Mobility, a critical factor in quality of life, is often rehabilitated using simplistic solutions, such as walkers. Exoskeletons (wearable robotics) offer a more sophisticated rehabilitation approach. However, non-adherence to externally worn mobility aids limits their efficacy. Here, we present the concept of a fully implantable assistive limb actuator that overcomes non-adherence constraints, and which can provide high-precision assistive force. In a bipedal animal model (fowl), we have developed a variable length isometric actuator (measuring ϕ9 x 30 mm) that is able to be directly implanted within the leg via a bone anchor and tendon fixation, replacing the lateral gastrocnemius muscle belly. The actuator is able to generate isometric force similar to the in vivo force of the native muscle, designed to generate assistive torque at the ankle and reduce muscular demand at no additional energy cost. The device has a stroke of 10 mm that operates up to 770 mm/s (77 stroke lengths/s), capable of acting as a clutch (disengaging when needed) and with a tunable slack length to modulate the timing and level of assistive force during gait. Surgical techniques to attach the actuator to the biological system, the Achilles tendon and tibia, have been established and validated using survival surgeries and cadaveric specimens.

The portrayed personality of virtual characters and agents is understood to influence how we perceive and engage with digital applications. Understanding how the features of speech and animation drive portrayed personality allows us to intentionally design characters to be more personalized and engaging. In this study, we use performance capture data of unscripted conversations from a variety of actors to explore the perceptual outcomes associated with the modalities of speech and motion. Specifically, we contrast full performance-driven characters to those portrayed by generated gestures and synthesized speech, analysing how the features of each influence portrayed personality according to the Big Five personality traits. We find that processing speech and motion can have mixed effects on such traits, with our results highlighting motion as the dominant modality for portraying extraversion and speech as dominant for communicating agreeableness and emotional stability. Our results can support the Extended Reality (XR) community in development of virtual characters, social agents and 3D User Interface (3DUI) agents portraying a range of targeted personalities.

Shape Memory Alloys (SMAs) are a type of smart material that are commonly used in compact and lightweight linear actuators. Paired with compliant motion conversion mechanisms, these alloys can be transformed into lightweight grippers ideal for applications such as drone deliveries. This work proposes to use the inherent stiffness of a compliant mechanism with an SMA coil, to design and size a self-biasing SMA gripper. In this paper, with the help of 2.5D design, a functional radial 4-prong gripper is designed and fabricated. While only weighing 17g, it has a gripping force and stroke of 1.8N and 4mm, respectively. Along with this functional prototype, an analytical model and design framework is presented allowing for the design and sizing of similar self-biasing SMA actuators for other different light-weight applications.

Research on creation of virtual humans enables increasing automatization of their behavior, including synthesis of verbal and nonverbal behavior. As the achievable realism of different aspects of agent design evolves asynchronously, it is important to understand if and how divergence in realism between behavioral channels can elicit negative user responses. Specifically, in this work, we investigate the question of whether autonomous virtual agents relying on synthetic text-to-speech voices should portray a corresponding level of realism in the non-verbal channels of motion and visual appearance, or if, alternatively, the best available realism of each channel should be used. In two perceptual studies, we assess how realism of voice, motion, and appearance influence the perceived match of speech and gesture motion, as well as the agent's likability and human-likeness. Our results suggest that maximizing realism of voice and motion is preferable even when this leads to realism mismatches, but for visual appearance, lower realism may be preferable. (A video abstract can be found at https://youtu.be/arfZZ-hxD1Y.)

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an important tool for studying gene regulatory proteins, such as transcription factors and histones. Peak calling is one of the first steps in the analysis of these data. Peak calling consists of two sub-problems: identifying candidate peaks and testing candidate peaks for statistical significance. We surveyed 30 methods and identified 12 features of the two sub-problems that distinguish methods from each other. We picked six methods GEM, MACS2, MUSIC, BCP, Threshold-based method (TM) and ZINBA] that span this feature space and used a combination of 300 simulated ChIP-seq data sets, 3 real data sets and mathematical analyses to identify features of methods that allow some to perform better than the others. We prove that methods that explicitly combine the signals from ChIP and input samples are less powerful than methods that do not. Methods that use windows of different sizes are more powerful than the ones that do not. For statistical testing of candidate peaks, methods that use a Poisson test to rank their candidate peaks are more powerful than those that use a Binomial test. BCP and MACS2 have the best operating characteristics on simulated transcription factor binding data. GEM has the highest fraction of the top 500 peaks containing the binding motif of the immunoprecipitated factor, with 50% of its peaks within 10 base pairs of a motif. BCP and MUSIC perform best on histone data. These findings provide guidance and rationale for selecting the best peak caller for a given application.

Abstract Summary: The large and growing number of genome-wide datasets highlights the need for high-performance feature analysis and data comparison methods, in addition to efficient data storage and retrieval techniques. We introduce BEDOPS, a software suite for common genomic analysis tasks which offers improved flexibility, scalability and execution time characteristics over previously published packages. The suite includes a utility to compress large inputs into a lossless format that can provide greater space savings and faster data extractions than alternatives. Availability: http://code.google.com/p/bedops/ includes binaries, source and documentation. Contact: [email protected] and [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.