Coculture of Marine Streptomyces sp. With Bacillus sp. Produces a New Piperazic Acid-Bearing Cyclic Peptide

Microbial culture conditions in the laboratory, which conventionally involve the cultivation of one strain in one culture vessel, are vastly different from natural microbial environments. Even though perfectly mimicking natural microbial interactions is virtually impossible, the cocultivation of multiple microbial strains is a reasonable strategy to induce the production of secondary metabolites, which enables the discovery of new bioactive natural products. Our coculture of marine Streptomyces and Bacillus strains isolated together from an intertidal mudflat led to discover a new metabolite, dentigerumycin E (1). Dentigerumycin E was determined to be a new cyclic hexapeptide incorporating three piperazic acids, N-OH-Thr, N-OH-Gly, β-OH-Leu, and a pyran-bearing polyketide acyl chain mainly by analysis of its NMR and MS spectroscopic data. The putative PKS-NRPS biosynthetic gene cluster for dentigerumycin E was found in the Streptomyces strain, providing clear evidence that this cyclic peptide is produced by the Streptomyces strain. The absolute configuration of dentigerumycin E was established based on the advanced Marfey's method, ROESY NMR correlations, and analysis of the amino acid sequence of the ketoreductase domain in the biosynthetic gene cluster. In biological evaluation of dentigerumycin E (1) and its chemical derivatives [2-N,16-N-deoxydenteigerumycin E (2) and dentigerumycin methyl ester (3)], only dentigerumycin E exhibited antiproliferative and antimetastatic activities against human cancer cells, indicating that N-OH and carboxylic acid functional groups are essential for the biological activity

Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach

How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix

Global trends and prospects of environmentally friendly transportation have helped to popularize electric vehicles (EVs). With the spread of EVs, vehicle-to-grid (V2G) technology is gaining importance for its role in connecting the electricity stored in the battery of EVs to a grid-like energy storage system (ESS). Electricity generation mix and battery for V2G energy storage have a decisive effect on the stabilization of a V2G system, but no attempt has been made. Therefore, this study analyzes consumer preference considering the electricity generation mix and battery for the V2G. We conduct a conjoint survey of a 1000 South Koreans and employ the multiple discrete-continuous extreme value model. The results show that drivers prefer plug-in hybrid- and battery EVs to other vehicles. Additionally, findings show that driver’s utility changes at 27.9% of the battery allowance for V2G system and it becomes positive after 55.7%. Furthermore, we conduct a scenario analysis considering the electricity generation mix (more traditional vs. renewable) and battery allowance. Based on this analysis, we suggest some policies and corporate strategies to support the success of the V2G market depending on energy policies and battery allowance level

A 31.2pJ/disparity?? pixel stereo matching processor with stereo SRAM for mobile UI application

An energy-efficient and high-speed stereo matching processor is proposed for smart mobile devices with proposed stereo SRAM (S-SRAM) and independent regional integral cost (IRIC). Cost generation unit (CGU) with the proposed S-SRAM reduces 63.2% of CGU power consumption. The proposed IRIC enables cost aggregation unit (CAU) to obtain 6.4?? of speed and 12.3% of the power reduction of CAU with pipelined integral cost generator (PICG). The proposed stereo matching processor, implemented in 65nm CMOS process, achieves 82fps and 31.2pJ/disparity-pixel energy efficiency at 30fps. Its energy efficiency is improved by 77.6% compared to the state-of-the-art

A keypoint-level parallel pipelined object recognition processor with gaze activation image sensor for mobile smart glasses system

In this paper, a low-power real-time gaze-activated object recognition processor is proposed for a battery-powered smart glasses system. For high energy efficiency, we propose keypoint-level pipelined architecture to increase the hardware utilziation which results in significant power reduction of the real-time recognition processor. In addition, low-power gaze-activation image sensor with mixed-mode architecture is proposed for the glass user's gaze estimation. Therefore, only the small image region where the glasses user is seeing needs to be processed by the recognition processor leading to further power reduction. As a result, the proposed object recognition processor shows 30fps real-time performance only with 75mW power consumption, which is 3.5x and 4.4x smaller power than the state-of-the-art works

A 2.71nJ/pixel 3D-stacked gaze-activated object-recognition system for low-power mobile HMD applications

Smart eyeglasses or head-mounted displays (HMDs) have been gaining traction as next-generation mainstream wearable devices. However, previous HMD systems [1] have had limited application, primarily due to their lacking a smart user interface (Ul) and user experience (UX). Since HMD systems have a small compact wearable platform, their Ul requires new modalities, rather than a computer mouse or a 2D touch panel. Recent speech-recognition-based Uls require voice input to reveal the user's intention to not only HMD users but also others, which raises privacy concerns in a public space. In addition, prior works [2-3] attempted to support object recognition (OR) or augmented reality (AR) in smart eyeglasses, but consumed considerable power, >381mW, resulting in <;6 hours operation time with a 2100mWh battery

A 2.71 nJ/Pixel Gaze-Activated Object Recognition System for Low-Power Mobile Smart Glasses

A low-power object recognition (OR) system with intuitive gaze user interface (UI) is proposed for battery-powered smart glasses. For low-power gaze UI, we propose a low-power single-chip gaze estimation sensor, called gaze image sensor (GIS). In GIS, a novel column-parallel pupil edge detection circuit (PEDC) with new pupil edge detection algorithm XY pupil detection (XY-PD) is proposed which results in 2.9x power reduction with 16x larger resolution compared to previous work. Also, a logarithmic SIMD processor is proposed for robust pupil center estimation, <1 pixel error, with low-power floating-point implementation. For OR, low-power multicore OR processor (ORP) is implemented. In ORP, task-level pipeline with keypoint-level scoring is proposed to reduce the number of cores as well as the operating frequency of keypoint-matching processor (KMP) for low-power consumption. Also, dual-mode convolutional neural network processor (CNNP) is designed for fast tile selection without external memory accesses. In addition, a pipelined descriptor generation processor (DGP) with LUT-based nonlinear operation is newly proposed for low-power OR. Lastly, dynamic voltage and frequency scaling (DVFS) for dynamic power reduction in ORP is applied. Combining both of the GIS and ORP fabricated in 65 nm CMOS logic process, only 75 mW average power consumption is achieved with real-time OR performance, which is 1.2x and 4.4x lower power than the previously published work

A 1.22 TOPS and 1.52mW/MHz Augmented Reality Multi-Core Processor with Neural Network NoC for HMD Applications

Augmented reality (AR) is being investigated in advanced displays for the augmentation of images in a real-world environment. Wearable systems, such as head-mounted display (HMD) systems, have attempted to support real-time AR as a next generation UI/UX [1-2], but have failed, due to their limited computing power. In a prior work, a chip with limited AR functionality was reported that could perform AR with the help of markers placed in the environment (usually 1D or 2D bar codes) [3]. However, for a seamless visual experience, 3D objects should be rendered directly on the natural video image without any markers. Unlike marker-based AR, markerless AR requires natural feature extraction, general object recognition, 3D reconstruction, and camera-pose estimation to be performed in parallel. For instance, markerless AR for a VGA input-test video consumes ~1.3W power at 0.2fps throughput, with TI&apos;s OMAP4430, which exceeds power limits for wearable devices. Consequently, there is a need for a high-performance energy-efficient markerless AR processor to realize a real-time AR system, especially for HMD applications