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3,827 result(s) for "Multimodality"
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Uninvited Guest in the Left Ventricle: Cardiac Lipoma
Cardiac lipoma is a rare primary tumor of the heart. With advances in diagnostic and treatment methods, an increasing number of cases have been reported. This trend suggests that the clinical presentation, previously believed to follow classic patterns, may actually exhibit atypical features. In such cases, multimodal imaging facilitates accurate diagnosis and the selection of the most appropriate treatment. This case report presents a 28-year-old female with progressive exertional dyspnea. Transthoracic and transesophageal echocardiography revealed a large mass in the left ventricle, originating from the posteromedial papillary muscle. The mass exhibited a low-density focus with a well-defined boundary and regular shape and, fortunately, had no significant effect on the valves or hemodynamics. Cardiac magnetic resonance imaging confirmed the diagnosis of a cardiac lipoma. Although surgical intervention was offered, the patient ultimately declined the procedure. Most patients diagnosed with cardiac lipoma are asymptomatic, and the diagnosis is often made incidentally. The use of multimodality imaging greatly aids in diagnosis. Echocardiography is a suitable modality for ongoing monitoring.
Позиционирование гостей в ток-шоу Майбрит Иллнер Schmitt R., Petrova A.A. Zur Positionierung Von Tolkshowgästen In Einer Sendung Von Maybrit Illner
In diesem Aufsatz werden Positionierungsverfahren analysiert, welche die Macher einer Talkshow einsetzen, um ihre Gäste den Fernsehzuschauern als relevante Gesprächspartner für das Thema „Steuerhinterziehung durch Prominente\" zu präsentieren. Es wird untersucht, wie es den Machern der Talkshow gelingt, die Gäste bereits bei der Erstvorstellung durch das Zusammenspiel einer Stimme aus dem Off und der Kameraführung als „prototypische Vertreter\" zu präsentieren und zueinander zu positionieren. Von den insgesamt fünf Teilnehmern der Talkshow werden zwei dieser Erstvorstellungen detailliert analysiert. Es handelt sich um die Präsentation zweier Gäste, die in einer deutlich antagonistischen Beziehung zueinander stehen. Diese Gäste werden unmittelbar hintereinander vorgestellt. Auf der Grundlage aller fünf Gastpräsentationen, die wir detailliert rekonstruiert haben, jedoch aus Platzgründen hier leider nicht ebenfalls präsentieren können, wird ein strukturiertes Positionierungsgeflecht deutlich. Dieses Geflecht weist im Zentrum die von uns rekonstruierte thematische und personelle „Gegnerschaft\" auf. In der Peripherie sind dann insgesamt vier Vertreter relevanter gesellschaftlicher Positionen zum Thema der Talkshow beigeordnet. Dabei handelt es sich um Vertreter der Rechtsprechung, der Politik, der Alltagsmoral und der Psychologie und Theologie. Die Analysen werden in theoretischer Hinsicht auf der Grundlage multimodaler Vorstellungen zur Positionierung und zum Recipient Design durchgeführt. In methodisch-methodologischer Perspektive orientiert sich die Analyse an der multimodalen Interaktionsanalyse. Schlüsselwörter: Multimodalität, Positionierung, Recipient Design, multimodale Interaktionsanalyse, Talk Show, interaktive Konstruktion von Gegnerschaft.
Toward multimodal virtual communication
The paper explores the concept of multimodality in contemporary stylistics of virtual communication, mainly focusing on the process of interpretation. After outlining the basics of multimodal meaning-making theory, it further develops it by incorporating ideas from the theory of media or other related disciplines. The next part describes the of immersive potential of virtual environment, tying it to the multisensorial nature of human epistemic existence. Finally, the last part introduces these ideas to the process of stylistic interpretation, emphasizing the changes and problems, which multimodal virtual communication introduces.
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook
In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and proteomics). While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice. This paper aims to present a detailed, practical, and solution-oriented perspective on the use of multimodal LLMs (M-LLMs) in the medical field. Our investigation spanned M-LLM foundational principles, current and potential applications, technical and ethical challenges, and future research directions. By connecting these elements, we aimed to provide a comprehensive framework that links diverse aspects of M-LLMs, offering a unified vision for their future in health care. This approach aims to guide both future research and practical implementations of M-LLMs in health care, positioning them as a paradigm shift toward integrated, multimodal data–driven medical practice. We anticipate that this work will spark further discussion and inspire the development of innovative approaches in the next generation of medical M-LLM systems.
Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California
Millennials tend to use a variety of travel modes more often than older birth cohorts. Two potential explanations for this phenomenon prevail in the literature. According to the first explanation, millennials often choose travel multimodality at least in part because of the effects of the economic crisis, which affected young adults more severely than their older counterparts. Another explanation points to the fact that millennials may have fundamentally different preferences from those of older birth cohorts. This paper presents an examination of millennials’ travel behavior as compared to the preceding Generation X, based on a survey of 1069 California commuters. It shows that millennials adopt multimodality more often than Gen Xers, on average. However, the analysis also points to substantial heterogeneity among millennials and indicates that, perhaps contrary to expectations and the stereotype in the media, the majority of millennials are monomodal drivers in California. The paper contributes to the literature on millennials’ mobility in several ways. First, it rigorously classifies various forms of travel multimodality (on a monthly basis and distinctively taking trip purpose into account) through the analysis of a rich dataset that includes individual attitudes and preferences; second, it explores gradual changes of multimodality across age and generation; and third, it analyzes the effects of various demographic, built environment, and attitudinal attributes on the adoption of multimodality.
An update on larynx cancer
Laryngeal cancer remains one of the most common tumors of the respiratory tract. Fortunately, significant advancements have been made over the past decade in the treatment of laryngeal cancer. Although surgery has been the historical mainstay for localized disease and still is an integral part of treatment, nonsurgical options like radiation and systemic therapy have emerged as viable options. In addition, in the metastatic setting, novel agents are showing promise for this patient population. The care for patients with laryngeal cancer continues to evolve and truly requires a multidisciplinary team-based approach. Unique morbidities, such as loss of natural voice, respiration, and airway protection during swallowing, are observed with this disease and require special consideration.
Quantum dots for cancer research: current status, remaining issues, and future perspectives
Cancer is a major threat to public health in the 21st century because it is one of the leading causes of death worldwide. The mechanisms of carcinogenesis, cancer invasion, and metastasis remain unclear. Thus, the development of a novel approach for cancer detection is urgent, and real-time monitoring is crucial in revealing its underlying biological mechanisms. With the optical and chemical advantages of quantum dots (QDs), QD-based nanotechnology is helpful in constructing a biomedical imaging platform for cancer behavior study. This review mainly focuses on the application of QD-based nanotechnology in cancer cell imaging and tumor microenvironment studies both in vivo and in vitro, as well as the remaining issues and future perspectives.
The Future of Digital Communication Research: Considering Dynamics and Multimodality
Digital communication, the electronic transmission of information, reflects and influences consumers’ perceptions, attitudes, behaviors, and shopping journeys. Thus, data stemming from digital communication is an important source of insights for retailers, manufacturers, and service firms alike. This article discusses emerging trends and recent advances in digital communication research, as well as its future opportunities for retail practice and research. The authors outline four consumer–retailer domains relevant to digital communication, which in turn frame their discussion of the properties of communication dynamics (e.g., trends, variations) within messages, communicators, and their interaction, as well as communication multimodality (i.e., numeric heuristics, text, audio, image, and video). These factors are critical for understanding and predicting consumers’ behaviors and market developments. Furthermore, this article delineates conceptual and methodological challenges for researchers working in contexts that feature dynamics and multimodality. Finally, this article proposes an agenda for continued research, with the goal of stimulating further efforts to unlock the “black boxes” of digital communication and gain insights into both consumers and markets.
Resonating with the Past: Mediation of Memory in Sound Sculptures
Sound sculptures can serve as resonant mediums of remembrance, reawakening historical soundscapes and evoking memories through the use of sound. In this way, they enable a sensory connection to the past. This paper explores how sound functions as a semiotic resource, interacting with other modalities within sound sculptures and the contexts in which they are placed. It examines how memory can be accessed through the sense of hearing and investigates cases where sound memorializes historical events. I draw on a social semiotic approach, particularly Günter Kress’s theory of multimodality, considering sound sculptures as multimodal texts and multisensory objects. Complementing this, I employ sound theory and acoustic ecology studies, primarily the works of Raymond Murray Schafer, which provide a language for describing the sound modality within sound sculptures. To ground this exploration, I analyze sonic objects and sonic events in the sound sculptures by artists Bill Fontana, Markus Kison, and Nikita Kadan through the lenses of the acoustic ecology framework. These works exemplify how sound can operate as both a symbol and a mnemonic trigger, how its sensory and emotional dimensions contribute to memory-making processes.
Recent trends in deep learning based personality detection
Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.