User evaluation of an interactive learning framework for single-arm and dual-arm robots

The final publication is available at link.springer.comSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett’s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.Peer ReviewedPostprint (author's final draft

Real World Bayesian Optimization Using Robots to Clean Liquid Spills

Developing robots that can contribute to cleaning could have a significant impact on the lives of many. Cleaning wet liquid spills is a particularly challenging task for a robotic system, and has several high impact applications. This is a hard task to physically model due to the complex interactions between cleaning materials and the surface. As such, to the authors' knowledge there has been no prior work in this area. A new method for finding optimal control parameters for the cleaning of liquid spills is required by developing a robotic system which iteratively learns to clean through physical experimentation. The robot creates a liquid spill, cleans and assesses performance and uses Bayesian optimization to find the optimal control parameters for a given size of liquid spill. The automation process enabled the experiment to be repeated more than 400 times over 20 hours to find the optimal wiping control parameters for many different conditions. We then show that these solutions can be extrapolated for different spill conditions. The optimized control parameters showed reliable and accurate performances, which in some cases, outperformed humans at the same task.This work was supported by BEKO PLC and Symphony Kitchens. We are especially thankful for the valuable inputs from Dr Graham Anderson and Dr Natasha Conway

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Multiple ATR-Chk1 Pathway Proteins Preferentially Associate with Checkpoint-Inducing DNA Substrates

The ATR-Chk1 DNA damage checkpoint pathway is a critical regulator of the cellular response to DNA damage and replication stress in human cells. The variety of environmental, chemotherapeutic, and carcinogenic agents that activate this signal transduction pathway do so primarily through the formation of bulky adducts in DNA and subsequent effects on DNA replication fork progression. Because there are many protein-protein and protein-DNA interactions proposed to be involved in activation and/or maintenance of ATR-Chk1 signaling in vivo, we systematically analyzed the association of a number of ATR-Chk1 pathway proteins with relevant checkpoint-inducing DNA structures in vitro. These DNA substrates included single-stranded DNA, branched DNA, and bulky adduct-containing DNA. We found that many checkpoint proteins show a preference for single-stranded, branched, and bulky adduct-containing DNA in comparison to undamaged, double-stranded DNA. We additionally found that the association of checkpoint proteins with bulky DNA damage relative to undamaged DNA was strongly influenced by the ionic strength of the binding reaction. Interestingly, among the checkpoint proteins analyzed the checkpoint mediator proteins Tipin and Claspin showed the greatest differential affinity for checkpoint-inducing DNA structures. We conclude that the association and accumulation of multiple checkpoint proteins with DNA structures indicative of DNA damage and replication stress likely contribute to optimal ATR-Chk1 DNA damage checkpoint responses

Power Law versus Exponential State Transition Dynamics: Application to Sleep-Wake Architecture

BACKGROUND: Despite the common experience that interrupted sleep has a negative impact on waking function, the features of human sleep-wake architecture that best distinguish sleep continuity versus fragmentation remain elusive. In this regard, there is growing interest in characterizing sleep architecture using models of the temporal dynamics of sleep-wake stage transitions. In humans and other mammals, the state transitions defining sleep and wake bout durations have been described with exponential and power law models, respectively. However, sleep-wake stage distributions are often complex, and distinguishing between exponential and power law processes is not always straightforward. Although mono-exponential distributions are distinct from power law distributions, multi-exponential distributions may in fact resemble power laws by appearing linear on a log-log plot. METHODOLOGY/PRINCIPAL FINDINGS: To characterize the parameters that may allow these distributions to mimic one another, we systematically fitted multi-exponential-generated distributions with a power law model, and power law-generated distributions with multi-exponential models. We used the Kolmogorov-Smirnov method to investigate goodness of fit for the "incorrect" model over a range of parameters. The "zone of mimicry" of parameters that increased the risk of mistakenly accepting power law fitting resembled empiric time constants obtained in human sleep and wake bout distributions. CONCLUSIONS/SIGNIFICANCE: Recognizing this uncertainty in model distinction impacts interpretation of transition dynamics (self-organizing versus probabilistic), and the generation of predictive models for clinical classification of normal and pathological sleep architecture

Cooperative control of striated muscle mass and metabolism by MuRF1 and MuRF2

The muscle-specific RING finger proteins MuRF1 and MuRF2 have been proposed to regulate protein degradation and gene expression in muscle tissues. We have tested the in vivo roles of MuRF1 and MuRF2 for muscle metabolism by using knockout (KO) mouse models. Single MuRF1 and MuRF2 KO mice are healthy and have normal muscles. Double knockout (dKO) mice obtained by the inactivation of all four MuRF1 and MuRF2 alleles developed extreme cardiac and milder skeletal muscle hypertrophy. Muscle hypertrophy in dKO mice was maintained throughout the murine life span and was associated with chronically activated muscle protein synthesis. During ageing (months 4–18), skeletal muscle mass remained stable, whereas body fat content did not increase in dKO mice as compared with wild-type controls. Other catabolic factors such as MAFbox/atrogin1 were expressed at normal levels and did not respond to or prevent muscle hypertrophy in dKO mice. Thus, combined inhibition of MuRF1/MuRF2 could provide a potent strategy to stimulate striated muscles anabolically and to protect muscles from sarcopenia during ageing

Disruption of a GATA4/Ankrd1 Signaling Axis in Cardiomyocytes Leads to Sarcomere Disarray: Implications for Anthracycline Cardiomyopathy

Doxorubicin (Adriamycin) is an effective anti-cancer drug, but its clinical usage is limited by a dose-dependent cardiotoxicity characterized by widespread sarcomere disarray and loss of myofilaments. Cardiac ankyrin repeat protein (CARP, ANKRD1) is a transcriptional regulatory protein that is extremely susceptible to doxorubicin; however, the mechanism(s) of doxorubicin-induced CARP depletion and its specific role in cardiomyocytes have not been completely defined. We report that doxorubicin treatment in cardiomyocytes resulted in inhibition of CARP transcription, depletion of CARP protein levels, inhibition of myofilament gene transcription, and marked sarcomere disarray. Knockdown of CARP with small interfering RNA (siRNA) similarly inhibited myofilament gene transcription and disrupted cardiomyocyte sarcomere structure. Adenoviral overexpression of CARP, however, was unable to rescue the doxorubicin-induced sarcomere disarray phenotype. Doxorubicin also induced depletion of the cardiac transcription factor GATA4 in cardiomyocytes. CARP expression is regulated in part by GATA4, prompting us to examine the relationship between GATA4 and CARP in cardiomyocytes. We show in co-transfection experiments that GATA4 operates upstream of CARP by activating the proximal CARP promoter. GATA4-siRNA knockdown in cardiomyocytes inhibited CARP expression and myofilament gene transcription, and induced extensive sarcomere disarray. Adenoviral overexpression of GATA4 (AdV-GATA4) in cardiomyocytes prior to doxorubicin exposure maintained GATA4 levels, modestly restored CARP levels, and attenuated sarcomere disarray. Interestingly, siRNA-mediated depletion of CARP completely abolished the Adv-GATA4 rescue of the doxorubicin-induced sarcomere phenotype. These data demonstrate co-dependent roles for GATA4 and CARP in regulating sarcomere gene expression and maintaining sarcomeric organization in cardiomyocytes in culture. The data further suggests that concurrent depletion of GATA4 and CARP in cardiomyocytes by doxorubicin contributes in large part to myofibrillar disarray and the overall pathophysiology of anthracycline cardiomyopathy

Early discontinuation of endocrine therapy for breast cancer: Who is at risk in clinical practice?

Purpose: Despite evidence supporting at least five years of endocrine therapy for early breast cancer, many women discontinue therapy early. We investigated the impact of initial therapy type and specific comorbidities on discontinuation of endocrine therapy in clinical practice. Methods We identified women in a population-based cohort with a diagnosis of early breast cancer and an incident dispensing of anastrozole, letrozole or tamoxifen from 2003-2008 (N = 1531). Pharmacy and health service data were used to determine therapy duration, treatment for pre-existing and post-initiation comorbidities (anxiety, depression, hot flashes, musculoskeletal pain, osteoporosis, vaginal atrophy), demographic and other clinical characteristics. Time to discontinuation of initial, and any, endocrine therapy was calculated. Cox regression determined the association of different characteristics on early discontinuation. Results Initial endocrine therapy continued for a median of 2.2 years and any endocrine therapy for 4.8 years. Cumulative probability of discontinuing any therapy was 17% after one year and 58% by five years. Initial tamoxifen, pre-existing musculoskeletal pain and newly-treated anxiety predicted shorter initial therapy but not discontinuation of any therapy. Early discontinuation of any therapy was associated with newly-treated hot flashes (HR = 2.1, 95%CI = 1.3-3.3), not undergoing chemotherapy (HR = 1.4, 95%CI = 1.1-1.8) and not undergoing mastectomy (HR = 1.5, 95%CI = 1.2-1.8). Conclusions Less than half of women completed five years of endocrine therapy. Women at greatest risk of stopping any therapy early were those with newly-treated hot flashes, no initial chemotherapy, or no initial mastectomy. This suboptimal use means that the reductions in recurrence demonstrated in clinical trials may not be realised in practice