Hasil untuk "Animal biochemistry"

Xuyang Fang, Sion Hannuna, Edwin Simpson et al.

Identifying individual animals in long-duration videos is essential for behavioral ecology, wildlife monitoring, and livestock management. Traditional methods require extensive manual annotation, while existing self-supervised approaches are computationally demanding and ill-suited for long sequences due to memory constraints and temporal error propagation. We introduce a highly efficient, self-supervised method that reframes animal identification as a global clustering task rather than a sequential tracking problem. Our approach assumes a known, fixed number of individuals within a single video -- a common scenario in practice -- and requires only bounding box detections and the total count. By sampling pairs of frames, using a frozen pre-trained backbone, and employing a self-bootstrapping mechanism with the Hungarian algorithm for in-batch pseudo-label assignment, our method learns discriminative features without identity labels. We adapt a Binary Cross Entropy loss from vision-language models, enabling state-of-the-art accuracy ($>$97\%) while consuming less than 1 GB of GPU memory per batch -- an order of magnitude less than standard contrastive methods. Evaluated on challenging real-world datasets (3D-POP pigeons and 8-calves feeding videos), our framework matches or surpasses supervised baselines trained on over 1,000 labeled frames, effectively removing the manual annotation bottleneck. This work enables practical, high-accuracy animal identification on consumer-grade hardware, with broad applicability in resource-constrained research settings. All code written for this paper are \href{https://huggingface.co/datasets/tonyFang04/8-calves}{here}.

Louis Odinakaose Ezediuno, Michael Asebake Ockiya, Luqman Oluwaseun Awoniyi et al.

Purpose Colorectal cancer (CRC) remains a significant global health burden, necessitating innovative approaches for prevention and treatment. This study proposes a multiepitope vaccine targeting carcinoembryonic antigen (CEA) and insulin-like growth factor-1 receptor (IGF-1R), two prominent biomarkers associated with CRC progression. Methods Sequences of CEA and IGF-1R proteins were retrieved from NCBI databank, the sequences were aligned on the MEGA5 tool to identify conserved regions. Immunological and structural predictive analysis which include antigenic potential prediction, cytotoxic T-lymphocytes (CTLs), helper-T lymphocytes (HTLs), B-cell epitopes predictions, and prediction of the vaccine secondary and tertiary structure were performed. The vaccine was evaluated to validate its physiochemical and immunological properties. To determine the binding energy and domain, the tertiary structure of the vaccine was docked to Toll-like receptor 4, and viewed on PyMOL and LigPlot+ tools. Results CEA and IGF-1R were revealed to be highly antigenic, and non-allergens demonstrating the capacity to elicit robust immune responses, which include CTLs, HTLs, and B cells activation. The secondary structure revealed a conformation closely resembling native protein, with alpha helices, beta sheets, and coils, indicative of favorable interactions. Tertiary structure prediction predicted five models, model 0 was selected and validated due its highest confidence, and validation revealed that 87.5% of residues were within favored regions, with a z-score of 4.03. Molecular docking predicted strong binding complex with low binding energy. Conclusion Based on our analysis, the proposed multiepitope vaccine holds promise as an effective preventive measure against colorectal cancer development.

K.P. Shajeem, Sajith Purushothaman, K. Ally et al.

A 90-day feeding trial was conducted to evaluate the effects of Saccharomyces cerevisiae postbiotics on  dry matter intake, nutrient digestibility and rumen fermentation pattern in early-lactating crossbred cows. Twenty-four crossbred cows (within 10 days of calving) were randomly assigned to three groups: a control group fed a standard diet and two treatment groups receiving the standard diet supplemented with either 3g or 6g of postbiotic per animal. All animals were managed uniformly and fed according to ICAR (2013) standards. Weekly average daily dry matter intake was similar across three groups (P0.05). A five-day digestibility trial conducted at the end of the experimental period revealed no significant differences (P0.05) among the groups in the digestibility coefficients of dry matter, crude protein, ether extract, crude fibre, nitrogen-free extract, neutral detergent fibre and acid detergent fibre. Rumen pH and ammonia nitrogen levels were similar and remained within the normal range for all three treatment groups. However, the acetate proportion was significantly higher (P

Ryan M. Tharp, Nathan J. Hostetter, Jeffrey A. Buckel

Abstract Home range is a vital component to understanding animal ecology and can vary with factors like species, body size, and habitat. Artificial reefs are increasingly used to supplement or enhance habitat for reef fish. Quantifying reef fish home range sizes and the factors affecting home ranges is thus critical to understanding the efficacy of artificial reefs to sustain communities that reflect those on natural reefs. We estimated home ranges of reef fishes at artificial reefs in the southeast United States, evaluated what factors affected those home ranges, and compared them to home ranges of similarly sized fish on natural reefs. From June–October 2021 and 2022, we deployed acoustic tags on five fishery targeted reef species, black sea bass (Centropristis striata), gag (Mycteroperca microlepis), greater amberjack (Seriola dumerili), almaco jack (S. rivoliana), and red snapper (Lutjanus campechanus), on four artificial reefs near Cape Lookout, North Carolina. Tagged fish were tracked using a fine-scale positioning system for ~ 120 days. Home ranges varied by species and fish size (i.e., total length). Black sea bass had the smallest home ranges (mean = 6266 m2), gag and red snapper had moderate home ranges (38,265 m2 and 53,553 m2, respectively), and almaco jack and greater amberjack had the largest (152,146 m2, and 414,107 m2, respectively). Black sea bass, gag, and red snapper displayed increased home range size with total length while greater amberjack and almaco jack home ranges remained relatively constant across lengths. Greater amberjack home ranges were further influenced by artificial reef complex area with an increase in reef area leading to a larger home range. Our data from artificial reefs showed considerable overlap in the relationship between home range and body size when compared to similarly sized predatory reef fish on natural reefs. This information will be vital in improving ecological understanding of how artificial reefs can influence area use of reef-associated species to help inform future artificial reef deployments. Moreover, these results provide an important comparison between artificial reefs and natural reefs as habitats for reef-associated species, a topic that will become increasingly important as the quantity of artificial structures in our oceans increases.

Monika Jotautaitė, Lucius Caviola, David A. Brewster et al.

As large language models (LLMs) become more widely deployed, it is crucial to examine their ethical tendencies. Building on research on fairness and discrimination in AI, we investigate whether LLMs exhibit speciesist bias -- discrimination based on species membership -- and how they value non-human animals. We systematically examine this issue across three paradigms: (1) SpeciesismBench, a 1,003-item benchmark assessing recognition and moral evaluation of speciesist statements; (2) established psychological measures comparing model responses with those of human participants; (3) text-generation tasks probing elaboration on, or resistance to, speciesist rationalizations. In our benchmark, LLMs reliably detected speciesist statements but rarely condemned them, often treating speciesist attitudes as morally acceptable. On psychological measures, results were mixed: LLMs expressed slightly lower explicit speciesism than people, yet in direct trade-offs they more often chose to save one human over multiple animals. A tentative interpretation is that LLMs may weight cognitive capacity rather than species per se: when capacities were equal, they showed no species preference, and when an animal was described as more capable, they tended to prioritize it over a less capable human. In open-ended text generation tasks, LLMs frequently normalized or rationalized harm toward farmed animals while refusing to do so for non-farmed animals. These findings suggest that while LLMs reflect a mixture of progressive and mainstream human views, they nonetheless reproduce entrenched cultural norms around animal exploitation. We argue that expanding AI fairness and alignment frameworks to explicitly include non-human moral patients is essential for reducing these biases and preventing the entrenchment of speciesist attitudes in AI systems and the societies they influence.

Lisai Zhang, Baohan Xu, Siqian Yang et al.

We present AniME, a director-oriented multi-agent system for automated long-form anime production, covering the full workflow from a story to the final video. The director agent keeps a global memory for the whole workflow, and coordinates several downstream specialized agents. By integrating customized Model Context Protocol (MCP) with downstream model instruction, the specialized agent adaptively selects control conditions for diverse sub-tasks. AniME produces cinematic animation with consistent characters and synchronized audio visual elements, offering a scalable solution for AI-driven anime creation.

Xuan Wang, Kai Ruan, Liyang Qian et al.

Text-driven motion generation has attracted increasing attention due to its broad applications in virtual reality, animation, and robotics. While existing methods typically model human and animal motion separately, a joint cross-species approach offers key advantages, such as a unified representation and improved generalization. However, morphological differences across species remain a key challenge, often compromising motion plausibility. To address this, we propose X-MoGen, the first unified framework for cross-species text-driven motion generation covering both humans and animals. X-MoGen adopts a two-stage architecture. First, a conditional graph variational autoencoder learns canonical T-pose priors, while an autoencoder encodes motion into a shared latent space regularized by morphological loss. In the second stage, we perform masked motion modeling to generate motion embeddings conditioned on textual descriptions. During training, a morphological consistency module is employed to promote skeletal plausibility across species. To support unified modeling, we construct UniMo4D, a large-scale dataset of 115 species and 119k motion sequences, which integrates human and animal motions under a shared skeletal topology for joint training. Extensive experiments on UniMo4D demonstrate that X-MoGen outperforms state-of-the-art methods on both seen and unseen species.

Yuyan Wu, Sanjay Giridharan, Leixin Ma et al.

Underwater marine animal monitoring is essential for assessing biodiversity, evaluating ecosystem health, and understanding the effects of offshore structures. Traditional approaches such as tagging, sonar, and camera systems are often invasive, energy-intensive, or limited by poor visibility and water turbidity. Inspired by the hydrodynamic sensing of seal whiskers, wavy whisker vibration sensors have been developed for flow velocity and angle-of-attack detection. However, most prior work has focused on sensor characterization and only forward modeling, with limited exploration of the inverse problem of inferring animal movement. Moreover, current sensor sensitivity to vortex street wakes generated by swimming animals remains insufficient for practical monitoring. To address this gap, we develop a whisker-inspired sensor with a spiral-perforated base that amplifies vibrations within frequency ranges relevant to animal-induced wakes. We further characterize the influence of spiral parameters on the sensitive frequency band, enabling adaptation of the design to specific species. We evaluated the amplification effect of the spiral-perforated design using frequency response simulations of the whisker-base structure under harmonic water pressure. Results show up to 51x enhancement in root mean squared displacement at the target sensor location within frequency bands associated with animal-induced wakes compared to the baseline design, confirming the effectiveness of the amplification.

Yuanfeng Xu, Yuhao Chen, Zhongzhan Huang et al.

Recent video editing advancements rely on accurate pose sequences to animate subjects. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differs greatly from that of a pig due to differences in body structure). In this paper, we present AnimateZoo, a zero-shot diffusion-based video generator to address this challenging cross-species animation issue, aiming to accurately produce animal animations while preserving the background. The key technique used in our AnimateZoo is subject alignment, which includes two steps. First, we improve appearance feature extraction by integrating a Laplacian detail booster and a prompt-tuning identity extractor. These components are specifically designed to capture essential appearance information, including identity and fine details. Second, we align shape features and address conflicts from differing subjects by introducing a scale-information remover. This ensures accurate cross-species animation. Moreover, we introduce two high-quality animal video datasets featuring a wide variety of species. Trained on these extensive datasets, our model is capable of generating videos characterized by accurate movements, consistent appearance, and high-fidelity frames, without the need for the pre-inference fine-tuning that prior arts required. Extensive experiments showcase the outstanding performance of our method in cross-species action following tasks, demonstrating exceptional shape adaptation capability. The project page is available at https://justinxu0.github.io/AnimateZoo/.

Vojtěch Cermak, Lukas Picek, Lukáš Adam et al.

We propose a new method - WildFusion - for individual identification of a broad range of animal species. The method fuses deep scores (e.g., MegaDescriptor or DINOv2) and local matching similarity (e.g., LoFTR and LightGlue) to identify individual animals. The global and local information fusion is facilitated by similarity score calibration. In a zero-shot setting, relying on local similarity score only, WildFusion achieved mean accuracy, measured on 17 datasets, of 76.2%. This is better than the state-of-the-art model, MegaDescriptor-L, whose training set included 15 of the 17 datasets. If a dataset-specific calibration is applied, mean accuracy increases by 2.3% percentage points. WildFusion, with both local and global similarity scores, outperforms the state-of-the-art significantly - mean accuracy reached 84.0%, an increase of 8.5 percentage points; the mean relative error drops by 35%. We make the code and pre-trained models publicly available5, enabling immediate use in ecology and conservation.

K. Thulasi , K. Devada, H. Shameem et al.

The present study evaluated the PCR-RFLP of ITS-2+genes for species differentiation of three major amphistomes namely Gastrothylax crumenifer, Fischoederius cobboldi and Paramphistomum spp. Molecular analysis using PCR yielded amplicons of 515 bp for each species. The nucleotide differences among the sequences of the three species of amphistomes at different positions were further used for designing suitable RFLP. The amphistome parasites were distinguished taking into account the differences in the recognition sequences of TspRI on the ITS-2+ region by PCR-RFLP. The enzyme cleaved F.cobboldi at two recognition sites, CAGTG and CACTG and yielded 331, 93 and 91 bp products. Gastrothylax crumenifer had one recognition site CAGTG and resulted in 331 bp and 184 bp fragments. Since no recognition sequence was found in Paramphistomum spp., there was no cleavage. It is concluded that PCRRFLP was a promising molecular tool for species identification of amphistomes.

Dmitrii S. Vasilev, Anastasiia D. Shcherbitskaia, Natalia L. Tumanova et al.

The effect of the homocysteine toxicity on both mother and embryo is known to induce disruption of placental blood flow and disturbances of the brain formation in offspring. The mechanisms of these effects are poorly understood and should be studied. The effects of prenatal hyperhomocysteinemia (pHHC) on the expression of some neuronal genes, neural tissue maturation and neuronal migration were analyzed in this study. Hyperhomocysteinemia was induced in female rats by per os administration of 0.15% aqueous methionine solution during pregnancy. On P5–P20 some features of developmental delay were observed in both cortical and hippocampus tissue ultrastructure in pHHC pups, accompanied by a retardation in body weight and motor development. In hippocampus tissue of P20 pHHC pups of synaptic glomeruli were absence suggesting more essential tissue immaturity compared to the cortical one. In pHHC pupst was shown decreased number and disturbed positioning of the neuronal cells labeled on E14 or E18, suggesting decrease in generation of cortical neuroblasts and disturbance in their radial migration into the cortical plate. On E14 the expression of the <i>Kdr</i> gene (an angiogenesis system component) was decreased in pHHC fetus brains. The content of SEMA3E and the MMP-2 activity level was increased. On E20 the increase in proBDNF/mBDNF ratio was also shown in pHHC pups, it might affect positioning maturation and viability of neuronal cell. The activation of caspase-3 accompanied by decrease in the level of procaspase-8 in the brain tissue of E20 pHHC fetuses may suggest the presence of cell apoptosis. It can be concluded that pHHC disturbs the mechanisms of early brain development and delay in brain tissue maturation in both neocortex and hippocampus of pups during early postnatal ontogenesis.

Min-Young Jeon, Min-Young Jeon, Jee Eun Han et al.

IntroductionThe binary PirA/PirB toxin expressed by Vibrio parahaemolyticus (PirABVp) is a virulent complex that causes acute hepatopancreatic necrosis disease (AHPND) in shrimps, affecting the global shrimp farming industry. AHPND is currently diagnosed by detecting pirA and pirB genes by PCR; however, several V. parahaemolyticus strains do not produce the two toxins as proteins. Thus, an immunoassay using antibodies may be the most effective tool for detecting toxin molecules. In this study, we report a sandwich ELISA-based immunoassay for the detection of PirABVp.MethodsWe utilized a single-chain variable fragment (scFv) antibody library to select scFvs against the PirA or PirB subunits. Phage display panning rounds were conducted to screen and identify scFv antibodies directed against each recombinant toxin subunit. Selected scFvs were converted into IgGs to develop a sandwich immunoassay to detect recombinant and bacterial PirABVp.ResultsAntibodies produced as IgG forms showed sub-nanomolar to nanomolar affinities (KD), and a pair of anti-PirA antibody as a capture and anti-PirB antibody as a detector showed a limit of detection of 201.7 ng/mL for recombinant PirABVp. The developed immunoassay detected PirABVp in the protein lysates of AHPND-causing V. parahaemolyticus (VpAHPND) and showed a significant detectability in moribund or dead shrimp infected with a VpAHPND virulent strain compared to that in non-infected shrimp.DiscussionThese results indicate that the developed immunoassay is a reliable method for diagnosing AHPND by detecting PirABVp at the protein level and could be further utilized to accurately determine the virulence of extant or newly identified VpAHPND in the global shrimp culture industry.

Siméon Pierre Chegaing Fodouop, Sedric Donald Tala, Lunga Paul Keilah et al.

Veikka Immonen, Ekaterina Nepovinnykh, Tuomas Eerola et al.

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks.

A. Imbs, E. Ermolenko, V. Grigorchuk et al.

Marine invertebrates are a paraphyletic group that comprises more than 90% of all marine animal species. Lipids form the structural basis of cell membranes, are utilized as an energy reserve by all marine invertebrates, and are, therefore, considered important indicators of their ecology and biochemistry. The nutritional value of commercial invertebrates directly depends on their lipid composition. The lipid classes and fatty acids of marine invertebrates have been studied in detail, but data on their lipidomes (the profiles of all lipid molecules) remain very limited. To date, lipidomes or their parts are known only for a few species of mollusks, coral polyps, ascidians, jellyfish, sea anemones, sponges, sea stars, sea urchins, sea cucumbers, crabs, copepods, shrimp, and squid. This paper reviews various features of the lipid molecular species of these animals. The results of the application of the lipidomic approach in ecology, embryology, physiology, lipid biosynthesis, and in studies on the nutritional value of marine invertebrates are also discussed. The possible applications of lipidomics in the study of marine invertebrates are considered.

Junjie Han, Janice Siegford, Gustavo de los Campos et al.

Understanding factors affecting social interactions among animals is important for applied animal behavior research. Thus, there is a need to elicit statistical models to analyze data collected from pairwise behavioral interactions. In this study, we propose treating social interaction data as dyadic observations and propose a statistical model for their analysis. We performed posterior predictive checks of the model through different validation strategies: stratified 5-fold random cross-validation, block-by-social-group cross-validation, and block-by-focal-animals validation. The proposed model was applied to a pig behavior dataset collected from 797 growing pigs freshly remixed into 59 social groups that resulted in 10,032 records of directional dyadic interactions. The response variable was the duration in seconds that each animal spent delivering attacks on another group mate. Generalized linear mixed models were fitted. Fixed effects included sex, individual weight, prior nursery mate experience, and prior littermate experience of the two pigs in the dyad. Random effects included aggression giver, aggression receiver, dyad, and social group. A Bayesian framework was utilized for parameter estimation and posterior predictive model checking. Prior nursery mate experience was the only significant fixed effect. In addition, a weak but significant correlation between the random giver effect and the random receiver effect was obtained when analyzing the attacking duration. The predictive performance of the model varied depending on the validation strategy, with substantially lower performance from the block-by-social-group strategy than other validation strategies. Collectively, this paper demonstrates a statistical model to analyze interactive animal behaviors, particularly dyadic interactions.

Yunseok Jang, Yale Song, Gunhee Kim

Animated GIFs are becoming increasingly popular in online communication. People use them to express emotion, share their interests and enhance (or even replace) short-form texting; they are a new means to tell visual stories. Some creative animated GIFs are highly addictive to watch, and eventually become viral -- they circulate rapidly and widely within the network. What makes certain animated GIFs go viral? In this paper, we study the virality of animated GIFs by analyzing over 10 months of complete data logs (more than 1B posts and 12B reblogs) on Tumblr, one of the largest repositories of animated GIFs on the Internet. We conduct a series of quantitative and comparative studies on Tumblr data, comparing major types of online content -- text, images, videos, and animated GIFs. We report on a number of interesting, new findings on animated GIFs. We show that people tend to make animated GIFs easily searchable and discoverable by adding more hashtags than other content types. We also show that animated GIFs tend to go more viral than images and videos on Tumblr. With more in-depth analysis, we present that animated GIFs tend to get reblogged more and followed more from non-followers, while animated GIFs have more recurrence of a post. Lastly, we show that the virality of animated GIFs is more easily predictable than that of images and videos.

Hua Zhang, Jinjin Tong, Yonghong Zhang et al.

Objective In the present study, an liquid chromatography/mass spectrometry (LC/MS) metabolomics approach was performed to investigate potential biomarkers of milk production in high- and low-milk-yield dairy cows and to establish correlations among rumen fluid metabolites. Methods Sixteen lactating dairy cows with similar parity and days in milk were divided into high-yield (HY) and low-yield (LY) groups based on milk yield. On day 21, rumen fluid metabolites were quantified applying LC/MS. Results The principal component analysis and orthogonal correction partial least squares discriminant analysis showed significantly separated clusters of the ruminal metabolite profiles of HY and LY groups. Compared with HY group, a total of 24 ruminal metabolites were significantly greater in LY group, such as 3-hydroxyanthranilic acid, carboxylic acids, carboxylic acid derivatives (L-isoleucine, L-valine, L-tyrosine, etc.), diazines (uracil, thymine, cytosine), and palmitic acid, while the concentrations of 30 metabolites were dramatically decreased in LY group compared to HY group, included gentisic acid, caprylic acid, and myristic acid. The metabolite enrichment analysis indicated that protein digestion and absorption, ABC transporters and unsaturated fatty acid biosynthesis were significantly different between the two groups. Correlation analysis between the ruminal microbiome and metabolites revealed that certain typical metabolites were exceedingly associated with definite ruminal bacteria; Firmicutes, Actinobacteria, and Synergistetes phyla were highly correlated with most metabolites. Conclusion These findings revealed that the ruminal metabolite profiles were significantly different between HY and LY groups, and these results may provide novel insights to evaluate biomarkers for a better feed digestion and may reveal the potential mechanism underlying the difference in milk yield in dairy cows.

Xiaoying Chen, Xu Han, Bruno Blanchi et al.

Abstract Rett syndrome (RTT) is a progressive neurodevelopmental disorder, mainly caused by mutations in MeCP2 and currently with no cure. We report here that neurons from R106W MeCP2 RTT human iPSCs as well as human embryonic stem cells after MeCP2 knockdown exhibit consistent and long-lasting impairment in maturation as indicated by impaired action potentials and passive membrane properties as well as reduced soma size and spine density. Moreover, RTT-inherent defects in neuronal maturation could be pan-neuronal and occurred in neurons with both dorsal and ventral forebrain features. Knockdown of MeCP2 led to more severe neuronal deficits as compared to RTT iPSC-derived neurons, which appeared to retain partial function. Strikingly, consistent deficits in nuclear size, dendritic complexity and circuitry-dependent spontaneous postsynaptic currents could only be observed in MeCP2 knockdown neurons but not RTT iPSC-derived neurons. Both neuron-intrinsic and circuitry-dependent deficits of MeCP2-deficient neurons could be fully or partially rescued by re-expression of wild type or T158M MeCP2, strengthening the dosage dependency of MeCP2 on disease phenotypes and also the partial function of the mutant. Our findings thus reveal stable neuronal maturation deficits and unexpectedly, graded sensitivities of neuron-inherent and neural transmission phenotypes towards the extent of MeCP2 deficiency, which is informative for future therapeutic development.