The synthesis of dendritic architectures has steadily advanced to overcome challenges associated with biological applications. Key issues-including reduced toxicity, enhanced target specificity, improved stability, and efficient delivery of drugs, vaccines, and genes-have posed significant hurdles, particularly when scalability for therapeutic translation is required. Among these, optimizing dendrimer systems to engage in carbohydrate-related biological functions has remained a central focus since the introduction of glycodendrimers. The emergence of glycodendrimers has proven especially effective in contexts demanding multivalent binding interactions. This review highlights advancements in the design of multivalent carbohydrate-based nanomaterials, tracing their evolution from the early development of glycopolymers to the emergence of glycodendrimers. It presents key developments in the use of scaffolds featuring an expanded array of surface functional groups alongside variations in the chemical building blocks traditionally employed in classical dendrimer synthesis-culminating in the so-called "onion-peel strategy". These innovations are further complemented by the integration of orthogonal ligation techniques. Additionally, efforts to streamline the synthesis of multivalent glycoarchitectures have led to the emergence of simplified methodologies, including transition-metal-templated assembly and the self-organization of glycodendrimersomes.
Artificial intelligence (AI) introduces a new paradigm that challenges established information systems (IS) frameworks, prompting a reassessment of IS use in AI-driven systems. To address this, we conducted a three-iteration multi-approach review of 52 articles centered on AI facets and system-level properties as drivers of AI use. Our review identifies and classifies AI’s facets (anthropomorphism, autonomy, inscrutability, and learning) and system-level properties (explainability, transparency, and reliability). We propose working definitions to resolve conceptual issues. Building on these findings, we inductively develop a framework that positions AI use as a process in which AI facets disrupt or enable user interactions, while system-level properties mediate these effects, individually or in combination. Together, these elements shape established and emerging forms of AI use and outcomes. Leveraging our framework and iterations insights, we critically assess extant understanding and propose a research agenda with actionable paths to support future IS research addressing AI use complexities.
The growing integration of artificial intelligence (AI) into organizational workflows is fundamentally reshaping work and how individuals delegate tasks. This paper examines how workers strive to preserve their personal control through cognitive and behavioral processes shaped by contextual congruence, a concept we introduce to capture the alignment between workers' preference for AI delegation and their work environment. We develop the human-in-control (HiC) theoretical model, which conceptualizes personal control as a dynamic process rooted in the experience of AI delegation. The model identifies four adaptation pathways through which workers negotiate how direct task control is distributed between them and the AI systems. Each pathway is characterized by a distinctive configuration of primary and secondary control processes that workers mobilize to maintain, restore, or enhance their personal control. This research contributes to IS scholarship by offering a comprehensive, integrated framework that explains how workers reconfigure their sense of personal control in contexts that encourage or limit AI delegation. It extends adaptation theory beyond scenarios where technology is integrated into work, addressing conditions where AI delegation is constrained against workers' preferences, thereby creating distinct forms of disruption through deprivation. Through these contributions, the HiC model provides researchers with a novel perspective for studying delegation to AI, bridging personal control theory with existing adaptation frameworks by addressing the control tensions in human-AI work.
Exhaustive long-term and large-scale ice jam records are scarce in most cold river environments. Many discrete events occur in small, sparsely populated river systems and are poorly represented in open-source databases. These observation biases are transferred to predictive models of ice jams and the collective understanding of their formation mechanisms. This study addresses these observation biases by using land use as a proxy for ice jam observation probability and by combining direct human observations with dendrochronological records of ice jam activity. The probability of observing an ice jam directly by a witness or indirectly by a tree-ring dated tree scar increases with the density of urban and forest cover, respectively. The annual probability of occurrence for ice jams calculated from direct observational or dendrochronological records alone correlated poorly with geomorphological factors known to cause ice jams. Correcting the observational biases in individual records with their respective land use densities improved the correlation with the Ice jam Predisposition Index (IJPI), a spatial predictor of the probability of occurrence for ice jams. Correcting the observation bias with land use and combining multisource data (direct and dendrochronological observations) further improved the correlation between ice jams and the IJPI. A multi-source approach thus partly overcomes the observation bias of individual records. This work highlights the potential impacts of observation biases in direct and indirect (dendrochronological) ice jam records and shows that bias-corrected, multisource ice jam records could benefit the calibration and validation of ice jam prediction model.
Medical assistance in dying (MAiD) is gaining legal and social acceptance; yet it remains ethically controversial and challenging for healthcare professionals. This functional MRI study examines how social norms and empathy influence MAiD decisions in 59 Australian medical students while evaluating hypothetical assisted-dying scenarios. Participants' decisions generally aligned with the legal framework. MAiD was approved when eligibility criteria were met (normative cases) and denied when they were not (nonnormative cases). Nonnormative scenarios elicited greater activation in frontoparietal brain regions involved in response selection and inhibition, consistent with increased decision difficulty. These scenarios elicited heightened activity in the precuneus, temporoparietal junction, and angular gyrus, along with stronger functional connectivity between the anterior hippocampus and the precuneus, suggesting greater reliance on memory retrieval and mentalizing. Normative scenarios were associated with increased amygdala activity, particularly among less religious participants, suggesting a role for negative affective salience. Greater activity in the ventromedial prefrontal cortex, and connectivity between the anterior cingulate cortex and this region, suggest positive feelings related to compassion when a clinician can legally approve an assisted dying request. Normative scenarios were also associated with reduced connectivity between the anterior cingulate cortex and the anterior insula, particularly in those with higher trait affective empathy, suggesting that doctors might feel a reduction in their patients' pain. The findings provide the first empirical evidence of the neural mechanisms underlying decision-making in bioethical cases involving death as the outcome, highlighting distinct contributions and potential risk factors for medical practitioners in normative and nonnormative MAiD clinical situations.
