Conceptual design for spacelab pool boiling experiment

A pool boiling heat transfer experiment to be incorporated with a larger two-phase flow experiment on Spacelab was designed to confirm (or alter) the results of earth-normal gravity experiments which indicate that the hydrodynamic peak and minimum pool boiling heat fluxes vanish at very low gravity. Twelve small sealed test cells containing water, methanol or Freon 113 and cylindrical heaters of various sizes are to be built. Each cell will be subjected to one or more 45 sec tests in which the surface heat flux on the heaters is increased linearly until the surface temperature reaches a limiting value of 500 C. The entire boiling process will be photographed in slow-motion. Boiling curves will be constructed from thermocouple and electric input data, for comparison with the motion picture records. The conduct of the experiment will require no more than a few hours of operator time

Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

Background:There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.Methods:In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.Results:We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap.Conclusions:Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the inter-active knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data

Insulin Stimulates the Phosphorylation of the Exocyst Protein Sec8 in Adipocytes

The signal transduction pathway leading from the insulin receptor to stimulate the fusion of vesicles containing the glucose transporter GLUT4 with the plasma membrane in adipocytes and muscle cells is not completely understood. Current evidence suggests that in addition to the Rab GTPase-activating protein AS160, at least one other substrate of Akt (also called protein kinase B), which is as yet unidentified, is required. Sec8 is a component of the exocyst complex that has been previously implicated in GLUT4 trafficking. In the present study, we report that insulin stimulates the phosphorylation of Sec8 on Ser-32 in 3T3-L1 adipocytes. On the basis of the sequence around Ser-32 and the finding that phosphorylation is inhibited by the PI3K (phosphoinositide 3-kinase) inhibitor wortmannin, it is likely that Akt is the kinase for Ser-32. We examined the possible role of Ser-32 phosphorylation in the insulin-stimulated trafficking of GLUT4, as well as the TfR (transferrin receptor), to the plasma membrane by determining the effects of overexpression of the non-phosphorylatable S32A mutant of Sec8 and the phosphomimetic S32E mutant of Sec8. Substantial overexpression of both mutants had no effect on the amount of GLUT4 or TfR at the cell surface in either the untreated or insulin-treated states. These results indicate that insulin-stimulated phosphorylation of Sec8 is not part of the mechanism by which insulin enhances the fusion of vesicles with the plasma membrane

Topology and Evolution of Technology Innovation Networks

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the US Patent and Trademark Office (USPTO). We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such attachment kernel is shared by scientific citation networks, thus indicating an universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.Comment: 6 pages, 5 figures, submitted to Physical Review

Numerical Capture and Validation of a Massively Separated Bluff-Body Wake

A flow over a bluff-body is numerically investigated and validated using a Detached-Eddy Simulation (DES) technique at Re=21,400. An incompressible solver that is nominally second-order accurate employing an implicit constant backward time-stepping scheme with blended upwind-central differencing spatial discretization is used to study the massively separated wake that is generated. Measurements are taken up to 6 downstream characteristic lengths, evaluating the wake time-averaged first- and second-moment statistics alongside near-wall boundary layer quantities and surface-force integrals. Results advocate the use of DES methods, which are found to be significantly more accurate for capturing wake statistics, compared to two different Reynolds-Averaged (RANS) models calibrated with an identical grid. Although comparative accuracy can be obtained with the RANS techniques for the boundary layer and surface-forces, these techniques are unsuitable for modeling wake statistics as they are inherently dissipative, evident through early velocity recovery when evaluated against experimental data

Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. Iso-Tools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools

A New Hybrid Debugging Architecture for Eclipse

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-14125-1_11[EN] During many years, print debugging has been the most used method for debugging. Nowadays, however, industrial languages come with a trace debugger that allows programmers to trace computations step by step using breakpoints and state viewers. Almost all modern programming environments include a trace debugger that allows us to inspect the state of a computation in any given point. Nevertheless, this debugging method has been criticized for being completely manual and time-consuming. Other debugging techniques have appeared to solve some of the problems of Trace Debugging, but they suffer from other problems such as scalability. In this work we present a new hybrid debugging technique. It is based on a combination of Trace Debugging, Algorithmic Debugging and Omniscient Debugging to produce a synergy that exploits the best properties and strong points of each technique. We describe the architecture of our hybrid debugger and our implementation that has been integrated into Eclipse as a plugin.This work has been partially supported by the Spanish Ministerio de Economía y Competitividad (Secretaria de Estado de Investigación, Desarrollo e Innovación) under grant TIN2008-06622-003-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052. David Insa was partially supported by the Spanish Ministerio de Educación under FPU grant AP2010-4415.González, J.; Insa Cabrera, D.; Silva Galiana, JF. (2013). A New Hybrid Debugging Architecture for Eclipse. En Logic-Based Program Synthesis and Transformation. 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