A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application.

Occupational ApplicationDigital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments.

TECHNICAL ABSTRACTBackground: With any type of human movement, there is the potential for a collision with other objects. In addition to the objects presented in the environment surrounding one's body and surrounding the objects to be manipulated, one's own body can become an obstacle. Therefore, consideration of the methods available for avoiding obstacles is necessary to comprehensively describe the way human movements are planned.Purpose: This paper evaluates a collision avoidance algorithm for human motion prediction based on the perceived risk of collision, specifically the application to human motion prediction.Method: Human motion prediction is formulated as an optimization problem with dynamic effort as the cost function, and the perceived risk of collision is considered as one constraint among other constraints. Performance using the new formulation was compared to observed performance from an experiment.Result: Based on the results, the new formulation can account for the suboptimal behavior observed in real subjects while still optimizing biomechanical cost. The predicted motion is much more realistic compared with that from purely biomechanically optimized formulation.Application: The developed collision avoidance algorithm can be applied to optimization-based manual movement prediction in which obstacles need to be navigated.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development.

OCCUPATIONAL APPLICATIONSDigital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models.

TECHNICAL ABSTRACTBackground: Humans perform daily tasks such as reaching around an obstacle with ease, even though the complexities of such behavior are largely hidden from those performing them. Optimization-based motion prediction has employed numerical methods in order to predict human movements. However, these movements are heavily constrained, such that the planning of the motion is explicitly provided in the optimization formulation of the problem. This implies that for each task a unique optimization formulation is needed, which relies heavily on the experience of the code developer to provide these constraints.Purpose: Cognitive psychology has focused on the reasoning or motivation behind the planning of movements and provides an opportunity for digital human modeling to adopt these theories to provide a more general or versatile motion prediction framework. Humans tend to overestimate the risk associated with colliding with objects during movement. We present the formulation of a collision avoidance algorithm that considers the perceived risk, for future use in a human motion prediction application.Methods: An experiment was completed to evaluate human performance when avoiding obstacles during movement. Using Bayesian inference, perceived risk was modeled and minimized for use in human motion prediction.Results: The experimental results were used to derive a formula in which the perceived risk associated with the task could be quantified in a gain/loss context. Overestimation of risk by a subject was modeled using the observed behavior and the results of simulations based on the parameterized risk model are presented.Conclusions: The algorithm presented, based on the perceived risk of collision, can be integrated into human motion prediction to generate realistic human motion considering collision avoidance.

LYSMD3: A mammalian pattern recognition receptor for chitin.

Chitin, a major component of fungal cell walls, has been associated with allergic disorders such as asthma. However, it is unclear how mammals recognize chitin and the principal receptor(s) on epithelial cells that sense chitin remain to be determined. In this study, we show that LYSMD3 is expressed on the surface of human airway epithelial cells and demonstrate that LYSMD3 is able to bind chitin, as well as ß-glucan, on the cell walls of fungi. Knockdown or knockout of LYSMD3 also sharply blunts the production of inflammatory cytokines by epithelial cells in response to chitin and fungal spores. Competitive inhibition of the LYSMD3 ectodomain by soluble LYSMD3 protein, multiple ligands, or antibody against LYSMD3 also blocks chitin signaling. Our study reveals LYSMD3 as a mammalian pattern recognition receptor (PRR) for chitin and establishes its role in epithelial cell inflammatory responses to chitin and fungi.

Innate Immunity Induced by the Major Allergen Alt a 1 From the Fungus Alternaria Is Dependent Upon Toll-Like Receptors 2/4 in Human Lung Epithelial Cells.

Allergens are molecules that elicit a hypersensitive inflammatory response in sensitized individuals and are derived from a variety of sources. Alt a 1 is the most clinically important secreted allergen of the ubiquitous fungus, Alternaria. It has been shown to be a major allergen causing IgE-mediated allergic response in the vast majority of Alternaria-sensitized individuals. However, no studies have been conducted in regards to the innate immune eliciting activities of this clinically relevant protein. In this study, recombinant Alt a 1 was produced, purified, labeled, and incubated with BEAS-2B, NHBE, and DHBE human lung epithelial cells. Alt a 1 elicited strong induction of IL-8, MCP-1, and Gro-a/b/g. Using gene-specific siRNAs, blocking antibodies, and chemical inhibitors such as LPS-RS, it was determined that Alt a 1-induced immune responses were dependent upon toll-like receptors (TLRs) 2 and 4, and the adaptor proteins MYD88 and TIRAP. Studies utilizing human embryonic kidney cells engineered to express single receptors on the cell surface such as TLRs, further confirmed that Alt a 1-induced innate immunity is dependent upon TLR4 and to a lesser extent TLR2.

Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

Iron acquisition and oxidative stress response in aspergillus fumigatus.

BACKGROUND: Aspergillus fumigatus is a ubiquitous airborne fungal pathogen that presents a life-threatening health risk to individuals with weakened immune systems. A. fumigatus pathogenicity depends on its ability to acquire iron from the host and to resist host-generated oxidative stress. Gaining a deeper understanding of the molecular mechanisms governing A. fumigatus iron acquisition and oxidative stress response may ultimately help to improve the diagnosis and treatment of invasive aspergillus infections. RESULTS: This study follows a systems biology approach to investigate how adaptive behaviors emerge from molecular interactions underlying A. fumigatus iron regulation and oxidative stress response. We construct a Boolean network model from known interactions and simulate how changes in environmental iron and superoxide levels affect network dynamics. We propose rules for linking long term model behavior to qualitative estimates of cell growth and cell death. These rules are used to predict phenotypes of gene deletion strains. The model is validated on the basis of its ability to reproduce literature data not used in model generation. CONCLUSIONS: The model reproduces gene expression patterns in experimental time course data when A. fumigatus is switched from a low iron to a high iron environment. In addition, the model is able to accurately represent the phenotypes of many knockout strains under varying iron and superoxide conditions. Model simulations support the hypothesis that intracellular iron regulates A. fumigatus transcription factors, SreA and HapX, by a post-translational, rather than transcriptional, mechanism. Finally, the model predicts that blocking siderophore-mediated iron uptake reduces resistance to oxidative stress. This indicates that combined targeting of siderophore-mediated iron uptake and the oxidative stress response network may act synergistically to increase fungal cell killing.

Biochemical and genetic characterization of Arabidopsis flavanone 3beta-hydroxylase.

Flavanone 3beta-hydroxylase (F3H; EC 1.14.11.9) is a 2-oxoglutarate dependent dioxygenase that catalyzes the synthesis of dihydrokaempferol, the common precursor for three major classes of 3-hydroxy flavonoids, the flavonols, anthocyanins, and proanthocyanidins. This enzyme also competes for flux into the 3-deoxy flavonoid branch pathway in some species. F3H genes are increasingly being used, often together with genes encoding other enzymes, to engineer flavonoid synthesis in microbes and plants. Although putative F3H genes have been cloned in a large number of plant species, only a handful have been functionally characterized. Here we describe the biochemical properties of the Arabidopsis thaliana F3H (AtF3H) enzyme and confirm the activities of gene products from four other plant species previously identified as having high homology to F3H. We have also investigated the surprising "leaky" phenotype of AtF3H mutant alleles, uncovering evidence that two related flavonoid enzymes, flavonol synthase (EC 1.14.11.23) and anthocyanidin synthase (EC 1.14.11.19), can partially compensate for F3H in vivo. These experiments further indicate that the absence of F3H in these lines enables the synthesis of uncommon 3-deoxy flavonoids in the Arabidopsis seed coat.