Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existing methods only apply to human figures, which usually do not generalize well on anthropomorphic characters commonly used in industries like gaming and entertainment. Furthermore, previous methods could only generate videos with static backgrounds, which limits the realism of the videos. For the first challenge, our in-depth analysis suggests to attribute this limitation to their insufficient modeling of motion, which is unable to comprehend the movement pattern of the driving video, thus imposing a pose sequence rigidly onto the target character. To this end, this paper proposes Animate-X++, a universal animation framework based on DiT for various character types, including anthropomorphic characters. To enhance motion representation, we introduce the Pose Indicator, which captures comprehensive motion pattern from the driving video through both implicit and explicit manner. The former leverages CLIP visual features of a driving video to extract its gist of motion, like the overall movement pattern and temporal relations among motions, while the latter strengthens the generalization of DiT by simulating possible inputs in advance that may arise during inference. For the second challenge, we introduce a multi-task training strategy that jointly trains the animation and TI2V tasks. Combined with the proposed partial parameter training, this approach achieves not only character animation but also text-driven background dynamics, making the videos more realistic. Moreover, we introduce a new Animated Anthropomorphic Benchmark (A2Bench) to evaluate the performance of Animate-X++ on universal and widely applicable animation images. Extensive experiments demonstrate the superiority and effectiveness of Animate-X++.
Understanding how animals learn is a central challenge in neuroscience, with growing relevance to the development of animal- or human-aligned artificial intelligence. However, existing approaches tend to assume fixed parametric forms for the learning rule (e.g., Q-learning, policy gradient), which may not accurately describe the complex forms of learning employed by animals in realistic settings. Here we address this gap by developing a framework to infer learning rules directly from behavioral data collected during de novo task learning. We assume that animals follow a decision policy parameterized by a generalized linear model (GLM), and we model their learning rule -- the mapping from task covariates to per-trial weight updates -- using a deep neural network (DNN). This formulation allows flexible, data-driven inference of learning rules while maintaining an interpretable form of the decision policy itself. To capture more complex learning dynamics, we introduce a recurrent neural network (RNN) variant that relaxes the Markovian assumption that learning depends solely on covariates of the current trial, allowing for learning rules that integrate information over multiple trials. Simulations demonstrate that the framework can recover ground-truth learning rules. We applied our DNN and RNN-based methods to a large behavioral dataset from mice learning to perform a sensory decision-making task and found that they outperformed traditional RL learning rules at predicting the learning trajectories of held-out mice. The inferred learning rules exhibited reward-history-dependent learning dynamics, with larger updates following sequences of rewarded trials. Overall, these methods provide a flexible framework for inferring learning rules from behavioral data in de novo learning tasks, setting the stage for improved animal training protocols and the development of behavioral digital twins.
Corina Duda-Seiman, Liliana Mititelu-Tartau, Simona Biriescu
et al.
Background: With growing interest in healthy lifestyles, protein bars have gained popularity. However, many commercial bars contain excessive calories, sugar, and artificial additives that undermine their health benefits. This study aimed to develop a protein bar using natural ingredients with a balanced macronutrient profile. Method: The protein bar formulation used soy protein extract, a plant-based protein source, known for its complete amino acid profile but limited in methionine, which was complemented by oats to nutritionally balance this deficiency. A database was created to evaluate the cost-effectiveness of commercially available protein bars based on consumer feedback. The experimental bar was tested for nutritional value, shelf life, and physiological impact, using only natural ingredients for texture, flavor, and stability. Results: The experimental protein bar had higher protein and fiber content than a selected commercial bar but a shorter shelf life (7 days vs. 90 days) due to the absence of preservatives. The database helped identify target consumer groups and ensure the product was affordable and nutritionally effective. Conclusion: This study demonstrates that using natural, complementary ingredients can create a protein bar with a more balanced nutrient profile while avoiding harmful additives. The final product supports muscle protein synthesis through its high-quality protein content and promotes glycemic control and satiety via its fiber-rich, low-sugar formulation and metabolic processes, offering a healthier alternative to commercial options, with a focus on consumer health and cost-effectiveness.
Arghavan Jalayer, Masoud Jalayer, Mehdi Khakzand
et al.
Overpopulation in animal shelters contributes to increased disease spread and higher expenses on animal healthcare, leading to fewer adoptions and more shelter deaths. Additionally, one of the greatest challenges that shelters face is the noise level in the dog kennel area, which is physically and physiologically hazardous for both animals and staff. This paper proposes a multi-criteria optimization framework to automatically design cage layouts that maximize shelter capacity, minimize tension in the dog kennel area by reducing the number of cages facing each other, and ensure accessibility for staff and visitors. The proposed framework uses a Genetic Algorithm (GA) to systematically generate and improve layouts. A novel graph theory-based algorithm is introduced to process solutions and calculate fitness values. Additionally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and sort the layouts in each iteration. The graph-based algorithm calculates variables such as cage accessibility and shortest paths to access points. Furthermore, a heuristic algorithm is developed to calculate layout scores based on the number of cages facing each other. This framework provides animal shelter management with a flexible decision-support system that allows for different strategies by assigning various weights to the TOPSIS criteria. Results from cats' and dogs' kennel areas show that the proposed framework can suggest optimal layouts that respect different priorities within acceptable runtimes.
Animal populations worldwide are rapidly declining, and a technology that can accurately count endangered species could be vital for monitoring population changes over several years. This research focused on fine-tuning object detection models for drone images to create accurate counts of animal species. Hundreds of images taken using a drone and large, openly available drone-image datasets were used to fine-tune machine learning models with the baseline YOLOv8 architecture. We trained 30 different models, with the largest having 43.7 million parameters and 365 layers, and used hyperparameter tuning and data augmentation techniques to improve accuracy. While the state-of-the-art YOLOv8 baseline had only 0.7% accuracy on a dataset of safari animals, our models had 95% accuracy on the same dataset. Finally, we deployed the models on the Jetson Orin Nano for demonstration of low-power real-time species detection for easy inference on drones.
Animals perceive the world to plan their actions and interact with other agents to accomplish complex tasks, demonstrating capabilities that are still unmatched by AI systems. To advance our understanding and reduce the gap between the capabilities of animals and AI systems, we introduce a dataset of pet egomotion imagery with diverse examples of simultaneous egomotion and multi-agent interaction. Current video datasets separately contain egomotion and interaction examples, but rarely both at the same time. In addition, EgoPet offers a radically distinct perspective from existing egocentric datasets of humans or vehicles. We define two in-domain benchmark tasks that capture animal behavior, and a third benchmark to assess the utility of EgoPet as a pretraining resource to robotic quadruped locomotion, showing that models trained from EgoPet outperform those trained from prior datasets.
Raphael Reme, Alasdair Newson, Elsa Angelini
et al.
Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the evaluation and improvement of such tracking algorithms. To address this, we developed SINETRA, a versatile simulator that generates synthetic tracking data for particles on a deformable background, closely mimicking live animal recordings. This simulator produces annotated 2D and 3D videos that reflect the intricate movements seen in behaving animals like Hydra Vulgaris. We evaluated four state-of-the-art tracking algorithms highlighting the current limitations of these methods in challenging scenarios and paving the way for improved cell tracking techniques in dynamic biological systems.
Qing Jiang, Kenichi Nagano, Takeshi Moriishi
et al.
Background: Zinc finger-containing transcription factor Osterix/Specificity protein-7 (Sp7) is an essential transcription factor for osteoblast differentiation. However, its functions in differentiated osteoblasts remain unclear and the effects of osteoblast-specific Sp7 deletion on osteocytes have not been sufficiently studied. Methods: Sp7floxneo/floxneo mice, in which Sp7 expression was 30 % of that in wild-type mice because of disturbed splicing by neo gene insertion, and osteoblast-specific knockout (Sp7fl/fl;Col1a1−Cre) mice using 2.3-kb Col1a1 enhanced green fluorescent protein (EGFP)-Cre were examined by micro-computed tomography (micro-CT), bone histomorphometry, serum markers, and histological analyses. The expression of osteoblast and osteocyte marker genes was examined by real-time reverse transcription (RT)-PCR analysis. Osteoblastogenesis, osteoclastogenesis, and regulation of the expression of collagen type I alpha 1 chain (Col1a1) were examined in primary osteoblasts. Results: Femoral trabecular bone volume was higher in female Sp7floxneo/floxneo and Sp7fl/fl;Col1a1−Cre mice than in the respective controls, but not in males. Bromodeoxyuridine (BrdU)-positive osteoblastic cells were increased in male Sp7fl/fl;Col1a1−Cre mice, and osteoblast number and the bone formation rate were increased in tibial trabecular bone in female Sp7fl/fl;Col1a1−Cre mice, although osteoblast maturation was inhibited in female Sp7fl/fl;Col1a1−Cre mice as shown by the increased expression of an immature osteoblast marker gene, secreted phosphoprotein 1 (Spp1), and reduced expression of a mature osteoblast marker gene, bone gamma-carboxyglutamate protein/bone gamma-carboxyglutamate protein 2 (Bglap/Bglap2). Furthermore, alkaline phosphatase activity was increased but mineralization was reduced in the culture of primary osteoblasts from Sp7fl/fl;Col1a1−Cre mice. Therefore, the accumulated immature osteoblasts in Sp7fl/fl;Col1a1−Cre mice was likely compensated for the inhibition of osteoblast maturation at different levels in males and females. Vertebral trabecular bone volume was lower in both male and female Sp7fl/fl;Col1a1−Cre mice than in the controls and the osteoblast parameters and bone formation rate in females were lower in Sp7fl/fl;Col1a1−Cre mice than in Sp7fl/fl mice, suggesting differential regulatory mechanisms in long bones and vertebrae. The femoral cortical bone was thin and porous in Sp7floxneo/floxneo and Sp7fl/fl;Col1a1−Cre mice of both sexes, the number of canaliculi was reduced, and terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling (TUNEL)-positive lacunae and the osteoclasts were increased, whereas the bone formation rate was similar in Sp7fl/fl;Col1a1−Cre and Sp7fl/fl mice. The serum levels of total procollagen type 1 N-terminal propeptide (P1NP), a marker for bone formation, were similar, while those of tartrate-resistant acid phosphatase 5b (TRAP5b), a marker for bone resorption, were higher in Sp7fl/fl;Col1a1−Cre mice. Osteoblasts were less cuboidal, the expression of Col1a1 and Col1a1-EGFP-Cre was lower in Sp7fl/fl;Col1a1−Cre mice, and overexpression of Sp7 induced Col1a1 expression. Conclusions: Our studies indicated that Sp7 inhibits the proliferation of immature osteoblasts, induces osteoblast maturation and Col1a1 expression, and is required for osteocytes to acquire a sufficient number of processes for their survival, which prevents cortical porosity. The translational potential of this article: This study clarified the roles of Sp7 in differentiated osteoblasts in proliferarion, maturation, Col1a1 expression, and osteocyte process formation, which are required for targeting SP7 in the development of therapies for osteoporosis.
Abstract Background Researches have used intra-compartmental infusion and ballon tourniquest to create high intra-compartmental pressure in animal models of Acute Compartment Syndrome (ACS). However, due to the large differences in the modeling methods and the evaluation criteria of ACS, further researches of its pathophysiology and pathogenesis are hindered. Currently, there is no ideal animal model for ACS and this study aimed to establish a reproducible, clinically relevant animal model. Methods Blunt trauma and fracture were caused by the free falling of weights (0.5 kg, 1 kg, 2 kg) from a height of 40 cm onto the lower legs of rats, and the application of pressures of 100 mmHg, 200 mmHg, 300 mmHg and 400 mmHg to the lower limbs of rats using a modified pressurizing device for 6 h. The intra-compartmental pressure (ICP) and the pressure change (ΔP) of rats with single and combined injury were continuously recorded, and the pathophysiology of the rats was assessed based on serum biochemistry, histological and hemodynamic changes. Results The ΔP caused by single injury method of different weights falling onto the lower leg did not meet the diagnosis criteria for ACS (< 30 mmHg). On the other hand, a combined injury method of a falling weight of 1.0 kg and the use of a pressurizing device with pressure of 300 mmHg or 400 mmHg for 6 h resulted in the desired ACS diagnosis criteria with a ΔP value of less than 30 mmHg. The serum analytes, histological damage score, and fibrosis level of the combined injury group were significantly increased compared with control group, while the blood flow was significantly decreased compared with control group. Conclusion We successfully established a new preclinical ACS-like rat model, by the compression of the lower leg of rats with 300 mmHg pressure for 6 h and blunt trauma by 1.0 kg weight falling.
Antonio Carlos Costa, Gautam Sridhar, Claire Wyart
et al.
Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales, which hampers quantitative reasoning and the identification of general principles. Here, we combine data analysis and theory to investigate the relationship between behavioral plasticity and heavy-tailed statistics often observed in animal behavior. Specifically, we first leverage high-resolution recordings of C. elegans locomotion to show that stochastic transitions among long-lived behaviors exhibit heavy-tailed first passage time distributions and correlation functions. Such heavy tails can be explained by slow adaptation of behavior over time. This particular result motivates our second step of introducing a general model where we separate fast dynamics on a quasi-stationary multi-well potential, from non-ergodic, slowly varying modes. We then show that heavy tails generically emerge in such a model, and we provide a theoretical derivation of the resulting functional form, which can become a power law with exponents that depend on the strength of the fluctuations. Finally, we provide direct support for the generality of our findings by testing them in a C. elegans mutant where adaptation is suppressed and heavy tails thus disappear, and recordings of larval zebrafish swimming behavior where heavy tails are again prevalent.
We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, our model requires no pose annotations or parametric shape models for training; it learns purely from a collection of unlabeled web video clips, leveraging semantic correspondences distilled from self-supervised image features. At the core of our method is a video Photo-Geometric Auto-Encoding framework that decomposes each training video clip into a set of explicit geometric and photometric representations, including a rest-pose 3D shape, an articulated pose sequence, and texture, with the objective of re-rendering the input video via a differentiable renderer. This decomposition allows us to learn a generative model over the underlying articulated pose sequences akin to a Variational Auto-Encoding (VAE) formulation, but without requiring any external pose annotations. At inference time, we can generate new motion sequences by sampling from the learned motion VAE, and create plausible 4D animations of an animal automatically within seconds given a single input image.
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wildlife sound available has massively increased, and automatic classification through deep learning now shows strong results. However, bioacoustics is not a single task but a vast range of small-scale tasks (such as individual ID, call type, emotional indication) with wide variety in data characteristics, and most bioacoustic tasks do not come with strongly-labelled training data. The standard paradigm of supervised learning, focussed on a single large-scale dataset and/or a generic pre-trained algorithm, is insufficient. In this work we recast bioacoustic sound event detection within the AI framework of few-shot learning. We adapt this framework to sound event detection, such that a system can be given the annotated start/end times of as few as 5 events, and can then detect events in long-duration audio -- even when the sound category was not known at the time of algorithm training. We introduce a collection of open datasets designed to strongly test a system's ability to perform few-shot sound event detections, and we present the results of a public contest to address the task. We show that prototypical networks are a strong-performing method, when enhanced with adaptations for general characteristics of animal sounds. We demonstrate that widely-varying sound event durations are an important factor in performance, as well as non-stationarity, i.e. gradual changes in conditions throughout the duration of a recording. For fine-grained bioacoustic recognition tasks without massive annotated training data, our results demonstrate that few-shot sound event detection is a powerful new method, strongly outperforming traditional signal-processing detection methods in the fully automated scenario.
Takeo Matsumoto, Takuma Suzuki, Mitsuhiro Nakamura
et al.
Abstract Background Since the human papillomavirus vaccines do not eliminate preexisting infections, nonsurgical alternative approaches to cervical intraepithelial neoplasia (CIN) have been required. We previously reported that FOXP4 (forkhead box transcription factor P4) promoted proliferation and inhibited squamous differentiation of CIN1‐derived W12 cells. Since it was reported that FOXP expressions were regulated by the androgen/androgen receptor (AR) complex and AR was expressed on the CIN lesions, in this study we examined the effects of androgen on CIN progression. Methods Since AR expression was negative in W12 cells and HaCaT cells, a human male skin‐derived keratinocyte cell line, we transfected AR to these cell lines and investigated the effects of dihydrotestosterone (DHT) on their proliferation and squamous differentiation. We also examined the immunohistochemical expression of AR in CIN lesions. Results DHT reduced the intranuclear expression of FOXP4, attenuating cell proliferation and promoting squamous differentiation in AR‐transfected W12 cells. Si‐RNA treatments showed that DHT induced the expression of squamous differentiation‐related genes in AR‐transfected W12 cells via an ELF3‐dependent pathway. DHT also reduced FOXP4 expression in AR‐transfected HaCaT cells. An immunohistochemical study showed that AR was expressed in the basal to parabasal layers of the normal cervical epithelium. In CIN1 and 2 lesions, AR was detected in atypical squamous cells, whereas AR expression had almost disappeared in the CIN3 lesion and was not detected in SCC, suggesting that androgens do not act to promote squamous differentiation in the late stages of CIN. Conclusion Androgen is a novel factor that regulates squamous differentiation in the early stage of CIN, providing a new strategy for nonsurgical and hormone‐induced differentiation therapy against CIN1 and CIN2.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
The lack of annotated training data in bioacoustics hinders the use of large-scale neural network models trained in a supervised way. In order to leverage a large amount of unannotated audio data, we propose AVES (Animal Vocalization Encoder based on Self-Supervision), a self-supervised, transformer-based audio representation model for encoding animal vocalizations. We pretrain AVES on a diverse set of unannotated audio datasets and fine-tune them for downstream bioacoustics tasks. Comprehensive experiments with a suite of classification and detection tasks have shown that AVES outperforms all the strong baselines and even the supervised "topline" models trained on annotated audio classification datasets. The results also suggest that curating a small training subset related to downstream tasks is an efficient way to train high-quality audio representation models. We open-source our models at \url{https://github.com/earthspecies/aves}.
In this article, I put forward the idea that the neoplastic process (NP) has deep evolutionary roots and make specific predictions about the connection between cancer and the formation of the first embryo, which allowed for the evolutionary radiation of metazoans. My main hypothesis is that the NP is at the heart of cellular mechanisms responsible for animal morphogenesis and, given its embryological basis, also at the center of animal evolution. It is thus understood that NP-associated mechanisms are deeply rooted in evolutionary history and tied to the formation of the first animal embryo. In my consideration of these arguments, I expound on how cancer biology is perfectly intertwined with evolutionary biology. I describe essential cellular components of unicellular holozoans that served as a basis for the formation of the neoplastic functional module (NFM) and its subsequent exaptation, which brought forth two great biophysical revolutions within the first embryo. Finally, I examine the role of Physics in the modeling of the NFM and its contribution to morphogenesis to reveal the totipotency of the zygote.
Peter R. Thompson, Melodie Kunegel-Lion, Mark A. Lewis
Animal learning has interested ecologists and psychologists for over a century. Mathematical models that explain how animals store and recall information have gained attention recently. Central to this work is statistical decision theory (SDT), which relates information uptake in animals to Bayesian inference. SDT effectively explains many learning tasks in animals, but extending this theory to predict how animals will learn in changing environments still poses a challenge for ecologists. We addressed this shortcoming with a novel implementation of Bayesian Markov Chain Monte Carlo (MCMC) sampling to simulate how animals sample environmental information and learn as a result. We applied our framework to an individual-based model simulating complex foraging tasks encountered by wild animals. Simulated ``animals" learned behavioral strategies that optimized foraging returns simply by following the principles of an MCMC sampler. In these simulations, behavioral plasticity was most conducive to efficient foraging in unpredictable and uncertain environments. Our model suggests that animals prioritize highly concentrated resources even when these resources are less available overall, in line with existing knowledge on optimal foraging and ideal free distribution theory. Our innovative computational modelling framework can be applied more widely to simulate the learning of many other tasks in animals and humans.