QT interval shortening after bariatric surgery depends on the applied heart rate correction equation

A shortening of electrocardiographic QT interval has been observed in obese subjects after weight loss, but previous results may have been biased by inappropriate heart rate (HR) correction.Methods Electrocardiography (ECG) recordings of 49 (35 females) severely obese patients before and 12 months after Roux-en-Y gastric bypass (RYGB) surgery were analysed. QT interval (QTc) was calculated by using four different equations, i.e. Bazett, Fridericia, Framingham and Hodges.Results Irrespectively of the used correction formula, QTc interval length was reduced after the surgery (QTcBazett −31 ± 18 ms; QTcFridericia −12 ± 15 ms; QTcFramingham −14 ± 15 ms; QTcHodges −9 ± 15 ms; all Ps Bazett reduction was significantly greater than the reduction in QTc calculated upon the other three equations (all Ps Bazett (P Fridericia, QTcFramingham and QTcHodges (all Ps > 0.05) were significantly correlated with concurrent changes in HR. Multivariate regression analyses revealed a significant independent association of serum insulin levels with QTcFridericia, QTcFramingham and QTcHodges values (all Ps < 0.05) preoperatively, whilst changes in QTc interval length after the surgery were not consistently associated to concurrent changes in metabolic traits.Conclusions Our data show that the extent of weight loss-associated QTc interval shortening largely depends on the applied HR correction equation and appears to be overestimated when the most popular Bazett’s equation is used

Interoperability and FAIRness through a novel combination of Web technologies

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs

Automated extraction of potential migraine biomarkers using a semantic graph

Problem Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. However, searching these enormous knowledge reservoirs and integrating findings across heterogeneous sources is costly and difficult. Here we demonstrate how semantically integrated knowledge, extracted from biomedical literature and structured databases, can be used to automatically identify potential migraine biomarkers. Method We used a knowledge graph containing more than 3.5 million biomedical concepts and 68.4 million relationships. Biochemical compound concepts were filtered and ranked by their potential as biomarkers based on their connections to a subgraph of migraine-related concepts. The ranked results were evaluated against the results of a systematic literature review that was performed manually by migraine researchers. Weight points were assigned to these reference compounds to indicate their relative importance. Results Ranked results automatically generated by the knowledge graph were highly consistent with results from the manual literature review. Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out of the 644 (85%) weight points assigned to the reference compounds. For reference compounds that were not in the top of the list, an extensive error analysis has been performed. When evaluating the overall performance, we obtained a ROC-AUC of 0.974. Discussion Semantic knowledge graphs composed of information integrated from multiple and varying sources can assist researchers in identifying potential disease biomarkers

A goal-oriented method for FAIRification planning

The FAIR Principles provide guidance on how to improve the Findability, Accessibility, Interoperability, and Reusability of digital resources. Since the publication of the principles in 2016, several workflows have been proposed to support the process of making data FAIR (FAIRification). However, to respect the uniqueness of different communities, both the principles and the available workflows have been deliberately designed to remain agnostic in terms of standards, tools, and related implementation choices. Consequently, FAIRification needs to be properly planned in advance, and implementation details must be discussed with stakeholders and aligned with FAIRification objectives. To support this, this paper describes a method for identifying and refining FAIRification objectives. Leveraging on best practices and techniques from requirements and ontology engineering, the method aims at incrementally elaborating the most obvious aspects of the domain (e.g. the initial set of elements to be collected) into complex and comprehensive objectives. The definition of clear objectives enables stakeholders to communicate effectively and make informed implementation decisions, such as defining achievement criteria for distinct principles and identifying relevant metadata to be collected.</p

FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services

2D/3D Heterostructure for Semitransparent Perovskite Solar Cells with Engineered Bandgap Enables Efficiencies Exceeding 25% in Four‐Terminal Tandems with Silicon and CIGS

Wide-bandgap perovskite solar cells (PSCs) with optimal bandgap (E$_{g}$) and high power conversion efficiency (PCE) are key to high-performance perovskite-based tandem photovoltaics. A 2D/3D perovskite heterostructure passivation is employed for double-cation wide-bandgap PSCs with engineered bandgap (1.65 eV ≤ E$_{g}$ ≤ 1.85 eV), which results in improved stabilized PCEs and a strong enhancement in open-circuit voltages of around 45 mV compared to reference devices for all investigated bandgaps. Making use of this strategy, semitransparent PSCs with engineered bandgap are developed, which show stabilized PCEs of up to 25.7% and 25.0% in fourterminal perovskite/c-Si and perovskite/CIGS tandem solar cells, respectively. Moreover, comparable tandem PCEs are observed for a broad range of perovskite bandgaps. For the first time, the robustness of the four-terminal tandem configuration with respect to variations in the perovskite bandgap for two state-of-the-art bottom solar cells is experimentally validated

The implicitome: A resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing