University of Udine
Archivio istituzionale della ricerca - Università degli Studi di UdineNot a member yet
91472 research outputs found
Sort by
3D sound radiation reconstruction from camera measurements
Get PDFIn general, the measurement of the sound radiation field by machinery and partitions requires time-consuming tests, which should be carried out in specially dedicated anechoic/reverberant facilities with calibrated sensors and complex acquisition and post processing equipment. This article introduces a two-step method for the identification from optical measurements of the free-field sound radiation generated by flexural vibrations of closed shells. In the first step, the flexural vibration of the shell is reconstructed with a frequency domain triangulation technique based on short multi-view video acquisitions made with a single high-resolution, high-speed camera. In the second step, the free-field sound radiation is derived from a discretized boundary integral formulation. The study is focused on the identification of the sound radiation from the flexural vibration of a baffled cylinder model structure. The vibration and sound fields reconstructed from the camera measurements are validated against direct measurements taken with a laser scanner vibrometer and a microphone array, respectively. Overall, this research demonstrates that optical methods based on camera measurements can be suitably employed to produce fast and accurate full-field measurements of sound radiation of closed shells (without the need for a dedicated measurement environment, e.g. reverberant, anechoic chambers)
Innovative strategies to detect antibiotic resistance in food sector
No full textAntibiotic resistance is a rapidly growing global issue that poses significant threats to animal and human health. This Ph.D. thesis aimed to determine the presence of antibiotic-resistant bacteria in raw food materials and to develop a rapid and low-cost method for detecting antibiotic resistance genes. The research focuses on two key bacterial species, Staphylococcus aureus and Escherichia coli and addresses the same topics in parallel for both microorganisms, as major contributors to antibiotic resistance, selected due to their presence in hospitals and agri-food chain.
The bacteria were isolated from raw cow milk, raw pork and beef meat that can act as vehicles for transmission of resistant bacteria to humans. Currently, the most widely used method for identifying antibiotic resistance is the gold standard antibiotic susceptibility test which involves isolation of the microorganism and exposure to specific antibiotics. Based on this technique, S. aureus isolates showed high levels of resistance to ampicillin, cefoxitin and tetracycline, while E. coli isolates to ampicillin. Since the bacterial cell surface is the primary interface with the surrounding environment and human host cells, and alterations in surface properties can impact both antibiotic susceptibility and virulence, the relation between antibiotic resistance in S. aureus isolates, bacterial cell surface characteristics and the interaction with human Caco-2 intestinal epithelial cell line were investigated. Multi-drug-resistant S. aureus did not show toxicity against Caco-2, but modified their surface, such as reducing permeability to block antibiotic entry or altering their surface charge. These findings underscore the need for further research to fully understand the risks associated with foodborne S. aureus, particularly its capability to evade antibiotic treatment and adapt its surface properties. Although effective, these methods required several days to provide results and were costly. To improve the detection of resistant genes, molecular techniques were employed to identify the most prevalent genes responsible for methicillin-resistance (mecA gene), tetracycline-resistance (tetK gene) and β-lactam-resistance (blaTEM gene). PCR and multiplex PCR were applied to isolates’ DNAs for the detection of target genes providing results within a few hours. These methods require specialized laboratories and expert operators and often the time before obtaining results is still high, in fact, the first hours from the infection are the critical ones for identifying the presence of resistance and choosing the appropriate therapy. Biosensors offered a promising solution to reduce costs and speed up the analysis. These innovative, small-size devices combine a recognition element for detecting the target with an electronic component for signal transduction. Biosensors are versatile, compact, fast, easy to use and ideal for self-testing systems and in situ applications, providing a faster and more accessible approach to detect antibiotic resistance. In this project, DNA sequences of mecA, tetK and blaTEM genes were used as templates to design single-stranded DNA probes, which worked as the recognition element for the biosensor. The probes were evaluated in silico using AmplifX, OligoAnalyzer and BLAST (Basic Local Alignment Search Tool) programs for their specificity further confirmed through dot blot testing. These newly designed probes were successfully applied in an electrochemical genosensor, allowing the detection of mecA, tetK and blaTEM genes in 20 minutes on the isolates’ DNAs.
Optimization of both dot blot and biosensor protocols was crucial for improving their practical application, in fact, lastly, these methods were directly applied to the whole DNA extracted from food matrices with promising results on their effectiveness
The Functional Clustering of the Mortality Gender Gap: A Multi-Country Analysis
No full textDemographic literature is rich of empirical analyses showing how women have historically experienced lower mortality rates than men. In this paper, we consider a measure of the gender gap in mortality rates, the Gender Gap Ratio, across a wide range of populations collected in the Human Mortality Database. With the aim of highlighting similarities and differences between the countries considered, we apply a functional clustering method to the multivariate time series of Gender Gap. We reconstruct the functional form of the trends from the available discrete observations and derive the curves through non-parametric smoothing. Results for 65-years-old people from 1965 to 2014 are presented and discussed
AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images
Get PDFImportance: Artificial intelligence (AI) assistance in magnetic resonance imaging (MRI) assessment for prostate cancer shows promise for improving diagnostic accuracy but lacks large-scale observational evidence. Objective: To evaluate whether use of AI-assisted assessment for diagnosing clinically significant prostate cancer (csPCa) on MRI is superior to unassisted readings. Design, Setting, and Participants: This diagnostic study was conducted between March and July 2024 to compare unassisted and AI-assisted diagnostic performance using the AI system developed within the international Prostate Imaging-Cancer AI (PI-CAI) Consortium. The study involved 61 readers (34 experts and 27 nonexperts) from 53 centers across 17 countries. Readers assessed prostate magnetic resonance images both with and without AI assistance, providing Prostate Imaging Reporting and Data System (PI-RADS) annotations from 3 to 5 (higher PI-RADS indicated a higher likelihood of csPCa) and patient-level suspicion scores ranging from 0 to 100 (higher scores indicated a greater likelihood of harboring csPCa). Biparametric prostate MRI examinations were included for 780 men from the PI-CAI study who were included in the newly-conducted observer study. All men within the PI-CAI study had suspicion of harboring prostate cancer, sufficient diagnostic image quality, and no prior clinically significant cancer findings. Disease presence was defined by histopathology, and absence was determined by 3 or more years of follow-up. The AI system was recalibrated using 420 Dutch examinations to generate lesion-detection maps, with AI scores ranging from 1 to 10, in which 10 indicates the highest likelihood of csPCa. The remaining 360 examinations, originating from 3 Dutch centers and 1 Norwegian center, were included in the observer study. Main Outcomes and Measures: The primary outcome was diagnosis of csPCa, evaluated using the area under the receiver operating characteristic curve and sensitivity and specificity at a PI-RADS threshold of 3 or more. The secondary outcomes included analysis at alternate operating points and reader expertise. Results: Among the 360 examinations of 360 men (median age, 65 years [IQR, 62-70 years]) who were included for testing, 122 (34%) harbored csPCa. AI assistance was associated with significantly improved performance, achieving a 3.3% increase in the area under the receiver operating characteristic curve (95% CI, 1.8%-4.9%; P <.001), from 0.882 (95% CI, 0.854-0.910) in unassisted assessments to 0.916 (95% CI, 0.893-0.938) with AI assistance. Sensitivity improved by 2.5% (95% CI, 1.1%-3.9%; P <.001), from 94.3% (95% CI, 91.9%-96.7%) to 96.8% (95% CI, 95.2%-98.5%), and specificity increased by 3.4% (95% CI, 0.8%-6.0%; P =.01), from 46.7% (95% CI, 39.4%-54.0%) to 50.1% (95% CI, 42.5%-57.7%), at a PI-RADS score of 3 or more. Secondary analyses demonstrated similar performance improvements across alternate operating points and a greater benefit of AI assistance for nonexpert readers. Conclusions and Relevance: The findings of this diagnostic study of patients suspected of harboring prostate cancer suggest that AI assistance was associated with improved radiologic diagnosis of clinically significant disease. Further research is required to investigate the generalization of outcomes and effects on workflow improvement within prospective settings
Model-free kinematic control for robotic systems
No full textWe propose a new model-free approach for kinematic robot control, where both the model and its parameters are partially unknown, which is inspired by the model-free plant tuning framework. The proposed method only relies on the assumption that the relationship between control inputs and outputs is a smooth and static unknown function, whose partial derivatives have lower and upper bounds that are approximately known. Notably, our approach does not require a learning phase, and it is flexible enough to be applied to a wide class of robotic structures. To showcase the methodology, two distinct types of robotic challenges are considered: the control of a family of cable-driven parallel robots and the control of a tendon-driven soft robot. We devise a task-independent approach to synthesize controllers that enable robots to achieve their goals, with minimum prior knowledge of the nonlinear system. Experimental results are presented for the cable-driven parallel robot, while simulations are conducted for the soft robot case
Usefulness of a hub and spoke TDM-guided expert clinical pharmacological advice program of dalbavancin for optimizing very long-term curative or suppressive treatment of chronic staphylococcal infections
Get PDFA hub and spoke model for optimizing long-term treatment of chronic staphylococcal infections with dalbavancin based on therapeutic drug monitoring (TDM)-guided expert clinical pharmacological advice (ECPA) was implemented. This multicentric retrospective cohort study included patients receiving dalbavancin monotherapy lasting >6 weeks at different spoke hospitals having treatment optimized by means of a TDM-guided ECPA program at a hub hospital. Optimal pharmacokinetic/pharmacodynamic target against staphylococci with an MIC up to 0.125 mg/L was defined as dalbavancin concentrations >8.04 mg/L. Patients received dalbavancin therapy for curative (curative group) or suppressive (suppressive group) purposes. Clinical outcome was assessed by means of repeated ambulatory visits. A total of 12 spoke hospitals applied for 414 TDM-based ECPA for 101 patients, of whom 64.4% (65/101) were treated for curative and 35.6% (36/101) were for suppressive purposes. In the curative and suppressive groups, TDM-based ECPA optimized treatment for up to 14 and 28 months, respectively, and ensured median optimal exposure of 95.7% and 100%, respectively. In the curative group, having <70% of treatment time with concentrations above the optimal target increased failure risk [odds ratio (OR), 6.71; confidence interval (CI), 0.97–43.3; P = 0.05]. In the suppressive group, infective endocarditis was associated with an increased risk of ineffective treatment (OR, 8.65; CI, 1.29–57.62; P = 0.046). Mild adverse events were reported in 4.5% (5/101) of cases. A hub and spoke TDM-guided ECPA program of dalbavancin may be cost-effective for optimizing long-term treatment of chronic staphylococcal infections and for patients admitted to hospitals lacking in-house MD clinical pharmacologists
Lime-induced iron deficiency stimulates a stronger response in tolerant grapevine rootstocks compared to low iron availability
Get PDFIron (Fe) is abundant in soil, but its bioavailability can be limited by environmental factors, negatively impacting plant growth and productivity. While root mechanisms for enhancing Fe uptake are well-studied in some model plants, the responses of tolerant and susceptible grapevine rootstocks to low Fe availability remain poorly understood. This study examined the responses of two grapevine rootstocks, Fercal (tolerant) and 3309C (susceptible), to three Fe conditions: direct Fe deficiency (−Fe), induced Fe deficiency through the addition of bicarbonate (+Fe+BIC), and control (+Fe). Our main findings include: 1) more severe leaf symptoms in 3309C than in Fercal independent of the type of stress, 2) overall growth reduction due to direct Fe deficiency (−Fe), while under induced Fe deficiency (+Fe+BIC) Fercal strongly increased root biomass. This observation is supported by the increased expression of root-development related genes VviSAUR66 and VviZAT6, 3) enhanced organic acid contents under induced Fe deficiency (+Fe+BIC) and different organic acids profiles depending on applied stress and genotype, and 4) stronger modulation of gene expression in Fercal root tips, including enhanced expression of Fe mobilization and transport genes (VviOPT3, VviIREG3, VviZIF1). Overall, bicarbonate-induced Fe deficiency (+Fe+BIC) had greater negative effects than direct Fe deficiency (−Fe), with Fercal showing a higher adaptive capability to maintain Fe homeostasis
Real-Time Anomaly Detection in Docker Containers: A Continuous Learning Approach Using SF-SOINN
No full textAs cyber threats are becoming more sophisticated than ever with the rapid expansion of internet-connected systems and increased use of containerized environments, we present Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), a novel approach to unsupervised anomaly detection in containerized platforms. Whereas traditional Intrusion Detection Systems (IDS) utilize supervised learning that uses known attack signatures for training, SF-SOINN employs a continuous learning approach to adapt dynamically to new data patterns, thereby eliminating the need for labeled datasets. This capability enables effective real-time detection of zero-day threats in dynamic environments. SF-SOINN have demonstrated efficacy in identifying malicious attacks on the real-world NSL-KDD dataset, and we extended its application to containerized environments, using the KubAnomaly framework. Our benchmark results reveal that SF-SOINN outperforms traditional supervised models like Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and unsupervised KubAnomaly, particularly in scenarios involving complex attacks. The performance metric considered here focused on the optimization of False Positive Rate (FPR), while balancing other key performance metrics like accuracy, recall, and precision to achieve best results - and we anticipate this approach will lay a strong foundation for developing robust anomaly IDS in future
Production and characterization of bioaerogel particles from strawberries and their application as oil structuring ingredient in low-saturated fat cocoa spreads
Get PDFBioaerogels bear high potential in the development of fat-replacers, due to their oil-structuring capacity. However, current aerogel preparation from biopolymeric gels requires a complex and resource-intensive process, which might limit their adoption as oil-structuring food ingredients. A simpler and more sustainable process to produce bioaerogels could be based on their direct preparation from plant tissues rather than from biopolymeric gels. Similar to gels, also in plant tissues, water is embedded into a fibrous network, so water removal while preserving tissue structure can lead to porous materials with bioaerogel properties, avoiding biopolymer extraction, purification and sol-gel steps. This work aimed to demonstrate the potential of tissue-derived aerogels as fat-replacement ingredients in cocoa spreads. To this aim, strawberry pulp was subjected to water-to-ethanol exchange, wet milling, and supercritical-CO2-drying. This process resulted in bioaerogel particles with high mesopore volume (0.69 cm3 g−1), low density (0.03 g cm−3) and high surface area (233 m2 g−1). The particles showed an oil absorption capacity higher than 90%, leading to a self-standing material retaining 80% oil upon centrifugation. Strawberry bioaerogel particles were used to formulate low-saturated fat cocoa spreads. A bioaerogel particle amount as low as 0.2–0.4 g/100 gspread was enough to obtain spreads covering a wide range of rheological and spreadability properties. Preliminary techno-economic assessment demonstrated the technical and economic feasibility of the proposed process to produce bioaerogels from plant tissues intended as fat-replacement ingredients