'Deadman' and 'Passcode' microbial kill switches for bacterial containment

Biocontainment systems that couple environmental sensing with circuit-based control of cell viability could be used to prevent escape of genetically modified microbes into the environment. Here we present two engineered safeguard systems known as the 'Deadman' and 'Passcode' kill switches. The Deadman kill switch uses unbalanced reciprocal transcriptional repression to couple a specific input signal with cell survival. The Passcode kill switch uses a similar two-layered transcription design and incorporates hybrid LacI-GalR family transcription factors to provide diverse and complex environmental inputs to control circuit function. These synthetic gene circuits efficiently kill Escherichia coli and can be readily reprogrammed to change their environmental inputs, regulatory architecture and killing mechanism.United States. Defense Threat Reduction Agency (Grant HDTRA1-14-1-0006)Howard Hughes Medical InstituteUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110725)United States. Air Force Office of Scientific Research (Grant FA9550-14-1-0060

Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components

The recent Zika virus outbreak highlights the need for low-cost diagnostics that can be rapidly developed for distribution and use in pandemic regions. Here, we report a pipeline for the rapid design, assembly, and validation of cell-free, paper-based sensors for the detection of the Zika virus RNA genome. By linking isothermal RNA amplification to toehold switch RNA sensors, we detect clinically relevant concentrations of Zika virus sequences and demonstrate specificity against closely related Dengue virus sequences. When coupled with a novel CRISPR/Cas9-based module, our sensors can discriminate between viral strains with single-base resolution. We successfully demonstrate a simple, field-ready sample-processing workflow and detect Zika virus from the plasma of a viremic macaque. Our freeze-dried biomolecular platform resolves important practical limitations to the deployment of molecular diagnostics in the field and demonstrates how synthetic biology can be used to develop diagnostic tools for confronting global health crises.Defense Threat Reduction Agency (DTRA) (HDTRA1-14-1-0006)United States. National Institutes of Health (NIH AI100190

Marker-Based 3D Position-Prediction Algorithm of Mobile Vertiport for Cabin-Delivery Mechanism of Dual-Mode Flying Car

This paper presents an image-processing technique for cabin delivery employing local localization and docking in a mobile station, which is a mobile vertiport for the use of dual-mode flying cars. A dual-mode flying automobile with an aerial electric vehicle (AEV), a ground electric vehicle (GEV), and a cabin is a future method of transportation that can be used in both the air and on the ground. To enable AEVs to land in a specific position, a landing site is necessary. The proposed AEV uses vertical take-off and landing, and a vertiport landing site is required. As vertical take-off and landing sites require a lot of space and are challenging to operate in multiple positions, we suggest a mobile vertiport that can fit into a small space. A mobile station is appropriate for dual-mode flying cars since it includes critical activities such as transporting AEVs from the ground and charging as well as a cabin-delivery system. The mobile station can generate a path to the AEV by calculating the relative position using the markers attached to the AEV and estimating the position of the landing AEV. The mobile station detects a marker for precise positioning correction, followed by exact position correction for cabin delivery, to travel to the accurate position of the AEV. To increase the success rate of cabin delivery, docking markers are identified and the angle position error between the mobile station and cabin is computed and corrected to rectify the position between the cabin and the mobile station for cabin delivery. In addition, the experimental results revealed a mechanically correctable error range that encompassed all experimental values. Consequently, this study showed that image processing may be used to create a mobile station for dual-mode flying automobiles

Marker-Based 3D Position-Prediction Algorithm of Mobile Vertiport for Cabin-Delivery Mechanism of Dual-Mode Flying Car

This paper presents an image-processing technique for cabin delivery employing local localization and docking in a mobile station, which is a mobile vertiport for the use of dual-mode flying cars. A dual-mode flying automobile with an aerial electric vehicle (AEV), a ground electric vehicle (GEV), and a cabin is a future method of transportation that can be used in both the air and on the ground. To enable AEVs to land in a specific position, a landing site is necessary. The proposed AEV uses vertical take-off and landing, and a vertiport landing site is required. As vertical take-off and landing sites require a lot of space and are challenging to operate in multiple positions, we suggest a mobile vertiport that can fit into a small space. A mobile station is appropriate for dual-mode flying cars since it includes critical activities such as transporting AEVs from the ground and charging as well as a cabin-delivery system. The mobile station can generate a path to the AEV by calculating the relative position using the markers attached to the AEV and estimating the position of the landing AEV. The mobile station detects a marker for precise positioning correction, followed by exact position correction for cabin delivery, to travel to the accurate position of the AEV. To increase the success rate of cabin delivery, docking markers are identified and the angle position error between the mobile station and cabin is computed and corrected to rectify the position between the cabin and the mobile station for cabin delivery. In addition, the experimental results revealed a mechanically correctable error range that encompassed all experimental values. Consequently, this study showed that image processing may be used to create a mobile station for dual-mode flying automobiles

Development and Application of an Integrated Management System for Off-Site Construction Projects

The off-site construction (OSC) method has attracted the interest of experts to resolve productivity stagnation and lack of skilled workforce and to reduce greenhouse gas emissions in the construction industry. Due to the unique characteristics of OSC projects, wherein building elements are produced in a factory, transported, and installed in the field, a management approach that differs from the management techniques of previous construction projects is required. Accordingly, with this study, we examined the characteristics of OSC projects and derived key management items through literature review, case analysis, and expert meetings to develop an integrated management system for OSC projects (OSC-IMS). The proposed system, OSC-IMS, integrates the entire supply chain of the OSC project. It includes the following functions: drawing management, scheduling and planning, site installation planning, production planning, production monitoring, shipping and transportation, delivery and inspection, site installation monitoring, and progress payment management. To verify the applicability and effectiveness of OSC-IMS, it was implemented in four projects. The application of the system to the case studies demonstrated the improvements in work efficiency and accuracy and decreased waste time in every work step. The findings indicate that the system can enhance project performance. This study contributes to the identification of the features and key elements of OSC management such that these factors can be linked with managing system development. This work describes the overall effect of the proposed system on real projects

Estimation and Prediction of Record Values Using Pivotal Quantities and Copulas

Recently, the area of sea ice is rapidly decreasing due to global warming, and since the Arctic sea ice has a great impact on climate change, interest in this is increasing very much all over the world. In fact, the area of sea ice reached a record low in September 2012 after satellite observations began in late 1979. In addition, in early 2018, the glacier on the northern coast of Greenland began to collapse. If we are interested in record values of sea ice area, modeling relationships of these values and predicting future record values can be a very important issue because the record values that consist of larger or smaller values than the preceding observations are very closely related to each other. The relationship between the record values can be modeled based on the pivotal quantity and canonical and drawable vine copulas, and the relationship is called a dependence structure. In addition, predictions for future record values can be solved in a very concise way based on the pivotal quantity. To accomplish that, this article proposes an approach to model the dependence structure between record values based on the canonical and drawable vine. To do this, unknown parameters of a probability distribution need to be estimated first, and the pivotal-based method is provided. In the pivotal-based estimation, a new algorithm to deal with a nuisance parameter is proposed. This method allows one to reduce computational complexity when constructing exact confidence intervals of functions with unknown parameters. This method not only reduces computational complexity when constructing exact confidence intervals of functions with unknown parameters, but is also very useful for obtaining the replicated data needed to model the dependence structure based on canonical and drawable vine. In addition, prediction methods for future record values are proposed with the pivotal quantity, and we compared them with a time series forecasting method in real data analysis. The validity of the proposed methods was examined through Monte Carlo simulations and analysis for Arctic sea ice data

Improved Manufacturability and In Vivo Comparative Pharmacokinetics of Dapagliflozin Cocrystals in Beagle Dogs and Human Volunteers

Dapagliflozin (DAP), which improves glycemic control in patients with type 2 diabetes mellitus, has poor physical properties against heat and moisture, thus hindering its manufacturing potential. The superior physicochemical properties of a recently developed cocrystal of DAP and citric acid (DAP cocrystal) in comparison with those of DAP and Forxiga®, a patented solvate form with propandiol monohydrate, were identified via structural analysis and moisture sorption isotherm. For the first time, the formulation, manufacturability, and in vivo bioavailability of DAP cocrystals were successfully investigated to develop oral dosage forms that substitute Forxiga®. The intrinsic dissolution rate of DAP cocrystal was controlled by varying particle size distribution. Unlike the direct compression (DC), roller compaction (RC) was more preferable to obtain good flowability of dry granules for a continuous manufacturing system. The cocrystal structure was maintained throughout the stability assessment period. In Vitro dissolution pattern differences of the optimized DAP cocrystal tablet with RC and the reference tablet, Forxiga® 10 mg, were pharmaceutically equivalent within 5% in four different media. Furthermore, comparative pharmacokinetic analysis confirmed that a 10 mg DAP cocrystal tablet with RC was bioequivalent to a 10 mg Forxiga® tablet, as assessed in beagle dogs and human volunteers