Transesophageal Echocardiography During Cardiopulmonary Resuscitation Is Associated With Shorter Compression Pauses Compared With Transthoracic Echocardiography.

STUDY OBJECTIVE: Point-of-care ultrasonography provides diagnostic information in addition to visual pulse checks during cardiopulmonary resuscitation (CPR). The most commonly used modality, transthoracic echocardiography, has unfortunately been repeatedly associated with prolonged pauses in chest compressions, which correlate with worsened neurologic outcomes. Unlike transthoracic echocardiography, transesophageal echocardiography does not require cessation of compressions for adequate imaging and provides the diagnostic benefit of point-of-care ultrasonography. To assess a benefit of transesophageal echocardiography, we compare the duration of chest compression pauses between transesophageal echocardiography, transthoracic echocardiography, and manual pulse checks on video recordings of cardiac arrest resuscitations. METHODS: We analyzed 139 pulse check CPR pauses among 25 patients during cardiac arrest. RESULTS: Transesophageal echocardiography provided the shortest mean pulse check duration (9 seconds [95% confidence interval {CI} 5 to 12 seconds]). Mean pulse check duration with transthoracic echocardiography was 19 seconds (95% CI 16 to 22 seconds), and it was 11 seconds (95% CI 8 to 14 seconds) with manual checks. Intraclass correlation coefficient between abstractors for a portion of individual and average times was 0.99 and 0.99, respectively (P<.001 for both). CONCLUSION: Our study suggests that pulse check times with transesophageal echocardiography are shorter versus with transthoracic echocardiography for ED point-of-care ultrasonography during cardiac arrest resuscitations, and further emphasizes the need for careful attention to compression pause duration when using transthoracic echocardiography for point-of-care ultrasonography during ED cardiac arrest resuscitations.

SBOL Visual: A Graphical Language for Genetic Designs.

Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.

Simulated radiographic bone and joint modeling from 3D ankle MRI: feasibility and comparison with radiographs and 2D MRI.

PURPOSE: The purpose of this work is to simulate radiographs from isotropic 3D MRI data, compare relationship of angle and joint space measurements on simulated radiographs with corresponding 2D MRIs and real radiographs (XR), and compare measurement times among the three modalities. MATERIALS AND METHODS: Twenty-four consecutive ankles were included, eight males and 16 females, with a mean age of 46 years. Segmented joint models simulating radiographs were created from 3D MRI data sets. Three readers independently performed blinded angle and joint space measurements on the models, corresponding 2D MRIs, and XRs at two time points. Linear mixed models and the intraclass correlation coefficient (ICC) was ascertained, with p values less than 0.05 considered significant. RESULTS: Simulated radiograph models were successfully created in all cases. Good agreement (ICC > 0.65) was noted among all readers across all modalities and among most measurements. Absolute measurement values differed between modalities. Measurement time was significantly greater (p < 0.05) on 2D versus simulated radiographs for most measurements and on XR versus simulated radiographs (p < 0.05) for nearly half the measurements. CONCLUSIONS: Simulated radiographs can be successfully generated from 3D MRI data; however, measurements differ. Good inter-reader and moderate-to-good intra-reader reliability was observed and measurements obtained on simulated radiograph models took significantly less time compared to measurements with 2D and generally less time than XR.

Ensemble Detection of DNA Engineering Signatures.

Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.

Engineered yeast genomes accurately assembled from pure and mixed samples.

Yeast whole genome sequencing (WGS) lacks end-to-end workflows that identify genetic engineering. Here we present Prymetime, a tool that assembles yeast plasmids and chromosomes and annotates genetic engineering sequences. It is a hybrid workflow-it uses short and long reads as inputs to perform separate linear and circular assembly steps. This structure is necessary to accurately resolve genetic engineering sequences in plasmids and the genome. We show this by assembling diverse engineered yeasts, in some cases revealing unintended deletions and integrations. Furthermore, the resulting whole genomes are high quality, although the underlying assembly software does not consistently resolve highly repetitive genome features. Finally, we assemble plasmids and genome integrations from metagenomic sequencing, even with 1 engineered cell in 1000. This work is a blueprint for building WGS workflows and establishes WGS-based identification of yeast genetic engineering.

TASBE Flow Analytics: A Package for Calibrated Flow Cytometry Analysis.

Flow cytometry is a powerful method for high-throughput precision measurement of cell fluorescence and size. Effective use of this tool for quantification of synthetic biology devices and circuits, however, generally requires careful application of complex multistage workflows for calibration, filtering, and analysis with appropriate statistics. The TASBE Flow Analytics package provides a free, open, and accessible implementation of such workflows in a form designed for high-throughput analysis of large synthetic biology data sets. Given a set of experimental samples and controls, this package can process them to output calibrated data, quantitative analyses and comparisons, automatically generated figures, and detailed debugging and diagnostic reports in both human-readable and machine-readable forms. TASBE Flow Analytics can be used through a simple user-friendly interactive Excel interface, as a library supporting Matlab, Octave, or Python interactive sessions, or as a component integrated into automated workflows.

Managing bioengineering complexity with AI techniques.

Our capabilities for systematic design and engineering of biological systems are rapidly increasing. Effectively engineering such systems, however, requires the synthesis of a rapidly expanding and changing complex body of knowledge, protocols, and methodologies. Many of the problems in managing this complexity, however, appear susceptible to being addressed by artificial intelligence (AI) techniques, i.e., methods enabling computers to represent, acquire, and employ knowledge. Such methods can be employed to automate physical and informational "routine" work and thus better allow humans to focus their attention on the deeper scientific and engineering issues. This paper examines the potential impact of AI on the engineering of biological organisms through the lens of a typical organism engineering workflow. We identify a number of key opportunities for significant impact, as well as challenges that must be overcome.

Accurate predictions of genetic circuit behavior from part characterization and modular composition.

A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIP's precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.

Automated selection of synthetic biology parts for genetic regulatory networks.

Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching ensures that the mapping satisfies the regulatory relationships in the abstract design. Signal matching ensures that the expression levels of functional units are compatible. Finally, part matching finds a DNA part sequence that can implement the design. Our software tool MatchMaker implements these three steps.

An end-to-end workflow for engineering of biological networks from high-level specifications.

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.