Using evolutionary robotics to estimate maximum running speeds under non-Earth gravitational conditions

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P2

Predicting the metabolic energy costs of bipedalism using evolutionary robotics

To understand the evolution of bipedalism among the homnoids in an ecological context we need to be able to estimate theenerrgetic cost of locomotion in fossil forms. Ideally such an estimate would be based entirely on morphology since, except for the rare instances where footprints are preserved, this is hte only primary source of evidence available. In this paper we use evolutionary robotics techniques (genetic algoritms, pattern generators and mechanical modeling) to produce a biomimentic simulation of bipedalism based on human body dimensions. The mechnaical simulation is a seven-segment, two-dimensional model with motive force provided by tension generators representing the major muscle groups acting around the lower-limb joints. Metabolic energy costs are calculated from the muscel model, and bipedal gait is generated using a finite-state pattern generator whose parameters are produced using a genetic algorithm with locomotor economy (maximum distance for a fixed energy cost) as the fitness criterion. The model is validated by comparing the values it generates with those for modern humans. The result (maximum efficiency of 200 J m-1) is within 15% of the experimentally derived value, which is very encouraging and suggests that this is a useful analytic technique for investigating the locomotor behaviour of fossil forms. Initial work suggests that in the future this technique could be used to estimate other locomotor parameters such as top speed. In addition, the animations produced by this technique are qualitatively very convincing, which suggests that this may also be a useful technique for visualizing bipedal locomotion

Evaluating alternative gait strategies using evolutionary robotics

Evolutionary robitics is a branch of artificial intelligence concerned with the automatic generation of autonomous robots. Usually the form of the robit is predefined an various computational techniques are used to control the machine's behaviour. One aspect is the spontaneous generation of walking in legged robots and this can be used to investigate the mechanical requiements for efficient walking in bipeds. This paper demonstrates a bipedal simulator that spontaneously generates walking and running gaits. The model can be customized to represent a range of hominoid morphologies and used to predict performance paramets such as preferred speed and metabolic energy cost. Because it does not require any motion capture data it is particularly suitable for investigating locomotion in fossil animals. The predictoins for modern humans are highly accurate in terms of energy cost for a given speend and thus the values predicted for other bipeds are likely to be good estimates. To illustrate this the cost of transport is calculated for Australopithecus afarensis. The model allows the degree of maximum extension at the knee to be varied causing the model to adopt walking gaits varying from chimpanzee-like to human=like. The energy costs associated with these gait choices can thus be calculated and this information used to evaluate possible locomotor strategies in early hominid

Evaluating alternative gait strategies using evolutionary robotics

Evolutionary robitics is a branch of artificial intelligence concerned with the automatic generation of autonomous robots. Usually the form of the robit is predefined an various computational techniques are used to control the machine's behaviour. One aspect is the spontaneous generation of walking in legged robots and this can be used to investigate the mechanical requiements for efficient walking in bipeds. This paper demonstrates a bipedal simulator that spontaneously generates walking and running gaits. The model can be customized to represent a range of hominoid morphologies and used to predict performance paramets such as preferred speed and metabolic energy cost. Because it does not require any motion capture data it is particularly suitable for investigating locomotion in fossil animals. The predictoins for modern humans are highly accurate in terms of energy cost for a given speend and thus the values predicted for other bipeds are likely to be good estimates. To illustrate this the cost of transport is calculated for Australopithecus afarensis. The model allows the degree of maximum extension at the knee to be varied causing the model to adopt walking gaits varying from chimpanzee-like to human=like. The energy costs associated with these gait choices can thus be calculated and this information used to evaluate possible locomotor strategies in early hominid

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer

Fc-Optimized Anti-CD25 Depletes Tumor-Infiltrating Regulatory T Cells and Synergizes with PD-1 Blockade to Eradicate Established Tumors

CD25 is expressed at high levels on regulatory T (Treg) cells and was initially proposed as a target for cancer immunotherapy. However, anti-CD25 antibodies have displayed limited activity against established tumors. We demonstrated that CD25 expression is largely restricted to tumor-infiltrating Treg cells in mice and humans. While existing anti-CD25 antibodies were observed to deplete Treg cells in the periphery, upregulation of the inhibitory Fc gamma receptor (FcγR) IIb at the tumor site prevented intra-tumoral Treg cell depletion, which may underlie the lack of anti-tumor activity previously observed in pre-clinical models. Use of an anti-CD25 antibody with enhanced binding to activating FcγRs led to effective depletion of tumor-infiltrating Treg cells, increased effector to Treg cell ratios, and improved control of established tumors. Combination with anti-programmed cell death protein-1 antibodies promoted complete tumor rejection, demonstrating the relevance of CD25 as a therapeutic target and promising substrate for future combination approaches in immune-oncology

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies