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Physics-constrained data-driven computing is a hybrid approach that integrates universal physical laws with data-based models of experimental data to enhance the scientific computing. A new data-driven simulation approach enriched with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance accuracy and robustness against noise and outliers in data sets in the data-driven computing. In this approach, for a given state obtained by the physical simulation, the corresponding optimum experimental solution is sought by projecting the state onto the associated local convex manifold reconstructed based on the nearest experimental data. This learning process of local data structure is less sensitivity to noisy data and consequently yields better accuracy. A penalty relaxation is also introduced to recast the local learning solver in the context of non-negative least squares that can be solved effectively. The reproducing kernel approximation with stabilized nodal integration are employed for the solution of the physical manifold to allow reduced stress-strain data at the discrete points for enhanced effectiveness in the LCDD learning solver. Due to the inherent manifold learning properties, LCDD performs well for high-dimensional data sets that are relatively sparse in real-world engineering applications. Numerical tests demonstrated that LCDD enhances nearly one order of accuracy compared to the standard distance-minimization data-driven scheme when dealing with noisy database, and a linear exactness is achieved when local stress-strain relation is linear.

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Gastrointestinal bacteria and epithelia contribute to systemic inflammation and infections in critically ill patients, but the gut microbiota in these diseases has not been analyzed dynamically by molecular fingerprinting methods. This study aimed to identify ileal flora dysbiosis pattern and bacterial species that changed significantly in a rat model of intestinal ischemia and reperfusion and illustrate time courses of both epithelial alterations and gut flora variations in the same injury. Forty-eight rats were randomized into eight groups (n = 6/group). Six groups underwent superior mesenteric artery occlusion for 30 min and were killed at 1, 3, 6, 12, 24, and 72 h following reperfusion, respectively; a group of rats were killed just after anesthesia (control), and a sham-operated group received 12-h reperfusion. Denaturing gradient gel electrophoresis of ileal microbiota showed that gut flora pattern changed early after intestinal ischemia and reperfusion, differed significantly at 12 h of reperfusion, and then started to recover toward normal pattern. The specific dysbiosis were characterized by Escherichia coli proliferation and Lachnospiraceae and Lactobacilli reduction. These bacteria that contributed most were identified by principal component analysis and sequencing and confirmed by real-time polymerase chain reaction. In addition, alterations of ileal microbiota followed epithelial changes in the time course of reperfusion.

Persistent pulmonary hypertension of the newborn (PPHN) is a clinical syndrome characterized by increased medial and adventitial thickness of the lung vasculature. The underlying mechanisms that regulate the cell phenotype alteration during PPHN remodeling are largely unknown. We randomly selected newborn rats that were exposed to hypoxia (10-12%) or room air for 2 weeks and used a microarray to identify the lung tissue microRNAs (miRNAs) involved in PPHN progression. The role of a key miRNA that affects the endothelial-to-mesenchymal transition (EndMT) in primary cultured rat pulmonary microvascular endothelial cells (RPMECs) was investigated. The expression of miR-126a-5p was elevated in the PPHN model according to microarray analysis. The relative expression of miR-126a-5p in RPMECs increased when they were exposed to hypoxia (P<0.05), consistent with the microarray results. Pecam1 expression decreased, whereas alpha-smooth muscle actin (α-SMA) increased in the hypoxic RPMECs. Knockdown of miR-126a-5p in RPMECs followed by treatment with hypoxia for 48 h resulted in a significant increase in the expression of Pecam1 and a reduction in α-SMA expression, with a simultaneous increase in PI3K (p85β) and phosphorylation of AKT at serine 473 compared with the negative control. Finally, the circulating miR-126a-5p concentration was upregulated in the PPHN model compared with healthy neonates. We concluded that hypoxia changed the cell homeostasis and that miR-126a-5p was upregulated in PPHN, which is partly responsible for hypoxia-induced EndMT. The mechanism underlying the upregulation of miR-126a-5p by hypoxia probably acts through the p85-β/p-AKT pathway.

Advection-dispersion equations (ADEs) are an important class of partial differential equations (PDEs) that are used to describe transport phenomena in the fields of hydrology (Ingham & Ma, 2005) and hydrogeology (Patil & Chore, 2014).We consider both forward and backward ADEs. In the former case, the initial conditions at time t 0 as well as boundary conditions are specified, and the solutions of forward ADEs are found for later times. This is a well-posed problem with well-established numerical methods. Nevertheless, there are some challenges in numerically solving forward ADEs, mainly associated with the advection-dominated problems. In backward ADEs, the concentration is known at later (terminal) times and solutions are sought for earlier times. Backward ADEs arise in the source identification problems (Neupauer & Wilson, 1999;Wilson & Liu, 1994) and could lead to numerically unstable grid-based solutions that require some form of regularization (Xiong et al., 2006) or should be solved as an inverse problem that is computationally more expensive because it requires solving forward problems multiple times (Atmadja & Bagtzoglou, 2001).Numerical discretization-based methods, including the finite element (FE) and finite difference (FD) methods, are commonly used for solving the Darcy flow equation and ADE. Discretization-based methods approximate the PDE solution with its values at a set of grid points distributed over the spatiotemporal domain. The discrete solution is obtained by discretizing the time and spatial derivatives of state variables. It is worth noting that the space-dependent parameters such as hydraulic conductivity are usually given not as a continuous field but as a set of values at the grid points.The combination of an advection (first-order) term and a dispersion (second-order) term in ADEs present several challenges for numerical methods. For example, for advection-dominated transport (i.e., Péclet number Pe ≫ 1), the numerical solutions can develop oscillations (over-or undershoot) or numerical dispersion (Huyakorn, 2012;Pinder & Gray, 2013). These two numerical issues are closely related, and a numerical scheme developed to reduce numerical dispersion generally causes oscillation, whereas the suppression of oscillation comes at the cost of increased numerical dispersion (Wang & Lacroix, 1997).

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Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age-related