Framing right-wing populist satire: the case-study of Ghisberto's cartoons in Italy

Over the last few years, right-wing populism has increased its popularity and political weight, successfully merging with Euro-scepticism, nationalism, xenophobia, religious symbolism, and aggressive forms of conservatism (e.g., anti-feminism, homophobia, and, in general, patriarchal politics). Several studies have focused on the communication strategies of contemporary populism, examining the latter’s use of traditional and new media. So far, however, little attention has been paid to the role and language of right-wing populist satire. Our study draws on the ideational approach to populism to explore how right-wing populism is expressed in satirical cartoons. This approach perceives populism as a thin-centered ideology, based on a Manichean division between ‘good people’ and ‘evil elites,’ which regularly combines with other ideological components (e.g., nationalism, Euroscepticism, xenophobia). Our analysis focuses on the Italian cartoonist Ghisberto, known for his provocative and frequently controversial work. We examine a sample of Ghisberto’s vignettes using multimodal analysis tools and Greimas’s notion of isotopy. The aim is to investigate how right-wing populist satire constructs its different targets (the EU, left-wingers, migrants, NGOs, women, etc.) and how populist ideology exploits cartoons’ communicative resources and power

An experimental Critical Multimodal Discourse Study to the AI-driven sentiment analysis of online crisis communication

In response to the challenges of crisis management and communication in business digital scenarios (Umar et al. 2022; Catenaccio 2021), this case study presents an example of the evolution and implications of online crisis communication and discursive practices that combine human input and AI for the management of crisis events and trust repair.Reliability of communication in web-based business scenarios cannot be easily achieved because of the huge amount of data. In addition, reputation management and trust are constantly put under threat by misinformation and misunderstanding (Garzone, Giordano 2018). With a view to countering these risks, companies are increasingly outsourcing digital marketing services (Palttala et al. 2012) based on AI methods to analyse consumers’ online needs and behaviour (Schwaiger et al. 2021), even though research has recently raised some concerns on the over-reliance on AI-based tools (Tam, Kim 2019).We intend to show how multimodal critical discourse studies (Djonov, Zhao 2014) can help identify and understand the potentials and limits of social media listening tools that are designed for crisis prediction and management (van Zoonen, van der Meer 2015). To do so, we present a case study that we engaged with during our research traineeship at Digital Trails, a B2B company dealing with online visibility, digital marketing and reputation analysis.In reporting the outcomes of online reputation analysis to gauge possible damages after a crisis event, we present the comparison between the AI-driven sentiment analysis (conducted via Meltwater) and the human-based revision and fine-tuning of AI-driven sentiment analysis. Our aim is to discuss the potentials and the criticalities of AI-driven sentiment detection. Among the latter, we highlight the unrecognizability of languages other than English, and the flaw in interpreting pragmatic aspects, as well as multimodal digital artefacts. Consistent with our findings, we argue that AI models, based on a unimodal and decontextualized architecture still require human validation. We conclude by indicating research directions for the detection of sentiment polarity, which include higher collaboration between IT developers and multimodal discourse analysts so that multimodally-informed models can assist crisis communication and management more efficiently

Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images

The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis

Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising

We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broadband red-shifted Stokes pulses, covering the whole fingerprint region (400-1800 cm-1), employing supercontinuum generation in a bulk crystal. Our system reaches state-of-the-art acquisition speed (<1 ms/pixel) and unprecedented sensitivity of ≈14.1 mmol/L when detecting dimethyl sulfoxide in water. To further improve the performance of the system and to enhance the signal-to-noise ratio of the CARS spectra, we designed a convolutional neural network for spectral denoising, coupled with a post-processing pipeline to distinguish different chemical species of biological tissues

EMI and Beyond

This collection presents the state of the art on English-medium instruction (EMI) / Integrating content and language (ICL) in Italian higher education, drawing attention to different critical aspects of the teaching/learning experience and highlighting the perspectives of various educational stakeholders regarding the effectiveness of tertiary study in a foreign language. The chapters draw on a range of methodologies, from multimodal participant observation, to action research, to video-stimulated recall (VSR), to questionnaires and interviews, in examining language policies and practices across various educational settings. Overall, the volume suggests that internationalisation succeeds best when the form of lessons (language) and the content of lessons (disciplinary concepts) are constructively aligned in curriculum planning and delivery. This integration process requires the strategic support of educators to guarantee the quality of learning in multilingual education.

Noninvasive morpho-molecular imaging reveals early therapy-induced senescence in human cancer cells

Anticancer therapy screening in vitro identifies additional treatments and improves clinical outcomes. Systematically, although most tested cells respond to cues with apoptosis, an appreciable portion enters a senescent state, a critical condition potentially driving tumor resistance and relapse. Conventional screening protocols would strongly benefit from prompt identification and monitoring of therapy-induced senescent (TIS) cells in their native form. We combined complementary all-optical, label-free, and quantitative microscopy techniques, based on coherent Raman scattering, multiphoton absorption, and interferometry, to explore the early onset and progression of this phenotype, which has been understudied in unperturbed conditions. We identified TIS manifestations as early as 24 hours following treatment, consisting of substantial mitochondrial rearrangement and increase of volume and dry mass, followed by accumulation of lipid vesicles starting at 72 hours. This work holds the potential to affect anticancer treatment research, by offering a label-free, rapid, and accurate method to identify initial TIS in tumor cells

EMI and Beyond

This collection presents the state of the art on English-medium instruction (EMI) / Integrating content and language (ICL) in Italian higher education, drawing attention to different critical aspects of the teaching/learning experience and highlighting the perspectives of various educational stakeholders regarding the effectiveness of tertiary study in a foreign language. The chapters draw on a range of methodologies, from multimodal participant observation, to action research, to video-stimulated recall (VSR), to questionnaires and interviews, in examining language policies and practices across various educational settings. Overall, the volume suggests that internationalisation succeeds best when the form of lessons (language) and the content of lessons (disciplinary concepts) are constructively aligned in curriculum planning and delivery. This integration process requires the strategic support of educators to guarantee the quality of learning in multilingual education.

Full-Spectrum CARS Microscopy of Cells and Tissues with Ultrashort White-Light Continuum Pulses

Coherent anti-StokesRaman scattering (CARS) microscopyis an emergingnonlinear vibrational imaging technique that delivers label-free chemicalmaps of cells and tissues. In narrowband CARS, two spatiotemporallysuperimposed picosecond pulses, pump and Stokes, illuminate the sampleto interrogate a single vibrational mode. Broadband CARS (BCARS) combinesnarrowband pump pulses with broadband Stokes pulses to record broadvibrational spectra. Despite recent technological advancements, BCARSmicroscopes still struggle to image biological samples over the entireRaman-active region (400-3100 cm(-1)). Here,we demonstrate a robust BCARS platform that answers this need. Oursystem is based on a femtosecond ytterbium laser at a 1035 nm wavelengthand a 2 MHz repetition rate, which delivers high-energy pulses usedto produce broadband Stokes pulses by white-light continuum generationin a bulk YAG crystal. Combining such pulses, pre-compressed to sub-20fs duration, with narrowband pump pulses, we generate a CARS signalwith a high (<9 cm(-1)) spectral resolution inthe whole Raman-active window, exploiting both the two-color and three-colorexcitation mechanisms. Aided by an innovative post-processing pipeline,our microscope allows us to perform high-speed (approximate to 1 ms pixeldwell time) imaging over a large field of view, identifying the mainchemical compounds in cancer cells and discriminating tumorous fromhealthy regions in liver slices of mouse models, paving the way forapplications in histopathological settings

Label-free multimodal nonlinear optical microscopy reveals features of bone composition in pathophysiological conditions

Bone tissue features a complex microarchitecture and biomolecular composition, which determine biomechanical properties. In addition to state-of-the-art technologies, innovative optical approaches allowing the characterization of the bone in native, label-free conditions can provide new, multi-level insight into this inherently challenging tissue. Here, we exploited multimodal nonlinear optical (NLO) microscopy, including co-registered stimulated Raman scattering, two-photon excited fluorescence, and second-harmonic generation, to image entire vertebrae of murine spine sections. The quantitative nature of these nonlinear interactions allowed us to extract accurate biochemical, morphological, and topological information on the bone tissue and to highlight differences between normal and pathologic samples. Indeed, in a murine model showing bone loss, we observed increased collagen and lipid content as compared to the wild type, along with a decreased craniocaudal alignment of bone collagen fibres. We propose that NLO microscopy can be implemented in standard histopathological analysis of bone in preclinical studies, with the ambitious future perspective to introduce this technique in the clinical practice for the analysis of larger tissue sections