In dry lowland regions of the world, sorghum (Sorghum bicolor (L) Moench) is a significant crop extensively farmed for food, feed, fodder, and fuel. Despite extensive breeding work, sorghum production and productivity remain low in Ethiopia. To create superior genotypes in breeding programs, it is necessary to comprehend the existence and extent of genetic diversity. Therefore, this research aimed to evaluate the importance of genetic diversity, heritability, and genetic advance in sorghum genotypes. Using an alpha lattice design with two replications, a total of 72 genotypes were assessed during the main cropping season of 2020 in Miesso in Eastern Ethiopia and Kobo in Northern Ethiopia. For every trait, a combined analysis of variance revealed a significant difference (p < 0.01) between the genotypes. Grain yield, the number of heads per plot, and the number of stands at harvest had the highest genotypic and phenotypic coefficients of variation, while the number of days to flowering, days to maturity, grain filling period, leaf length, and leaf width had the lowest. Genetic advance as a percentage of the mean (GAM) varied from 2.28 % for the number of days to maturity to 97.04 % for grain yield, while broad sense heritability ranged from 26.46 % for panicle width to 89.67 % for grain yield. The genotypes ETSC14804-4-2 (4.97 t/ha), ETSC14695-1-2 (4.7 t/ha), and ETSC14715-3-1 (4.46 t/ha) were found to be high-yielders in comparison to the others based on the current data. Still, more research is required to make better recommendations.
The present study is dedicated to exploring key factors impacting US pet owners’ preferences for brand, price, country of origin, and health and nutrition claims as important extrinsic and credence attributes. Pet engagement and subjective and objective knowledge, as well as varying forms of pet humanisation behaviour, were thought to be suitable factors. The study is of an explorative and quantitative nature, rooted in an online consumer survey, descriptive statistics, and partial least squares structural equation modelling (PLS-SEM). To strengthen the PLS-SEM model, relative preference shares derived from a best–worst analysis were integrated into the model. The results with the strongest effect sizes indicate that US pet owners’ objective knowledge is positively associated with pet non-humanisation behaviour, those who actively engage with their pet are positively associated with loving humanisation behaviour, and that health and nutritional claims on pet food are less important for those reporting non-humanisation behaviours. The analysis between the varying types of pet humanisation behaviours and the best–worst-derived relative preferences for extrinsic and credence attributes provides a diverse picture. Together, the results suggest that pet engagement and both subjective and objective knowledge are associated with pet humanisation behaviour, which are differentially linked to the importance of pet food product attributes. Best practice recommendations for marketers in the pet food industry are provided.
Animated movies are captivating for their unique character designs and imaginative storytelling, yet they pose significant challenges for existing recognition systems. Unlike the consistent visual patterns detected by conventional face recognition methods, animated characters exhibit extreme diversity in their appearance, motion, and deformation. In this work, we propose an audio-visual pipeline to enable automatic and robust animated character recognition, and thereby enhance character-centric understanding of animated movies. Central to our approach is the automatic construction of an audio-visual character bank from online sources. This bank contains both visual exemplars and voice (audio) samples for each character, enabling subsequent multi-modal character recognition despite long-tailed appearance distributions. Building on accurate character recognition, we explore two downstream applications: Audio Description (AD) generation for visually impaired audiences, and character-aware subtitling for the hearing impaired. To support research in this domain, we introduce CMD-AM, a new dataset of 75 animated movies with comprehensive annotations. Our character-centric pipeline demonstrates significant improvements in both accessibility and narrative comprehension for animated content over prior face-detection-based approaches. For the code and dataset, visit https://www.robots.ox.ac.uk/~vgg/research/animated_ad/.
Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, challenges like visual consistency, stylistic coherence, and ethical considerations persist. Additionally, this paper explores future directions and advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation
Neural Style Transfer (NST) is a technique for applying the visual characteristics of one image onto another while preserving structural content. Traditionally used for artistic transformations, NST has recently been adapted, e.g., for domain adaptation and data augmentation. This study investigates the use of this technique for enhancing animal facial landmark detectors training. As a case study, we use a recently introduced Ensemble Landmark Detector for 48 anatomical cat facial landmarks and the CatFLW dataset it was trained on, making three main contributions. First, we demonstrate that applying style transfer to cropped facial images rather than full-body images enhances structural consistency, improving the quality of generated images. Secondly, replacing training images with style-transferred versions raised challenges of annotation misalignment, but Supervised Style Transfer (SST) - which selects style sources based on landmark accuracy - retained up to 98% of baseline accuracy. Finally, augmenting the dataset with style-transferred images further improved robustness, outperforming traditional augmentation methods. These findings establish semantic style transfer as an effective augmentation strategy for enhancing the performance of facial landmark detection models for animals and beyond. While this study focuses on cat facial landmarks, the proposed method can be generalized to other species and landmark detection models.
Lukáš Praus, Jaromír Ducháček, Tomáš Mrština
et al.
Selenium (Se) supplementation is a common practice in dairy nutrition. However, the use of biofortified feedstuffs remains a not fully realized strategy to enhance the Se content of animal derived products. This study explored an on-farm biofortification approach by incorporating Se-enriched maize silage into the total mixed ration (TMR) of dairy cows. Sixty Holstein cows were divided into a control group (CON), receiving a conventional diet with selenite supplementation (0.6 mg/kg Se in TMR), and an experimental group (EXP), in which conventional silage was replaced with high-Se silage (0.9 mg/kg Se in TMR). The trial lasted 22 weeks, including one week of adaptation and four weeks after supplementation, when Se concentrations in milk, Se transfer efficiency, and key milk components critical for the production of Se-enriched dairy products were assessed. The higher Se concentration in the TMR had no adverse effects on milk composition or antioxidant status. Milk Se concentration in the EXP group increased rapidly, reaching 68 µg/l within two weeks, significantly higher (P < 0.005) than in the CON group (27 µg/l). Se transfer efficiency to milk was also higher in the EXP group (13.9%) compared to the CON group (8.8%). The diverse Se species in biofortified silage, confirmed through the speciation analysis, may have contributed to these outcomes. However, the gradual decline in milk Se after the initial peak warrants further investigation into physiological factors or changes in silage Se speciation during storage.
Hareeshma Babu, Laiju M. Philip, Syam K. Venugopal
et al.
A study was conducted in twelve dogs showing at least two signs of otitis externa, to evaluate the efficacy of ozone therapy in treatment of otitis externa in dogs. The selected dogs were randomly divided into two groups with six animals each. Group I underwent otic flushing with 0.9 per cent normal saline under general anaesthesia, while the other group was treated with 55 μgmL ozonated distilled water in the same manner. All the animals received antibiotic therapy based on the results of antibiogram of otic discharge, along with topical antibiotic and antifungal therapy. Maximum reduction in otitis index score, pruritus severity scale and pain severity score were achieved by day 21 of treatment in all the animals. Radiographically ear canal appeared air filled and widened on fourth week of treatment, suggestive of absence of exudate and oedema in both groups. On clinical and otoscopic examination ulcerations, oedema, erythema, pain and otorrhea was found to be reduced by second week of treatment in Group I, compared to third week in Group I. Even though there was reduction in total viable bacterial count between day one and day seven of treatment in both groups, a marked reduction was observed by day 14 in Group II. Hence, ozone therapy may be considered as an adjunct to the management of otitis externa in dogs along with systemic and topical antibiotic therapy.
This study aimed to investigate the hormone levels of melatonin (Mel), estradiol (E2), vitellogenin (VTG), and growth hormone (GH) in inducing reproduction cycles in female African catfish, Clarias gariepinus, to develop actionable strategies that directly contribute to the efficiency and sustainability of African catfish farming. The treatments involved in this study are three photoperiod variations (L8:D16, L4:D20, and L0:D24) combined with two different temperatures (28 °C and 32 °C) during 90 days of culture (doc). Serum hormone levels were measured using ELISA, and egg diameter was measured using a microscope every 30 days. The results showed that the biological rhythm of the reproductive cycle of African catfish was accelerated by constant exposure to a photoperiod of L0:D24 and 28 °C for 30 days. In this condition, the levels of hormones involved in the reproductive such as Mel (89.82 ± 5.49 ng/mL), E2 (1.66 ± 0.02 ng/mL), VTG (100.96 ± 0.27 ng/mL) and GH (0.33 ± 0.02 ng/mL) with an average egg diameter of 1.15 ± 0.07 mm. These results highlight the complex interplay between photoperiod, temperature, and reproductive physiology in African catfish, suggesting that environmental manipulation could be a valuable tool for optimizing breeding conditions in aquaculture. The conclusion of this study is that manipulating the photoperiod and temperature is an effective and economical approach to stimulate fish spawning. These findings have important implications for African catfish breeding practices, as they provide a clear and actionable strategy for improving reproductive performance.
Sanja Đurđević, Igor Tomašević, Steva Lević
et al.
Research background. The food industry is constantly searching for solutions to reduce the sodium content in meat products as the world is facing an increased risk of diseases caused by a greater intake of sodium from salt through processed foods, including minced meat products.
Experimental approach. The aim of this work is to determine potential use of chia mucilage in different mass fractions (2 and 4 %) in traditional products with reduced salt mass fraction (by 15 and 30 %) and to evaluate its impact on technological properties, colour, texture and sensory parameters of minced meat product ćevap. Given its water-binding and gelling properties, chia mucilage may exert a similar functional effect as salt in minced meat products, particularly in improving water retention and texture.
Results and conclusions. The results showed that replacement of sodium chloride with chia mucilage did not have a significant effect on some technological properties, such as pH and cooking loss, but textural parameters were affected, producing softer and stickier product in general. A treatment in which sodium chloride was reduced by 15 % and 2 % chia mucilage were added was preferred in terms of appearance, juiciness and overall acceptability, while higher chia mucilage mass fractions led to lower scores in taste and saltiness perception as shown in sensory analysis.
Novelty and scientific contribution. As a conclusion, it was established that chia mucilage can help reduce the salt content, but with careful reformulation so that it does not change the sensory qualities.
In recent years, facial video generation models have gained popularity. However, these models often lack expressive power when dealing with exaggerated anime-style faces due to the absence of high-quality anime-style face training sets. We propose a facial animation platform that enables real-time conversion from real human faces to cartoon-style faces, supporting multiple models. Built on the Gradio framework, our platform ensures excellent interactivity and user-friendliness. Users can input a real face video or image and select their desired cartoon style. The system will then automatically analyze facial features, execute necessary preprocessing, and invoke appropriate models to generate expressive anime-style faces. We employ a variety of models within our system to process the HDTF dataset, thereby creating an animated facial video dataset.
Youssouf El Hadj, Sidi Hamady, Saad Bouh Sidaty
et al.
(1) Background: Milk is an important product in Mauritania both for livelihood and food and nutritional security. (2) Methods: In this study, we examine the economic performance of cattle farms in the Hodh Chargui region using data from a cross-sectional survey of 50 farms conducted in 2023. (3) Results: The results showed that the most significant cost item was animal feed (54%), followed by labor (40%) and charges induced by health and watering (3% each). In terms of income, the sale of young males accounted for the largest portion (48%), followed by the sales of dry cows (20%), lactating cows (17%), adult males (8%), and milk (7%). The average gross margin was MRU 131,697.82 ± 509,571.84 (equivalent to USD 3300.69 ± 12,771.22) per breeder per year. Out of the farms surveyed, 46% (23/50) reported a positive gross margin, while 54% reported a negative gross margin. (4) Conclusions: These results indicate a wide range of profitability, from lowest to highest, and suggest the need for improved management of cattle farms in Hodh Chargui in order to enhance their economic efficiency.
Tiffany A. Kosch, María Torres-Sánchez, H. Christoph Liedtke
et al.
Abstract Amphibians represent a diverse group of tetrapods, marked by deep divergence times between their three systematic orders and families. Studying amphibian biology through the genomics lens increases our understanding of the features of this animal class and that of other terrestrial vertebrates. The need for amphibian genomic resources is more urgent than ever due to the increasing threats to this group. Amphibians are one of the most imperiled taxonomic groups, with approximately 41% of species threatened with extinction due to habitat loss, changes in land use patterns, disease, climate change, and their synergistic effects. Amphibian genomic resources have provided a better understanding of ontogenetic diversity, tissue regeneration, diverse life history and reproductive modes, anti-predator strategies, and resilience and adaptive responses. They also serve as essential models for studying broad genomic traits, such as evolutionary genome expansions and contractions, as they exhibit the widest range of genome sizes among all animal taxa and possess multiple mechanisms of genetic sex determination. Despite these features, genome sequencing of amphibians has significantly lagged behind that of other vertebrates, primarily due to the challenges of assembling their large, repeat-rich genomes and the relative lack of societal support. The emergence of long-read sequencing technologies, combined with advanced molecular and computational techniques that improve scaffolding and reduce computational workloads, is now making it possible to address some of these challenges. To promote and accelerate the production and use of amphibian genomics research through international coordination and collaboration, we launched the Amphibian Genomics Consortium (AGC, https://mvs.unimelb.edu.au/amphibian-genomics-consortium ) in early 2023. This burgeoning community already has more than 282 members from 41 countries. The AGC aims to leverage the diverse capabilities of its members to advance genomic resources for amphibians and bridge the implementation gap between biologists, bioinformaticians, and conservation practitioners. Here we evaluate the state of the field of amphibian genomics, highlight previous studies, present challenges to overcome, and call on the research and conservation communities to unite as part of the AGC to enable amphibian genomics research to “leap” to the next level.
Mapping Land Use and Land cover Changes (LULC) and detecting changes using remote sensing and Geographic Information System (GIS) techniques is a cost-effective way of gaining a good understanding of the land cover alteration processes caused by land use change and their effects. This study assessed the transformation of the Wayanad district landscape over a period of 23 years. LANDSAT satellite images (of 30 m resolution) encompassing the area at three epochs were classified into nine classes (coffee dominated mixed crop, built-up, evergreen forest, deciduous forest, grassland, mixed crop with built-up, paddy, tea plantation, and waterbody) using the maximum likelihood algorithm, resulting in classes for each land use. The results showed that over the past 23 years, coffee-dominated mixed crops have increased by 0.84% in 2014 and 11.84% in 2022 compared to 1999; deciduous forest area decreased 3.6% and increased 0.6% in 2014 and 2022, respectively. Tea plantations increased by 0.9% in 2014 and by 0.49% in 2022, which decreased by 0.41% compared to 2014. Built-up has increased by 6.42% and 6.02% in 2014 and 2022 respectively, and slightly decreased by 0.4% compared to 2014. Evergreen forest areas increased in 2014 by 6.68% and 2.28% in 2022 and decreased by 4.44% compared to the previous time period. Grass land areas have decreased by 6.25% and 5.33%, respectively, and mixed crops with built-up areas has decreased by 0.74% and remained the same in the last two epochs, while paddy and waterbodies have decreased by 3.4%, 15.79%, and 0.07 and 0.19%, respectively, in 2014 and 2022 of the total geographical area Keywords: Remote sensing, GIS techniques, LULC, forest cover
Paula Caride, Josefina Garrido, Mauro Rivas-Ferreiro
et al.
Akanthomyces dipterigenus is a species of entomopathogenic fungus (Hypocreales: Cordycipitaceae) that has undergone several reclassifications in recent decades. While it has been utilized for biological control, information on its distribution and ecology remains limited. Furthermore, this species has only recently been reported in continental European countries. Our research provides the first report of A. dipterigenus in the Iberian Peninsula. Fungal isolation was achieved via a modified baiting technique featuring Rhopalosiphum padi aphids. Results suggest that the isolation methodologies and ecological traits of the fungus may either aid or hinder its detection. Additional research is necessary to assess its distribution throughout the Peninsula and neighbouring European countries, and to examine its potential as a biological control agent.
Current image-based keypoint detection methods for animal (including human) bodies and faces are generally divided into full-supervised and few-shot class-agnostic approaches. The former typically relies on laborious and time-consuming manual annotations, posing considerable challenges in expanding keypoint detection to a broader range of keypoint categories and animal species. The latter, though less dependent on extensive manual input, still requires necessary support images with annotation for reference during testing. To realize zero-shot keypoint detection without any prior annotation, we introduce the Open-Vocabulary Keypoint Detection (OVKD) task, which is innovatively designed to use text prompts for identifying arbitrary keypoints across any species. In pursuit of this goal, we have developed a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM). This framework synergistically combines vision and language models, creating an interplay between language features and local keypoint visual features. KDSM enhances its capabilities by integrating Domain Distribution Matrix Matching (DDMM) and other special modules, such as the Vision-Keypoint Relational Awareness (VKRA) module, improving the framework's generalizability and overall performance.Our comprehensive experiments demonstrate that KDSM significantly outperforms the baseline in terms of performance and achieves remarkable success in the OVKD task.Impressively, our method, operating in a zero-shot fashion, still yields results comparable to state-of-the-art few-shot species class-agnostic keypoint detection methods.We will make the source code publicly accessible.
Jessica K. Hodgins, Wayne L. Wooten, David C. Brogan
et al.
This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondary motion to the animations with spring-mass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.
We consider minimal-perimeter lattice animals, providing a set of conditions which are sufficient for a lattice to have the property that inflating all minimal-perimeter animals of a certain size yields (without repetitions) all minimal-perimeter animals of a new, larger size. We demonstrate this result on the two-dimensional square and hexagonal lattices. In addition, we characterize the sizes of minimal-perimeter animals on these lattices that are not created by inflating members of another set of minimal-perimeter animals.
Niamh Mimnagh, Iuri Ferreira, Luciano Verdade
et al.
We propose a modelling framework which allows for the estimation of abundances from trace counts. This indirect method of estimating abundance is attractive due to the relative affordability with which it may be carried out, and the reduction in possible risk posed to animals and humans when compared to direct methods for estimating animal abundance. We assess these methods by performing simulations which allow us to examine the accuracy of model estimates. The models are then fitted to several case studies to obtain abundance estimates for collared peccaries in Brazil, kit foxes in Arizona, red foxes in Italy and sika deer in Scotland. Simulation results reveal that these models produce accurate estimates of abundance at a range of sample sizes. In particular, this modelling framework produces accurate estimates when data is very scarce. The use of vestige counts in estimating abundance allows for the monitoring of species which may otherwise go undetected due to their reclusive nature. Additionally, the efficacy of these models when data is collected at very few transects will allow for the use of small-scale data collection programmes which may be carried out at reduced cost, when compared to larger-scale data collection.
Cartoons and animation domain videos have very different characteristics compared to real-life images and videos. In addition, this domain carries a large variability in styles. Current computer vision and deep-learning solutions often fail on animated content because they were trained on natural images. In this paper we present a method to refine a semantic representation suitable for specific animated content. We first train a neural network on a large-scale set of animation videos and use the mapping to deep features as an embedding space. Next, we use self-supervision to refine the representation for any specific animation style by gathering many examples of animated characters in this style, using a multi-object tracking. These examples are used to define triplets for contrastive loss training. The refined semantic space allows better clustering of animated characters even when they have diverse manifestations. Using this space we can build dictionaries of characters in an animation videos, and define specialized classifiers for specific stylistic content (e.g., characters in a specific animation series) with very little user effort. These classifiers are the basis for automatically labeling characters in animation videos. We present results on a collection of characters in a variety of animation styles.