNAL BEAM RADIATION THERAPY- A POPULATION-BASED STUDY Paolo Dell’Oglio, Hiba Abou-Haidar, Sami-Ramzi Leyh-Bannurah, Zhe Tian, Giorgio Gandaglia, Shahrokh Shariat, Umberto Capitanio, Alberto Briganti, Francesco Montorsi, Markus Graefen, Fred Saad, and Pierre Karakiewicz Paolo Dell’OglioPaolo Dell’Oglio More articles by this author , Hiba Abou-HaidarHiba Abou-Haidar More articles by this author , Sami-Ramzi Leyh-BannurahSami-Ramzi Leyh-Bannurah More articles by this author , Zhe TianZhe Tian More articles by this author , Giorgio GandagliaGiorgio Gandaglia More articles by this author , Shahrokh ShariatShahrokh Shariat More articles by this author , Umberto CapitanioUmberto Capitanio More articles by this author , Alberto BrigantiAlberto Briganti More articles by this author , Francesco MontorsiFrancesco Montorsi More articles by this author , Markus GraefenMarkus Graefen More articles by this author , Fred SaadFred Saad More articles by this author , and Pierre KarakiewiczPierre Karakiewicz More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.394AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The National Comprehensive Cancer Network and the European Association of Urology guidelines recommend using radiation therapy with androgen deprivation therapy (RT-ADT) for the treatment of high-risk localized prostate cancer and locally advanced prostate cancer patients. The objective of the current study was to examine the rate of adherence to these guidelines within a large-scale population-based data repository. We hypothesized near-perfect guideline adherence. METHODS Between 2003 and 2009, in the Surveillance Epidemiology and End Results (SEER)-Medicare database, 14,180 patients were diagnosed with high-risk (T1-T2 with WHO histological grade 3), or locally advanced (T3-T4) prostatic adenocarcinoma. We assessed the annual rates of adherence to guidelines with respect to use of RT-ADT in the overall population and after stratification according to stage-grade groupings (T1-T2 G3 vs. T3-T4 any grade), age (66-69 vs. 70-74 vs. 75-79 vs. ≥80), Charlson comorbidity index-CCI (0 vs. 1 vs. ≥2) and pre-existing baseline cardiovascular (CV) risk. Linear regression model was used to test the slope of rates of combination treatment over time. Multivariate logistic regression analyses were performed to assess the predictors of RT-ADT use. RESULTS RT-ADT rates, and guideline adherence, were 58.1 to 75.0%, with the highest rate (75.0%) in 2003, and the lowest (58.1%) in 2009. When stratified according to stage-grade groupings, age, CCI and pre-existing baseline CV risk, similar results were obtained. In multivariate analyses, more recent year of diagnosis (OR 0.90, p<0.001), younger patients (OR 1.03; p<0.001), stages T1-T2 G3 disease (OR 0.35, p<0.001), CCI of 0 (OR 0.87, p=0.007), African-American race (OR 0.85, p=0.004), high socioeconomic status (OR 0.90, p=0.009), unknown marital status (OR 0.77, p<0.001), urban residence (OR 0.64, p<0.001), and residence in the Midwest, South, and West (OR 0.51, OR 0.63, OR 0.60, respectively; all p<0.001) were associated with lower RT-ADT use. CONCLUSIONS The rate of guideline adherence regarding the use of RT-ADT is suboptimal and decreases with time instead of increasing. Moreover, individual patient characteristics indicate random variability. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e656-e657 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Paolo Dell’Oglio More articles by this author Hiba Abou-Haidar More articles by this author Sami-Ramzi Leyh-Bannurah More articles by this author Zhe Tian More articles by this author Giorgio Gandaglia More articles by this author Shahrokh Shariat More articles by this author Umberto Capitanio More articles by this author Alberto Briganti More articles by this author Francesco Montorsi More articles by this author Markus Graefen More articles by this author Fred Saad More articles by this author Pierre Karakiewicz More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE).

Increased brain volume is a consistent finding in young children with autism spectrum disorders (ASD); however, the regional specificity and developmental course of abnormal brain structure are less clear. Small sample sizes, particularly among voxel-based morphometry (VBM) investigations, likely contribute to this difficulty. Recently established large-scale neuroimaging data repositories have helped clarify the neuroanatomy of neuropsychiatric disorders such as schizophrenia and may prove useful in ASD. Structural brain images from the Autism Brain Imaging Database Exchange (ABIDE), which contains over 1100 participants, were analyzing using DARTEL VBM to investigate total brain and tissue volumes, and regional brain structure abnormalities in ASD. Two, overlapping cohorts were analyzed; an 'All Subjects' cohort (n = 833) that included all individuals with usable MRI data, and a 'Matched Samples' cohort (n = 600) comprised of ASD and TD individuals matched, within each site, on age and sex. Total brain and grey matter volumes were enlarged by approximately 1-2% in ASD; however, the effect reached statistical significance in only the All Subjects cohort. Within the All Subjects cohort, VBM analysis revealed enlargement of the left anterior superior temporal gyrus in ASD. No significant regional changes were detected in the Matched Samples cohort. There was a non-significant reduction in the correlation between IQ and TBV in ASD compared to TD. Brain structure abnormalities in ASD individuals age 6 and older consists of a subtle increase in total brain volume due to enlargement of grey matter with little evidence of regionally specific effects.