Treating moderate-to-severe atopic dermatitis with benralizumab: results from the HILLIER study, a plain language summary.

WHAT IS THIS SUMMARY ABOUT?: Atopic dermatitis (AD) is a chronic (long-lasting) skin disease that leads to dry, itchy, and swollen red spots, which can also be painful and flare up at any time. Some people with AD have a high number of eosinophils, a type of white blood cell, which are associated with worse disease. Medicated creams and lotions, prescribed by health care providers, are meant to reduce the symptoms of AD. For some people, these creams and lotions do not work. Benralizumab injection is a medication that reduces and removes eosinophils. A clinical trial called HILLER tested benralizumab to see if there was a difference in symptoms of AD after reducing or removing eosinophils. This article explains how benralizumab reduced eosinophils and the effect it had on AD symptoms in the HILLIER study. WHAT WERE THE MAIN CONCLUSIONS REPORTED BY THE RESEARCHERS?: Benralizumab reduced blood eosinophil numbers. However, benralizumab showed no evidence of treatment benefit on signs, symptoms, or severity of AD, as measured by three skin assessments compared with placebo. Benralizumab was well tolerated and had a safety profile that was consistent with previous studies. The five most commonly reported side effects were COVID-19 infection, upper respiratory tract infection, headache, swelling of the lymph nodes, and pink eye (conjunctivitis) in patients who received either benralizumab or placebo. WHAT ARE THE KEY TAKEAWAYS?: Benralizumab lowered the number of blood eosinophils without improving AD symptoms and was well tolerated.

Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes.

Although secondary metabolites are typically associated with competitive or pathogenic interactions, the high bioactivity of endophytic fungi in the Xylariales, coupled with their abundance and broad host ranges spanning all lineages of land plants and lichens, suggests that enhanced secondary metabolism might facilitate symbioses with phylogenetically diverse hosts. Here, we examined secondary metabolite gene clusters (SMGCs) across 96 Xylariales genomes in two clades (Xylariaceae s.l. and Hypoxylaceae), including 88 newly sequenced genomes of endophytes and closely related saprotrophs and pathogens. We paired genomic data with extensive metadata on endophyte hosts and substrates, enabling us to examine genomic factors related to the breadth of symbiotic interactions and ecological roles. All genomes contain hyperabundant SMGCs; however, Xylariaceae have increased numbers of gene duplications, horizontal gene transfers (HGTs) and SMGCs. Enhanced metabolic diversity of endophytes is associated with a greater diversity of hosts and increased capacity for lignocellulose decomposition. Our results suggest that, as host and substrate generalists, Xylariaceae endophytes experience greater selection to diversify SMGCs compared with more ecologically specialised Hypoxylaceae species. Overall, our results provide new evidence that SMGCs may facilitate symbiosis with phylogenetically diverse hosts, highlighting the importance of microbial symbioses to drive fungal metabolic diversity.

Alleviating Environmental Health Disparities Through Community Science and Data Integration.

Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these "wicked" dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO).

Planet Microbe: a platform for marine microbiology to discover and analyze interconnected 'omics and environmental data.

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each 'omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.

PuMA: A papillomavirus genome annotation tool.

High-throughput sequencing technologies provide unprecedented power to identify novel viruses from a wide variety of (environmental) samples. The field of 'viral metagenomics' has dramatically expanded our understanding of viral diversity. Viral metagenomic approaches imply that many novel viruses will not be described by researchers who are experts on (the genomic organization of) that virus family. We have developed the papillomavirus annotation tool (PuMA) to provide researchers with a convenient and reproducible method to annotate and report novel papillomaviruses. PuMA currently correctly annotates 99% of the papillomavirus genes when benchmarked against the 655 reference genomes in the papillomavirus episteme. Compared to another viral annotation pipeline, PuMA annotates more viral features while being more accurate. To demonstrate its general applicability, we also developed a preliminary version of PuMA that can annotate polyomaviruses. PuMA is available on GitHub (https://github.com/KVD-lab/puma) and through the iMicrobe online environment (https://www.imicrobe.us/#/apps/puma).

Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity.

Infections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or even replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. All three classifiers identified the organisms present in their default databases from a mock bacterial community of 20 organisms, but only Centrifuge had no false positives. In addition, Centrifuge required far less computational resources and time for analysis. Centrifuge analysis of metagenomes obtained from samples of VAP, infected DFUs, and FN showed Centrifuge identified pathogenic bacteria and one virus that were corroborated by culture or a clinical PCR assay. Importantly, in both diabetic foot ulcer patients, metagenomic sequencing identified pathogens 4-6 weeks before culture. Finally, we show that Centrifuge results were minimally affected by elimination of time-consuming read quality control and host screening steps.

NCBI's Virus Discovery Hackathon: Engaging Research Communities to Identify Cloud Infrastructure Requirements.

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

In-Orbit Performance of the GRACE Follow-on Laser Ranging Interferometer.

The Laser Ranging Interferometer (LRI) instrument on the Gravity Recovery and Climate Experiment (GRACE) Follow-On mission has provided the first laser interferometric range measurements between remote spacecraft, separated by approximately 220 km. Autonomous controls that lock the laser frequency to a cavity reference and establish the 5 degrees of freedom two-way laser link between remote spacecraft succeeded on the first attempt. Active beam pointing based on differential wave front sensing compensates spacecraft attitude fluctuations. The LRI has operated continuously without breaks in phase tracking for more than 50 days, and has shown biased range measurements similar to the primary ranging instrument based on microwaves, but with much less noise at a level of 1 nm/sqrt[Hz] at Fourier frequencies above 100 mHz.

iMicrobe: Tools and data-dreaiven discovery platform for the microbiome sciences.

BACKGROUND: Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. FINDINGS: The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation-supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). CONCLUSIONS: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons.

Background: Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. Results: We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. Conclusions: A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.

The public cancer radiology imaging collections of The Cancer Imaging Archive.

The Cancer Imaging Archive (TCIA) is the U.S. National Cancer Institute's repository for cancer imaging and related information. TCIA contains 30.9 million radiology images representing data collected from approximately 37,568 subjects. This data is organized into collections by tumor-type with many collections also including analytic results or clinical data. TCIA staff carefully de-identify and curate all incoming collections prior to making the information available via web browser or programmatic interfaces. Each published collection within TCIA is assigned a Digital Object Identifier that references the collection. Additionally, researchers who use TCIA data may publish the subset of information used in their analysis by requesting a TCIA generated Digital Object Identifier. This data descriptor is a review of a selected subset of existing publicly available TCIA collections. It outlines the curation and publication methods employed by TCIA and makes available 15 collections of cancer imaging data.

Beach safety education for primary school children.

Childhood drowning remains a serious public health problem worldwide. The Australian Water Safety Council has set as one of its highest priorities the reduction of drowning deaths in children aged 0-14 years. However, concerns have recently been raised that many students completing primary school still lack the ability to recognize potential aquatic risks, cope with emergencies or assist someone else in danger. In this study, 107 primary school children aged 11-12 completed a one day training programme led by surf lifesaving instructors. Pre, post and eight week follow-up measures showed statistically significant improvements in recognition of the red 'beach closed' flag, aquatic safety signs, how to identify a rip current and choosing the safest place to swim at a beach that included a rip current in the picture. Following training students were more willing to provide first aid assistance to family members and friends in an emergency situation. Findings reinforce the value of school-based training that provides a general foundation for aquatic safety, with the caveat that current programmes must be evaluated to ensure their content has a robust prevention focus.

iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure.

Microbes affect nutrient and energy transformations throughout the world's ecosystems, yet they do so under viral constraints. In complex communities, viral metagenome (virome) sequencing is transforming our ability to quantify viral diversity and impacts. Although some bottlenecks, for example, few reference genomes and nonquantitative viromics, have been overcome, the void of centralized data sets and specialized tools now prevents viromics from being broadly applied to answer fundamental ecological questions. Here we present iVirus, a community resource that leverages the CyVerse cyberinfrastructure to provide access to viromic tools and data sets. The iVirus Data Commons contains both raw and processed data from 1866 samples and 73 projects derived from global ocean expeditions, as well as existing and legacy public repositories. Through the CyVerse Discovery Environment, users can interrogate these data sets using existing analytical tools (software applications known as 'Apps') for assembly, open reading frame prediction and annotation, as well as several new Apps specifically developed for analyzing viromes. Because Apps are web based and powered by CyVerse supercomputing resources, they enable scalable analyses for a broad user base. Finally, a use-case scenario documents how to apply these advances toward new data. This growing iVirus resource should help researchers utilize viromics as yet another tool to elucidate viral roles in nature.

Computational prospecting the great viral unknown.

Bacteriophages play an important role in host-driven biological processes by controlling bacterial population size, horizontally transferring genes between hosts and expressing host-derived genes to alter host metabolism. Metagenomics provides the genetic basis for understanding the interplay between uncultured bacteria, their phage and the environment. In particular, viral metagenomes (viromes) are providing new insight into phage-encoded host genes (i.e. auxiliary metabolic genes; AMGs) that reprogram host metabolism during infection. Yet, despite deep sequencing efforts of viral communities, the majority of sequences have no match to known proteins. Reference-independent computational techniques, such as protein clustering, contig spectra and ecological profiling are overcoming these barriers to examine both the known and unknown components of viromes. As the field of viral metagenomics progresses, a critical assessment of tools is required as the majority of algorithms have been developed for analyzing bacteria. The aim of this paper is to offer an overview of current computational methodologies for virome analysis and to provide an example of reference-independent approaches using human skin viromes. Additionally, we present methods to carefully validate AMGs from host contamination. Despite computational challenges, these new methods offer novel insights into the diversity and functional roles of phages in diverse environments.

Gramene: A Resource for Comparative Analysis of Plants Genomes and Pathways.

Gramene is an integrated informatics resource for accessing, visualizing, and comparing plant genomes and biological pathways. Originally targeting grasses, Gramene has grown to host annotations for economically important and research model crops, including wheat, potato, tomato, banana, grape, poplar, and Chlamydomonas. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. This chapter outlines system requirements for end users and database hosting, data types and basic navigation within Gramene, and provides examples of how to (1) view a phylogenetic tree for a family of transcription factors, (2) explore genetic variation in the orthologues of a gene with a known trait association, and (3) upload, visualize, and privately share end user data into a new genome browser track.Moreover, this is the first publication describing Gramene's new web interface-intended to provide a simplified portal to the most complete and up-to-date set of plant genome and pathway annotations.

Emergency response readiness for primary school children.

Objective The aim of the present study was to determine whether a 1-day basic life support (BLS) training program can significantly increase emergency response readiness for primary school children. Methods One hundred and seven children aged 11-12 years completed a program led by surf lifesaving instructors. A 50-item quiz was administered 1 week before and 1 and 8 weeks after training. Results Significant improvements were gained in knowledge of cardiopulmonary resuscitation (CPR; P<0.001), the response sequence for emergency situations (DRSABCD action plan) and various emergency scenarios, including choking (P<0.001) and severe bleeding (P<0.001). Knowledge and understanding were retained at the 8-week follow-up. Students reported increased confidence in assisting others after training, consistent with previous studies. Conclusions A 1-day training program can significantly increase BLS knowledge and confidence to provide assistance in an emergency situation. Findings reinforce the value of school-based training that provides a general foundation for emergency response readiness. What is known about this topic? The importance and value of teaching BLS to school children is well established in the US, UK and Europe. However, in the past 20 years there has been little or no published Australian evaluation research in this area, despite thousands of training programs running each year around the country for children in first aid, CPR and water safety. What does this paper add? This paper confirms that Australian primary school children can benefit significantly from short, targeted BLS training programs that provide the basic skills and confidence for them to respond in an emergency situation. What are the implications for practitioners? The paper provides a training and evaluation framework that can be used by health educators for age-appropriate BLS programs. The study shows that making training real-world and relevant, especially having hands-on CPR practice with manikins, can address common barriers to performing first aid and CPR reported by young people.

A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial.

The National Cancer Institute (NCI), in conjunction with blinded university, provides a mechanism to enable public access to the study data, CT radiology images, and pathology images from the National Lung Screening Trial (NLST). Access to the data and images is through the NCI-sponsored, blinded university-hosted The Cancer Imaging Archive (TCIA), a repository of more than 40 study collections of cancer images. Once access to the NLST data has been granted by NCI, a Query Tool within TCIA is used to access the NLST data and images. The Query Tool is a simple-to-use menu-driven database application designed to quickly pose queries and retrieve/save results (from 53,452 NLST participants), download CT images (~20 million available), and view pathology images (~1200 available). NLST study data are contained in 17 Query Tool tables with ~370 variables to query. This paper describes Query Tool design, functionality, and usefulness for researchers, clinicians, and software developers to query data, save query results, and download/view images.

Psychotic traits in comedians.

BACKGROUND: The popular belief that creativity is associated with madness has increasingly become the focus of research for many psychologists and psychiatrists. However, despite being prime examples of creative thinking, comedy and humour have been largely neglected. AIMS: To test the hypothesis that comedians would resemble other creative individuals in showing a higher level of psychotic characteristics related to both schizophrenia and manic depression. METHOD: A group of comedians (n = 500+) and a control sample of actors (n = 350+) completed an online questionnaire containing the short version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE), with scales measuring four dimensions of psychotic traits. Scores were compared with general population norms. RESULTS: Comedians scored significantly above O-LIFE norms on all four scales. Actors also differed from the norms but on only three of the scales. Most striking was the comedians' high score on both introverted anhedonia and extraverted impulsiveness. CONCLUSIONS: This unusual personality structure may help to explain the facility for comedic performance.

Ensembl Genomes 2013: scaling up access to genome-wide data.

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. This article provides an update to the previous publications about the resource, with a focus on recent developments. These include the addition of important new genomes (and related data sets) including crop plants, vectors of human disease and eukaryotic pathogens. In addition, the resource has scaled up its representation of bacterial genomes, and now includes the genomes of over 9000 bacteria. Specific extensions to the web and programmatic interfaces have been developed to support users in navigating these large data sets. Looking forward, analytic tools to allow targeted selection of data for visualization and download are likely to become increasingly important in future as the number of available genomes increases within all domains of life, and some of the challenges faced in representing bacterial data are likely to become commonplace for eukaryotes in future.