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Monoclonal antibodies can provide important pre- or post-exposure protection against disease for those not yet vaccinated or in individuals that fail to mount a protective immune response after vaccination. A key concern in use of monotherapy monoclonal antibody products lies in the high risk of mutagenic escape. Inmazeb (REGN-EB3), a three-antibody cocktail against Ebola virus, demonstrated efficacy in lessening disease course and improving survival in a randomized, controlled trial. Here we present the cryoEM structure at 3.1 Å of the Ebola virus glycoprotein, determined without symmetry averaging, in a simultaneous complex with eight Fab fragments of antibodies in the Inmazeb cocktail. This structure allows modeling of previously disordered portions of the glycan cap, maps the non-overlapping epitopes of Inmazeb, and illuminates the basis for complementary activities, as well as residues that are critical for resistance to escape by each component of this cocktail and other clinically relevant antibodies. We also provide direct evidence that, unlike monotherapy treatments, including those targeting conserved epitopes, the Inmazeb protects against the rapid emergence of EBOV escape mutants and supports the benefit of the combination approach.

Motivation Genome sequencing projects have revealed frequent gains and losses of genes between species. Previous versions of our software, Computational Analysis of gene Family Evolution (CAFE), have allowed researchers to estimate parameters of gene gain and loss across a phylogenetic tree. However, the underlying model assumed that all gene families had the same rate of evolution, despite evidence suggesting a large amount of variation in rates among families. Results Here, we present CAFE 5, a completely re-written software package with numerous performance and user-interface enhancements over previous versions. These include improved support for multithreading, the explicit modeling of rate variation among families using gamma-distributed rate categories, and command-line arguments that preclude the use of accessory scripts. Availability and implementation CAFE 5 source code, documentation, test data and a detailed manual with examples are freely available at https://github.com/hahnlab/CAFE5/releases. Supplementary information Supplementary data are available at Bioinformatics online.

Background Metastatic urothelial cancer (UC) is the eighth most common cause of cancer death in the UK. Standard first-line treatment, for most patients, is cytotoxic chemotherapy. Although UC is initially sensitive to chemotherapy, relapse is almost inevitable after which outcomes are poor, with median overall survival 8 months. There is, therefore, an urgent need for novel therapies to improve outcomes for this patient group. Methods ATLANTIS is a randomised phase II, adaptive design trial of maintenance therapy in biomarker defined subgroups of patients with advanced UC. The primary end-point is progression-free survival (PFS) and the study involves over 30 UK cancer centres. Discussion ATLANTIS is the first study in the UK to employ a precision medicine approach to UC patients for maintenance treatment. Agents with positive efficacy signal will proceed to randomised phase III trials to confirm activity of novel biologically stratified therapies in UC.

Many members of the current generation of students and researchers are accustomed to intuitive computing devices and never had to learn how to use command-line based systems, which comprise the majority of high-performance computing environments in use. In the 2013-14 time frame, both Indiana University and Purdue university separately launched virtual desktop front-ends for their high performance computing clusters with the aim of offering an easier on-ramp to new users. In the last five years we iterated on and refined these approaches, and we now have over two thousand annual active users combined. Over 75% of those users say that the desktop services are either moderately or extremely important for their ability to use HPC resources. In this paper, we share our experience bootstrapping this new service framework, bringing in the end-users, dealing with runaway success, and making this service a sustainable offering. This paper offers a comprehensive picture of the driving motivations for desktops at each institution, reasons users like desktops, and ways of getting started.

Many universities invest substantial resources in the design, deployment, and maintenance of campus-based cyberinfrastructure (CI). To justify the expense, it is important that university administrators and others understand and communicate the value of these internal investments in terms of scholarly impact. This paper introduces two visualizations and their usage in the Value Analytics (VA) module for Open XD metrics on demand (XDMoD), which enable analysis of external grant funding income, scholarly publications, and collaboration networks. The VA module was developed by Indiana University’s (IU) Research Technologies division, Pervasive Technology Institute, and the CI for Network Science Center (CNS), in conjunction with the University at Buffalo’s Center for Computational Research. It provides diverse visualizations of measures of information technology (IT) usage, external funding, and publications in support of IT strategic decision-making. This paper details the data, analysis workflows, and visual mappings used in two VA visualizations that aim to communicate the value of different IT usage in terms of NSF and NIH funding, resulting publications, and associated research collaborations. To illustrate the feasibility of measuring IT values on research, we measured its financial and academic impact from the period between 2012 and 2017 for IU. The financial return on investment (ROI) is measured in terms of IU funding, totaling $339,013,365 for 885 NIH and NSF projects associated with IT usage, and the academic ROI constitutes 968 publications associated with 83 of these NSF and NIH awards. In addition, the results show that Medical Specialties, Brain Research, and Infectious Diseases are the top three scientific disciplines ranked by the number of publications during the given time period.

Understanding the financial and intellectual value of campus-based cyberinfrastructure (CI) to the institutions that invest in such CI is intrinsically difficult. Given today's financial pressures, there is often administrative pressure questioning the value of campus-based and campus-funded CI resources. In this paper we describe new financial analytics capabilities being added to the widely used system analysis tool Open XDMoD (XSEDE Metrics on Demand) to create a new realm of metrics that will allow us to correlate usage of high performance computing with funding and publications. The capabilities to be added will eventually allow CI centers to view metrics relevant to both scientific output in terms of publications, and financial data in terms of awarded grants.The creation of Open XDMoD Value Analytics was funded by the National Science Foundation as a two year project. We are now nearing the end of the first year of this award, during which we focused on financial analytics. During the second year of this project we will focus on analytics of intellectual output. This module will allow the same sorts of analyses about systems and users as the financial analytics module, but in terms of intellectual outputs such as number of publications, citations to publications, and H indices. This module will also have capabilities to visualize such data, integrated with financial data. We plan to present these tools at PEARC '18.

In 1997, Indiana University (IU) began a purposeful and steady drive to expand the use of supercomputers and what we now call cyberinfrastructure. In 2001, IU implemented the first 1 TFLOPS supercomputer owned by and operated for a single US University. In 2013, IU made an analogous investment and achievement at the 1 PFLOPS level: Big Red II, a Cray XE6/XK7, was the first supercomputer capable of 1 PFLOPS (theoretical) performance that was a dedicated university resource. IU's high performance computing (HPC) resources have fostered innovation in disciplines from biology to chemistry to medicine. Currently, 185 disciplines and sub disciplines are represented on Big Red II with a wide variety of usage needs. Quantitative data suggest that investment in this supercomputer has been a good value to IU in terms of academic achievement and federal grant income. Here we will discuss how investment in Big Red II has benefited IU, and argue that locally-owned computational resources (scaled appropriately to needs and budgets) may be of benefit to many colleges and universities. We will also discuss software tools under development that will aid others in quantifying the benefit of investment in high performance computing to their campuses.

Utilizing the enormous computing resources of high performance computing systems is anything but a trivial task. Performance analysis tools are designed to assist developers in this challenging task by helping to understand the application behavior and identify critical performance issues. In this paper we share our efforts and experiences in analyzing and optimizing Trinity, a well-established framework for the de novo reconstruction of transcriptomes from RNA-seq reads. Thereby, we try to reflect all aspects of the ongoing performance engineering: the identification of optimization targets, the code improvements resulting in 22 % overall runtime reduction, as well as the challenges we encountered getting there.

The Royal College of Physicians and Society of Acute Medicine have proposed the introduction of Acute Care Quality Indicators. These include the need for rapid initial patient assessment and instigation of a management plan.1 Reductions in junior doctor working hours may impact on our ability to meet these standards. We present the result of a service review of 297 consecutive admissions to a large acute medical unit. This has identified a marked dichotomy between waiting times during daytime and out-of-hours, despite appropriate response to initial triage. We conclude that further service redevelopment is required to reach the standards proposed in the current Acute Care Quality Indicators.

Chromosomal rearrangements leading to fusion transcripts represent a class of oncogenic aberrations that are of high interest for understanding cancer biology, treating cancer patients, and as targets for the development of new therapies. Transcriptome sequencing via RNA-Seq coupled with downstream bioinformatics software applications offers an effective method for identifying candidate fusion transcripts, and is more targeted and cost-effective than whole genome sequencing. Although many such fusion detection software tools have recently been made available, they often differ greatly in prediction accuracy, execution times, computational requirements, installation complexity, and in not being readily accessible to non-bioinformatician cancer researchers. Here we present the Trinity Cancer Transcriptome Analysis Toolkit (CTAT), a newly developed suite of RNA-Seq targeted fusion detection tools leveraging a combination of reference genome read-mapping and de novo transcriptome assembly, coupled to in silico fusion transcript validation, annotation, and visualization. We show that components of our Trinity CTAT fusion detection toolkit, including STAR-Fusion and FusionInspector, yield improved accuracy and run times as compared to alternative leading tools. As a demonstration of the utility of Trinity CTAT, we explore fusion transcript discovery in a large cohort of ∼300 chronic lymphocytic leukemia (CLL) patient samples. We identify several novel recurrent fusions that may represent drivers of CLL etiology and provide new avenues to therapies. In addition to fusion transcript identification, Trinity CTAT aims to provide cancer researchers with easy access to methods for analyzing cancer RNA-Seq, including transcript reconstruction, identification of mutations, expression analysis, and tumor heterogeneity. Trinity CTAT is freely available open source software and readily accessible to all cancer researchers via our public Galaxy web portal: https://galaxy.ncgas-trinity.indiana.edu/. Citation Format: Brian Haas, Timothy Tickle, Nathalie Pochet, Jing Sun, Peggy Hsu, Chip Stewart, Carrie Ganote, Ben Fulton, Catherine Wu, Aviv Regev. Fast and accurate fusion transcript detection using the Trinity Cancer Transcriptome Analysis Toolkit. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5266.