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Understanding the immune microenvironment in pediatric low-grade glioma (pLGG) patients may help in identification of the patients who benefit from anti-tumor immunotherapies. However, surgical resection is not feasible for many pLGG tumors in certain anatomical locations. Therefore, developing non-invasive tools that characterize the tumor microenvironment prior to therapeutic interventions could contribute to stratification and enrollment of the patients into relevant clinical trials. In this work, we derived radiomic signatures of immune profiles (radioimmunomics) based on machine learning (ML) analysis of readily available conventional MRI scans. Transcriptomic data for a cohort of 197 subjects was retrospectively collected from Open Pediatric Brain Tumor Atlas (OpenPBTA). The patients were categorized into three groups (Group1-3) based on their immunological profiles using consensus clustering algorithm. This analysis revealed greater immune cell infiltration in non-BRAF mutated pLGGs. Group1 showed more enrichment in M1 macrophages, and microenvironment and immune scores compared to Group2 and Group3. Elevated tumor inflammation score (TIS), as a predictor of clinical response to anti-PD-1 blockade, was observed in Group1 compared to Group2 (p= 1.4e-7) and Group3 (p= 0.0054). Radiomic features, including volumetric, morphologic, histogram, and texture descriptors, were extracted from the segmented tumor regions on multiparametric MRI (mpMRI) scans of 71 (of 197) patients. Multivariate ML models were trained to predict the three immunological groups based on radiomic features using cross-validated random forest classifier along with recursive feature elimination, which yielded AUC of 0.72 for this multi-class classification problem. Our findings indicate the presence of distinct immunological groups in pLGG tumors, with possibly more favorable response to immunotherapies in Group1 tumors. Furthermore, we developed radioimmunomic signatures based on pre-operative conventional mpMRI that can potentially stratify the patients based on their immune tumor microenvironment. Based on these initial promising results, we are exploring additional features to increase the accuracy of radioimmunomics model.

PurposeLibrary Hi Tech is one of the most influential journals that publish leading research in library and information science (LIS). The present study aims to understand the scholarly communication in Library Hi Tech by profiling its historic footprint, emerging trends and knowledge diffusion.Design/methodology/approachA total of 3,131 bibliographic records between 1995 and 2018 were collected from the Web of Science. Text mining, graph analysis and data visualization were used to analyze subject category assignment, domain-level citation trends, co-occurrence of keywords, keyword bursts, networks of document co-citation and landmark articles.FindingsFindings indicated that published research in the journal was largely influenced by the psychology, education and social domain as a unidisciplinary discipline. Knowledge of the journal has been disseminated into multiple domains such as LIS, computer science and education. Dominant thematic concentrations were also identified: (1) library services in academic libraries and related to digital libraries, (2) adoption of new information technologies and (3) information-seeking behavior in these contexts. Additionally, the journal has exhibited an increased research emphasis on mixed-method user-centered studies and investigations into libraries' use of new media.Originality/valueThis study provides a promising approach to understand scientific trends and the intellectual growth of journals. It also helps Library Hi Tech to become more self-explanatory with a detailed bibliometric profile and to identify future directions in editorship and readership. Finally, researchers in the community can better position their studies within the emerging trends and current challenges of the journal.

Pediatric central nervous system (CNS) cancers are the leading disease-related cause of death in children and there is urgent need for curative therapeutic strategies for these tumors. To address the urgency, Children’s Brain Tumor Network (CBTN) has advanced an open science model to accelerate the research discovery for pediatric brain tumors. In first phase of Open Pediatric Brain Tumor Atlas (OpenPBTA) effort CBTN together with Pacific Pediatric Neuro-Oncology Consortium (PNOC) with support of Gabriella Miller Kids First Data Resource Center (KFDRC) created and comprehensively characterized over 1000 clinically annotated pediatric brain tumors. In the second phase of the OpenPBTA effort, through resource awards and collaboration across KFDRC, the NCI Childhood Cancer Data Initiative (CCDI), NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), NCI Center for Cancer Research and additional partnered institutions and foundations, CBTN has expanded OpenPBTA to support high throughput molecular characterization for an additional 1900 pediatric brain tumor patients and their families. This includes the processing and characterization of over 8000 specimens across >50 brain tumor diagnoses. The cohort expansion builds on >1000 previously characterized samples with a portfolio of multimodal data including whole genome sequencing, RNA sequencing, miRNA sequencing, methylation sequencing, proteomics, lipidomics and/or metabolomics. Molecular data is linked to patient longitudinal clinical data, imaging data (MRIs and radiology reports), histology slide images, and pathology reports. To inform novel discovery and clinical implementation of genomic approaches for diagnostic/therapeutic purposes, the data is deposited the cloud-based research environment of the NCI’s CCDI and the KFDRC to provide near real-time integration, dissemination, processing, and sharing of associated petabyte-scale harmonized data. The approach leverages the DRC platform’s cloud-based computational environment in CAVATICA. Processed annotations are facilitated via CAVATICA-enabled shareable pipelines and can be explored through PedcBioPortal, a data visualization/analysis application further integrating additional public and deposited datasets. This expansion phase of OpenPBTA is released with no embargo period and provides one of the largest deeply characterized cohorts of pediatric brain tumor samples and associated clinical data for >3000 pediatric brain tumor patients. CBTN’s open-science, rapid-release model aims to advance novel biomarkers and therapeutic exploratory research, supporting new clinical trial development and accelerated discovery on behalf of changing the outcome for kids with brain tumors. Citation Format: Mateusz P. Koptyra, Komal Rahti, Yuankun Zhu, Bailey Farrow, Daniel Miller, Adam Kraya, Yiran Guo, Peter Madsen, Nicholas Van Kuren, Xiaoyan Huang, Miguel A. Brown, Jennifer L. Mason, Meen Chul Kim, Allison P. Heath, Brian M. Ennis, Bo Zhang, Jena V. Lilly, Jo Lynne Rokita, Christopher Friedman, Ximena P. Cuellar, Catherine A. Sullivan, Noel Coleman, Trang Duros, Thinh Q. Nguyen, Emmett C. Drake, Zeinab Helili, Beth A. Frenkel, Gerri R. Trooskin, Ariana Familiar, Karthik Viswanathan, Christopher M. Beck, Madison L. Hollawell, Valerie P. Baubet, Cassie Kline, Mariarita Santi, Tatiana S. Patton, Stephanie Stefankiewicz, Arya Kamnaa, Ryan A. Velasco, Dani Cardona, Phillip J. Storm, Adam C. Resnick, o/b/o Children's Brain Tumor Network. Expansion of the Pediatric Brain Tumor Atlas: Children's Brain Tumor Network, Kids First Data Resource and Childhood Cancer Data Initiative Open Science effort. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3566.

Brain tumors are the leading cause of disease-related death in children and young adults ages 0-19 in largely populated countries such as the United States. In one year alone, 4,000 children and young adults will be diagnosed with a brain or central nervous system tumor in the United States. Brain tumors are complex and difficult to treat in growing children, with current treatments oftentimes causing significant and lifelong side effects. Furthermore, there have only been five drugs in the last 20 years approved by the FDA to treat pediatric brain tumors. Founded in 2011, the Children’s Brain Tumor Network (CBTN) is focused on accelerating the pace of translational research, the discovery of new treatments, and informing precision medicine for children diagnosed with brain tumors. CBTN comprises 32 member institutions/hospitals having over 4700 patient subjects enrolled, spanning 30+ brain tumor diagnoses, over 66,000 biobanked samples and 150 preclinical models. Longitudinal clinical data is also collected for every subject that is enrolled in the observational protocol. Through large scale data generation efforts funded by the NCI and foundational support, CBTN has whole genome, RNA-seq and other molecular characterization for over half of the enrolled patient population. Additionally, efforts have been underway to collect all pathology and radiology imaging and reporting for the subjects. With sequencing being done by multiple vendors, imaging protocols being different across multiple hospitals, and complex clinical treatment and longitudinal follow up data being translated from EHR systems, CBTN has created a rich, but complex, data landscape that is the largest of its kind in the world. In order to accelerate the process of going from data to cures, the data needs to be centralized, organized, and easily distributable. To do this, CBTN has built a first of its kind data workflow that acts as the inventory system for its various data assets. Using a modern data stack including dbt, PostgresSQL, Meltano and AWS, combined with utilization of FHIR as an interchange standard, data from multiple disparate sources such as REDcap, EHR systems, and PAC systems flow in near “real-time” to be utilized as integrated data resources The result of this modern, multimodal, and multi-institutional warehouse allows CBTN to distribute data quickly and accurately to translational researchers around the world and contribute data to key research efforts such as AACR Project Genie, Kids First Data Resource Center, NCI Childhood Cancer Data Initiative, and the NCI’s Open Targets Platform. Citation Format: Bailey K. Farrow, Nicholas Van Kuren, Nathan Young, Christopher Friedman, Meen Chul Kim, Alex Lubneuski, Jennifer Mason, Thinh (Bin) Nguyen, Zeinab Helili, Elizabeth Frenkel, Catherine Sullivan, Ariana Familiar, Yuankun Zhu, Mateusz Koptyra, Tatiana Patton, Jena Lilly, Phillip B. Storm, Adam Resnick, Allison P. Heath. Establishing a multimodal data warehousing platform to accelerate discoveries in pediatric brain tumors for the Children’s Brain Tumor Network. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3565.

Pediatric low-grade glioma (pLGG) encompasses a variety of tumor subtypes with heterogeneous treatment response and relatively long progression-free survival (PFS). Radiomics may serve as a non-invasive and in-vivo tool for early prediction of PFS as a surrogate marker for treatment response and to objectively gauge the efficacy of novel treatment strategies. Here, we present a multivariate model based on radiomic features and clinical variables for risk stratification of pLGGs in terms of PFS and seek associations of the predicted risk groups and mutations in key molecular markers using data from PedCBioportal. Pre-operative multi-parametric MRI scans (T1-pre, T1-post, T2, T2-FLAIR) of 129 patients with newly diagnosed pLGG (median age, 7.76, range, 0.35-19.58 years; median PFS, 28.5, range, 1.1-124.8 months) were collected and quantitative radiomic features (n = 881) were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted based on clinical (age, sex, and extent of tumor resection) and radiomic variables using 4-fold cross-validation. A subset of radiomic features (n = 27) that were most predictive of PFS was selected by applying Elastic Net regularization penalty during Cox-PH model fitting. High-, medium- and low-risk groups were determined based on model predictions. Cox-PH modeling showed excellent performance for prediction of PFS as suggested by the concordance index of 0.78. Radiogenomic assessment (data available in 94/129 patients) showed more enrichment of mutations in NF1 and RB1 genes in the high-risk group, as compared to the low- and medium-risk groups. We showed the potential value of radiomics in providing upfront prediction of PFS, which may further be used as an added treatment arm for early assessment of treatment response of the pLGG patients enrolled in the clinical trials. In the next step of this work, we will expand the cohort and cross-validate these results in an external cohort.

Recent studies have shown preliminary evidence for differentiation of the tumor microenvironment (TME) and immune landscape between molecularly-defined medulloblastoma (MB) subtypes. Identifying radiological correlates of these TME patterns could establish a non-invasive method of immune profile characterization for guiding patient-centered therapies. Here, we examine immune profiles between MB subtypes using data from Open Pediatric Brain Tumor Atlas (OpenPBTA), and their relationship to tumor measurements from pre-operative MRIs. We identified a retrospective cohort of 94 pediatric MB patients with available molecular subtyping and immune profiles (36 cell types) from bulk gene expression data. A random forest analysis was used to classify the four MB subtypes based on immune profiles. Four cell types had high impact on classification performance: plasmacytoid dendritic cells (PDC; 25.8% accuracy decrease when randomized), hematopoietic stem cells (HSC; 21.9%), plasma B cells (20.3%), and cancer associated fibroblasts (18.8%). Pairwise comparisons revealed SHH and WNT tumors had significantly higher numbers of fibroblasts and HSCs compared to Group3/Group4. We also found novel evidence for significantly lower amounts of plasma B cells in the SHH group, and high PDC levels in Group4, followed by Group3, and low PDC in SHH/WNT. Multi-parametric MRI scans for 39 patients were used to segment tumor volumes. Overall tumor volume was significantly correlated with composite stroma scores (R = 0.34, p = 0.036). Additionally, patients with higher volumes of gadolinium contrast-enhancing compared to non-enhancing components had higher immune (R = 0.42, p = 0.009) and microenvironment (summed immune and stromal cell types; R = 0.44, p = 0.006) scores, regardless of their molecular subtype. Together, our results demonstrate: (1) the use of rich immune profiles for differentiating molecular subtypes of MB and their unique TME characterization; and (2) initial evidence for radiological correlates of these profiles based on pre-operative imaging collected through standard practices.

Academic health centers, CTSA hubs, and hospital libraries experience similar funding challenges and charges to do more with less. In recent years academic health center and hospital librarians have risen to these challenges by examining their service models, and beyond that, examining their patron base and users' needs. To meet the needs of employees, patients, and those who assist patients, hospital librarians can employ the CTS Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health. The Persona profiles, which outline the motivations, goals, pain points, wants, and needs of twelve employees and two patients in translational science, provide vital information and insights that can inform everything from designing software tools and educational services, to advertising these services, to designing impactful and collaborative library spaces.

Objective The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. Materials and Methods We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. Results The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain’s growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. Discussion Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. Conclusion The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

The Pediatric Brain Tumor Atlas (PBTA), created as a multi-center effort by the Children’s Brain Tumor Tissue Consortium (CBTTC) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) and supported by the Gabriella Miller Kids First Data Resource Center (DRC), has the initial goal of comprehensively characterizing over 1,600 clinically annotated pediatric brain tumor samples and making the data publicly available to cancer researchers worldwide in near real time and without embargo. PBTA comprises comprehensive clinical data in addition to whole exome sequencing, whole genome sequencing (WGS), RNA sequencing (RNASeq), miRNA sequencing, and proteomics. To address the need to inform novel discovery and clinical implementation of genomic approaches for diagnostic/therapeutic purposes, PBTA utilizes the cloud-based research environment of the Gabriella Miller Kids First DRC to provide near real-time integration, dissemination, processing, and sharing of associated petabyte-scale harmonized data. The PBTA initiative leverages the DRC platform’s users to access their dbGAP approved use of TARGET and other pediatric cancer datasets hosted by NCI’s Cancer Genomics’ Cloud and Genomic Data Commons, allowing for cross-disease studies. Processed annotations is further enabled for the PBTA via PedcBioPortal, a data visualization/analysis application further integrating additional public and deposited datasets. In its first data release are over 30 different types of pediatric brain tumors representing over 1,000 subjects. This data is available on the Kids First DRC Portal and PedcBioPortal and users can seamlessly move between applications. Data types include those for matched tumor/normal samples, such as WGS, RNASeq, proteomics, longitudinal clinical data, imaging data (MRIs and radiology reports), histology slide images, and pathology reports. CBTTC/PNOC promote real-time data release and collaborative discovery via shared resources. The combination of cloud-based analytic platforms with data from both genomics and clinical practice serves to define a new paradigm for accelerated pediatric cancer translational research.

Background: With an increase in the number of disciplines contributing to health literacy scholarship, we sought to explore the nature of interdisciplinary research in the field. Objective: This study sought to describe disciplines that contribute to health literacy research and to quantify how disciplines draw from and contribute to an interdisciplinary evidence base, as measured by citation networks. Methods: We conducted a literature search for health literacy articles published between 1991 and 2015 in four bibliographic databases, producing 6,229 unique bibliographic records. We employed a scientometric tool (CiteSpace [Version 4.4.R1]) to quantify patterns in published health literacy research, including a visual path from cited discipline domains to citing discipline domains. Key Results: The number of health literacy publications increased each year between 1991 and 2015. Two spikes, in 2008 and 2013, correspond to the introduction of additional subject categories, including information science and communication. Two journals have been cited more than 2,000 times—the Journal of General Internal Medicine ( n = 2,432) and Patient Education and Counseling ( n = 2,252). The most recently cited journal added to the top 10 list of cited journals is the Journal of Health Communication ( n = 989). Three main citation paths exist in the health literacy data set. Articles from the domain “medicine, medical, clinical” heavily cite from one domain (health, nursing, medicine), whereas articles from the domain “psychology, education, health” cite from two separate domains (health, nursing, medicine and psychology, education, social). Conclusions: Recent spikes in the number of published health literacy articles have been spurred by a greater diversity of disciplines contributing to the evidence base. However, despite the diversity of disciplines, citation paths indicate the presence of a few, self-contained disciplines contributing to most of the literature, suggesting a lack of interdisciplinary research. To address complex and evolving challenges in the health literacy field, interdisciplinary team science, that is, integrating science from across multiple disciplines, should continue to grow. [ Health Literacy Research and Practice . 2017;1(4):e182–e191.] Plain Language Summary: The addition of diverse disciplines conducting health literacy scholarship has spurred recent spikes in the number of publications. However, citation paths suggest that interdisciplinary research can be strengthened. Findings directly align with the increasing emphasis on team science, and support opportunities and resources that incentivize interdisciplinary health literacy research.

Our previous scientometric review of regenerative medicine provides a snapshot of the fast-growing field up to the end of 2011. The new review identifies emerging trends and new developments appearing in the literature of regenerative medicine based on relevant articles and reviews published between 2000 and the first month of 2014.Multiple datasets of publications relevant to regenerative medicine are constructed through topic search and citation expansion to ensure adequate coverage of the field. Networks of co-cited references representing the literature of regenerative medicine are constructed and visualized based on a combined dataset of 71,393 articles published between 2000 and 2014. Structural and temporal dynamics are identified in terms of most active topical areas and cited references. New developments are identified in terms of newly emerged clusters and research areas. Disciplinary-level patterns are visualized in dual-map overlays.While research in induced pluripotent stem cells remains the most prominent area in the field of regenerative medicine, research related to clinical and therapeutic applications in regenerative medicine has experienced a considerable growth. In addition, clinical and therapeutic developments in regenerative medicine have demonstrated profound connections with the induced pluripotent stem cell research and stem cell research in general. A rapid adaptation of graphene-based nanomaterials in regenerative medicine is evident. Both basic research represented by stem cell research and application-oriented research typically found in tissue engineering are now increasingly integrated in the scientometric landscape of regenerative medicine. Tissue engineering is an interdisciplinary field in its own right. Advances in multiple disciplines such as stem cell research and graphene research have strengthened the connections between tissue engineering and regenerative medicine.

Introduction: The literature of orphan drugs and rare diseases between 2000 and 2014 is reviewed. The overall structure of its intellectual landscape is characterized in terms of thematic concentra...

Gabriella Miller Kids First Pediatric Research Program (GMKF) is a nation-wide, multi-year initiative focused on the integration of large-scale clinically annotated genomic data for childhood cancers and structural birth defects supported by the NIH Common Fund. Awarded by GMKF, the Kids First Data Resource Center (DRC) is tasked to build infrastructure and workflows for data intaking, harmonization, integration and access authorization to empower collaborative discoveries across the GMKF and other integrated datasets. A key challenge for uniform analyses and empowered discovery of large-scale genomic data relates to the diverse genomic processing workflows and methods employed across the sequencing and bioinformatics community. The DRC genomic harmonization team aims to provide “analysis ready” datasets that are “functionally equivalent” across the Kids First datasets and other large-scale genomic data initiatives in order to accelerate the discovery process. Paired with the cloud-based workspace environments of the DRC, such harmonized dataset provide unprecedented opportunities for shared, reproducible discovery by a diverse, collaborative network of researchers. As such, DRC initial pipelines are developed with BWA-MEM alignment on genome build GRCh38 followed by the GATK best practices for germline variant calling and joint genotyping. Common Workflow Language (CWL) is used as the main workflow specification, while Docker technology has been applied to containerize all the tools used by the workflow. Our current workflows are tasked with data harmonization across a number of different experimental platforms including whole genome sequencing, exome sequencing, and RNA-seq. The data processing is done via CAVATICA, an Amazon Web Services (AWS) based cloud computing platform associated with the Kids First DRC Portal co-developed by Seven Bridges Genomics, where workflows feature scatter-gather parallelization and AWS resource optimization. By utilizing such a framework, the DRC team has harmonized over 10,000 WGS and 1,000 RNA-Seq samples across 12 study cohorts within 8 months. This dataset in its current release includes samples from 40 pediatric brain cancers as well as 8 childhood birth defects with the outcome of delivering 150TB harmonized CRAM and 60TB gVCF. With a highly optimized bioinformatics pipeline powered by an efficient cloud-based execution workflow, The DRC platform processes one genome in about 11 hours with an average compute cost of $15 for whole genome alignment and germline variant calling. Here we present our observed challenges and identified opportunities in the analysis and integration of multi-disease pediatric genomic data on a large scale. Citation Format: Yuankun Zhu, Miguel Brown, Batsal Devkota, Bailey Farrow, Bogdan Gavrilovic, Allison Heath, Kyle Hernandez, Avi Kelman, Parimala Killada, Meen Chul Kim, Daniel Kolbman, Mateusz Koptyra, Milan Kovacevic, Maarten Leerkes, Alex Lubneuski, Michele Mattioni, Pichai Raman, Adam Resnick, Nikola Skundric, Deanne Taylor, Junjun Zhang, Bo Zhang, Phillip B. Storm. Genomic harmonization of the Data Resource Center for Gabriella Miller Kids First Pediatric Research Program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2465.

The Pediatric Brain Tumor Atlas (PBTA) is a cloud-based, cross-platform, and data-rich collaborative effort to accelerate discoveries for therapeutic intervention in children diagnosed with brain tumors. Pediatric brain tumors are the leading cause of disease-related death in children and despite advances in therapy, morbidity and mortality rates remain poor. There have been large scale efforts to genomically profile these cancers in the past, however public access to the resulting datasets are limited at best. In contrast, the PBTA, created as a multi-center effort by Children’s Brain Tumor Tissue Consortium (CBTTC) and Pacific Pediatric Neuro-Oncology Consortium (PNOC), has a goal of characterizing over 1,600 pediatric brain tumor samples and making the data publicly available to cancer researchers world-wide. PBTA comprises comprehensive clinical data in addition to whole exome sequencing, whole genome sequencing (WGS), RNA sequencing (RNASeq), miRNA sequencing, and proteomics. To address the need to inform novel discovery and clinical implementation of genomic approaches for diagnostic/therapeutic purposes, PBTA utilizes a cloud-based scientific environment, the CAVATICA portal. CAVATICA provides near real-time integration, dissemination, processing, and sharing of associated PetaByte-sized data by leveraging Amazon Web Services and powerful genomic workflows. It also enables dbGaP approved users to access TCGA and other datasets hosted by NCI’s Cancer Genomics’ Cloud, allowing for cross-disease studies. While CAVATICA manages raw sequencing data, processed annotations and biospecimen querying is enabled for PBTA via PedcBioPortal, a data visualization/analysis application further integrating additional public and deposited datasets. PBTA data can further be accessed via the KidsFirst platform, which integrates both CAVATICA and PedcBioPortal, and allows for querying of cancer and non-cancer genomic data for subsequent analytics, visualization, and discovery. The 1st dataset release of PBTA occurred on September 10th, 2018. In this release there are over 30 different types of pediatric brain tumors representing over 1,000 subjects. This data is available on CAVATICA, KidsFirst, and PedcBioPortal and users can seamlessly move between applications. Data types include those for matched tumor/normal samples, such as WGS, RNASeq, proteomics, longitudinal clinical data, imaging data (MRIs and radiology reports), histology slide images, and pathology reports. CBTTC/PNOC promote real time data release with no embargo period, allowing PBTA to have is up-to-date data releases with no embargo. The combination of cloud-based analytic platforms such as KidsFirst/CAVATICA/PedcBioPortal with data from both genomics and clinical practice serves to define a new paradigm for pediatric cancer research and collaborative discovery. Citation Format: Yuankun Zhu, Yiran Guo, Allison P. Heath, Pichai Raman, Elizabeth Appert, Jennifer Mason, Bo Zhang, Karthik Kalletla, Miguel A. Brown, Natasha Singh, Bailey K. Farrow, Parimala Killada, Meen Chul Kim, Alex Felmeister, Mateusz P. Koptyra, Sabine Mueller, Michael Prados, Jena V. Lilly, Rishi Lulla, Adam C. Resnick, Javad Nazarian, Phillip B. Storm, Angela J. Waanders. The Pediatric Brain Tumor Atlas - Transforming the landscape of research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3667.

In spite of various efforts to resolve digital divide of people with disabilities, it still has a long way to go because there lack of identifying information needs of people with disabilities and potential users including their family and the related, and integrated information channels such as a portal, and so on. In this context, this study aims at analyzing the users’ unique information behavior and evaluating usability of domestic web information services for people with disabilities which are currently servi ced. Increasingly, we suggest new functional requirements of building a library portal for pe ople with disabilities.키워드: 장애인, 정보격차, 정보이용행태, 장애인도서관 포털서비스people with disabilities, digital divide, information behavior, library portal for people with disabilities *** 연세대학교 문헌정보학과 대학원(andrewevans@yonsei.ac.kr) ( 제1저자)연세대학교 문헌정보학과 교수(jlee01@yonsei.ac.kr) ( 교신저자) ￭￭ 논문접수일자:2013년 5월 20일 ￭ 최초심사일자:2013년 6월 7일 ￭ 게재확정일자:2013년 6월 12일정보관리학회지, 30(2), 169-188, 2013. [http ://dx.doi.org/10. 3743/KOSIM.2013.30.2.169]

The advent of Web 2.0 and social media is taking a leading role of emerging big data. At the same time, however, informational dysfunction such as infri ngement of one's rights and violation of social order has been increasing sharply. This stu dy, therefore, aims at defining malicious usage, identifying malicious feature, and devising an automated method for classifying them. In particular, the rule-based experiment reveals statisti cally significant performance enhancement. 키워드 : 소셜미디어 마이닝 , 트위터 , 악의적 이용 , 자동분류 Social Media Mining, Twitter, Malicious Usage, Automatic Classification ********************* 본 연구는 2012년 정부(교육과학기술부)의 재원으로 한국연구재단 한국사회과학연구지원사업(SSK)의 지원을 받아 수행된 것임 (NRF-2012-2012S1A3A2033291).연세대학교 문헌정보학과 대학원(andrewevans@yonsei.ac.kr)연세대학교 문헌정보학과 석사과정(moomyungz@naver.com)연세대학교 문헌정보학과 학사과정(hng88@naver.com)연세대학교 문헌정보학과 석사과정(arcoiris@yonsei.ac.kr)연세대학교 문헌정보학과 부교수(min.song@yonsei.ac.kr)논문접수일자: 2013년 1월 15일 최초심사일자 : 2013년 1월 21일 게재확정일자 : 2013년 2월 2일한국문헌정보학회지, 47(1): 269-286, 2013. [http ://dx.doi.org/10. 4275/KSLIS.2013.47.1.269]

Patients are facing complex health care decisions everyday. They are given many different treatment paths and often do not understand or question if these are the best path for their health conditions. These patients often turn to the internet, including authorative healthcare web sites and social media, to explore which option is the best for them [1][2]. In their exploration of different treatment options, however, they often notice gaps in research. Much literature supports these dissatisfactory experiences of healthcare consumers [4][6][7]. Therefore, less healthcare research reaches the benefiting stage. Eventually, although patients are concerned and interested in research, they often do not have a way to connect to researchers. To properly tackle this problematic issue, we propose an online platform called Act Together and Connect for patient-centered outcome research or ACTONNECT in short. ACTONNECT is based on the grounded theory of action-based matchmaking and engagement [3][5]. Meaning, if there are established actions from both partners, the matchmaking process is more likely to be successful. Based on previous research, there are three levels of engagements in the matchmaking process: 1) The initial willingness from both parties, one who is looking to be connected with the other to develop rigorous research studies, 2) The two groups are engaged by actions, physically sharing in the activities of creating research studies, protocol and publishing's, and 3) The Engaged partnership, which is literally the 1 +1 > 2 partnership. Based on this concept, ACTONNECT allows the matchmaking at the highest level by allowing patients and researchers who share a common ground. Hence, we view ACTONNECT as a common platform for both patients and researchers. With the action-based matchmaking, the partnership between patients and researchers can be more sustainable. To properly tackle this problematic issue, we propose an online platform called Act Together and Connect for patient-centered outcome research or ACTONNECT in short. ACTONNECT is based on the grounded theory of action-based matchmaking and engagement [3][5]. Meaning, if there are established actions from both partners, the matchmaking process is more likely to be successful. Based on previous research, there are three levels of engagements in the matchmaking process: 1) The initial willingness from both parties, one who is looking to be connected with the other to develop rigorous research studies, 2) The two groups are engaged by actions, physically sharing in the activities of creating research studies, protocol and publishing's, and 3) The Engaged partnership, which is literally the 1 +1 > 2 partnership. Based on this concept, ACTONNECT allows the matchmaking at the highest level by allowing patients and researchers who share a common ground. Hence, we view ACTONNECT as a common platform for both patients and researchers. With the action-based matchmaking, the partnership between patients and researchers can be more sustainable. In order to facilitate dynamic interaction among various stakeholders around healthcare issues, ACTONNECT features following functions: 1) healthcare heterogeneous network visualization, 2) ACTONNECTION publisher, 3) ACTONNECTION matchmaker, and 4) research article recommender. Figure 1 illustrate the user interface of the system. First, stakeholders can explore the healthcare social media through investigating the extracted healthcare heterogeneous network and identifying related research articles form authoritative healthcare sites such as WebMD and Pub Med. A healthcare heterogeneous network [8] is a graph consisting of nodes and links, where. Each node belongs to one particular type from, such as Drug (R), Disease (D), Adverse Drug Reactionss (A), Treatment (T), Diagnostics (G), Users (U), etc. Each link belongs to one particular relation from, such as treat between T and D, cause between R and A, use between U and R, etc. We construct the healthcare heterogeneous network by extracting the healthcare related discussion threads in social media, annotating the messages, and computing the strength of association between the extracted entities. Users can click on the nodes and links to find relevant discussions on diseases, drugs, treatments, etc. In this work, the social media content is crawled from a healthcare social media site, MedHelp.org. Through researching the social media and authorative healthcare information, users find out the experience of other patients on various drug and treatment options as well as some other scientific findings. Patients and researchers are able to post their insights through pubishing their own ACTONNECTION in the system. The ACTONNECT system will recommend other Connections, based on the document similarity. Hence, users view the insights of other patients, caregivers, physicians and researchers. Users may connect to each other to discuss their findings and establish collaborations of new research problems.

The advent of social media is changing the existing information behavior by letting users access to real-time online information channels without the constraints of time and space. It also generates a huge amount of data worth discovering novel knowledge. Social media, therefore, has created an enormous challenge for scientists trying to keep pace with developments in their field. Most of the previous studies have adopted broad-brush approaches which tend to result in providing limited analysis. To handle these problems properly, we introduce our real-time Twitter trend mining system, RT2M, which operates in real-time to process big stream datasets available on Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, Topic Modeling to keep track of changes of topical trend, and analysis on mention-based user networks. We also demonstrate an empirical study on 2012 Korean presidential election. The case study reveals Twitter could be a useful source to detect and predict the advent and changes of social issues, and analysis of mention-based user networks could show different aspects of user behaviors.