Art consumption is part of human sociality. Embedded in myriad think pieces and social media posts about the #MeToo movement were questions about how people should view artwork by artists accused of sexual transgressions. Central to think pieces about R Kelly, Woody Allen, and so on, is a search for answers: "Should we now discard once-beloved artworks?" Beyond being of great public interest, such questions about people's reappraisal may have both implications for (a) other cultural products (e.g., scientific findings, vaccines) and (b) economic impacts for those in the multibillion-dollar art and entertainment industries. Across four studies, we produce some of the first work empirically describing how Americans reappraise artists' works considering criminal accusations. Across four studies, Americans (N = 1,527; 58.68% women), appraised artworks, learned new information that the artist was accused of a crime-including sexual assault and murder-and reappraised that work. Upon reappraisal, Americans more strongly endorsed censuring art (e.g., preventing its display) by artists accused of sexual assault than other crimes, Cohen's d = 0.30-0.61. Americans' liking of the artwork did not change upon reappraisal. This effect held whether art was novel or familiar and beloved, when controlling for participant political orientation or art knowledge, and even as participants deemed murder more morally wrong. Discussion includes possible explanations rooted in evolutionary psychology, such as coordinated condemnation of morally reprehensible behaviors.
The rapid growth of biological data and experimental complexity has motivated increasing interest in artificial intelligence (AI) systems that extend beyond static prediction toward autonomous reasoning and action. While recent computational models achieve strong predictive performance, they largely operate as passive tools within human-driven research workflows. In contrast, AI agents integrate reasoning, planning, tool invocation, and feedback-driven refinement, enabling more adaptive and interactive forms of biological analysis. This survey provides a systematic synthesis of recent progress in biological AI agents by reviewing over 100 representative studies across clinical analytics, molecular and drug design, multi-omics analysis, and knowledge discovery. We introduce a unified 5D taxonomy that organizes existing work along task domains, system architectures, interaction modes, evaluation strategies, and resource integration. Building on this framework, we analyze common design patterns, highlight emerging capabilities enabled by agentic paradigms, and identify key open challenges, including reliability, privacy, scalability, and standardized evaluation. Collectively, this survey clarifies the conceptual and methodological landscape of biological AI agents and outlines directions toward more robust, transparent, and collaborative agent-based systems for biological research. To serve as a living resource for the community, we curated a GitHub repository that includes resources and benchmark summaries, available at https://github.com/MineSelf2016/biological_agents_survey.
PurposeMost people associate ownership with the ability to control something. In the USA, employee share (or stock) ownership plans (ESOPs) are one of the principal forms of employee ownership. However, most ESOPs give employees very limited rights of control over the company they own. This paper explore this conflict by examining theories of property and ownership to determine whether the right to participate in decision-making is inherent in the idea of ownership as it is generally understood. Ultimately, the author argues that the law governing ESOPs should be revised to give employees a larger role in the governance of their companies.Design/methodology/approachThis paper considers the concept of ownership both historically and analytically. The author examines the roots of property theory in the work of John Locke and contemporary theorists, as well as contemporary theorizing about ownership.FindingsThere are two kinds of ownership: legal ownership and psychological ownership. In legal ownership, the right to participation is inherent but alienable, so one can legally be an owner of something but have no right of participation. Psychological ownership primarily arises from a sense of control. Legal ownership confers some part of the bundle of rights associated with property. Psychological ownership conveys a feeling of efficacy, responsibility and control, but no formal rights. The author argues that, for employee ownership to be more than mere property-holding, it must include meaningful participation in decision-making, including governance.Research limitations/implicationsThis paper is only concerned with ESOPs in the USA. Although the findings may be applicable, it does not address other forms of employee ownership or employee ownership outside of the USA.Practical implicationsPeople associate ownership with the ability to control something, so when workers are told they own their company but then find they have few control rights, it may undermine their sense of ownership. This then has negative implications for the company's success. To ensure meaningful levels of governance rights, policy-makers should revise the laws governing ESOPs to require greater involvement by employees.Social implicationsClarifying ambiguities around ownership will help support arguments for affording employee-owners greater control rights in their companies, which will have various spill-over effects.Originality/valuePractitioners and scholars alike deploy the term, “ownership” but ascribe different meanings to it. The distinction between legal and psychological ownership is largely lacking in the ESOP literature. Clarifying this distinction will help to move the discussion forward regarding employee participation in ESOPs. In addition, the paper provides an original analysis of property that demonstrates the importance of the right to control, showing that the traditional ESOP structure may violate important aspects of that right.
Accurately modeling the binding between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is essential for guiding immunotherapy development and personalized vaccine design. However, the vast diversity of TCR repertoires and the scarcity of experimentally validated interactions make generalization to unseen epitopes challenging. This paper proposes TIDE, a cross-attention-driven dual-encoder framework that leverages large protein and molecular language models to learn discriminative representations of TCRs and peptides. In TIDE, TCR sequences are encoded using Evolutionary Scale Modeling (ESM), while peptides are transformed into SMILES strings and processed by MolFormer to capture chemical and structural properties. Multi-layer cross-attention then refines and integrates these embeddings, highlighting interaction-relevant patterns without requiring explicit structural alignment. Evaluated on the TCHard benchmark under both zero-shot and few-shot settings, TIDE achieves superior predictive accuracy and robustness compared to state-of-the-art baselines such as ChemBERTa, TITAN, and NetTCR. These results demonstrate that combining pretrained language models with cross-attention fusion offers a powerful approach for TCR-pMHC binding prediction and paves the way for more reliable computational immunology applications.
We develop a compact transmon emitter/detector (TED) superconducting circuit and demonstrate its dual functionality as a single-photon source and detector. In our setup, photons emitted by a source TED are transmitted via a meter-long coaxial cable, routed through a circulator, and captured by a measurement TED. Both TED modules operate with nominally identical parameters, highlighting the flexibility of this novel architecture. Furthermore, we introduce an efficient microwave photon detection scheme tailored to the TED. Using this setup, we detect 60
