Thermodynamic analysis and subscale modeling of space-based orbit transfer vehicle cryogenic propellant resupply

The resupply of the cryogenic propellants is an enabling technology for spacebased orbit transfer vehicles. As part of the NASA Lewis ongoing efforts in microgravity fluid management, thermodynamic analysis and subscale modeling techniques were developed to support an on-orbit test bed for cryogenic fluid management technologies. Analytical results have shown that subscale experimental modeling of liquid resupply can be used to validate analytical models when the appropriate target temperature is selected to relate the model to its prototype system. Further analyses were used to develop a thermodynamic model of the tank chilldown process which is required prior to the no-vent fill operation. These efforts were incorporated into two FORTRAN programs which were used to present preliminary analyticl results

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell

Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing

Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5–5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing.National Institutes of Health (U.S.) (Intramural Research Program)National Human Genome Research Institute (U.S.)Charles University (program UNCE 204011)Charles University (program PRVOUK-P24/LF1/3)Czech Republic. Ministry of Education, Youth, and Sports (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant LH12015)National Institutes of Health (U.S.) (Harvard Digestive Diseases Center, grant DK34854

Disposition of quinapril and quinaprilat in the isolated perfused rat kidney

An isolated perfused rat kidney model was used to probe the renal disposition of quinapril and quinaprilat after separate administration of each drug species. Control studies were performed with drug-free perfusate ( n=8 ) and perfusate containing quinapril ( n=9 ) quinaprilat ( n=7 ) at initial drug concentrations of 1000 ng/ml (including corresponding tracer levels of tritiated drug). Physiologic parameters were within the normal range of values for this technique and were stable for the duration of each experiment. Quinapril and quinaprilat concentrations were determined in perfusate, urine, and perfusate ultrafiltrate using a specific and sensitive reversed-phase HPLC procedure with radiochemical detection, coupled to liquid scintillation spectrometry. Perfusate protein binding was determined using an ultrafiltration method at 37°C. The total renal learance of quinapril ( CLr ) was calculated as Dose/AUC (0-∞), and is represented by the sum of its urinary and metabolic clearances. The urinary clearances ( CLe ) of quinapril and quinaprilat were calculated as urinary excretion rate divided by midpoint perfusate concentration for each respective species. Of the total renal clearance for quinapril ( CLr =4.49 ml/min), less than 0.1% was cleared as unchanged drug ( CLe =0.004 ml/min); over 99% of the drug was cleared as quinaprilat formed in the kidney. The clearance ratio of quinapril [ CR=CLr/(fu·GFR )] was 41.0, a value representing extensive tubular secretion into the renal cells. Following quinaprilat administration, the clearance ratio of metabolite [ CR=CLe/(fu β GFR) ] was 3.85, indicating a net secretion process for renal elimination.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45050/1/10928_2006_Article_BF02354286.pd

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

<div><p>Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.</p></div

Performing image segmentation with deep convolutional neural networks.

<p>(a) Image segmentation can be recast as an image classification task that is amenable to a supervised machine learning approach. A manually annotated image is converted into a training dataset by sampling regions around boundary, interior, and background pixels. These sample images are then used to train an image classifier that can then be applied to new images. (b) The mathematical structure of a conv-net. A conv-net can be broken down into two components. The first component is dimensionality reduction through the iterative application of three operations—convolutions, a transfer function, and down sampling. The second component is a classifier that uses the representation and outputs scores for each class.</p

Sample images from live-cell experiments that were segmented using conv-nets.

<p>Images of bacterial and mammalian cells were segmented using trained conv-nets and additional downstream processing. Thresholding for bacterial cells and an active contour based approach for mammalian cells were used to convert the conv-net prediction into a segmentation mask.</p