AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit.

Neural network based integration of assays to assess pathogenic potential.

Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, however, are generated with a specific objective and which makes integration challenging.

A single institutional experience with suction thrombectomy in acute mesenteric ischemia.

Acute mesenteric ischemia (AMI) is typically treated by open surgery or hybrid techniques. Catheter-based aspiration thrombectomy represents another minimally invasive alternative with a potential additional safety benefit of minimizing the bleeding risk associated with thrombolytics. In this institutional case series, we present five clinical cases of aspiration thrombectomy for high-risk AMI using the Penumbra aspiration system.

A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments.

RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST).

Motivation: Transcriptomics is a common approach to identify changes in gene expression induced by a disease state. Standard transcriptomic analyses consider differentially expressed genes (DEGs) as indicative of disease states so only a few genes would be treated as signals when the effect size is small, such as in brain tissue. For tissue with small effect sizes, if the DEGs do not belong to a pathway known to be involved in the disease, there would be little left in the transcriptome for researchers to follow up with.

Prediction of whole-cell transcriptional response with machine learning.

Motivation: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations.

Situated Precision Healthcare in the Smart Medical Home: Bringing NASA's Research Strategy down to Earth.

This special issue is ambitious in that it calls for strategic transformation in research on Alzheimer's Disease (AD) and related dementias, including innovation in both research design and value delivery, through lifestyle interventions that implicitly relate to a much broader range of comorbidities and diseases of aging. One response to this challenge is to venture beyond the boundaries of research that supports the healthcare industry. Toward this end, we introduce opportunities for research translation and knowledge transfer from NASA to the healthcare industry.

Validation of a preoperative prediction model for mortality within 1 year after endovascular aortic aneurysm repair of intact aneurysms.

Objective: Most would agree that at least 1-year survival is necessary after intact abdominal aortic aneurysm (AAA) repair to appropriately justify the cost and risk of the procedure. No validated clinical decision instruments exist to predict survival after endovascular aneurysm repair (EVAR) beyond the perioperative period. The purpose of this analysis was to create a preoperative prediction model for 1-year mortality after EVAR for intact AAA in the Society for Vascular Surgery Vascular Quality Initiative.

Outcomes of patients with type I endoleak at completion of endovascular abdominal aneurysm repair.

Background: Type I endoleak (TIE) during endovascular aneurysm repair (EVAR) is usually identified and treated intraoperatively. We evaluated the outcomes of patients who, despite possible treatment, had TIE at completion of EVAR.

Methods: We examined consecutive EVAR for nonruptured abdominal aortic aneurysm (AAA) within the Vascular Study Group of New England database (2003-2012) and compared the outcomes of patients who had TIE at completion with those who did not.

A randomized comparison of a 3-week and 6-week vascular surgery simulation course on junior surgical residents' performance of an end-to-side anastomosis.

Objective: We assessed the effect of an open vascular simulation course on the surgical skill of junior surgical residents in performing a vascular end-to-side anastomosis and determined the course length required for effectiveness. We hypothesized that a 6-week course would significantly increase the surgical skill of junior residents in performing an end-to-side anastomosis, while a 3-week course would not.

Methods: We randomized 37 junior residents (postgraduate year 1 to 3) to a course consisting of three (short course, n = 18) or six (long course, n = 19) consecutive weekly 1-hour teaching sessions.