American University Discourse in the COVID-19 Pandemic: Multimodal Aspect

American University Discourse in the COVID-19 Pandemic: Multimodal AspectThis paper examines the transformations that have taken place in the multimodal representation of American university discourse during the COVID-19 pandemic. The first part of the paper investigates how the traditional realization of American university discourse has changed during the coronavirus pandemic. The second part of the paper focuses on the structure, content, role, and characteristic features of the multimodal texts which have been created by American universities as social institutions and have been published on their websites and social media accounts during the pandemic. Overall, it is assumed that during the pandemic the content of the analyzed texts has gradually changed from active propaganda for complete isolation at home to the cautious promotion of reconnecting in a secure surrounding on campus. Amerykański dyskurs uniwersytecki w pandemii COVID-19: aspekt multimodalnyAutorka analizuje przemiany, które zaszły w multimodalnej reprezentacji amerykańskiego dyskursu uniwersyteckiego podczas pandemii COVID-19. W pierwszej część artykułu bada, jak tradycyjna realizacja amerykańskiego dyskursu uniwersyteckiego zmieniła się podczas pandemii koronawirusa. W drugiej części artykułu skupia się na strukturze, treści, roli i charakterystycznych cechach tekstów multimodalnych, które zostały wytworzone przez amerykańskie uniwersytety jako instytucje społeczne i opublikowane na własnych stronach internetowych i kontach w mediach społecznościowych w czasie pandemii. Na podstawie analizowanych tekstów autorka zauważa, że ich treść zmieniała się stopniowo: od aktywnego propagowania całkowitej izolacji w domu do ostrożnego promowania ponownego łączenia się w bezpiecznym otoczeniu na terenie kampusu.

Development of Novel Deep Multimodal Representation Learning-based Model for the Differentiation of Liver Tumors on B-Mode Ultrasound Images

Recently, multimodal representation learning for images and other information such as numbers or language has gained much attention due to the possibility of combining latent features using a single distribution. The aim of the current study was to analyze the diagnostic performance of deep multimodal representation model-based integration of tumor image, patient background, and blood biomarkers for the differentiation of liver tumors observed using B-mode ultrasonography (US). First, we applied supervised learning with a convolutional neural network (CNN) to 972 liver nodules in the training and development sets (479 benign and 493 malignant nodules), to develop a predictive model using segmented B-mode tumor images. Additionally, we also applied a deep multimodal representation model to integrate information about patient background or blood biomarkers to B-mode images. We then investigated the performance of the models in an independent test set of 108 liver nodules, including 53 benign and 55 malignant tumors. Using only the segmented B-mode images, the diagnostic accuracy and area under the curve (AUC) values were 68.52% and 0.721, respectively. As the information about patient background such as age or sex and blood biomarkers was integrated, the diagnostic performance increased in a stepwise manner. The diagnostic accuracy and AUC value of the multimodal DL model (which integrated B-mode tumor image, patient age, sex, AST, ALT, platelet count, and albumin data) reached 96.30% and 0.994, respectively. Integration of patient background and blood biomarkers in addition to US image using multimodal representation learning outperformed the CNN model using US images. We expect that the deep multimodal representation model could be a feasible and acceptable tool that can effectively support the definitive diagnosis of liver tumors using B-mode US in daily clinical practice.