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Summary: The novel betacoronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) causes a form of severe pneumonia disease called coronavirus disease 2019 (COVID-19). To develop human neutralizing anti-SARS-CoV-2 antibodies, antibody gene libraries from convalescent COVID-19 patients were constructed and recombinant antibody fragments (scFv) against the receptor-binding domain (RBD) of the spike protein were selected by phage display. The antibody STE90-C11 shows a subnanometer IC50 in a plaque-based live SARS-CoV-2 neutralization assay. The in vivo efficacy of the antibody is demonstrated in the Syrian hamster and in the human angiotensin-converting enzyme 2 (hACE2) mice model. The crystal structure of STE90-C11 Fab in complex with SARS-CoV-2-RBD is solved at 2.0 Å resolution showing that the antibody binds at the same region as ACE2 to RBD. The binding and inhibition of STE90-C11 is not blocked by many known emerging RBD mutations. STE90-C11-derived human IgG1 with FcγR-silenced Fc (COR-101) is undergoing Phase Ib/II clinical trials for the treatment of moderate to severe COVID-19.

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user's standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user's perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.

The quality of biodiversity data publicly accessible via aggregators such as GBIF (Global Biodiversity Information Facility), the ALA (Atlas of Living Australia), iDigBio (Integrated Digitized Biocollections), and OBIS (Ocean Biogeographic Information System) is often questioned, especially by the research community. The Data Quality Interest Group, established by Biodiversity Information Standards (TDWG) and GBIF, has been engaged in four main activities: developing a framework for the assessment and management of data quality using a fitness for use approach; defining a core set of standardised tests and associated assertions based on Darwin Core terms; gathering and classifying user stories to form contextual-themed use cases, such as species distribution modelling, agrobiodiversity, and invasive species; and developing a standardised format for building and managing controlled vocabularies of values. Using the developed framework, data quality profiles have been built from use cases to represent user needs. Quality assertions can then be used to filter data suitable for a purpose. The assertions can also be used to provide feedback to data providers and custodians to assist in improving data quality at the source. A case study, using two different implementations of tests and assertions based around the Darwin Core "Event Date" terms, were also tested against GBIF data, to demonstrate that the tests are implementation agnostic, can be run on large aggregated datasets, and can make biodiversity data more fit for typical research uses. [ABSTRACT FROM AUTHOR]

Introduction As childhood mortality from infectious diseases falls across sub-Saharan Africa (SSA), the burden of disease attributed to surgical conditions is increasing. However, limited data exist on paediatric surgical outcomes in SSA. We compared the outcomes of five common paediatric surgical conditions in SSA with published benchmark data from high-income countries (HICs).Methods A multicentre, international, prospective cohort study was undertaken in hospitals providing paediatric surgical care across SSA. Data were collected on consecutive children (birth to 16 years), presenting with gastroschisis, anorectal malformation, intussusception, appendicitis or inguinal hernia, over a minimum of 1 month, between October 2016 and April 2017. Participating hospitals completed a survey on their resources available for paediatric surgery.The primary outcome was all-cause in-hospital mortality. Mortality in SSA was compared with published benchmark mortality in HICs using χ2 analysis. Generalised linear mixed models were used to identify patient-level and hospital-level factors affecting mortality. A p

Introduction: The Biodiversity informatics community has made important achievements regarding digitizing, integrating and publishing standardized data about global biodiversity. However, the assessment of the quality of such data and the determination of the fitness for use of those data in different contexts remain a challenge. To tackle such problem using a common approach and conceptual base, the TDWG Biodiversity Data Quality Interest Group - BDQ-IG (https://github.com/tdwg/bdq) has proposed a conceptual framework to define the necessary components to describe Data Quality (DQ) needs, DQ solutions, and DQ reports. It supports a consistent description of the meaning of DQ in specific contexts and how to assess and manage DQ in a global and collaborative environment Veiga 2016, Veiga et al. 2017. Based on the common ground provided by this conceptual framework, we implemented a prototype of a Fitness for Use Backbone (FFUB) as a Node.js module (h ttps://nodejs.org/api/modules.html) for registering and retrieving instances of the framework concepts. Material and methods: This prototype was built using Node.js, an asynchronous eventdriven JavaScript runtime, which uses a non-blocking I/O model that makes it lightweight and efficient to build scalable network applications (https://nodejs.org). In order to facilitate the reusability of the module, we registered it in the NPM package manager (https:// www.npmjs.com). To foster collaboration on the development of the module, the source code was made available in the GitHub (https://github.com) version control system. To test the module, we have developed a simple mechanism for measuring, validating and amending the quality of datasets and records, called BDQ-Toolkit. The source code of the FFUB module can be found at https://github.com/BioComp-USP/ffub. Installing and using the module requires Node.js version 6 or higher. Instructions for installing and using the FFUB module can be found at https://www.npmjs.com/package/ffub. Results: The implemented prototype is organized into three main types of functions: registry, retrieve and print. Registry functions enable the creation instances of concepts of the conceptual framework, as illustrated in Fig. 1, such as use cases, information elements, dimensions, criteria, enhancements, specifications, mechanisms, assertions (measure, validation, and amendment) and DQ profiles. As a prototype, these instances are not persisted, but they are stored in an in-memory JSON object. Retrieve functions are used to get instances of the framework concepts, such as DQ reports, based on the in-memory JSON object. Print functions are used to write in the console the concepts stored in the inmemory JSON object in a formatted way. Inside the FFUB module, we implemented a test which registers a set of instances of the framework concepts, including a simple DQ profile, specifications and mechanisms and a set of assertions applied to a sample dataset and its records. Based on these registries, it is possible to retrieve and print DQ reports, presenting the current status of DQ of the sample dataset and its records according to the defined DQ profile. Final remarks: This module provides a practical interface to the proposed conceptual framework. It allows the input of instances of concepts and generates, as output, information which allows the DQ assessment and management. Future work includes creating a RESTful API, based on the functions developed in this prototype, using sophisticated methods of data retrieving based on NoSQL databases. [ABSTRACT FROM AUTHOR]

The Online Pollen Catalogs Network (RCPol) (http://rcpol.org.br) was conceived to promote interaction among researchers and the integration of data from pollen collections, herbaria and bee collections. In order to structure RCPol work, researchers and collaborators have organized information on Palynology in four branches: palynoecology, paleopalynology, palynotaxonomy and spores. This information is collaboratively digitized and managed using standardized Google Spreadsheets. These datasets are assessed by the RCPol palynology experts and when a dataset is compliant with the RCPol data quality policy, it is published to http://chaves.rcpol.org.br. Data quality assessment used to be performed manually by the experts and was timeconsuming and inconsistent in detecting data quality problemas such as incomplete and inconsistent information. In order to support data quality assessment in a more automated and effective way, we are developing a data quality tool which implements a series of mechanisms to measure, validate and improve completeness, consistency, conformity, accessibility and uniqueness of data, prior to a manual expert assessment. The system was designed according to the conceptual framework proposed by Task Group 1 of the Biodiversity Data Quality Interest Group Veiga et al. 2017. For each sheet in the Google Spreadsheet, the system generates a set of assertions of measures, validations and amendments for the records (rows) and datasets (sheets), according to a profile defined for RCPol. The profile follows the policies of data quality measurement, validation and enhancement. The data quality measurement policy encompassess the dimensions of completeness, consistency, conformity, accessibility and uniqueness. RCPol uses a quality assurance approach: only data that are compliant with all the quality requirements are published in the system. Therefore, its data quality validation policy only considers datasets with 100% completeness, consistency, conformity, accessibility and uniqueness. In order to improve the quality in each relevant dimension, a set of enhancements was defined in the data quality enhancement policy. Based on this RCPol profile, the system is able to generate reports that contain measures, validations and amendments assertions with the method and tool used to generate the assertion. This web-based system can be tested at h ttp://chaves.rcpol.org.br/admin/data-quality with the dataset https://docs.google.com/spreadsheets/u/1/d/1gH0aa2qqnAgfAixGom3Gnx6Qp 91ZvWhUHPb_QeoIreQ. This system is able to assure that only data compliant with the data quality profile defined by RCPol are fit for use and can be published. This system contributes significantly to decreasing the workload of the experts. Some data may still contain values that cannot be easily automatically assessed, e.g. validate if the content of an image matches the respective scientific name, so expert manual assessment remains necessary. After the system reports that data are compliant with the profile, a manual assessment must be performed by the experts, using the data quality report as support, and only after that will the data be published. The next steps include archival of the data quality reports in a database, improving the web interface to enable searching and sorting of assertions, and to provide a machine readable interface for the data quality reports. [ABSTRACT FROM AUTHOR]