Conspectus The enhanced catalytic activity of Pd–Au catalysts originates from ensemble effects related to the local composition of Pd and Au. The study of Pd–Au planar model catalysts in an ultrahigh ...vacuum (UHV) environment allows the observation of molecular level catalytic reactions between the Pd–Au surface and target molecules. Recently, there has been progress in understanding the behavior of simple molecules (H2, O2, CO, etc.) employing UHV surface science techniques, the results of which can be applied not only to heterogeneous catalysis but also to electro- and photochemical catalysis. Employing UHV methods in the investigation of Pd–Au model catalysts has shown that single Pd atoms can dissociatively adsorb H2 molecules. The recombinative desorption temperature of H2 varies with Pd ensemble size, which allows the use of H2 as a probe molecule for quantifying surface composition. In particular, H2 desorption from Pd–Au interface sites (or small Pd ensembles) is observed from 150–300 K, which is between the H2 desorption temperature from pure Au (∼110 K) and Pd (∼350 K) surfaces. When the Pd ensembles are large enough to form Pd(111)-like islands, H2 desorption occurs from 300–400 K, as with pure Pd surfaces. The different H2 desorption behavior, which depends on Pd ensemble size, has also been applied to the analysis of dehydrogenation mechanisms for potential liquid storage mediums for H2, namely formic acid and ethanol. In both cases, the Pd–Au interface is the main reaction site for generating H2 from formic acid and ethanol with less overall decomposition of the two molecules (compared to pure Pd). The chemistry behind O2 activation has also been informed through the control of Pd ensembles on a gold model catalyst for acetaldehyde and ethanol oxidation reactions under UHV conditions. O2 molecules molecularly adsorbed on continuous Pd clusters can be dissociated into O adatoms above 180 K. This O2 activation process is improved by coadsorbed H2O molecules. It is also possible to directly (through a precursor mechanism) introduce O adatoms on the Pd–Au surface by exposure to O2 at 300 K. The quantity of dissociatively adsorbed O adatoms is proportional to the Pd coverage. However, the O adatoms are more reactive on a less Pd covered surface, especially at the Pd–Au interface sites, which can initiate CO oxidation at temperatures as low as 140 K. Acetaldehyde molecules can be selectively oxidized to acetic acid on the Pd–Au surface with O adatoms, in which the selectivity toward acetic acid originates from preventing the decarboxylation of acetate species. Moreover, the O adatoms on the Pd–Au surface accelerate ethanol dehydrogenation, which causes the increase in acetaldehyde production. Hydrogen is continuously abstracted from the formed acetaldehyde and remaining ethanol molecules, and they ultimately combine as ethyl acetate on the Pd–Au surface. Using Pd–Au model catalysts under UHV conditions allows the discovery of molecular level mechanistic details regarding the catalytic behavior of H and O adatoms with other molecules. We also expect that these findings will be applicable regarding other chemistry on Pd–Au catalysts.
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We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by ...breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.
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Low-intensity, low-frequency ultrasound (LILFU) is the next-generation, non-invasive brain stimulation technology for treating various neurological and psychiatric disorders. However, the underlying ...cellular and molecular mechanism of LILFU-induced neuromodulation has remained unknown. Here, we report that LILFU-induced neuromodulation is initiated by opening of TRPA1 channels in astrocytes. The Ca2+ entry through TRPA1 causes a release of gliotransmitters including glutamate through Best1 channels in astrocytes. The released glutamate activates NMDA receptors in neighboring neurons to elicit action potential firing. Our results reveal an unprecedented mechanism of LILFU-induced neuromodulation, involving TRPA1 as a unique sensor for LILFU and glutamate-releasing Best1 as a mediator of glia-neuron interaction. These discoveries should prove to be useful for optimization of human brain stimulation and ultrasonogenetic manipulations of TRPA1.
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•Ultrasound-induced neuromodulation is initiated by opening of TRPA1 in astrocytes•The Ca2+ entry through TRPA1 causes a release of glutamate through Best1 channels•The released glutamate activates NMDA receptors in neighboring neurons
Oh et al. show that TRPA1 is the molecular sensor and transducer for low-intensity, low-frequency ultrasound (LILFU). With TRPA1’s unique co-localization and cooperation with the glutamate-releasing Ca2+-activated Best1 at the microdomains of astrocytes, LILFU is capable of eliciting neuromodulation as a consequence of neuronal NMDAR activation.
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Afferent signals recorded from the dorsal root ganglion can be used to extract sensory information to provide feedback signals in a functional electrical stimulation (FES) system. The goal of this ...study was to propose an efficient feature projection method for detecting sensory events from multiunit activity-based feature vectors of tactile afferent activity. Tactile afferent signals were recorded from the L4 dorsal root ganglion using a multichannel microelectrode for three types of sensory events generated by mechanical stimulation on the rat hind paw. The multiunit spikes (MUSs) were extracted as multiunit activity-based feature vectors and projected using a linear feature projection method which consisted of projection pursuit and negentropy maximization (PP/NEM). Finally, a multilayer perceptron classifier was used to detect sensory events. The proposed method showed a detection accuracy superior to those of other linear and nonlinear feature projection methods and all processes were completed within real-time constraints. Results suggest that the proposed method could be useful to detect sensory events in real time. We have demonstrated the methodology for an efficient feature projection method to detect real-time sensory events from the multiunit activity of dorsal root ganglion recordings. The proposed method could be applied to provide real-time sensory feedback signals in closed-loop FES systems.
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The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in ...studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.
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Heart rate variability (HRV) is closely related to changes in the autonomic nervous system (ANS) associated with stress and pain. In this study, we investigated whether HRV could be used to assess ...cancer pain in mice with peritoneal metastases. At 12 days after cancer induction, positive indicators of pain such as physiological characteristics, appearance, posture, and activity were observed, and time- and frequency-domain HRV parameters such as mean R-R interval, square root of the mean squared differences of successive R-R intervals, and percentage of successive R-R interval differences greater than 5 ms, low frequency (LF), high frequency (HF), and ratio of LF and HF power, were found to be significantly decreased. These parameters returned to normal after analgesic administration. Our results indicate that overall ANS activity was decreased by cancer pain and that HRV could be a useful tool for assessing pain.
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Since the emergence of Virtual Reality technology, it has been adopted in the field of neurology. While Virtual Reality has contributed to various rehabilitation approaches, its potential advantages, ...especially in diagnosis, have not yet been fully utilized. Moreover, new tides of the Metaverse are approaching rapidly, which will again boost public and research interest and the importance of immersive Virtual Reality technology. Nevertheless, accessibility to such technology for people with neurological disorders has been critically underexplored. Through this perspective paper, we will briefly look over the current state of the technology in neurological studies and then propose future research directions, which hopefully facilitate beneficial Virtual Reality studies on a wider range of topics in neurology.
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As a new class of magnetic materials, metal-organic framework (MOF) has received a significant attention due to their functionality and porosity that can provide diverse magnetic phenomena by ...utilizing host-guest chemistry. For Fe-MOF-74, we here find using density functional calculations that the O2 and C2H4 adsorptions result in the ferromagnetic (FM) and antiferromagnetic (AFM) orderings along the 1D chain of an hexagonal MOF framework, respectively, while their adsorption energies, pi-complexation, and geometrical changes are all similar upon binding. We reveal that this different magnetism behavior is attributed to the different electronic effects, where the adsorbed O2 greatly withdraws a minor spin electron from the Fe centers. The latter significant back donation opens a new channel for superexchange interactions that can enhance the FM coupling between Fe centers, where the strength of calculated intrachain FM coupling constrant (Jin) in O2 adsorbed Fe-MOF-74 is more than 10 times enhanced compared to bare Fe-MOF-74. This prediction suggests a possibility for the conceptual usage of Fe-MOF-74 as a gas sensor based on its magnetic changes caused by the adsorbed gases. Furthermore, the suggested mechanism might be used to control the magnetic properties of MOFs using the guest molecules, although concrete strategies to enhance such magnetic interactions to be used in practical applications would require further significant investigation.
Model induction is one of the most popular methods to extract information to better understand AI’s decisions by estimating the contribution of input features for a class of interest. However, we ...found a potential issue: most model induction methods, especially those that compute class activation maps, rely on arbitrary thresholding to mute some of their computed attribution scores, which can cause the severe quality degradation of model induction. Therefore, we propose a new threshold fine-tuning (TFT) procedure to enhance the quality of input attribution based on model induction. Our TFT replaces arbitrary thresholding with an iterative procedure to find the optimal cut-off threshold value of input attribution scores using a new quality metric. Furthermore, to remove the burden of computing optimal threshold values on a per-input basis, we suggest an activation fine-tuning (AFT) framework using a tuner network attached to the original convolutional neural network (CNN), retraining the tuner-attached network with auxiliary data produced by TFT. The purpose of the tuner network is to make the activations of the original CNN less noisy and thus better suited for computing input attribution scores based on class activation maps from the activations. In our experiments, we show that the per-input optimal thresholding of attribution scores using TFT can significantly improve the quality of input attribution, and CNNs fine-tuned with our AFT can be used to produce improved input attribution matching the quality of TFT-tuned input attribution without requiring costly per-input threshold optimization.
The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying ...locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.
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