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Intraluminal thrombus: Simple bystander or even element in abdominal aortic aneurysm pathogenesis?
Traditional single model based soft sensors may have poor performance on quality prediction for batch processes because of the strong nonlinearity, multiple-phase, and time-varying characteristics. Therefore, a phase partition based ensemble learning framework upon least squares support vector regression (LSSVR) is proposed for soft sensor modeling. Firstly, multiway principal component analysis (MPCA) is employed to handle high-dimensional datasets and extract essential correlation information. Then, different operation phases of the process can be identified by the phase partition strategy based on Gaussian mixture model (GMM) method. Meanwhile, the optimal Gaussian component number is determined by Bayesian information criterion (BIC) technique. Further, multiple localized LSSVR models are constructed to characterize the various dynamic relationships between quality and process variables for local regions, while the grid search (GS) and ten-fold cross-validation methods are introduced to parameter optimization for each local model. Finally, the posterior probability for each test sample with respect to different phases can be estimated by Bayesian inference strategy, and local outputs are integrated to produce the final quality prediction results. Feasibility and superiority of the proposed soft sensor are validated through a case study for penicillin fermentation process. It can achieve satisfactory prediction accuracy and effectively tackle nonlinear and multi-phase modeling problems in chemical and biological processes.High-density urban habitats provide a hotbed for the rapid spread of infectious diseases. School children densely aggregate in classrooms. So schools are high incidence area of infectious diseases. This paper aims at investigating the transmission of influenza-like-illness within households with a school child using a survey study of fourth grade elementary school students in Shanghai, China. We found that the pairwise transmission probability within a household is only 0.172, which implies that the average number of infections caused by a single infectious individual in a household in Shanghai is only 0.304. Thus, the majority of transmission must occur outside of a household for a disease to cause an outbreak.Here we study how the structure and growth of a cellular population vary with the distribution of maturation times from each stage. We consider two cell cycle stages. The first represents early G1. The second includes late G1, S, G2, and mitosis. Passage between the two reflects passage of an important cell cycle checkpoint known as the restriction point. We model the population as a system of partial differential equations. After establishing the existence of solutions, we characterize the maturation rates and derive the steady-state age and stage distributions as well as the asymptotic growth rates for models with exponential and inverse Gaussian maturation time distributions. We find that the stable age and stage distributions, transient dynamics, and asymptotic growth rates are substantially different for these two maturation models. We conclude that researchers modeling cellular populations should take care when choosing a maturation time distribution, as the population growth rate and stage structure can be heavily impacted by this choice. Furthermore, differences in the models' transient dynamics constitute testable predictions that can help further our understanding of the fundamental process of cellular proliferation. We hope that our numerical methods and programs will provide a scaffold for future research on cellular proliferation.Purpose In order to classify different types of health data collected in clinical practice of hernia surgery more effectively and improve the classification performance of support vector machine (SVM). Methods A prospective randomized study was conducted. Sixty patients undergoing hernia repair under general anesthesia were randomly divided into two groups, PLMA group (n = 30) and ETT group (n = 30), for airway management. Heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory parameters and the incidence of complications related to ProSeal laryngeal mask airway (PLMA) and endotracheal tube (ETT) were collected in clinical experiments in order to evaluate the operation condition. On the basis of this experiment, at first, expert credibility is introduced to process the index value; secondly, the classification weight of the index is objectively determined by the information entropy output of the index itself; finally, a comprehensive classification model of support vector machine based on key sample set is proposed and its advantages are evaluated. Result After classifying the experimental data, we found that SVM can accurately judge the effect of surgery by data. In this experiment, PLMA method is better than ETT method in xenon repair operation. Discussion SVM has great accuracy and practicability in judging the outcome of xenon repair operation. Conclusion The proposed index classification weight model can deal with the uncertainties caused by uncertain information and give the confidence of the uncertain information. Compared with the traditional SVM method, the proposed method based on SVM and key sample set greatly reduces the number of samples that misjudge the effect of samples, and improves the practicability of SVM method. It is concluded that PLMA is superior to the ETT technique to hernia surgical. The idea of constructing classification model based on key sample set proposed in this paper can also be used for reference in other data mining methods.In this paper, we modify the HBV model proposed in [1] to include the spatial variations of free antibody, virus-antibody complexes, and free virus. By using comparison arguments and theory of uniform persistence, we can show that the persistene/extinction of HBV can be determined by the reproduction number(s).Markovian model is widely used to study cardiac electrophysiology and drug screening. Due to the stiffness of Markov model for single-cell simulation, it is prone to induce instability by using large time-steps. Hybrid operator splitting (HOS) and uniformization (UNI) methods were devised to solve Markovian models with fixed time-step. Recently, it is shown that these two methods combined with Chen-Chen-Luo's quadratic adaptive algorithm (CCL) can save markedly computation cost with adaptive time-step. However, CCL determines the time-step size solely based on the membrane potential. The voltage changes slowly to increase the step size rapidly, while the values of state variables of Markov sodium channel model still change dramatically. As a result, the system is not stable and the errors of membrane potential and sodium current exceed 5%. To resolve this problem, we propose a multi-variable CCL method (MCCL) in which state occupancies of Markov model are included with membrane potential as the control quadratic parameters to determine the time-step adaptively. Using fixed time-step RK4 as a reference, MCCL combined with HOS solver has 17.2 times speedup performance with allowable errors 0.6% for Wild-Type Na+ channel with 9 states (WT-9) model, and it got 21.1 times speedup performance with allowable errors 3.2% for WildType Na+ channel with 8 states (WT-8) model. It is concluded that MCCL can improve the simulation instability problem induced by a large time-step made with CCL especially for high stiff Markov model under allowable speed tradeoff.Kidney tubules are lined with flow-sensing structures, yet information about the flow itself is not easily obtained. We aim to generate a multiscale biomechanical model for analyzing fluid flow and fluid-structure interactions within an elastic kidney tubule when the driving pressure is pulsatile. We developed a two-dimensional macroscopic mathematical model of a single fluid-filled tubule corresponding to a distal nephron segment and determined both flow dynamics and wall strains over a range of driving frequencies and wall compliances using finite-element analysis. The results presented here demonstrate good agreement with available analytical solutions and form a foundation for future inclusion of elastohydrodynamic coupling by neighboring tubules. Overall, we are interested in exploring the idea of dynamic pathology to better understand the progression of chronic kidney diseases such as Polycystic Kidney Disease.Prostate cancer (PCa) is one of the most common cancer in males. Previous studies indicated that MIR22HG was a tumor suppressor in various cancers. However, the expression pattern and functional roles of MIR22HG in PCa remained to be further investigated. In this study, we for the first time showed MIR22HG was down-regulated in PCa. Furthermore, we observed the lower expression levels of MIR22HG were significantly related to higher Gleason score and T stage. Of note, we found that higher MIR22HG expression was associated with better disease-free survival and overall survival time in PCa. Moreover, we constructed a MIR22HG mediated co-expression network. Bioinformatics analysis showed MIR22HG was associated with regulating inflammatory response, regulation of transcription, cellular response to tumor necrosis factor, neutrophil chemotaxis, cell-cell signaling, and TNF signaling pathway. These results showed that MIR22HG could serve as a novel biomarker for prostate cancer.The incubation period for Hepatitis B virus (HBV) within the human is epidemiologically significant because it is typically of long duration (1.5∼6 months) and the disease transmission possibility may be increased due to more contact from the patients in this period. In this paper, we investigate an SEICRV epidemic model with time delay to research the transmission dynamics of Hepatitis B disease. The basic reproductive number $mathcal R_0$ is derived and can determine the dynamics of the model. The disease-free equilibrium is globally asymptotically stable if $mathcal R_01$, the model admits a unique endemic equilibrium which is locally asymptotically stable. The endemic equilibrium is globally asymptotically stable when the vertical transmission is ignored. Numerically, we study the Hepatitis B transmission case in Xinjiang, China. Using the Hepatitis B data from Xinjiang, the basic reproductive number is estimated as 1.47 (95% CI 1.34-1.50). By the end of 2028, the cumulative number of Hepatitis B cases in Xinjiang will be estimated about 700,000 if there is no more effective preventive measures. SB415286 mouse The sensitivity analysis of $mathcal R_0$ in terms of parameters indicates prevention and treatment for chronic patients are key measures in controlling the spread of Hepatitis B in Xinjiang.We consider a population dynamics model in investigating data from controlled experiments with aphids in broccoli patches surrounded by different margin types (bare or weedy ground) and three levels of insecticide spray (no, light, or heavy spray). The experimental data is clearly aggregate in nature. In previous efforts [1], the aggregate nature of the data was ignored. In this paper, we embrace this aspect of the experiment and correctly model the data as aggregate data, comparing the results to the previous approach. We discuss cases in which the approach may provide similar results as well as cases in which there is a clear difference in the resulting fit to the data.
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