Hasil untuk "Animal culture"

V. Hernández-Gea, S. Friedman

S. Kirksey, S. Helmreich

Anthropologists have been committed, at least since Franz Boas, to investigating relationships between nature and culture. At the dawn of the 21st century, this enduring interest was inflected with some new twists. An emergent cohort of “multispecies ethnographers” began to place a fresh emphasis on the subjectivity and agency of organisms whose lives are entangled with humans. Multispecies ethnography emerged at the intersection of three interdisciplinary strands of inquiry: environmental studies, science and technology studies (STS), and animal studies. Departing from classically ethnobiological subjects, useful plants and charismatic animals, multispecies ethnographers also brought understudied organisms—such as insects, fungi, and microbes—into anthropological conversations. Anthropologists gathered together at the Multispecies Salon, an art exhibit, where the boundaries of an emerging interdiscipline were probed amidst a collection of living organisms, artifacts from the biological sciences, and surprising biopolitical interventions.

K. Yamada, E. Cukierman

H. Eagle

R. Bartenschlager, V. Lohmann

D. Nebert, T. Dalton, A. Okey et al.

The mammalian CYP1A1, CYP1A2, and CYP1B1 genes (encoding cytochromes P450 1A1, 1A2, and 1B1, respectively) are regulated by the aromatic hydrocarbon receptor (AHR). The CYP1 enzymes are responsible for both metabolically activating and detoxifying numerous polycyclic aromatic hydrocarbons (PAHs) and aromatic amines present in combustion products. Many substrates for CYP1 enzymes are AHR ligands. Differences in AHR affinity between inbred mouse strains reflect variations in CYP1 inducibility and clearly have been shown to be associated with differences in risk of toxicity or cancer caused by PAHs and arylamines. Variability in the human AHR affinity exists, but differences in human risk of toxicity or cancer related to AHR activation remain unproven. Mouse lines having one or another of the Cyp1 genes disrupted have shown paradoxical effects; in the test tube or in cell culture these enzymes show metabolic activation of PAHs or arylamines, whereas in the intact animal these enzymes are sometimes more important in the role of detoxification than metabolic potentiation. Intact animal data contradict pharmaceutical company policies that routinely test drugs under development; if a candidate drug shows CYP1 inducibility, further testing is generally discontinued for fear of possible toxic or carcinogenic effects. In the future, use of “humanized” mouse lines, containing a human AHR or CYP1 allele in place of the orthologous mouse gene, is one likely approach to show that the AHR and the CYP1 enzymes in human behave similarly to that in mouse.

M. Itzstein, Wen-Yang Wu, G. Kok et al.

J. Taylor, J. Hardy, K. Fischbeck

S. Weiss, S. Navas-Martín

N. Stephens, L. Di Silvio, Illtud Dunsford et al.

Background Cultured meat forms part of the emerging field of cellular agriculture. Still an early stage field it seeks to deliver products traditionally made through livestock rearing in novel forms that require no, or significantly reduced, animal involvement. Key examples include cultured meat, milk, egg white and leather. Here, we focus upon cultured meat and its technical, socio-political and regulatory challenges and opportunities. Scope and approach The paper reports the thinking of an interdisciplinary team, all of whom have been active in the field for a number of years. It draws heavily upon the published literature, as well as our own professional experience. This includes ongoing laboratory work to produce cultured meat and over seventy interviews with experts in the area conducted in the social science work. Key findings and conclusions Cultured meat is a promising, but early stage, technology with key technical challenges including cell source, culture media, mimicking the in-vivo myogenesis environment, animal-derived and synthetic materials, and bioprocessing for commercial-scale production. Analysis of the social context has too readily been reduced to ethics and consumer acceptance, and whilst these are key issues, the importance of the political and institutional forms a cultured meat industry might take must also be recognised, and how ambiguities shape any emergent regulatory system.

D. Talan, D. Citron, F. Abrahamian et al.

S. Vartoukian, R. Palmer, W. Wade

M. Gosset, F. Berenbaum, S. Thirion et al.

J. Valk, D. Brunner, K. D. Smet et al.

T. Ludwig, Veit Bergendahl, M. Levenstein et al.

Harsha Koduri

Monitoring animal populations in natural environments requires systems that can interpret both visual data and human language queries. This work introduces ViLLa (Vision-Language-Logic Approach), a neuro-symbolic framework designed for interpretable animal monitoring. ViLLa integrates three core components: a visual detection module for identifying animals and their spatial locations in images, a language parser for understanding natural language queries, and a symbolic reasoning layer that applies logic-based inference to answer those queries. Given an image and a question such as "How many dogs are in the scene?" or "Where is the buffalo?", the system grounds visual detections into symbolic facts and uses predefined rules to compute accurate answers related to count, presence, and location. Unlike end-to-end black-box models, ViLLa separates perception, understanding, and reasoning, offering modularity and transparency. The system was evaluated on a range of animal imagery tasks and demonstrates the ability to bridge visual content with structured, human-interpretable queries.

Chowdhury Shahriar Muzammel, Maria Spichkova, James Harland

Requirements Engineering (RE) is one of the most interaction-intensive phases of software development. This means that RE activities might be especially impacted by stakeholders' national culture. Software development projects increasingly have a very diverse range of stakeholders. To future-proof RE activities, we need to help RE practitioners avoid misunderstandings and conflicts that might arise from not understanding potential Cultural Influences (CIs). Moreover, an awareness of CIs supports diversity and inclusion in the IT profession. Bangladesh has a growing IT sector with some unique socio-cultural characteristics, and has been largely overlooked in this research field. In this study, we aim to investigate how the RE process is adopted in the context of Bangladeshi culture and what cultural influences impact overall RE activities.

Eric J. W. Orlowski, Hakim Norhashim, Tristan Koh Ly Wey

While cultural alignment has increasingly become a focal point within AI research, current approaches relying predominantly on quantitative benchmarks and simplistic proxies fail to capture the deeply nuanced and context-dependent nature of human cultures. Existing alignment practices typically reduce culture to static demographic categories or superficial cultural facts, thereby sidestepping critical questions about what it truly means to be culturally aligned. This paper argues for a fundamental shift towards integrating interpretive qualitative approaches drawn from social sciences into AI alignment practices, specifically in the context of Large Language Models (LLMs). Drawing inspiration from Clifford Geertz's concept of "thick description," we propose that AI systems must produce outputs that reflect deeper cultural meanings--what we term "thick outputs"-grounded firmly in user-provided context and intent. We outline three necessary conditions for successful cultural alignment: sufficiently scoped cultural representations, the capacity for nuanced outputs, and the anchoring of outputs in the cultural contexts implied within prompts. Finally, we call for cross-disciplinary collaboration and the adoption of qualitative, ethnographic evaluation methods as vital steps toward developing AI systems that are genuinely culturally sensitive, ethically responsible, and reflective of human complexity.

Sachin R. Pendse, Ben Rochford, Neha Kumar et al.

The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.

Jing Yao, Xiaoyuan Yi, Jindong Wang et al.

As Large Language Models (LLMs) are deployed across diverse regions, aligning them with pluralistic cultures is crucial for improving user engagement and mitigating cultural conflicts. Recent work has curated, either synthesized or manually annotated, culture-specific corpora for alignment. Nevertheless, inspired by cultural theories, we recognize they face two key challenges. (1) Representativeness: These corpora inadequately capture the target culture's core characteristics, causing insufficient cultural coverage and redundancy; (2) Distinctiveness: They fail to distinguish the unique nuances of the target culture from patterns shared across relevant ones, hindering precise culture modeling. To handle these challenges, we introduce CAReDiO, a novel data optimization framework that alternately optimizes culture-sensitive questions and responses according to two information-theoretic objectives in an in-context manner, enhancing both cultural representativeness and distinctiveness of constructed data. Extensive experiments on 15 cultures demonstrate that CAReDiO can create high-quality data with richer cultural information and enable efficient alignment of small open-source or large proprietary LLMs with as few as 200 training samples, consistently outperforming previous datasets in both multi-choice and open-ended benchmarks.