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2,070 results on '"Schirinzi, A."'

1. Assessment of Spectral based Solutions for the Detection of Floating Marine Debris

Author

Alì, Muhammad, Razzano, Francesca, Vitale, Sergio, Ferraioli, Giampaolo, Pascazio, Vito, Schirinzi, Gilda, and Ullo, Silvia

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation., Comment: 5 pages, 3 figures, submitted and accepted for 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)

Published
2024

5. Psychometric properties and clinical correlates of the Frontal Behaviour Inventory in progressive supranuclear palsy: data from the PSP-NET

Author

Cappiello, Arianna, Cuoco, Sofia, De Micco, Rosa, Satolli, Sara, Di Biasio, Francesca, Markushi, Tiziana Benzi, Sambati, Luisa, Pilotto, Andrea, Costanzo, Matteo, Longo, Chiara, Schirinzi, Tommaso, Del Prete, Eleonora, Frosini, Daniela, Stefani, Alessandro, Malaguti, Maria Chiara, Fabbrini, Giovanni, Padovani, Alessandro, Calandra-Buonaura, Giovanna, Marchese, Roberta, Tessitore, Alessandro, Barone, Paolo, and Picillo, Marina

Published
2024
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7. AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission

Author

Razzano, Francesca, Di Stasio, Pietro, Mauro, Francesco, Meoni, Gabriele, Esposito, Marco, Schirinzi, Gilda, and Ullo, Silvia L.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing. The objective is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature. Moreover, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. The specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency (ESA) OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants monitoring on the Phisat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are discussed and in progress and future work introduced., Comment: 11 pages, 9 figures, submitted to IEEE JSTARS

Published
2024

8. CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data

Author

Yang, Wenyu, Vitale, Sergio, Aghababaei, Hossein, Ferraioli, Giampaolo, Pascazio, Vito, and Schirinzi, Gilda

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tropical forests are a key component of the global carbon cycle. With plans for upcoming space-borne missions like BIOMASS to monitor forestry, several airborne missions, including TropiSAR and AfriSAR campaigns, have been successfully launched and experimented. Typical Synthetic Aperture Radar Tomography (TomoSAR) methods involve complex models with low accuracy and high computation costs. In recent years, deep learning methods have also gained attention in the TomoSAR framework, showing interesting performance. Recently, a solution based on a fully connected Tomographic Neural Network (TSNN) has demonstrated its effectiveness in accurately estimating forest and ground heights by exploiting the pixel-wise elements of the covariance matrix derived from TomoSAR data. This work instead goes beyond the pixel-wise approach to define a context-aware deep learning-based solution named CATSNet. A convolutional neural network is considered to leverage patch-based information and extract features from a neighborhood rather than focus on a single pixel. The training is conducted by considering TomoSAR data as the input and Light Detection and Ranging (LiDAR) values as the ground truth. The experimental results show striking advantages in both performance and generalization ability by leveraging context information within Multiple Baselines (MB) TomoSAR data across different polarimetric modalities, surpassing existing techniques., Comment: Submitted to IEEE TGRS, under review

Published
2024

12. Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data

Author

Razzano, Francesca, Mauro, Francesco, Di Stasio, Pietro, Meoni, Gabriele, Esposito, Marco, Schirinzi, Gilda, and Ullo, Silvia Liberata

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting water quality. Its accurate assessment is crucial for safeguarding ecosystems and human consumption, demanding meticulous attention and action. For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A. Traditional methods are labor-intensive while CatBoost offers an efficient solution, excelling in predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data through the Google Earth Engine (GEE), our study contributes to scalable and precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong contaminants monitoring stations enriches our study, providing region-specific insights. Results showcase the viability of this integrated approach, laying the foundation for adopting advanced techniques in global water quality management., Comment: 4 pages, 3 figures, IGARSS2024

Published
2024

13. Safety and efficacy of tixagevimab/cilgavimab for pre-exposure prophylaxis in kidney transplant recipients: a multicenter retrospective cohort study

Author

Simone, Simona, Pronzo, Virginia, Pesce, Francesco, Bavaro, Davide Fiore, Infante, Barbara, Mercuri, Silvia, Schirinzi, Annalisa, Panaro, Antonella, Conte, Eleonora, Belati, Alessandra, Troise, Dario, Pontrelli, Paola, Conserva, Francesca, Gallo, Pasquale, Panico, Maddalena, Spilotros, Marco, Lucarelli, Giuseppe, Saracino, Annalisa, Stallone, Giovanni, Di Serio, Francesca, Ditonno, Pasquale, and Gesualdo, Loreto

Published
2024
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16. Sex hormones shape EEG-based functional connectivity in early-stage Parkinson’s disease patients

Author

Matteo Conti, Roberta Bovenzi, Mariangela Pierantozzi, Clara Simonetta, Valerio Ferrari, Jacopo Bissacco, Rocco Cerroni, Claudio Liguori, Francesca Di Giuliano, Nicola Biagio Mercuri, Tommaso Schirinzi, and Alessandro Stefani

Subjects
Parkinson’s disease, Functional connectivity, Resting-state network, HD-EEG, Sex hormones, Sex dimorphism, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Parkinson’s disease (PD) epidemiology and clinical features are sexually dimorphic. However, there are no data based on EEG functional connectivity (FC). Likewise, the contribution of sex hormones on brain FC has never been evaluated. Here, we aimed to investigate the association between biological sex and sex hormones on cortical FC changes in PD using high-density EEG. This study involved 69 early-stage PD patients (F/M 27/42) and 69 age-matched healthy controls (HC) (F/M 30/39). Sex hormone levels (total-testosterone (TT), estradiol (E2), follicle-stimulating hormone (FSH), and luteinizing hormone (LH)) were assessed in PD patients. Data were recorded with a 64-channel EEG system. Source reconstruction method was used to identify brain activity. Cortico-cortical FC was analysed based on the weighted phase-lag index (wPLI) in θ-α-β-low γ bands. Network-based statistic (NBS) was used to compare FC between genders in HC and PD and to study the relationship between FC and sex hormones in PD. PD exhibited a hypoconnected network at θ and α bands and a hyperconnected network at β band compared to HC. Male HC showed a hyperconnected network at α-band compared to female HC. Conversely, males with PD showed a hypoconnected network at α-band compared to females with PD. In females and males with PD, E2 positively correlated with α-FC, while gonadotropins positively correlated with β-FC. TT positively correlated with the θ-FC only in males. Sex hormones shape EEG-FC in both males and females with PD, supporting their major influence on PD pathophysiology.

Published
2025
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17. Does thyroid diseases contribute to the natural history of idiopathic adult-onset dystonia? Data from the Italian Dystonia Registry

Author

Idrissi, Sarah, Velucci, Vittorio, Esposito, Marcello, Trinchillo, Assunta, Habestwallner, Francesco, Belvisi, Daniele, Fabbrini, Giovanni, Ferrazzano, Gina, Rizzo, Vincenzo, Terranova, Carmen, Girlanda, Paolo, Pellicciari, Roberta, Avanzino, Laura, Di Biasio, Francesca, Marchese, Roberta, Bono, Francesco, Idone, Giovanni, Laterza, Vincenzo, Lettieri, Christian, Rinaldo, Sara, Eleopra, Roberto, Castagna, Anna, Altavista, Maria Concetta, Moschella, Vincenzo, Erro, Roberto, Barone, Paolo, Barbero, Pierangelo, Ceravolo, Roberto, Mazzucchi, Sonia, Mascia, Marcello Mario, Ercoli, Tommaso, Muroni, Antonella, Zibetti, Maurizio, Lopiano, Leonardo, Scaglione, Cesa Lorella Maria, Bentivoglio, Anna Rita, Petracca, Martina, Magistrelli, Luca, Cotelli, Maria Sofia, Cossu, Giovanni, Squintani, Giovanna Maddalena, De Santis, Tiziana, Schirinzi, Tommaso, Misceo, Salvatore, Pisani, Antonio, Berardelli, Alfredo, and Defazio, Giovanni

Published
2024
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32. Stage-dependent phosphoproteome remodeling of Parkinson's disease blood cells

Author

Giorgia Massacci, Veronica Venafra, Maximilian Zwiebel, Maria Wahle, Rocco Cerroni, Jacopo Bissacco, Livia Perfetto, Vito Michienzi, Alessandro Stefani, Nicola Biagio Mercuri, Tommaso Schirinzi, and Francesca Sacco

Subjects
Parkinson's disease, Peripheral blood, Phosphoproteomic, Biomarker, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The complexity and heterogeneity of PD necessitate advanced diagnostic and prognostic tools to elucidate its molecular mechanisms accurately. In this study, we addressed this challenge by conducting a pilot phospho-proteomic analysis of peripheral blood mononuclear cells (PBMCs) from idiopathic PD patients at varying disease stages to delineate the functional alterations occurring in these cells throughout the disease course and identify key molecules and pathways contributing to PD progression. By integrating clinical data with phospho-proteomic profiles across various PD stages, we identify potential stage-specific molecular signatures indicative of disease progression. This integrative approach allows for the discernment of distinct disease states and enhances our understanding of PD heterogeneity.

Published
2024
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33. Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis

Author

Shahab Ul Islam, Shahab Zaib, Giampaolo Ferraioli, Vito Pascazio, Gilda Schirinzi, and Ghassan Husnain

Subjects
plant disease, tomato, convolutional neural network, machine learning, deep learning, Agriculture (General), S1-972, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.

Published
2024
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36. Multidisciplinary care use in neurodegenerative complex diseases: The example of progressive supranuclear palsy and advanced Parkinson's disease in real-life

Author

Serio, Marina, Zibetti, Maurizio, Bissacco, Jacopo, Satolli, Sara, Sambati, Luisa, Bacchin, Ruggero, Longo, Chiara, Marchese, Roberta, Panetta, Salvatore, Magistrelli, Luca, Contaldi, Elena, Moschella, Vincenzo, Altavista, Maria Concetta, Costanzo, Matteo, Fabbrini, Giovanni, Olivola, Enrica, Modugno, Nicola, Cincotta, Massimo, Piccininni, Maristella, Capecci, Marianna, Baldini, Nicolò, Ciammola, Andrea, Ticozzi, Nicola, Raffaella, Di Giacopo, De Togni, Laura, Sala, Francesca, Nicoletti, Alessandra, Cicero, Edoardo, Ceravolo, Roberto, Del Prete, Eleonora, Fabbri, Margherita, Ledda, Claudia, Schirinzi, Tommaso, Artusi, Carlo Alberto, Avallone, Anna Rosa, Zenuni, Henri, De Micco, Rosa, Aloisio, Simone, Cani, Ilaria, Malaguti, Maria Chiara, Di Biasio, Francesca, Calandra-Buonaura, Giovanna, Stefani, Alessandro, Lopiano, Leonardo, Barone, Paolo, and Picillo, Marina

Published
2024
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41. SAR interferometry

Author

Fornaro, Gianfranco, primary, Schirinzi, Gilda, additional, Pauciullo, Antonio, additional, Pascazio, Vito, additional, and Reale, Diego, additional

Published
2024
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43. 2D SAR focusing

Author

Fornaro, Gianfranco, primary, Schirinzi, Gilda, additional, Pauciullo, Antonio, additional, Pascazio, Vito, additional, and Reale, Diego, additional

Published
2024
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44. SAR tomography

Author

Fornaro, Gianfranco, primary, Schirinzi, Gilda, additional, Pauciullo, Antonio, additional, Pascazio, Vito, additional, and Reale, Diego, additional

Published
2024
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45. Introduction

Author

Fornaro, Gianfranco, primary, Schirinzi, Gilda, additional, Pauciullo, Antonio, additional, Pascazio, Vito, additional, and Reale, Diego, additional

Published
2024
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47. Scene characterization

Author

Fornaro, Gianfranco, primary, Schirinzi, Gilda, additional, Pauciullo, Antonio, additional, Pascazio, Vito, additional, and Reale, Diego, additional

Published
2024
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48. Beneficial effects of recombinant CER-001 high-density lipoprotein infusion in sepsis: results from a bench to bedside translational research project

Author

Alessandra Stasi, Marco Fiorentino, Rossana Franzin, Francesco Staffieri, Sabrina Carparelli, Rosa Losapio, Alberto Crovace, Luca Lacitignola, Maria Teresa Cimmarusti, Francesco Murgolo, Monica Stufano, Cesira Cafiero, Giuseppe Castellano, Fabio Sallustio, Chiara Ferrari, Mario Ribezzi, Nicola Brienza, Annalisa Schirinzi, Francesca Di Serio, Salvatore Grasso, Paola Pontrelli, Cyrille Tupin, Ronald Barbaras, Constance Keyserling-Peyrottes, Antonio Crovace, and Loreto Gesualdo

Subjects
ApoA-I complexes, Sepsis, Multi-organ dysfunction, Cytokine storm, Medicine
Abstract

Abstract Background Sepsis is characterized by a dysregulated immune response and metabolic alterations, including decreased high-density lipoprotein cholesterol (HDL-C) levels. HDL exhibits beneficial properties, such as lipopolysaccharides (LPS) scavenging, exerting anti-inflammatory effects and providing endothelial protection. We investigated the effects of CER-001, an engineered HDL-mimetic, in a swine model of LPS-induced acute kidney injury (AKI) and a Phase 2a clinical trial, aiming to better understand its molecular basis in systemic inflammation and renal function. Methods We carried out a translational approach to study the effects of HDL administration on sepsis. Sterile systemic inflammation was induced in pigs by LPS infusion. Animals were randomized into LPS (n = 6), CER20 (single dose of CER-001 20 mg/kg; n = 6), and CER20 × 2 (two doses of CER-001 20 mg/kg; n = 6) groups. Survival rate, endothelial dysfunction biomarkers, pro-inflammatory mediators, LPS, and apolipoprotein A-I (ApoA-I) levels were assessed. Renal and liver histology and biochemistry were analyzed. Subsequently, we performed an open-label, randomized, dose-ranging (Phase 2a) study included 20 patients with sepsis due to intra-abdominal infection or urosepsis, randomized into Group A (conventional treatment, n = 5), Group B (CER-001 5 mg/kg BID, n = 5), Group C (CER-001 10 mg/kg BID, n = 5), and Group D (CER-001 20 mg/kg BID, n = 5). Primary outcomes were safety and efficacy in preventing AKI onset and severity; secondary outcomes include changes in inflammatory and endothelial dysfunction markers. Results CER-001 increased median survival, reduced inflammatory mediators, complement activation, and endothelial dysfunction in endotoxemic pigs. It enhanced LPS elimination through the bile and preserved liver and renal parenchyma. In the clinical study, CER-001 was well-tolerated with no serious adverse events related to study treatment. Rapid ApoA-I normalization was associated with enhanced LPS removal and immunomodulation with improvement of clinical outcomes, independently of the type and gravity of the sepsis. CER-001-treated patients had reduced risk for the onset and progression to severe AKI (stage 2 or 3) and, in a subset of critically ill patients, a reduced need for organ support and shorter ICU length of stay. Conclusions CER-001 shows promise as a therapeutic strategy for sepsis management, improving outcomes and mitigating inflammation and organ damage. Trial registration The study was approved by the Agenzia Italiana del Farmaco (AIFA) and by the Local Ethic Committee (N° EUDRACT 2020–004202-60, Protocol CER-001- SEP_AKI_01) and was added to the EU Clinical Trials Register on January 13, 2021.

Published
2023
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