Hasil untuk "Animal biochemistry"

Mohammad Mehdi Ommati, Samira Sabouri, Zilong Sun et al.

Benjamin Antieau

We give a direct proof of the fact that the animation of the opposite of the category of finite sets is a 1-category.

Chaoyue Song, Xiu Li, Fan Yang et al.

Modern interactive applications increasingly demand dynamic 3D content, yet the transformation of static 3D models into animated assets constitutes a significant bottleneck in content creation pipelines. While recent advances in generative AI have revolutionized static 3D model creation, rigging and animation continue to depend heavily on expert intervention. We present Puppeteer, a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects. Our system first predicts plausible skeletal structures via an auto-regressive transformer that introduces a joint-based tokenization strategy for compact representation and a hierarchical ordering methodology with stochastic perturbation that enhances bidirectional learning capabilities. It then infers skinning weights via an attention-based architecture incorporating topology-aware joint attention that explicitly encodes inter-joint relationships based on skeletal graph distances. Finally, we complement these rigging advances with a differentiable optimization-based animation pipeline that generates stable, high-fidelity animations while being computationally more efficient than existing approaches. Extensive evaluations across multiple benchmarks demonstrate that our method significantly outperforms state-of-the-art techniques in both skeletal prediction accuracy and skinning quality. The system robustly processes diverse 3D content, ranging from professionally designed game assets to AI-generated shapes, producing temporally coherent animations that eliminate the jittering issues common in existing methods.

Shijie Sheng, Jianghao Wu, Renxuan Bo et al.

Existing research has yet to reach a consensus on whether and how small flying animals utilize elastic energy storage mechanisms to reduce flight energy expenditure, and there is a lack of systematic and universal methods for assessment. To address these gaps, this study proposes a method to evaluate elastic energy storage capacity based on wing kinematic parameters (flapping amplitude and flapping frequency), grounded in the hypothesis that animals tend to minimize flight energy expenditure. By establishing a simplified power model, the study calculates the optimal kinematic parameters corresponding to the minimum mechanical power requirements under two extreme conditions: no elastic energy storage and complete elastic energy storage. These optimal parameters are then compared with measured data from various small flying animals. The results show that the measured parameters of hummingbirds, ladybugs, and rhinoceros beetles are close to the no-storage optimum, indicating relatively weak elastic energy storage capacity; whereas hoverflies, bumblebees, and honeybees align closely with the complete-storage optimum, suggesting strong elastic energy storage ability. Furthermore, the wing kinematic adjustment strategies these animals employ in response to changes in load or air density are consistent with the predicted elastic storage capacities. This study provides a systematic new approach for assessing biological elastic energy storage capacity and offers a theoretical basis for the low-power design of flapping wing micro air vehicles.

Paolo Cermelli, Silvia Marchese, Laura Sacerdote et al.

We study here the social network generated by the asynchronous visits, to a fixed set of sites, of mobile agents modelled as independent random walks on the plane lattice. The social network is constructed by assuming that a group of agents are associated if they have visited the same set of sites within a finite time interval. This construction is an instance of a random intersection graph, and has been used in the literature to study association networks in a number of animal species. We characterize the mathematical structure of these networks, which we view as one-mode projections of suitable bipartite graphs or, equivalently, as 2-sections of the corresponding hypergraphs. We determine analytically the probability distribution of the random bipartite graphs and hypergraphs associated to this construction, and suggest that association networks generated by the use of common resources are better described by hypergraphs rather than simple projected graphs, that miss important information regarding the actual associations among the agents.

Kanwal, Okezie Emmanuel, Rozina et al.

Addressing the dual challenges of greenhouse gas emissions and fossil fuel depletion requires sustainable and cost-effective energy solutions. This study investigates biodiesel production from non-edible Calotropis gigantea L. seed oil using a novel copper oxide (CuO) nano-catalyst synthesized from the green pods of C. gigantea. CuO nanoparticles were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). Optimal biodiesel production conditions were achieved at a methanol-to-oil molar ratio of 9:1, reaction temperature of 80 °C, reaction time of 105 min, and catalyst loading of 0.74 wt%, resulting in a 90 % yield. The synthesized biodiesel was characterized through FT-IR spectroscopy, and gas chromatography-mass spectrometry (GC–MS). Physicochemical analysis demonstrated compliance with both European (EN 14214) and American (ASTM D 6751) biodiesel standards, exhibiting favorable properties including density (0.792 kg/L), acid value (0.34 mg KOH/g), kinematic viscosity (6 mm2/s), flash point (91 °C), cloud point (−10 °C), pour point (−8 °C), and minimal sulphur content (0.00097 wt%). These findings establish the viability of converting toxic, non-edible C. gigantea seeds into high-quality biodiesel, presenting a promising pathway toward sustainable energy production while potentially fostering regional socioeconomic development through valorization of agricultural waste.

Bastien Arcelin, Nicolas Chaverou

Creating realistic or stylized facial and lip sync animation is a tedious task. It requires lot of time and skills to sync the lips with audio and convey the right emotion to the character's face. To allow animators to spend more time on the artistic and creative part of the animation, we present Audio2Rig: a new deep learning based tool leveraging previously animated sequences of a show, to generate facial and lip sync rig animation from an audio file. Based in Maya, it learns from any production rig without any adjustment and generates high quality and stylized animations which mimic the style of the show. Audio2Rig fits in the animator workflow: since it generates keys on the rig controllers, the animation can be easily retaken. The method is based on 3 neural network modules which can learn an arbitrary number of controllers. Hence, different configurations can be created for specific parts of the face (such as the tongue, lips or eyes). With Audio2Rig, animators can also pick different emotions and adjust their intensities to experiment or customize the output, and have high level controls on the keyframes setting. Our method shows excellent results, generating fine animation details while respecting the show style. Finally, as the training relies on the studio data and is done internally, it ensures data privacy and prevents from copyright infringement.

Lucas Invernizzi, Jean-françois Lemaître, Mathieu Douhard

In its initial form, the expensive son hypothesis postulates that sons from male-biased sexually dimorphic species require more food during growth than daughters, which ultimately incur fitness costs for mothers predominantly producing and rearing sons. We first dissect the evolutionary framework in which the expensive son hypothesis is rooted, and we provide a critical reappraisal of its differences from other evolutionary theories proposed in the field of sex allocation. Then, we synthesize the current (and absence of) support for the costs of producing and rearing sons on maternal fitness components (future reproduction and survival). Regarding the consequences in terms of future reproduction, we highlight that species with pronounced sexual size dimorphism display a higher cost of sons than of daughters on subsequent reproductive performance, at least in mammals. However, in most studies, the relative fitness costs of producing and rearing sons and daughters can be due to sex-biased maternal allocation strategies rather than differences in energetic demands of offspring, which constitutes an alternative mechanism to the expensive son hypothesis stricto sensu. We observe that empirical studies investigating the differential costs of sons and daughters on maternal survival in non-human animals remain rare, especially for long-term survival. Indeed, most studies have investigated the influence of offspring sex (or litter sex ratio) at year $T$ on survival at year $T+1$, and they rarely provide a support to the expensive son hypothesis. On the contrary, in humans, most studies have focused on the relationship between proportion of sons and maternal lifespan, but these results are inconsistent. Our study highlights new avenues for future research that should provide a comprehensive view of the expensive son hypothesis, by notably disentangling the effects of offspring behaviour from the effect of sex-specific maternal allocation. Moreover, we emphasize that future studies should also embrace the mechanistic side of the expensive son hypothesis, largely neglected so far, by deciphering the physiological pathways linking son's production to maternal health and fitness.

Lukas Picek, Lukas Neumann, Jiri Matas

We propose a method that robustly exploits background and foreground in visual identification of individual animals. Experiments show that their automatic separation, made easy with methods like Segment Anything, together with independent foreground and background-related modeling, improves results. The two predictions are combined in a principled way, thanks to novel Per-Instance Temperature Scaling that helps the classifier to deal with appearance ambiguities in training and to produce calibrated outputs in the inference phase. For identity prediction from the background, we propose novel spatial and temporal models. On two problems, the relative error w.r.t. the baseline was reduced by 22.3% and 8.8%, respectively. For cases where objects appear in new locations, an example of background drift, accuracy doubles.

Yudong Jiang, Baohan Xu, Siqian Yang et al.

Animation has gained significant interest in the recent film and TV industry. Despite the success of advanced video generation models like Sora, Kling, and CogVideoX in generating natural videos, they lack the same effectiveness in handling animation videos. Evaluating animation video generation is also a great challenge due to its unique artist styles, violating the laws of physics and exaggerated motions. In this paper, we present a comprehensive system, AniSora, designed for animation video generation, which includes a data processing pipeline, a controllable generation model, and an evaluation benchmark. Supported by the data processing pipeline with over 10M high-quality data, the generation model incorporates a spatiotemporal mask module to facilitate key animation production functions such as image-to-video generation, frame interpolation, and localized image-guided animation. We also collect an evaluation benchmark of 948 various animation videos, with specifically developed metrics for animation video generation. Our entire project is publicly available on https://github.com/bilibili/Index-anisora/tree/main.

Caroline Ismeurt-Walmsley, Patrizia Giannoni, Florence Servant et al.

The gut-brain axis has emerged as a key player in the regulation of brain function and cognitive health. Gut microbiota dysbiosis has been observed in preclinical models of Alzheimer's disease and patients. Manipulating the composition of the gut microbiota enhances or delays neuropathology and cognitive deficits in mouse models. Accordingly, the health status of the animal facility may strongly influence these outcomes. In the present study, we longitudinally analysed the faecal microbiota composition and amyloid pathology of 5XFAD mice housed in a specific opportunistic pathogen-free (SOPF) and a conventional facility. The composition of the microbiota of 5XFAD mice after aging in conventional facility showed marked differences compared to WT littermates that were not observed when the mice were bred in SOPF facility. The development of amyloid pathology was also enhanced by conventional housing. We then transplanted faecal microbiota (FMT) from both sources into wild-type (WT) mice and measured memory performance, assessed in the novel object recognition test, in transplanted animals. Mice transplanted with microbiota from conventionally bred 5XFAD mice showed impaired memory performance, whereas FMT from mice housed in SOPF facility did not induce memory deficits in transplanted mice. Finally, 18 weeks of housing SOPF-born animals in a conventional facility resulted in the reappearance of specific microbiota compositions in 5XFAD vs WT mice. In conclusion, these results show a strong impact of housing conditions on microbiota-associated phenotypes and question the relevance of breeding preclinical models in specific pathogen-free (SPF) facilities.

Bohan Zeng, Xuhui Liu, Sicheng Gao et al.

Face animation has achieved much progress in computer vision. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion deformation. In this paper, we propose a Face Animation framework with an attribute-guided Diffusion Model (FADM), which is the first work to exploit the superior modeling capacity of diffusion models for photo-realistic talking-head generation. To mitigate the uncontrollable synthesis effect of the diffusion model, we design an Attribute-Guided Conditioning Network (AGCN) to adaptively combine the coarse animation features and 3D face reconstruction results, which can incorporate appearance and motion conditions into the diffusion process. These specific designs help FADM rectify unnatural artifacts and distortions, and also enrich high-fidelity facial details through iterative diffusion refinements with accurate animation attributes. FADM can flexibly and effectively improve existing animation videos. Extensive experiments on widely used talking-head benchmarks validate the effectiveness of FADM over prior arts.

Jack Saunders, Steven Caulkin, Vinay Namboodiri

The ability to accurately capture and express emotions is a critical aspect of creating believable characters in video games and other forms of entertainment. Traditionally, this animation has been achieved with artistic effort or performance capture, both requiring costs in time and labor. More recently, audio-driven models have seen success, however, these often lack expressiveness in areas not correlated to the audio signal. In this paper, we present a novel approach to facial animation by taking existing animations and allowing for the modification of style characteristics. Specifically, we explore the use of a StarGAN to enable the conversion of 3D facial animations into different emotions and person-specific styles. We are able to maintain the lip-sync of the animations with this method thanks to the use of a novel viseme-preserving loss.

Yutong Chen, Junhong Zhao, Wei-Qiang Zhang

It is in high demand to generate facial animation with high realism, but it remains a challenging task. Existing approaches of speech-driven facial animation can produce satisfactory mouth movement and lip synchronization, but show weakness in dramatic emotional expressions and flexibility in emotion control. This paper presents a novel deep learning-based approach for expressive facial animation generation from speech that can exhibit wide-spectrum facial expressions with controllable emotion type and intensity. We propose an emotion controller module to learn the relationship between the emotion variations (e.g., types and intensity) and the corresponding facial expression parameters. It enables emotion-controllable facial animation, where the target expression can be continuously adjusted as desired. The qualitative and quantitative evaluations show that the animation generated by our method is rich in facial emotional expressiveness while retaining accurate lip movement, outperforming other state-of-the-art methods.

Jose Luis Ponton

Real-time animation of virtual characters has traditionally been accomplished by playing short sequences of animations structured in the form of a graph. These methods are time-consuming to set up and scale poorly with the number of motions required in modern virtual environments. The ever-increasing need for highly-realistic virtual characters in fields such as entertainment, virtual reality, or the metaverse has led to significant advances in the field of data-driven character animation. Techniques like Motion Matching have provided enough versatility to conveniently animate virtual characters using a selection of features from an animation database. Data-driven methods retain the quality of the captured animations, thus delivering smoother and more natural-looking animations. In this work, we researched and developed a Motion Matching technique for the Unity game engine. In this thesis, we present our findings on how to implement an animation system based on Motion Matching. We also introduce a novel method combining body orientation prediction with Motion Matching to animate avatars for consumer-grade virtual reality systems.

Takashi Yazawa, Mohammad Sayful Islam, Yoshitaka Imamichi et al.

During mammalian gestation, large amounts of progesterone are produced by the placenta and circulate for the maintenance of pregnancy. In contrast, primary plasma estrogens are different between species. To account for this difference, we compared the expression of ovarian and placental steroidogenic genes in various mammalian species (mouse, guinea pig, porcine, ovine, bovine, and human). Consistent with the ability to synthesize progesterone, CYP11A1/Cyp11a1, and bi-functional HSD3B/Hsd3b genes were expressed in all species. CYP17A1/Cyp17a1 was expressed in the placenta of all species, excluding humans. CYP19A/Cyp19a1 was expressed in all placental estrogen-producing species, whereas estradiol-producing HSD17B1 was only strongly expressed in the human placenta. The promoter region of <i>HSD17B1</i> in various species possesses a well-conserved SP1 site that was activated in human placental cell line JEG-3 cells. However, DNA methylation analyses in the ovine placenta showed that the SP1-site in the promoter region of <i>HSD17B1</i> was completely methylated. These results indicate that epigenetic regulation of HSD17B1 expression is important for species-specific placental sex steroid production. Because human HSD17B1 showed strong activity for the conversion of androstenedione into testosterone, similar to HSD17B1/Hsd17b1 in other species, we also discuss the biological significance of human placental HSD17B1 based on the symptoms of aromatase-deficient patients.

Noe Kawamoto, Akira Sakai

A spread-out lattice animal is a finite connected set of edges in $\{ \{x,y\} \subset \mathbb{Z}^d:0<||x-y||\le L \}$. A lattice tree is a lattice animal with no loops.The best estimate on the critical point $p_c$ so far was achieved by Penrose(JSP,77(1994):3-15): $p_c=1/e+O(L^{-2d/7}\log L)$ for both models for all $d\ge1$. In this paper, we show that $p_c=1/e+CL^{-d}+O(L^{-d-1})$ for all $d>8$, where the model-dependent constant $C$ has the random-walk representation $C_\mathrm{LT}=\sum_{n=2}^\infty\frac{n+1}{2e}U^{*n}(o)$ and $C_\mathrm{LA}=C_\mathrm{LT}-\frac1{2e^2}\sum_{n=3}^\infty U^{*n}(o)$, where $U^{*n}$ is the $n$-fold convolution of the uniform distribution on the $d$-dimensional ball $\{x\in \mathbb{R}^d:\|x\|\le1\}$. The proof is based on a novel use of the lace expansion for the two-point function and detailed analysis of the 1-point function at a certain value of $p$ that is designed to make the analysis extreamly simple.

Vladimir R. Chechetkin, Vasily V. Lobzin

A world-wide COVID-19 pandemic intensified strongly the studies of molecular mechanisms related to the coronaviruses. The origin of coronaviruses and the risks of human-to-human, animal-to-human, and human-to-animal transmission of coronaviral infections can be understood only on a broader evolutionary level by detailed comparative studies. In this paper, we studied ribonucleocapsid assembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic human coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-229E, and HCoV-NL63 and compared them with RNAPS in the genomes of the related animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV, Bat-CoV MOP1, TGEV, and one of camel alphacoronaviruses. RNAPS in the genomes of coronaviruses were evolved due to weakly specific interactions between genomic RNA and N proteins in helical nucleocapsids. Combining transitional genome mapping and Jaccard correlation coefficients allows us to perform the analysis directly in terms of underlying motifs distributed over the genome. In all coronaviruses RNAPS were distributed quasi-periodically over the genome with the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer and shorter than that of SARS-CoV, respectively. The comparison with the experimentally verified packaging signals for MERS-CoV, MHV, and TGEV proved that the distribution of particular motifs is strongly correlated with the packaging signals. We also found that many motifs were highly conserved in both characters and positioning on the genomes throughout the lineages that make them promising therapeutic targets. The mechanisms of encapsidation can affect the recombination and co-infection as well.

Matthew S. Bull, Vivek N. Prakash, Manu Prakash

Effective organismal behavior responds appropriately to changes in the surrounding environment. Attaining this delicate balance of sensitivity and stability is a hallmark of the animal kingdom. By studying the locomotory behavior of a simple animal (\textit{Trichoplax adhaerens}) without muscles or neurons, here, we demonstrate how monociliated epithelial cells work collectively to give rise to an agile non-neuromuscular organism. Via direct visualization of large ciliary arrays, we report the discovery of sub-second ciliary reorientations under a rotational torque that is mediated by collective tissue mechanics and the adhesion of cilia to the underlying substrate. In a toy model, we show a mapping of this system onto an "active-elastic resonator". This framework explains how perturbations propagate information in this array as linear speed traveling waves in response to mechanical stimulus. Next, we explore the implications of parametric driving in this active-elastic resonator and show that such driving can excite mechanical 'spikes'. These spikes in collective mode amplitudes are consistent with a system driven by parametric amplification and a saturating nonlinearity. We conduct extensive numerical experiments to corroborate these findings within a polarized active-elastic sheet. These results indicate that periodic and stochastic forcing are valuable for increasing the sensitivity of collective ciliary flocking. We support these theoretical predictions via direct experimental observation of linear speed traveling waves which arise from the hybridization of spin and overdamped density waves. We map how these ciliary flocking dynamics result in agile motility via coupling between an amplified resonator and a tuning (Goldstone-like) mode of the system. This sets the stage for how activity and elasticity can self-organize into behavior which benefits the organism as a whole.

Harrish Thasarathan, Mehran Ebrahimi

There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads to foreign countries where labor is inexpensive and long hours are common. Automating part of this process can be incredibly useful for reducing costs and creating manageable workloads for major animation studios and outsourced artists. We present a method for automating line art colorization by keeping artists in the loop to successfully reduce this workload while staying true to an artist's vision. By incorporating color hints and temporal information to an adversarial image-to-image framework, we show that it is possible to meet the balance between automation and authenticity through artist's input to generate colored frames with temporal consistency.