This article interrogates the Amsterdam Plaskrul (pee curl) not merely as a functional amenity, but as a material archive of ontological sorting. Departing from the critiques of gendered exclusion, this study reads the pee curl as a philosophically generative case study through an infrastructural thick description that interprets design as a material text to argue that the pee curl’s promise of welfare inclusion is undercut by the very conditions of its provision. The analysis proceeds through three layers. First, a spatial analysis reveals how the pee curl’s design prioritizes urban circulation over inhabitation, offering inclusion only by reducing the subject to a unit of biological throughput. Second, an ontological inquiry shows that the removal of privacy strips the user of political subjecthood, re-enacting the colonial distinction between “Human” and “Animal”. Third, an affective analysis examines how the infrastructure functions as a technology of micro-discipline by offering relief while withholding the conditions necessary for dignity. Ultimately, the article contends that the pee curl operates as an infrastructural script that does not simply fail to provide care but successfully stages a hierarchy of being.
With the widespread adoption of Large Language Models (LLMs), respecting indigenous cultures becomes essential for models' culturally safety and responsible global applications. Existing studies separately consider cultural safety and cultural knowledge and neglect that the former should be grounded by the latter. This severely prevents LLMs from yielding culture-specific respectful responses. Consequently, adaptive cultural safety remains a formidable task. In this work, we propose to jointly model cultural safety and knowledge. First and foremost, cultural-safety and knowledge-paired data serve as the key prerequisite to conduct this research. However, the cultural diversity across regions and the subtlety of cultural differences pose significant challenges to the creation of such paired evaluation data. To address this issue, we propose a novel framework that integrates authoritative cultural knowledge descriptions curation, LLM-automated query generation, and heavy manual verification. Accordingly, we obtain a dataset named AdaCultureSafe containing 4.8K manually decomposed fine-grained cultural descriptions and the corresponding 48K manually verified safety- and knowledge-oriented queries. Upon the constructed dataset, we evaluate three families of popular LLMs on their cultural safety and knowledge proficiency, via which we make a critical discovery: no significant correlation exists between their cultural safety and knowledge proficiency. We then delve into the utility-related neuron activations within LLMs to investigate the potential cause of the absence of correlation, which can be attributed to the difference of the objectives of pre-training and post-alignment. We finally present a knowledge-grounded method, which significantly enhances cultural safety by enforcing the integration of knowledge into the LLM response generation process.
Intermittent fertilization intensity (IFI) is closely related to higher fertilization in chicken hens. Recent studies have identified genes influencing sperm-oocyte interactions and immune regulation that impact the fertilization process in chickens. This research aims to identify key candidate genes and variants regulating IFI through transcriptomic analysis and dual luciferase assays. Our study's transcriptomic analysis of 12 individuals exhibiting extreme IFI revealed several key candidate genes. Validation using quantitative PCR highlighted PRSS12, DNER, WIF1, and NRXN1 as potential contributors to variations in IFI. Notably, we observed significant differences in the expression of LMO4, located 247.2 kb downstream of IFI-associated genomic regions. To explore variants potentially involved in the regulation of LMO4, we conducted short variant annotation and SV-GWAS, but found no significant associations with IFI. Further motif analysis and dual luciferase validation uncovered three regulatory elements within the associated region that exhibited enhanced promoter or enhancer activity following significant SNP mutations. In conclusion, our findings indicate that LMO4, PRSS12, DNER, WIF1, and NRXN1 serve as primary candidate genes for regulating IFI. Additionally, three regulatory elements significantly associated with IFI were identified upstream of LMO4. These variants hold promise for use in selecting low-IFI lines.
The Yangtze finless porpoise (<i>Neophocaena asiaeorientalis asiaeorientalis</i>, YFP) is the only freshwater cetacean species currently found in China’s Yangtze River. To accurately evaluate its genetic diversity and provide reliable molecular markers for population genetic studies, this study developed a highly efficient and reproducible method for identifying polymorphic microsatellite loci using whole-genome sequencing data. Using this method, we identified and validated a set of highly polymorphic microsatellite markers, which were then used to analyze the genetic diversity of the YFP populations in Poyang Lake to evaluate their effectiveness. Our results demonstrated that the screening pipeline successfully identified 220 tetranucleotide repeat microsatellite loci. Based on the principle of uniform chromosomal distribution, 190 loci were randomly selected for experimental validation, of which 19 exhibited stable amplification, high polymorphism, and a low genotyping error rate. Genetic diversity analysis based on these markers revealed significant genetic variation among YFP populations in Poyang Lake, confirming the effectiveness of the developed markers. The polymorphic microsatellite molecular marker system developed in this study demonstrates high reliability and applicability for assessing YFP genetic diversity. This system provides a critical technical foundation for future research in conservation genetics, genetic resource preservation, and the development of genetic management strategies for the species.
The Audio Mostly (AM) conference has long been a platform for exploring the intersection of sound, technology, and culture. Despite growing interest in sonic cultures, discussions on the role of cultural diversity in sound design and sonification remain limited. This paper investigates the implicit biases and gaps within the discourse on music and sound aesthetics, challenging the notion of music as a 'universal language'. Through a historical and cross-cultural analysis of musicology and ethnomusicology, the profound influence of cultural context on auditory perception and aesthetic appraisal is highlighted. By drawing parallels between historical music practices and contemporary sound design, the paper advocates for a more inclusive approach that recognizes the diversity of sonic traditions. Using music as a case study, we underscore broader implications for sound design and sonification, emphasizing the need to integrate cultural perspectives into auditory design practices. A reevaluation of existing frameworks in sound design and sonification is proposed, emphasizing the necessity of culturally informed practices that resonate with global audiences. Ultimately, embracing cultural diversity in sound design is suggested to lead to richer, more meaningful auditory experiences and to foster greater inclusivity within the field.
This paper introduces a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of LLMs. By decomposing the evaluation along the dimensions of linguistic medium and cultural context, this framework enables a nuanced analysis of LLMs' ability to process questions within both native and cross-cultural contexts cross-lingually. Extensive evaluations are conducted on a wide range of models, revealing a notable "CulturalLinguistic Synergy" phenomenon, where models exhibit better performance when questions are culturally aligned with the language. This phenomenon is further explored through interpretability probing, which shows that a higher proportion of specific neurons are activated in a language's cultural context. This activation proportion could serve as a potential indicator for evaluating multilingual performance during model training. Our findings challenge the prevailing notion that LLMs, primarily trained on English data, perform uniformly across languages and highlight the necessity of culturally and linguistically model evaluations. Our code can be found at https://yingjiahao14. github.io/Dual-Evaluation/.
Accurate and scalable quantification of animal pose and appearance is crucial for studying behavior. Current 3D pose estimation techniques, such as keypoint- and mesh-based techniques, often face challenges including limited representational detail, labor-intensive annotation requirements, and expensive per-frame optimization. These limitations hinder the study of subtle movements and can make large-scale analyses impractical. We propose Pose Splatter, a novel framework leveraging shape carving and 3D Gaussian splatting to model the complete pose and appearance of laboratory animals without prior knowledge of animal geometry, per-frame optimization, or manual annotations. We also propose a rotation-invariant visual embedding technique for encoding pose and appearance, designed to be a plug-in replacement for 3D keypoint data in downstream behavioral analyses. Experiments on datasets of mice, rats, and zebra finches show Pose Splatter learns accurate 3D animal geometries. Notably, Pose Splatter represents subtle variations in pose, provides better low-dimensional pose embeddings over state-of-the-art as evaluated by humans, and generalizes to unseen data. By eliminating annotation and per-frame optimization bottlenecks, Pose Splatter enables analysis of large-scale, longitudinal behavior needed to map genotype, neural activity, and behavior at high resolutions.
Anis Suttan Shahrir, Zakiah Ayop, Syarulnaziah Anawar
et al.
Durian plantation suffers from animal intrusions that cause crop damage and financial loss. The traditional farming practices prove ineffective due to the unavailability of monitoring without human intervention. The fast growth of machine learning and Internet of Things (IoT) technology has led to new ways to detect animals. However, current systems are limited by dependence on single object detection algorithms, less accessible notification platforms, and limited deterrent mechanisms. This research suggests an IoT-enabled animal detection system for durian crops. The system integrates YOLOv5 and SSD object detection algorithms to improve detection accuracy. The system provides real-time monitoring, with detected intrusions automatically reported to farmers via Telegram notifications for rapid response. An automated sound mechanism (e.g., tiger roar) is triggered once the animal is detected. The YOLO+SSD model achieved accuracy rates of elephant, boar, and monkey at 90%, 85% and 70%, respectively. The system shows the highest accuracy in daytime and decreases at night, regardless of whether the image is still or a video. Overall, this study contributes a comprehensive and practical framework that combines detection, notification, and deterrence, paving the way for future innovations in automated farming solutions.
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, most rely on fixed skeletal templates, which prevent them from generalizing to skeletons with different or perturbed topologies. We address the core limitation of current motion generation methods - the combined lack of large-scale heterogeneous animal motion data and unified generative frameworks capable of jointly modeling arbitrary skeletal topologies and textual conditions. To this end, we introduce OmniZoo, a large-scale animal motion dataset spanning 140 species and 32,979 sequences, enriched with multimodal annotations. Building on OmniZoo, we propose a generalized autoregressive motion generation framework capable of producing text-driven motions for arbitrary skeletal topologies. Central to our model is a Topology-aware Skeleton Embedding Module that encodes geometric and structural properties of any skeleton into a shared token space, enabling seamless fusion with textual semantics. Given a text prompt and a target skeleton, our method generates temporally coherent, physically plausible, and semantically aligned motions, and further enables cross-species motion style transfer.
Reconstructing the 3D geometry, pose, and motion of animals is a long-standing problem, which has a wide range of applications, from biology, livestock management, and animal conservation and welfare to content creation in digital entertainment and Virtual/Augmented Reality (VR/AR). Traditionally, 3D models of real animals are obtained using 3D scanners. These, however, are intrusive, often prohibitively expensive, and difficult to deploy in the natural environment of the animals. In recent years, we have seen a significant surge in deep learning-based techniques that enable the 3D reconstruction, in a non-intrusive manner, of the shape and motion of dynamic objects just from their RGB image and/or video observations. Several papers have explored their application and extension to various types of animals. This paper surveys the latest developments in this emerging and growing field of research. It categorizes and discusses the state-of-the-art methods based on their input modalities, the way the 3D geometry and motion of animals are represented, the type of reconstruction techniques they use, and the training mechanisms they adopt. It also analyzes the performance of some key methods, discusses their strengths and limitations, and identifies current challenges and directions for future research.
Animals flexibly recombine a finite set of core motor motifs to meet diverse task demands, but existing behavior segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To better capture the continuous structure of behavior generation, we introduce motif-based continuous dynamics (MCD) discovery, a framework that (1) uncovers interpretable motif sets as latent basis functions of behavior by leveraging representations of behavioral transition structure, and (2) models behavioral dynamics as continuously evolving mixtures of these motifs. We validate MCD on a multi-task gridworld, a labyrinth navigation task, and freely moving animal behavior. Across settings, it identifies reusable motif components, captures continuous compositional dynamics, and generates realistic trajectories beyond the capabilities of traditional discrete segmentation models. By providing a generative account of how complex animal behaviors emerge from dynamic combinations of fundamental motor motifs, our approach advances the quantitative study of natural behavior.
Nisha Pillai, Aditi Virupakshaiah, Harrison W. Smith
et al.
Animal health monitoring and population management are critical aspects of wildlife conservation and livestock management that increasingly rely on automated detection and tracking systems. While Unmanned Aerial Vehicle (UAV) based systems combined with computer vision offer promising solutions for non-invasive animal monitoring across challenging terrains, limited availability of labeled training data remains an obstacle in developing effective deep learning (DL) models for these applications. Transfer learning has emerged as a potential solution, allowing models trained on large datasets to be adapted for resource-limited scenarios such as those with limited data. However, the vast landscape of pre-trained neural network architectures makes it challenging to select optimal models, particularly for researchers new to the field. In this paper, we propose a reinforcement learning (RL)-based transfer learning framework that employs an upper confidence bound (UCB) algorithm to automatically select the most suitable pre-trained model for animal detection tasks. Our approach systematically evaluates and ranks candidate models based on their performance, streamlining the model selection process. Experimental results demonstrate that our framework achieves a higher detection rate while requiring significantly less computational time compared to traditional methods.
Sharifa Sultana, Hafsah Mahzabin Chowdhury, Zinnat Sultana
et al.
Reproductive well-being education in the Global South is often challenged as many communities perceive many of its contents as misinformation, misconceptions, and language-inappropriate. Our ten-month-long ethnographic study (n=41) investigated the impact of sociocultural landscape, cultural beliefs, and healthcare infrastructure on Bangladeshi people's access to quality reproductive healthcare and set four design goals: combating misinformation, including culturally appropriate language, professionals' accountable moderation, and promoting users' democratic participation. Building on the model of `\textit{Distributive Justice,}' we designed and evaluated \textit{`Socheton,'} a culturally appropriate AI-mediated tool for reproductive well-being that includes healthcare professionals, AI-language teachers, and community members to moderate and run the activity-based platform. Our user study (n=28) revealed that only combating misinformation and language inappropriateness may still leave the community with a conservative mob culture and patronize reproductive care-seeking. This guides well-being HCI design toward being culturally appropriate in the context of reproductive justice with sensitive marginalized communities.
As an important pillar of underwater intelligence, Marine Animal Segmentation (MAS) involves segmenting animals within marine environments. Previous methods don't excel in extracting long-range contextual features and overlook the connectivity between discrete pixels. Recently, Segment Anything Model (SAM) offers a universal framework for general segmentation tasks. Unfortunately, trained with natural images, SAM does not obtain the prior knowledge from marine images. In addition, the single-position prompt of SAM is very insufficient for prior guidance. To address these issues, we propose a novel feature learning framework, named Dual-SAM for high-performance MAS. To this end, we first introduce a dual structure with SAM's paradigm to enhance feature learning of marine images. Then, we propose a Multi-level Coupled Prompt (MCP) strategy to instruct comprehensive underwater prior information, and enhance the multi-level features of SAM's encoder with adapters. Subsequently, we design a Dilated Fusion Attention Module (DFAM) to progressively integrate multi-level features from SAM's encoder. Finally, instead of directly predicting the masks of marine animals, we propose a Criss-Cross Connectivity Prediction (C$^3$P) paradigm to capture the inter-connectivity between discrete pixels. With dual decoders, it generates pseudo-labels and achieves mutual supervision for complementary feature representations, resulting in considerable improvements over previous techniques. Extensive experiments verify that our proposed method achieves state-of-the-art performances on five widely-used MAS datasets. The code is available at https://github.com/Drchip61/Dual_SAM.
Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the wild. Our predictions based on Emergenets built only using 220,151 Hemagglutinnin (HA) sequences consistently outperform WHO seasonal vaccine recommendations for H1N1/H3N2 subtypes over two decades (average match-improvement: 3.73 AAs, 28.40\%), and are at par with state-of-the-art approaches that use more detailed phenotypic annotations. Finally, our generative models are used to scalably calculate the current odds of emergence of animal strains not yet in human circulation, which strongly correlates with CDC's expert-assessed Influenza Risk Assessment Tool (IRAT) scores (Pearson's $r = 0.721, p = 10^{-4}$). A minimum five orders of magnitude speedup over CDC's assessment (seconds vs months) then enabled us to analyze 6,354 animal strains collected post-2020 to identify 35 strains with high emergence scores ($> 7.7$). The Emergenet framework opens the door to preemptive pandemic mitigation through targeted inoculation of animal hosts before the first human infection.
The cultural landscape of interactions with dialogue agents is a compelling yet relatively unexplored territory. It's clear that various sociocultural aspects -- from communication styles and beliefs to shared metaphors and knowledge -- profoundly impact these interactions. To delve deeper into this dynamic, we introduce cuDialog, a first-of-its-kind benchmark for dialogue generation with a cultural lens. We also develop baseline models capable of extracting cultural attributes from dialogue exchanges, with the goal of enhancing the predictive accuracy and quality of dialogue agents. To effectively co-learn cultural understanding and multi-turn dialogue predictions, we propose to incorporate cultural dimensions with dialogue encoding features. Our experimental findings highlight that incorporating cultural value surveys boosts alignment with references and cultural markers, demonstrating its considerable influence on personalization and dialogue quality. To facilitate further exploration in this exciting domain, we publish our benchmark publicly accessible at https://github.com/yongcaoplus/cuDialog.
Kate L. Hepworth‐Warren, Krista Estell, Bobby Cowles
et al.
Abstract Background Serum amyloid A (SAA) is a major acute phase protein in horses which could be a useful tool for assessing clinical response to treatment of bacterial pneumonia in adult horses. Objectives To monitor SAA concentration in response to treatment and identify associations among SAA concentration, WBC and neutrophil counts, and fibrinogen in bacterial pneumonia in adult horses. Animals Eighteen adult horses with bacterial pneumonia. Methods Prospective clinical study. Horses hospitalized with bacterial pneumonia were enrolled and SAA concentration and vital signs were assessed daily. SAA concentration was measured by a handheld meter. CBC and plasma fibrinogen were assessed on days 0, 1, and 2, then every 3 days until discharge. Data were not normally distributed and therefore were log transformed. Log‐transformed data were analyzed and comparisons were performed on LSMeans by the 2‐sided Student's t‐test at the 5% level of significance. Results Geometric mean SAA concentration on day 0 was 537 μg/mL (SE 383 μg/mL). Geometric mean SAA concentration decreased significantly over time (P = .0001), peaking at day 2 (geomean 1038 μg/mL, SE 261.7 μg/mL) and decreasing until discharge. Plasma concentration of fibrinogen (P = .06), neutrophil count (P = .48), and WBC count (P = .07) did not change significantly over time. Conclusions and Clinical Importance SAA concentration decreased significantly over the course of treatment and correlated with clinical improvement of pneumonia whereas fibrinogen, neutrophil, and WBC counts did not.
Sonal Gupta, Gaurav Tripathi, Kartikey Patel
et al.
Buffalo cloning is a powerful assisted reproductive tool for multiplying elite buffalo germplasm. However, the live off-spring production efficiency is low due to aberrant epigenetic reprogramming. Aberrant epigenetic marks can be modified by culturing donor cells and/or one cell stage fused embryo or both with epigenetic modifiers alone or in combination. In the present study, we examined the effect of oxamflatin (OxF), ascorbic acid (AA), and their combined (OxF+AA) effect on in vitro developmental competence, quality, and pregnancy establishment rate of buffalo cloned embryos. Oxamflatin is a histone deacetylase inhibitor, whereas ascorbic acid is a hypomethylating agent. To achieve this aim of the study, reconstructs (fused two enucleated ooplasm + donor cell) were cultured for 8 h, i.e., 4 h post-fusion and 4 h post-activation with 1 μM oxamflatin (OxF), 50 μM ascorbic acid (AA), and there combined (OxF+AA) treatment. There was no significant (p<0.05) difference in cleavage rates when reconstructs were treated with oxamflatin (81.34±0.81%), ascorbic acid (82.76±0.51%), combined treatment (82.17±0.54%) compared with control (82.87±0.63%). The blastocyst production rate was significantly higher (p<0.05) in combined treatment OxF+AA (41.64±0.95%) as compared to OxF (34.88±1.22%), AA (38.99±0.69%) and control (30.29±0.77%). The TUNEL assay showed a significantly lower (p<0.05) apoptotic index in combined (OxF+AA) treatment (1.43±0.43) as compared to oxamflatin (3.54±0.46), ascorbic acid (3.24±0.49) and control (5.06±0.48). The cloned embryos were transferred to the synchronized recipient (n=15 to18 buffaloes in each group), and the conception rate was observed better in combined treatment (OxF+AA) (46.66%) than oxamflatin (16.66%) and ascorbic acid (12.50%). At the same time, no pregnancy was reported in the control group. In conclusion, the combined treatment with oxamflatin and ascorbic acid improves the in vitro and in vivo developmental potential in buffalo-cloned embryos, which could probably be due to decreased methylation and increased acetylation of the embryos.
As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial. Despite advancements in multilingual capabilities, models are not designed with appropriate cultural nuances. In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture. We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures. CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations. Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis, where we find concerning cases of stereotyping and cultural unfairness. We further test their text-infilling performance, revealing the incapability of appropriate adaptation to Arab cultural contexts. Finally, we analyze 6 Arabic pre-training corpora and find that commonly used sources such as Wikipedia may not be best suited to build culturally aware LMs, if used as they are without adjustment. We will make CAMeL publicly available at: https://github.com/tareknaous/camel
Culture is a collection of connected and potentially interactive patterns that characterize a social group or a passed-on idea that people acquire as members of society. While offline activities can provide a better picture of the geographical association of cultural traits than online activities, gathering such data on a large scale has been challenging. Here, we use multi-decade longitudinal records of cultural events from Meetup.com, the largest event-based social networking service, to examine the landscape of offline cultural events. We analyze the temporal and categorical event dynamics driven by cultural diversity using over 2 million event logs collected over 17 years in 90 countries. Our results show that the national economic status explains 44.6 percent of the variance in total event count, while cultural characteristics such as individualism and long-term orientation explain 32.8 percent of the variance in topic categories. Furthermore, our analysis using hierarchical clustering reveals cultural proximity between the topics of socio-cultural activities (e.g., politics, leisure, health, technology). We expect that this work provides a landscape of social and cultural activities across the world, which allows us to better understand their dynamical patterns as well as their associations with cultural characteristics.