A Two-Layer Gene Circuit for Decoupling Cell Growth from Metabolite Production

SummaryWe present a synthetic gene circuit for decoupling cell growth from metabolite production through autonomous regulation of enzymatic pathways by integrated modules that sense nutrient and substrate. The two-layer circuit allows Escherichia coli to selectively utilize target substrates in a mixed pool; channel metabolic resources to growth by delaying enzymatic conversion until nutrient depletion; and activate, terminate, and re-activate conversion upon substrate availability. We developed two versions of controller, both of which have glucose nutrient sensors but differ in their substrate-sensing modules. One controller is specific for hydroxycinnamic acid and the other for oleic acid. Our hydroxycinnamic acid controller lowered metabolic stress 2-fold and increased the growth rate 2-fold and productivity 5-fold, whereas our oleic acid controller lowered metabolic stress 2-fold and increased the growth rate 1.3-fold and productivity 2.4-fold. These results demonstrate the potential for engineering strategies that decouple growth and production to make bio-based production more economical and sustainable

Complete genome sequence of Middle East respiratory syndrome coronavirus KOR/KNIH/002_05_2015, isolated in South Korea

The full genome sequence of a Middle East respiratory syndrome coronavirus (MERS-CoV) was identified from cultured and isolated in Vero cells. The viral genome sequence has high similarity to 53 human MERS-CoVs, ranging from 99.5% to 99.8% at the nucleotide level. © 2015 Kim et al.

Regulation of synaptic Rac1 activity, long-term potentiation maintenance, and learning and memory by BCR and ABR Rac GTPase-activating proteins

Rho family small GTPases are important regulators of neuronal development. Defective Rho regulation causes nervous system dysfunctions including mental retardation and Alzheimer's disease. Rac1, a member of the Rho family, regulates dendritic spines and excitatory synapses, but relatively little is known about how synaptic Rac1 is negatively regulated. Breakpoint cluster region (BCR) is a Rac GTPase-activating protein known to form a fusion protein with the c-Abl tyrosine kinase in Philadelphia chromosome-positive chronic myelogenous leukemia. Despite the fact that BCR mRNAs are abundantly expressed in the brain, the neural functions of BCR protein have remained obscure. We report here that BCR and its close relative active BCR-related (ABR) localize at excitatory synapses and directly interact with PSD-95, an abundant postsynaptic scaffolding protein. Mice deficient for BCR or ABR show enhanced basal Rac1 activity but only a small increase in spine density. Importantly, mice lacking BCR or ABR exhibit a marked decrease in the maintenance, but not induction, of long-term potentiation, and show impaired spatial and object recognition memory. These results suggest that BCR and ABR have novel roles in the regulation of synaptic Rac1 signaling, synaptic plasticity, and learning and memory, and that excessive Rac1 activity negatively affects synaptic and cognitive functions.This work was supported by the National Creative Research Initiative Program of the Korean Ministry of Education, Science and Technology (E.K.), Neuroscience Program Grant 2009-0081468 (S.-Y.C.), 21st Century Frontier R&D Program in Neuroscience Grant 2009K001284 (H.K.), Basic Science Research Program Grant R13-2008-009-01001-0 (Y.C.B.), and United States Public Health Service Grants HL071945 (J.G.) and HL060231 (J.G., N.H.)

Locally Specified CPT Soil Classification Based on Machine Learning Techniques

Cone penetration tests (CPTs) can provide highly accurate and detailed information and characteristics relevant to the stiffness, strength, and consolidation of tested geomaterials, but they do not directly recover real soil samples. Thus, when CPT results are applied to soil classification, experience-based classification charts or tables are generally used. However, such charts or tables have the inherent drawback of being derived from the test data applied to each classification method, which promotes their failure to cover the engineering features of soils from other places. This study proposes a machine learning approach using C4.5 decision tree algorithm to develop a locally specified CPT-based soil classification system. The findings demonstrate that a locally specified soil classification scheme can be attained by utilizing a simple and trained decision tree model with appropriate combinations of training data and input attributes. Additionally, it is confirmed that oversampling the minor classes makes the classification accuracy for data with highly unbalanced classes appear more balanced for each class

Bayesian Network-Based High-Level Context Recognition for Mobile Context Sharing in Cyber-Physical System

With the recent proliferation of smart phones, they become useful tools to implement high-confidence cyber-physical systems. Among many applications, context sharing systems in mobile environment attract attention with the popularization of social media. Mobile context sharing systems can share more information than web-based social network services because they can use a variety of information from mobile sensors. To share high-level contexts such as activity, emotion, and user relationship, a user had to annotate them manually in previous works. This paper proposes a mobile context sharing system that can recognize high-level contexts automatically by using Bayesian networks based on mobile logs. We have developed a ContextViewer application which consists of a phonebook and a map browser to show the feasibility of the system. Experiments of evaluating Bayesian networks and performing the SUS test confirm that the proposed system is useful

Geo-Proxy-Based Site Classification for Regional Zonation of Seismic Site Effects in South Korea

Seismic site effects and topographic effects related to ground motion occur during an earthquake due to site-specific geotechnical or geological characteristics, including the geological or geographical structure and the characteristics of near-surface sub-soil layers. Site-specific site effects due to geological conditions have been confirmed in recent earthquake events. Earthquake-induced damage has mainly occurred at accumulated soft soil layers under basins or along coasts and rivers. An alternative method has recently been proposed for evaluating regional seismic site effects and amplification factors using digital elevation models (DEM). High-quality DEMs at high resolutions may be employed to resolve finer-scale variations in topographic gradients and consequently, correlated site response parameters. Because there are many regions in South Korea lacking borehole datasets, which are insufficient for site classification only using borehole datasets, a DEM-based proxy for seismic zonation can be effective. Thus, in this study, geo-proxy-based site classification was proposed based on empirical correlations with site response parameters and conducted for regional zonation of seismic site effects to identify the amplification of characteristics in the western metropolitan areas of South Korea, depending on the site-specific geo-spatial conditions

Site-Specific Zonation of Seismic Site Effects by Optimization of the Expert GIS-Based Geotechnical Information System for Western Coastal Urban Areas in South Korea

Abstract Earthquake-induced disasters are often more severe over soft soils than over firm soils or rocks owing to the seismic site effects related to the amplification of ground motion. On a regional scale, such differences can be estimated by spatially predicting the subsurface soil thickness over the entire target area. Generally, soil deposits are deeper in coastal or riverside areas than in inland regions. In this study, the seismic site effects in the coastal metropolitan areas of Incheon and Bucheon, South Korea, were assessed to provide information on seismic hazards. Spatial prediction of geotechnical layers was performed for the entire study area within an advanced GIS framework. Approximately 7500 existing borehole records in the Incheon and Bucheon areas were gathered and archived into a GIS database. Surface geotechnical data were acquired from a walk-over survey. Based on the optimized geo-data, spatial zoning maps of site-specific seismic response parameters, based on multiscale geospatial modeling, were created and presented for use in a regional seismic mitigation strategy. Seismic zonation was also performed to determine site coefficients for seismic design over the entire target area and to compare them with each other. We verified that the geotechnical data based spatial zonation would be useful for seismic hazard mitigation