Hasil untuk "Animal culture"

N. Iscove, F. Sieber, K. Winterhalter

D. Russell, S. Snyder

Xinyu Shi, Li-Yi Wei, Nanxuan Zhao et al.

We introduce the concept of notational animating, an interaction paradigm for animation authoring where users sketch high-level notations over static drawings to indicate intended motions, which are then interpreted by automatic methods (e.g., GenAI models) to generate animation keyframes. Sketched notations have long served as cognitive instruments for animators, capturing forces, poses, dynamics, paths, and other animation features. However, such notations are often context-dependent, non-categorical, ambiguous, and composable based on our analysis of real-world animator-produced sketches. To facilitate interpretation, we first formalize these notations into a structured animation representation (i.e., source, path, and target). We then built an animation authoring system that translates high-level notations into the formalized intended animation, provides dynamic UI widgets for fine-grained parameter control, and establishes a closed feedback loop to resolve ambiguity. Finally, through a preliminary study with animators, we assess the usability of notational animating, reflect its affordance, and identify its contexts of use.

Jingyang Ke, Feiyang Wu, Jiyi Wang et al.

Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals. In natural environments, animals exhibit more complex, long-term behaviors driven by intrinsic motivations that are often unobservable. Recent works in time-varying inverse reinforcement learning (IRL) aim to capture shifting motivations in long-term, freely moving behaviors. However, a crucial challenge remains: animals make decisions based on their history, not just their current state. To address this, we introduce SWIRL (SWitching IRL), a novel framework that extends traditional IRL by incorporating time-varying, history-dependent reward functions. SWIRL models long behavioral sequences as transitions between short-term decision-making processes, each governed by a unique reward function. SWIRL incorporates biologically plausible history dependency to capture how past decisions and environmental contexts shape behavior, offering a more accurate description of animal decision-making. We apply SWIRL to simulated and real-world animal behavior datasets and show that it outperforms models lacking history dependency, both quantitatively and qualitatively. This work presents the first IRL model to incorporate history-dependent policies and rewards to advance our understanding of complex, naturalistic decision-making in animals.

Thi Thu Thuy Nguyen, Duc Thanh Nguyen

Smart data selection is becoming increasingly important in data-driven machine learning. Active learning offers a promising solution by allowing machine learning models to be effectively trained with optimal data including the most informative samples from large datasets. Wildlife data captured by camera traps are excessive in volume, requiring tremendous effort in data labelling and animal detection models training. Therefore, applying active learning to optimise the amount of labelled data would be a great aid in enabling automated wildlife monitoring and conservation. However, existing active learning techniques require that a machine learning model (i.e., an object detector) be fully accessible, limiting the applicability of the techniques. In this paper, we propose a model-agnostic active learning approach for detection of animals captured by camera traps. Our approach integrates uncertainty and diversity quantities of samples at both the object-based and image-based levels into the active learning sample selection process. We validate our approach in a benchmark animal dataset. Experimental results demonstrate that, using only 30% of the training data selected by our approach, a state-of-the-art animal detector can achieve a performance of equal or greater than that with the use of the complete training dataset.

John E. Linhoss, Jordan D. Gruber, Janet C. Remus et al.

Summary: Elevated ammonia (NH₃) emissions from litter are a concern in modern broiler production and can negatively influence performance and welfare. While commercially available litter amendments are commonly used to mitigate ammonia emissions, their effectiveness is often short-lived, prompting interest in more sustainable alternatives such as biochar. The objective of this project was to investigate the effects of different biochar application treatments (surface-applied vs mixed) on NH₃ volatilizations from used broiler litter. Biochar was surface-applied to litter at rates of 0.48, 0.97, 1.46, and 1.95 kg/m2 and mixed in at rates of 7.5, 15.0, 22.5, and 30.0 % v/v. Poultry Litter Treatment® (PLT) was surface-applied at a rate of 0.73 kg/m2 and a control of non-amended litter was also included. Treatments were replicated three times using a total of thirty 13.2 L plastic vessels. A constant air flow of 1.5 LPM was supplied to each vessel and either exhausted to the atmosphere or a photoacoustic NH₃ gas analyzer. NH3 was measured in each vessel nine times daily during a 12 d study. Mixing biochar into the litter provided enhanced contact with litter profile and led to significantly lower overall NH₃ concentrations than the surface-applied treatment (126 ppm vs 146.6 ppm, respectively). The 22.5 and 30 % v/v mixed applications resulted in the lowest NH3 concentrations (P ≤ 0.05) for the biochar treatments. However, NH₃ concentrations from all the biochar application treatments were significantly higher than PLT (65.0 ppm). This study shows that mixing biochar into broiler litter can reduce NH₃ volatilization. However, it does not seem to be competitive with PLT in terms of NH₃ reductions alone.

Abraham Akinyemi, Olumide Ajani, Olumide Akinniyi

Monosodium glutamate (MSG) is a widespread flavour enhancer linked to health risks, including male reproductive dysfunction. This study investigated tiger nut (Cyperus esculentus) as a potential protective agent against MSG-induced reproductive issues in male Wistar rats. Forty adult rats were divided into four groups: control, MSG-only (2 mg/g), tiger nut-only (500 mg/kg), and MSG+tiger nut combination (2 mg/g MSG + 500 mg/kg tiger nut). Treatments were administered orally for 28 days, with analyses conducted at days 14 and 28. Results showed significant variations in sperm parameters. At 14 days, the tiger nut group showed highest sperm motility (88.60±4.04%) and count (100.60±3.21×106/mL), while MSG reduced sperm viability (70.00±4.69%). By 28 days, MSG significantly decreased sperm motility (41.80±4.92%) and viability (54.80±6.76%). MSG increased sperm abnormalities at 14 days (13.60±2.51%) but normalized by 28 days. The MSG+tiger nut combination eliminated certain sperm abnormalities like coiled tail and tail-without-head. Gonadometric parameters remained stable throughout the study, indicating tiger nut's ability to maintain testicular architecture despite MSG exposure. Initial body weight increases in the MSG group normalized by weeks 3-4. The study concludes that tiger nut juice significantly protects against MSG-induced low sperm quality in male Wistar rats, suggesting its potential as a protective supplement for populations with unavoidable MSG exposure. Future research should explore long-term effects and cellular mechanisms.

Mubin Ul Haque, Joel Janek Dabrowski, Rebecca M. Rogers et al.

Detecting flying animals (e.g., birds, bats, and insects) using weather radar helps gain insights into animal movement and migration patterns, aids in management efforts (such as biosecurity) and enhances our understanding of the ecosystem.The conventional approach to detecting animals in weather radar involves thresholding: defining and applying thresholds for the radar variables, based on expert opinion. More recently, Deep Learning approaches have been shown to provide improved performance in detection. However, obtaining sufficient labelled weather radar data for flying animals to build learning-based models is time-consuming and labor-intensive. To address the challenge of data labelling, we propose a self-supervised learning method for detecting animal movement. In our proposed method, we pre-train our model on a large dataset with noisy labels produced by a threshold approach. The key advantage is that the pre-trained dataset size is limited only by the number of radar images available. We then fine-tune the model on a small human-labelled dataset. Our experiments on Australian weather radar data for waterbird segmentation show that the proposed method outperforms the current state-of-the art approach by 43.53% in the dice co-efficient statistic.

Yingxue Yu, Vidit Vidit, Andrey Davydov et al.

Animal Re-ID is crucial for wildlife conservation, yet it faces unique challenges compared to person Re-ID. First, the scarcity and lack of diversity in datasets lead to background-biased models. Second, animal Re-ID depends on subtle, species-specific cues, further complicated by variations in pose, background, and lighting. This study addresses background biases by proposing a method to systematically remove backgrounds in both training and evaluation phases. And unlike prior works that depend on pose annotations, our approach utilizes an unsupervised technique for feature alignment across body parts and pose variations, enhancing practicality. Our method achieves superior results on three key animal Re-ID datasets: ATRW, YakReID-103, and ELPephants.

Chen-Chi Chang, Han-Pi Chang, Hung-Shin Lee

In an era where cultural preservation is increasingly intertwined with technological innovation, this study introduces a groundbreaking approach to promoting and safeguarding the rich heritage of Taiwanese Hakka culture through the development of a Retrieval-Augmented Generation (RAG)-enhanced chatbot. Traditional large language models (LLMs), while powerful, often fall short in delivering accurate and contextually rich responses, particularly in culturally specific domains. By integrating external databases with generative AI models, RAG technology bridges this gap, empowering chatbots to not only provide precise answers but also resonate deeply with the cultural nuances that are crucial for authentic interactions. This study delves into the intricate process of augmenting the chatbot's knowledge base with targeted cultural data, specifically curated to reflect the unique aspects of Hakka traditions, language, and practices. Through dynamic information retrieval, the RAG-enhanced chatbot becomes a versatile tool capable of handling complex inquiries that demand an in-depth understanding of Hakka cultural context. This is particularly significant in an age where digital platforms often dilute cultural identities, making the role of culturally aware AI systems more critical than ever. System usability studies conducted as part of our research reveal a marked improvement in both user satisfaction and engagement, highlighting the chatbot's effectiveness in fostering a deeper connection with Hakka culture. The feedback underscores the potential of RAG technology to not only enhance user experience but also to serve as a vital instrument in the broader mission of ethnic mainstreaming and cultural celebration.

Qianyi Deng, Oishi Deb, Amir Patel et al.

Animal pose estimation (APE) aims to locate the animal body parts using a diverse array of sensor and modality inputs (e.g. RGB cameras, LiDAR, infrared, IMU, acoustic and language cues), which is crucial for research across neuroscience, biomechanics, and veterinary medicine. By evaluating 176 papers since 2011, APE methods are categorised by their input sensor and modality types, output forms, learning paradigms, experimental setup, and application domains, presenting detailed analyses of current trends, challenges, and future directions in single- and multi-modality APE systems. The analysis also highlights the transition between human and animal pose estimation, and how innovations in APE can reciprocally enrich human pose estimation and the broader machine learning paradigm. Additionally, 2D and 3D APE datasets and evaluation metrics based on different sensors and modalities are provided. A regularly updated project page is provided here: https://github.com/ChennyDeng/MM-APE.

Amith Ananthram, Elias Stengel-Eskin, Mohit Bansal et al.

Vision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things. For example, individuals from Western cultures focus more on the central figure in an image while individuals from East Asian cultures attend more to scene context. In this work, we characterize the Western bias of VLMs in image understanding and investigate the role that language plays in this disparity. We evaluate VLMs across subjective and objective visual tasks with culturally diverse images and annotations. We find that VLMs perform better on the Western split than on the East Asian split of each task. Through controlled experimentation, we trace one source of this bias in image understanding to the lack of diversity in language model construction. While inference in a language nearer to a culture can lead to reductions in bias, we show it is much more effective when that language was well-represented during text-only pre-training. Interestingly, this yields bias reductions even when prompting in English. Our work highlights the importance of richer representation of all languages in building equitable VLMs.

Jui-Fang Kuo, Yin-Hua Cheng, Chun-Wei Tung et al.

Abstract Background Fipronil (FPN) is a broad-spectrum pesticide and commonly known as low toxicity to vertebrates. However, increasing evidence suggests that exposure to FPN might induce unexpected adverse effects in the liver, reproductive, and nervous systems. Until now, the influence of FPN on immune responses, especially T-cell responses has not been well examined. Our study is designed to investigate the immunotoxicity of FPN in ovalbumin (OVA)-sensitized mice. The mice were administered with FPN by oral gavage and immunized with OVA. Primary splenocytes were prepared to examine the viability and functionality of antigen-specific T cells ex vivo. The expression of T cell cytokines, upstream transcription factors, and GABAergic signaling genes was detected by qPCR. Results Intragastric administration of FPN (1–10 mg/kg) for 11 doses did not show any significant clinical symptoms. The viability of antigen-stimulated splenocytes, the production of IL-2, IL-4, and IFN-γ by OVA-specific T cells, and the serum levels of OVA-specific IgG1 and IgG2a were significantly increased in FPN-treated groups. The expression of the GABAergic signaling genes was notably altered by FPN. The GAD67 gene was significantly decreased, while the GABAR β2 and GABAR δ were increased. Conclusion FPN disturbed antigen-specific immune responses by affecting GABAergic genes in vivo. We propose that the immunotoxic effects of FPN may enhance antigen-specific immunity by dysregulation of the negative regulation of GABAergic signaling on T cell immunity.

Michael Perez, Corey Toler-Franklin

Classifying the behavior of humans or animals from videos is important in biomedical fields for understanding brain function and response to stimuli. Action recognition, classifying activities performed by one or more subjects in a trimmed video, forms the basis of many of these techniques. Deep learning models for human action recognition have progressed significantly over the last decade. Recently, there is an increased interest in research that incorporates deep learning-based action recognition for animal behavior classification. However, human action recognition methods are more developed. This survey presents an overview of human action recognition and pose estimation methods that are based on convolutional neural network (CNN) architectures and have been adapted for animal behavior classification in neuroscience. Pose estimation, estimating joint positions from an image frame, is included because it is often applied before classifying animal behavior. First, we provide foundational information on algorithms that learn spatiotemporal features through 2D, two-stream, and 3D CNNs. We explore motivating factors that determine optimizers, loss functions and training procedures, and compare their performance on benchmark datasets. Next, we review animal behavior frameworks that use or build upon these methods, organized by the level of supervision they require. Our discussion is uniquely focused on the technical evolution of the underlying CNN models and their architectural adaptations (which we illustrate), rather than their usability in a neuroscience lab. We conclude by discussing open research problems, and possible research directions. Our survey is designed to be a resource for researchers developing fully unsupervised animal behavior classification systems of which there are only a few examples in the literature.

Kylie J. Trettner, Jeremy Hsieh, Weikun Xiao et al.

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison to the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

M. Sakalem, M. D. De Sibio, Felipe Allan Da Silva Da Costa et al.

An impressive percentage of biomedical advances were achieved through animal research and cell culture investigations. For drug testing and disease researches, both animal models and preclinical trials with cell cultures are extremely important, but present some limitations, such as ethical concern and inability of representing complex tissues and organs. 3D cell cultures arise providing a more realistic in vitro representation of tissues and organs. Environment and cell type in 3D cultures can represent in vivo conditions and thus provide accurate data on cell‐to‐cell interactions, and cultivation techniques are based on a scaffold, usually hydrogel or another polymeric material, or without scaffold, such as suspended microplates, magnetic levitation, and microplates for spheroids with ultra‐low fixation coating.

Jonathan Zong, Josh Pollock, Dylan Wootton et al.

We present Animated Vega-Lite, a set of extensions to Vega-Lite that model animated visualizations as time-varying data queries. In contrast to alternate approaches for specifying animated visualizations, which prize a highly expressive design space, Animated Vega-Lite prioritizes unifying animation with the language's existing abstractions for static and interactive visualizations to enable authors to smoothly move between or combine these modalities. Thus, to compose animation with static visualizations, we represent time as an encoding channel. Time encodings map a data field to animation keyframes, providing a lightweight specification for animations without interaction. To compose animation and interaction, we also represent time as an event stream; Vega-Lite selections, which provide dynamic data queries, are now driven not only by input events but by timer ticks as well. We evaluate the expressiveness of our approach through a gallery of diverse examples that demonstrate coverage over taxonomies of both interaction and animation. We also critically reflect on the conceptual affordances and limitations of our contribution by interviewing five expert developers of existing animation grammars. These reflections highlight the key motivating role of in-the-wild examples, and identify three central tradeoffs: the language design process, the types of animated transitions supported, and how the systems model keyframes.

Jaime Cofre

Cytasters have been underestimated in terms of their potential relevance to embryonic development and evolution. From the perspective discussed herein, structures such as the multiciliated cells of comb rows and balancers supporting mineralized statoliths and macrocilia in Beroe ovata point to a past event of multiflagellate fusion in the origin of metazoans. These structures, which are unique in evolutionary history, indicate that early animals handled basal bodies and their duplication in a manner consistent with a "developmental program" originated in the Ctenophora. Furthermore, the fact that centrosome amplification leads to spontaneous tumorigenesis suggests that the centrosome regulation process was co-opted into a neoplastic functional module. Multicilia, cilia, and flagella are deeply rooted in the evolution of animals and Neoplasia. The fusion of several flagellated microgametes into a cell with a subsequent phase of zygotic (haplontic) meiosis might have been at the origin of both animal evolution and the neoplastic process. In the Ediacaran ocean, we also encounter evolutionary links between the Warburg effect and Neoplasia.

LALMUANSANGI, ISHANI ROY, MOKIDUR RAHMAN et al.

In present study, lactation specific demographic analysis was carried out on 1728 records of Jersey crossbred cows maintained at Eastern Regional Station (ERS), ICAR-NDRI, Kalyani, West Bengal over a period of 40 years (1980-2019). The survival rate was 79% in first lactation and followed a decreasing trend with lactation order. The stayability for first lactation was one; decreased in subsequent lactations. Stayability was noted as 0.365, 0.052 and 0.015 at fourth, eighth and eleventh lactation respectively, indicating that only 36.5, 5.2 and 1.5 % of cows remained in the herd in these lactation period. Approximately 79% of total cows present in the herd belonged to first 4 lactations while less than 3% cows belonged to 9 and above parities indicating abundance of younger cows. The expected herd life was observed as 2.75, 2.222 and 1.721 in first, third and fifth lactation and decreased with parity order. The probability of cow being lost from the herd after first lactation was 21%. The present study concluded that the herd comprised of younger cows with a required annual replacement rate of 21% to keep the herd size constant.

Kursa Olimpia, Tomczyk Grzegorz, Sawicka-Durkalec Anna

Ornithobacterium rhinotracheale (ORT) causes significant economic losses to the poultry industry around the world. The bacterium often affects poultry as part of multiple infections causing very serious clinical signs that are usually not limited only to the respiratory system. This study’s main objective was the retrospective detection and identification of ORT in turkey flocks.