While large vision-language models can generate motion graphics animations from text prompts, they regularly fail to include all spatio-temporal properties described in the prompt. We introduce MoVer, a motion verification DSL based on first-order logic that can check spatio-temporal properties of a motion graphics animation. We identify a general set of such properties that people commonly use to describe animations (e.g., the direction and timing of motions, the relative positioning of objects, etc.). We implement these properties as predicates in MoVer and provide an execution engine that can apply a MoVer program to any input SVG-based motion graphics animation. We then demonstrate how MoVer can be used in an LLM-based synthesis and verification pipeline for iteratively refining motion graphics animations. Given a text prompt, our pipeline synthesizes a motion graphics animation and a corresponding MoVer program. Executing the verification program on the animation yields a report of the predicates that failed and the report can be automatically fed back to LLM to iteratively correct the animation. To evaluate our pipeline, we build a synthetic dataset of 5600 text prompts paired with ground truth MoVer verification programs. We find that while our LLM-based pipeline is able to automatically generate a correct motion graphics animation for 58.8% of the test prompts without any iteration, this number raises to 93.6% with up to 50 correction iterations. Our code and dataset are at https://mover-dsl.github.io.
Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained control at the layer level. To bridge this gap, we introduce LayerAnimate, a novel video diffusion framework with layer-aware architecture that empowers the manipulation of layers through layer-level controls. The development of a layer-aware framework faces a significant data scarcity challenge due to the commercial sensitivity of professional animation assets. To address the limitation, we propose a data curation pipeline featuring Automated Element Segmentation and Motion-based Hierarchical Merging. Through quantitative and qualitative comparisons, and user study, we demonstrate that LayerAnimate outperforms current methods in terms of animation quality, control precision, and usability, making it an effective tool for both professional animators and amateur enthusiasts. This framework opens up new possibilities for layer-level animation applications and creative flexibility. Our code is available at https://layeranimate.github.io.
Speech-driven 3D facial animation has attracted increasing interest since its potential to generate expressive and temporally synchronized digital humans. While recent works have begun to explore emotion-aware animation, they still depend on explicit one-hot encodings to represent identity and emotion with given emotion and identity labels, which limits their ability to generalize to unseen speakers. Moreover, the emotional cues inherently present in speech are often neglected, limiting the naturalness and adaptability of generated animations. In this work, we propose LSF-Animation, a novel framework that eliminates the reliance on explicit emotion and identity feature representations. Specifically, LSF-Animation implicitly extracts emotion information from speech and captures the identity features from a neutral facial mesh, enabling improved generalization to unseen speakers and emotional states without requiring manual labels. Furthermore, we introduce a Hierarchical Interaction Fusion Block (HIFB), which employs a fusion token to integrate dual transformer features and more effectively integrate emotional, motion-related and identity-related cues. Extensive experiments conducted on the 3DMEAD dataset demonstrate that our method surpasses recent state-of-the-art approaches in terms of emotional expressiveness, identity generalization, and animation realism. The source code will be released at: https://github.com/Dogter521/LSF-Animation.
Fitrya Fitrya, Elfita Elfita, Ferlinahayati
et al.
Background: Artocarpus altilis is a traditional medicine proven to be efficacious in several pharmacological tests using animal models. However, there have been no reports regarding its safety so far. Therefore, this study aimed to evaluate the toxicity of ethanol extract of A. altilis (EAA) in experimental animals. Methods: An acute toxicity test was carried out based on OECD 423 guidelines with a single dose of 10,000 mg/kg BW. Subchronic toxicity was tested for 28 days based on OECD 407 guidelines, with 200 and 400 mg/kg BW doses. Mortality, variations in body weight, hematological and biochemical markers, organ index, and the histology of vital organs were all used to study toxic symptoms. Results: The results showed that the ethanol extract of A. altilis leaves was practically nontoxic with an LD50 value greater than 10,000 mg/kg. A subchronic toxicity study showed that the extract did not cause death or weight loss in rats and had no toxic effects on hematology or serum biochemistry. However, the subchronic toxicity study at a dose of 400 mg/kg BW revealed changes in the liver index and histology, although these did not affect the biochemical parameters of the organ. Conclusion: The LD50 value of the extract was greater than 10,000 mg/kg BW. The ethanol extract of A. altilis leaves at a≤ 400 mg/kg dose showed no toxicity in the subchronic phase. The results of this study support the safety of using EAA as a herbal medicine candidate.
Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms -- which include tree-based models, deep neural networks, and graphical models -- differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species -- fly, mouse, and human -- we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.
Sacha Inchi seeds <i>(Plukenetia huayllabambana</i>) are highly regarded for their nutritional richness, specifically their high omega-3 content. Chia seed (<i>Salvia hispanica</i> L.) mucilage is recognized for its emulsion abilities. There is growing demand for innovative mayonnaise formulations using healthier, plant-based alternatives. This study developed a plant-based mayonnaise (PBM) by replacing egg yolks with chia seed mucilage (CSM) and using Sacha Inchi seed oil (SIO), achieving sensory qualities similar to traditional mayonnaise. Five formulations of PBM were evaluated, with variations in CSM content (1% to 3%) and water content (43% to 45%) and using salt (0.5%), oil (48%), pepper (0.5%) and lemon juice (5%). PBM was evaluated based on omega-3 (%) content, total fat (%) content, stability of emulsion (%), rheology and physicochemical properties. Formulation with 3% of CSM was the optimal option due to its emulsion stability (98.56%) and rheology, very similar to those of traditional mayonnaise (99.13%). PBM formulation with 3% CSM showed the highest omega-3 fatty acid content of 55.36% for 100 g fat, compared with the 0.27% found in traditional mayonnaise. The PBM formulation with 3% CSM also showed important characteristics such as phenolic content (310.814 µg GAE/g ms), antioxidant activity (1991.79 µg Trolox/g ms), Ph (4.24), a peroxide index (11.92 meq-O<sub>2</sub>/Kg oil), an acidity index (3.59 mg KOH/g), a shelf life study and proximal composition. This study underscores the potential of CSM and SIO in mayonnaise formulations, addressing concerns with traditional options.
AbstractHematology, plasma biochemistry, body composition, and bone mineral density (BMD) were analyzed for 30 captive Ceratophrys cranwelli (C. cranwelli) to establish the reference intervals. There was no significant difference between males and females in all blood routine tests. Blood biochemistry included 17 analytes, and only total bile acid (TBA), calcium (CA), and phosphorus (PHOS) showed significant differences. Male TBA levels were higher than females, while female CA and PHOS levels were higher than males. The body composition and BMD of males and females were similar, except for bone area, which showed a significant gender difference, with females having higher values than males. The data obtained in this study can help with the medical management of diseased individuals and serve as a reference for health assessments of future populations.
There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.
Twenty bitches with previous history of subfertility presented for breeding management at University Veterinary Hospital, Kokkalai were selected for the study. The stage of oestrous cycle was determined and breeding scheduled based predominantly on anuclear cell index (ACI) and serum progesterone assay, after giving due weightage to behaviour and clinical signs. First mating was considered as day 0 and the second as day 3. The mean ACI of the bitches under study was 56.4 ± 1.53 per cent and the mean serum progesterone levels was recorded as 2.82± 0.16 ng/mL three days prior to breeding (day 3). Since, ACI had highly positive correlation with serum progesterone level (Pearson’s correlation coefficient being 0.880) in subfertile bitches three days prior to first breeding, it could be inferred that, when the mean ACI reached more than 55 per cent, serum progesterone levels reach values corresponding to presumably the day of LH surge and by considering both the variables, the day of ovulation could be predicted. Hence, the correlation analysis of ACI and progesterone assay can be considered as reliable method to schedule optimum breeding time in subfertile bitches.
Babatunde Adewale, Hammed Mogaji, Joshua Balogun
et al.
Nigeria remains the most endemic country in sub-Saharan Africa (SSA) for soil-transmitted helminthiases (STH). In line with ongoing monitoring plans, we present findings from a recent analysis of STH epidemiological data in Borgu, one of the non-endemic implementation units for STH in the northcentral region of Nigeria. An overall prevalence of 8.8% was recorded for STH infection, which corresponds to a 51.9% decline from the 18.3% reported in 2013. All the infected participants (36 out of 410) had a low intensity of infection. However, more than two-thirds (69%) of the children do not have access to latrine facilities, and 45% of them walk barefoot. Prevalence was significantly associated with community, age, and parental occupation. About 21–25% reduced odds were reported in some of the study communities, and children whose parents were traders had 20 times lower odds of infection compared to those whose parents were farmers. The ongoing preventive chemotherapy program for lymphatic filariasis in the area could be responsible for the huge reduction in prevalence and intensity estimates for STH. It is therefore important to invest in monitoring transmission dynamics in other non-endemic areas to arrest emerging threats through the provision of complementary interventions including WASH facilities and other health educational tools.
Zuzanna Wiśniewska, Paweł Kołodziejski, Ewa Pruszyńska
et al.
ABSTRACT: The aim of this study was to determine the effect of emulsifier and multicarbohydrase enzyme supplementation on performance, nutrient utilization, and apparent metabolizable energy—nitrogen (AMEN) value of broiler diets containing rapeseed meal (RSM) as well as their influence on the gut morphological structures, excretion of total and free sialic acid, and cecum concentration of short-chain fatty acids (SCFAs) in broiler chickens. A total of 384 male broiler chicks were assigned to four dietary treatments. The diet of the control treatment (CON) consisted of soybean, maize, and RSM (5% in starter, 7% in grower, 15% in finisher) with soybean and palm oils. The diets used for the experimental treatments were the control diet supplemented with an emulsifier (EMU), enzyme (ENZ), or both (EMU + ENZ). The duodenum (n = 10/treatment) and ileum (n = 10/treatment) digesta samples were assessed to determine nutrient digestibility: crude protein (CP), ether extract (EE), starch, Ca. Throughout the experimental period, EMU + ENZ treatment indicated the lowest total average feed intake and feed conversion ratio, with the highest average weight gain among the studied treatments (P < 0.05). The EMU + ENZ treatment also resulted in higher (P < 0.05): apparent prececal digestibility (APD) of CP, total tract neutral detergent fibre (NDF) degradation, apparent total tract digestibility (ATTD) of EE, villus height to crypt depth ratio (P < 0.1). The highest APD of EE was noted in the EMU treatment (P < 0.05). No significant differences were found in the AMEN values of the diets. A greater jejunum villi surface area was found in groups supplemented by enzyme compared to CON (P < 0.05). The EMU + ENZ treatment presented lower sialic acid excretion in the ileum and concentration of cecum SCFAs compared to the CON treatment (P < 0.05). The obtained results indicate that simultaneous usage of additives had beneficial effect on production parameters, nutrient digestibility, NDF degradation, as well as gut mucosa morphology. Based on the SCFAs concentration results, separate or simultaneous addition of emulsifier or/and enzyme did not provoke excessive fermentation activity of cecal bacteria.
Laura P. Schaposnik, Sheryl Hsu, Robin I. M. Dunbar
In recent years it has become evident the need of understanding how failure of coordination imposes constraints on the size of stable groups that highly social mammals can live in. We examine here the forces that keep animals together as a herd and others that drive them apart. Different phenotypes (e.g. genders) have different rates of gut fill, causing them to spend different amounts of time performing activities. By modeling a group as a set of semi-coupled oscillators on a disc, we show that the members of the group may become less and less coupled until the group dissolves and breaks apart. We show that when social bonding creates a stickiness, or gravitational pull, between pairs of individuals, fragmentation is reduced.
Ileana O Jelescu, Francesco Grussu, Andrada Ianus
et al.
Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental in transferring motion from driving videos to still images. However, such approaches fail in case the source image and driving video encompass large appearance variation. Moreover, the extraction of structure information requires additional modules that endow the animation-model with increased complexity. Deviating from such models, we here introduce the Latent Image Animator (LIA), a self-supervised autoencoder that evades need for structure representation. LIA is streamlined to animate images by linear navigation in the latent space. Specifically, motion in generated video is constructed by linear displacement of codes in the latent space. Towards this, we learn a set of orthogonal motion directions simultaneously, and use their linear combination, in order to represent any displacement in the latent space. Extensive quantitative and qualitative analysis suggests that our model systematically and significantly outperforms state-of-art methods on VoxCeleb, Taichi and TED-talk datasets w.r.t. generated quality.
This paper presents a novel approach for exploring diverse and expressive motions that are physically correct and interactive. The approach combining user participation in with the animation development process using crowdsourcing to remove the need for data-driven libraries while address aesthetic limitations. A core challenge for character animation solutions that do not use pre-recorded data is they are constrained to specific actions or appear unnatural and out of place (compared to real-life movements). Character movements are very subjective to human perception (easily identify underlying unnatural or strange patterns with simple actions, such as walking or climbing). We present an approach that leverage's crowdsourcing to reduce these uncanny artifacts within generated character animations. Crowdsourcing animations is an uncommon practice due to the complexities of having multiple people working in parallel on a single animation. A web-based solution for analysis and animation is presented in this paper. It allows users to optimize and evaluate complicated character animation mechanism conveniently on-line. The context of this paper introduces a simple animation system, which is integrated into a web-based solution (JavaScript/HTML5). Since Web browser are commonly available on computers, the presented application is easy to use on any platform from any location (easy to maintain and share). Our system combines the expressive power of web pages for visualising content on-the-fly with a fully fledged interactive (physics-based) animation solution that includes a rich set of libraries.
Modeling real-world sound is a fundamental problem in the creative use of machine learning and many other fields, including human speech processing and bioacoustics. Transformer-based generative models and some prior work (e.g., DDSP) are known to produce realistic sound, although they have limited control and are hard to interpret. As an alternative, we aim to use modular synthesizers, i.e., compositional, parametric electronic musical instruments, for modeling non-music sounds. However, inferring synthesizer parameters given a target sound, i.e., the parameter inference task, is not trivial for general sounds, and past research has typically focused on musical sound. In this work, we optimize a differentiable synthesizer from TorchSynth in order to model, emulate, and creatively generate animal vocalizations. We compare an array of optimization methods, from gradient-based search to genetic algorithms, for inferring its parameters, and then demonstrate how one can control and interpret the parameters for modeling non-music sounds.
The decisive role of Embryology in understanding the evolution of animal forms is founded and deeply rooted in the history of science. It is recognized that the emergence of multicellularity would not have been possible without the formation of the first embryo. We speculate that biophysical phenomena and the surrounding environment of the Ediacaran ocean were instrumental in co-opting a neoplastic functional module (NFM) within the nucleus of the first zygote. Thus, the neoplastic process, understood here as a biological phenomenon with profound embryologic implications, served as the evolutionary engine that favored the formation of the first embryo and cancerous diseases and allowed to coherently create and recreate body shapes in different animal groups during evolution. In this article, we provide a deep reflection on the Physics of the first embryogenesis and its contribution to the exaptation of additional NFM components, such as the extracellular matrix. Knowledge of NFM components, structure, dynamics, and origin advances our understanding of the numerous possibilities and different innovations that embryos have undergone to create animal forms via Neoplasia during evolutionary radiation. The developmental pathways of Neoplasia have their origins in ctenophores and were consolidated in mammals and other apical groups.
Josefin Söder, Sara Wernersson, Katja Höglund
et al.
Abstract Background The gut microbiota and its metabolic end-products act in close collaboration with the nutrient metabolism of the animal. A relationship between excess adiposity and alterations in gut microbiota composition has been identified in humans and rodents, but data are scarce for overweight dogs. This study compared composition and temporal variations of gut microbiota in healthy lean and spontaneously overweight dogs. The analysis was based on three individual fresh faeces samples from each dog during a 10-day period. Twenty-seven healthy and intact male Labrador retriever dogs were included, 12 of which were classified as lean (body condition score (BCS) 4–5 on a 9-point scale) and 15 as overweight (BCS 6–8). Gut microbiota was analysed by Illumina sequencing of the V3-V4 region of the 16S rRNA gene. Results Lean and overweight groups of dogs were not separated by principal coordinate analysis (PCoA), analysis of similarity (one-way ANOSIM, P = 0.99) or species indicator analysis (IndVal) using operational taxonomic units (OTU) data. Gut microbial taxa at phylum, family or genus level did not differ between lean and overweight dogs in mixed-model repeated measures analyses. Short-term stability, evaluated by similarity index, did not differ between lean and overweight dogs over the 10-day period. Pooled Firmicutes/Bacteroidetes (F/B) ratio was 3.1 ± 3.7 in overweight dogs and 2.1 ± 1.2 in lean dogs (P = 0.83). Individual dogs, irrespective of body condition (lean or overweight), displayed variation in mean alpha diversity (Chao-1 index range 122–245, Shannon index range 2.6–3.6) and mean similarity index (range 44–85%). Conclusions Healthy lean and spontaneously overweight Labrador retriever dogs had comparable gut microbiota composition and short-term stability over a 10-day sampling period. There were no alterations in microbial diversity or in relative abundance of specific taxa at phylum, family or genus level in overweight compared to lean dogs. Our findings suggest that there are few detectable differences in gut microbiota composition between healthy spontaneously overweight and lean dogs by the current method. Future application of metagenomic or metabolomic techniques could be used to investigate microbial genes or microbial end-products that may differ even when microbiota compositional analyses fail to detect a significant difference between lean and overweight dogs.
Insect meals are considered among the most promising feed materials in fish nutrition due to their sustainability and possibility of fish meal replacement. The present study is the first application of full-fat black soldier fly larvae (BSFL) meal in brown trout (Salmo trutta m. fario) diets. Two experiments were performed on 240 brown trout fingerlings (average body mass 4.85 g) distributed into four groups (12 tanks for the growth performance experiment, 10 fish/tank; and 12 metabolic tanks for the digestibility test, 10 fish/tank). The experimental group design was conducted as follows: control diet, with no BSFL and 35% fish meal, and experimental diets: BSFL5 – with 5% BSFL full-fat meal and 32.5% fish meal; BSFL10 – with 10% BSFL full-fat meal and 30% fish meal; and BSFL20 – with 20% BSFL full-fat meal and 25% fish meal. No effects were recorded in the case of growth performance and feed utilization parameters. The environmental sustainability of the usage of insect meals in fish diets was proven – due to the lower fish meal inclusion, the fish-in-fish-out ratio decreased by 31% in BSFL20. In the case of the viscerosomatic index, increases in BSFL5 and BSFL20 were reported. In all experimental groups, decreases in hepatosomatic index values were observed. Crude protein digestibility decreased in BSFL5 and BSFL20, while crude fat digestibility decreased only in the BSFL20 group. The effect of including BSFL full-fat meal in a brown trout diet on serum biochemical parameters was reported. The aspartate transaminase concentration increased in BSFL10 and BSFL20, while the gamma-glutamyl transpeptidase values decreased in BSFL20. In the case of total cholesterol, higher values were observed in BSFL10 and BSFL20. The albumin content decreased in the BSFL20 group, while globulin showed the highest values in the control group. The microbiota composition was not affected by insect meal inclusion. In conclusion, the results of the present study showed the high potential of BSFL full-fat meal application of up to 20% in a brown trout diet.