Hasil untuk "Animal biochemistry"

Fei Wang, Lianchi Wu, Aixin Hu et al.

The diversity and stability of the gut microbiota, along with various microbial and host–microbe interactions, are crucial factors in maintaining a healthy state. In this study, a total of 12 healthy 1–2 years old cats of similar weight were recruited and divided into two groups according to the experimental design and breed: the British shorthair (BS) group and the nulla luctus felis (NLF) group. After 21 days of the same diet, we analyzed and compared the gut microbiota of BS and NLF. Our results showed that the values of the serum biochemical indicators of the BS and NLF selected for this experiment were within the normal range. The Venn diagram showed that the two groups had 310 common operational taxonomic units. Significant differences in beta diversity (P < 0.05), but not in alpha diversity (P > 0.05), distinguished the two groups. Comparative analysis revealed the NLF group was enriched in Lactobacillus and Bacillus, but depleted in Enterococcus at the genus level (P < 0.05). Furthermore, 59 taxa were established as biomarkers based on a linear discriminant analysis score greater than 3.5. According to PICRUSt2 function analyses, the BS group and NLF group had a ratio of 77.11% and 76.55% for metabolism at level 1, respectively. At level 3, the NLF group significantly increased 15 metabolism pathways, while decreasing 13 metabolism pathways (P < 0.05). Finally, NLF-P1, which was screened from the feces of NLF, exhibited a good antibacterial effect on three strains of pet-associated pathogens, and the evolutionary tree was constructed to show that it may be Lactobacillus paracasei or Lactobacillus casei. In conclusion, there were significant differences in intestinal microbiota composition between BS and NLF, and NLF-P1 has research and application potential.

Axiu Mao, Meilu Zhu, Lei Shen et al.

With the rapid advancements in deep learning techniques, wearable sensor-aided animal activity recognition (AAR) has demonstrated promising performance, thereby improving livestock management efficiency as well as animal health and welfare monitoring. However, existing research often prioritizes overall performance, overlooking the fact that classification accuracies for specific animal behavioral categories may remain unsatisfactory. This issue typically stems from suboptimal sampling rates or class imbalance problems. To address these challenges and achieve high classification accuracy across all individual behaviors in farm animals, we propose a novel Individual-Behavior-Aware Network (IBA-Net). This network enhances the recognition of each specific behavior by simultaneously customizing features and calibrating the classifier. Specifically, considering that different behaviors require varying sampling rates to achieve optimal performance, we design a Mixture-of-Experts (MoE)-based Feature Customization (MFC) module. This module adaptively fuses data from multiple sampling rates, capturing customized features tailored to various animal behaviors. Additionally, to mitigate classifier bias toward majority classes caused by class imbalance, we develop a Neural Collapse-driven Classifier Calibration (NC3) module. This module introduces a fixed equiangular tight frame (ETF) classifier during the classification stage, maximizing the angles between pair-wise classifier vectors and thereby improving the classification performance for minority classes. To validate the effectiveness of IBA-Net, we conducted experiments on three public datasets covering goat, cattle, and horse activity recognition. The results demonstrate that our method consistently outperforms existing approaches across all datasets.

Yongjian Ma, Meng Yan, Zhenfei Xie et al.

Abstract Genomic imprinting is required for sexual reproduction and embryonic development of mammals, in which, differentially methylated regions (DMRs) regulate the parent-specific monoallelic expression of imprinted genes. Numerous studies on imprinted genes have highlighted their critical roles in development. However, what imprinting network is essential for development is still unclear. Here, we establish a stepwise system to reconstruct a development-related imprinting network, in which diploid embryonic stem cells (ESCs) are derived by fusing between parthenogenetic (PG)- and androgenetic (AG)-haploid embryonic stem cells (haESCs) with different DMR deletions (termed Ha-Ha-fusion system), followed by tetraploid complementation to produce all-haESC fetuses. Diploid ESCs fused between PG-haESCs carrying 8 maternally-derived DMR deletions and AG-haESCs with 2 paternally-derived DMR deletions give rise to live pups efficiently, among which, one lives to weaning. Strikingly, diploid ESCs derived from the fusion of PG-haESCs with 7 maternal DMR deletions and AG-haESCs with 2 paternal DMR deletions and maternal Snrpn-DMR deletion also support full-term embryonic development. Moreover, embryos reconstructed by injection of AG-haESCs with hypomethylated H19-DMR into oocytes with H19-DMR deletion develop into live mice sustaining inverted allelic gene expression. Together, our findings indicate that restoration of monoallelic expression of 10 imprinted regions is adequate for the full-term development of all-haESC pups, and it works irrespective of their parental origins. Meanwhile, Ha-Ha-fusion system provides a useful tool for deciphering imprinting regulation networks during embryonic development.

Ghulam Abbas, Abdullah Hassan Hashmi, Muhammad Saeed Imran et al.

Chitosan (a chitin derived biopolymer) has gained enormous attention due to its potential uses in broiler and layer production. Chitosan is a natural, nontoxic, and biodegradable substance that has several benefits in poultry, i.e., it improves poultry growth, health, and overall productivity. The present review paper explores the versatile roles of chitosan in poultry, exploring its use as an immune-modulator, growth promoter, and natural antimicrobial and antioxidant feed additive. Chitosan biopolymer is reported to boost the immunity FCR (feed conversion ratio) and reduce the chances of diseases working as an antimicrobial compound. Chitosan enhances nutrient digestibility and absorption, maintains gut health, and controls the intestinal microflora. Dietary use of chitosan is well documented to improve meat quality, feather growth, and egg quality as an alternative to synthetic growth promoters and antibiotics. Therefore, this review intends to gather scientific evidence about using chitosan on production performance, health and well-being of poultry and minimizing the impact of production on the environment for sustainable and environmentally healthy production. Moreover, the prospects for research on chitosan in poultry production are also discussed, stressing the need to optimize chitosan applications and address existing limitations.

Jack Goffinet, Youngjo Min, Carlo Tomasi et al.

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.

Keyi Chen, Mingze Sun, Zhenyu Liu et al.

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.

Jiyi Wang, Jingyang Ke, Bo Dai et al.

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.

Ziqi Li, Abderraouf Amrani, Shri Rai et al.

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.

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.

Marc Benedí San Millán, Angela Dai, Matthias Nießner

Animation of humanoid characters is essential in various graphics applications, but requires significant time and cost to create realistic animations. We propose an approach to synthesize 4D animated sequences of input static 3D humanoid meshes, leveraging strong generalized motion priors from generative video models -- as such video models contain powerful motion information covering a wide variety of human motions. From an input static 3D humanoid mesh and a text prompt describing the desired animation, we synthesize a corresponding video conditioned on a rendered image of the 3D mesh. We then employ an underlying SMPL representation to animate the corresponding 3D mesh according to the video-generated motion, based on our motion optimization. This enables a cost-effective and accessible solution to enable the synthesis of diverse and realistic 4D animations.

C. E. Samuel

Yujie Hou, Guohua Deng, Pengfei Cui et al.

Human infections with the H7N9 influenza virus have been eliminated in China through vaccination of poultry; however, the H7N9 virus has not yet been eradicated from poultry. Carefully analysis of H7N9 viruses in poultry that have sub-optimal immunity may provide a unique opportunity to witness the evolution of highly pathogenic avian influenza virus in the context of vaccination. Between January 2020 and June 2023, we isolated 16 H7N9 viruses from samples we collected during surveillance and samples that were sent to us for disease diagnosis. Genetic analysis indicated that these viruses belonged to a single genotype previously detected in poultry. Antigenic analysis indicated that 12 of the 16 viruses were antigenically close to the H7-Re4 vaccine virus that has been used since January 2022, and the other four viruses showed reduced reactivity with the vaccine. Animal studies indicated that all 16 viruses were nonlethal in mice, and four of six viruses showed reduced virulence in chickens upon intranasally inoculation. Importantly, the H7N9 viruses detected in this study exclusively bound to the avian-type receptors, having lost the capacity to bind to human-type receptors. Our study shows that vaccination slows the evolution of H7N9 virus by preventing its reassortment with other viruses and eliminates a harmful characteristic of H7N9 virus, namely its ability to bind to human-type receptors.

Ahmed F. Abdelkhalek, Janet M. Daly

A comparative medicine approach, whereby similarities and differences in biology between human and veterinary species are used to enhance understanding for the benefit of both, is highly relevant to the development of viral vaccines. Human and equine influenza share many similarities in pathogenesis and immune responses. The DNA vaccine approach offers potential advantages for responding rapidly and effectively to outbreaks or pandemics in both humans and animals, especially in under-resourced regions. The European and American vaccine regulatory authorities require demonstration of vaccine efficacy in animal models. However, mice, the most widely used model, are not naturally infected with influenza viruses, resulting in different pathobiology. Additionally, mice as a model for DNA vaccine testing appear to overestimate the humoral immune response compared to other mammalian species. In this review, we propose that testing of DNA vaccines against influenza type A viruses (and other shared pathogens) in the horse can provide valuable knowledge for the development of human DNA vaccines.

Pingping Zhang, Tianyu Yan, Yang Liu et al.

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.

Kevin Yuanbo Wu, Jin Li, Aaron Esser-Kahn et al.

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.

Carolina Khosrawipour, Jakub Nicpoń, Zdzisław Kiełbowicz et al.

For decades, intraperitoneal chemotherapy (IPC) was used as a liquid solution for the treatment of peritoneal metastasis. Due to its advantageous physical properties, foam-based intraperitoneal chemotherapy (FBIC) was recently proposed as a treatment for peritoneal metastasis. For the first time, this study intends to examine the feasibility, expansion, drug distribution, and penetration of FBIC in vivo. Three swine received contrast-enhanced FBIC doxorubicin delivered using a bicarbonate carrier system. During the procedure, intraoperative blood analyses and periumbilical diameter, as well as foam distribution, penetration, and expansion of the FBIC were analyzed. The swine received an abdominal CT scan to evaluate the contrast distribution. Furthermore, a hematoxylin-eosin (HE) staining of peritoneal samples was performed, and fluorescence microscopy was conducted. FBIC was performed without complications. The periumbilical diameter peaked after 5 min and then decreased. Blood analyses showed changes in blood parameters, with a reduction in the pH levels of serum calcium and potassium. CT scan detected contrast-enhanced FBIC throughout the abdominal cavity. Fluorescence microscopy confirmed that all areas were exposed to doxorubicin and no pathologies were detected in the HE histology. Our preliminary results are quite encouraging and indicate that FBIC is a feasible approach. However, in order to discuss possible clinical applications, further studies are required to investigate the pharmacologic, pharmacodynamic, and physical properties of FBIC.

LI Han, ZHANG Xiaorui, ZHANG Chengfang

ObjectiveTo explore the beneficial role and potential mechanism of intermittent fasting in olanzapine-induced metabolic disorders.MethodsC57BL/6J mice were randomly divided into four groups: Saline + ad libitum (Saline+Ad libitum), Saline + intermittent fasting (Saline +IF), olanzapine administration + ad libitum (Olanzapine+ Ad libitum), and olanzapine administration + intermittent fasting (Olanzapine+IF)， with eight mice in each group. The IF group adopted the 5∶2 scheme, that is, fasting on Monday and Thursday every week, and eating freely in the rest of the time. Ad libitum feeding as the control of intermittent fasting, Saline gavage as the control of olanzapine administration. The experiment lasted for 12 weeks. The differences of body mass, liver mass and epididymal adipose tissue mass were compared between the olanzapine-treated group and the control group after IF intervention. The body fat mass, lean body mass, and visceral fat infiltration of mice were analyzed by nuclear magnetic resonance and HE staining, respectively. Furthermore, the levels of fasting blood glucose, insulin, and insulin resistance index (HOMA-IR) in the process of glucose metabolism were also measured by glucose oxidase method and radioimmunoassay, respectively. The effects of IF on H2O2 release and the level of cytochrome C mRNA, a marker related to mitochondrial damage, were detected by ELISA and real-time fluorescence quantitative PCR.ResultsAfter 12 weeks of treatment, olanzapine induced a significant increase in body mass, body fat, lean body mass and visceral fat infiltration (P<0.05), as well as fasting blood glucose, insulin, and HOMA-IR (P<0.05); however, IF significantly reduced the above indicators (P<0.05). Further studies showed that the release of H2O2 and the expression of Cytochrome C mRNA in adipose tissue of mice after intermittent fasting treatment were significantly decreased (P<0.05).ConclusionIntermittent fasting therapy can alleviate olanzapine-induced metabolic disorders in mice. The underlying mechanism may involve the inhibition of oxidative stress level and the maintenance of mitochondrial functions.

M. R. Krishna Nath, N. Madhavan Unny, Sindhu K. Rajan et al.

The present study was conducted to evaluate the alterations in oxidative stress parameters in dogs suffering from haemorrhagic gastroenteritis (HGE). Dogs presented with vomiting and diarrhoea were screened and fifteen animals with signs suggestive of HGE were included in the study. The oxidative stress parameters, serummalondialdehyde (MDA) level, total antioxidant status (TAS) and plasma glutathione peroxidase (GSH-Px) activity were studied. The values were compared with the values from six apparently healthy dogs. A significant increase was noticed in the mean values of serum MDA and TAS of diseased animals at the time of presentation when compared to healthy animals whereas the activity of plasma GSH-Px was found to be lower than in healthy dogs. Supplementation with N-acetyl cysteine @ 70 mg/kg or five days was found effective in managing the oxidative injury in the affected animals.

Vasiliki Kompoura, Ioanna Prapa, Paraskevi B. Vasilakopoulou et al.

The present study aimed at investigating the possible benefits of a dietary intervention with Corinthian currants, a rich source of phenolic compounds, on type 1 diabetes (T1D) using the animal model of the streptozotocin-(STZ)-induced diabetic rat. Male Wistar rats were randomly assigned into four groups: control animals, which received a control diet (CD) or a diet supplemented with 10% <i>w</i>/<i>w</i> Corinthian currants (CCD), and diabetic animals, which received a control diet (DCD) or a currant diet (DCCD) for 4 weeks. Plasma biochemical parameters, insulin, polar phenolic compounds, and inflammatory factors were determined. Microbiota populations in tissue and intestinal fluid of the caecum, as well as fecal microbiota populations and short-chain fatty acids (SCFAs), were measured. Fecal microbiota was further analyzed by 16S rRNA sequencing. The results of the study showed that a Corinthian currant-supplemented diet restored serum polar phenolic compounds and decreased interleukin-1b (IL-1b) (<i>p</i> < 0.05) both in control and diabetic animals. Increased caecal lactobacilli counts (<i>p</i> < 0.05) and maintenance of enterococci levels within normal range were observed in the intestinal fluid of the DCCD group (<i>p</i> < 0.05 compared to DCD). Higher acetic acid levels were detected in the feces of diabetic rats that received the currant diet compared to the animals that received the control diet (<i>p</i> < 0.05). Corinthian currant could serve as a beneficial dietary component in the condition of T1D based on the results coming from the animal model of the STZ-induced T1D rat.