Hasil untuk "Animal biochemistry"

Lucas Martini, Alexander Lappe, Anna Bognár et al.

The recognition of dynamic and social behavior in animals is fundamental for advancing ethology, ecology, medicine and neuroscience. Recent progress in deep learning has enabled automated behavior recognition from video, yet an accurate reconstruction of the three-dimensional (3D) pose and shape has not been integrated into this process. Especially for non-human primates, mesh-based tracking efforts lag behind those for other species, leaving pose descriptions restricted to sparse keypoints that are unable to fully capture the richness of action dynamics. To address this gap, we introduce the $\textbf{Big Ma}$ca$\textbf{Q}$ue 3D Motion and Animation Dataset ($\texttt{BigMaQ}$), a large-scale dataset comprising more than 750 scenes of interacting rhesus macaques with detailed 3D pose descriptions. Extending previous surface-based animal tracking methods, we construct subject-specific textured avatars by adapting a high-quality macaque template mesh to individual monkeys. This allows us to provide pose descriptions that are more accurate than previous state-of-the-art surface-based animal tracking methods. From the original dataset, we derive BigMaQ500, an action recognition benchmark that links surface-based pose vectors to single frames across multiple individual monkeys. By pairing features extracted from established image and video encoders with and without our pose descriptors, we demonstrate substantial improvements in mean average precision (mAP) when pose information is included. With these contributions, $\texttt{BigMaQ}$ establishes the first dataset that both integrates dynamic 3D pose-shape representations into the learning task of animal action recognition and provides a rich resource to advance the study of visual appearance, posture, and social interaction in non-human primates. The code and data are publicly available at https://martinivis.github.io/BigMaQ/ .

Weihan Li, Jingyang Ke, Yule Wang et al.

Understanding animal behavior from video is essential for neuroscience research. Modern laboratories typically collect two complementary data streams: skeletal keypoints from pose estimation tools and raw video recordings. Keypoint-based methods are efficient but suffer from geometric ambiguity, environmental blindness, and sensitivity to occlusions. Video-based methods capture rich context but require processing every frame, making them impractical for the hundreds of hours of recordings that modern experiments produce. We introduce LookAgain, a multimodal framework that combines the efficiency of keypoints with the representational power of video through on-demand visual grounding. During training, LookAgain uses dense visual features to pretrain a motion encoder and to train a gating module that learns which frames require visual context. During inference, this gating module activates visual processing only when keypoint signals are ambiguous, while maintaining performance comparable to using all frames. Experiments on single-animal and multi-animal benchmarks show that LookAgain achieves strong performance with significantly reduced computational cost, enabling high-quality behavior analysis on long-duration recordings.

Wilhelm Kerle-Malcharek, Karsten Klein, Martin Wikelski et al.

Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research. Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbing the animals. This study aims to explore how selective data transmission, guided by machine learning, can reduce the energy consumption of bio-loggers, thereby extending their operational lifespan without requiring hardware modifications.

Depanshu Sani, Mehar Khurana, Saket Anand

Animal Re-ID has recently gained substantial attention in the AI research community due to its high impact on biodiversity monitoring and unique research challenges arising from environmental factors. The subtle distinguishing patterns, handling new species and the inherent open-set nature make the problem even harder. To address these complexities, foundation models trained on labeled, large-scale and multi-species animal Re-ID datasets have recently been introduced to enable zero-shot Re-ID. However, our benchmarking reveals significant gaps in their zero-shot Re-ID performance for both known and unknown species. While this highlights the need for collecting labeled data in new domains, exhaustive annotation for Re-ID is laborious and requires domain expertise. Our analyses show that existing unsupervised (USL) and AL Re-ID methods underperform for animal Re-ID. To address these limitations, we introduce a novel AL Re-ID framework that leverages complementary clustering methods to uncover and target structurally ambiguous regions in the embedding space for mining pairs of samples that are both informative and broadly representative. Oracle feedback on these pairs, in the form of must-link and cannot-link constraints, facilitates a simple annotation interface, which naturally integrates with existing USL methods through our proposed constrained clustering refinement algorithm. Through extensive experiments, we demonstrate that, by utilizing only 0.033% of all annotations, our approach consistently outperforms existing foundational, USL and AL baselines. Specifically, we report an average improvement of 10.49%, 11.19% and 3.99% (mAP) on 13 wildlife datasets over foundational, USL and AL methods, respectively, while attaining state-of-the-art performance on each dataset. Furthermore, we also show an improvement of 11.09%, 8.2% and 2.06% for unknown individuals in an open-world setting.

Ian Noronha, Advait Prasad Jawaji, Juan Camilo Soto et al.

Animal-robot interaction (ARI) remains an unexplored challenge in robotics, as robots struggle to interpret the complex, multimodal communication cues of animals, such as body language, movement, and vocalizations. Unlike human-robot interaction, which benefits from established datasets and frameworks, animal-robot interaction lacks the foundational resources needed to facilitate meaningful bidirectional communication. To bridge this gap, we present the MBE-ARI (Multimodal Bidirectional Engagement in Animal-Robot Interaction), a novel multimodal dataset that captures detailed interactions between a legged robot and cows. The dataset includes synchronized RGB-D streams from multiple viewpoints, annotated with body pose and activity labels across interaction phases, offering an unprecedented level of detail for ARI research. Additionally, we introduce a full-body pose estimation model tailored for quadruped animals, capable of tracking 39 keypoints with a mean average precision (mAP) of 92.7%, outperforming existing benchmarks in animal pose estimation. The MBE-ARI dataset and our pose estimation framework lay a robust foundation for advancing research in animal-robot interaction, providing essential tools for developing perception, reasoning, and interaction frameworks needed for effective collaboration between robots and animals. The dataset and resources are publicly available at https://github.com/RISELabPurdue/MBE-ARI/, inviting further exploration and development in this critical area.

Nantikan Joyroy, Lukana Ngiwsara, Siriporn Wannachat et al.

Background Lawsonia inermis L., commonly known as henna, is a traditional medicinal Indian plant used for anti-dandruff and antifungal purposes. The plant is rich in phytochemicals and is believed to have significant bioactivity potential. However, limited information is available on the phytochemical compositions of L. inermis cultivars in Thailand. Therefore, this study aims to assess the phytochemical constituents and investigate the bioactivity of L. inermis extract. Methods L. inermis leaf extracts were prepared by macerating in ethanol (HenE), methanol (HenM), chloroform (HenC), hexane (HenH), and water boiling (HenW). The phenolic and flavonoid contents were determined by Folin-Ciocalteu and aluminum chloride colorimetric methods. High-performance liquid chromatography (HPLC) was performed to qualify polyphenolic contents. Antioxidant activities were evaluated by using 2,2-Diphenyl-1-picrylhydrazyl (DPPH), 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and ferric reducing antioxidant power (FRAP) methods. Moreover, antibacterial activity was tested against two gram-positive and four gram-negative bacteria by the agar well diffusion and the broth dilution methods, and antifungal activity was carried out using the poisoned food technique. Additionally, the cytotoxicity of the extracts against MDA-MB-231, SW480, A549 and A549RT-eto cancer cell lines was determined by using (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (MTT) assay. The scratch wound healing assay was performed to determine the effect of anti-migration on A549 cells. Results Quantitative analysis revealed that HenE and HenM extracts had high phenolic and flavonoid contents. Gallic acid, catechin, ellagic acid, apigetrin, lawsone and quercetin were identified by HPLC. The HenE and HenM extracts exhibited strong antioxidant properties, and the extracts showed different inhibition growth against bacteria tested, especially B. cereus and S. aureus. In addition, all extracts had potential inhibitory activity to all fungal strains, especially HenE and Hen M, which exhibited strong antifungus activity against Penicillium sp. All extracts showed cytotoxic effects in the cell lines MDA-MB-231, SW480, A549 and A549RT-eto, except HenH. The HenE and HenM exhibited the best IC50 values of 57.33 ± 5.56 µg/ml and 65.00 ± 7.07 µg/ml against SW480 cells, respectively. The HenC, HenW, and HenH were found to suppress A549 cells migration. Discussion and Conclusion This study revealed that the L. inermis extracts, particularly those obtained from polar solvents (HenE and HenM), had a strong potency for antioxidant, antibacterial, and anticancer properties. Our findings highlight the valuable biological properties of extracts that can be promoted through additional investigation into their applications in Thailand for medicinal and industrial purposes.

Alaa H. Sewid, Alaa H. Sewid, Mohamed Sharaf et al.

IntroductionEnterococcus faecalis, a common inhabitant of the feline gastrointestinal tract, has emerged as a significant pathogen causing urinary tract infections (UTIs) in domestic cats. The rise of multidrug-resistant E. faecalis strains and their propensity to form biofilms pose significant challenges in treatment. This study investigated the antibacterial and antibiofilm activities of hexagonal zinc oxide nanoparticles (ZnONPs) alone and in combination with streptomycin and Moringa oleifera leaf extract (MOLe) against multidrug-resistant E. faecalis isolates from feline UTIs.MethodsAntimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method. Biofilm formation was assessed using the crystal violet assay, and biofilm-associated genes (sprE, gelE, fsrABC) were detected by PCR. ZnONPs, Str/ZnONPs (streptomycin-loaded ZnONPs), and Str/MOLe@ZnONPs (streptomycin and MOLe-loaded ZnONPs) were characterized using FTIR, DLS, TEM, and SEM. The antibacterial and antibiofilm activities of the synthesized nanoparticles were evaluated through time-kill assays, well diffusion assays, and gene expression analysis.ResultsA high prevalence of multidrug resistance was observed among the E. faecalis isolates, with significant resistance to ampicillin, vancomycin, and streptomycin. Characterization studies revealed the successful encapsulation of streptomycin and MOLe within the ZnONPs.In vitro assays demonstrated that Str/MOLe@ZnONPs exhibited potent antibacterial and antibiofilm activities against the tested E. faecalis strains, significantly reducing bacterial growth and biofilm formation.DiscussionThe emergence of multidrug-resistant E. faecalis strains necessitates the development of novel therapeutic strategies. This study demonstrates the promising potential of ZnONPs, particularly those loaded with streptomycin and MOLe, in combating biofilm-forming E. faecalis. The synergistic effects of the combined formulation may offer a novel approach to overcome antibiotic resistance and improve the treatment outcomes of E. faecalis UTIs in domestic cats.

Alexander Rezvykh, Daniil Shteinberg, Evgeny Bronovitsky et al.

Boyang Wang, Fengyu Yang, Xihang Yu et al.

While real-world anime super-resolution (SR) has gained increasing attention in the SR community, existing methods still adopt techniques from the photorealistic domain. In this paper, we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First, we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead, we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline, we introduce the Anime Production-oriented Image (API) dataset. In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition, we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark, showing our method outperforms state-of-the-art anime dataset-trained approaches.

Shuai Tan, Biao Gong, Xiang Wang et al.

Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize well on anthropomorphic characters commonly used in industries like gaming and entertainment. Our in-depth analysis suggests to attribute this limitation to their insufficient modeling of motion, which is unable to comprehend the movement pattern of the driving video, thus imposing a pose sequence rigidly onto the target character. To this end, this paper proposes Animate-X, a universal animation framework based on LDM for various character types (collectively named X), including anthropomorphic characters. To enhance motion representation, we introduce the Pose Indicator, which captures comprehensive motion pattern from the driving video through both implicit and explicit manner. The former leverages CLIP visual features of a driving video to extract its gist of motion, like the overall movement pattern and temporal relations among motions, while the latter strengthens the generalization of LDM by simulating possible inputs in advance that may arise during inference. Moreover, we introduce a new Animated Anthropomorphic Benchmark (A^2Bench) to evaluate the performance of Animate-X on universal and widely applicable animation images. Extensive experiments demonstrate the superiority and effectiveness of Animate-X compared to state-of-the-art methods.

M. Drisya, E. D. Benjamin, C. Jayakumar et al.

The current study was conducted to evaluate the quality of specific fractions of three Large White Yorkshire (LWY) boar semen samples. Fifteen semen samples were collected by gloved hand method as an initial 10 mL of sperm rich fraction (SRF- F1), remaining SRF (F2) and whole semen. Samples were evaluated for colour, volume, pH, sperm concentration and progressive motility. The first 10 mL of SRF of boar semen contained 25 per cent of the total ejaculate spermatozoa and was able to withstand cooling, freezing and thawing better than the spermatozoa of the bulk ejaculate. A significant difference between the fractions was observed in the volume of semen ejaculated, pH and sperm concentration across different fractions of the same ejaculate. The differences in semen fractions were attributed to the differences in the composition of seminal plasma of the initial fraction of SRF Keywords: Boar semen, sperm rich fraction, semen evaluation

Vorthon Sawaswong, Prangwalai Chanchaem, Pavit Klomkliew et al.

Abstract Tuberculosis (TB) is an infectious disease caused by the Mycobacterium tuberculosis complex (Mtbc), which develops from asymptomatic latent TB to active stages. The microbiome was purposed as a potential factor affecting TB pathogenesis, but the study was limited. The present study explored the association between gut-pharyngeal microbiome and TB stages in cynomolgus macaques using the full-length 16S rDNA amplicon sequencing based on Oxford Nanopore Technologies. The total of 71 macaques was divided into TB (−) control, TB (+) latent and TB (+) active groups. The differential abundance analysis showed that Haemophilus hemolyticus was decreased, while Prevotella species were increased in the pharyngeal microbiome of TB (+) macaques. In addition, Eubacterium coprostanoligenes in the gut was enriched in TB (+) macaques. Alteration of these bacteria might affect immune regulation and TB severity, but details of mechanisms should be further explored and validated. In summary, microbiota may be associated with host immune regulation and affect TB progression. The findings suggested the potential mechanisms of host-microbes interaction, which may improve the understanding of the role of microbiota and help develop therapeutics for TB in the future.

Nehal Kamel, Dina Bashir, Ebtihal El-Leithy et al.

Polyethylene terephthalate nanoplastics (PET-NPs) are utilized in the production of medical bionic materials and the packaging of beverages. Betaine is a ubiquitous natural constituent present in organisms such as plants, animals, and microorganisms. So, the current investigation tries to find out if PET-NPs could seriously harm mice's kidneys and whether betaine could have any ameliorative effects. In this study, a total of 40 mice were separated into four groups (ten mice in each): Group I (performed as the control group), Group II (received 1000 mg/kg betaine intraperitoneally), Group III (received 200 mg/kg PET-NPs orally), and Group IV (was given betaine first, and after 1 hour, PET-NPs were given at dosages that were the same as those given to groups II and III, respectively) daily for a month. Serum and kidney samples were collected and processed for biochemical and histological assessments. The current study found that PET-NPs significantly increased blood urea nitrogen (BUN), creatinine, and malondialdehyde levels (MDA), while reducing glutathione (GSH) levels. The histological examination revealed multiple histopathological alterations. The PET-NPs-exposed group demonstrated renal corpuscle hypotrophy, a loss of cellular structure in some proximal convoluted tubules (PCT) and distal convoluted tubules (DCT). The renal medulla exhibits hyalinization, congestion, and degeneration of collecting tubules. Conversely, the pre-administration of betaine results in a decline in BUN, creatinine, and MDA concentrations. Furthermore, there is a rise in GSH levels, and the group pretreated with betaine showed significant improvement in kidney architecture, with the renal cortex showing almost normal architecture and the collecting tubules in the renal medulla slightly improving. In conclusion, betaine showed a promising nephroprotective effect against PET-NP-induced toxicity

Maria Luz Fernandez, M. Luisa Bonet, Francisco J Pérez Cano et al.

In submitting conference proceedings to <i>Biology and Life Sciences Forum</i>, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Yen-Chi Cheng, Chieh Hubert Lin, Chaoyang Wang et al.

Toward unlocking the potential of generative models in immersive 4D experiences, we introduce Virtual Pet, a novel pipeline to model realistic and diverse motions for target animal species within a 3D environment. To circumvent the limited availability of 3D motion data aligned with environmental geometry, we leverage monocular internet videos and extract deformable NeRF representations for the foreground and static NeRF representations for the background. For this, we develop a reconstruction strategy, encompassing species-level shared template learning and per-video fine-tuning. Utilizing the reconstructed data, we then train a conditional 3D motion model to learn the trajectory and articulation of foreground animals in the context of 3D backgrounds. We showcase the efficacy of our pipeline with comprehensive qualitative and quantitative evaluations using cat videos. We also demonstrate versatility across unseen cats and indoor environments, producing temporally coherent 4D outputs for enriched virtual experiences.

Ahmet Haydar Ornek, Deniz Sen, Esmanur Civil

The main aim of this study is to demonstrate how innovative view synthesis and 3D reconstruction techniques can be used to create models of endangered species using monocular RGB images. To achieve this, we employed SyncDreamer to produce unique perspectives and NeuS and NeRF to reconstruct 3D representations. We chose four different animals, including the oriental stork, frog, dragonfly, and tiger, as our subjects for this study. Our results show that the combination of SyncDreamer, NeRF, and NeuS techniques can successfully create 3D models of endangered animals. However, we also observed that NeuS produced blurry images, while NeRF generated sharper but noisier images. This study highlights the potential of modeling endangered animals and offers a new direction for future research in this field. By showcasing the effectiveness of these advanced techniques, we hope to encourage further exploration and development of techniques for preserving and studying endangered species.

Divya P. D., Shynu M., Jayavardhanan K.K. et al.

In the present study, next generation sequencing was employed to identify and explore the differential expression profiles of microRNAs (miRNAs) in peripheral blood mononuclear cells (PBMCs) of crossbred (B. taurus x B. indicus) and Vechur (B. indicus) cattle in response to the bacterial endotoxin-lipopolysaccharide (LPS). The PBMCs from adult apparently healthy female crossbred cows and Vechur cattle, a native cattle breed of Kerala, India were stimulated with 10 μg/mL of LPS for 6 h. Among the differentially expressed miRNAs, the expression of 13 miRNAs showed statistically significant up regulation while, significant decrease in the expression of 15 miRNAs was noticed in LPS treated PBMCs of Vechur cattle compared to crossbred cows. The expression profiling of miRNA, bta-miR-375, expression of which was found to be significantly down regulated in LPS treated PBMCs of Vechur cattle with respect to crossbred cattle by the NGS studies, is presented in the present manuscript. The decrease in expression of bta-miR-375 noticed by NGS was in accordance with the results of quantitative real time PCR assay. Functional gene enrichment analysis and pathway analysis revealed significant enrichment of predicted targets of bta-miR-375 in many immune related and cell signalling mechanisms. In addition, over representation of targets of bta-miR-375 was also noticed in pathogenesis of many of the bovine diseases. The study could also identify differences in the expression of cytokines, viz. Tumour Necrosis Factor Alpha (TNFα), Interleukin 4 (IL-4) and Interferon-γ (IFNγ) between LPS treated and untreated PBMCs of crossbred and Vechur cattle.

Vimudha Muralidharan, Sridevi Janardhanam, Saravanan Palanivel et al.

Pawel Romanczuk, Bryan C. Daniels

Collective behaviors exhibited by animal groups, such as fish schools, bird flocks, or insect swarms are fascinating examples of self-organization in biology. Concepts and methods from statistical physics have been used to argue theoretically about the potential consequences of collective effects in such living systems. In particular, it has been proposed that such collective systems should operate close to a phase transition, specifically a (pseudo-)critical point, in order to optimize their capability for collective computation. In this chapter, we will first review relevant phase transitions exhibited by animal collectives, pointing out the difficulties of applying concepts from statistical physics to biological systems. Then we will discuss the current state of research on the "criticality hypothesis", including methods for how to measure distance from criticality and specific functional consequences for animal groups operating near a phase transition. We will highlight the emerging view that de-emphasizes the optimality of being exactly at a critical point and instead explores the potential benefits of living systems being able to tune to an optimal distance from criticality. We will close by laying out future challenges for studying collective behavior at the interface of physics and biology.

Reza Arablouei, Ziwei Wang, Greg J. Bishop-Hurley et al.

In this paper, we examine the use of data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extract three new features from the GNSS data, namely, distance from water point, median speed, and median estimated horizontal position error. We combine the information available from the accelerometry and GNSS data via two approaches. The first approach is based on concatenating the features extracted from both sensor data and feeding the concatenated feature vector into a multi-layer perceptron (MLP) classifier. The second approach is based on fusing the posterior probabilities predicted by two MLP classifiers. The input to each classifier is the features extracted from the data of one sensing mode. We evaluate the performance of the developed multimodal animal behavior classification algorithms using two real-world datasets collected via smart cattle collar tags and ear tags. The leave-one-animal-out cross-validation results show that both approaches improve the classification performance appreciably compared with using data of only one sensing mode. This is more notable for the infrequent but important behaviors of walking and drinking. The algorithms developed based on both approaches require little computational and memory resources hence are suitable for implementation on embedded systems of our collar tags and ear tags. However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.