Hasil untuk "Animal biochemistry"

A. K. Azkur, M. Akdiş, D. Azkur et al.

As a zoonotic disease that has already spread globally to several million human beings and possibly to domestic and wild animals, eradication of coronavirus disease 2019 (COVID‐19) appears practically impossible. There is a pressing need to improve our understanding of the immunology of this disease to contain the pandemic by developing vaccines and medicines for the prevention and treatment of patients. In this review, we aim to improve our understanding on the immune response and immunopathological changes in patients linked to deteriorating clinical conditions such as cytokine storm, acute respiratory distress syndrome, autopsy findings and changes in acute‐phase reactants, and serum biochemistry in COVID‐19. Similar to many other viral infections, asymptomatic disease is present in a significant but currently unknown fraction of the affected individuals. In the majority of the patients, a 1‐week, self‐limiting viral respiratory disease typically occurs, which ends with the development of neutralizing antiviral T cell and antibody immunity. The IgM‐, IgA‐, and IgG‐type virus‐specific antibodies levels are important measurements to predict population immunity against this disease and whether cross‐reactivity with other coronaviruses is taking place. High viral load during the first infection and repeated exposure to virus especially in healthcare workers can be an important factor for severity of disease. It should be noted that many aspects of severe patients are unique to COVID‐19 and are rarely observed in other respiratory viral infections, such as severe lymphopenia and eosinopenia, extensive pneumonia and lung tissue damage, a cytokine storm leading to acute respiratory distress syndrome, and multiorgan failure. Lymphopenia causes a defect in antiviral and immune regulatory immunity. At the same time, a cytokine storm starts with extensive activation of cytokine‐secreting cells with innate and adaptive immune mechanisms both of which contribute to a poor prognosis. Elevated levels of acute‐phase reactants and lymphopenia are early predictors of high disease severity. Prevention of development to severe disease, cytokine storm, acute respiratory distress syndrome, and novel approaches to prevent their development will be main routes for future research areas. As we learn to live amidst the virus, understanding the immunology of the disease can assist in containing the pandemic and in developing vaccines and medicines to prevent and treat individual patients.

D. Graves

K. Timmis

J. Duruibe, Oguwuegbu M.O.C., J. Egwurugwu

G. Hutvagner, T. Du, Zuoshang Xu et al.

J. Galagan, S. Calvo, K. Borkovich et al.

Neurospora crassa is a central organism in the history of twentieth-century genetics, biochemistry and molecular biology. Here, we report a high-quality draft sequence of the N. crassa genome. The approximately 40-megabase genome encodes about 10,000 protein-coding genes—more than twice as many as in the fission yeast Schizosaccharomyces pombe and only about 25% fewer than in the fruitfly Drosophila melanogaster. Analysis of the gene set yields insights into unexpected aspects of Neurospora biology including the identification of genes potentially associated with red light photobiology, genes implicated in secondary metabolism, and important differences in Ca2+ signalling as compared with plants and animals. Neurospora possesses the widest array of genome defence mechanisms known for any eukaryotic organism, including a process unique to fungi called repeat-induced point mutation (RIP). Genome analysis suggests that RIP has had a profound impact on genome evolution, greatly slowing the creation of new genes through genomic duplication and resulting in a genome with an unusually low proportion of closely related genes.

C. Ratledge, L. Dover

J. Thompson

Tianyu Sun, Zhoujie Fu, Bang Zhang et al.

The demand for realistic and versatile character animation has surged, driven by its wide-ranging applications in various domains. However, the animation generation algorithms modeling human pose with 2D or 3D structures all face various problems, including low-quality output content and training data deficiency, preventing the related algorithms from generating high-quality animation videos. Therefore, we introduce MVAnimate, a novel framework that synthesizes both 2D and 3D information of dynamic figures based on multi-view prior information, to enhance the generated video quality. Our approach leverages multi-view prior information to produce temporally consistent and spatially coherent animation outputs, demonstrating improvements over existing animation methods. Our MVAnimate also optimizes the multi-view videos of the target character, enhancing the video quality from different views. Experimental results on diverse datasets highlight the robustness of our method in handling various motion patterns and appearances.

H. Maurer

A. Merrill, E. Schmelz, D. Dillehay et al.

The "sphingosin" backbone of sphingolipids was so named by J. L. W. Thudichum in 1884 for its enigmatic ("Sphinx-like") properties. Although still an elusive class of lipids, research on the involvement of sphingolipids in the signal transduction pathways that mediate cell growth, differentiation, multiple cell functions, and cell death has been rapidly expanding our understanding of these compounds. In addition to the newly discovered role of ceramide as an intracellular second messenger for tumor necrosis factor-alpha, IL-1beta, and other cytokines, sphingosine, sphingosine-1-phosphate, and other sphingolipid metabolites have recently been demonstrated to modulate cellular calcium homeostasis and cell proliferation. Perturbation of sphingolipid metabolism using synthetic and naturally occurring inhibitors of key enzymes of the biosynthetic pathways is aiding the characterization of these processes; for examples, inhibition of cerebroside synthase has indicated a role for ceramide in cellular stress responses including heat shock, and inhibition of ceramide synthase (by fumonisins) has revealed the role of disruption of sphingolipid metabolism in several animal diseases. Fumonisins are currently the focus of a FDA long-term tumor study. This review summarizes recent research on (i) the role of sphingolipids as important components of the diet, (ii) the role of sphingoid base metabolites and the ceramide cycle in expression of genes regulating cell growth, differentiation, and apoptosis, (iii) the use of cerebroside synthase inhibitors as tools for understanding the role of sphingolipids as mediators of cell cycle progression, renal disease, and stress responses, and (iv) the involvement of disrupted sphingolipid metabolism in animal disease and cellular deregulation associated with exposure to inhibitors of ceramide synthase and serine palmitoyltransferase, key enzymes in de novo sphingolipid biosynthesis. These findings illustrate how an understanding of the function of sphingolipids can help solve questions in toxicology and this is undoubtedly only the beginning of this story.

Suresh Patel, Satish Patel, Ashvin Kotadiya et al.

Background Wistar rats are extensively used as the model for assessing toxicity and efficacy in preclinical research. Hematological and biochemical laboratory data are essential for evaluating specific variations in the physiological and functional profile of a laboratory animal. Establishing hematological and biochemical reference values for Wistar (han) rats at various age intervals was the goal of this work. Male and female Wistar rats (n = 660) of ages 6–8 weeks, 10–14 weeks and > 6 months were used in the experiment. Blood and serum were collected from these rats under fasting conditions. Results We observed that the majority of hematological and biochemical parameters were significantly influenced by sex and age. Hematological changes were significantly correlated to aging were increased red blood cells, hemoglobin, hematocrit, neutrophils, monocytes and eosinophils in both sexes, as well as decreased platelet, mean corpuscular volume, mean corpuscular hemoglobin and lymphocytes in both sexes. White blood cells of male rats were considerably higher than those of female rats in all age ranges. For biochemistry, increase in glucose, total protein and creatinine were seen in both sexes, along with increases in urea in females and alanine aminotransferase in males. Age was significantly associated with decreased alkaline phosphatase in both sexes. Conclusions When using Wistar rats as a model, these reference values may be useful in evaluating the results.

Jihyeon Park, Jiyoon Myung, Seone Shin et al.

Designers often encounter friction when animating static SVG graphics, especially when the visual structure does not match the desired level of motion detail. Existing tools typically depend on predefined groupings or require technical expertise, which limits designers' ability to experiment and iterate independently. We present Decomate, a system that enables intuitive SVG animation through natural language. Decomate leverages a multimodal large language model to restructure raw SVGs into semantically meaningful, animation-ready components. Designers can then specify motions for each component via text prompts, after which the system generates corresponding HTML/CSS/JS animations. By supporting iterative refinement through natural language interaction, Decomate integrates generative AI into creative workflows, allowing animation outcomes to be directly shaped by user intent.

Harrison Jesse Smith, Nicky He, Yuting Ye

Drawing is a fun and intuitive way to create a character, accessible even to small children. However, animating 2D figure drawings is a much more challenging task, requiring specialized tools and skills. Bringing 2D figures to 3D so they can be animated and consumed in immersive media poses an even greater challenge. Moreover, it is desirable to preserve the unique style and identity of the figure when it is being animated and viewed from different perspectives. In this work, we present an approachable and easy-to-create 2.5D character model and retargeting technique that can apply complex 3D skeletal motion, including rotation within the transverse plane, onto a single childlike figure drawing in a style-preserving manner in realtime. Because our solution is view-dependent, the resulting character is well-suited for animation in both 2D and 3D contexts. We also present a novel annotation study motivating our system design decisions and a pair of user studies validating the usefulness and appeal of our solution. We showcase the generality of our system in a range of 2D and 3D applications.

Jingyu Liu, Zijie Xin, Yuhan Fu et al.

Sketch animation, which brings static sketches to life by generating dynamic video sequences, has found widespread applications in GIF design, cartoon production, and daily entertainment. While current methods for sketch animation perform well in single-object sketch animation, they struggle in multi-object scenarios. By analyzing their failures, we identify two major challenges of transitioning from single-object to multi-object sketch animation: object-aware motion modeling and complex motion optimization. For multi-object sketch animation, we propose MoSketch based on iterative optimization through Score Distillation Sampling (SDS) and thus animating a multi-object sketch in a training-data free manner. To tackle the two challenges in a divide-and-conquer strategy, MoSketch has four novel modules, i.e., LLM-based scene decomposition, LLM-based motion planning, multi-grained motion refinement, and compositional SDS. Extensive qualitative and quantitative experiments demonstrate the superiority of our method over existing sketch animation approaches. MoSketch takes a pioneering step towards multi-object sketch animation, opening new avenues for future research and applications.

Anne Lundgaard Hansen, Seung Jung Lee

This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in trading decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered trading advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias.

Duan Li, Xinyuan Guo, Xinhuan Shu et al.

Animating objects' movements is widely used to facilitate tracking changes and observing both the global trend and local hotspots where objects converge or diverge. Existing methods, however, often obscure critical local hotspots by only considering the start and end positions of objects' trajectories. To address this gap, we propose RouteFlow, a trajectory-aware animated transition method that effectively balances the global trend and local hotspots while minimizing occlusion. RouteFlow is inspired by a real-world bus route analogy: objects are regarded as passengers traveling together, with local hotspots representing bus stops where these passengers get on and off. Based on this analogy, animation paths are generated like bus routes, with the object layout generated similarly to seat allocation according to their destinations. Compared with state-of-the-art methods, RouteFlow better facilitates identifying the global trend and locating local hotspots while performing comparably in tracking objects' movements.

Martyna Frątczak, Mikołaj Kaczmarski, Katarzyna Szkudelska et al.

Due to their high sensitivity to hormonal agents, in recent years, amphibians have been proposed as bioindicators for the presence of endocrine-disrupting compounds (EDCs) in the environment. EDCs are a chemically diverse group of compounds, mainly of anthropogenic origin, that can interfere with hormone receptors. The escalating global environmental pollution with endocrine-disrupting compounds (EDCs) poses a significant threat to wildlife and human health. Amphibians are at high risk of exposure to EDCs in the environment, as they produce eggs without a protective shell, possess highly permeable skin, and most of them undergo an aquatic life phase, where they are chronically exposed to EDCs in the water. This exposure raises concerns about the contribution of EDCs to the dramatic decline of amphibian populations and underscores the necessity of environmental-relevant studies in this area. Despite the critical need, amphibians have attracted relatively little research focus in this regard. Although above 200 experimental studies on the topic of EDCs and sex, reproduction, and related traits in amphibians have been conducted, a significant portion of them rely on model species from the Xenopus genus, which do not fully represent the diverse group of amphibians. Additionally, these studies often use strict laboratory conditions that fail to mimic natural exposure scenarios. Our extensive review highlights the research gaps and emphasizes the importance of incorporating a broader range of amphibian species to understand the true impact of EDCs. We advocate for more studies in environmentally relevant settings and the use of native species to enhance the ecological validity of findings.

Charlotte Lorand, Léa Bouet, Olivier Devineau et al.

Abstract Background In wildlife studies, animal behavior serves as a key indicator of the impact of environmental changes and anthropogenic disturbances. However, wild animals are elusive and traditional GPS studies only provide limited insight into their daily activities. To address this issue, behavior classification models have increasingly been used to detect specific behaviors in wildlife equipped with tri-axial accelerometers. Such models typically need to be trained on data from the target species. The present study focuses on developing a behavioral classification model tailored to the grey wolf (Canis lupus) and encompassing a variety of ecologically relevant behaviors. Methods We collected data from nine captive wolves equipped with collar-mounted tri-axial accelerometers recording continuous acceleration at 32 Hz (“fine-scale”) and averaged acceleration over 5-min intervals (“activity”). Using simultaneous video observations, we trained Random Forest models to classify wolf acceleration data into specific behaviors. We investigated the potential limits to the generalizability of these models to unlabeled data through individual-based cross-validation. Results We present: (1) a model classifying fine-scale acceleration data (32 Hz) into 12 distinct behaviors (lying, trotting, stationary, galloping, walking, chewing, sniffing, climbing, howling, shaking, digging and jumping) with a class recall of 0.77–0.99 (0.01–0.91 in cross-validation), (2) a model classifying activity data (5-min averages) into 3 behavior categories (static, locomotion and miscellaneous) with a class recall of 0.43–0.91 (0.39–0.92 in cross-validation). Although classification performance decreased following cross-validation, recall scores for lying, trotting, stationary, galloping, walking and chewing individual behaviors (as well as static and locomotion categories) remained above 0.6. Classification performance was consistently poorer for rare behaviors, which constituted less than 1.1% of the training dataset. Conclusions We demonstrate the use of collar-mounted accelerometer to distinguish between 12 behaviors and 3 behavior categories in captive wolves, at fine-scale (32 Hz) and averaged 5-min resolutions, respectively. We also discuss the generalizability of the two models to free-ranging settings. These models can be employed to support future behavioral studies examining questions such as conflict mitigation, wolf responses to human disturbances, or specific activity budgets.

Gareth Lamb, Ching Hei Lo, Jin Wu et al.

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong.