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Transcutaneous neuromuscular electrical stimulation (NMES) can be used to activate the quadriceps femoris muscle to produce knee extension torque via seven distinct motor points, defined as the most sensitive locations on the muscle belly to electrical stimuli. However, it remains unclear how much individual motor points of the quadriceps femoris muscle contribute to the knee joint torque. Here we systematically investigated the contribution of each motor point of the quadriceps femoris muscle to the knee joint torque produced by paired electrical stimuli. Ten able-bodied individuals participated in this study. Paired electrical stimuli was applied by delivering electrical impulses on the motor points in all combinations among seven motor points (i.e., totaling to 127 combinations) at two different stimulation intensities (i.e., 25% and 50% of the maximum) while recording isometric knee joint torque. The contribution of individual motor points was estimated using statistical analyses. We found that a linear addition of twitch torques induced by single motor point stimulus overestimated the twitch torques induced by multiple motor point stimulations, suggesting overlaps in muscle fibres activated by each motor point. Using multiple linear regressions, we identified the average contribution of each motor point to the knee extension torque during paired electrical stimuli and found significant differences between these torque contributions. We demonstrated that seven distinct motor points can be activated for the quadriceps muscle group using paired electrical stimuli and identified the contribution of each motor point to knee extension torque during twitch muscle contraction; these findings provide useful information to design rehabilitation using NMES on quadriceps femoris muscles.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

The pieces in this Amerasia Journal Forum provides first-person retrospectives on the multiethnic, multi-generational, and transnational Free Chol Soo Lee movement from many of the principals involved in it. Derrick Lim was a member of the Chol Soo Lee Defense Committee

We present a quasi-static model of the electromagnetic interaction between dipolar emitting molecule and a Plasmon-metal nano-sphere. Based on the image theory of dielectric sphere, we model the Plasmon nano-spheres by off-centered dipole images. The retardation effect is taken into account by electrodynamical modifications on spherical polarizability and dipole radiation field. The modifications of the radiative rate, total decay rate and the quantum yield for the molecules are derived. The image model indicates the strong distance dependence of the enhancement on both radiative and total decay rates. The comparison with the exact electrodynamical model and other simplified models indicate that the off-center image provide accurate predictions on radiative and total decay rates, even in close distances.

We present an automated electrical burning process to selectively eliminate conducting paths due to metallic and high-leakage ambipolar single-walled carbon nanotubes in aligned and randomly networked configurations of varying dimensions. Our algorithm is hysterisis immune and adjusts gate bias in between successive burn pulses to facilitate optimized electrical burning results. In back-gated devices, we show improvements in on/off ratio better than three orders of magnitude while achieving on-state current reduction of less than 25%. We also discuss important considerations that must be taken into account for obtaining good electrical burning results in top-gated devices.