Feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic RRAM array and CMOSbased neuron chip has been experimentally demonstrated. Learning capability of a neuromorphic system comprising RRAM synapses and CMOS neurons has been confirmed experimentally, for the first time.

Although surrogate measures to quantify pain intensity have been commercialised, there is a need to develop a new index with improved accuracy. The aim of this study was to develop a new analgesic index using nasal photoplethysmography data. The specially designed sensor was placed between the columella and the nasal septum to acquire nasal photoplethysmography in surgical patients. Nasal photoplethysmography and Surgical Pleth Index (GE Healthcare) data were obtained for 14 min both in the absence (pre-operatively) or presence (postoperatively) of pain in a group of surgical patients, each patient acting as their own control. Various dynamic photoplethysmography variables were extracted to quantify pain intensity; the most accurate index was selected using logistic regression as a classifier. The area under the curve of the receiver-operating characteristic curve was measured to evaluate the accuracy of final model predictions. In total, 12,012 heart beats from 89 patients were used to develop a new Nasal Photoplethysmography Index for analgesic depth quantification. The two-variable model (a combination of diastolic peak point variation and heart beat interval variation) was most accurate in discriminating between the presence and absence of pain (numerical rating scale (NRS) ≥ 3). The accuracy and area under the curve of the receiver-operating characteristic curve for the Nasal Photoplethysmography Index were 75.3% and 0.8018, respectively, and 64.8% and 0.7034, respectively, for the Surgical Pleth Index. The Nasal Photoplethysmography Index clearly distinguished pain (NRS ≥ 3) in awake surgical patients with postoperative pain. The Nasal Photoplethysmography Index performed better than the Surgical Pleth Index. Further validation studies are needed to evaluate its feasibility to quantify pain intensity during general anaesthesia.

This paper describes a novel micro electrical impedance spectroscopy (µEIS) device with cell traps, which can discriminate between normal and cancerous human urothelial cells. Three-dimensional traps with electrodes were employed for effective capture of target cells. This allows cells to be automatically captured, while the electrodes on the slanted area of the trap accurately characterize the electrical impedance of the cells. The device is equipped with five traps of various dimensions in a single device, which can capture various types of cells regardless of their size and deformability. The µEIS device accurately distinguished cancerous human urothelial cells (TCCSUP) from normal cells (SV-HUC-1); the average differences in the phase angle and resistance between the target cells were 1.8° at 119 kHz and 416.1 Ω at 109 kHz, respectively.