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Abstract The increasing adoption of tokens on the Ethereum blockchain has given rise to many distinct economic communities whose activity history is publicly accessible. In this paper we study the communities of Ethereum fungible and non-fungible tokens, regulated, respectively, by the ERC-20 and ERC-721 standards. In particular, we focus on token transfers and consider the top 100 largest ERC-20 and ERC-721 ecosystems by number of transfers, modeling them as networks where nodes correspond to participants and edges represent token transfers. We analyze their main topological properties and conduct a clustering-based study to identify groups of graphs with similar topologies. Subsequently, we classify the networks based on the application domain of their corresponding token and investigate whether graphs with similar topologies correspond to tokens within the same domain. We also conduct a temporal analysis of token popularity based on the historical transfer activity. Our findings highlight the existence of common topological properties (e.g., absence of small world effect) across both types of tokens. In contrast, the clustering analysis indicates no evident connection between the token application domain and the structure of the induced transfer networks, with the exception of non-fungible tokens associated with spamming activities.

IntroductionData-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.MethodsWe propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.ResultsThis infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.ConclusionThe anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

Abstract Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully confirm manual analyses by experts in the field. We hereby present RecombinHunt, an original data-driven method for the identification of recombinant genomes, capable of recognizing recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy and within reduced turn-around times. ReconbinHunt shows high specificity and sensitivity, compares favorably with other state-of-the-art methods, and faithfully confirms manual analyses by experts. RecombinHunt identifies recombinant viral genomes from the recent monkeypox epidemic in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus.

BackgroundSince the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. ObjectiveThis study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19–related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. MethodsWe considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications’ abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. ResultsWe built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. ConclusionsOur approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.

We propose an efficient quantum subroutine for matrix multiplication that computes a state vector encoding the entries of the product of two matrices in superposition. The subroutine exploits efficient state preparation techniques and shows a potential speed-up with respect to classical methods. The most important benefit of our subroutine is that it encodes the entries of the matrix product directly in the state vector, which can be used for further computations within the same quantum circuit. All scenarios involving the computation of non-homomorphic functions of the product of two matrices can benefit from our technique. As a possible application, we discuss the computation of the variance of the entries of a matrix product, which can be a useful tool for some machine learning algorithms.

Abstract In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving unconventional side effects, rather than exchanging value. For instance, dust might be linked to on-chain services or to malicious activity, such as dust attacks attempting to break users’ pseudonymity. To study this phenomenon, this paper presents an in depth analysis of Bitcoin transactions involving dust, showing how dust is created and consumed. We identify the top dust creators and consumers and discuss how consumption has evolved over time. Finally, we use the data to identify transactions suspected of being part of dust attacks and quantify their impact on address deanonymization. Our results show that dust is mainly related to on-chain betting services. Transactions likely to be part of dust attacks are a minority of dust creating transactions but, despite their relatively low number, they have a disproportionately high effect on helping attackers to break address pseudonymity.

Abstract Background Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. Results To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. Conclusions As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.

Abstract Background Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. Results In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the “ontological unpacking” method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. Conclusions We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the “I” in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.

Abstract Background Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. Results Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. Conclusions The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.

Abstract Since the outbreak of the COVID-19 pandemic, many research organizations have studied the genome of the SARS-CoV-2 virus; a body of public resources have been published for monitoring its evolution. While we experience an unprecedented richness of information in this domain, we also ascertained the presence of several information quality issues. We hereby propose CoV2K, an abstract model for explaining SARS-CoV-2-related concepts and interactions, focusing on viral mutations, their co-occurrence within variants, and their effects. CoV2K provides a clear and concise route map for understanding different connected types of information related to the virus; it thus drives a process of data and knowledge integration that aggregates information from several current resources, harmonizing their content and overcoming incompleteness and inconsistency issues. CoV2K is available for exploration as a graph that can be queried through a RESTful API addressing single entities or paths through their relationships. Practical use cases demonstrate its application to current knowledge inquiries.

The inflation of SARS-CoV-2 lineages with a high number of accumulated mutations (such as the recent case of Omicron) has risen concerns about the evolutionary capacity of this virus. Here, we propose a computational study to examine non-synonymous mutations gathered within genomes of SARS-CoV-2 from the beginning of the pandemic until February 2022. We provide both qualitative and quantitative descriptions of such corpus, focusing on statistically significant co-occurring and mutually exclusive mutations within single genomes. Then, we examine in depth the distributions of mutations over defined lineages and compare those of frequently co-occurring mutation pairs. Based on this comparison, we study mutations’ convergence/divergence on the phylogenetic tree. As a result, we identify 1,818 co-occurring pairs of non-synonymous mutations showing at least one event of convergent evolution and 6,625 co-occurring pairs with at least one event of divergent evolution. Notable examples of both types are shown by means of a tree-based representation of lineages, visually capturing mutations’ behaviors. Our method confirms several well-known cases; moreover, the provided evidence suggests that our workflow can explain aspects of the future mutational evolution of SARS-CoV-2.

BackgroundSARS-CoV-2 viremia has been found to be a potential prognostic factor in patients hospitalized for COVID-19.ObjectiveWe aimed to assess the association between SARS-CoV-2 viremia and mortality in COVID-19 hospitalized patients during different epidemic periods.MethodsA prospective COVID-19 registry was queried to extract all COVID-19 patients with an available SARS-CoV-2 viremia performed at hospital admission between March 2020 and January 2022. SARS-CoV-2 viremia was assessed by means of GeneFinderTM COVID-19 Plus RealAmp Kit assay and SARS-CoV-2 ELITe MGB® Kit using ResultsFour hundred and forty-five out of 2,822 COVID-19 patients had an available SARS-CoV-2 viremia, prevalently males (64.9%) with a median age of 65 years (IQR 55-75). Patients with a positive SARS-CoV-2 viremia (86/445; 19.3%) more frequently presented with a severe or critical disease (67.4% vs 57.1%) when compared to those with a negative SARS-CoV-2 viremia. Deceased subjects (88/445; 19.8%) were older [75 (IQR 68-82) vs 63 (IQR 54-72)] and showed more frequently a detectable SARS-CoV-2 viremia at admission (60.2% vs 22.7%) when compared to survivors. In univariable analysis a positive SARS-CoV-2 viremia was associated with a higher odd of death [OR 5.16 (95% CI 3.15-8.45)] which was confirmed in the multivariable analysis adjusted for age, biological sex and, disease severity [AOR 6.48 (95% CI 4.05-10.45)]. The association between positive SARS-CoV-2 viremia and death was consistent in the period 1 February 2021-31 January 2022 [AOR 5.86 (95% CI 3.43-10.16)] and in subgroup analysis according to disease severity: mild/moderate [AOR 6.45 (95% CI 2.84-15.17)] and severe/critical COVID-19 patients [AOR 6.98 (95% CI 3.68-13.66)].ConclusionsSARS-CoV-2 viremia resulted associated to COVID-19 mortality and should be considered in the initial assessment of COVID-19 hospitalized patients.

Abstract Since its emergence in late 2019, the diffusion of SARS-CoV-2 is associated with the evolution of its viral genome. The co-occurrence of specific amino acid changes, collectively named ‘virus variant’, requires scrutiny (as variants may hugely impact the agent’s transmission, pathogenesis, or antigenicity); variant evolution is studied using phylogenetics. Yet, never has this problem been tackled by digging into data with ad hoc analysis techniques. Here we show that the emergence of variants can in fact be traced through data-driven methods, further capitalizing on the value of large collections of SARS-CoV-2 sequences. For all countries with sufficient data, we compute weekly counts of amino acid changes, unveil time-varying clusters of changes with similar—rapidly growing—dynamics, and then follow their evolution. Our method succeeds in timely associating clusters to variants of interest/concern, provided their change composition is well characterized. This allows us to detect variants’ emergence, rise, peak, and eventual decline under competitive pressure of another variant. Our early warning system, exclusively relying on deposited sequences, shows the power of big data in this context, and concurs to calling for the wide spreading of public SARS-CoV-2 genome sequencing for improved surveillance and control of the COVID-19 pandemic.

In many blockchain networks, light nodes (e.g. mobile clients) with few computational resources must rely on more powerful full nodes to retrieve transactions from the chain. However, in this untrusted environment a malicious full node could deliver altered or incomplete information, requiring query authentication techniques to ensure the integrity of the results. To this aim, we study an authentication mechanism for spatial information (i.e. data representing the location, size, and shape of objects in a geographical coordinate system). We assume that light nodes issue range queries to obtain data from a single block. To enable authentication, we propose to construct a Merkle R-tree for each block and embed its root into the corresponding header, so that full nodes can exploit it to fetch information and construct a proof of integrity for lightweight clients. We also develop a new algorithm based on sorting and partitioning for constructing Merkle R-trees from a set of spatial transactions and employ space-filling curves to preserve the locality of elements. We examine its theoretical complexity, evaluate it experimentally on a real data set and compare it against other popular construction strategies. Results show that, as queries become more selective, trees generated with our solution improve query performance and reduce verification times with respect to other approaches. Moreover, we observe that the overhead induced by the tree construction is negligible if compared to the average inter-block time of popular blockchain protocols such as Bitcoin and Ethereum.

Genomic data are growing at unprecedented pace, along with new protocols, update polices, formats and guidelines, terminologies and ontologies, which are made available every day by data providers. In this continuously evolving universe, enforcing quality on data and metadata is increasingly critical. While many aspects of data quality are addressed at each individual source, we focus on the need for a systematic approach when data from several sources are integrated, as such integration is an essential aspect for modern genomic data analysis. Data quality must be assessed from many perspectives, including accessibility, currency, representational consistency, specificity, and reliability.In this article we review relevant literature and, based on the analysis of many datasets and platforms, we report on methods used for guaranteeing data quality while integrating heterogeneous data sources. We explore several real-world cases that are exemplary of more general underlying data quality problems and we illustrate how they can be resolved with a structured method, sensibly applicable also to other biomedical domains. The overviewed methods are implemented in a large framework for the integration of processed genomic data, which is made available to the research community for supporting tertiary data analysis over Next Generation Sequencing datasets, continuously loaded from many open data sources, bringing considerable added value to biological knowledge discovery.

Since the beginning of 2020, the COVID-19 pandemic has posed unprecedented challenges to viral data analysis and connected host disease diagnostic methods. We propose VirusLab, a flexible system for analysing SARS-CoV-2 viral sequences and relating them to metadata or clinical information about the host. VirusLab capitalizes on two existing resources: ViruSurf, a database of public SARS-CoV-2 sequences supporting metadata-driven search, and VirusViz, a tool for visual analysis of search results. VirusLab is designed for taking advantage of these resources within a server-side architecture that: (i) covers pipelines based on approaches already in use (ARTIC, Galaxy) but entirely cutomizable upon user request; (ii) predigests analysis of raw sequencing data from different platforms (Oxford Nanopore and Illumina); (iii) gives access to public archives datasets; (iv) supplies user-friendly reporting – making it a tool that can also be integrated into a business environment. VirusLab can be installed and hosted within the premises of any organization where information about SARS-CoV-2 sequences can be safely integrated with information about hosts (e.g., clinical metadata). A system such as VirusLab is not currently available in the landscape of similar providers: our results show that VirusLab is a powerful tool to generate tabular/graphical and machine readable reports that can be integrated in more complex pipelines. We foresee that the proposed system can support many research-oriented and therapeutic scenarios within hospitals or the tracing of viral sequences and their mutational processes within organizations for viral surveillance.

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

Next Generation Sequencing technologies have produced a substantial increase of publicly available genomic data and related clinical/biospecimen information. New models and methods to easily access, integrate and search them effectively are needed. An effort was made by the Genomic Data Commons (GDC), which defined strict procedures for harmonizing genomic and clinical data of cancer, and created the GDC data portal with its application programming interface (API). In this work, we enhance GDC harmonization by applying a state of the art data model (called Genomic Data Model) made of two components: the genomic data, in Browser Extensible Data (BED) format, and the related metadata, in a tab-delimited key-value format. Furthermore, we extend the GDC genomic data with information extracted from other public genomic databases (e.g., GENCODE, HGNC and miRBase). For metadata, we implemented automatic procedures to extract and normalize them, recognizing and eliminating redundant ones, from both Clinical/Biospecimen Supplements and GDC Data Model, that are present on the two sources of GDC (i.e., data portal and API). We developed and released the OpenGDC software, which is able to extract, integrate, extend, and standardize genomic and clinical data of The Cancer Genome Atlas (TCGA) from the GDC. Additionally, we created a publicly accessible repository, containing such homogenized and enhanced TCGA data (resulting in about 1.3 TB). Our approach, implemented in the OpenGDC software, provides a step forward to the effective and efficient management of big genomic and clinical data of cancer. The strong usability of our data model and utility of our work is demonstrated through the application of the GenoMetric Query Language (GMQL) on the transformed TCGA data from the GDC, achieving promising results, facilitating information retrieval and knowledge discovery analyses.

Any attempt to find connections between mathematical properties and complexity has a strong relevance to the field of Complexity Theory. This is due to the lack of mathematical techniques to prove lower bounds for general models of computation. This work represents a step in this direction: we define a combinatorial property that makes Boolean functions `` hard '' to compute in constant depth and show how the harmonic analysis on the hypercube can be applied to derive new lower bounds on the size complexity of previously unclassified Boolean functions.

Any attempt to find connections between mathematical properties and complexity has a strong relevance to the field of Complexity Theory. This is due to the lack of mathematical techniques to prove lower bounds for general models of computation.\par This work represents a step in this direction: we define a combinatorial property that makes Boolean functions ''\emphhard'' to compute in constant depth and show how the harmonic analysis on the hypercube can be applied to derive new lower bounds on the size complexity of previously unclassified Boolean functions.