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Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context' to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR') and another condition where the two objects are always dissimilar (‘FAR'). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR' condition (where objects are potentially confusable) than in ‘FAR' condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context' during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

Traditional cancer treatments use nonspecific drugs and monoclonal antibodies to target tumor cells. Chimeric antigen receptor (CAR)-T cell therapy, however, leverages the immune system's T-cells to recognize and attack tumor cells. T-cells are isolated from patients and modified to target tumor-associated antigens. CAR-T therapy has achieved FDA approval for treating blood cancers like B-cell acute lymphoblastic leukemia, large B-cell lymphoma, and multiple myeloma by targeting CD-19 and B-cell maturation antigens. Bi-specific chimeric antigen receptors may contribute to mitigating tumor antigen escape, but their efficacy could be limited in cases where certain tumor cells do not express the targeted antigens. Despite success in blood cancers, CAR-T technology faces challenges in solid tumors, including lack of reliable tumor-associated antigens, hypoxic cores, immunosuppressive tumor environments, enhanced reactive oxygen species, and decreased T-cell infiltration. To overcome these challenges, current research aims to identify reliable tumor-associated antigens and develop cost-effective, tumor microenvironment-specific CAR-T cells. This review covers the evolution of CAR-T therapy against various tumors, including hematological and solid tumors, highlights challenges faced by CAR-T cell therapy, and suggests strategies to overcome these obstacles, such as utilizing single-cell RNA sequencing and artificial intelligence to optimize clinical-grade CAR-T cells.

The interaction of visual exploration and auditory processing is central to early cognitive development, supporting object discrimination, categorization, and word learning. Research has shown visual-auditory interactions to be complex, created from multiple processes and changing over multiple timescales. To better understand these interactions, we generalize a formal neural process model of early word learning to two studies examining how words impact 9- to 22-month-olds' attention to novelty. These simulations clarify the origin and nature of previously demonstrated effects of labels on visual exploration and the basis of mutual exclusivity effects in word learning. We use our findings to discuss key questions for this special section: what makes a good theory and how should formal theories interface with empirical paradigms and findings?

Cervical cancer is still the leading cause of cancer mortality worldwide even after introduction of vaccine against Human papillomavirus (HPV), due to low vaccine coverage, especially in the developing world. Cervical cancer is primarily treated by Chemo/Radiotherapy, depending on the disease stage, with Carboplatin/Cisplatin-based drug regime. These drugs being non-specific, target rapidly dividing cells, including normal cells, so safer options are needed for lower off-target toxicity. Natural products offer an attractive option compared to synthetic drugs due to their well-established safety profile and capacity to target multiple oncogenic hallmarks of cancer like inflammation, angiogenesis, etc. In the current study, we investigated the effect of Bergenin (C-glycoside of 4-O-methylgallic acid), a natural polyphenol compound that is isolated from medicinal plants such as Bergenia crassifolia, Caesalpinia digyna, and Flueggea leucopyrus. Bergenin has been shown to have anti-inflammatory, anti-ulcerogenic, and wound healing properties but its anticancer potential has been realized only recently. We performed a proteomic analysis of cervical carcinoma cells treated with bergenin and found it to influence multiple hallmarks of cancers, including apoptosis, angiogenesis, and tumor suppressor proteins. It was also involved in many different cellular processes unrelated to cancer, as shown by our proteomic analysis. Further analysis showed bergenin to be a potent-angiogenic agent by reducing key angiogenic proteins like Galectin 3 and MMP-9 (Matrix Metalloprotease 9) in cervical carcinoma cells. Further understanding of this interaction was carried out using molecular docking analysis, which indicated MMP-9 has more affinity for bergenin as compared to Galectin-3. Cumulatively, our data provide novel insight into the anti-angiogenic mechanism of bergenin in cervical carcinoma cells by modulation of multiple angiogenic proteins like Galectin-3 and MMP-9 which warrant its further development as an anticancer agent in cervical cancer. [ABSTRACT FROM AUTHOR]

plore some productive areas of overlap between cognitive

Recent empirical studies have affirmed the fundamental roleof attention and memory processes in statistical word learningtasks. These processes interact in complex ways to guidespontaneous looking behaviors of learners as well asdetermine their overall learning performance. On themodelling side, studies have made it clear that computationalmodels must provide process-based rather than onlycomputational accounts of word learning, because these canconnect to the empirically observed behaviors at a moment-to-moment timescale. Thus, here we present a neurally-grounded process model of word learning called WOLVES(Word-Object Learning Via Visual Exploration in Space) thatintegrates visual dynamics and word-object binding acrossmultiple timescales. WOLVES integrates multiple establisheddynamic neural field models to allow fine-grained indexing ofcomponent processes driving the looking-learning loop. Wereport simulation results for three empirical cross-situationalword learning experiments to validate the model.