Dihydrosphingosine influenced enrichment associated with sphingolipids attenuates TGFβ caused collagen combination throughout heart failure fibroblasts.
 When an alternating voltage is applied to the conductive layer of a capacitive touchscreen, an oscillating electroadhesive force (also known as electrovibration) is generated between the human finger and its surface in the normal direction. This electroadhesive force causes an increase in friction between the sliding finger and the touchscreen. Although the practical implementation of this technology is quite straightforward, the physics behind voltage-induced electroadhesion and the resulting contact interactions between human finger and the touchscreen are still under investigation. In this paper, we first present the results of our experimental study conducted with a custom-made tribometer to investigate the effect of input voltage on the tangential forces acting on the finger due to electroadhesion during sliding. We then support our experimental results with a contact mechanics model developed for estimating voltage-induced frictional forces between human finger and a touchscreen as a function of the applied normal force. The unknown parameters of the model were estimated via optimization by minimizing the error between the measured tangential forces and the ones generated by the model. The estimated model parameters show a good agreement with the ones reported in the literature.There are conflicting objectives between required characteristics of haptic interfaces (HIs) such as maximum force feedback capability versus back-drive friction, which can be optimally traded-off in a redundant HI; a redundant HI has more degrees of freedom than minimally required ones for a given task. In this paper, a contact-aware null-space control approach for redundant HIs is proposed to address these trade-offs. First, we introduce a task-dependent null-space controller in which the internal motion of the redundant HI is appropriately controlled to achieve a desired performance. Next, a transition method is developed to facilitate the adaptation of the null-space controller's varying objectives according to the varying nature of the task. The transition method prevents discontinuities in the null-space control signal. This transition method is informed by a proposed actuator saturation observer that monitors the distance of joint torques from their saturation levels. The overall outcome is an ability to recreate the feelings of soft contacts and hard contacts with higher fidelity compared to what a conventional non-redundant HI can achieve. Experimental results are reported to verify the effectiveness of the proposed control strategies. It is shown that the proposed controller can perform well in the soft-contact, hard-contact, and transition phases.It can be useful to display information about numerosity haptically. For instance, to display the time of day or distances when visual or auditory feedback is not possible or desirable. Here we investigated the possibility of displaying numerosity information by means of a sequence of vibration pulses. From previous studies on numerosity perception in vision, haptics and audition it is known that numerosity judgment can be facilitated by grouping. Therefore, we investigated whether perception of the number of vibration pulses in a sequence can be facilitated by temporally grouping the pulses.  https://www.selleckchem.com/  We found that indeed temporal grouping can lead to considerably smaller errors and lower error rates indicating that this facilitated the task, but only when participants knew in advance whether the pulses would be temporally grouped. When grouped and ungrouped series of pulses were presented randomly interleaved, there was no difference in performance. This means that temporally grouping vibration sequences can allow the sequence to be displayed at a faster rate while it remains possible to perceive the number of vibration pulses accurately if the users is aware of the temporal grouping.Conventional cochlear implants using periodic sampling are power consuming and incapable of capturing the amplitude and phase of the input acoustic signal simultaneously.  https://www.selleckchem.com/  This paper presents an asynchronous event-driven encoder chip for cochlear implants capable of extracting the temporal fine structure. The chip architecture is based on asynchronous delta modulation (ADM) where the signal peak/trough crossing events are captured and digitized intrinsically, which has the advantages of significantly reduced power consumption, reduced circuit area, and the elimination of dedicated data compression circuitry. An 8-channel prototype chip was fabricated in 0.18 μm 1P6M CMOS process, occupying an area of 0.15 ×1.7 mm2 and has a power consumption of 36.2 μW from a 0.6V supply. A 16-channel stimulation encoding system was built by integrating two test chips, capable of processing the entire audible frequency range from 100 Hz to 10 kHz. Experimental characterization using the human voice is provided to corroborate functionality in the application environment.The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an important role in functional connectivity classification, among which convolutional neural network (CNN) based methods become a new hot topic since they can extract topological features in the brain network. However, the conventional CNN-based methods haven't taken sparse connectivity patterns (SCPs) of the human brain into consideration, which may lead to redundancy of the topological features, and limit their performance and generalization. To solve it, we propose a novel CNN-based model with graphical Lasso (CNNGLasso) to extract sparse topological features for brain disease classification. Firstly, we develop a novel graphical Lasso model for revealing the SCPs at group-level. Then, the SCPs are used to guide the topological feature extraction. Finally, the obtained sparse topological features are used to classify the patients from normal controls. The experiment results on the ABIDE dataset demonstrate that the CNNGLasso outperforms the others on various performances. Besides, the abnormal brain regions derived from the trained model are consistent with the previous investigations, which further proves the application prospect of the CNNGLasso.