Functionality-Driven Musculature Retargeting

We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure

Infection of Enteromyxum leei in cultured starry flounder Platichthys stellatus

Enteromyxum leei has been identified as the causative agent of emaciation disease in a wide range of marine fish hosts. In this study, we aimed to determine the effect of the parasitic infection of Enteromyxum species on starry flounder that were cultured in aquaculture farms of Jeju island in Korea. As the mortality of cultured olive flounder Paralichthys olivaceus because of E. leei infection increased, some fish farms on Jeju island attempted to culture the starry flounder Platichthys stellatus, as an alternative. Myxosporeans with a developmental stage similar to E. leei were found in the intestines of cultured starry flounders. The partial 18S rDNA of myxosporeans showed 100% similarity with E. leei. To reveal the effect of E. leei infection on starry flounder, the intensity of E. leei infection measured using quantitative polymerase chain reaction, and the condition factor (CF) of fish were measured and analyzed statistically. The results showed that high-intensity E. leei infection significantly decreased the CF of the starry flounder. However, the pathogenicity of E. leei to starry flounder is low, considering its mortality and clinical signs

Viperin mutation is linked to immunity, immune cell dynamics, and metabolic alteration during VHSV infection in zebrafish

Viperin is a prominent antiviral protein found in animals. The primary function of Viperin is the production of 3’-deoxy-3’,4’-didehydro-cytidine triphosphate (ddhCTP), an inhibitory nucleotide involved in viral RNA synthesis. Studies in mammalian models have suggested that ddhCTP interferes with metabolic proteins. However, this hypothesis has yet to be tested in teleost. In this study, the role of Viperin in regulating metabolic alterations during viral hemorrhagic septicemia virus (VHSV) infection was tested. When infected with VHSV, viperin-/- fish showed considerably higher mortality rates. VHSV copy number and the expression of the NP gene were significantly increased in viperin-/- fish. Metabolic gene analysis revealed significant differences in soda, hif1a, fasn, and acc expression, indicating their impact on metabolism. Cholesterol analysis in zebrafish larvae during VHSV infection showed significant upregulation of cholesterol production without Viperin. In vitro analysis of ZF4 cells suggested a considerable reduction in lipid production and a significant upregulation of reactive oxygen species (ROS) generation with the overexpression of viperin. Neutrophil and macrophage recruitment were significantly modulated in viperin-/- fish compared to the wild-type (WT) fish. Thus, we have demonstrated that Viperin plays a role in interfering with metabolic alterations during VHSV infection

Functional characterization and expression analysis of c-type and g-like-type lysozymes in yellowtail clownfish (Amphiprion clarkii)

Lysozymes are well-known antibacterial enzymes that mainly target the peptidoglycan layer of the bacterial cell wall. Animal lysozymes are mainly categorized as g-type, c-type, and i-type based on protein sequence and structural differences. In this study, c-type (AcLysC) and g-like-type (AcLysG-like) lysozymes from Amphiprion clarkii were characterized in silico via expressional and functional approaches. According to in silico analysis, open reading frames of AcLysC and AcLysG-like were 429 bp and 570 bp, respectively, encoding the corresponding polypeptide chains with 142 and 189 amino acids. Elevated expression levels of AcLysC and AcLysG-like were observed in the liver and the heart tissues, respectively, as evidenced by quantitative real-time polymerase chain reaction assays. AcLysC and AcLysG-like transcript levels were upregulated in gills, head kidney, and blood cells following experimental immune stimulation. Recombinant AcLysC exhibited potent lytic activity against Vibrio anguillarum, whereas recombinant AcLysG-like showed remarkable antibacterial activity against Vibrio harveyi and Streptococcus parauberis, which was further evidenced by scanning electron microscopic imaging of destructed bacterial cell walls. The findings of this study collectively suggest the potential roles of AcLysC and AcLysG-like in host immune defense

The role of the anterior cingulate cortex in prediction error and signaling surprise

In the past two decades, reinforcement learning (RL) has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of a stimulus, and many other interpretations. Anterior cingulate cortex (ACC) has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex. This paper reviews the brain mechanisms responsible for surprise; focusing on the Anterior Cingulate Cortex (ACC), long-known to play a role in behavioral-error, with a recently-expanded role in predicting likely' events and signaling deviations between expected and observed events. It argues for ACC's role in in surprise and learning, based on recent modelling work. As such, the paper provides the neuroscience complement to the psychological and computational proposals of other papers in the volume

A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

Predictive processing simplified: the infotropic machine

On a traditional view of cognition, we see the agent acquiring stimuli, interpreting these in some way, and producing behavior in response. An increasingly popular alternative is the predictive processing framework. This sees the agent as continually generating predictions about the world, and responding productively to any errors made. Partly because of its heritage in the Bayesian brain theory, predictive processing has generally been seen as an inherently Bayesian process. The `hierarchical prediction machine' which mediates it is envisaged to be a specifically Bayesian device. But as this paper shows, a specification for this machine can also be derived directly from information theory, using the metric of predictive payoff as an organizing concept. Hierarchical prediction machines can be built along purely information-theoretic lines, without referencing Bayesian theory in any way; this simplifies the account to some degree. The present paper describes what is involved and presents a series of working models. An experiment involving the conversion of a Braitenberg vehicle to use a controller of this type is also described