Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment

Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

A combined interpolation - 3D Level Set Method (I+3DLSM) based segmentation process is presented. The performance in terms of accuracy of the 3-dimensional (3D) level set method (LSM) in the segmentation of throat regions from highly anisotropic magnetic resonance imaging (MRI) volumes, with and without an interpolation step is evaluated. Qualitative and quantitative results from real MRI data suggest that performing interpolation, to reconstruct isotropic MRI volumes, prior to 3D LSM improves the accuracy of the segmentation results, compared to interpolation post 3D LSM and no interpolation at all

RotoStep: A Chromosome Dynamics Simulator Reveals Mechanisms of Loop Extrusion

ChromoShake is a three-dimensional simulator designed to explore the range of configurational states a chromosome can adopt based on thermodynamic fluctuations of the polymer chain. Here, we refine ChromoShake to generate dynamic simulations of a DNA-based motor protein such as condensin walking along the chromatin substrate. We model walking as a rotation of DNA-binding heat-repeat proteins around one another. The simulation is applied to several configurations of DNA to reveal the consequences of mechanical stepping on taut chromatin under tension versus loop extrusion on single-tethered, floppy chromatin substrates. These simulations provide testable hypotheses for condensin and other DNA-based motors functioning along interphase chromosomes. Our model reveals a novel mechanism for condensin enrichment in the pericentromeric region of mitotic chromosomes. Increased condensin dwell time at centromeres results in a high density of pericentric loops that in turn provide substrate for additional condensin

AI-Assisted Forward Modeling of Biological Structures

The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures

Validation of a magnetic resonance imaging-based auto-contouring software tool for gross tumour delineation in head and neck cancer radiotheraphy planning

To perform statistical validation of a newly developed magnetic resonance imaging (MRI) auto-contouring software tool for gross tumour volume (GTV) delineation in head and neck tumours to assist in radiotherapy planning. Axial MRI baseline scans were obtained for 10 oropharyngeal and laryngeal cancer patients. GTV was present on 102 axial slices and auto-contoured using the modified fuzzy c-means clustering integrated with level set method (FCLSM). Peer reviewed (C-gold) manual contours were used as the reference standard to validate auto-contoured GTVs (C-auto) and mean manual contours (C-manual) from 2 expert clinicians (C1 and C2). Multiple geometrical metrics, including Dice Similarity Coefficient (DSC) were used for quantitative validation. A DSC ≥0.7 was deemed acceptable. Inter-and intra-variabilities amongst the manual contours were also validated. The 2-dimension (2D) contours were then reconstructed in 3D for GTV volume calculation, comparison and 3D visualisation. The mean DSC between C-gold and C-auto was 0.79. The mean DSC bet ween C-gold and C-manual was 0.79 and that between C1 and C2 was 0.80. The average time for GTV auto-contouring per patient was 8 minutes (range 6-13mins; mean 45seconds per axial slice) compared to 15 minutes (range 6-23mins; mean 88 seconds per axial slice) for C1. The average volume concordance between C-gold and C-auto volumes was 86. 51% compared to 74.16% between C-gold and C-manual. The average volume concordance between C1 and C2 volumes was 86.82%. This newly-designed MRI-based auto-contouring software tool shows initial acceptable results in GTV delineation of oropharyngeal and laryngeal tumours using FCLSM. This auto-contouring software tool may help reduce inter-and intra- variability and can assist clinical oncologists with time-consuming, complex radiotherapy planning

Geometric partitioning of cohesin and condensin is a consequence of chromatin loops

SMC (structural maintenance of chromosomes) complexes condensin and cohesin are crucial for proper chromosome organization. Condensin has been reported to be a mechanochemical motor capable of forming chromatin loops, while cohesin passively diffuses along chromatin to tether sister chromatids. In budding yeast, the pericentric region is enriched in both condensin and cohesin. As in higher-eukaryotic chromosomes, condensin is localized to the axial chromatin of the pericentric region, while cohesin is enriched in the radial chromatin. Thus, the pericentric region serves as an ideal model for deducing the role of SMC complexes in chromosome organization. We find condensin-mediated chromatin loops establish a robust chromatin organization, while cohesin limits the area that chromatin loops can explore. Upon biorientation, extensional force from the mitotic spindle aggregates condensin-bound chromatin from its equilibrium position to the axial core of pericentric chromatin, resulting in amplified axial tension. The axial localization of condensin depends on condensin's ability to bind to chromatin to form loops, while the radial localization of cohesin depends on cohesin's ability to diffuse along chromatin. The different chromatin-tethering modalities of condensin and cohesin result in their geometric partitioning in the presence of an extensional force on chromatin

Genotoxic stress induces Sca‐1‐expressing metastatic mammary cancer cells

We describe a cell damage‐induced phenotype in mammary carcinoma cells involving acquisition of enhanced migratory and metastatic properties. Induction of this state by radiation required increased activity of the Ptgs2 gene product cyclooxygenase 2 (Cox2), secretion of its bioactive lipid product prostaglandin E2 (PGE2), and the activity of the PGE2 receptor EP4. Although largely transient, decaying to low levels in a few days to a week, this phenotype was cumulative with damage and levels of cell markers Sca‐1 and ALDH1 increased with treatment dose. The Sca‐1+, metastatic phenotype was inhibited by both Cox2 inhibitors and PGE2 receptor antagonists, suggesting novel approaches to radiosensitization

Value at Risk models with long memory features and their economic performance

We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms