This highly regarded book remains a standard work for both students and professionals in the meat industry. Its basic theme remains the central importance of biochemistry in understanding the production, storage, processing and eating quality of meat. At a time when so much controversy surrounds meat production and nutrition, Meat Science provides a clear guide, which takes the reader from the growth and development of meat animals, through the conversion of muscle to meat, to the point of consumption. This new edition incorporates significant advances in meat science during the past ten years.
Rumen fermentation affects ruminants productivity and the environmental impact of ruminant production. The release to the atmosphere of methane produced in the rumen is a loss of energy and a cause of climate change, and the profile of volatile fatty acids produced in the rumen affects the post-absorptive metabolism of the host animal. Rumen fermentation is shaped by intracellular and intercellular flows of metabolic hydrogen centered on the production, interspecies transfer, and incorporation of dihydrogen into competing pathways. Factors that affect the growth of methanogens and the rate of feed fermentation impact dihydrogen concentration in the rumen, which in turn controls the balance between pathways that produce and incorporate metabolic hydrogen, determining methane production and the profile of volatile fatty acids. A basic kinetic model of competition for dihydrogen is presented, and possibilities for intervention to redirect metabolic hydrogen from methanogenesis toward alternative useful electron sinks are discussed. The flows of metabolic hydrogen toward nutritionally beneficial sinks could be enhanced by adding to the rumen fermentation electron acceptors or direct fed microbials. It is proposed to screen hydrogenotrophs for dihydrogen thresholds and affinities, as well as identifying and studying microorganisms that produce and utilize intercellular electron carriers other than dihydrogen. These approaches can allow identifying potential microbial additives to compete with methanogens for metabolic hydrogen. The combination of adequate microbial additives or electron acceptors with inhibitors of methanogenesis can be effective approaches to decrease methane production and simultaneously redirect metabolic hydrogen toward end products of fermentation with a nutritional value for the host animal. The design of strategies to redirect metabolic hydrogen from methane to other sinks should be based on knowledge of the physicochemical control of rumen fermentation pathways. The application of new –omics techniques together with classical biochemistry methods and mechanistic modeling can lead to exciting developments in the understanding and manipulation of the flows of metabolic hydrogen in rumen fermentation.
Lead-contaminated soils are becoming an ecological risk to the environment because of producing low-quality food which is directly causing critical health issues in humans and animals. We hypothesized that incorporation of dicalcium phosphate (DCP), eggshell powder (ESP) and biochar (BH) at diverse rates into a Pb-affected soil can proficiently immobilize Pb and decline its bioavailability to spinach (Spinacia oleracea L.). A soil was artificially spiked with Pb concentration (at 600 mg kg-1) and further amended with DCP, ESP, and BH (as sole treatments at 2% and in concoctions at 1% each) for immobilization of Pb in the soil. The interlinked effects of applied treatments on Pb concentrations in shoots and roots, biomass, antioxidants, biochemistry, and nutrition of spinach were also investigated. Results depicted that the highest reduction in DTPA-extractable Pb and the concentrations of Pb in shoots and roots was achieved in DCP1%+BH1% treatment that was up to 58%, 66%, and 53%, respectively over control. Likewise, the DCP1%+BH1% treatment also showed the maximum shoot and root dry weight (DW), chlorophyll-a (Chl-a) and chlorophyll-b (Chl-b) contents and relative water content (RWC) in spinach up to 92%, 121%, 60%, 65%, and 30%, respectively, compared to control. Likewise, DCP1%+BH1% treatment noticeably improved antioxidant enzymes, biochemistry, and nutrition in the leaves. Moreover, the DCP1%+BH1% treatment depicted mostly enhanced activities of dehydrogenase, catalase, acid phosphatase, alkaline phosphatase, phosphomonoesterase, urease, protease and B-glucosidase in the post-harvested soil up to 118%, 345%, 55%, 92%, 288%, 107%, 53% and 252%, respectively over control.
Reem Ahmed Mahmoud, Hamdy Hassanine, Ali Ashry
et al.
Abstract Punica granatum (pomegranate) peel is recognized for its rich phytochemical profile and traditional use in medicinal applications, making it a promising source of anticancer agents. In this study, an aqueous pomegranate peel extract (PPE) was prepared and encapsulated into chitosan nanoparticles (CSPPE) using the ionic gelation method. The resulting CSPPE nanoparticles were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM), revealing nanoscale size distribution (Z-average 408.8 ± 98.8 nm by DLS, TEM mean 51.5 ± 12.6 nm) and good colloidal stability, with positive zeta potential (+ 15–35 mV) supporting electrostatic stabilization. Fourier-transform infrared spectroscopy (FTIR) indicated successful physical entrapment of PPE within the chitosan matrix, with characteristic functional groups preserved. The cytotoxic activity of both PPE and CSPPE was assessed against human hepatocellular carcinoma (HepG2) cells using the MTT assay. PPE showed moderate cytotoxicity only at high concentrations (IC₅₀ = 921.8 ± 18.6 µg/mL), while CSPPE induced markedly stronger, concentration-dependent cytotoxic effects across all tested doses (IC₅₀ = 12.38 ± 0.28 µg/mL PPE-equivalent), representing approximately a 75-fold increase in potency. Blank chitosan nanoparticles without PPE exhibited minimal cytotoxicity (> 90% cell viability), confirming that the observed enhancement is attributable to PPE encapsulation. Microscopic observations corroborated the dose-dependent cytotoxic effects, showing reduced cell density, loss of adhesion, and morphological changes consistent with cell death. These findings demonstrate that nanoencapsulation significantly enhances the bioactivity of pomegranate peel extract against hepatocellular carcinoma cells. While this study provides strong preliminary evidence, further in vitro mechanistic assays and in vivo investigations are warranted to elucidate the mode of cell death, assess selectivity towards cancer versus normal hepatocytes, and confirm therapeutic potential and safety.
Bertille Carine Djapoum Theno, Gisele Aurelie Foko Dadji, Francis Zeukeng
et al.
Vector-based surveillance is crucial for monitoring arboviral diseases such as dengue, chikungunya, yellow fever, and Zika. In Cameroon, data on the distribution and bionomics of Aedes species in medium-sized cities within the equatorial forest region are limited. This study assessed the distribution, larval habitats, and bionomics of immature Aedes spp. stages across four mid-sized cities of southern Cameroon. Larval surveys were conducted from March 2022 to March 2023 in Bertoua, Kribi, Sangmelima, and Abong-Mbang during rainy and dry seasons. Water-holding containers were inspected using the dipping method, and collected larvae and pupae were reared to adults for morphological identification. Entomological indices were used to estimate arboviral transmission risk, and species co-occurrence in larval habitats was recorded. A generalized linear model was used to compare abundance and distribution among sites, seasons, and breeding site. From 758 larval habitats inspected (214 in Bertoua, 213 in Sangmelima, 184 in Kribi, 147 in Abong-Mbang), 3820 mosquitoes belonging to four genera and 11 species were emerged. Aedes spp. mosquitoes were the most abundant (n = 2903), with Aedes albopictus and Aedes aegypti as dominant species that often co-occurred. Seasonal variation in the abundance of all mosquito genera was significant, with peak Aedes spp. abundance in the rainy season (n = 2359; 81.3%). Tires were the most common larval habitats, mostly located within 10 m of human dwellings. Kribi and Bertoua exhibited the highest container, house, and Breteau indices, suggesting high potential risk. The study supports the need for regular vector-based surveillance to control arboviral disease in Cameroon. Such studies could also provide critical information for strengthening integrated vector management.
Animating metaphoric visualizations brings data to life, enhancing the comprehension of abstract data encodings and fostering deeper engagement. However, creators face significant challenges in designing these animations, such as crafting motions that align semantically with the metaphors, maintaining faithful data representation during animation, and seamlessly integrating interactivity. We propose a human-AI co-creation workflow that facilitates creating animations for SVG-based metaphoric visualizations. Users can initially derive animation clips for data elements from vision-language models (VLMs) and subsequently coordinate their timelines based on entity order, attribute values, spatial layout, or randomness. Our design decisions were informed by a formative study with experienced designers (N=8). We further developed a prototype, DataSway, and conducted a user study (N=14) to evaluate its creativity support and usability. A gallery with 6 cases demonstrates its capabilities and applications in web-based hypermedia. We conclude with implications for future research on bespoke data visualization animation.
While state-of-the-art background removal models excel at realistic imagery, they frequently underperform in specialized domains such as anime-style content, where complex features like hair and transparency present unique challenges. To address this limitation, we collected and annotated a custom dataset of 1,228 high-quality anime images of characters and objects, and fine-tuned the open-sourced BiRefNet model on this dataset. This resulted in marked improvements in background removal accuracy for anime-style images, increasing from 95.3% to 99.5% for our newly introduced Pixel Accuracy metric. We are open-sourcing the code, the fine-tuned model weights, as well as the dataset at: https://github.com/MatteoKartoon/BiRefNet.
Many STEM concepts pose significant learning challenges to students due to their inherent complexity and abstract nature. Visualizing complex problems through animations can significantly enhance learning outcomes. However, the creation of animations can be time-consuming and inconvenient. Hence, many educators illustrate complex concepts by hand on a board or a digital device. Although static graphics are helpful for understanding, they are less effective than animations. The free and open-source Python package Manim enables educators to create visually compelling animations easily. Python's straightforward syntax, combined with Manim's comprehensive set of built-in classes and methods, greatly simplifies implementation. This article presents a series of examples that demonstrate how Manim can be used to create animated video lessons for a variety of topics in computer science and mathematics. In addition, it analyzes viewer feedback collected across multiple social media platforms to evaluate the effectiveness and accessibility of these visualizations. The article further explores broader potentials of the Manim Python library by showcasing demonstrations that extend its applications to subject areas beyond computer science and mathematics.
Generating high-fidelity garment animations through traditional workflows, from modeling to rendering, is both tedious and expensive. These workflows often require repetitive steps in response to updates in character motion, rendering viewpoint changes, or appearance edits. Although recent neural rendering offers an efficient solution for computationally intensive processes, it struggles with rendering complex garment animations containing fine wrinkle details and realistic garment-and-body occlusions, while maintaining structural consistency across frames and dense view rendering. In this paper, we propose a novel approach to directly synthesize garment animations from body motion sequences without the need for an explicit garment proxy. Our approach infers garment dynamic features from body motion, providing a preliminary overview of garment structure. Simultaneously, we capture detailed features from synthesized reference images of the garment's front and back, generated by a pre-trained image model. These features are then used to construct a neural radiance field that renders the garment animation video. Additionally, our technique enables garment recoloring by decomposing its visual elements. We demonstrate the generalizability of our method across unseen body motions and camera views, ensuring detailed structural consistency. Furthermore, we showcase its applicability to color editing on both real and synthetic garment data. Compared to existing neural rendering techniques, our method exhibits qualitative and quantitative improvements in garment dynamics and wrinkle detail modeling. Code is available at \url{https://github.com/wrk226/GarmentAnimationNeRF}.
Animating various character drawings is an engaging visual content creation task. Given a single character drawing, existing animation methods are limited to flat 2D motions and thus lack 3D effects. An alternative solution is to reconstruct a 3D model from a character drawing as a proxy and then retarget 3D motion data onto it. However, the existing image-to-3D methods could not work well for amateur character drawings in terms of appearance and geometry. We observe the contour lines, commonly existing in character drawings, would introduce significant ambiguity in texture synthesis due to their view-dependence. Additionally, thin regions represented by single-line contours are difficult to reconstruct (e.g., slim limbs of a stick figure) due to their delicate structures. To address these issues, we propose a novel system, DrawingSpinUp, to produce plausible 3D animations and breathe life into character drawings, allowing them to freely spin up, leap, and even perform a hip-hop dance. For appearance improvement, we adopt a removal-then-restoration strategy to first remove the view-dependent contour lines and then render them back after retargeting the reconstructed character. For geometry refinement, we develop a skeleton-based thinning deformation algorithm to refine the slim structures represented by the single-line contours. The experimental evaluations and a perceptual user study show that our proposed method outperforms the existing 2D and 3D animation methods and generates high-quality 3D animations from a single character drawing. Please refer to our project page (https://lordliang.github.io/DrawingSpinUp) for the code and generated animations.
Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is possible to achieve striking half-body human animation while simplifying unnecessary conditions. To this end, we propose a half-body human animation method, dubbed EchoMimicV2, that leverages a novel Audio-Pose Dynamic Harmonization strategy, including Pose Sampling and Audio Diffusion, to enhance half-body details, facial and gestural expressiveness, and meanwhile reduce conditions redundancy. To compensate for the scarcity of half-body data, we utilize Head Partial Attention to seamlessly accommodate headshot data into our training framework, which can be omitted during inference, providing a free lunch for animation. Furthermore, we design the Phase-specific Denoising Loss to guide motion, detail, and low-level quality for animation in specific phases, respectively. Besides, we also present a novel benchmark for evaluating the effectiveness of half-body human animation. Extensive experiments and analyses demonstrate that EchoMimicV2 surpasses existing methods in both quantitative and qualitative evaluations.
We present DreamBeast, a novel method based on score distillation sampling (SDS) for generating fantastical 3D animal assets composed of distinct parts. Existing SDS methods often struggle with this generation task due to a limited understanding of part-level semantics in text-to-image diffusion models. While recent diffusion models, such as Stable Diffusion 3, demonstrate a better part-level understanding, they are prohibitively slow and exhibit other common problems associated with single-view diffusion models. DreamBeast overcomes this limitation through a novel part-aware knowledge transfer mechanism. For each generated asset, we efficiently extract part-level knowledge from the Stable Diffusion 3 model into a 3D Part-Affinity implicit representation. This enables us to instantly generate Part-Affinity maps from arbitrary camera views, which we then use to modulate the guidance of a multi-view diffusion model during SDS to create 3D assets of fantastical animals. DreamBeast significantly enhances the quality of generated 3D creatures with user-specified part compositions while reducing computational overhead, as demonstrated by extensive quantitative and qualitative evaluations.
Abstract Background Hepcidin is the master regulator of iron homeostasis. Hepcidin downregulation has been demonstrated in the brains of Alzheimer’s disease (AD) patients. However, the mechanism underlying the role of hepcidin downregulation in cognitive impairment has not been elucidated. Methods In the present study, we generated GFAP-Cre-mediated hepcidin conditional knockout mice (Hamp GFAP cKO) to explore the effect of hepcidin deficiency on hippocampal structure and neurocognition. Results We found that the Hamp GFAP cKO mice developed AD-like brain atrophy and memory deficits. In particular, the weight of the hippocampus and the number of granule neurons in the dentate gyrus were significantly reduced. Further investigation demonstrated that the morphological change in the hippocampus of Hamp GFAP cKO mice was attributed to impaired neurogenesis caused by decreased proliferation of neural stem cells. Regarding the molecular mechanism, increased iron content after depletion of hepcidin followed by an elevated level of the inflammatory factor tumor necrosis factor-α accounted for the impairment of hippocampal neurogenesis in Hamp GFAP cKO mice. These observations were further verified in GFAP promoter-driven hepcidin knockdown mice and in Nestin-Cre-mediated hepcidin conditional knockout mice. Conclusions The present findings demonstrated a critical role for hepcidin in hippocampal neurogenesis and validated the importance of iron and associated inflammatory cytokines as key modulators of neurodevelopment, providing insights into the potential pathogenesis of cognitive dysfunction and related treatments. Graphical Abstract
Creating realistic 3D facial animation is crucial for various applications in the movie production and gaming industry, especially with the burgeoning demand in the metaverse. However, prevalent methods such as blendshape-based approaches and facial rigging techniques are time-consuming, labor-intensive, and lack standardized configurations, making facial animation production challenging and costly. In this paper, we propose a novel self-supervised framework, Versatile Face Animator, which combines facial motion capture with motion retargeting in an end-to-end manner, eliminating the need for blendshapes or rigs. Our method has the following two main characteristics: 1) we propose an RGBD animation module to learn facial motion from raw RGBD videos by hierarchical motion dictionaries and animate RGBD images rendered from 3D facial mesh coarse-to-fine, enabling facial animation on arbitrary 3D characters regardless of their topology, textures, blendshapes, and rigs; and 2) we introduce a mesh retarget module to utilize RGBD animation to create 3D facial animation by manipulating facial mesh with controller transformations, which are estimated from dense optical flow fields and blended together with geodesic-distance-based weights. Comprehensive experiments demonstrate the effectiveness of our proposed framework in generating impressive 3D facial animation results, highlighting its potential as a promising solution for the cost-effective and efficient production of facial animation in the metaverse.