1,721,065 research outputs found
Nero's thirst
No full textThe two references to thirst that we read in Suetonius’ Life of Nero (34, 4 ; 48, 3-4) have generally been overlooked by critics, and their function in the narrative framework of the episodes containing them is far from clear. Here I try to explain them through the ancient medical tradition, which interpreted thirst as a specific psychosomatic reaction to
external impulses similar to those faced by Nero in two decisive moments of his biography
Recensione a J. Stover, G. Woudhuysen, The lost history of Sextus Aurelius Victor. Edinburgh studies in later Latin literature. Edinburgh: Edinburgh University Press, 2023. Pp. 552. ISBN 9781474492874.
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Domenico di Bandino. Recollecte Lucani
No full textLo studio dell’attività del Domenico di Bandino auctorista, interprete di classici “antichi” e “moderni” come Valerio Massimo, Lucano, la Rhetorica ad Herennium e Dante, è stato finora pressoché completamente oscurato a vantaggio di quello della sua opera di maggior respiro, la monumentale enciclopedia intitolata Fons memorabilium uniuersi. Questa edizione del commento dell’umanista aretino al Bellum Ciuile di Lucano, finora inedito, si propone di colmare tale rilevante lacuna: gettando uno sguardo nella classe stessa del magister – il testo ci è stato infatti tramandato nella forma di recollecte, appunti presi da uno studente a lezione – si riesce a far luce su molti aspetti di assoluto interesse relativi all’insegnamento di Lucano nel primo umanesimo, dalla forte continuità con l’esegesi medievale alla riscoperta e implementazione di testi come Tacito e i Commentarii cesariani, dall’introduzione di nuovi approcci interpretativi al rapporto con altre lecturae coeve.
L’edizione, corredata da un’ampia introduzione, un ricco apparato delle fonti e indici analitici, è inoltre il primo contributo ecdotico di vasto respiro alla scoliastica lucanea dopo il completamento della pubblicazione del Supplementum Adnotationum super Lucanum a cura di Giuseppe Angelo Cavajoni (1979-1990)
Recensione a V. D’Urso, Viuit post proelia Magnus. Commento a Lucano, Bellum Ciuile VIII
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Lucano, Bellum Civile VIII. Introduzione, testo, traduzione e commento
No full textIn the flourishing revival of commentaries on Lucan's Bellum Civile over the last two decades, Book VIII had not yet received an interest commensurate with its decisive importance in the overall structure of the poem: until now, the only comprehensive exegetical tools available to scholars remained those of J. P. Postgate (1917) and R. Mayer (1981), now inevitably dated.
This new commentary, accompanied by an extensive introduction, a critically revised text and a 'service' translation conceived as a first interpretive approach, aims to fill this gap not only by taking due account of the renewed scholarly debate on numerous aspects of Lucan's poem, but also by making the minute analysis of the text an opportunity to reconsider in a new light some traditional themes of Lucan's bibliography, from the relationship with historical sources to that with the practice of declamation, from the poet's political positions to his technical-scientific skills.
For these reasons, the volume is an opportunity for comparison and in-depth study not only for those who deal with Latin epics or the literature of the Neronian age, but also for historians and scholars of rhetoric
Machine Learning in clinical biology and medicine: from prediction of multidrug resistant infections in humans to pre-mRNA splicing control in Ciliates
Full text linkMachine Learning methods have broadly begun to infiltrate the clinical literature in
such a way that the correct use of algorithms and tools can facilitate both diagnosis and
therapies. The availability of large quantities of high-quality data could lead to an
improved understanding of risk factors in community and healthcare-acquired
infections. In the first part of my PhD program, I refined my skills in Machine Learning
by developing and evaluate with a real antibiotic stewardship dataset, a model useful
to predict multi-drugs resistant urinary tract infections after patient hospitalization9
.
For this purpose, I created an online platform called DSaaS specifically designed for
healthcare operators to train ML models (supervised learning algorithms). These
results are reported in Chapter 2.
In the second part of the PhD thesis (Chapter 3) I used my new skills to study the
genomic variants, in particular the phenomenon of intron splicing. One of the important
modes of pre-mRNA post-transcriptional modification is alternative intron splicing,
that includes intron retention (unsplicing), allowing the creation of many distinct
mature mRNA transcripts from a single gene. An accurate interpretation of genomic
variants is the backbone of genomic medicine. Determining for example the causative
variant in patients with Mendelian disorders facilitates both management and potential
downstream treatment of the patient’s condition, as well as providing peace of mind
and allowing more effective counselling for the wider family. Recent years have seen a surge in bioinformatics tools designed to predict variant
impact on splicing, and these offer an opportunity to circumvent many limitations of
RNA-seq based approaches. An increasing number of these tools rely on machine
learning computational approaches that can identify patterns in data and use this
knowledge to speculate on new data.
I optimized a pipeline to extract and classify introns from genomes and transcriptomes
and I classified them into retained (Ris) and constitutively spliced (CSIs) introns. I used
data from ciliates for the peculiar organization of their genomes (enriched of coding
sequences) and because they are unicellular organisms without cells differentiated into
tissues. That made easier the identification and the manipulation of introns. In
collaboration with the PhD colleague dr. Leonardo Vito, I analyzed these intronic
sequences in order to identify “features” to predict and to classify them by Machine
Learning algorithms. We also developed a platform useful to manipulate FASTA, gtf,
BED, etc. files produced by the pipeline tools. I named the platform: Biounicam (intron
extraction tools) available at http://46.23.201.244:1880/ui.
The major objective of this study was to develop an accurate machine-learning model
that can predict whether an intron will be retained or not, to understand the key-features
involved in the intron retention mechanism, and provide insight on the factors that drive
IR. Once the model has been developed, the final step of my PhD work will be to
expand the platform with different machine learning algorithms to better predict the
retention and to test new features that drive this phenomenon. These features hopefully
will contribute to find new mechanisms that controls intron splicing. The other additional papers and patents I published during my PhD program are in
Appendix B and C. These works have enriched me with many useful techniques for
future works and ranged from microbiology to classical statistics
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