Dynamic motion planning of 3D human locomotion using gradient-based optimization

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.     

3D treatment planning for heavy charged particles

Summary The comments herein describe, at a necessarily superficial level, a number of issues which must be addressed in developing plans for heavy charged particle therapy. Programs now exist which provide the needed capabilities. The challenge now is to make the planning process easier and faster- and possibly more efective. It seems likely that this will be achieved in the next few years.Invited paper given on the fourth workshop on Heavy Charged Particles in Biology and Medicine GSI, Darmstadt, FRG, September 23–25, 1991     

Modelling glioma invasion using 3D bioprinting and scaffold-free 3D culture

Glioma is a highly aggressive form of brain cancer, with some subtypes having 5-year survival rates of less than 5%. Tumour cell invasion into the surrounding parenchyma seems to be the primary driver of these poor outcomes, as most gliomas recur within 2 cm of the original surgically-resected tumour. Many current approaches to the development of anticancer therapy attempt to target genetic weaknesses in a particular cancer, but may not take into account the microenvironment experienced by a tumour and the patient-specific genetic differences in susceptibility to treatment. Here we demonstrate the use of complementary approaches, 3D bioprinting and scaffold-free 3D tissue culture, to examine the invasion of glioma cells into neural-like tissue with 3D confocal microscopy. We found that, while both approaches were successful, the use of 3D tissue culture for organoid development offers the advantage of broad accessibility. As a proof-of-concept of our approach, we developed a system in which we could model the invasion of human glioma cells into mouse neural progenitor cell-derived spheroids. We show that we can follow invasion of human tumour cells using cell-tracking dyes and 3D laser scanning confocal microscopy, both in real time and in fixed samples. We validated these results using conventional cryosectioning. Our scaffold-free 3D approach has broad applicability, as we were easily able to examine invasion using different neural progenitor cell lines, thus mimicking differences that might be observed in patient brain tissue. These results, once applied to iPSC-derived cerebral organoids that incorporate the somatic genetic variability of patients, offer the promise of truly personalized treatments for brain cancer.     

Improving wildlife tracking using 3D information

The monitoring of wildlife populations is of growing importance due to the worldwide endangerment of many species, global climate change, and land cover change. Wildlife monitoring by camera traps is an established and non-invasive standard approach to quantify species diversity, estimate occupancy and relative abundance and track animal behaviour in 24/7 documentation. We propose a novel wildlife-specific 3D multi-object tracking workflow using inexpensive stereo camera traps. By embedding carefully efficient 2D methods into the overall 3D workflow, we avoid, on the one hand, costly processing of complex 3D data structures, i.e., 3D point clouds but on the other hand outperform significantly typical 2D tracking approaches with our overall 3D workflow in terms of international established multi-object tracking metrics, i.e., with respect to the reliability and accuracy of the tracking results. The code is available at https://github.com/m-klasen/3d_wildlife-tracking     

Sensitivity improvement in 2D and 3D HCCH spectroscopy using heteronuclear cross-polarization

Summary A new method, which employs a sequence of heteronuclear-homonuclear-heteronuclear Hartmann-Hahn (HEHOHEHAHA) cross-polarization steps for obtaining through-bond H-C-C-H correlations in larger proteins (M_r > 15 kDa), is presented. The method has significantly higher sensitivity compared to INEPTHOHAHA-INEPT-based techniques. An additional feature of this experiment is that well-phaseable spectra may be obtained with a minimal (4-step) phase cycle and, consequently, experimental time can be utilized towards obtaining high resolution in indirect dimensions. Results from 2D and 3D HEHOHEHAHA experiments on T4-lysozyme are presented.     

Polymer chain packing analysis using molecular modeling

This article describes the methodology used in applying molecular modeling to investigate the chain packing of polymers. Models for polyethylene and Uans-polyacetylene were developed in order to study the cell packing energy as a function of the chain setting angles. This approach was found to yield chain setting angle values that corresponded to those determined experimentally by other authors. The limitations of the method are also discussed.     

Protein-protein docking using region-based 3D Zernike descriptors

Background  

Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur.     

Protein function prediction using local 3D templates

The prediction of a protein's function from its 3D structure is becoming more and more important as the worldwide structural genomics initiatives gather pace and continue to solve 3D structures, many of which are of proteins of unknown function. Here, we present a methodology for predicting function from structure that shows great promise. It is based on 3D templates that are defined as specific 3D conformations of small numbers of residues. We use four types of template, covering enzyme active sites, ligand-binding residues, DNA-binding residues and reverse templates. The latter are templates generated from the target structure itself and scanned against a representative subset of all known protein structures. Together, the templates provide a fairly thorough coverage of the known structures and ensure that if there is a match to a known structure it is unlikely to be missed. A new scoring scheme provides a highly sensitive means of discriminating between true positive and false positive template matches. In all, the methodology provides a powerful new tool for function prediction to complement those already in use.     

3D tree-ring analysis using helical X-ray tomography

The current state-of-the-art of tree-ring analysis and densitometry is still mainly limited to two dimensions and mostly requires proper treatment of the surface of the samples. In this paper we elaborate on the potential of helical X-ray computed tomography for 3D tree-ring analysis. Microdensitometrical profiles are obtained by processing of the reconstructed volumes. Correction of the structure direction, taking into account the angle of growth rings and grain, results in very accurate microdensity and precise ring width measurements. Both a manual as well as an automated methodology is proposed here, of which the MATLAB^© code is available. Examples are given for pine (Pinus sylvestris L.), oak (Quercus robur L.) and teak (Tectona grandis L.). In all, the methodologies applied here on the 3D volumes are useful for growth related studies, enabling a fast and non-destructive analysis.