Human Sensation of Transcranial Electric Stimulation.

Noninvasive transcranial electric stimulation is increasingly being used as an advantageous therapy alternative that may activate deep tissues while avoiding drug side-effects. However, not only is there limited evidence for activation of deep tissues by transcranial electric stimulation, its evoked human sensation is understudied and often dismissed as a placebo or secondary effect. By systematically characterizing the human sensation evoked by transcranial alternating-current stimulation, we observed not only stimulus frequency and electrode position dependencies specific for auditory and visual sensation but also a broader presence of somatic sensation ranging from touch and vibration to pain and pressure. We found generally monotonic input-output functions at suprathreshold levels, and often multiple types of sensation occurring simultaneously in response to the same electric stimulation. We further used a recording circuit embedded in a cochlear implant to directly and objectively measure the amount of transcranial electric stimulation reaching the auditory nerve, a deep intercranial target located in the densest bone of the skull. We found an optimal configuration using an ear canal electrode and low-frequency (<300 Hz) sinusoids that delivered maximally ~1% of the transcranial current to the auditory nerve, which was sufficient to produce sound sensation even in deafened ears. Our results suggest that frequency resonance due to neuronal intrinsic electric properties need to be explored for targeted deep brain stimulation and novel brain-computer interfaces

A surface-enhanced Raman scattering (SERS)-active optical fiber sensor based on a three-dimensional sensing layer

AbstractTo fabricate a new surface-enhanced Raman scattering (SERS)-active optical fiber sensor, the design and preparation of SERS-active sensing layer is one of important topics. In this study, we fabricated a highly sensitive three-dimensional (3D) SERS-active sensing layer on the optical fiber terminal via in situ polymerizing a porous polymer material on a flat optical fiber terminal through thermal-induced process, following with the photochemical silver nanoparticles growth. The polymerized polymer formed a 3D porous structure with the pore size of 0.29–0.81μm, which were afterward decorated with abundant silver nanoparticles with the size of about 100nm, allowing for higher SERS enhancement. This SERS-active optical fiber sensor was applied for the determination of 4-mercaptopyridine, crystal violet and maleic acid The enhancement factor of this SERS sensing layer can be reached as about 108. The optical fiber sensor with high sensitive SERS-active porous polymer is expected for online analysis and environment detection

(S)-2-Amino-1-(pyrrolidinium-2-ylmeth­yl)pyridinium dibromide

In the title compound, C10H17N3 2+·2Br−, the pyrrolidinium ring displays an envelope conformation, with the flap N atom lying 0.564 (6) Å from the mean plane of the remaining four C atoms. The attached methyl­ene C atom, which connects the pyrrolidinium ring and the 2-amino­pyridine group, is displaced from the plane of the four pyrrolidinium C atoms by 0.811 (8) Å in the same direction as the pyrrolidinium N atom. The amine N lies on the opposite side of this plane

HULL SHAPE OPTIMIZATION OF SMALL UNDERWATER VEHICLE BASED ON KRIGING-BASED RESPONSE SURFACE METHOD AND MULTI-OBJECTIVE OPTIMIZATION ALGORITHM

Small underwater vehicles have unique advantages in ocean exploration. The resistance and volume of a vehicle are key factors affecting its operation time underwater. This paper aims to develop an effective method to obtain the optimal hull shape of a small underwater vehicle using Kriging-based response surface method (RSM) and multi-objective optimization algorithm. Firstly, the hydrodynamic performance of a small underwater vehicle is numerically investigated using computational fluid dynamics (CFD) method and the value range of related design variables is determined. The mesh convergence is verified to ensure the accuracy of the calculation results. Then, by means of the Latin hypercube sampling (LHS) design of simulation, the Kriging-based RSM model is developed according to the relation between each design variable of the vehicle and the output parameters applied to the vehicle. Based on the Kriging-based RSM model, the optimal hull shape of the vehicle is determined by using Screening and MOGA. As results, the vehicle resistance reduces and volume increases obviously