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Objective To describe the design, implementation, and utilization of electronic health record (EHR)–based digital health surveillance strategies used to manage the coronavirus disease 2019 (COVID-19) pandemic and to ensure delivery of high-quality clinical care, such as case identification, remote monitoring, telemedicine services, and recruitment to clinical trials at Mayo Clinic. Methods The design and implementation work described in this report was performed at Mayo Clinic, a large multistate integrated health care system with more than 1.5 million annual patient visits that uses the Epic EHR system. Rule-based live registries were designed in the EHR system to classify patients who currently test positive for COVID-19, patients who test positive but have recovered from COVID-19, patients who are thought to have COVID-19 but do not yet meet clinical diagnostic criteria, patients who test negative for COVID-19, and patients who exceed a risk score for serious complications from COVID-19. Results By use of registries, custom dashboards and operational reports were developed to provide a daily high-level summary for clinical practice use and up-to-date information to manage individual patients affected by COVID-19, including support of case identification, contact isolation, and other care management tasks. Conclusion We developed and implemented a systematic approach to the use of EHR patient registries to manage the COVID-19 pandemic that proved feasible and useful in a large multistate group clinical practice. The key to harnessing the potential of digital surveillance tools to promote patient-centered care during the COVID-19 pandemic was to use the registry data, reports, and dashboards as informatics tools to inform decision-making.

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

Glycans are one of the major biological polymers found in the mammalian body. They play a vital role in a number of physiologic and pathologic conditions. Glycan microarrays allow a plethora of information to be obtained on protein–glycan binding interactions. In this review, we describe the intricacies of the generation of glycan microarray data and the experimental methods for studying binding. We highlight the importance of this knowledge before moving on to the data analysis. We then highlight a number of tools for the analysis of glycan microarray data such as data repositories, data visualization and manual analysis tools, automated analysis tools and structural informatics tools.

Objective In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence–based methods with unstructured patient data collected through telehealth visits. Materials and Methods After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding–based convolutional neural network for predicting COVID-19 test results based on patients’ self-reported symptoms. Results Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. Conclusions Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.

Introduction New informatics tools can transform evidence-based information to individualized predictive reports to serve shared decisions in clinic. We developed a web-based system to collect patient-reported outcomes (PROs) and medical risk factors and to compare responses to national registry data. The system generates predicted outcomes for individual patients and a report for use in clinic to support decisions. We present the report development, presentation, and early experience implementing this PRO-based, shared decision report for knee and hip arthritis patients seeking orthopedic evaluation. Methods Iterative patient and clinician interviews defined report content and visual display. The web-system supports: (a) collection of PROs and risk data at home or in office, (b) automated statistical processing of responses compared to national data, (c) individualized estimates of likely pain relief and functional gain if surgery is elected, and (d) graphical reports to support shared decisions. The system was implemented at 12 sites with 26 surgeons in an ongoing cluster randomized trial. Results Clinicians and patients recommended that pain and function as well as clinical risk factors (e.g., BMI, smoking) be presented to frame the discussion. Color and graphics support patient understanding. To date, 7891 patients completed the assessment before the visit and 56% consented to study participation. Reports were generated for 98% of patients and 68% of patients recalled reviewing the report with their surgeon. Conclusions Informatics solutions can generate timely, tailored office reports including PROs and predictive analytics. Patients successfully complete the pre-visit PRO assessments and clinicians and patients value the report to support shared surgical decisions.

Objective The objective of this project was to enable poison control center (PCC) participation in standards-based health information exchange (HIE). Previously, PCC participation was not possible due to software noncompliance with HIE standards, lack of informatics infrastructure, and the need to integrate HIE processes into workflow. Materials and Methods We adapted the Health Level Seven Consolidated Clinical Document Architecture (C-CDA) consultation note for the PCC use case. We used rapid prototyping to determine requirements for an HIE dashboard for use by PCCs and developed software called SNOWHITE that enables poison center HIE in tandem with a poisoning information system. Results We successfully implemented the process and software at the PCC and began sending outbound C-CDAs from the Utah PCC on February 15, 2017; we began receiving inbound C-CDAs on October 30, 2018. Discussion With the creation of SNOWHITE and initiation of an HIE process for sending outgoing C-CDA consultation notes from the Utah Poison Control Center, we accomplished the first participation of PCCs in standards-based HIE in the US. We faced several challenges that are also likely to be present at PCCs in other states, including the lack of a robust set of patient identifiers to support automated patient identity matching, challenges in emergency department computerized workflow integration, and the need to build HIE software for PCCs. Conclusion As a multi-disciplinary, multi-organizational team, we successfully developed both a process and the informatics tools necessary to enable PCC participation in standards-based HIE and implemented the process at the Utah PCC.

As participants in the California Medicaid 1115 waiver, the University of California San Diego Health (UCSDH) used population health informatics tools to address health disparities. This case study describes a modern application of health informatics to improve data capture, describe health disparities through demographic stratification, and drive reliable care through electronic medical record-based registries. We provide a details in our successful approach using (1) standardized collection of race, ethnicity, language, sexual orientation, and gender identity data, (2) stratification of 8 quality measures by demographic profile, and (3) improved quality performance through registries for wellness, social determinants of health, and chronic disease. A strong population health platform paired with executive support, physician leadership, education and training, and workflow redesign can improve the representation of diversity and drive reliable processes for care delivery that improve health equity.

Clinical studies, especially randomized, controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long‐standing concern when applying trial results to real‐world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic review to understand the practice of generalizability assessment. We identified 187 relevant articles and systematically organized these studies in a taxonomy with three dimensions: (i) data availability (i.e., before or after trial (a priori vs. a posteriori generalizability)); (ii) result outputs (i.e., score vs. nonscore); and (iii) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but

ObjectivesInformatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).Materials and MethodsWe collected feature requests and workflow improvement suggestions from the end-users of HMVV version 1. Over 1.5 years, we iteratively implemented these features in five sequential updates to HMVV version 3.ResultsWe improved the performance and data throughput of the application while reducing the opportunity for manual data entry errors. We enabled end-user workflows for pipeline monitoring, variant interpretation and annotation, and integration with our laboratory information system. System maintenance was improved through enhanced defect reporting, heightened data security, and improved modularity in the code and system environments.Discussion and ConclusionValidation of each HMVV update was performed according to expert guidelines. We enabled an 8× reduction in the bioinformatics pipeline computation time for our longest running assay. Our molecular pathologists can interpret the assay results at least 2 days sooner than was previously possible. The application and pipeline code are publicly available at https://github.com/hmvv.

Nowadays, large amounts of omics data have been generated and contributed to increasing knowledge about associated biological mechanisms. A new challenge coming along is how to identify the active pathways and extract useful insights from these data with huge background information and noise. Although biologically meaningful modules can often be detected by many existing informatics tools, it is still hard to interpret or make use of the results towards in silico hypothesis generation and testing. To address this gap, we previously developed the IMPRes (Integrative MultiOmics Pathway Resolution) v 1.0 algorithm, a new step-wise active pathway detection method using a dynamic programming approach. This approach enables the network detection one step at a time, making it easy for researchers to trace the pathways, and leading to more accurate drug design and more effective treatment strategies. In this paper, we present IMPRes-Pro, an enhancement to IMPRes v1.0 by integrating proteomics data along with transcriptomics data and constructing a heterogeneous background network. The evaluation experiment conducted on human primary breast cancer dataset has shown the advantage over the original IMPRes v1.0 method. Furthermore, a case study on human metastatic breast cancer dataset was performed and we have provided several insights regarding the selection of optimal therapy strategy. IMPRes-Pro algorithm and visualization tool is available as a web service at http://digbio.missouri.edu/impres.

MotivationIn time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not, and consequently has now turned into the primary bottleneck. Modern computational hardware are capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, for example, is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices.ResultsIn an effort to stimulate the development of a new generation of faster sequence analysis tools, We developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 4 minutes while the original version took 1.5 hours, using the same storage solution.Availability and ImplementationOpen source and freely available athttps://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high performance informatics tools, either directly boosting their performance if they are currently dataaccess bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.Contactyi.qiao@genetics.utah.edu.

Glycosylation is important in biology, contributing to both protein conformation and function. Structurally, glycosylation is complex and diverse. This complexity is reflected in the topology, composition, monosaccharide linkages, and isomerism of each oligosaccharide. Glycoanalytics is a discipline that addresses the understanding and characterization of this complexity and its correlation with biology. It includes analytical steps such as sample preparation, instrument measurements, and data analyses. Of these, data analysis has emerged as a critical bottleneck because data collection has increasingly become high-throughput. This has resulted in data-rich workflows that lack rapid and automated data analytics. To address this issue, the field has been developing software for interpretation of quantitative glycomics studies. Here, we describe a protocol using available informatics tools for analysis of data from analysis of released glycans using high-/ultraperformance liquid chromatography (H/UPLC) coupled with mass spectrometry (MS).

BackgroundProgress in therapeutic research is slowed by the regulatory burden of clinical trials, which provide the best evidence for guiding treatment. There is a long delay from evidence generation to adoption, highlighting the need for designs that link evidence generation to implementation.ObjectiveTo identify clinical trial designs that confer minimal risk above that inherent in clinical care, to obviate the need for cumbersome consenting processes to enrol patients in prospective clinical research studies. These designs extend the scope of the Learning Healthcare System, a framework for leveraging retrospective ‘big data’ to advance clinical research, to include data collected from prospective controlled trials.SummaryPragmatic trials may use simplified eligibility criteria, unblinded interventions and objective outcome measures that can all be monitored through the electronic health records (EHR), to reduce costs and speed study conduct. Most pragmatic trials continue to suffer from substantial regulatory burden. Written consent to participate in research can be waived only if the research produces minimal risk above what is encountered in everyday life. However, the ‘consent’ processes for prescribing Federal Drug Administration-approved medications in clinical medicine are informal, even when they involve decisions of uncertain benefit and higher levels of risk. We propose that trial designs that mimic clinical decision-making in areas of uncertainty (clinical equipoise) and in which no data are generated outside of usual care (ideally by EHR embedding) confer minimal additional risk. Trial designs meeting this standard could, therefore, be conducted with minimal documentation of consent, even when interventions contain different risks. To align with risk encountered in clinical practice, allocation to treatment arms should change (adaptive randomisation) as data are collected and analysed. Embedding of informatics tools into the EHR has the additional benefit that, as adaptive randomisation progresses, evidence-generation transitions into implementation via decision-support tools—the ultimate realisation of the Learning Healthcare System.

The ongoing pandemic (COVID-19) has prompted a fresh wave of clinical informatics tools deployed in the fight against the novel coronavirus. They span a range of functions, including triage applications, screening protocols, and exposure notification programs, many of which have been well-characterized in the literature and media alike. However, parallel efforts to apply informatics tools to a policy tracking context have as yet received scant attention. These would effectively “track the trackers,” enabling comparison between seemingly disparate interventions and imposing a sense of methodological order across the potpourri of COVID-19 laws, rules, and guidance across the United States. This editorial discusses the practical utility of developing these informatics and data visualization tools, before outlining a framework for doing so. They stand to offer key decisionmakers an implement for formulating targeted policy and augmenting existing initiatives, and as such should be actively promoted across the healthcare space.

Proteomics is a powerful tool for obtaining information on a large number of proteins with regard to their expression levels, interactions with other molecules, and posttranslational modifications. Whereas nontargeted, discovery proteomics uncovers differences in the proteomic landscape under different conditions, targeted proteomics has been developed to overcome the limitations of this approach with regard to quantitation. In addition to technical advances in instruments and informatics tools, the advent of the synthetic proteome composed of synthetic peptides or recombinant proteins has advanced the adoption of targeted proteomics across a wide range of research fields. Targeted proteomics can now be applied to measurement of the dynamics of any proteins of interest under a variety of conditions as well as to estimation of the absolute abundance or stoichiometry of proteins in a given network. Multiplexed targeted proteomics assays of high reproducibility and accuracy can provide insight at the quantitative level into entire networks that govern biological phenomena or diseases. Such assays will establish a new paradigm for data-driven science.

Translational healthcare (TH) can be defined as a form of precision medicine used as a prevention and treatment approach for an individual by taking into consideration the subject’s associated variability factors along with standard clinical care. It roughly translates into the process of transforming a huge amount of data into healthcare using advanced computational tools. The TH spectrum stretches at one end where informatics tools link biological units to their clinical output, i.e., from bench to bedside, while at the other end it is to gain knowledge from information translated from the input of biomedical informatics. It is an integrated, comprehensive approach toward diagnostic and therapeutic study that broadly includes clinical big data analysis for genomic discovery and pharmacogenomics to provide routine healthcare, using the collected data through omics approach to design specific drugs and personalized genome testing of the individual through next-generation sequencing analysis to provide customized treatment. The TH attempts to provide individual solutions to singular patient based on combining their genomic, environmental, and clinical profiles that allow us to use this holistic approach in patient care. Academic centers, pharmaceutical companies, biotech companies, and health centers all around the world are developing and implementing various discovery projects whose main objective is to allow workers access a common, homogenous, and detailed data of de identified specimens. This initiative is serving as a repository for augmenting translational research which would lead us to finding firm clinical solutions. This chapter discusses the perspectives of various elements which are involved in translational computational tools that drive us toward realizing precision medicine as an actuality.

Facing the COVID-19 pandemic, healthcare organizations across the world worked expeditiously to prepare for and manage the outbreak. In the modern era, clinical information systems (CIS) and the electronic health record (EHR) are essential tools that can support patient care and enhance healthcare delivery. Researchers out of the University of California, San Diego, were the first to describe in detail the rapid development and implementation of EHR based tools designed specifically to support the management of COVID-19. Drs. Jeff Reeves and Chris Longhurst will describe how CIS are utilized in response to COVID-19, challenges to successful implementation of informatics tools, and the future of informatics in pandemic management.

The present study encompasses the role of information science in the biological field. In recent years there has been a great influence of information science in different sectors and it has become one of the important components in biological processes. The role of informatics can be traded in biology right from the introduction of bioinformatics as a discipline to the use of advanced software to detect and monitor analytes. The use of software like BLAST, GROMACS, EMBOSS, etc are customized to execute the functions and attenuate the desire applications. The informatics tools have also influenced in analytical techniques like MALDI-TOF, MASS Spectroscopy, and Transmission electron microscopy for accurate measurement of the analytes to provide detailed information. Based on the gaining popularity of the integration of informatics tools with the biology and health care sector, the present study is presented as mini-review to highlight the important findings and provides insight on emerging roles. The presented mini-review is unique with scanty reports available on the compilation of informatics advancement and its hinging in biology.

Objective We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. Materials and Methods We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. Results Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. Discussion Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. Conclusions The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.

Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multi-task learning and meta-learning are proposed. A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.

The huge amount of textual information that exists on social networks added by users through comments, has aroused a great interest in companies and research groups, which seek to use this information to identify trends and acceptance levels of brands, products, and services. A technique to know the level of acceptance or rejection of a particular topic, in an automated way, is the Sentiment Analysis. Some informatics tools incorporate this technique, however, there are few contributions to texts in Spanish. It is because of the difficulty of identifying different contexts, dialects, complex grammatical structures, and semantic language variances in each region. This article presents a web tool for the analysis of sentiments in texts written in Spanish that include Ecuadorian dialect or idioms. The tool was developed in R-Shiny with an approach lexicon-based. The tool allows the customization of the lexicons based on context and facilitates the automatic download of tweets according to search criteria such as the place, dates, and topic. To evaluate the effectiveness of the application, their result was compared with two commercial tools (Azure Text Analytics and IBM Watson NLU) and a manual score carried out by a group of people. The tests include the analysis of three corpora created from tweets. The results show the effectiveness of the tool to identify the sentiment polarity, especially in texts that include dialects, colloquial words, and negative expressions.

BACKGROUND: Electronic health record templates have served a variety of functions, such as improving documentation for reliable reporting of health care outcomes. Standardizing template documentation has been important for accurately capturing case workload and supporting quality improvement initiatives. METHODS: North Florida/South Georgia Veterans Health System consists of 2 Florida-based hospitals: Malcom Randall Veterans Affairs Medical Center (MRVAMC) in Gainesville, and Lake City Veterans Affairs Medical Center (LCVAMC). In the first quarter of fiscal year 2017, MRVAMC and LCVAMC had a below-average case severity index (CSI) of 0.76 and 0.81, respectively, compared with that of the 0.96 national average for the Veterans Health Administration (VHA). An innovative history and physical template design with embedded informatics tools was created to improve clinical documentation. RESULTS: Compliance with standardized history and physical medicine template use was monitored for about 1 year after standardized template implementation. Compliance improved from 43.2% in June 2018 to 89.9% in June 2019 at MRVAMC and increased from 48.2% in June 2018 to 96.9% in June 2019 at LCVAMC. CSI improved to 0.97 at MRVAMC and 1.07 at LCVAMC in the first quarter of fiscal year 2019, which exceeded the VHA national average of 0.89 during the same period. CONCLUSIONS: NF/SGVHS integrated informatics solutions within template design was associated with an increase in CSI via improved clinical documentation.

Next-generation sequencing (NGS) is rapidly expanding into routine oncology practice. Genetic variations in both the cancer and inherited genomes are informative for hereditary cancer risk, prognosis, and treatment strategies. Herein, we focus on the clinical perspective of integrating NGS results into patient care to assist with therapeutic decision making. Five key considerations are addressed for operationalization of NGS testing and application of results to patient care as follows: (1) NGS test ordering and workflow design; (2) result reporting, curation, and storage; (3) clinical consultation services that provide test interpretations and identify opportunities for molecularly guided therapy; (4) presentation of genetic information within the electronic health record; and (5) education of providers and patients. Several of these key considerations center on informatics tools that support NGS test ordering and referencing back to the results for therapeutic purposes. Clinical decision support tools embedded within the electronic health record can assist with NGS test utilization and identifying opportunities for targeted therapy including clinical trial eligibility. Challenges for project and change management in operationalizing NGS-supported, evidence-based patient care in the context of current information technology systems with appropriate clinical data standards are discussed, and solutions for overcoming barriers are provided.

La dimensión cultural de la informática médica se establece a partir de la unidad dialéctica que existe con la sistematización de la informatización en Salud ya que los profesionales de la salud van apropiándose, desde su cultura, de un conjunto de conocimientos, habilidades, valores y valoraciones a través de las herramientas informáticas, a su vez permite ir profundizando en el empleo de cada herramienta informática para luego generalizar su utilización. La dinámica del proceso formativo de la informática médica se desarrolla a través de una doble relación dialéctica: la primera, entre la apropiación de la cultura infotecnológica médica y la contextualización médica de la cultura informática y la segunda, entre la gestión de datos médicos informatizados y la interpretación médica de la información tecnológica que se sintetizan en la sistematización de la informatización en Salud desde una cultura informática médica y la generalización de la infotecnología médica.

The extensible neuroimaging archive toolkit (XNAT) is a common platform for storing and distributing neuroimaging data and is used by many key repositories of public neuroimaging data. Some examples include the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://nitrc.org/), the ConnectomeDB for the Human Connectome Project (https://db.humanconnectome.org/), and XNAT Central (https://central.xnat.org/). We introduce Rxnat (https://github.com/adigherman/Rxnat), an open-source R package designed to interact with any XNAT-based repository. The program has similar capabilities with PyXNAT and XNATpy, which were developed for Python users. Rxnat was developed to address the increased popularity of R among neuroimaging researchers. The Rxnat package can query multiple XNAT repositories and download all or a specific subset of images for further processing. This provides a lingua franca for the large community of R analysts to interface with multiple XNAT-based publicly available neuroimaging repositories. The potential of Rxnat is illustrated using an example of neuroimaging data normalization from two neuroimaging repositories, NITRC and HCP.

Board certified clinical informaticians provide expertise in leveraging health IT (HIT) and health data for patient care and quality improvement. Clinical Informatics experts possess the requisite skills and competencies to make systems-level improvements in care delivery using HIT, workflow and data analytics, knowledge acquisition, clinical decision support, data visualization, and related informatics tools. However, these physicians lack structured and sustained funding because they have no billing codes. The sustainability and growth of this new and promising medical subspecialty is threatened by outdated and inconsistent funding models that fail to support the education and professional growth of clinical informaticians. The Clinical Informatics Program Directors' Community is calling upon the Centers for Medicare and Medicaid Services to consider novel funding structures and programs through its Innovation Center for Clinical Informatics Fellowship training. Only through structural and sustained funding for Clinical Informatics fellows will be able to fully develop the potential of electronic health records to improve the quality, safety, and cost of clinical care.

ObjectiveUsing the case of barcode medication administration (BCMA), our objective is to describe the challenges nurses face when informatics tools are not designed to accommodate the full complexity of their work.Materials and MethodsAutonomy is associated with nurse satisfaction and quality of care. BCMA organizes patient information and verifies medication administration. However, it presents challenges to nurse autonomy. Qualitative fieldwork, including observations of everyday work and interviews, was conducted during the implementation of BCMA in a large academic medical center. Fieldnotes and interview transcripts were coded and analyzed to describe nurses’ perspectives on medication safety.ResultsNurses adopt orienting frames to structure work routines and require autonomy to ensure safe task completion. Nurses exerted agency by trusting their own judgment over system information when the system did not consider workload complexity. Our results indicate that the system’s rigidity clashed with adaptive needs embodied by nurses’ orienting frames.DiscussionDespite the fact that the concept of nurse as knowledge worker is foundational to informatics, nurses may be perceived as doers, rather than knowledge workers. In practice, nurses not only make decisions, but also engage in highly complex task-related work that is not well supported by process-oriented information technology tools.ConclusionsInformation technology developers and healthcare organization managers should engage and better understand nursing work in order to develop technological and social systems to support it.

The human epidermal growth factor receptor 2 (HER2) is a well-studied oncoprotein that is overexpressed in a considerable proportion of breast cancer patients. The increased expression of this tyrosine kinase receptor is usually associated with poor clinical prognosis in female patients with breast cancer. In these patients, specific response of immune system against HER2 had been observed. This suggests that immunotherapy approaches can be employed for enhancing the response of tumor infiltrating lymphocytes against HER2 in susceptible tumor microenvironment. In this regard, peptide vaccines are considered one of the most affordable immunotherapy modalities due to their low production cost and long-term effect. For this purpose, we have screened the extracellular domain of HER2 crystal for potential B-cells and T-cells epitopes by using different immuno-informatics tools. The output peptides were then refined and filtered according to their antigenicity, allergenicity and vulnerability to selected proteases. Here, we present multiple B-cells and T-cells epitope candidates against HER2 extracellular domain with high antigenicity, low allergenicity and good resistance for selected proteolytic enzymes. These filtered epitopes can be used for design and construction of anti-HER2 peptide vaccine for potential use in HER2 positive breast cancer patients. Additionally, the sequence of linear B-cells epitopes can be used for the design of monoclonal antibody variable region against HER2 extracellular domain.

Large scale, complex biobanking of biofluids for immunology research and testingRohit K. Gupta1, Vanitha Sampath2, Kari C. Nadeau2, Holden T. Maecker3,1 Biospecimen Resource Program, Office of Research, University of California, San Francisco, CA, USA.2 Sean N. Parker Center for Allergy and Asthma Research at Stanford University and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.3 Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, CA, USA.Corresponding Author: Prof. Holden Maecker, Department of Microbiology and Immunology, Fairchild Science Building, room D039, 299 Campus Drive, Stanford, CA 94305-5124, USA, Email: maecker@stanford.edu, Phone: (650) 723-1671Funding: Sean N. Parker Center for Allergy and Asthma Research at Stanford University and Sunshine Foundation.Conflict of Interest: Dr. Nadeau reports grants from National Institute of Allergy and Infectious Diseases (NIAID), Food Allergy Research & Education (FARE), End Allergies Together (EAT), Allergenis, and Ukko Pharma; Grant awardee at NIAID, National Institute of Environmental Health Sciences (NIEHS), National Heart, Lung, and Blood Institute (NHLBI), and the Environmental Protection Agency (EPA); is involved in Clinical trials with Regeneron, Genentech, AImmune Therapeutics, DBV Technologies, AnaptysBio, Adare Pharmaceuticals, and Stallergenes-Greer; Research Sponsorship by Novartis, Sanofi, Astellas, Nestle; Data and Safety Monitoring Board member at Novartis and NHLBI; Cofounded Before Brands, Alladapt, ForTra, and Iggenix; Chief Intellectual Office at FARE, Director of the World Allergy Organization (WAO) Center of Excellence at Stanford, Personal fees from Regeneron, Astrazeneca, ImmuneWorks, and Cour Pharmaceuticals; Consultant and Advisory Board Member at European Academy of Allergy and Clinical Immunology (EAACI) Research and Outreach Committee, Ukko, Before Brands, Alladapt, IgGenix, Probio, Vedanta, Centecor, Seed, Novartis, NHBLI, EPA, National Scientific Committee of Immune Tolerance Network (ITN) and NIH Programs; US patents for basophil testing, multifood immunotherapy and prevention, monoclonal antibody from plasmoblasts, and device for diagnostics. HM, RG, and VS indicate no conflict of interest.Author Contributions : All authors wrote and edited the final manuscript.Word Count: 978To the Editor:Biobanks have evolved from simple localized storage of samples in individual labs and clinics to large industrialized repositories with sophisticated sample life cycle infrastructure. By enabling collaborations between researchers working on different aspects of a disease, biobanks can bridge the gap between clinical care and research, accelerating medical care towards precision medicine. The concomitant advances in trans-omic technologies, big data analytics, and biorepositories make possible a coordinated, robust systems biology approach. Biobanks can be envisioned as a central hub responsible for compliant custodianship of specimens and associated clinical and biological data. Operationally, biobanks should strive to provide universal consent, standardized processing, cold-chain management, and quality control checks. Here, we discuss biobanks with respect to optimal utilization of biofluid derivatives, such as cells, supernatants, and genomic material, for immunology research and testing.A number of parameters need to be considered for specimen optimization and standardization based on sample type and downstream assays to be performed. Choices begin with the blood collection tubes to be used. For DNA and RNA analysis, EDTA anticoagulated blood is most common, as heparin can inhibit downstream polymerase reactions. However, at least one source suggests that citrate may provide higher quality RNA and DNA than other stabilizers.1For immunoassays, either serum or plasma can be effectively used, but there are subtle differences for some cytokine analytes.2 As such, a minimal requirement should be to use the same matrix (serum or plasma) and same anticoagulant if using plasma, for all samples to be compared in a study. For metabolome and lipidome studies, a report by Yin et al suggests EDTA plasma as the preferred matrix, since clotting in serum tubes activates additional processes, including the release of metabolites and enzymes from activated platelets.3For cellular assays such as flow cytometry, CyTOF, or single-cell RNAseq, viably cryopreserved peripheral blood mononuclear cells (PBMCs) or other cells of interest are key. Protocols for cryopreservation are readily available, but careful attention to both freezing and thawing protocols is particularly important to maintain viability and recovery. Traditionally, heparinized blood is used for Ficoll isolation of PBMCs, but other anticoagulants are generally equivalent for functionality of cryopreserved PBMC. Concomitant use of the whole blood for other purposes (e.g., stimulation or DNA isolation) may dictate the optimal anticoagulant—for example, EDTA would inhibit T cell receptor-based stimulation, but would be compatible with molecular assays). There are also variations to traditional Ficoll protocols, using Cell Preparation Tubes (CPTs) or SepMate tubes.4 These should be considered, as they can save time and labor and overcome some hurdles for standardization of the Ficoll procedure. The main drawbacks are a slight reduction in yield or increase in erythrocyte contamination. Importantly, training and protocol adherence are still important to prevent, for example, breakage of CPT from improper centrifuge holders, inadequate PBMC separation from improper spin speed, or loss of separation if CPT are shipped in very cold temperatures. Another variable to be considered is time to processing5, which is of course highly related to whether samples are shipped prior to processing (see Figure 1). This is particularly relevant to functional cellular assays. An alternative to overnight shipping and PBMC cryopreservation for functional assays is to perform on-site stimulation and stabilization of whole blood (e.g., Smart Tube Inc., http://smarttubeinc.com); however, proper monitoring of cold-chain storage is critical to ensure frozen specimens are not compromised. For example, when using the Smart Tube system, biobanks must maintain the frozen samples at -80°C, as micro-fluctuations in temperature can cause the specimens to coagulate, rendering them unusable. In any case, there are a number of potential variables that can be detrimental to downstream analysis and even reproducibility; biobanks should strive to harmonize collection, processing, and storage of samples related to biofluids.Research institutes often have multiple laboratories, each of which may be supporting various collections of human specimens. Unfortunately, most labs have employed their own data solutions to track and search for specimens, which has led to fragmented processes and inconsistent ontologies. Utilization of biospecimens that have been collected for scientific purposes continues to be problematic and may be more effective when paired with informatics tools that enable researchers to track, annotate, and interrogate.6 Biobanks should have a sample management system (SMS) which permits labs to accurately register, label (Figure 2), and track biospecimen inventory related to study participants7; in addition, the software should be configurable to align with lab workflows, while maintaining best practices for biobanking and ensuring governance can be maintained by the individual laboratory or institute. Further, for bioinventory tracking, it is critical to connect de-identified clinical attributes from electronic health records to biological assays following analysis of specimens in a central ecosystem; this enables researchers to rapidly search and request specimens for further analysis.8 To date, although many solutions have been developed to support virtual sample catalogs, most require extensive software engineering support in order to be deployed and require data to be migrated to a central database; robust and innovative solutions for identifying unused biospecimens in the life sciences are still desired.Long thought of as freezer farms, a biobank’s primary role has always been to provide proper cold-chain storage and logistics related to biospecimens. While much literature exists on optimal storage conditions and management,9 biobanks have evolved to now facilitate research in the life sciences that extend from the physical management of the sample life cycle to supporting standardized processing, assay optimization, and modernized data infrastructure. As compliant use of biospecimens continues to be a major component being addressed through community engagement, biobanks are poised to play an important role in medical research with increasing demand for high quality biospecimens. However, a number of questions and challenges exist regarding standardization, classification, management, sustainability, as well as ethical considerations including ownership and informed consent. Ultimately, improving how biospecimens are utilized for downstream analysis can accelerate our understanding of biological mechanisms and fuel a better tomorrow.Rohit K. Gupta1Vanitha Sampath2Kari C. Nadeau2Holden T. Maecker31 Biospecimen Resource Program, Office of Research, University of California, San Francisco, CA, USA.2 Sean N. Parker Center for Allergy and Asthma Research at Stanford University and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.3 Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, CA, USA.

17 pages, 7 figures, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2020.00384/full#supplementary-material.-- All datasets generated for this study are included in the article/Supplementary Material, The scientific literature available on deep-sea biodiversity is ample and covers a wide array of objectives, geographic areas, and topics. It also explores the links between ecosystem functioning and productivity as well as modeling, management, and exploitation. New statistical analytical tools now allow the comprehensive monitoring of the status of deep-sea research to highlight global research topics and their trends, which deserve further development and economic investments. Here, we used a science mapping approach to provide a global and systematic bibliometric synthesis of these current research topics and their trends to identify the size, growth, trajectory, and geographic distribution of scientific efforts as well as to highlight the emerging topics. A total of 1287 deep-sea biodiversity publications were retrieved from the Scopus database from 1993 to the present. Both established and emerging research topics were identified: (i) biogeochemical, microbial, and molecular analyses; (ii) biodiversity assessments; (iii) ecosystem conservation and management; and, finally, (iv) zoology and taxocoenosis. The temporal change in research activity (which was assessed by subdividing publications into blocks from 1993 to 2010 and 2011 to 2019) demonstrated that the ¿biogeochemical, microbial, and molecular analyses¿ cluster was not present from 1993 to 2010 since it was included in the cluster for ¿biodiversity assessments,¿ which it eventually diverged from in the following couple of decades. The United States took the dominant role in research, followed by the United Kingdom; Germany and France were also evidenced. China was particularly associated with the United States, RD was supported by the DG ENV project IDEM (Implementation of the MSFD to the Deep Mediterranean Sea; contract EU No 11.0661/2017/750680/SUB/EN V.C2), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

The human brain undergoes rapid development during childhood, with significant improvement in a wide spectrum of cognitive and affective functions. Mapping domain- and age-specific brain activity patterns has important implications for characterizing the development of children’s cognitive and affective functions. The current mainstay of brain templates is primarily derived from structural magnetic resonance imaging (MRI), and thus is not ideal for mapping children’s cognitive and affective brain development. By integrating task-dependent functional MRI data from a large sample of 250 children (aged 7 to 12) across multiple domains and the latest easy-to-use and transparent preprocessing workflow, we here created a set of age-specific brain functional activity maps across four domains: attention, executive function, emotion, and risky decision-making. Moreover, we developed a toolbox named Developmental Brain Functional Activity maps across multiple domains that enables researchers to visualize and download domain- and age-specific brain activity maps for various needs. This toolbox and maps have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse website (http://www.nitrc.org/projects/dbfa). Our study provides domain- and age-specific brain activity maps for future developmental neuroimaging studies in both healthy and clinical populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-021-00650-7.

Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of our society, including a burgeoning role in medical research and clinical practice. As the volume of medical data grows at an apparently exponential rate, particularly since the human genome project laid the foundation for modern genetic inquiry, informatics tools like machine learning are becoming crucial in analyzing these data to provide meaningful tools for diagnostic, prognostic, and therapeutic purposes. Within medicine, hematologic diseases can be particularly challenging to understand and treat given the increasingly complex and intercalated genetic, epigenetic, immunologic, and regulatory pathways that must be understood to optimize patient outcomes. In acute myeloid leukemia (AML), new developments in machine learning algorithms have enabled a deeper understanding of disease biology and the development of better prognostic and predictive tools. Ongoing work in the field brings these developments incrementally closer to clinical implementation.

Over the years, glasses have become very popular, and since they are a permanent used product, many issues concerning frames have been present. Because of size and weight under which they are manufactured, pain and pressure marks over face areas and the head (nasal bone, temporal bone, temple, and nape) are shown. In this paper, it is proposed an improvement in frames by taking advantage of the technological advances. The study has in focus the students from the Faculty of Applied Sciences and Engineering at the Technical University of Cotopaxi-Ecuador that require the use of glasses due to different types of vision problems. Our work aims: to identify the need for improvement on glasses frames by exploring most common problems; to address these issues by giving a specific solution—new frame model based on 3D scanning; and to compare the existing data with the new data generated through informatics tools. Our findings appear to support well the general problem, and we believe that we have designed an innovative solution to the commercial frames.

Ion mobility-mass spectrometry (IM-MS) combines complementary size- and mass-selective separations into a single analytical platform. This chapter provides context for both the instrumental arrangements and key application areas that are commonly encountered in bioanalytical settings. New advances in these high-throughput strategies are described with description of complementary informatics tools to effectively utilize these data-intensive measurements. Rapid separations such as these are especially important in systems, synthetic, and chemical biology in which many small molecules are transient and correspond to various biological classes for integrated omics measurements. This chapter highlights the fundamentals of IM-MS and its applications toward biomolecular separations and discusses methods currently being used in the fields of proteomics, lipidomics, and metabolomics.

Support from family members is an important determinant of health. In this work, we probe opportunities for facilitating family support with TableChat, a chat-based mobile application for food journaling. Leveraging food as a test case of family support, TableChat virtually extends the experience of bonding over the dinner table. We surveyed 158 people about their existing family support practices and deployed TableChat with 10 families in the field. We found that tangible support was the most common form of support shared in TableChat and also the most appreciated by participants. However, we found that participants valued not only supportive actions taken by their family members, but also those deliberately not taken (e.g., not buying junk food). Finally, families reported that journaling meals eaten apart aided the exchange of support, satisfied curiosity, and provided a "check-in" that everything was alright, whereas journaling meals eaten together felt redundant. We conclude with a framework that illustrates how informatics tools can be designed to complement rather than compete with existing family interactions.

The use of Web-based informatics tools such as eBird, iNaturalist, and NatureAtlas that allow anyone to find and share occurrences and observations of organisms in nature could readily be integrated with the time-honored specimen collection and field journaling components of taxon-based natural history courses. We find, however, that fewer than 2% of such courses have used any such tool. Consequently, the far majority of students receive no formal exposure to the 21st-century technologies and concepts that are transforming the data landscapes of the very fields the courses should be preparing them to enter. We conducted a seven-year, empirical assessment of the integration of such technology with coursework, and our results reveal why recognizing and correcting this shortcoming is critical. Our data indicate that such technology can enhance student engagement and student perception of learning, and that its broader integration with coursework could be a boon to regional and global efforts to document and conserve biodiversity. We conclude that the academic community is missing a tremendous opportunity to better engage future biologists and potential citizen-scientists in a critically important, emergent paradigm in biodiversity informatics.

Data science and informatics tools have been proliferating recently within the computational materials science and catalysis fields. This proliferation has spurned the creation of various frameworks for automated materials screening, discovery, and design. Underpinning these frameworks are surrogate models with uncertainty estimates on their predictions. These uncertainty estimates are instrumental for determining which materials to screen next, but the computational catalysis field does not yet have a standard procedure for judging the quality of such uncertainty estimates. Here we present a suite of figures and performance metrics derived from the machine learning community that can be used to judge the quality of such uncertainty estimates. This suite probes the accuracy, calibration, and sharpness of a model quantitatively. We then show a case study where we judge various methods for predicting density-functional-theory-calculated adsorption energies. Of the methods studied here, we find that the best performer is a model where a convolutional neural network is used to supply features to a Gaussian process regressor, which then makes predictions of adsorption energies along with corresponding uncertainty estimates., 24 pages, 7 figures Submitted to Machine Learning: Science & Technology journal of IOP

OBJECTIVE: To discuss the use of Electronic Health Record (EHR), clinical data, that informs Clinical Databases and Clinical Decision Support Systems (CDSS) tools in critical care nursing practice to guide patient care. BACKGROUND: Critical care nurses practice in a challenging environment that requires responses to patients with complex, often unstable health conditions. EHR, access to clinical data, and CDSS informed by data from Clinical Databases are informatics tools designed to work together to facilitate decision-making in nursing practice. INFORMATICS TOOLS IN CRITICAL CARE: EHR incorporates many levels of clinical data and includes CDSS. Together, these informatics tools are designed to support nursing practice. Critical care nurses care for patients with a broad number of diseases. EHR, clinical data, and CDSS are informatics tools that can provide important support for critical care nurses’ decision-making. CONCLUSIONS: The complex decision-making environment of critical care requires informatics tools that support nursing practice through integration of current evidence with clinical data. Future research recommendations include continuing efforts towards the development of clinical decision support tools based on patient data that include predictive models to support increased patient safety.

A variety of existing perioperative informatics tools offer clinicians and researchers the opportunity to improve the delivery of care and clinical outcomes for patients undergoing cardiac and vascular surgery. Many of these tools can be used to improve the reliability of the care delivery process through the application of clinical decision support tools and/or quality improvement methodologies at a number of junctures. In this review, the authors will offer a concise overview of the existing perioperative informatics literature, with a focus on tools considered to be of utility in confronting the unique challenges inherent to cardiac and vascular surgery. The authors also highlight areas that they believe are of interest for future targeted inquiry.

The rapid drop in the cost of DNA sequencing led to the availability of multi-gene panels, which test 25 or more cancer susceptibility genes for a low cost. Clinicians and genetic counselors need a tool to interpret results, understand risk of various cancers, and advise on a management strategy. This is challenging as there are multiple studies regarding each gene, and it is not possible for clinicians and genetic counselors to be aware of all publications, nor to appreciate the relative accuracy and importance of each. Through an extensive literature review, we have identified reliable studies and derived estimates of absolute risk. We have also developed a systematic mechanism and informatics tools for (1) data curation, (2) the evaluation of quality of studies, and (3) the statistical analysis necessary to obtain risk. We produced the risk prediction clinical decision support tool ASK2ME (All Syndromes Known to Man Evaluator). It provides absolute cancer risk predictions for various hereditary cancer susceptibility genes. These predictions are specific to patients' gene carrier status, age, and history of relevant prophylactic surgery. By allowing clinicians to enter patient information and receive patient-specific cancer risks, this tool aims to have a significant impact on the quality of precision cancer prevention and disease management activities relying on panel testing. It is important to note that this tool is dynamic and constantly being updated, and currently, some of its limitations include (1) for many gene-cancer associations risk estimates are based on one study rather than meta-analysis, (2) strong assumptions on prior cancers, (3) lack of uncertainty measures, and (4) risk estimates for a growing set of gene-cancer associations which are not always variant specific. All of these concerns are being addressed on an ongoing basis, aiming to make the tool even more accurate.

Increasing amounts of systems toxicology data, including omics results, are becoming publically available and accessible in databases. Data-driven and informatics-tool supported pipeline schemas for fitting such data into Adverse Outcome Pathway (AOP) descriptions could potentially aid the development of nonanimal-based hazard and risk assessment methods. We devised a 6-step workflow that integrated diverse types of toxicology data into a novel AOP scheme for pulmonary fibrosis. Mining of literature references and diverse data sources covering previous pathway descriptions and molecular results were coupled in a stepwise manner with informatics tools applications that enabled gene linkage and pathway identification in molecular interaction maps. Ultimately, a network of functional elements coupled 64 pulmonary fibrosis-associated genes into a novel, open-source AOP-linked molecular pathway, now available for commenting and improvements in WikiPathways (WP3624). Applying in silico-based knowledge extraction and modeling, the pipeline enabled screening and fusion of many different complex data types, including the integration of omics results. Overall, the taken, stepwise approach should be generally useful to construct novel AOP descriptions as well as to enrich developing AOP descriptions in progress.

Making effective use of patient-generated data (PGD) is challenging for both patients and providers. Designing systems to support collaborative and individual use of PGD is a topic of importance in CSCW, considering the limitations of informatics tools. To inform better system design, we conducted a study including focus groups, observations and interviews with patients and providers to understand how PGD is interpreted and used. We found that while PGD is useful for identifying and solving disease-related problems, the following differences in patient-provider perceptions challenge its effective use - different perceptions about what is a problem, selecting what kinds of problems to focus on, and using different data representations. Drawing on these insights, we reflect on two specific conceptualizations of disease management behavior (sensemaking and problem-solving) as they relate to data specific activities of patients and providers and provide design suggestions for tools to support collaborative and individual use of PGD.

Purpose of review Perioperative informatics tools continue to be developed at a rapid pace and offer clinicians the potential to greatly enhance clinical decision making. The goal of this review is to bring the reader updates on perioperative information management and discuss future research directions in the field. Recent findings Clinical decision support tools become more timely, accurate, and, in some instances, have been shown to improve patient outcomes. When correctly implemented, they are critical tools for optimization of perioperative care. Summary Perioperative informaticians continue to test new and innovative ways to enhance the delivery of anesthesia care, improving the safety and efficacy of perioperative management. Future work will continue to refine tools to ensure that perioperative informatics provides clinicians timely and accurate feedback, with demonstrable evidence that a decision support system improves patient outcomes.

Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDXs repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world's largest open PDX database containing millions of phenomic information from over 4300 models (www.pdxfinder.org, PMID: 30535239). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines metadata necessary to describe models (PMID: 29092942). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive search and faceted search interface allow users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data detail pages where all available information can be browsed and downloaded. To further facilitate user's model selection, we are linking key external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDX variation data (COSMIC, CIViC, OncoMx, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatics tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modeling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximizing the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01 (ended 31Aug2020), U24 CA253539, and R01 CA089713. Citation Format: Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: An open and global catalogue of patient-derived xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB017.

Diagnostic genetic testing programmes based on next-generation DNA sequencing have resulted in the accrual of large datasets of targeted raw sequence data. Most diagnostic laboratories process these data through an automated variant-calling pipeline. Validation of the chosen analytical methods typically depends on confirming the detection of known sequence variants. Despite improvements in short-read alignment methods, current pipelines are known to be comparatively poor at detecting large insertion/deletion mutations. We performed clinical validation of a local reassembly tool, ABRA (assembly-based realigner), through retrospective reanalysis of a cohort of more than 2000 hereditary cancer cases. ABRA enabled detection of a 96-bp deletion, 4-bp insertion mutation in PMS2 that had been initially identified using a comparative read-depth approach. We applied an updated pipeline incorporating ABRA to the entire cohort of 2000 cases and identified one previously undetected pathogenic variant, a 23-bp duplication in PTEN. We demonstrate the effect of read length on the ability to detect insertion/deletion variants by comparing HiSeq2500 (2 × 101-bp) and NextSeq500 (2 × 151-bp) sequence data for a range of variants and thereby show that the limitations of shorter read lengths can be mitigated using appropriate informatics tools. This work highlights the need for ongoing development of diagnostic pipelines to maximize test sensitivity. We also draw attention to the large differences in computational infrastructure required to perform day-to-day versus large-scale reprocessing tasks.

An antileukemic agent prescribed for pediatric oncology patients during the maintenance phase of therapy for acute lymphoblastic leukemia, 6-mercaptopurine (6-MP), is highly influenced by genetic variations in the thiopurine S-methyltransferase enzyme. As such, 6-MP must be dosed so that patients with 1 or 2 inactive thiopurine S-methyltransferase alleles will not incur an increased risk for myelosuppression or other toxicities. Informatics tools such as clinical decision support systems are useful for the application of this and similar pharmacogenetics information to the realm of nursing and clinical practice for safe and effective patient care. This article will discuss pharmacogenetics and the associated use of 6-MP; present implications for nursing practice; identify informatics tools such as clinical decision support systems, which can greatly enhance the care of patients whose treatment is based on critical genetic information; and examine the relationship of genetics, nursing practice, and informatics for 6-MP dosing in pediatric oncology.

Omic data analyses pose great informatics challenges. As an emerging subfield of bioinformatics, omics informatics focuses on analyzing multi-omic data efficiently and effectively, and is gaining momentum. There are two underlying trends in the expansion of omics informatics landscape: the explosion of scattered individual omics informatics tools with each of which focuses on a specific task in both single- and multi- omic settings, and the fast-evolving integrated software platforms such as workflow management systems that can assemble multiple tools into pipelines and streamline integrative analysis for complicated tasks. In this survey, we give a holistic view of omics informatics, from scattered individual informatics tools to integrated workflow management systems. We not only outline the landscape and challenges of omics informatics, but also sample a number of widely used and cutting-edge algorithms in omics data analysis to give readers a fine-grained view. We survey various workflow management systems (WMSs), classify them into three levels of WMSs from simple software toolkits to integrated multi-omic analytical platforms, and point out the emerging needs for developing intelligent workflow management systems. We also discuss the challenges, strategies and some existing work in systematic evaluation of omics informatics tools. We conclude by providing future perspectives of emerging fields and new frontiers in omics informatics.

In September of 2014, the American College of Radiology joined a number of other organizations in sponsoring the 2015 National Academy of Medicine report, Improving Diagnosis In Health Care. Our presentation to the Academy emphasized that although diagnostic errors in imaging are commonly considered to result only from failures in disease detection or misinterpretation of a perceived abnormality, most errors in diagnosis result from failures in information gathering, aggregation, dissemination and ultimately integration of that information into our patients’ clinical problems. Diagnostic errors can occur at any point on the continuum of imaging care from when imaging is first considered until results and recommendations are fully understood by our referring physicians and patients. We used the concept of the Imaging Value Chain and the ACR’s Imaging 3.0 initiative to illustrate how better information gathering and integration at each step in imaging care can mitigate many of the causes of diagnostic errors. Radiologists are in a unique position to be the aggregators, brokers and disseminators of information critical to making an informed diagnosis, and if radiologists were empowered to use our expertise and informatics tools to manage the entire imaging chain, diagnostic errors would be reduced and patient outcomes improved. Heath care teams should take advantage of radiologists’ ability to fully manage information related to medical imaging, and simultaneously, radiologists must be ready to meet these new challenges as health care evolves. The radiology community stands ready work with all stakeholders to design and implement solutions that minimize diagnostic errors.

The Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the integration of experiments and simulations within a data-aware/enabling framework. To realize this vision, MGI recognizes the need for the creation of a new kind of workforce capable of creating and/or deploying advanced informatics tools and methods into the materials discovery/development cycle. An interdisciplinary team at Texas A&M seeks to address this challenge by creating an interdisciplinary program that goes beyond MGI in that it incorporates the discipline of engineering systems design as an essential component of the new accelerated materials development paradigm. The Data-Enabled Discovery and Development of Energy Materials (D3EM) program seeks to create an interdisciplinary graduate program at the intersection of materials science, informatics, and design. In this paper, we describe the rationale for the creation of such a program, present the pedagogical model that forms the basis of the program, and describe some of the major elements of the program.