Gut microbiota community shift with coronary artery disease (CAD) has been reported in several limited cohorts during the past several years. However, whether the enriched or decreased microbiota taxa with CAD can be reproducible deserves further investigation and validation. In this study, 78 human subjects were recruited. Of these, 19 were diagnosed without stenosis in coronary artery (control group, referred to herein as Ctrl), 14 with stenosis less than 50% (LT50), and 45 with stenosis greater than 50% (GT50). Fecal samples were collected and DNA was extracted to perform 16S rRNA gene sequencing. The operational taxonomic units (OTUs) were analyzed to identify taxa specific to different groups; next, multivariate logistic regression was employed to test whether the defined taxa could independently predict CAD risk. We found that Deltaproteobacteria, Fusobacterium, Bilophila, Actinomyces, and Clostridium XIX were enriched in Ctrl; Prevotellaceae, Parabacteriodes, and Butyricicoccus were enriched in LT50; and Roseburia and Butyricimonas were enriched in GT50. Further analysis revealed that increased populations of Deltaproteobacteria, Fusobacterium, Bilophila, and Desulfovibrionaceae were associated with a 0.26-fold, 0.21-fold, 0.18-fold, and 0.26-fold decreased risk of CAD, respectively (p < 0.05), and an increased Prevotellaceae population was associated with a 5.63-fold increased risk of CAD (p < 0.01). A combination of the 20 microbial taxa achieved an area under the receiver operating characteristic (ROC) curve of higher than 0.88 for all discriminations between LT50 vs Ctrl, GT50 vs Ctrl, LT50 + GT50 vs Ctrl, and GT50 vs Ctrl + LT50. However, the microbial taxa previously reported as enriched in CAD patients or healthy controls could not be observed in our cohort except for Bacteroides. In conclusion, CAD patients showed a different microbial taxa signature than the healthy controls. However, the non-reproducibility of the microbiota taxa enriched in CAD across different cohorts limits the use of this signature in early diagnosis and prevention. Only decreased Bacteroides abundance was found to be a reliable marker to indicate CAD progression.
Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.
This paper proposes a scanner–stage synchronized approach emphasizing a novel control structure for the laser polishing of Inconel 718 components manufactured by selective laser melting in order to address increasing demands for high surface quality in metal additive manufacturing. The proposed synchronized control system is composed of a motion decomposition module and an error synthesis module. The experimental results show that stitching errors can be avoided thanks to continuous motion during laser processing. Moreover, in comparison with the existing step-scan method, the processing efficiency of the proposed method is improved by 38.22% and the surface quality of the laser-polished area is significantly enhanced due to a more homogeneous distribution of the laser energy during the material phase change. The proposed synchronized system paves the way for high-speed, high-precision, and large-area laser material processing without stitching errors.
In this paper, self-piercing riveting (SPR) and friction self-piercing riveting (F-SPR) processes were employed to join aluminum alloy AA5182-O sheets. Parallel studies were carried out to compare the two processes in terms of joint macrogeometry, tooling force, microhardness, quasi-static mechanical performance, and fatigue behavior. The results indicate that the F-SPR process formed both rivet-sheet interlocking and sheet-sheet solid-state bonding, whereas the SPR process only contained rivet-sheet interlocking. For the same rivet flaring, the F-SPR process required 63% less tooling force than the SPR process because of the softening effect of frictional heat and the lower rivet hardness of F-SPR. The decrease in the switch depth of the F-SPR resulted in more hardening of the aluminum alloy surrounding the rivet. The higher hardness of aluminum and formation of solid-state bonding enhanced the F-SPR joint stiffness under lap-shear loading, which contributed to the higher quasi-static lap-shear strength and longer fatigue life compared to those of the SPR joints.
Dissolved oxygen (DO) is an important indicator of aquaculture, and its accurate forecasting can effectively improve the quality of aquatic products. In this paper, a new DO hybrid forecasting model is proposed that includes three stages: multi-factor analysis, adaptive decomposition, and an optimization-based ensemble. First, considering the complex factors affecting DO, the grey relational (GR) degree method is used to screen out the environmental factors most closely related to DO. The consideration of multiple factors makes model fusion more effective. Second, the series of DO, water temperature, salinity, and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform (EWT) method. Then, five benchmark models are utilized to forecast the sub-series of EWT decomposition. The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm (PSOGSA). Finally, a multi-factor ensemble model for DO is obtained by weighted allocation. The performance of the proposed model is verified by time-series data collected by the pacific islands ocean observing system (PacIOOS) from the WQB04 station at Hilo. The evaluation indicators involved in the experiment include the nash-sutcliffe efficiency (NSE), kling-gupta efficiency (KGE), mean absolute percent error (MAPE), standard deviation of error (SDE), and coefficient of determination (R2). Example analysis demonstrates that: ① the proposed model can obtain excellent DO forecasting results; ② the proposed model is superior to other comparison models; and ③ the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.
Modeling and simulation have emerged as an indispensable approach to create numerical experiment platforms and study engineering systems. However, the increasingly complicated systems that engineers face today dramatically challenge state-of-the-art modeling and simulation approaches. Such complicated systems, which are composed of not only continuous states but also discrete events, and which contain complex dynamics across multiple timescales, are defined as generalized hybrid systems (GHSs) in this paper. As a representative GHS, megawatt power electronics (MPE) systems have been largely integrated into the modern power grid, but MPE simulation remains a bottleneck due to its unacceptable time cost and poor convergence. To address this challenge, this paper proposes the numerical convex lens approach to achieve state-discretized modeling and simulation of GHSs. This approach transforms conventional time-discretized passive simulations designed for pure-continuous systems into state-discretized selective simulations designed for GHSs. When this approach was applied to a largescale MPE-based renewable energy system, a 1000-fold increase in simulation speed was achieved, in comparison with existing software. Furthermore, the proposed approach uniquely enables the switching transient simulation of a largescale megawatt system with high accuracy, compared with experimental results, and with no convergence concerns. The numerical convex lens approach leads to the highly efficient simulation of intricate GHSs across multiple timescales, and thus significantly extends engineers' capability to study systems with numerical experiments.
With the advantages of better mimicking the specificity of natural tissues, three-dimensional (3D) cell culture plays a major role in drug development, toxicity testing, and tissue engineering. However, existing scaffolds or microcarriers for 3D cell culture are often limited in size and show suboptimal performance in simulating the vascular complexes of living organisms. Therefore, we present a novel hierarchically inverse opal porous scaffold made via a simple microfluidic approach for promoting 3D cell co-culture techniques. The designed scaffold is constructed using a combined concept involving an emulsion droplet template and inert polymer polymerization. This work demonstrates that the resultant scaffolds ensure a sufficient supply of nutrients during cell culture, so as to achieve large-volume cell culture. In addition, by serially planting different cells in the scaffold, a 3D co-culture system of endothelial-cell-encapsulated hepatocytes can be developed for constructing certain functional tissues. It is also demonstrated that the use of the proposed scaffold for a co-culture system helps hepatocytes to maintain specific in vivo functions. These hierarchically inverse opal scaffolds lay the foundation for 3D cell culture and even the construction of biomimetic tissues.
This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) method in the image frequency domain. The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks. In order to improve the training efficiency, images are first transformed into the frequency domain during a preprocessing phase. The algorithm is then calibrated using the flattened frequency data. LSTM is used to improve the performance of the developed network for long sequence data. The accuracy of the developed model is 99.05%, 98.9%, and 99.25%, respectively, for training, validation, and testing data. An implementation framework is further developed for future application of the trained model for large-scale images. The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time. The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.
Hepatitis C virus (HCV) is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (HCC) worldwide. Among the structural proteins of HCV, the HCV core protein has the ability to regulate gene transcription, lipid metabolism, cell proliferation, apoptosis, and autophagy, all of which are closely related to the development of HCC. Transgenic mice carrying the HCV core gene exhibited age-dependent insulin resistance, hepatic steatosis, and HCC that resembled the clinical characteristics of chronic hepatitis C patients. Several dietary modifications, including calorie restriction and diets rich in saturated fatty acids (SFAs), trans fatty acids (TFAs), or cholesterol, were found to influence hepatic steatogenesis and tumorigenesis in HCV core gene transgenic mice. These strategies modulated hepatocellular stress and proliferation, in addition to hepatic fibrotic processes and the microenvironment, thereby corroborating a close interconnection between dietary habits and steatosis-related hepatocarcinogenesis. In this review, we summarize the findings obtained from mouse models transgenic for the HCV genome, with a special focus on HCV core gene transgenic mice, and discuss the mechanisms of steatogenesis and hepatocarcinogenesis induced by the HCV core protein and the impact of dietary habits on steatosis-derived HCC development.