Motion and emotion estimation for robotic autism intervention.

Robots have recently emerged as a novel approach to treating autism spectrum disorder (ASD). A robot can be programmed to interact with children with ASD in order to reinforce positive social skills in a non-threatening environment. In prior work, robots were employed in interaction sessions with ASD children, but their sensory and learning abilities were limited, while a human therapist was heavily involved in “puppeteering” the robot. The objective of this work is to create the next-generation autism robot that includes several new interactive and decision-making capabilities that are not found in prior technology. Two of the main features that this robot would need to have is the ability to quantitatively estimate the patient’s motion performance and to correctly classify their emotions. This would allow for the potential diagnosis of autism and the ability to help autistic patients practice their skills. Therefore, in this thesis, we engineered components for a human-robot interaction system and confirmed them in experiments with the robots Baxter and Zeno, the sensors Empatica E4 and Kinect, and, finally, the open-source pose estimation software OpenPose. The Empatica E4 wristband is a wearable device that collects physiological measurements in real time from a test subject. Measurements were collected from ASD patients during human-robot interaction activities. Using this data and labels of attentiveness from a trained coder, a classifier was developed that provides a prediction of the patient’s level of engagement. The classifier outputs this prediction to a robot or supervising adult, allowing for decisions during intervention activities to keep the attention of the patient with autism. The CMU Perceptual Computing Lab’s OpenPose software package enables body, face, and hand tracking using an RGB camera (e.g., web camera) or an RGB-D camera (e.g., Microsoft Kinect). Integrating OpenPose with a robot allows the robot to collect information on user motion intent and perform motion imitation. In this work, we developed such a teleoperation interface with the Baxter robot. Finally, a novel algorithm, called Segment-based Online Dynamic Time Warping (SoDTW), and metric are proposed to help in the diagnosis of ASD. Social Robot Zeno, a childlike robot developed by Hanson Robotics, was used to test this algorithm and metric. Using the proposed algorithm, it is possible to classify a subject’s motion into different speeds or to use the resulting SoDTW score to evaluate the subject’s abilities

The EFF-1A Cytoplasmic Domain Influences Hypodermal Cell Fusions in C. elegans But Is Not Dependent on 14-3-3 Proteins.

BACKGROUND: Regulatory and biophysical mechanisms of cell-cell fusion are largely unknown despite the fundamental requirement for fused cells in eukaryotic development. Only two cellular fusogens that are not of clear recent viral origin have been identified to date, both in nematodes. One of these, EFF-1, is necessary for most cell fusions in Caenorhabditis elegans. Unregulated EFF-1 expression causes lethality due to ectopic fusion between cells not developmentally programmed to fuse, highlighting the necessity of tight fusogen regulation for proper development. Identifying factors that regulate EFF-1 and its paralog AFF-1 could lead to discovery of molecular mechanisms that control cell fusion upstream of the action of a membrane fusogen. Bioinformatic analysis of the EFF-1A isoform\u27s predicted cytoplasmic domain (endodomain) previously revealed two motifs that have high probabilities of interacting with 14-3-3 proteins when phosphorylated. Mutation of predicted phosphorylation sites within these motifs caused measurable loss of eff-1 gene function in cell fusion in vivo. Moreover, a human 14-3-3 isoform bound to EFF-1::GFP in vitro. We hypothesized that the two 14-3-3 proteins in C. elegans, PAR-5 and FTT-2, may regulate either localization or fusion-inducing activity of EFF-1. METHODOLOGY/PRINCIPAL FINDINGS: Timing of fusion events was slightly but significantly delayed in animals unable to produce full-length EFF-1A. Yet, mutagenesis and live imaging showed that phosphoserines in putative 14-3-3 binding sites are not essential for EFF-1::GFP accumulation at the membrane contact between fusion partner cells. Moreover, although the EFF-1A endodomain was required for normal rates of eff-1-dependent epidermal cell fusions, reduced levels of FTT-2 and PAR-5 did not visibly affect the function of wild-type EFF-1 in the hypodermis. CONCLUSIONS/SIGNIFICANCE: Deletion of the EFF-1A endodomain noticeably affects the timing of hypodermal cell fusions in vivo. However, prohibiting phosphorylation of candidate 14-3-3-binding sites does not impact localization of the fusogen. Hypodermal membrane fusion activity persists when 14-3-3 expression levels are reduced

Genetic Labeling of Synapses

A major challenge in neuroscience is to unravel how the synaptic contacts between neurons give rise to brain circuits. A number of techniques have been developed to visualize the synaptic organization of neurons. In this chapter, we focus on genetic methods to mark specific types of synapses so that synaptic sites can be visualized throughout the entire dendritic or axonal arbor of single neurons. Genetic synaptic labeling can be achieved by cell-type-specific viral or transgenic delivery of synaptic proteins tagged by fluorescent proteins. Sparse genetic labeling of neurons permits semiautomated quantification of the distribution and densities of selected types of synapses in segregated domains of the axonal and dendritic trees. These approaches can reduce the complexity and ambiguity of attributing synaptic sites to unravel principles of the synaptic organization of identified neuronal types in the circuit

Insulin Acts through FOXO3a to Activate Transcription of Plasminogen Activator Inhibitor Type 1

Plasminogen activator inhibitor-1 (PAI-1) is an important regulator of fibrinolysis. PAI-1 levels are elevated in type 2 diabetes, and this elevation correlates with macro- and microvascular complications of diabetes. However, the mechanistic link between insulin and up-regulation of PAI-1 is unclear. Here we demonstrate that overexpression of Forkhead-related transcription factor (Fox)O1, FoxO3a, and FoxC1 augment insulin’s ability to activate the PAI-1 promoter. In addition, insulin treatment promotes the phosphorylation of nuclear and cytoplasmic Fox03a and an increase of cytoplasmic Fox03a. In contrast, insulin treatment led to the accumulation of phospho-Fox01 only in the cytoplasm. Furthermore, insulin also increased the ability of chimeric LexA-FoxO1, LexA-FoxO3a, and LexA-FoxC1 proteins to increase the activity of a LexA reporter, suggesting that the effect of insulin on FoxO3a was direct. Using small interfering RNA to specifically deplete each of the Fox transcription factors tested, we demonstrate that only reduction of FoxO3a inhibits insulin-increased PAI-1-Luc expression and PAI-1 mRNA accumulation. Finally, chromatin immunoprecipitation assays confirm the presence of FoxO3a on the PAI-1 promoter. These results suggest that FoxO3a mediates insulin-increased PAI-1 gene expression