Can Sophie's Choice Be Adequately Captured by Cold Computation of Minimizing Losses? An fMRI Study of Vital Loss Decisions

The vast majority of decision-making research is performed under the assumption of the value maximizing principle. This principle implies that when making decisions, individuals try to optimize outcomes on the basis of cold mathematical equations. However, decisions are emotion-laden rather than cool and analytic when they tap into life-threatening considerations. Using functional magnetic resonance imaging (fMRI), this study investigated the neural mechanisms underlying vital loss decisions. Participants were asked to make a forced choice between two losses across three conditions: both losses are trivial (trivial-trivial), both losses are vital (vital-vital), or one loss is trivial and the other is vital (vital-trivial). Our results revealed that the amygdala was more active and correlated positively with self-reported negative emotion associated with choice during vital-vital loss decisions, when compared to trivial-trivial loss decisions. The rostral anterior cingulate cortex was also more active and correlated positively with self-reported difficulty of choice during vital-vital loss decisions. Compared to the activity observed during trivial-trivial loss decisions, the orbitofrontal cortex and ventral striatum were more active and correlated positively with self-reported positive emotion of choice during vital-trivial loss decisions. Our findings suggest that vital loss decisions involve emotions and cannot be adequately captured by cold computation of minimizing losses. This research will shed light on how people make vital loss decisions

Understanding Human Cognitive Control via fMRI Analysis

Cognitive control is the essential high-order information processing system of human brains. Understanding cognitive control helps improve the diagnostic and treatment of various neurological disorders. We focus on learning the uncertainty representation in cognitive control, namely, how human brains react to the same task with different levels of uncertainty, using task-evoked function MRI images. The learning includes two tasks: identification of key brain regions and brain connectivities. We propose an interpretable convolutional neural network, called ROI-reweight 3D CNN, to identify key brain regions. We train a classifier for task-evoked fMRI images, which also locates crucial ROIs based on a reweight layer. Brain connectivity analysis can be formulated as a graph inference problem, in which the edges in the graph indicate relations between ROIs. We propose a neural architecture based on Markov Random Fields (MRF) for the brain network learning task. The neural network learns a graphical model as brain connectivity pattern. Furthermore, we are interested in learning the differential connectivity patterns under different uncertainty conditions. We design a neural network architecture which learns to decide whether two input images are from the same class (uncertainty level). The key is to identify an underlying graphical model (MRF) that captures the difference between different uncertainty levels

Composing tree graphical models with persistent homology features for clustering mixed-type data

Clustering data with both continuous and discrete attributes is a challenging task. Existing methods often lack a principled probabilistic formulation. In this paper, we propose a clustering method based on a tree-structured graphical model to describe the generation process of mixed-type data. Our tree-structured model factorizes into a product of pairwise interactions, and thus localizes the interaction between feature variables of different types. To provide a robust clustering method based on the tree-model, we adopt a topographical view and compute peaks of the density function and their attractive basins for clustering. Furthermore, we leverage the theory from topology data analysis to adaptively merge trivial peaks into large ones in order to achieve meaningful clusterings. Our method outperforms state-of-the-art methods on mixed-type data

How Live Streaming Interactions and Their Visual Stimuli Affect Users’ Sustained Engagement Behaviour—A Comparative Experiment Using Live and Virtual Live Streaming

With the massive expansion in live streaming, enhancing the sustained engagement of users has become a key issue in ensuring its success. This study examines the relationship between real-time interaction, user perceptions, user intention to keep using live streaming, and whether this relationship differs between a live and a virtual live streaming environment. Using partial least squares (PLS) structural equation modelling (SEM), this paper analyses 240 valid questionnaire responses and finds that there is a link between real-time interactions, visual stimuli, and users’ sustained engagement. This shows that users’ active interactions while watching live streaming videos significantly affect their perceptions of social presence and trust, which in turn, affect their sustained engagement behaviour. These effects were found to vary with differences in the live streaming environment. The findings of this paper will play a positive role in understanding the differences between various live streaming environments, in optimizing the design of live streaming content and in improving the perceptions of emotional warmth by live streaming users

Integrated transcriptomics, proteomics, and functional analysis to characterize the tissue‐specific small extracellular vesicle network of breast cancer

Abstract Small extracellular vesicles (sEVs) are essential mediators of intercellular communication within the tumor microenvironment (TME). Although the biological features of sEVs have been characterized based on in vitro culture models, recent evidence indicates significant differences between sEVs derived from tissue and those derived from in vitro models in terms of both content and biological function. However, comprehensive comparisons and functional analyses are still limited. Here, we collected sEVs from breast cancer tissues (T‐sEVs), paired normal tissues (N‐sEVs), corresponding plasma (B‐sEVs), and tumor organoids (O‐sEVs) to characterize their transcriptomic and proteomic profiles. We identified the actual cancer‐specific sEV signatures characterized by enriched cell adhesion and immunomodulatory molecules. Furthermore, we revealed the significant contribution of cancer‐associated fibroblasts in the sEV network within the TME. In vitro model‐derived sEVs did not entirely inherit the extracellular matrix‐ and immunity regulation‐related features of T‐sEVs. Also, we demonstrated the greater immunostimulatory ability of T‐sEVs on macrophages and CD8+ T cells compared to O‐sEVs. Moreover, certain sEV biomarkers derived from noncancer cells in the circulation exhibited promising diagnostic potential. This study provides valuable insights into the functional characteristics of tumor tissue‐derived sEVs, highlighting their potential as diagnostic markers and therapeutic agents for breast cancer

CD19 CAR T cells following autologous transplantation in poor-risk relapsed and refractory B-cell non-Hodgkin lymphoma

High-dose chemotherapy and autologous stem cell transplantation (HDT-ASCT) is the standard of care for relapsed or primary refractory (rel/ref) chemorefractory diffuse large B-cell lymphoma. Only 50% of patients are cured with this approach. We investigated safety and efficacy of CD19-specific chimeric antigen receptor (CAR) T cells administered following HDT-ASCT. Eligibility for this study includes poor-risk rel/ref aggressive B-cell non-Hodgkin lymphoma chemosensitive to salvage therapy with: (1) positron emission tomography–positive disease or (2) bone marrow involvement. Patients underwent standard HDT-ASCT followed by 19-28z CAR T cells on days +2 and +3. Of 15 subjects treated on study, dose-limiting toxicity was observed at both dose levels (5 × 106 and 1 × 107 19-28z CAR T per kilogram). Ten of 15 subjects experienced CAR T-cell–induced neurotoxicity and/or cytokine release syndrome (CRS), which were associated with greater CAR T-cell persistence (P = .05) but not peak CAR T-cell expansion. Serum interferon-γ elevation (P < .001) and possibly interleukin-10 (P = .07) were associated with toxicity. The 2-year progression-free survival (PFS) is 30% (95% confidence interval, 20% to 70%).  Subjects given decreased naive-like (CD45RA+CCR7+) CD4+ and CD8+ CAR T cells experienced superior PFS (P = .02 and .04, respectively). There was no association between CAR T-cell peak expansion, persistence, or cytokine changes and PFS. 19-28z CAR T cells following HDT-ASCT were associated with a high incidence of reversible neurotoxicity and CRS. Following HDT-ASCT, effector CD4+ and CD8+ immunophenotypes may improve disease control. This trial was registered at www.clinicaltrials.gov as #NCT01840566. •19-28z CAR T cells following HDT-ASCT resulted in an incidence of severe neurotoxicity of 67%.•19-28z CAR T grafts with increased effector immunophenotypes trended toward protection from POD following HDT-ASCT. [Display omitted