2,135 research outputs found
Valutare la comprensione lessicale in età prescolare: Un confronto tra la BVL_4-12 e il PPVT-R
No full textl presente lavoro di ricerca mira a valutare l’efficacia della prova di Comprensione Lessicale in Età Prescolare della Batteria per la Valutazione del Linguaggio in Bambini dai 4 ai 12 anni (BVL_4-12; Marini et al., 2015) confrontando la prestazione di un gruppo di bambini a questo test con quella ottenuta somministrando una prova, il Peabody Picture Vocabulary Test – Revised (PPVT-R; Dunn & Dunn, 1981, versione italiana a cura di Stella, Pizzoli & Tressoldi, 2001) per bambini dai 3 ai 12 anni di età, di consolidato uso nella pratica clinica. La differenza più evidente tra i due test risiede nella maggiore rapidità di somministrazione del test della BVL_4-12 e nella sua maggiore semplicità di scoring. Per questo studio sono stati reclutati 20 bambini frequentanti il primo anno della scuola dell’infanzia. Di questi, 15 bambini erano caratterizzati da uno sviluppo tipico del linguaggio, mentre i rimanenti 5 erano sottoposti a trattamento logopedico. I risultati di analisi quantitative e qualitative confermano che i due test forniscono informazioni equivalenti in bambini in età prescolare
"The love that made hell, paradise." Ouida re-writing the Paolo and Francesca theme in Held in Bondage
No full textThe bestselling Victorian author Ouida reveals in her novels, and, in particular, Held in Bondage, an extraordinary knowledge od Dante, by using characters and themes from the Commedia. The Paolo and Francesca theme actually constitutes part of the plot of the novel and is to be found in many of her other works, short stories and non-fiction writing
Generative AI as a support tool for the histopathological diagnosis of Hirschsprung disease
No full textopenBackground: La diagnosi di malattia di Hirschsprung (HD) viene confermata attraverso l'analisi istopatologica delle biopsie rettali. Tuttavia, l'esame manuale dei vetrini istologici è un processo laborioso e soggetto ad errori, il che evidenzia la necessità di utilizzare l'Intelligenza Artificiale (AI) come strumento di supporto decisionale. L'applicazione del Machine Learning (ML) nella patologia digitale è spesso limitata dalla ridotta disponibilità di ampi set di immagini di alta qualità.
Scopo: Questo studio propone un approccio innovativo il cui scopo è la creazione di un dataset ottimizzato di immagini istologiche reali da usare come base per la definizione di un modello generativo di immagini sintetiche analoghe al fine di aumentare il numero di immagini disponibili e migliorare indirettamente le prestazioni diagnostiche dei modelli di ML nella malattia di Hirschsprung.
Materiali e metodi: I campioni fissati in formalina sono stati annotati manualmente evidenziando le cellule gangliari e le strutture nervose. Successivamente, sono state applicate tecniche di preprocessing standard, come lo spatial filtering, la normalizzazione dei colori e la segmentazione, per migliorare la qualità delle immagini e creare le maschere. Successivamente, è stato addestrato un modello generativo basato sulla diffusione denoising con l'obiettivo di sintetizzare immagini contenenti cellule gangliari.
Risultati: Abbiamo visionato, selezionato e digitalizzato 108 slides provenienti da pazienti sottoposti ad intervento tra gennaio 2010 e gennaio 2022. Successivamente queste immagini sono state annotate e segmentate per allenare la Generative AI. Il dataset è stato ampliato, grazie all’augumentation, ed è stato suddiviso in piccole sezioni che corrispondono alle dimensioni di ingresso della rete. La rete generativa ha prodotto immagini inedite contenenti cellule gangliari.
Conclusione: L’integrazione di immagini sintetiche e reali, insieme a un’ottimizzazione del preprocessing e della segmentazione, rappresenta una possibile strategia per migliorare le prestazioni del modello nell’identificazione delle caratteristiche diagnostiche di HD. Questo studio evidenzia il potenziale dell’uso combinato di immagini istologiche sintetiche nel training dei sistemi automatici con l’obiettivo di accelerare l’analisi nella diagnosi di HD e ridurre il carico della valutazione manuale.Background: The diagnosis of Hirschsprung disease (HD) is definitively confirmed through the histopathological analysis of rectal biopsies. However, the manual examination of histological slides is a labor-intensive process prone to errors, highlighting the need to utilize Artificial Intelligence (AI) as a decision- support tool. The application of Machine Learning (ML) in digital pathology is often limited by the scarcity of large datasets of high-quality images.
Aim: This study proposes an innovative approach whose aim is to create an optimized dataset of real histological images to be used as the basis for establishing a generative model of similar synthetic images in order to increase the number of available images and indirectly improve the diagnostic performance of ML models in Hirschsprung's disease.
Materials and Methods: Formalin-fixed samples were manually annotated to highlight ganglion cells and nerve structures. Subsequently, standard preprocessing techniques such as spatial filtering, color normalization, and segmentation were applied to improve image quality and create masks. A denoising diffusion-based generative model was then trained with the objective of synthesizing images containing ganglion cells.
Results: We reviewed, selected, and digitized 108 slides from patients who underwent surgery between January 2010 and January 2022. These images were then annotated and segmented to train the Generative AI. The dataset was expanded through augmentation, which were divided into smaller sections corresponding to the input size of the network. The generative network produced novel images containing ganglion cells.
Conclusion: The integration of synthetic and real images, coupled with optimized preprocessing and segmentation, represents a potential strategy to improve the model's performance in detecting the diagnostic features of HD. This study highlights the potential of using synthetic histological images in training automated systems with the goal of accelerating analysis in HD diagnosis and reducing the burden of manual evaluation
HERStory Makers 2023: Francesca Fotheringham
No full textFrancesca Fotheringham is a postdoctoral research associate at the University of Edinburgh studying educational psychology with a focus on neurodiversity. She took part in HERStory Makers 2023.What is HERStory Makers?HERStory Makers is a social media competition for female-identifying early career researchers to share their research, their career journeys, and to inspire the next generation. Winners are selected by public vote. HERStory Makers is also part of EXPLORATHON, Scotland's contribution to European Researchers' Night.In 2022-23, EXPLORATHON Francescasupported by the Engineering & Physical Sciences Research Council [grant number EP/X020762/1].Author contributions to contentFrancesca conceived, planned, and recorded the video content. Kirsty Ross edited the video content to insert HERStory Maker credits, added subtitles, and reduce video length to below Twitter/X limit of 2 mins and 20 secs.</p
Efficacy of ozonation on microbial counts in used brines for cheesemaking
No full textDue to their prolonged use, cheese brines are progressively enriched with organic substances, which allows microorganisms arising from the curds to achieve high concentrations. In this study, the feasibility of using gaseous ozone to reduce the microbial contamination of used brines was evaluated. The antimicrobial effect of ozone increases with the concentration and treatment time, reducing the total bacterial counts by 4.61logcfumL-1, the microstaphylococci counts by 3.37logcfumL-1 and the yeast counts by 2.70logcfumL-1, in the case of the stronger treatments. The antimicrobial effect of ozone was influenced by the protein content of brines. However, in brines that contain high amounts of protein, the inactivation effect of ozone was still significant. The Weibull model accurately estimated both the inactivation and the concavity exhibited in the survival curves for treatments at a concentration of 0.40mgL-1 of ozone for 240min. © 2015 Elsevier Ltd
Medicina illuminata. La Biblioteca Lancisiana di Roma
No full textL'articolo presenta i codici miniati della Biblioteca Lancisiana di Roma. La prima parte, del coautore, è dedicata alla Biblioteca. La seconda parte, di F. Manzari, tratta dei manoscritti miniati, costituiti da due codici con le opere di Avicenna e dal Liber fraternitatis della Confraternita dell'Ospedale di Santo Spirito in Sassia a Roma.The article introduces the illuminated manuscripts of the Biblioteca Lancisiana in Rome. The first part of the article, by the co-author, is dedicated to the Library. The second part, by Francesca Manzari, illustrates the manuscipts; these are two manuscripts with the works of Avicenna and the Liber fraternitatis of the Confraternity of the Hospital of Santo Spirito in Sassia in Rome
Occupational Risk Factors for SARS-CoV-2 Infection in Hospital Health Care Workers: A Prospective Nested Case-Control Study
No full textIntroduction: Health Care Workers (HCWs) are at a particular high risk of SARS-CoV-2 infection due to direct and indirect exposure to COVID-19 patients and Aerosol-Generating Procedures (AGPs). The aim of the study was to assess the risk factors for SARS-CoV-2 infection in HCWs exposed to COVID-19 patients, to evaluate the adherence and effectiveness of Infection Prevention and Control (IPC) measures, to describe the clinical presentation for SARS-CoV-2 infection in HCWs and to determine serological responses in HCWs. Methods: HCWs exposed to COVID-19 patients during the previous 14 days with a confirmed case status were recruited as cases; HCWs exposed to COVID-19 patients during the previous 14 days in the same ward without a suspected/probable/confirmed case status were recruited as controls. Serum samples were collected as soon as possible and after 21–28 days from all participants. Data were collected with a WHO standardized questionnaire as soon as possible and after 21–28 days. Results: All social, occupational and personal variables considered were not associated with an increased risk of SARS-CoV-2 infection. Conclusions: Our study showed a high knowledge of IPC measures and very high PPE use among HCWs
A DH-Leavened Musicological Toolbox
No full textGraduate-level training in music research methodologies tends to ignore digital humanities work and overlook the use of digital tools created in support of new forms of reading. Training instead focuses on source material in the student’s area of interest. This material includes secondary and primary (archival) resources, as well as information resources, such as: monuments of music and critical editions; indexes; bibliographies and thematic catalogs; dictionaries and encyclopedias; digital libraries of scores or editions; and databases of period-specific newspapers or journals. Graduate students taking research methods courses already have a toolbox built from their experiences as musicians and students of music, including the ability to read and interpret music notation, to understand theoretical and analytical concepts in music, as well as a command of music history, including the canon of musical works.
Digital humanities has become a major area of academic endeavor at the “interface of technological development, epistemological change and methodological concerns." An important characteristic of digital humanities research has been its interdisciplinarity. We argue that graduate training in musicology needs to include coverage of methodologies applied by digital humanists in support of new forms of reading, not only to broaden the canon of research topics in musicology, but also to build common ground with researchers of other disciplines. We propose that librarians are well positioned to provide this expertise and training
A Twitter Case Study for Assessing Digital Sound
No full textAcademic and cultural heritage institutions around the world have made measurable strides in the development of digital sound archives oriented towards research and access, but their impact on scholarship and society has been little studied. Traditionally, impact has been measured by citations; yet these are problematic metrics for non-traditional outputs like sound recordings. Social media data provide a promising avenue of investigation for measuring scholarly as well as societal impact. Twitter in particular has been shown to provide a high number of references for cultural and research outputs in all disciplines. This study analyzes Twitter references pertaining to the collections of five digital sound archives: British Library Sounds, Europeana Sounds, the Internet Archive Audio Archive, PennSound and UbuWeb. Using text analysis methods to identify high frequency events and trends, and labeling them with a rubric designed for measuring the impact of digital heritage resources, this study provides preliminary insights on user values as they relate to digital sound collections. Despite the limitations of using social media data, the evidence gathered in this case study characterizes aspects of the use of digital sound collections, and may point to future priorities for the digital preservation of sound.Peer reviewe
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