Hasil untuk "Animal culture"

Boyang Zhang, A. Korolj, B. Lai et al.

R. Harun, Manjinder Singh, G. Forde et al.

Li‐Shu Wang, G. Stoner

A. Kunnumakkara, P. Anand, B. Aggarwal

M. Post, S. Levenberg, D. Kaplan et al.

Cellular agriculture is an emerging branch of biotechnology that aims to address issues associated with the environmental impact, animal welfare and sustainability challenges of conventional animal farming for meat production. Cultured meat can be produced by applying current cell culture practices and biomanufacturing methods and utilizing mammalian cell lines and cell and gene therapy products to generate tissue or nutritional proteins for human consumption. However, significant improvements and modifications are needed for the process to be cost efficient and robust enough to be brought to production at scale for food supply. Here, we review the scientific and social challenges in transforming cultured meat into a viable commercial option, covering aspects from cell selection and medium optimization to biomaterials, tissue engineering, regulation and consumer acceptance. Producing meat without the drawbacks of conventional animal agriculture would greatly contribute to future food and nutrition security. This Review Article covers biological, technological, regulatory and consumer acceptance challenges in this developing field of biotechnology.

N. Auersperg, A. Wong, Kyung-Chul Choi et al.

B. Barres, I. Hart, H. Coles et al.

Bin Liu, Jau-Shyong Hong

S. Haynesworth, J. Goshima, V. Goldberg et al.

Mel Y. Chen

J. Iredale

A. Haraga, Maikke B. Ohlson, Samuel I. Miller

Pranjul Shah, Joëlle V. Fritz, E. Glaab et al.

Changes in the human gastrointestinal microbiome are associated with several diseases. To infer causality, experiments in representative models are essential, but widely used animal models exhibit limitations. Here we present a modular, microfluidics-based model (HuMiX, human–microbial crosstalk), which allows co-culture of human and microbial cells under conditions representative of the gastrointestinal human–microbe interface. We demonstrate the ability of HuMiX to recapitulate in vivo transcriptional, metabolic and immunological responses in human intestinal epithelial cells following their co-culture with the commensal Lactobacillus rhamnosus GG (LGG) grown under anaerobic conditions. In addition, we show that the co-culture of human epithelial cells with the obligate anaerobe Bacteroides caccae and LGG results in a transcriptional response, which is distinct from that of a co-culture solely comprising LGG. HuMiX facilitates investigations of host–microbe molecular interactions and provides insights into a range of fundamental research questions linking the gastrointestinal microbiome to human health and disease. Research on the interactions between the gut microbiota and human cells would greatly benefit from improved in vitro models. Here, Shah et al. present a modular microfluidics-based model that allows co-culture of human and microbial cells followed by 'omic' molecular analyses of the two cell contingents.

Nupura S. Bhise, V. Manoharan, Solange Massa et al.

P. Hunt

Caenorhabditis elegans is a small nematode that can be maintained at low cost and handled using standard in vitro techniques. Unlike toxicity testing using cell cultures, C. elegans toxicity assays provide data from a whole animal with intact and metabolically active digestive, reproductive, endocrine, sensory and neuromuscular systems. Toxicity ranking screens in C. elegans have repeatedly been shown to be as predictive of rat LD50 ranking as mouse LD50 ranking. Additionally, many instances of conservation of mode of toxic action have been noted between C. elegans and mammals. These consistent correlations make the case for inclusion of C. elegans assays in early safety testing and as one component in tiered or integrated toxicity testing strategies, but do not indicate that nematodes alone can replace data from mammals for hazard evaluation. As with cell cultures, good C. elegans culture practice (GCeCP) is essential for reliable results. This article reviews C. elegans use in various toxicity assays, the C. elegans model's strengths and limitations for use in predictive toxicology, and GCeCP. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons Ltd.

Anisha Palat

The normative definitions of human (and humanity) and animal (and animality) are most often linked to and underscored by Western philosophical and scientific views. Zakiyyah Iman Jack son’s Becoming Human: Mat ter and Meaning in an Antiblack World centralizes the question of these views, emphasizing that a rupture of these foundations might be the way forward in understanding the larger fields of humanity and animality, as well as their links to the fields of black studies, ecopolitics and biopolitics, posthumanism, animal studies, material culture, and gender and sexuality studies. Jackson takes the reader through arguments relating to these fields, illustrating how the understandings of these fields are based on antiblackness. This illustration is especially highlighted with regard to the animalization of black(ened) people, where Jackson shows that the animalization then is reliant on notions like black female flesh.1 Jackson’s aims for this book are high and the scope of the book is extremely wide, covering cultural production from the African diaspora across continents and time frames. Through this wide reach, Jackson manages to make extremely nuanced and complicated arguments regarding bestialization, animalization, and the idea of humanity and its links to black(ened) society. Ultimately, this monograph expresses foundational thoughts in novel ways, questioning their ontologies, and conveying the way forward through which to think about these ideas.

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

Shourya Jain, Paras Chopra

Users should not be systemically disadvantaged by the language they use for interacting with LLMs; i.e. users across languages should get responses of similar quality irrespective of language used. In this work, we create a set of real-world open-ended questions based on our analysis of the WildChat dataset and use it to evaluate whether responses vary by language, specifically, whether answer quality depends on the language used to query the model. We also investigate how language and culture are entangled in LLMs such that choice of language changes the cultural information and context used in the response by using LLM-as-a-Judge to identify the cultural context present in responses. To further investigate this, we evaluate LLMs on a translated subset of the CulturalBench benchmark across multiple languages. Our evaluations reveal that LLMs consistently provide lower quality answers to open-ended questions in low resource languages. We find that language significantly impacts the cultural context used by the model. This difference in context impacts the quality of the downstream answer.

Maksim Eren, Eric Michalak, Brian Cook et al.

Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations. As LLMs are increasingly used for strategic decision-making, policy support, and document engineering tasks such as summarization, categorization, and compliance-oriented auditing, improving cultural alignment is important for ensuring that downstream analyses and recommendations reflect target-population value profiles rather than default model priors. Previous work introduced a survey-grounded cultural alignment framework and showed that culture-specific prompting can reduce misalignment, but it primarily evaluated proprietary models and relied on manual prompt engineering. In this paper, we validate and extend that framework by reproducing its social sciences survey based projection and distance metrics on open-weight LLMs, testing whether the same cultural skew and benefits of culture conditioning persist outside closed LLM systems. Building on this foundation, we introduce use of prompt programming with DSPy for this problem-treating prompts as modular, optimizable programs-to systematically tune cultural conditioning by optimizing against cultural-distance objectives. In our experiments, we show that prompt optimization often improves upon cultural prompt engineering, suggesting prompt compilation with DSPy can provide a more stable and transferable route to culturally aligned LLM responses.

Lingzhi Shen, Xiaohao Cai, Yunfei Long et al.

Cultural awareness in language models is the capacity to understand and adapt to diverse cultural contexts. However, most existing approaches treat culture as static background knowledge, overlooking its dynamic and evolving nature. This limitation reduces their reliability in downstream tasks that demand genuine cultural sensitivity. In this work, we introduce CALM, a novel framework designed to endow language models with cultural self-awareness. CALM disentangles task semantics from explicit cultural concepts and latent cultural signals, shaping them into structured cultural clusters through contrastive learning. These clusters are then aligned via cross-attention to establish fine-grained interactions among related cultural features and are adaptively integrated through a Mixture-of-Experts mechanism along culture-specific dimensions. The resulting unified representation is fused with the model's original knowledge to construct a culturally grounded internal identity state, which is further enhanced through self-prompted reflective learning, enabling continual adaptation and self-correction. Extensive experiments conducted on multiple cross-cultural benchmark datasets demonstrate that CALM consistently outperforms state-of-the-art methods.