8 research outputs found
Biblioteche della formazione per la pubblica amministrazione a Roma
No full textTraining libraries must and can follow a known path and be an active part of the changing process, offering a clear and useful contribution to change their status from training libraries to libraries for training
eGLU 1.0. Protocollo per l'esplorazione dei siti web delle Pubbliche Amministrazioni
No full textIl protocollo eGLU 1.0 consiste in una procedura passo-passo che mira a fornire alle Pubbliche Amministrazioni una guida per l’analisi esplorativa delle interfacce dei siti web.
Si rivolge principalmente alle redazioni dei siti delle PA e non presuppone conoscenze specialistiche. L’utilizzo del protocollo consente, mediante prove con utenti svolte a diversi gradi di analisi, di valutare in via preliminare gli elementi che incidono sulla semplicità d’uso, la facilità di comprensione e la soddisfazione da parte degli utenti delle interfacce web
Se non li trovi... non li riusi! Appunti sulla catalogazione dei learning object
No full textLearning objects need an indexation process and some metadata to be reusable. This presentation examines LOM and DC metadata schemes for learning object retrieval
Censimento sullo stato delle carriere all'interno delle biblioteche dell'Amministrazione centrale
No full textThe article is the result of one surveying on the situation of the careers of the librarians of the libraries of Italian Public Administration
Il Protocollo eGLU 2.1. Il Protocollo eGLU-M. Come realizzare test di usabilità semplificati per i siti web e i servizi online delle PA. Glossario dell'usabilità
No full textL’attività del Gruppo di Lavoro per l’Usabilità (GLU) del Dipartimento Funzione Pubblica - che viene presentata nella duplice forma di un aggiornamento del protocollo per la realizzazione di test di usabilità semplificati eGLU 2.1 e della sua prima versione per mobile, il protocollo eGLU-M, unitamente al glossario dell’usabilità di WikiPA - è tutta dentro l’indirizzo emergente delle politiche pubbliche del digitale. Anzi, va detto che ne è da circa tre anni una originale, fertile e, per molti versi, esemplare anticipazione dal basso. Dopo tre anni di attività del GLU, i due strumenti vengono resi disponibili e possono essere utilizzati dagli operatori pubblici del web a partire da questo lavoro. Con l’avvertenza ai destinatari che il vero output che avranno prodotto organizzando test semplificati di usabilità, che si tratti della procedura eGLU 2.1 per desktop o eGLU-M per dispositivi mobili, non sarà solo un report con un elenco di problemi e criticità delle interfacce - che è pure il risultato previsto da chi le procedure le ha scritte - quanto anche le numerose esternalità positive, generate come effetti collaterali delle attività di testing. Su tutte le quali spicca l’effetto inaspettato di quella sorta di quantitative easing di insight prodotto dai test
Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome
No full textAbstract Background Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome. Methods Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained. Results We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p Conclusions The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.</p
Pioniere del video
No full textUna ricognizione sulle pioniere del video in Italia (anche con riferimenti al panorama internazionale) nel campo della ricerca, dell'organizzazione e della promozione delle arti elettronich
A large-scale study across the avian clade identifies ecological drivers of neophobia
No full textCopyright: \ua9 2025 Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Neophobia, or aversion to novelty, is important for adaptability and survival as it influences the ways in which animals navigate risk and interact with their environments. Across individuals, species and other taxonomic levels, neophobia is known to vary considerably, but our understanding of the wider ecological drivers of neophobia is hampered by a lack of comparative multispecies studies using standardized methods. Here, we utilized the ManyBirds Project, a Big Team Science large-scale collaborative open science framework, to pool efforts and resources of 129 collaborators at 77 institutions from 24 countries worldwide across six continents. We examined both difference scores (between novel object test and control conditions) and raw data of latency to touch familiar food in the presence (test) and absence (control) of a novel object among 1,439 subjects from 136 bird species across 25 taxonomic orders incorporating lab, field, and zoo sites. We first demonstrated that consistent differences in neophobia existed among individuals, among species, and among other taxonomic levels in our dataset, rejecting the null hypothesis that neophobia is highly plastic at all taxonomic levels with no evidence for evolutionary divergence. We then tested for effects of ecological factors on neophobia, including diet, sociality, habitat, and range, while accounting for phylogeny. We found that (i) species with more specialist diets were more neophobic than those with more generalist diets, providing support for the Neophobia Threshold Hypothesis; (ii) migratory species were also more neophobic than nonmigratory species, which supports the Dangerous Niche Hypothesis. Our study shows that the evolution of avian neophobia has been shaped by ecological drivers and demonstrates the potential of Big Team Science to advance our understanding of animal behavior
