ABSTRACT This study investigates the effects of different crude protein (CP) levels on growth performance, serum biochemistry, organ indices, intestinal morphology, colonic volatile fatty acids, and gut microbiota in Ningxiang finishing pigs. Ninety‐six pigs (53.20 ± 0.53 kg) were randomly assigned to three dietary treatments: high‐protein (HP, 15.56% CP), medium‐protein (MP, 12.94% CP), and low‐protein (LP, 10.31% CP), with four replicate pens per treatment and eight pigs per pen. Results showed that dietary CP levels had no significant effects on growth performance. However, the LP diet significantly reduced serum urea nitrogen, liver weight, and relative liver weight (p < 0.05). Additionally, jejunal crypt depth showed a linear decrease in response to graded reductions in dietary CP levels (Linear, p < 0.05). The LP diet significantly decreased the contents of isobutyric, isovaleric, and branched‐chain fatty acids in colonic fermentation products (p < 0.05). Furthermore, 16S rRNA sequencing revealed that the relative abundances of Terrisporobacter, Marvinbryantia, Turicibacter, Lachnospiraceae_AC2044_group, unclassified_f_Peptostreptococcaceae, norank_f_Eubacter_coprostanoligenes_group, Lachnospiraceae_UCG‐007, and UCG‐009 were significantly higher in the LP group (p < 0.05). Spearman correlation analysis indicated that isobutyric acid and isovaleric acid were negatively correlated with Lactobacillus and positively correlated with Streptococcus. In conclusion, the LP diet improved colonic microbiota composition while maintaining growth performance in Ningxiang finishing pigs. These results advance our understanding of protein nutrition in indigenous fat‐type pig breeds, providing a theoretical foundation for optimizing dietary formulations specifically in Ningxiang pigs.
In this article we introduce the notion of a balloon animal map between broken toric varieties and construct several long exact sequences in cohomology related to them. We give a new proof of the deletion-contraction relation on hypertoric Hitchin systems of Dansco-Mcbreen-Shende and present some refinements of it. The end result is a formula for the Poincaré polynomial of any hypertoric Hitchin system associated to a graph with first Betti number 2 along with a recipe to calculate the Poincaré polynomial of any hypertoric Hitchin system, given knowledge of a finite number of base cases.
Reducing the number of experimental units is one of the three pillars of the 3R principles (Replace, Reduce, Refine) in animal research. At the same time, statistical error rates need to be controlled to enable reliable inferences and decisions. This paper proposes a novel measure to quantify the evidentiary value of one experimental unit for a given study design. The experimental unit information index (EUII) is based on power, Type-I error and sample size, and has attractive interpretations both in terms of frequentist error rates and Bayesian posterior odds. We introduce the EUII in simple statistical test settings and show that its asymptotic value depends only on the assumed relative effect size under the alternative. We then extend the definition to adaptive designs where early stopping for efficacy or futility may cause reductions in sample size. Applications to group-sequential designs and a recently proposed adaptive statistical test procedure show the usefulness of the approach when the goal is to maximize the evidentiary value of one experimental unit. A reanalysis of 2738 animal experiments with simulated results from (post-hoc) interim analyses illustrates the possible savings in sample size.
Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal movements. A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts. Because locomotion dynamics are seldom known, relying solely on mathematical models is insufficient; constructing a simulator that both reproduces real trajectories and supports reward-driven optimization remains an open problem. We introduce a data-driven simulator for multi-animal behavior based on deep reinforcement learning and counterfactual simulation. We address the ill-posed nature of the problem caused by high degrees of freedom in locomotion by estimating movement variables of an incomplete transition model as actions within an RL framework. We also employ a distance-based pseudo-reward to align and compare states between cyber and physical spaces. Validated on artificial agents, flies, newts, and silkmoth, our approach achieves higher reproducibility of species-specific behaviors and improved reward acquisition compared with standard imitation and RL methods. Moreover, it enables counterfactual behavior prediction in novel experimental settings and supports multi-individual modeling for flexible what-if trajectory generation, suggesting its potential to simulate and elucidate complex multi-animal behaviors.
In the era of foundation models, achieving a unified understanding of different dynamic objects through a single network has the potential to empower stronger spatial intelligence. Moreover, accurate estimation of animal pose and shape across diverse species is essential for quantitative analysis in biological research. However, this topic remains underexplored due to the limited network capacity of previous methods and the scarcity of comprehensive multi-species datasets. To address these limitations, we introduce AniMer+, an extended version of our scalable AniMer framework. In this paper, we focus on a unified approach for reconstructing mammals (mammalia) and birds (aves). A key innovation of AniMer+ is its high-capacity, family-aware Vision Transformer (ViT) incorporating a Mixture-of-Experts (MoE) design. Its architecture partitions network layers into taxa-specific components (for mammalia and aves) and taxa-shared components, enabling efficient learning of both distinct and common anatomical features within a single model. To overcome the critical shortage of 3D training data, especially for birds, we introduce a diffusion-based conditional image generation pipeline. This pipeline produces two large-scale synthetic datasets: CtrlAni3D for quadrupeds and CtrlAVES3D for birds. To note, CtrlAVES3D is the first large-scale, 3D-annotated dataset for birds, which is crucial for resolving single-view depth ambiguities. Trained on an aggregated collection of 41.3k mammalian and 12.4k avian images (combining real and synthetic data), our method demonstrates superior performance over existing approaches across a wide range of benchmarks, including the challenging out-of-domain Animal Kingdom dataset. Ablation studies confirm the effectiveness of both our novel network architecture and the generated synthetic datasets in enhancing real-world application performance.
Eneko Atxa Landa, Elena Lazkano, Igor Rodriguez
et al.
Animating realistic avatars requires using high quality animations for every possible state the avatar can be in. This includes actions like walking or running, but also subtle movements that convey emotions and personality. Idle animations, such as standing, breathing or looking around, are crucial for realism and believability. In games and virtual applications, these are often handcrafted or recorded with actors, but this is costly. Furthermore, recording realistic idle animations can be very complex, because the actor must not know they are being recorded in order to make genuine movements. For this reasons idle animation datasets are not widely available. Nevertheless, this paper concludes that both acted and genuine idle animations are perceived as real, and that users are not able to distinguish between them. It also states that handmade and recorded idle animations are perceived differently. These two conclusions mean that recording idle animations should be easier than it is thought to be, meaning that actors can be specifically told to act the movements, significantly simplifying the recording process. These conclusions should help future efforts to record idle animation datasets. Finally, we also publish ReActIdle, a 3 dimensional idle animation dataset containing both real and acted idle motions.
The last two centuries have seen the occurrence of several theoretical and scientific advancements that revolutionized plant science. From Mendel's intuitions to discovery of DNA toward up to the development of next generation sequencing technologies, it is nowadays possible to precisely investigate the genetic basis of traits of agricultural interest accelerating crop improvement. The availability of catalogues of thousands of genomic markers in many species, provides new opportunities to identify favourable allelic variants supporting genomic assisted breeding programs. In addition, the release of complete plant genomes sequences and the development of pangenomes make possible the identification of structural variants facilitating mining of favourable alleles. Beyond technological advancements, data integration is crucial for the comprehensive understanding of the biological processes underlying plant traits and their interactions with the environment. This perspective article illustrates how omics technologies can be used to tailor genomic tools for different breeders' needs to revolutionize crop improvement.
This study introduces markerless retro-identification of animals, a novel concept and practical technique to identify past occurrences of organisms in archived data, that complements traditional forward-looking chronological re-identification methods in longitudinal behavioural research. Identification of a key individual among multiple subjects may occur late in an experiment if it reveals itself through interesting behaviour after a period of undifferentiated performance. Often, longitudinal studies also encounter subject attrition during experiments. Effort invested in training software models to recognise and track such individuals is wasted if they fail to complete the experiment. Ideally, we would be able to select individuals who both complete an experiment and/or differentiate themselves via interesting behaviour, prior to investing computational resources in training image classification software to recognise them. We propose retro-identification for model training to achieve this aim. This reduces manual annotation effort and computational resources by identifying subjects only after they differentiate themselves late, or at an experiment's conclusion. Our study dataset comprises observations made of morphologically similar reed bees (\textit{Exoneura robusta}) over five days. We evaluated model performance by training on final day five data, testing on the sequence of preceding days, and comparing results to the usual chronological evaluation from day one. Results indicate no significant accuracy difference between models. This underscores retro-identification's value in improving resource efficiency in longitudinal animal studies.
Genevieve Patterson, Joost Daniels, Benjamin Woodward
et al.
Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We present the FathomVerse v0 detection dataset to push the limits of our field by exploring animals that rarely come in contact with people in the deep sea. These animals present a novel vision challenge. The FathomVerse v0 dataset consists of 3843 images with 8092 bounding boxes from 12 distinct morphological groups recorded at two locations on the deep seafloor that are new to computer vision. It features visually perplexing scenarios such as an octopus intertwined with a sea star, and confounding categories like vampire squids and sea spiders. This dataset can push forward research on topics like fine-grained transfer learning, novel category discovery, species distribution modeling, and carbon cycle analysis, all of which are important to the care and husbandry of our planet.
Consider a family of random masses $\mathbf{m}(v)$ indexed by vertices of the lattice $\mathbb Z^d$. In the case where the masses are i.i.d.\ and satisfy a certain moment condition, it is known that there exists a deterministic $A\ge 0$ such that the maximal mass $A_n$ of an animal containing $0$ with cardinal $n$ satisfies $A_n/n \rightarrow A$ when $n\to \infty$, almost surely. The same also goes for self-avoiding paths. We extend this result to the case where the family of masses is an ergodic marked point process, with a suitable definition for animals in this context. Special cases include the initial model with ergodic instead of i.i.d.\ masses and marked Poisson point processes. We also discuss some sufficient or necessary conditions for integrability.
In the study of animal behavior, researchers often record long continuous videos, accumulating into large-scale datasets. However, the behaviors of interest are often rare compared to routine behaviors. This incurs a heavy cost on manual annotation, forcing users to sift through many samples before finding their needles. We propose a pipeline to efficiently sample rare behaviors from large datasets, enabling the creation of training datasets for rare behavior classifiers. Our method only needs an unlabeled animal pose or acceleration dataset as input and makes no assumptions regarding the type, number, or characteristics of the rare behaviors. Our pipeline is based on a recent graph-based anomaly detection model for human behavior, which we apply to this new data domain. It leverages anomaly scores to automatically label normal samples while directing human annotation efforts toward anomalies. In research data, anomalies may come from many different sources (e.g., signal noise versus true rare instances). Hence, the entire labeling budget is focused on the abnormal classes, letting the user review and label samples according to their needs. We tested our approach on three datasets of freely-moving animals, acquired in the laboratory and the field. We found that graph-based models are particularly useful when studying motion-based behaviors in animals, yielding good results while using a small labeling budget. Our method consistently outperformed traditional random sampling, offering an average improvement of 70% in performance and creating datasets even when the behavior of interest was only 0.02% of the data. Even when the performance gain was minor (e.g., when the behavior is not rare), our method still reduced the annotation effort by half.
This case report describes, for the first time, a simultaneous occurrence of open-cervix pyometra and gangrene of the tongue in a ten-year-old intact female Griffon dog. The bitch had a three-week history of grayish black thin purulent vulvar discharge, severe licking of the external genitalia, polyuria, polydipsia, inappetence and lethargy. The owner acknowledged of estrous cycle since two months. Blood work revealed severe leukocytosis, neutrophilia, monocytosis and elevated level of globulins. Urinalysis revealed low specific gravity of the urine. Abdominal radiography showed fluid-filled and slightly enlarged uterus. Ultrasonography of the abdomen revealed enlarged uterus with a thickened uterine wall. The diameter, wall thickness, and luminal thickness of the uterus were 2.32 cm, 0.38 cm, and 1.94 cm, respectively. Accumulation of anechoic-hypoechoic pus inside the uterus was also noticed. The case was confirmed as open-cervix pyometra therefore, ovariohysterectomy was carried out. After 48 hours post-operative, the bitch developed dry gangrene of the tongue. The cranial fourth of tongue became cold, insensitive, dry and grayish white. Moreover, there were diminished lingual function and a clear line of demarcation between the healthy and gangrenous parts of the tongue. Partial glossectomy was carried out and tolerated by the bitch. The animal had acceptable and functional outcomes. In conclusion, transmission of infection from open-cervix pyometra to the tongue is possible leading to dry gangrene in dogs.
Zhuqing Liang, Tyler Ralph-Epps, Michael W. Schmidtke
et al.
Abstract Barth syndrome (BTHS) is a rare disorder caused by mutations in the TAFAZZIN gene. Previous studies from both patients and model systems have established metabolic dysregulation as a core component of BTHS pathology. In particular, features such as lactic acidosis, pyruvate dehydrogenase (PDH) deficiency, and aberrant fatty acid and glucose oxidation have been identified. However, the lack of a mechanistic understanding of what causes these conditions in the context of BTHS remains a significant knowledge gap, and this has hindered the development of effective therapeutic strategies for treating the associated metabolic problems. In the current study, we utilized tafazzin-knockout C2C12 mouse myoblasts (TAZ-KO) and cardiac and skeletal muscle tissue from tafazzin-knockout mice to identify an upstream mechanism underlying impaired PDH activity in BTHS. This mechanism centers around robust upregulation of pyruvate dehydrogenase kinase 4 (PDK4), resulting from hyperactivation of AMP-activated protein kinase (AMPK) and subsequent transcriptional upregulation by forkhead box protein O1 (FOXO1). Upregulation of PDK4 in tafazzin-deficient cells causes direct phospho-inhibition of PDH activity accompanied by increased glucose uptake and elevated intracellular glucose concentration. Collectively, our findings provide a novel mechanistic framework whereby impaired tafazzin function ultimately results in robust PDK4 upregulation, leading to impaired PDH activity and likely linked to dysregulated metabolic substrate utilization. This mechanism may underlie previously reported findings of BTHS-associated metabolic dysregulation.
Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.
In this paper, we define "animated affinoid algebras" and prove some basic properties of them. Then we generalize the result of the previous paper of the author and the result of Lucas Mann (fppf-descent for discrete rings or affinoid algebras) to discrete animated rings or animated affinoid algebras.
Aliaksandr Siarohin, Willi Menapace, Ivan Skorokhodov
et al.
We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects. Our method learns the 3D structure and dynamics of objects solely from single-view RGB videos, and can decompose them into semantically meaningful parts that can be tracked and animated. Using a 3D autodecoder framework, paired with a keypoint estimator via a differentiable PnP algorithm, our model learns the underlying object geometry and parts decomposition in an entirely unsupervised manner. This allows it to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. We primarily evaluate the framework on two video datasets: VoxCeleb $256^2$ and TEDXPeople $256^2$. In addition, on the Cats $256^2$ image dataset, we show it even learns compelling 3D geometry from still images. Finally, we show our model can obtain animatable 3D objects from a single or few images. Code and visual results available on our project website, see https://snap-research.github.io/unsupervised-volumetric-animation .
Ulcerative colitis (UC) is a chronic inflammatory manifestation of the human colon that is linked with colorectal cancer. Development of an appropriate animal model is crucial to study the immunopathophysiology of UC wherein chemical induction is the most popular method of choice. However, unavailability of an optimum experimental model limits the success of this method. The present study aims to establish an optimized model for acetic acid-induced colitis in Sprague Dawley rats. Response Surface Methodology (RSM) with a six-factors Box-Behnken design was employed to generate an improved method of inducing UC in rat, predicting the case statistics, apposite investigation of quadratic response surfaces, and construction of a second-order polynomial equation. UC was diagnosed through three responses viz. weight loss, severity of diarrhea, and appearance of blood in the stool. Analysis of variance alongside RSM jointly revealed that induction of UC can be achieved with highest probability using the combination of parameters that includes 120 gm body weight, 1.5 ml of 4% acetic-acid v/v in distilled water with a single dose of treatment for 24 h including a pre-induction of 5 mins. This optimized UC-induction model was validated in-vivo through disease scoring index and hematological assessments with satisfactory level of desirability. • An improved experimental method for inducing ulcerative colitis (UC) in Sprague Dawley rats has been developed. • Box-Behnken Design-fitted Response Surface Methodology (RSM) was implicated in optimizing the experimental parameters for generating UC. • This statistically optimized and experimentally validated method resembles the recipe for the generation of UC in animal model with the highest possible desirability.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the persistent coronavirus disease 2019 (COVID-19) pandemic, which has resulted in millions of deaths worldwide and brought an enormous public health and global economic burden. The recurring global wave of infections has been exacerbated by growing variants of SARS-CoV-2. In this study, the virological characteristics of the original SARS-CoV-2 strain and its variants of concern (VOCs; including Alpha, Beta, and Delta) in vitro, as well as differential transcriptomic landscapes in multiple organs (lung, right ventricle, blood, cerebral cortex, and cerebellum) from the infected rhesus macaques, were elucidated. The original strain of SARS-CoV-2 caused a stronger innate immune response in host cells, and its VOCs markedly increased the levels of subgenomic RNAs, such as N, Orf9b, Orf6, and Orf7ab, which are known as the innate immune antagonists and the inhibitors of antiviral factors. Intriguingly, the original SARS-CoV-2 strain and Alpha variant induced larger alteration of RNA abundance in tissues of rhesus monkeys than Beta and Delta variants did. Moreover, a hyperinflammatory state and active immune response were shown in the right ventricles of rhesus monkeys by the up-regulation of inflammation- and immune-related RNAs. Furthermore, peripheral blood may mediate signaling transmission among tissues to coordinate the molecular changes in the infected individuals. Collectively, these data provide insights into the pathogenesis of COVID-19 at the early stage of infection by the original SARS-CoV-2 strain and its VOCs.
Biology (General), Computer applications to medicine. Medical informatics
Ubaldo Richard Marín Castro, Fernando Cansino Jácome, José Arturo Olguín-Rojas
et al.
This study aimed to determine storage conditions for microparticles containing habanero pepper extracts with maltodextrin (MD) and a 95:5 <i>w</i>/<i>w</i> mixture with precipitated silica (MDSP) as wall materials. State diagrams (SD) using water adsorption isotherms and glass transition temperatures were created. Monolayer values were 6.17 g (MD) and 6.76 g (MDSP) of water/100 g d.s. Critical water activity values (a<sub>w</sub>C) were 0.49 for MD and 0.41 for MDSP. When stored at a<sub>w</sub> > a<sub>w</sub>C, both samples underwent physical transformations, with significant color changes (ΔE > 8). Conversely, storage below a<sub>w</sub>C resulted in minimal changes (ΔE < 4), consistent with the SD.