Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

Evaluating predictive markers for viral rebound and safety assessment in blood and lumbar fluid during HIV-1 treatment interruption

Background: Validated biomarkers to evaluate HIV-1 cure strategies are currently lacking, therefore requiring analytical treatment interruption (ATI) in study participants. Little is known about the safety of ATI and its long-term impact on patient health. Objectives: ATI safety was assessed and potential biomarkers predicting viral rebound were evaluated. Methods: PBMCs, plasma and CSF were collected from 11 HIV-1-positive individuals at four different timepoints during ATI (NCT02641756). Total and integrated HIV-1 DNA, cell-associated (CA) HIV-1 RNA transcripts and restriction factor (RF) expression were measured by PCR-based assays. Markers of neuroinflammation and neuronal injury [neurofilament light chain (NFL) and YKL-40 protein] were measured in CSF. Additionally, neopterin, tryptophan and kynurenine were measured, both in plasma and CSF, as markers of immune activation. Results: Total HIV-1 DNA, integrated HIV-1 DNA and CA viral RNA transcripts did not differ pre- and post-ATI. Similarly, no significant NFL or YKL-40 increases in CSF were observed between baseline and viral rebound. Furthermore, markers of immune activation did not increase during ATI. Interestingly, the RFs SLFN11 and APOBEC3G increased after ATI before viral rebound. Similarly, Tat-Rev transcripts were increased preceding viral rebound after interruption. Conclusions: ATI did not increase viral reservoir size and it did not reveal signs of increased neuronal injury or inflammation, suggesting that these well-monitored ATIs are safe. Elevation of Tat-Rev transcription and induced expression of the RFs SLFN11 and APOBEC3G after ATI, prior to viral rebound, indicates that these factors could be used as potential biomarkers predicting viral rebound

PROTEOMIC ANALYSIS OF TWO DIFFERENT STATES OF NAEGLERIA FOWLERI

Naegleria fowleri are free-living ameboflagellates found in soil and freshwater habitats throughout the world that cause a fatal disease in humans called Primary Amoebic Meningoencephalitis (PAM). Mechanisms of host resistance or susceptibility to infection have not been fully elucidated, and possible treatment methods are still sub optimal. The disease is diagnosed using specific laboratory tests available in only a few laboratories in the United States. Because of the rarity of infection and difficulty in initial detection, more than often PAM is misdiagnosed. Therefore, it is very important to find causative marker for early detection of an infection. The purpose of this study is to create a proteomic signature map using two-dimensional gel electrophoresis (2-D gel) and recommend a subset of proteins that may be directly linked to the pathogenic state of N. fowleri. Replicates of 2-D Gels were created for both strains of N. fowleri and the proteomic templates from these gels were compared with each other. Scatter Plots were generated measuring the density of protein spots from 2-D gels being analyzed for each study. For each strains of N. fowleri, the 2-D gels from each study were compared within and compared between the two studies for reproducibility in data. The resulting correlation values for all of the Scatter Plots were greater or equal to 0.90. Finally, the representative proteomic template for axenically grown N. fowleri and mouse passaged N. fowleri were compared and the correlation value of 0.60 was observed. This confirmed our theory that these two strains or states of N. fowleri have very different protein expressions, and we were able to identify a subset of proteins, both over expressed and newly synthesized, that may be linked to the highly pathogenic state of N. fowleri

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure.Comment: arXiv admin note: substantial text overlap with arXiv:1609.04861, arXiv:1711.00267, arXiv:1604.0006

Semiconservative quasispecies equations for polysomic genomes: The general case

This paper develops a formulation of the quasispecies equations appropriate for polysomic, semiconservatively replicating genomes. This paper is an extension of previous work on the subject, which considered the case of haploid genomes. Here, we develop a more general formulation of the quasispecies equations that is applicable to diploid and even polyploid genomes. Interestingly, with an appropriate classification of population fractions, we obtain a system of equations that is formally identical to the haploid case. As with the work for haploid genomes, we consider both random and immortal DNA strand chromosome segregation mechanisms. However, in contrast to the haploid case, we have found that an analytical solution for the mean fitness is considerably more difficult to obtain for the polyploid case. Accordingly, whereas for the haploid case we obtained expressions for the mean fitness for the case of an analogue of the single-fitness-peak landscape for arbitrary lesion repair probabilities (thereby allowing for non-complementary genomes), here we solve for the mean fitness for the restricted case of perfect lesion repair.Comment: 16 pages, 3 figure

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure

3D Microfluidic model for evaluating immunotherapy efficacy by tracking dendritic cell behaviour toward tumor cells

Immunotherapy efficacy relies on the crosstalk within the tumor microenvironment between cancer and dendritic cells (DCs) resulting in the induction of a potent and effective antitumor response. DCs have the specific role of recognizing cancer cells, taking up tumor antigens (Ags) and then migrating to lymph nodes for Ag (cross)-presentation to naïve T cells. Interferon-α-conditioned DCs (IFN-DCs) exhibit marked phagocytic activity and the special ability of inducing Ag-specific T-cell response. Here, we have developed a novel microfluidic platform recreating tightly interconnected cancer and immune systems with specific 3D environmental properties, for tracking human DC behaviour toward tumor cells. By combining our microfluidic platform with advanced microscopy and a revised cell tracking analysis algorithm, it was possible to evaluate the guided efficient motion of IFN-DCs toward drug-treated cancer cells and the succeeding phagocytosis events. Overall, this platform allowed the dissection of IFN-DC-cancer cell interactions within 3D tumor spaces, with the discovery of major underlying factors such as CXCR4 involvement and underscored its potential as an innovative tool to assess the efficacy of immunotherapeutic approaches

Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments

Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems