G ‐scores: A method for identifying disease‐causing pathogens with application to lower respiratory tract infections

Lower respiratory tract infections (LRTIs) are well known for the lack of a good diagnostic method. The main difficulty lies in the fact that there are a variety of pathogens causing LRTIs, and their management and treatment are quite different. The development of quantitative real‐time loop‐mediated isothermal amplification (qrt‐LAMP) made it possible to rapidly amplify and quantify multiple pathogens simultaneously. The question that remains to be answered is how accurate and reliable is this method? More importantly, how are qrt‐LAMP measurements utilized to inform/suggest medical decisions? When does a pathogen start to grow out of control and cause infection? Answers to these questions are crucial to advise treatment guidance for LRTIs and also helpful to design phase I/II trials or adaptive treatment strategies. In this article, our main contributions include the following two aspects. First, we utilize zero‐inflated mixture models to provide statistical evidence for the validity of qrt‐LAMP being used in detecting pathogens for LRTIs without the presence of a gold standard test. Our results on qrt‐LAMP suggest that it provides reliable measurements on pathogens of interest. Second, we propose a novel statistical approach to identify disease‐causing pathogens, that is, distinguish the pathogens that colonize without causing problems from those that rapidly grow and cause infection. We achieve this by combining information from absolute quantities of pathogens and their symbiosis information to form G ‐scores. Change‐point detection methods are utilized on these G ‐scores to detect the three phases of bacterial growth—lag phase, log phase, and stationary phase. Copyright © 2014 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107530/1/sim6129-sup-0001-supplemental_new.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107530/2/sim6129.pd

Topoisomerase Inhibitors Addressing Fluoroquinolone Resistance in Gram-Negative Bacteria.

Since their discovery over 5 decades ago, quinolone antibiotics have found enormous success as broad spectrum agents that exert their activity through dual inhibition of bacterial DNA gyrase and topoisomerase IV. Increasing rates of resistance, driven largely by target-based mutations in the GyrA/ParC quinolone resistance determining region, have eroded the utility and threaten the future use of this vital class of antibiotics. Herein we describe the discovery and optimization of a series of 4-(aminomethyl)quinolin-2(1H)-ones, exemplified by 34, that inhibit bacterial DNA gyrase and topoisomerase IV and display potent activity against ciprofloxacin-resistant Gram-negative pathogens. X-ray crystallography reveals that 34 occupies the classical quinolone binding site in the topoisomerase IV-DNA cleavage complex but does not form significant contacts with residues in the quinolone resistance determining region

Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network

In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters

A Novel Method for Matching Reservoir Parameters Based on Particle Swarm Optimization and Support Vector Machine

When the reservoir physical properties are distributed very dispersedly, the matching precision of these reservoir parameters is not good. We propose a novel method for matching the reservoir physical properties based on particle swarm optimization (PSO) and support vector machine (SVM) algorithm. First, the data structure characteristics of the reservoir physical properties are analyzed. Then, the particle swarm differential perturbation evolution algorithm is used to cluster and characterize the reservoir physical properties. Finally, by using the SVM algorithm for feature reorganization and the least squares matching of the extracted reservoir physical properties, the feature quantity of the reservoir physical properties can be accurately mined and the pressure matching precision is improved. The experimental results show that employing the proposed method to analyze and sample the data characteristics of the physical properties of the reservoir is better. The extracted parameters can effectively reflect the physical characteristics of oil reservoirs. The proposed method has potential applications in guiding the exploration and development of oil reservoirs

A new method to determine varying adsorbed density based on Gibbs isotherm of supercritical gas adsorption

In the calculation of the absolute adsorption of supercritical gas adsorbed on the microporous materials, most existing methods regard the adsorbed density as a constant, which is very unreasonable. In this study, an extended pressure point method combined with Langmuir adsorption model is proposed in which the varying adsorbed density under different pressures is considered at the same time. The utility of the proposed method to correlate accurately the experimental data for supercritical gas adsorption system is demonstrated by high-pressure methane adsorption measurements on two groups of shale samples. Taking advantage of the proposed method, we can obtain the adsorbed density and the adsorbed volume corresponding to different pressures. Compared with the conventional methods under the assumption of fixed and parameterized adsorbed density, the proposed method yields better fitting results with the experimental data. Our work should provide important fundamental understandings and insights into the supercritical gas adsorption system

GPU-Based Computation of Formation Pressure for Multistage Hydraulically Fractured Horizontal Wells in Tight Oil and Gas Reservoirs

A mathematical model for multistage hydraulically fractured horizontal wells (MFHWs) in tight oil and gas reservoirs was derived by considering the variations in the permeability and porosity of tight oil and gas reservoirs that depend on formation pressure and mixed fluid properties and introducing the pseudo-pressure; analytical solutions were presented using the Newman superposition principle. The CPU-GPU asynchronous computing model was designed based on the CUDA platform, and the analytic solution was decomposed into infinite summation and integral forms for parallel computation. Implementation of this algorithm on an Intel i5 4590 CPU and NVIDIA GT 730 GPU demonstrates that computation speed increased by almost 80 times, which meets the requirement for real-time calculation of the formation pressure of MFHWs