Generalized camera calibration model for trapezoidal patterns on the road

published_or_final_versio

A Discontinuous Control Volume Finite Element Method for Multi-Phase Flow in Heterogeneous Porous Media

We present a new, high-order, control-volume-finite-element (CVFE) method for multiphase porous media flow with discontinuous 1st-order representation for pressure and discontinuous 2nd-order representation for velocity. The method has been implemented using unstructured tetrahedral meshes to discretize space. The method locally and globally conserves mass. However, unlike conventional CVFE formulations, the method presented here does not require the use of control volumes (CVs) that span the boundaries between domains with differing material properties. We demonstrate that the approach accurately preserves discontinuous saturation changes caused by permeability variations across such boundaries, allowing efficient simulation of flow in highly heterogeneous models. Moreover, accurate solutions are obtained at significantly lower computational cost than using conventional CVFE methods. We resolve a long-standing problem associated with the use of classical CVFE methods to model flow in highly heterogeneous porous media

Current transport property of n-GaN/n-6H-SiC heterojunction: Influence of interface states

Heterostructures of n-GaNn-6H-SiC grown by hydride vapor phase epitaxy (HVPE) and molecular-beam epitaxy (MBE) are characterized with the current-voltage (I-V), capacitance-voltage (C-V), and deep level transient spectroscopy (DLTS) techniques. Using different contact configurations, the I-V results reveal a rectifying barrier in the n-GaNn-6H-SiC heterostructures. When GaN is negatively biased, the current is exponentially proportional to the applied voltage with the built-in barrier being 0.4-1.1 eV for the HVPE samples and 0.5 eV for the MBE sample. DLTS measurements reveal intense band-like deep level states in the interfacial region of the heterostructure, and the Fermi-level pinning by these deep level defects is invoked to account for the interfacial rectifying barrier of the heterostructures. © 2005 American Institute of Physics.published_or_final_versio

A force-balanced control volume finite element method for multi-phase porous media flow modelling

A novel method for simulating multi-phase flow in porous media is presented. The approach is based on a control volume finite element mixed formulation and new force-balanced finite element pairs. The novelty of the method lies in: (a) permitting both continuous and discontinuous description of pressure and saturation between elements; (b) the use of arbitrarily high-order polynomial representation for pressure and velocity and (c) the use of high-order flux-limited methods in space and to time avoid introducing non-physical oscillations while achieving high-order accuracy where and when possible. The model is initially validated for two-phase flow. Results are in good agreement with analytically obtained solutions and experimental results. The potential of this method is demonstrated by simulating flow in a realistic geometry composed of highly permeable meandering channels

Components of the Hematopoietic Compartments in Tumor Stroma and Tumor-Bearing Mice

Solid tumors are composed of cancerous cells and non-cancerous stroma. A better understanding of the tumor stroma could lead to new therapeutic applications. However, the exact compositions and functions of the tumor stroma are still largely unknown. Here, using a Lewis lung carcinoma implantation mouse model, we examined the hematopoietic compartments in tumor stroma and tumor-bearing mice. Different lineages of differentiated hematopoietic cells existed in tumor stroma with the percentage of myeloid cells increasing and the percentage of lymphoid and erythroid cells decreasing over time. Using bone marrow reconstitution analysis, we showed that the tumor stroma also contained functional hematopoietic stem cells. All hematopoietic cells in the tumor stroma originated from bone marrow. In the bone marrow and peripheral blood of tumor-bearing mice, myeloid populations increased and lymphoid and erythroid populations decreased and numbers of hematopoietic stem cells markedly increased with time. To investigate the function of hematopoietic cells in tumor stroma, we co-implanted various types of hematopoietic cells with cancer cells. We found that total hematopoietic cells in the tumor stroma promoted tumor development. Furthermore, the growth of the primary implanted Lewis lung carcinomas and their metastasis were significantly decreased in mice reconstituted with IGF type I receptor-deficient hematopoietic stem cells, indicating that IGF signaling in the hematopoietic tumor stroma supports tumor outgrowth. These results reveal that hematopoietic cells in the tumor stroma regulate tumor development and that tumor progression significantly alters the host hematopoietic compartment

Effects and Action Mechanisms of Berberine and Rhizoma coptidis on Gut Microbes and Obesity in High-Fat Diet-Fed C57BL/6J Mice

Gut microbes play important roles in regulating fat storage and metabolism. Rhizoma coptidis (RC) and its main active compound, berberine, have either antimicrobial or anti-obesity activities. In the present study, we hypothesize that RC exerts anti-obesity effects that are likely mediated by mechanisms of regulating gut microbes and berberine may be a key compound of RC. Gut microbes and glucose and lipid metabolism in high-fat diet-fed C57BL/6J (HFD) mice in vivo are investigated after RC and berberine treatments. The results show that RC (200 mg/kg) and berberine (200 mg/kg) significantly lower both body and visceral adipose weights, and reduce blood glucose and lipid levels, and decrease degradation of dietary polysaccharides in HFD mice. Both RC and berberine significantly reduce the proportions of fecal Firmicutes and Bacteroidetes to total bacteria in HFD mice. In the trial ex vivo, both RC and berberine significantly inhibit the growth of gut bacteria under aerobic and anaerobic conditions. In in vitro trials, both RC and berberine significantly inhibit the growth of Lactobacillus (a classical type of Firmicutes) under anaerobic conditions. Furthermore, both RC and berberine significantly increase fasting-induced adipose factor (Fiaf, a key protein negatively regulated by intestinal microbes) expressions in either intestinal or visceral adipose tissues. Both RC and berberine significantly increase mRNA expressions of AMPK, PGC1α, UCP2, CPT1α, and Hadhb related to mitochondrial energy metabolism, which may be driven by increased Fiaf expression. These results firstly suggest that antimicrobial activities of RC and berberine may result in decreasing degradation of dietary polysaccharides, lowering potential calorie intake, and then systemically activating Fiaf protein and related gene expressions of mitochondrial energy metabolism in visceral adipose tissues. Taken together, these action mechanisms may contribute to significant anti-obesity effects. Findings in the present study also indicate that pharmacological regulation on gut microbes can develop an anti-obesity strategy

A comparison of estimating crop residue cover from sentinel-2 data using empirical regressions and machine learning methods

© 2020 by the authors. Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2-B4)/(B2-B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC

Effects of AR7 Joint Complex on arthralgia for patients with osteoarthritis: Results of a three-month study in Shanghai, China

<p>Abstract</p> <p>Background</p> <p>Osteoarthritis-induced arthralgia is a common cause of morbidity in both men and women worldwide. AR7 Joint Complex is a nutritional supplement containing various ingredients including sternum collagen II and methylsulfonylmethane. The product has been marketed in United States for over a decade, but clinical data measuring the effectiveness of this supplement in relieving arthralgia is lacking. The goal of this study was to determine the effect of AR7 Joint Complex on osteoarthritis.</p> <p>Methods</p> <p>A total of 100 patients over the age of 50 who had osteoarthritis were recruited to the double-blind study and randomly assigned into either treatment or placebo control groups. The patients in the treatment group were given AR7 Joint Complex orally, 1 capsule daily for 12 weeks, while the patients in the control group were given a placebo for the same period of time. Prior to and at the end of the study, data including Quality of Life questionnaires (SF-36), visual analog scales (1 to 100 mm), and X-rays of affected joints were collected.</p> <p>Results</p> <p>A total of 89 patients completed the study: 44 from the treatment group and 45 from the control group. No significant change in X-ray results was found in either group after the study. However, there was a significant decrease in patients complaining of arthralgia and tenderness (P < 0.01) in the treatment group and there was also a significant difference between the treatment and control groups at the end of the study. In addition, for Quality of Life data, the body pain index (BP) in the treatment group was significantly improved (P < 0.05) compared to the control group. No significant toxicity was noted in either group.</p> <p>Conclusion</p> <p>AR7 Joint Complex appears to have short-term effects in relieving pain in patients with osteoarthritis. Whether such an effect is long-lasting remains to be seen.</p

Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments