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This paper presents the noise optimization of a novel switched-capacitor (SC) based neural interface architecture, and its circuit demonstration in a 0.13 [Formula see text] CMOS process. To reduce thermal noise folding ratio, and suppress kT/C noise, several noise optimization techniques are developed in the proposed architecture. First, one parasitic capacitance suppression scheme is developed to block noise charge transfer from parasitic capacitors to amplifier output. Second, one recording path-splitting scheme is proposed in the input sampling stage to selectively record local field potentials (LFPs), extracellular spikes, or both for reducing input noise floor, and total power consumption. Third, an auto-zero noise cancellation scheme is developed to suppress kT/C noise in the neural amplifier stage. A prototype neural interface chip was fabricated, and also verified in both bench-top, and In-Vivo experiments. Bench-top testings show the input-referred noise of the designed chip is 4.8 [Formula see text] from 1 [Formula see text] to 300 [Formula see text], and 2.3 [Formula see text] from 300 [Formula see text] to 8 kHz respectively, and In-Vivo experiments show the peak-to-peak amplitude of the total noise floor including neural activity, electrode interface noise, and the designed chip is only around 20 [Formula see text]. In comparison with conventional architectures through both circuit measurement and animal experiments, it is well demonstrated that the proposed noise optimization techniques can effectively reduce circuit noise floor, thus extending the application range of switched-capacitor circuits.Breast Cancer is a highly aggressive type of cancer generally formed in the cells of the breast. A good predictive model can help in correct prognosis prediction of breast cancer. Previous works rely mostly on uni-modal data (selected gene expression) for predictive model design. In recent years, however, multi-modal cancer data sets have become available (gene expression, copy number alteration and clinical). Motivated by the enhancement of deep-learning based models, in the current study, we propose to use some deep-learning based predictive models in a stacked ensemble framework to improve the prognosis prediction of breast cancer from available multi-modal data sets. One of the unique advantages of the proposed approach lies in the architecture of the model. It is a two-stage model. Stage one uses a convolutional neural network for feature extraction, while stage two uses the extracted features as input to the stack-based ensemble model. The predictive performance evaluated using different performance measures shows that this model produces a better result than already existing approaches. This model results in AUC value equals to 0.93 and accuracy equals to 90.2% at medium stringency level (Specificity = 95% and threshold = 0.4.In this study, we estimated the multi-directional ankle mechanical impedance in two degrees-of-freedom (DOF) during standing, and determined how the stiffness, damping, and inertia vary with ankle angle and ankle torque at different postures. Fifteen subjects stood on a vibrating instrumented platform in four stationary postures, while subjected to pulse train perturbations in both the sagittal and frontal planes of motion. The four stationary postures were selected to resemble stages within the stance phase of the gait cycle including post-heel-strike during the loading response, mid-stance, post-mid-stance, and just before the heel rises from the ground in terminal-stance phase. In general, the ankle stiffness and damping increased in all directions as the foot COP moved forward, and more torque is generated in plantarflexion. Interestingly, the multi-directional ankle impedance during standing showed a similar shape and major tilt axes to the results of non-loaded scenarios. However, there were notable differences in the impedance amplitude when the ankle was not under bodyweight loading. Selleck GSK2126458 Last, the stiffness during standing had similar amplitudes ranges to the time-varying ankle stiffness during the stance phase of dynamic walking estimated in previous studies. These results have implications on the design of new, less physically intense, biomechanics experiments aimed at people with neuromuscular disorders or other physical impairments who cannot complete a standard gait test.The design and preliminary assessment of a semi-powered hand and arm exoskeleton is described. The exoskeleton is designed to enable bimanual activities of daily living for individuals with chronic, upper-limb hemiparesis resulting from stroke. Specifically, the device augments the user’s grasp strength and ability to extend the affected hand for bimanual tasks and supplements wrist and elbow stability while conducting these tasks. The exoskeleton is battery-powered and self-contained with all electronics and power units placed within the device structure. A preliminary assessment of the exoskeleton was performed with three subjects having right-sided upper-limb motor deficit resulting from stroke. For subjects with limited hand and arm functionality, the exoskeleton increased grasp strength and improved the ability to perform representative bimanual tasks.Parkinson’s disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson’s disease. The thalamus relays signals from the cerebellum to the primary motor cortex previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex).