Stretchable Thin-Film Transistors Based on Wrinkled Graphene and MoS2 Heterostructures.

Two-dimensional (2D) materials are outstanding candidates for stretchable electronics, but a significant challenge is their heterogeneous integration into stretchable geometries on soft substrates. Here, we demonstrate a strategy for stretchable thin film transistors (2D S-TFT) based on wrinkled heterostructures on elastomer substrates where 2D materials formed the gate, source, drain, and channel and characterized them with Raman spectroscopy and transport measurements. The 2D S-TFTs had initial mobility of 4.9 ± 0.7 cm2/(V s). The wrinkling reduced the strain transferred into the 2D materials by a factor of 50, allowing a substrate stretch of up to 23% that could be cycled thousands of times without electrical degradation. The stretch did not alter the mobility but did lead to strain-induced threshold voltage shifts by ΔVT = -1.9 V. These 2D S-TFTs form the foundation for stretchable integrated circuits and enable investigations of the impact of heterogeneous strain on electron transport.

Promoted Overall Water Splitting Catalytic Activity and Durability of Ni3Fe Alloy by Designing N-Doped Carbon Encapsulation.

Combining an electrochemically stable material onto the surface of a catalyst can improve the durability of a transition metal catalyst, and enable the catalyst to operate stably at high current density. Herein, the contribution of the N-doped carbon shell (NCS) to the electrochemical properties is evaluated by comparing the characteristics of the Ni3Fe@NCS catalyst with the N-doped carbon shell, and the Ni3Fe catalyst. The synthesized Ni3Fe@NCS catalyst has a distinct overpotential difference from the Ni3Fe catalyst (ηOER = 468.8 mV, ηHER = 462.2 mV) at (200 and -200) mA cm-2 in 1 m KOH. In stability test at (10 and -10) mA cm-2, the Ni3Fe@NCS catalyst showed a stability of (95.47 and 99.6)%, while the Ni3Fe catalyst showed a stability of (72.4 and 95.9)%, respectively. In addition, the in situ X-ray Absorption Near Edge Spectroscopy (XANES) results show that redox reaction appeared in the Ni3Fe catalyst by applying voltages of (1.7 and -0.48) V. The decomposition of nickel and iron due to the redox reaction is detected as a high ppm concentration in the Ni3Fe catalyst through Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) analysis. This work presents the strategy and design of a next-generation electrochemical catalyst to improve the electrocatalytic properties and stability.

Use of functional magnetic resonance imaging to identify cortical loci for lower limb movements and their efficacy for individuals after stroke.

BACKGROUND: Identification of cortical loci for lower limb movements for stroke rehabilitation is crucial for better rehabilitation outcomes via noninvasive brain stimulation by targeting the fine-grained cortical loci of the movements. However, identification of the cortical loci for lower limb movements using functional MRI (fMRI) is challenging due to head motion and difficulty in isolating different types of movement. Therefore, we developed a custom-made MR-compatible footplate and leg cushion to identify the cortical loci for lower limb movements and conducted multivariate analysis on the fMRI data. We evaluated the validity of the identified loci using both fMRI and behavioral data, obtained from healthy participants as well as individuals after stroke. METHODS: We recruited 33 healthy participants who performed four different lower limb movements (ankle dorsiflexion, ankle rotation, knee extension, and toe flexion) using our custom-built equipment while fMRI data were acquired. A subgroup of these participants (Dataset 1; n = 21) was used to identify the cortical loci associated with each lower limb movement in the paracentral lobule (PCL) using multivoxel pattern analysis and representational similarity analysis. The identified cortical loci were then evaluated using the remaining healthy participants (Dataset 2; n = 11), for whom the laterality index (LI) was calculated for each lower limb movement using the cortical loci identified for the left and right lower limbs. In addition, we acquired a dataset from 15 individuals with chronic stroke for regression analysis using the LI and the Fugl-Meyer Assessment (FMA) scale. RESULTS: The cortical loci associated with the lower limb movements were hierarchically organized in the medial wall of the PCL following the cortical homunculus. The LI was clearer using the identified cortical loci than using the PCL. The healthy participants (mean ± standard deviation: 0.12 ± 0.30; range: - 0.63 to 0.91) exhibited a higher contralateral LI than the individuals after stroke (0.07 ± 0.47; - 0.83 to 0.97). The corresponding LI scores for individuals after stroke showed a significant positive correlation with the FMA scale for paretic side movement in ankle dorsiflexion (R2 = 0.33, p = 0.025) and toe flexion (R2 = 0.37, p = 0.016). CONCLUSIONS: The cortical loci associated with lower limb movements in the PCL identified in healthy participants were validated using independent groups of healthy participants and individuals after stroke. Our findings suggest that these cortical loci may be beneficial for the neurorehabilitation of lower limb movement in individuals after stroke, such as in developing effective rehabilitation interventions guided by the LI scores obtained for neuronal activations calculated from the identified cortical loci across the paretic and non-paretic sides of the brain.

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

A preliminary study about the potential risks of the UV-weathered microplastic: The proteome-level changes in the brain in response to polystyrene derived weathered microplastics.

The growing use of plastic materials has resulted in a constant increase in the risk associated with microplastics (MPs). Ultra-violet (UV) light and wind break down modify MPs in the environment into smaller particles known as weathered MPs (WMPs) and these processes increase the risk of MP toxicity. The neurotoxicity of weathered polystyrene-MPs remains unclear. Therefore, it is important to understand the risks posed by WMPs. We evaluated the chemical changes of WMPs generated under laboratory-synchronized environmentally mimetic conditions and compared them with virgin MPs (VMPs). We found that WMP had a rough surface, slight yellow color, reduced molecular weight, and structural alteration compared with those of VMP. Next, 2 µg of ∼100 µm in size of WMP and VMP were orally administered once a day for one week to C57BL/6 male mice. Proteomic analysis revealed that the WMP group had significantly increased activation of immune and neurodegeneration-related pathways compared with that of the VMP group. Consistently, in in vitro experiments, the human brain-derived microglial cell line (HMC-3) also exhibited a more severe inflammatory response to WMP than to VMP. These results show that WMP is a more profound inflammatory factor than VMP. In summary, our findings demonstrate the toxicity of WMPs and provide theoretical insights into their potential risks to biological systems and even humans in the ecosystem.

Cation-disordered rocksalt-type high-entropy cathodes for Li-ion batteries.

High-entropy (HE) ceramics, by analogy with HE metallic alloys, are an emerging class of solid solutions composed of a large number of species. These materials offer the benefit of large compositional flexibility and can be used in a wide variety of applications, including thermoelectrics, catalysts, superionic conductors and battery electrodes. We show here that the HE concept can lead to very substantial improvements in performance in battery cathodes. Among lithium-ion cathodes, cation-disordered rocksalt (DRX)-type materials are an ideal platform within which to design HE materials because of their demonstrated chemical flexibility. By comparing a group of DRX cathodes containing two, four or six transition metal (TM) species, we show that short-range order systematically decreases, whereas energy density and rate capability systematically increase, as more TM cation species are mixed together, despite the total metal content remaining fixed. A DRX cathode with six TM species achieves 307 mAh g-1 (955 Wh kg-1) at a low rate (20 mA g-1), and retains more than 170 mAh g-1 when cycling at a high rate of 2,000 mA g-1. To facilitate further design in this HE DRX space, we also present a compatibility analysis of 23 different TM ions, and successfully synthesize a phase-pure HE DRX compound containing 12 TM species as a proof of concept.

Role of Interfacial Interactions in the Graphene-Directed Assembly of Monolayer Conjugated Polymers.

Development of graphene-organic hybrid electronics is one of the most promising directions for next-generation electronic materials. However, it remains challenging to understand the graphene-organic semiconductor interactions right at the interface, which is key to designing hybrid electronics. Herein, we study the influence of graphene on the multiscale morphology of solution-processed monolayers of conjugated polymers (PII-2T, DPP-BTz, DPP2T-TT, and DPP-T-TMS). The strong interaction between graphene and PII-2T was manifested in the high fiber density and high film coverage of monolayer films deposited on graphene compared to plasma SiO2 substrates. The monolayer films on graphene also exhibited a higher relative degree of crystallinity and dichroic ratio or polymer alignment, i.e., higher degree of order. Raman spectroscopy revealed the increased backbone planarity of the conjugated polymers upon deposition on graphene as well as the existence of electronic interaction across the interface. This speculation was further substantiated by the results of photoelectron spectroscopy (XPS and UPS) of PII-2T, which showed a decrease in binding energy of several atomic energy levels, movement of the Fermi level toward HOMO, and an increase in work function, all of which indicate p-doping of the polymer. Our results provide a new level of understanding on graphene-polymer interactions at nanoscopic interfaces and the consequent impact on multiscale morphology, which will aid in the design of efficient graphene-organic hybrid electronics.

Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN.

Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments.

Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns.

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter.

Toxicity impact of hydrogen peroxide on the fate of zebrafish and antibiotic resistant bacteria.

Hydrogen peroxide (H2O2) is applied in various environments. It could be present at concentrations ranging from nanomolar to micromolar in a water system. It is produced through pollutants and natural activities. Since few studies have been conducted about the impact of naturally produced H2O2 on aquatic organisms, the objective of the present study was to monitor changes in responses of aquatic model organisms such as zebrafish and antibiotic-resistant bacteria to different exogenous H2O2 exposure. Increases in exposure concentration and time induced decreases in the perception of zebrafish larvae (up to 69%) and movement of adult zebrafish (average speed, average acceleration, movement distance, and activity time) compared to the control (non-exposed group). In addition, as a function of H2O2 exposure concentration (0-100,000 nM) and time, up to 20-fold increase (p = 5.00*10-6) of lipid peroxidation compared to control was observed. For microorganisms, biofilm, an indirect indicator of resistance to external stressors, was increased up to 68% and gene transfer was increased (p = 2.00*10-6) by more than 30% after H2O2 exposure. These results imply that naturally generated H2O2 could adversely affect aquatic environment organisms and public health. Thus, more careful attention is needed for H2O2 production in an aquatic system.

Mixed-effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies.

We report that regions-of-interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed-effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real-world dataset for the neuronal response to nicotine use was acquired using a custom-made MRI-compatible apparatus for the smoking of electronic cigarettes (e-cigarettes). Nineteen participants smoked e-cigarettes in an MRI scanner using the apparatus with two experimental conditions: e-cigarettes with nicotine (ECIG) and sham e-cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2 -test of the MEMA but not from the t-test, and the corresponding activations were significantly associated with the similarity scores (r = -.52, p = .041, confidence interval [CI] = [-0.78, -0.17]) and the urge-to-smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2 -tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = -.58, p = .01, CI = [-0.84, -0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.

Potential-Dependent Passivation of Zinc Metal in a Sulfate-Based Aqueous Electrolyte.

Owing to its abundance, high theoretical capacity, and low electrode potential, zinc is one of the most important metallic anodes for primary and secondary batteries such as alkaline and zinc-air batteries. In the operation of zinc-based batteries, passivation of the anode surface plays an essential role because the electrode potential of zinc is slightly below that of the hydrogen evolution reaction. Therefore, it is important to scrutinize the nature of the passivation film to achieve anticorrosion inside batteries. Herein, the potential-dependent formation and removal of the passivation film during the deposition and dissolution of zinc metal in aqueous electrolytes are detected via electrochemical quartz crystal microbalance analysis. Film formation was not noticeable in hydroxide-based electrolytes; however, sulfate-based electrolytes induced potential-dependent formation and removal of the passivation film, enabling a superior coulombic efficiency of 99.37% and significantly reducing the rate of corrosion of the zinc-metal anodes. These observations provide insights into the development of advanced electrolytes for safe and stable energy-storage devices based on zinc-metal anodes.

Focused ultrasound enhances the anesthetic effects of topical lidocaine in rats.

BACKGROUND: High-intensity ultrasound has been used to induce acoustic cavitation in the skin and subsequently enhances skin permeability to deliver hydrophobic topical medications including lidocaine. In contrast, instead of changing skin permeability, pulsed application of low-intensity focused ultrasound (FUS) has shown to non-invasively and temporarily disrupt drug-plasma protein binding, thus has potential to enhance the anesthetic effects of hydrophilic lidocaine hydrochloride through unbinding it from serum/interstitial α1-acid glycoprotein (AAG). METHODS: FUS, operating at fundamental frequency of 500 kHz, was applied pulse-mode (55-ms pulse duration, 4-Hz pulse repetition frequency) at a spatial-peak pulse-average intensity of 5 W/cm2. In vitro equilibrium dialysis was performed to measure the unbound concentration of lidocaine (lidocaine hydrochloride) from dialysis cassettes, one located at the sonication focus and the other outside the sonication path, all immersed in phosphate-buffered saline solution containing both lidocaine (10 µg/mL) and human AAG (5 mg/mL). In subsequent animal experiments (Sprague-Dawley rats, n = 10), somatosensory evoked potential (SSEP), elicited by electrical stimulations to the unilateral hind leg, was measured under three experimental conditions-applications of FUS to the unilateral thigh area at the site of administered topical lidocaine, FUS only, and lidocaine only. Skin temperature was measured before and after sonication. Passive cavitation detection was also performed during sonication to evaluate the presence of FUS-induced cavitation. RESULTS: Sonication increased the unbound lidocaine concentration (8.7 ± 3.3 %) from the dialysis cassette, compared to that measured outside the sonication path (P < 0.001). Application of FUS alone did not alter the SSEP while administration of lidocaine reduced its P23 component (i.e., a positive peak at 23 ms latency). The FUS combined with lidocaine resulted in a further reduction of the P23 component (in a range of 21.8 - 23.4 ms after the electrical stimulations; F(2,27) = 3.2 - 4.0, P < 0.05), indicative of the enhanced anesthetic effect of the lidocaine. Administration of FUS neither induced cavitation nor altered skin conductance or temperature, suggesting that skin permeability was unaffected. CONCLUSIONS: Unbinding lidocaine from the plasma proteins by exposure to non-thermal low-intensity ultrasound is attributed as the main mechanism behind the observation.

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder.

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images.

A Systematic Review of Location Aware Schemes in the Internet of Things.

The rapid development in wireless technologies is positioning the Internet of Things (IoT) as an essential part of our daily lives. Localization is one of the most attractive applications related to IoT. In the past few years, localization has been gaining attention because of its applicability in safety, health monitoring, environment monitoring, and security. As a result, various localization-based wireless frameworks are being presented to improve such applications' performances based on specific key performance indicators (KPIs). Therefore, this paper explores the recently proposed localization schemes in IoT. Initially, this paper explains the major KPIs of localization. After that, a thorough comparison of recently proposed localization schemes based on the KPIs is presented. The comparison includes an overview, architecture, network structure, performance parameters, and target KPIs. At the end, possible future directions are presented for the researchers working in this domain.

Long-term seasonal and temporal changes of hydrogen peroxide from cyanobacterial blooms in fresh waters.

In water, hydrogen peroxide (H2O2) is produced through abiotic and biotic reactions with organic matter, including algal cells. The production of H2O2 is influenced by harmful algal cell communities and toxicity. However, only a few studies have been conducted on H2O2 concentrations in natural water. Particularly, the seasonal and temporal patterns of H2O2 concentration suggest that H2O2 generation from aquatic microorganisms could be identified to compare of photochemical production from dissolved organic matter. Study area is a source of raw water and is a large artificial lake located near a metropolitan city. Due to various environmental conditions, harmful algal blooms frequently occur in summer. The purpose of this study was to trace the H2O2 concentration and water quality parameters of study area where algal bloom occurs and what factors directly affect the H2O2 concentration. Experiments were performed on the influencing factors via water samples from study area and lab-scale culture tank. The lake produces an average of 553 nM H2O2, which increases by more than three times (1460 nM) in summer compared the winter. The lake (18.6-23.8 nMh-1) produced more H2O2 than streams (7.4-9.0 nMh-1) during daylight hours. All water sites presented the lowest production rates in dark conditions (1.1-1.5 nMh-1). Daytime environment increased the generation rate more than the nighttime. The trend of H2O2 produced by algal cells was similar to that of the growth of algal cells. The exposure to external substances (heavy metals and antibiotics) increased the incidence by approximately five times; antibiotics were more influential than heavy metals.

Anti-colitic effects of the modified Bojanggunbi-tang on dextran sulfate sodium-induced mouse colitis model.

Bojanggunbi-tang (BGT) is a well-known and widely used herbal prescription in Korea for colon diseases, with well-documented pharmacological effects on the digestive system. The current study aimed to develop a new simple and effective prescription using the original prescription. mBGT, a modified BGT, was developed by mixing the extracts of Lonicera japonica Thunb., Alisma orientalis and Atractylodes macrocephala based on a literature review and screening of 16 kinds of component herbs of BGT. A colitis mouse (Male, BALB/c) model was induced using dextran sulfate sodium (5%). The effects of BGT and mBGT on body weight, histological damage, clinical score, macroscopic score and colon length were compared. The mechanisms of action were analyzed based on cytokine production in colon tissue. mBGT at 300mg/kg showed similar effectiveness to that of BGT on colon shortening (P<0.01), clinical score (P<0.05), macroscopic score (P<0.01) and histological damage (P<0.01). In addition, mBGT decreased cytokines, including Interleukin 1 beta, tumor necrosis factor alpha and Interleukin 17, in a dose-dependent manner. In conclusion, mBGT could be a substitute prescription for BGT in clinics and a candidate for the development of a new BGT-based therapeutic agent against colitis.

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.

A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking.

The naturalistic viewing of a video clip enables participants to obtain more information from the clip compared to conventional viewing of a static image. Because changing the field-of-view (FoV) allows new visual information to be obtained, we were motivated to investigate whether naturalistic viewing with varying FoV based on active eye movement can enhance the viewing experience of natural stimuli, such as those found in a video clip with a 360° FoV in an MRI scanner. To this end, we developed a novel naturalistic viewing paradigm based on real-time eye-gaze tracking while participants were watching a 360° panoramic video during fMRI acquisition. The gaze position of the participants was recorded using an eye-tracking computer and then transmitted to a stimulus presentation computer via a TCP/IP connection. The identified gaze position was then used to alter the participants' FoV of the video clip in real-time, so the participants could change their FoV to fully explore the 360° video clip (referred to in this paper as active viewing). The gaze position of one participant while watching a video was used to change the FoV of the same video clip for a paired participant (referred to as yoked or passive viewing). Four 360° panoramic videos were used as stimuli, divided into categories based on the brightness level (i.e., bright vs. dark) and location (i.e., nature vs. city). Each of the subjects participated in the active viewing of one of the two nature videos and one of the two city videos and then engaged in the passive viewing of the other video in each category, followed by conventional viewing with a fixed FoV (referred to as fixed viewing) after each of the active or passive viewings. Forty-eight healthy volunteers participated in the study, and data from 42 of these participants were used in the analysis. Representational similarity analysis (RSA) was conducted in a multiple regression framework using representational dissimilarity matrix (RDM) codes to accommodate all of the information regarding neuronal activations from fMRI analysis and the participants' subjective ratings of their viewing experience with the four video clips and with the two contrasting viewing conditions (i.e., "active-fixed" and "passive-fixed"). It was found that the participants' naturalistic viewing experience of the video clips was substantially more immersive with active viewing than with passive and fixed viewing. The RSA using the RDM codes revealed the brain regions associated with the viewing experience, including eye movement and spatial navigation in the superior frontal area (of Brodmann's area 6) and the inferior/superior parietal areas, respectively. Brain regions potentially associated with cognitive and affective processing during the viewing of the video, such as the default-mode networks and insular/Rolandic operculum areas, were also identified. To the best of our knowledge, this is the first study that has used the participants' eye movements to interactively change their FoV for 360° panoramic video clips in real-time. Our method of utilizing the MRI environment can be further extended to other environments such as electroencephalography and behavioral research. It would also be feasible to apply our method to virtual reality and/or augmented reality systems to maximize user experience based on their eye movement.

Artificial Intelligence in Health Care: Current Applications and Issues.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.