Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Open Science and Authorship of Supplementary Material. Evidence from a Research Community

Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned out to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. As a preliminary study, in this paper, we leverage the wealth of information contained in Open Science Graphs, such as OpenAIRE, and conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science (MES) research community. The results are promising and suggest our hypothesis is worth exploring further as we registered in 22% of the cases substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.Comment: 8 pages, 2 figures; accepted to the 26th International Conference on Science, Technology and Innovation Indicators (STI 2022

Will open science change authorship for good? Towards a quantitative analysis

Authorship of scientific articles has profoundly changed from early science until now. If once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. We argue that this can be computationally analysed by taking advantage of the wealth of information in model Open Science Graphs. Such a study can pave the way to understand better the dynamics and patterns of authorship in linked literature, research data and software, and how they evolved over the years.Comment: 6 pages, in Proceedings of the 18th Italian Research Conference on Digital Libraries (IRCDL 2022), http://ceur-ws.org/Vol-3160/short15.pdf. arXiv admin note: text overlap with arXiv:2207.0277

Modelling digital health data: The ExaMode ontology for computational pathology

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. Material and methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries

Empowering digital pathology applications through explainable knowledge extraction tools

Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET). Combining rule-based techniques and pre-trained ML models provides high accuracy results for knowledge extraction. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning. SKET is a practical and unsupervised approach to extracting knowledge from pathology reports, which opens up unprecedented opportunities to exploit textual and multimodal medical information in clinical practice. We also propose SKET eXplained (SKET X), a web-based system providing visual explanations about the algorithmic decisions taken by SKET. SKET X is designed/developed to support pathologists and domain experts in understanding SKET predictions, possibly driving further improvements to the system

Incidence, Risk Factors and Outcome of Pre-engraftment Gram-Negative Bacteremia after Allogeneic and Autologous Hematopoietic Stem Cell Transplantation: An Italian Prospective Multicenter Survey

Abstract BACKGROUND: Gram-negative bacteremia (GNB) is a major cause of illness and death after hematopoietic stem cell transplantation (HSCT), and updated epidemiological investigation is advisable. METHODS: We prospectively evaluated the epidemiology of pre-engraftment GNB in 1118 allogeneic HSCTs (allo-HSCTs) and 1625 autologous HSCTs (auto-HSCTs) among 54 transplant centers during 2014 (SIGNB-GITMO-AMCLI study). Using logistic regression methods. we identified risk factors for GNB and evaluated the impact of GNB on the 4-month overall-survival after transplant. RESULTS: The cumulative incidence of pre-engraftment GNB was 17.3% in allo-HSCT and 9% in auto-HSCT. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most common isolates. By multivariate analysis, variables associated with GNB were a diagnosis of acute leukemia, a transplant from a HLA-mismatched donor and from cord blood, older age, and duration of severe neutropenia in allo-HSCT, and a diagnosis of lymphoma, older age, and no antibacterial prophylaxis in auto-HSCT. A pretransplant infection by a resistant pathogen was significantly associated with an increased risk of posttransplant infection by the same microorganism in allo-HSCT. Colonization by resistant gram-negative bacteria was significantly associated with an increased rate of infection by the same pathogen in both transplant procedures. GNB was independently associated with increased mortality at 4 months both in allo-HSCT (hazard ratio, 2.13; 95% confidence interval, 1.45-3.13; P <.001) and auto-HSCT (2.43; 1.22-4.84; P = .01). CONCLUSIONS: Pre-engraftment GNB is an independent factor associated with increased mortality rate at 4 months after auto-HSCT and allo-HSCT. Previous infectious history and colonization monitoring represent major indicators of GNB. CLINICAL TRIALS REGISTRATION: NCT02088840