Giovan Battista Pigna, Gli Heroici

Scomparso dagli interessi di letterati e studiosi per quasi cinque secoli, il trattato Gli Heroici di Giovan Battista Pigna torna ora in edizione critica, per le cure di Marco De Masi e Stefano Jossa, per rimettere in circolazione una riflessione sulla poesia narrativa e una modalità di costruzione dell'egemonia culturale che segnò il passaggio dallo scrittore di corte al segretario del principe ed ebbe un'influenza decisiva sulla formazione di Torquato Tasso

A point process approach for the classification of noisy calcium imaging data

We study noisy calcium imaging data, with a focus on the classification of spike traces. As raw traces obscure the true temporal structure of neuron’s activity, we performed a tuned filtering of the calcium concentration using two methods: a biophysical model and a kernel mapping. The former characterizes spike trains related to a particular triggering event, while the latter filters out the signal and refines the selection of the underlying neuronal response. Transitioning from traditional time series analysis to point process theory, the study explores spike-time distance metrics and point pattern prototypes to describe repeated observations. We assume that the analyzed neuron’s firing events, i.e. spike occurrences, are temporal point process events. In particular, the study aims to categorize 47 point patterns by depth, assuming the similarity of spike occurrences within specific depth categories. The results highlight the pivotal roles of depth and stimuli in discerning diverse temporal structures of neuron firing events, confirming the point process approach based on prototype analysis is largely useful in the classification of spike traces

Explainable screening of oral cancer via deep learning and case-based reasoning

Oral Squamous Cell Carcinoma is characterized by significant mortality and morbidity. Dental professionals can play an important role in its early detection, thanks to the availability of embedded smart cameras for oral photos and remote screening supported by Deep Learning (DL). Despite the promising results of DL for automated detection and classification of oral lesions, its effectiveness is based on a clearly defined protocol, on the explainability of results, and on periodic cases collection. This paper proposes a novel method, combining DL and Case-Based Reasoning (CBR), to allow the post-hoc explanation of the system answer. The method uses explainability tools organized in a protocol defined in the Business Process Model and Notation (BPMN) to allow its experimental validation. A redesign of the Faster-R-CNN Feature Pyramid Networks (FPN) + DL architecture is also proposed for lesions detection and classification, fine-tuned on 160 cases belonging to three classes of oral ulcers. The DL system achieves state-of-the-art performance, i.e., 83% detection and 92% classification rate (98% for neoplastic vs. non-neoplastic binary classification). A preliminary experimentation of the protocol involved both resident and specialized doctors over selected difficult cases. The system and cases have been publicly released to foster collaboration between research centers

Shaping the emission directivity of single quantum dots in dielectric nanodisks exploiting Mie resonances

Manipulating the optical landscape of single quantum dots (QDs) is essential to increase the emitted photon output, enhancing their performance as chemical sensors and single-photon sources. Micro-optical structures are typically used for this task, with the drawback of a large size compared to the embedded single emitters. Nanophotonic architectures hold the promise to modify dramatically the emission properties of QDs, boosting light−matter interactions at the nanoscale, in ultracompact devices. Here, we investigate the interplay between gallium arsenide (GaAs) single QDs and aluminum gallium arsenide (AlGaAs) nanostructures, capitalizing on the Kerker condition for precise control of the QD emission directivity. An extensive analysis of the photo- luminescence spectra of several QDs embedded in nanodisks revealed a pronounced directivity enhancement due to the Kerker effect, confirmed by theoretical simulations, resulting in a 14-fold increase of emitted intensity. Angle-resolved spectroscopy experiments also proved that the integration of GaAs QDs within nanostructures determines a precise angled emission, offering a distinctive avenue for manipulating the spatial characteristics of emitted light by exploiting Mie resonances. This work contributes to the optimization of QD integration in nanostructures and suggests potential improvements for applications in optical communications

A novel approach to investigate severe asthma and COPD: the 3d ex vivo respiratory mucosa model

Purpose: This article illustrates the replication of asthma and COPD conditions in a laboratory setting and the potential applications of this methodology. Introduction: Biologic drugs have been shown to enhance the treatment of severe asthma and COPD. Monoclonal antibodies against specific targets have dramatically changed the management of these conditions. Although the inflammatory pathways of asthma and COPD have already been clearly outlined, alternative mechanisms of action remain mostly unexplored. They could provide additional insights into these diseases and their clinical management. Aims:In vivo or in vitro models have thus been developed to test alternative hypotheses. This study describes sophisticated ex vivo models that mimic the response of human respiratory mucosa to disease triggers, aiming to narrow the gap between laboratory studies and clinical practice. Results: These models successfully replicate crucial aspects of these diseases, such as inflammatory cell presence, cytokine production, and changes in tissue structure, offering a dynamic platform for investigating disease processes and evaluating potential treatments, such as monoclonal antibodies. The proposed models have the potential to enhance personalized medicine approaches and patient-specific treatments, helping to advance the understanding and management of respiratory diseases

A modified robustness index for assessing operational performance of drinking water treatment plants: a comparative study within a new regulatory framework

Drinking water treatment plants (DWTPs) are facing emerging challenges affecting raw water quality. In addition, the new regulatory framework (EU 2184/2020) sets stricter limits for turbidity and percentile statistics for continuous compliance, demanding greater robustness of the treatment processes. To achieve this aim, this study proposes a turbidity robustness index (TRI), named TRI95B, to be used as a warning tool for detecting deviations from water quality standards. TRI95B has been compared with the TRIs existing in the literature. Furthermore, the TRI95B validation has been performed by a three-year monitoring dataset of a full-scale DWTP. The proposed TRI95B index has two key novelties compared to the existing indices required for adapting to the new drinking water regulation: i. introduces the 95th percentile as a statistical indicator; ii. considers an additional term that sets an alert when a threshold value is exceeded. The comparison results suggest a better correspondence to the real plant performances of TRI95B than the other TRIs. Indeed, both the sensitivity and specificity of TRI95B were significantly higher than the other TRIs, indicating a better capacity to correctly classify both positive and negative cases. Moreover, while the previous TRIs identify a critical operating condition when the turbidity goal was significantly exceeded, TRI95B highlights a failure condition at a lower discrepancy. Therefore, TRI95B is also able to identify short-duration and low magnitude failures, thus coping with the purpose of the new regulation for drinking water

Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics

Background and Objective: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. Methods: This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN-derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methods based on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Results: Using a breast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: (i) CNN-derived features guarantee more robust accuracy when compared against ViT-derived and radiomic features; (ii) conventional visualization map methods for explanation present several pitfalls; (iii) Rad4XCNN does not sacrifice model accuracy for their explainability; (iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings. Conclusions: Our method can mitigate some concerns related to the explainability-accuracy trade-off. This study highlighted the importance of proposing new methods for model explanation without affecting their accuracy