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Abstract The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data‐driven insights, yet significant challenges persist in accurately interpreting users analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error‐prone, and time‐intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation. We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM‐driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics. 

Amyloid aggregation and microbial infection are considered as pathological risk factors for developing amyloid diseases, including Alzheimer's disease (AD), type II diabetes (T2D), Parkinson's disease (PD), and medullary thyroid carcinoma (MTC). Due to the multifactorial nature of amyloid diseases, single-target drugs and treatments have mostly failed to inhibit amyloid aggregation and microbial infection simultaneously, thus leading to marginal benefits for amyloid inhibition and medical treatments. Herein, we proposed and demonstrated a new “anti-amyloid and antimicrobial hypothesis” to discover two host-defense antimicrobial peptides of α-defensins containing β-rich structures (human neutrophil peptide of HNP-1 and rabbit neutrophil peptide of NP-3A), which have demonstrated multi-target, sequence-independent functions to (i) prevent the aggregation and misfolding of different amyloid proteins of amyloid-β (Aβ, associated with AD), human islet amyloid polypeptide (hIAPP, associated with T2D), and human calcitonin (hCT, associated with MTC) at sub-stoichiometric concentrations, (ii) reduce amyloid-induced cell toxicity, and (iii) retain their original antimicrobial activity upon the formation of complexes with amyloid peptides. Further structural analysis showed that the sequence-independent amyloid inhibition function of α-defensins mainly stems from their cross-interactions with amyloid proteins via β-structure interactions. The discovery of antimicrobial peptides containing β-structures to inhibit both microbial infection and amyloid aggregation greatly expands the new therapeutic potential of antimicrobial peptides as multi-target amyloid inhibitors for better understanding pathological causes and treatments of amyloid diseases. 

Ethylene complexes of gold( i ) have been stabilized by electron-rich, κ 2 -bound tris(pyrazolyl)borate ligands. Large up-field shifts of olefinic carbon NMR resonances and relatively long CC distances of gold bound ethylene are indicative of significant Au( i ) → ethylene π-backbonding relative to the analog supported by a weakly donating ligand, consistent with the computational data.