Rapid in-situ calibration of computational micro-spectrometer with few-shot meta-learning.

Computational micro-spectrometers comprised of detector arrays and encoding structure arrays, such as on-chip Fabry-Perot (FP) cavity filters, have great potential in many in-situ applications owing to their compact size and snapshot imaging ability. Given manufacturing deviation and environmental influence are inevitable, easy and effective calibration for spectrometer is necessary, especially for in-situ applications. Currently calibration strategies based on iterative algorithms or neural networks require accurate measurements of pixel-level (spectral) encoding functions through monochromator or large amounts of standard samples. These procedures are time-consuming and expensive, thereby impeding in-situ applications. Meta-learning algorithms with few-shot learning ability can address this challenge by incorporating the prior knowledge in the simulated dataset. In this work, we propose a meta-learning algorithm free of measuring encoding function or large amounts of standard samples to calibrate a micro-spectrometer with manufacturing deviation effectively. Our micro-spectrometer comprises 16 types of FP filters covering a wavelength range of 550-720 nm. The center wavelength of each filter type deviates from the design up to 6 nm. After calibration with 15 different color data, the average reconstruction error on the test dataset decreased from 7.2 × 10-3 to 1.2 × 10-3, and further decreased to 9.4 × 10-4 when the calibration data increased to 24. The performance is comparable to algorithms trained with measured encoding function both in reconstruction error and generalization ability. We estimated that the cost of in-situ calibration through reflectance measurements of color chart decreased to one percent of the cost through monochromator measurements. By exploiting prior deviation information in simulation data with meta-learning, the efficiency and cost of calibration are significantly improved, thereby facilitating the large-scale production and in-situ application of micro-spectrometers.

Fluorine-Containing Ionogels with Stretchable, Solvent-Resistant, Wide Temperature Tolerance, and Transparent Properties for Ionic Conductors.

Stretchable ionogels, as soft ion-conducting materials, have generated significant interest. However, the integration of multiple functions into a single ionogel, including temperature tolerance, self-adhesiveness, and stability in diverse environments, remains a challenge. In this study, a new class of fluorine-containing ionogels was synthesized through photo-initiated copolymerization of fluorinated hexafluorobutyl methacrylate and butyl acrylate in a fluorinated ionic liquid 1-butyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide. The resulting ionogels demonstrate good stretchability with a fracture strain of ~1300%. Owing to the advantages of the fluorinated network and the ionic liquid, the ionogels show excellent stability in air and vacuum, as well as in various solvent media such as water, sodium chloride solution, and hexane. Additionally, the ionogels display impressive wide temperature tolerance, functioning effectively within a wide temperature range from -60 to 350 °C. Moreover, due to their adhesive properties, the ionogels can be easily attached to various substrates, including plastic, rubber, steel, and glass. Sensors made of these ionogels reliably respond to repetitive tensile-release motion and finger bending in both air and underwater. These findings suggest that the developed ionogels hold great promise for application in wearable devices.

Loss of CPEB3 Upregulates MEGF10 to Impair Mosaic Development of ON Starburst Amacrine Cells.

Cytoplasmic polyadenylation element binding protein 3 (CPEB3) regulates target RNA translation in neurons. Here, we examined CPEB3 distribution and function in the mouse retina. CPEB3 is expressed in retinal neurons, including those located in the inner nuclear layer (INL) and ganglion cell layer (GCL) but not in cone and rod photoreceptors in the outer nuclear layer (ONL). A previous study found CPEB3 expressed in cholinergic starburst amacrine cells (SACs). We first examined these cells and observed aberrant SAC mosaicism in CPEB3-knockout (KO) retinas. Retinal neurons showed orderly spatial arrangements. Many individual subtypes are organized non-randomly in patterns called mosaics. Despite CPEB3 being expressed in both populations of SACs, OFF SACs in the INL and ON SACs in the GCL, aberrant mosaic regularity was observed in only ON SACs of CPEB3-KO retinas. Molecular characterization revealed that translation of multiple epidermal growth factor 10 (Megf10) RNA is suppressed by CPEB3 during the first week of postnatal development, when MEGF10 is primarily expressed in SACs and mediates homotypic repulsive interactions to define intercellular spacing of SACs. Thus, elevated MEGF10 expression in the absence of the translational repressor CPEB3 may account for the defective spatial organization of ON SACs. Our findings uncover for the first time that translational control plays a role in shaping retinal mosaic arrangement.

Spatial distribution of excitatory synapses on the dendrites of ganglion cells in the mouse retina.

Excitatory glutamatergic inputs from bipolar cells affect the physiological properties of ganglion cells in the mammalian retina. The spatial distribution of these excitatory synapses on the dendrites of retinal ganglion cells thus may shape their distinct functions. To visualize the spatial pattern of excitatory glutamatergic input into the ganglion cells in the mouse retina, particle-mediated gene transfer of plasmids expressing postsynaptic density 95-green fluorescent fusion protein (PSD95-GFP) was used to label the excitatory synapses. Despite wide variation in the size and morphology of the retinal ganglion cells, the expression of PSD95 puncta was found to follow two general rules. Firstly, the PSD95 puncta are regularly spaced, at 1-2 µm intervals, along the dendrites, whereby the presence of an excitatory synapse creates an exclusion zone that rules out the presence of other glutamatergic synaptic inputs. Secondly, the spatial distribution of PSD95 puncta on the dendrites of diverse retinal ganglion cells are similar in that the number of excitatory synapses appears to be less on primary dendrites and to increase to a plateau on higher branch order dendrites. These observations suggest that synaptogenesis is spatially regulated along the dendritic segments and that the number of synaptic contacts is relatively constant beyond the primary dendrites. Interestingly, we also found that the linear puncta density is slightly higher in large cells than in small cells. This may suggest that retinal ganglion cells with a large dendritic field tend to show an increased connectivity of excitatory synapses that makes up for their reduced dendrite density. Mapping the spatial distribution pattern of the excitatory synapses on retinal ganglion cells thus provides explicit structural information that is essential for our understanding of how excitatory glutamatergic inputs shape neuronal responses.

Adaptive mixed reality rehabilitation improves quality of reaching movements more than traditional reaching therapy following stroke.

BACKGROUND: Adaptive mixed reality rehabilitation (AMRR) is a novel integration of motion capture technology and high-level media computing that provides precise kinematic measurements and engaging multimodal feedback for self-assessment during a therapeutic task. OBJECTIVE: We describe the first proof-of-concept study to compare outcomes of AMRR and traditional upper-extremity physical therapy. METHODS: Two groups of participants with chronic stroke received either a month of AMRR therapy (n = 11) or matched dosing of traditional repetitive task therapy (n = 10). Participants were right handed, between 35 and 85 years old, and could independently reach to and at least partially grasp an object in front of them. Upper-extremity clinical scale scores and kinematic performances were measured before and after treatment. RESULTS: Both groups showed increased function after therapy, demonstrated by statistically significant improvements in Wolf Motor Function Test and upper-extremity Fugl-Meyer Assessment (FMA) scores, with the traditional therapy group improving significantly more on the FMA. However, only participants who received AMRR therapy showed a consistent improvement in kinematic measurements, both for the trained task of reaching to grasp a cone and the untrained task of reaching to push a lighted button. CONCLUSIONS: AMRR may be useful in improving both functionality and the kinematics of reaching. Further study is needed to determine if AMRR therapy induces long-term changes in movement quality that foster better functional recovery.

Functional expression of ionotropic glutamate receptors in the rabbit retinal ganglion cells.

It has been known that retinal ganglion cells (RGCs) with distinct morphologies have different physiological properties. It was hypothesized that different functions of RGCs may in part result from various expressions of N-methyl-d-aspartate (NMDA), α-amino-3-hydroxyl-5-methyl-isoxazole-4-propinoic acid (AMPA), and kainic acid (KA) receptors on their dendrites. In the present study, we aimed to characterize the functional expression of AMPA and NMDA receptors of morphologically identified RGCs in the wholemount rabbit retina. The agmatine (AGB) activation assay was used to reveal functional expression of ionotropic glutamate receptors after the RGCs were targeted by injecting Neurobiotin. To examine the excitability of these glutamate receptors in an agonist specific manner, the lower concentrations of AMPA (2 µM) and NMDA (100 µM) were chosen to examine G7 (ON-OFF direction selective ganglion cells) and G11 (alpha ganglion cells) types of RGCs. We found that less than 40% of G7 type RGCs had salient AGB activation when incubated with 2 µM AMPA or 100 µM NMDA. The G11 type RGCs also showed similar activation frequencies, except that all of the OFF subtype examined had no AGB permeation under the same AMPA concentration. These results suggest that RGCs with large somata (G7 and G11 types) may express various heterogeneous functional ionotropic glutamate receptors, thus in part rendering their functional diversity.

Exploring the bases for a mixed reality stroke rehabilitation system, Part II: design of interactive feedback for upper limb rehabilitation.

BACKGROUND: Few existing interactive rehabilitation systems can effectively communicate multiple aspects of movement performance simultaneously, in a manner that appropriately adapts across various training scenarios. In order to address the need for such systems within stroke rehabilitation training, a unified approach for designing interactive systems for upper limb rehabilitation of stroke survivors has been developed and applied for the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System. RESULTS: The AMRR system provides computational evaluation and multimedia feedback for the upper limb rehabilitation of stroke survivors. A participant's movements are tracked by motion capture technology and evaluated by computational means. The resulting data are used to generate interactive media-based feedback that communicates to the participant detailed, intuitive evaluations of his performance. This article describes how the AMRR system's interactive feedback is designed to address specific movement challenges faced by stroke survivors. Multimedia examples are provided to illustrate each feedback component. Supportive data are provided for three participants of varying impairment levels to demonstrate the system's ability to train both targeted and integrated aspects of movement. CONCLUSIONS: The AMRR system supports training of multiple movement aspects together or in isolation, within adaptable sequences, through cohesive feedback that is based on formalized compositional design principles. From preliminary analysis of the data, we infer that the system's ability to train multiple foci together or in isolation in adaptable sequences, utilizing appropriately designed feedback, can lead to functional improvement. The evaluation and feedback frameworks established within the AMRR system will be applied to the development of a novel home-based system to provide an engaging yet low-cost extension of training for longer periods of time.

A novel adaptive mixed reality system for stroke rehabilitation: principles, proof of concept, and preliminary application in 2 patients.

This article presents the principles of an adaptive mixed reality rehabilitation (AMRR) system, as well as the training process and results from 2 stroke survivors who received AMRR therapy, to illustrate how the system can be used in the clinic. The AMRR system integrates traditional rehabilitation practices with state-of-the-art computational and motion capture technologies to create an engaging environment to train reaching movements. The system provides real-time, intuitive, and integrated audio and visual feedback (based on detailed kinematic data) representative of goal accomplishment, activity performance, and body function during a reaching task. The AMRR system also provides a quantitative kinematic evaluation that measures the deviation of the stroke survivor's movement from an idealized, unimpaired movement. The therapist, using the quantitative measure and knowledge and observations, can adapt the feedback and physical environment of the AMRR system throughout therapy to address each participant's individual impairments and progress. Individualized training plans, kinematic improvements measured over the entire therapy period, and the changes in relevant clinical scales and kinematic movement attributes before and after the month-long therapy are presented for 2 participants. The substantial improvements made by both participants after AMRR therapy demonstrate that this system has the potential to considerably enhance the recovery of stroke survivors with varying impairments for both kinematic improvements and functional ability.

Design of a home-based adaptive mixed reality rehabilitation system for stroke survivors.

This paper presents the design of a home-based adaptive mixed reality system (HAMRR) for upper extremity stroke rehabilitation. The goal of HAMRR is to help restore motor function to chronic stroke survivors by providing an engaging long-term reaching task therapy at home. The system uses an intelligent adaptation scheme to create a continuously challenging and unique multi-year therapy experience. The therapy is overseen by a physical therapist, but day-to-day use of the system can be independently set up and completed by a stroke survivor. The HAMMR system tracks movement of the wrist and torso and provides real-time, post-trial, and post-set feedback to encourage the stroke survivor to self-assess his or her movement and engage in active learning of new movement strategies. The HAMRR system consists of a custom table, chair, and media center, and is designed to easily integrate into any home.

Interactive rehabilitation and dynamical analysis of scalp EEG.

Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).

A computational framework for constructing interactive feedback for assisting motor learning.

New motion capture technologies are allowing detailed, precise and complete monitoring of movement through real-time kinematic analysis. However, a clinically relevant understanding of movement impairment through kinematic analysis requires the development of computational models that integrate clinical expertise in the weighing of the kinematic parameters. The resulting kinematics based measures of movement impairment would further need to be integrated with existing clinical measures of activity disability. This is a challenging process requiring computational solutions that can extract correlations within and between three diverse data sets: human driven assessment of body function, kinematic based assessment of movement impairment and human driven assessment of activity. We propose to identify and characterize different sensorimotor control strategies used by normal individuals and by hemiparetic stroke survivors acquiring a skilled motor task. We will use novel quantitative approaches to further our understanding of how human motor function is coupled to multiple and simultaneous modes of feedback. The experiments rely on a novel interactive tasks environment developed by our team in which subjects are provided with rich auditory and visual feedback of movement variables to drive motor learning. Our proposed research will result in a computational framework for applying virtual information to assist motor learning for complex tasks that require coupling of proprioception, vision audio and haptic cues. We shall use the framework to devise a computational tool to assist with therapy of stroke survivors. This tool will utilize extracted relationships in a pre-clinical setting to generate effective and customized rehabilitation strategies.

A low cost, adaptive mixed reality system for home-based stroke rehabilitation.

This paper presents a novel, low-cost, real-time adaptive multimedia environment for home-based upper extremity rehabilitation of stroke survivors. The primary goal of this system is to provide an interactive tool with which the stroke survivor can sustain gains achieved within the clinical phase of therapy and increase the opportunity for functional recovery. This home-based mediated system has low cost sensing, off the shelf components for the auditory and visual feedback, and remote monitoring capability. The system is designed to continue active learning by reducing dependency on real-time feedback and focusing on summary feedback after a single task and sequences of tasks. To increase system effectiveness through customization, we use data from the training strategy developed by the therapist at the clinic for each stroke survivor to drive automated system adaptation at the home. The adaptation includes changing training focus, selecting proper feedback coupling both in real-time and in summary, and constructing appropriate dialogues with the stroke survivor to promote more efficient use of the system. This system also allows the therapist to review participant's progress and adjust the training strategy weekly.

Mixed reality rehabilitation for stroke survivors promotes generalized motor improvements.

This paper presents results from a clinical study of stroke survivors using an adaptive, mixed-reality rehabilitation (AMRR) system for reach and grasp therapy. The AMRR therapy provides audio and visual feedback on the therapy task, based on detailed motion capture, that places the movement in an abstract, artistic context. This type of environment promotes the generalizability of movement strategies, which is shown through kinematic improvements on an untrained reaching task and higher clinical scale scores, in addition to kinematic improvements in the trained task.

An adaptive mixed reality training system for stroke rehabilitation.

This paper presents a novel mixed reality rehabilitation system used to help improve the reaching movements of people who have hemiparesis from stroke. The system provides real-time, multimodal, customizable, and adaptive feedback generated from the movement patterns of the subject's affected arm and torso during reaching to grasp. The feedback is provided via innovative visual and musical forms that present a stimulating, enriched environment in which to train the subjects and promote multimodal sensory-motor integration. A pilot study was conducted to test the system function, adaptation protocol and its feasibility for stroke rehabilitation. Three chronic stroke survivors underwent training using our system for six 75-min sessions over two weeks. After this relatively short time, all three subjects showed significant improvements in the movement parameters that were targeted during training. Improvements included faster and smoother reaches, increased joint coordination and reduced compensatory use of the torso and shoulder. The system was accepted by the subjects and shows promise as a useful tool for physical and occupational therapists to enhance stroke rehabilitation.

Bioelectronic DNA detection of human papillomaviruses using eSensor: a model system for detection of multiple pathogens.

BACKGROUND: We used human papillomaviruses (HPV) as a model system to evaluate the utility of a nucleic acid, hybridization-based bioelectronic DNA detection platform (eSensor) in identifying multiple pathogens. METHODS: Two chips were spotted with capture probes consisting of DNA oligonucleotide sequences specific for HPV types. Electrically conductive signal probes were synthesized to be complementary to a distinct region of the amplified HPV target DNA. A portion of the HPV L1 region that was amplified by using consensus primers served as target DNA. The amplified target was mixed with a cocktail of signal probes and added to a cartridge containing a DNA chip to allow for hybridization with complementary capture probes. RESULTS: Two bioelectric chips were designed and successfully detected 86% of the HPV types contained in clinical samples. CONCLUSIONS: This model system demonstrates the potential of the eSensor platform for rapid and integrated detection of multiple pathogens.