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The newly identified pathogenic human coronavirus, SARS-CoV-2, led to an atypical pneumonia-like severe acute respiratory syndrome (SARS) outbreak called coronavirus disease 2019 (abbreviated as COVID-19). Currently, nearly 77 million cases have been confirmed worldwide with the highest numbers of COVID-19 cases in the United States. Individuals are getting vaccinated with recently approved vaccines, which are highly protective in suppressing COVID-19 symptoms but there will be a long way before the majority of individuals get vaccinated. In the meantime, safety precautions and effective disease control strategies appear to be vital for preventing the virus spread in public places. Due to the longevity of the virus on smooth surfaces, photocatalytic properties of \"self-disinfecting/cleaning\" surfaces appear to be a promising tool to help guide disinfection policies for controlling SARS-CoV-2 spread in high-traffic areas such as hospitals, grocery stores, airports, schools, and stadiums. Here, we explored the photocatalytic properties of nanosized TiO (TNPs) as induced by the UV radiation, towards virus deactivation. Our preliminary results using a close genetic relative of SAR-CoV-2, HCoV-NL63, showed the virucidal efficacy of photoactive TNPs deposited on glass coverslips, as examined by quantitative RT-qPCR and virus infectivity assays. Efforts to extrapolate the underlying concepts described in this study to SARS-CoV-2 are currently underway.

Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients. Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.

Electromyography (EMG) is the standard technology for monitoring muscle activity in laboratory environments, either using surface electrodes or fine wire electrodes inserted into the muscle. Due to limitations such as cost, complexity, and technical factors, including skin impedance with surface EMG and the invasive nature of fine wire electrodes, EMG is impractical for use outside of a laboratory environment. Mechanomyography (MMG) is an alternative to EMG, which shows promise in pervasive applications. The present study used an exerting squat-based task to induce muscle fatigue. MMG and EMG amplitude and frequency were compared before, during, and after the squatting task. Combining MMG with inertial measurement unit (IMU) data enabled segmentation of muscle activity at specific points: entering, holding, and exiting the squat. Results show MMG measures of muscle activity were similar to EMG in timing, duration, and magnitude during the fatigue task. The size, cost, unobtrusive nature, and usability of the MMG/IMU technology used, paired with the similar results compared to EMG, suggest that such a system could be suitable in uncontrolled natural environments such as within the home.

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, 'Corner', has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.

The cardiac electrical impulse depends on an orchestrated interplay of transmembrane ionic currents in myocardial cells. Two critical ionic current mechanisms are the inwardly rectifying potassium current (I K1 ), which is important for maintenance of the cell resting membrane potential, and the sodium current (I Na ), which provides a rapid depolarizing current during the upstroke of the action potential. By controlling the resting membrane potential, I K1 modifies sodium channel availability and therefore, cell excitability, action potential duration, and velocity of impulse propagation. Additionally, I K1 –I Na interactions are key determinants of electrical rotor frequency responsible for abnormal, often lethal, cardiac reentrant activity. Here, we have used a multidisciplinary approach based on molecular and biochemical techniques, acute gene transfer or silencing, and electrophysiology to show that I K1 –I Na interactions involve a reciprocal modulation of expression of their respective channel proteins (Kir2.1 and Na V 1.5) within a macromolecular complex. Thus, an increase in functional expression of one channel reciprocally modulates the other to enhance cardiac excitability. The modulation is model-independent; it is demonstrable in myocytes isolated from mouse and rat hearts and with transgenic and adenoviral-mediated overexpression/silencing. We also show that the p ost synaptic density, d iscs large, and z onula occludens-1 (PDZ) domain protein SAP97 is a component of this macromolecular complex. We show that the interplay between Na v 1.5 and Kir2.1 has electrophysiological consequences on the myocardium and that SAP97 may affect the integrity of this complex or the nature of Na v 1.5–Kir2.1 interactions. The reciprocal modulation between Na v 1.5 and Kir2.1 and the respective ionic currents should be important in the ability of the heart to undergo self-sustaining cardiac rhythm disturbances.

Significance The 1a subunit of the human ether-à-go-go–related gene (hERG) potassium channel is a critical component of cardiac repolarization and the cornerstone of safety screens for new drug development. A second subunit, 1b, coassembles with 1a and modifies channel gating and drug block sensitivity. Adoption of 1a/1b heteromers as a model of native hERG current, I Kᵣ, has been hampered by the absence of direct evidence that 1b contributes to human cardiac repolarization. This study provides the first functional evidence, to our knowledge, that 1a/1b channels rather than homomeric 1a channels mediate repolarization. Because heteromeric and homomeric hERG channels have different pharmacological profiles, these findings have implications for native I Kᵣ models and hERG-based drug safety tests that help protect against drug-induced sudden cardiac death. The human ether-àà-go-go–related gene ( hERG ; or KCNH2 ) encodes the voltage-gated potassium channel underlying I Kᵣ, a repolarizing current in the heart. Mutations in KCNH2 or pharmacological agents that reduce I Kᵣ slow action potential (AP) repolarization and can trigger cardiac arrhythmias associated with long QT syndrome. Two channel-forming subunits encoded by KCNH2 (hERG 1a and 1b) are expressed in cardiac tissue. In heterologous expression systems, these subunits avidly coassemble and exhibit biophysical and pharmacological properties distinct from those of homomeric hERG 1a channels. Despite these findings, adoption of hERG 1a/1b heteromeric channels as a model for cardiac I Kᵣ has been hampered by the lack of evidence for a direct functional role for the 1b subunit in native tissue. In this study, we measured I Kᵣ and APs at physiological temperature in cardiomyocytes derived from human induced pluripotent stem cells (iPSC-CMs). We found that specific knockdown of the 1b subunit using shRNA caused reductions in 1b mRNA, 1b protein levels, and I Kᵣ magnitude by roughly one-half. AP duration was increased and AP variability was enhanced relative to controls. Early afterdepolarizations, considered cellular substrates for arrhythmia, were also observed in cells with reduced 1b expression. Similar behavior was elicited when channels were effectively converted from heteromers to 1a homomers by expressing a fragment corresponding to the 1a-specific N-terminal Per–Arnt–Sim domain, which is omitted from hERG 1b by alternate transcription. These findings establish that hERG 1b is critical for normal repolarization and that loss of 1b is proarrhythmic in human cardiac cells.

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human-machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human-robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about 750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

Biobased aerogels were used as environmentally friendly replacement for synthetic polymers as electrolyte carrier membranes in quantum dot-sensitized solar cell (QDSC). Integration of polymeric components in solar cells has received increased attention for sustainable energy generation. In this context, biobased aerogels were fabricated to apply as freestanding, porous and eco-friendly electrolyte holding membranes in QDSC. Bacterial cellulose (BC), cellulose nanofibers (CNF), chitin nanofibers (ChNF) and TEMPO-oxidized CNF (TOCNF) were selected because of their fibrilar structures and water-holding capability to investigate their inherent differences in terms of surface groups and electrostatic charge on the electrolyte redox reaction and the photocell function. BC, CNF, ChNF and TOCNF were selected due to different surface functional groups (hydroxyl, N -acetylglucosamine and carboxyl units) and fibrilar structures that can form highly interconnected and robust network. These aerogels enabled easy handling, effective electrolyte filling and efficient redox reactions, while keeping the solar cell performance on par to that of traditional reference cells without membranes. The aerogel membranes maintained the photocell performance since they took only a very small space of the electrolyte volume, which allowed efficient charge transfer. The results indicated that aerogels did not interfere with the cell operation, as confirmed by quartz crystal microgravimetry with bio-interphases in contact with the polysulfide-based electrolyte. The electrochemical measurements also suggested that the respective functional groups (hydroxyl, N -acetylglucosamine and carboxyl units) did not interfere with the redox reaction of the polysulfide electrolyte.