From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows.

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Vance, T. C., Wengren, M., Burger, E., Hernandez, D., Kearns, T., Medina-Lopez, E., Merati, N., O'Brien, K., O'Neil, J., Potemrag, J. T., Signell, R. P., & Wilcox, K. From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows. Frontiers in Marine Science, 6(211), (2019), doi:10.3389/fmars.2019.00211.Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.This is PMEL contribution 4873

High Sensitivity Troponin T and NT-proBNP in Patients Receiving Chimeric Antigen Receptor (CAR) T-Cell Therapy

Retrospective studies suggest that chimeric antigen receptor T-cell (CAR T) therapy may lead to cardiac injury, but this has not been assessed systematically or prospectively. In this prospective study of 40 patients who received CAR T, we systematically measured high-sensitivity troponin T (hsTropT) and N-terminal pro-B natriuretic peptide (NTproBNP) at baseline and on day 1, days 7, and 21 after CAR T. Biomarker elevations with respect to timepoint and cytokine release syndrome (CRS) status were examined using repeated measure analysis of variance. hsTropT did not differ with time or with the presence of grade 2 CRS. Median hsTropT was 12.1 ng/L [interquartile range (IQR): 9.2, 20.1] at baseline, 13.1 ng/L (IQR: 9.6, 24.2) at day 1, 11.9 ng/L (IQR: 9.6, 18.0) at day 7, and 15.3 ng/L (10.8, 20.2) at day 21. In contrast, NTproBNP rose on day 1 (PWilcox = 0.0002) and day 7 (PWilcox = 2.7 × 10−5), and the degree of elevation differed by the presence of grade 2 CRS (Pinteraction = 0.002). Median NTproBNP was 179 pg/mL (IQR: 116, 325) at baseline, 357 pg/mL (IQR: 98, 813) at day 1, 420 pg/mL (IQR: 239, 1242) at day 7, and 177 pg/mL (IQR: 80, 278) at day 21. In conclusion, hsTropT l did not differ across timepoints after CAR T therapy, but NTproBNP rose at day 7, the prognostic implications of which should be the target of future research, as the indications for this therapy expand

Targeting transcription regulation in cancer with a covalent CDK7 inhibitor

Tumour oncogenes include transcription factors that co-opt the general transcriptional machinery to sustain the oncogenic state, but direct pharmacological inhibition of transcription factors has so far proven difficult. However, the transcriptional machinery contains various enzymatic cofactors that can be targeted for the development of new therapeutic candidates, including cyclin-dependent kinases (CDKs). Here we present the discovery and characterization of a covalent CDK7 inhibitor, THZ1, which has the unprecedented ability to target a remote cysteine residue located outside of the canonical kinase domain, providing an unanticipated means of achieving selectivity for CDK7. Cancer cell-line profiling indicates that a subset of cancer cell lines, including human T-cell acute lymphoblastic leukaemia (T-ALL), have exceptional sensitivity to THZ1. Genome-wide analysis in Jurkat T-ALL cells shows that THZ1 disproportionally affects transcription of RUNX1 and suggests that sensitivity to THZ1 may be due to vulnerability conferred by the RUNX1 super-enhancer and the key role of RUNX1 in the core transcriptional regulatory circuitry of these tumour cells. Pharmacological modulation of CDK7 kinase activity may thus provide an approach to identify and treat tumour types that are dependent on transcription for maintenance of the oncogenic state.National Institutes of Health (U.S.) (Grant HG002668)National Institutes of Health (U.S.) (Grant CA109901

Measurement of the W-boson mass in pp collisions at √s=7 TeV with the ATLAS detector

A measurement of the mass of the W boson is presented based on proton–proton collision data recorded in 2011 at a centre-of-mass energy of 7 TeV with the ATLAS detector at the LHC, and corresponding to 4.6 fb−1 of integrated luminosity. The selected data sample consists of 7.8×106 candidates in the W→μν channel and 5.9×106 candidates in the W→eν channel. The W-boson mass is obtained from template fits to the reconstructed distributions of the charged lepton transverse momentum and of the W boson transverse mass in the electron and muon decay channels, yielding mW=80370±7 (stat.)±11(exp. syst.) ±14(mod. syst.) MeV =80370±19MeV, where the first uncertainty is statistical, the second corresponds to the experimental systematic uncertainty, and the third to the physics-modelling systematic uncertainty. A measurement of the mass difference between the W+ and W−bosons yields mW+−mW−=−29±28 MeV

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

2019 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

The International Liaison Committee on Resuscitation has initiated a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation science. This is the third annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. It addresses the most recent published resuscitation evidence reviewed by International Liaison Committee on Resuscitation Task Force science experts. This summary addresses the role of cardiac arrest centers and dispatcher-assisted cardiopulmonary resuscitation, the role of extracorporeal cardiopulmonary resuscitation in adults and children, vasopressors in adults, advanced airway interventions in adults and children, targeted temperature management in children after cardiac arrest, initial oxygen concentration during resuscitation of newborns, and interventions for presyncope by first aid providers. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the certainty of the evidence on the basis of the Grading of Recommendations, Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence to Decision Framework Highlights sections. The task forces also listed priority knowledge gaps for further research

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Comparative analysis of A-to-I editing in human and non-human primate brains reveals conserved patterns and context-dependent regulation of RNA editing

Abstract A-to-I RNA editing is an important process for generating molecular diversity in the brain through modification of transcripts encoding several proteins important for neuronal signaling. We investigated the relationships between the extent of editing at multiple substrate transcripts (5HT2C, MGLUR4, CADPS, GLUR2, GLUR4, and GABRA3) in brain tissue obtained from adult humans and rhesus macaques. Several patterns emerged from these studies revealing conservation of editing across primate species. Additionally, variability in the human population allows us to make novel inferences about the co-regulation of editing at different editing sites and even across different brain regions