Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies

Ocular disorders are currently affecting the developed world, causing loss of productivity in adults and children. While the cause of such disorders is not clear, neurological issues are often considered as the biggest possibility. Treatment of strabismus and vergence requires an invasive surgery or clinic-based vision therapy that has been used for decades due to the lack of alternatives such as portable therapeutic tools. Recent advancement in electronic packaging and image processing techniques have opened the possibility for optics-based portable eye tracking approaches, but several technical and safety hurdles limit the implementation of the technology in wearable applications. Here, we introduce a fully wearable, wireless soft electronic system that offers a portable, highly sensitive tracking of eye movements (vergence) via the combination of skin-conformal sensors and a virtual reality system. Advancement of material processing and printing technologies based on aerosol jet printing enables reliable manufacturing of skin-like sensors, while a flexible electronic circuit is prepared by the integration of chip components onto a soft elastomeric membrane. Analytical and computational study of a data classification algorithm provides a highly accurate tool for real-time detection and classification of ocular motions. In vivo demonstration with 14 human subjects captures the potential of the wearable electronics as a portable therapy system, which can be easily synchronized with a virtual reality headset

Validation of Biomarker-Based ABCD Score in Atrial Fibrillation Patients with a Non-Gender CHA2DS2-VASc Score 0-1:A Korean Multi-Center Cohort

PURPOSE: Atrial fibrillation (AF) patients with low to intermediate risk, defined as non-gender CHA(2)DS(2)-VASc score of 0–1, are still at risk of stroke. This study verified the usefulness of ABCD score [age (≥60 years), B-type natriuretic peptide (BNP) or N-terminal pro-BNP (≥300 pg/mL), creatinine clearance (<50 mL/min/1.73 m(2)), and dimension of the left atrium (≥45 mm)] for stroke risk stratification in non-gender CHA(2)DS(2)-VASc score 0–1. MATERIALS AND METHODS: This multi-center cohort study retrospectively analyzed AF patients with non-gender CHA(2)DS(2)-VASc score 0–1. The primary endpoint was the incidence of stroke with or without antithrombotic therapy (ATT). An ABCD score was validated. RESULTS: Overall, 2694 patients [56.3±9.5 years; female, 726 (26.9%)] were followed-up for 4.0±2.8 years. The overall stroke rate was 0.84/100 person-years (P-Y), stratified as follows: 0.46/100 P-Y for an ABCD score of 0; 1.02/100 P-Y for an ABCD score ≥1. The ABCD score was superior to non-gender CHA(2)DS(2)-VASc score in the stroke risk stratification (C-index=0.618, p=0.015; net reclassification improvement=0.576, p=0.040; integrated differential improvement=0.033, p=0.066). ATT was prescribed in 2353 patients (86.5%), and the stroke rate was significantly lower in patients receiving non-vitamin K antagonist oral anticoagulant (NOAC) therapy and an ABCD score ≥1 than in those without ATT (0.44/100 P–Y vs. 1.55/100 P-Y; hazard ratio=0.26, 95% confidence interval 0.11–0.63, p=0.003). CONCLUSION: The biomarker-based ABCD score demonstrated improved stroke risk stratification in AF patients with non-gender CHA(2)DS(2)-VASc score 0–1. Furthermore, NOAC with an ABCD score ≥1 was associated with significantly lower stroke rate in AF patients with non-gender CHA(2)DS(2)-VASc score 0–1

Effect of dentate gyrus disruption on remembering what happened where

Our previous studies using Bax knockout (Bax-KO) mice, in which newly generated granule cells continue to accumulate, disrupting neural circuitry specifically in the dentate gyrus (DG), suggest the involvement of the DG in binding the internally-generated spatial map with sensory information on external landmarks (spatial map object association) in forming a distinct spatial context for each environment. In order to test whether the DG is also involved in binding the internal spatial map with sensory information on external events (spatial map-event association), we tested the behavior of Bax-KO mice in a delayed-non-match-to-place task. Performance of Bax-KO mice was indistinguishable from that of wild-type mice as long as there was no interruption during the delay period (tested up to 5 min), suggesting that on-line maintenance of working memory is intact in Bax-KO mice. However, Bax-KO mice showed profound performance deficits when they were removed from the maze during the delay period (interruption condition) with a sufficiently long (65 s) delay, suggesting that episodic memory was impaired in Bax-KO mice. Together with previous findings, these results suggest the role of the DG in binding spatial information derived from dead reckoning and nonspatial information, such as external objects and events, in the process of encoding episodic memory1111Nsciescopu

Polarized and Stage-Dependent Distribution of Immunoreactivity for Novel PDZ-Binding Protein Preso1 in Adult Neurogenic Regions

BackgroundAdult neural stem cells have the potential for self-renewal and differentiation into multiple cell lineages via symmetric or asymmetric cell division. Preso1 is a recently identified protein involved in the formation of dendritic spines and the promotion of axonal growth in developing neurons. Preso1 can also bind to cell polarity proteins, suggesting a potential role for Preso1 in asymmetric cell division.MethodsTo investigate the distribution of Preso1, we performed immunohistochemistry with adult mouse brain slice. Also, polarized distribution of Preso1 was assessed by immunocytochemistry in cultured neural stem cells.ResultsImmunoreactivity for Preso1 (Preso1-IR) was strong in the rostral migratory stream and subventricular zone, where proliferating transit-amplifying cells and neuroblasts are prevalent. In cultured neural stem cells, Preso1-IR was unequally distributed in the cell cytosol. We also observed the distribution of Preso1 in the subgranular zone of the hippocampal dentate gyrus, another neurogenic region in the adult brain. Interestingly, Preso1-IR was transiently observed in the nuclei of doublecortin-expressing neuroblasts immediately after asymmetric cell division.ConclusionOur study demonstrated that Preso1 is asymmetrically distributed in the cytosol and nuclei of neural stem/progenitor cells in the adult brain, and may play a significant role in cell differentiation via association with cell polarity machinery

Functional Test Scales for Evaluating Cell-Based Therapies in Animal Models of Spinal Cord Injury

Recently, spinal cord researchers have focused on multifaceted approaches for the treatment of spinal cord injury (SCI). However, as there is no cure for the deficits produced by SCI, various therapeutic strategies have been examined using animal models. Due to the lack of standardized functional assessment tools for use in such models, it is important to choose a suitable animal model and precise behavioral test when evaluating the efficacy of potential SCI treatments. In the present review, we discuss recent evidence regarding functional recovery in various animal models of SCI, summarize the representative models currently used, evaluate recent cell-based therapeutic approaches, and aim to identify the most precise and appropriate scales for functional assessment in such research

Non-contact long-range magnetic stimulation of mechanosensitive ion channels in freely moving animals

Among physical stimulation modalities, magnetism has clear advantages, such as deep penetration and untethered interventions in biological subjects. However, some of the working principles and effectiveness of existing magnetic neurostimulation approaches have been challenged, leaving questions to be answered. Here we introduce m-Torquer, a magnetic toolkit that mimics magnetoreception in nature. It comprises a nanoscale magnetic torque actuator and a circular magnet array, which deliver piconewton-scale forces to cells over a working range of similar to 70 cm. With m-Torquer, stimulation of neurons expressing bona fide mechanosensitive ion channel Piezo1 enables consistent and reproducible neuromodulation in freely moving mice. With its long working distance and cellular targeting capability, m-Torquer provides versatility in its use, which can range from single cells to in vivo systems, with the potential application in large animals such as primates.11Nsciescopu

CNN-Based Multimodal Human Recognition in Surveillance Environments

In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research being performed on indoor human recognition. Previous research on indoor recognition has mainly focused on face recognition because the camera is usually closer to a person in an indoor environment than an outdoor environment. However, due to the nature of indoor surveillance cameras, which are installed near the ceiling and capture images from above in a downward direction, people do not look directly at the cameras in most cases. Thus, it is often difficult to capture front face images, and when this is the case, facial recognition accuracy is greatly reduced. To overcome this problem, we can consider using the face and body for human recognition. However, when images are captured by indoor cameras rather than outdoor cameras, in many cases only part of the target body is included in the camera viewing angle and only part of the body is captured, which reduces the accuracy of human recognition. To address all of these problems, this paper proposes a multimodal human recognition method that uses both the face and body and is based on a deep convolutional neural network (CNN). Specifically, to solve the problem of not capturing part of the body, the results of recognizing the face and body through separate CNNs of VGG Face-16 and ResNet-50 are combined based on the score-level fusion by Weighted Sum rule to improve recognition performance. The results of experiments conducted using the custom-made Dongguk face and body database (DFB-DB1) and the open ChokePoint database demonstrate that the method proposed in this study achieves high recognition accuracy (the equal error rates of 1.52% and 0.58%, respectively) in comparison to face or body single modality-based recognition and other methods used in previous studies

Age Estimation Robust to Optical and Motion Blurring by Deep Residual CNN

Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL) and craniofacial longitudinal morphological face database (MORPH) databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network

Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods