Numerical comparison of pipe-column-separation models

Results comparing six column-separation numerical models for simulating localized vapor cavities and distributed vaporous cavitation in pipelines are presented. The discrete vapor-cavity model (DVCM) is shown to be quite sensitive to selected input parameters. For short pipeline systems, the maximum pressure rise following column separation can vary markedly for small changes in wave speed, friction factor, diameter, initial velocity, length of pipe, or pipe slope. Of the six numerical models, three perform consistently over a broad number of reaches. One of them, the discrete gas-cavity model, is recommended for general use as it is least sensitive to input parameters or to the selected discretization of the pipeline. Three models provide inconsistent estimates of the maximum pressure rise as the number of reaches is increased; however, these models do give consistent results provided the ratio of maximum cavity size to reach volume is kept below 10%.Angus R. Simpson and Anton Bergan

A bioprinted cardiac patch composed of cardiac-specific extracellular matrix and progenitor cells for heart repair

Congenital heart defects are present in 8 of 1000 newborns and palliative surgical therapy has increased survival. Despite improved outcomes, many children develop reduced cardiac function and heart failure requiring transplantation. Human cardiac progenitor cell (hCPC) therapy has potential to repair the pediatric myocardium through release of reparative factors, but therapy suffers from limited hCPC retention and functionality. Decellularized cardiac extracellular matrix hydrogel (cECM) improves heart function in animals, and human trials are ongoing. In the present study, a 3D-bioprinted patch containing cECM for delivery of pediatric hCPCs is developed. Cardiac patches are printed with bioinks composed of cECM, hCPCs, and gelatin methacrylate (GelMA). GelMA-cECM bioinks print uniformly with a homogeneous distribution of cECM and hCPCs. hCPCs maintain >75% viability and incorporation of cECM within patches results in a 30-fold increase in cardiogenic gene expression of hCPCs compared to hCPCs grown in pure GelMA patches. Conditioned media from GelMA-cECM patches show increased angiogenic potential (>2-fold) over GelMA alone, as seen by improved endothelial cell tube formation. Finally, patches are retained on rat hearts and show vascularization over 14 d in vivo. This work shows the successful bioprinting and implementation of cECM-hCPC patches for potential use in repairing damaged myocardium

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Structural analysis of DNA binding by C.Csp231I, a member of a novel class of R-M controller proteins regulating gene expression

In a wide variety of bacterial restriction–modification systems, a regulatory `controller' protein (or C-protein) is required for effective transcription of its own gene and for transcription of the endonuclease gene found on the same operon. We have recently turned our attention to a new class of controller proteins (exemplified by C.Csp231I) that have quite novel features, including a much larger DNA-binding site with an 18 bp (∼60 Å) spacer between the two palindromic DNA-binding sequences and a very different recognition sequence from the canonical GACT/AGTC. Using X-ray crystallography, the structure of the protein in complex with its 21 bp DNA-recognition sequence was solved to 1.8 Å resolution, and the molecular basis of sequence recognition in this class of proteins was elucidated. An unusual aspect of the promoter sequence is the extended spacer between the dimer binding sites, suggesting a novel interaction between the two C-protein dimers when bound to both recognition sites correctly spaced on the DNA. A U-bend model is proposed for this tetrameric complex, based on the results of gel-mobility assays, hydrodynamic analysis and the observation of key contacts at the interface between dimers in the crystal.</jats:p

Pipeline column separation flow regimes

A generalized set of pipeline column separation equations is presented describing all conventional types of low-pressure regions. These include water hammer zones, distributed vaporous cavitation, vapor cavities, and shocks (that eliminate distributed vaporous cavitation zones). Numerical methods for solving these equations are then considered, leading to a review of three numerical models of column separation. These include the discrete vapor cavity model, the discrete gas cavity model, and the generalized interface vaporous cavitation model. The generalized interface vaporous cavitation model enables direct tracking of actual column separation phenomena (e.g., discrete cavities, vaporous cavitation zones), and consequently, better insight into the transient event. Numerical results from the three column separation models are compared with results of measurements for a number of flow regimes initiated by a rapid closure of a downstream valve in a sloping pipeline laboratory apparatus. Finally, conclusions are drawn about the accuracy of the modeling approaches. A new classification of column separation (active or passive) is proposed based on whether the maximum pressure in a pipeline following column separation results in a short-duration pressure pulse that exceeds the magnitude of the Joukowsky pressure rise for rapid valve closure.Anton Bergant and Angus R. Simpso

Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling

Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach called the rollout or Pilot method. These methods may be seen as meta-heuristics that can enhance (any) heuristic solution, by repetitively modifying a master solution: similarly to what is done in game tree search, better choices are identified using lookahead, based on solutions obtained by repeatedly using a greedy heuristic. This paper first illustrates how the Pilot method improves upon some simple well known dispatch heuristics for the job-shop scheduling problem. The Pilot method is then shown to be a special case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the Pilot method, MCTS methods use random completion of partial solutions to identify promising branches of the tree. The Pilot method and a simple version of MCTS, using the $\varepsilon$-greedy exploration paradigms, are then compared within the same framework, consisting of 300 scheduling problems of varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012

Oblique Confinement and Phase Transitions in Chern-Simons Gauge Theories

We investigate non-perturbative features of a planar Chern-Simons gauge theory modeling the long distance physics of quantum Hall systems, including a finite gap M for excitations. By formulating the model on a lattice, we identify the relevant topological configurations and their interactions. For M bigger than a critical value, the model exhibits an oblique confinement phase, which we identify with Lauglin's incompressible quantum fluid. For M smaller than the critical value, we obtain a phase transition to a Coulomb phase or a confinement phase, depending on the value of the electromagnetic coupling.Comment: 8 pages, harvmac, DFUPG 91/94 and MPI-PhT/94-9

Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review

OBJECTIVE: Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data. DESIGN: Methodological review of existing literature SETTING: We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomised longitudinal health data through quasi-experimental analysis. RESULTS: Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for time-dependent confounding. Other less frequently used methods included prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis and negative control outcomes. CONCLUSIONS: Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in non-randomised, longitudinal studies, but researchers often fail to take full advantage of available longitudinal information. A range of promising new methods have been developed, but further studies are needed to understand their relative performance in different contexts before they can be recommended for widespread use