Macrophage M1/M2 polarization Yunna, Chen; Mengru, Hu; Lei, Wang ...
European journal of pharmacology,
06/2020, Volume:
877
Journal Article
Peer reviewed
Macrophages can be affected by a variety of factors to change their phenotype and thus affect their function. Activated macrophages are usually divided into two categories, M1-like macrophages and ...M2-like macrophages. Both M1 macrophages and M2 macrophages are closely related to inflammatory responses, among which M1 macrophages are mainly involved in pro-inflammatory responses and M2 macrophages are mainly involved in anti-inflammatory responses. Improving the inflammatory environment by modulating the activation state of macrophages is an effective method for the treatment of diseases. In this review, we analyzed the mechanism of macrophage polarization from the tumor microenvironment, nanocarriers, nuclear receptor PPARγ, phagocytosis, NF-κB signaling pathways, and other pathways.
In this paper, fast algorithms for the extrapolation of band-limited signals are presented by the sampling theorem and Fourier series in the case of over sampling. Assume the band-limited signal is ...known in a finite interval. We update the signal outside the interval by the Shannon sampling theorem in the case of over sampling. Then we obtain a fast algorithm in the form of Fourier series instead of the Fourier transform in the Papoulis–Gerchberg algorithm. Gibbs phenomena is analyzed in the method. An algorithm is presented to control the Gibbs phenomena, and some examples are given in the experimental results.
In this paper, the ill-posedness of derivative interpolation is discussed, and a regularized derivative interpolation for band-limited signals is presented. The ill-posedness is analyzed by the ...Shannon sampling theorem. The convergence of the regularized derivative interpolation is studied by the combination of a regularized Fourier transform and the Shannon sampling theorem. The error estimation is given, and high-order derivatives are also considered. The algorithm of the regularized derivative interpolation is compared with derivative interpolation using some other algorithms.
The deep mechanisms (deterministic and/or stochastic processes) underlying community assembly are a central challenge in microbial ecology. However, the relative importance of these processes in ...shaping riverine microeukaryotic biogeography is still poorly understood. Here, we compared the spatiotemporal and biogeographical patterns of microeukaryotic community using high-throughput sequencing of 18S rRNA gene and multivariate statistical analyses from a subtropical river during wet and dry seasons.
Our results provide the first description of biogeographical patterns of microeukaryotic communities in the Tingjiang River, the largest river in the west of Fujian province, southeastern China. The results showed that microeukaryotes from both wet and dry seasons exhibited contrasting community compositions, which might be owing to planktonic microeukaryotes having seasonal succession patterns. Further, all components of the microeukaryotic communities (including total, dominant, always rare, and conditionally rare taxa) exhibited a significant distance-decay pattern in both seasons, and these communities had a stronger distance-decay relationship during the dry season, especially for the conditionally rare taxa. Although several variables had a significant influence on the microeukaryotic communities, the environmental and spatial factors showed minor roles in shaping the communities. Importantly, these microeukaryotic communities were strongly driven by stochastic processes, with 89.9%, 88.5%, and 89.6% of the community variation explained by neutral community model during wet, dry, and both seasons, respectively. The neutral community model also explained a large fraction of the community variation across different taxonomic groups and levels. Additionally, the microeukaryotic taxa, which were above and below the neutral prediction, were ecologically and taxonomically distinct groups, which might be interactively structured by deterministic and stochastic processes.
This study demonstrated that stochastic processes are sufficient in shaping substantial variation in river microeukaryotic metacommunity across different hydrographic regimes, thereby providing a better understanding of spatiotemporal patterns, processes, and mechanisms of microeukaryotic community in waters.
Mangrove ecosystems are vulnerable due to the exotic Spartina alterniflora (S. alterniflora) invasion in China. However, little is known about mangrove sediment microbial community assembly processes ...and interactions under S. alterniflora invasion. Here, we investigated the assembly processes and co-occurrence networks of the archaeal and bacterial communities under S. alterniflora invasion along the coastlines of Fujian province, southeast China.
Assembly of overall archaeal and bacterial communities was driven predominantly by stochastic processes, and the relative role of stochasticity was stronger for bacteria than archaea. Co-occurrence network analyses showed that the network structure of bacteria was more complex than that of the archaea. The keystone taxa often had low relative abundances (conditionally rare taxa), suggesting low abundance taxa may significantly contribute to network stability. Moreover, S. alterniflora invasion increased bacterial and archaeal drift process (part of stochastic processes), and improved archaeal network complexity and stability, but decreased the network complexity and stability of bacteria. This could be attributed to S. alterniflora invasion influenced microbial communities diversity and dispersal ability, as well as soil environmental conditions.
This study fills a gap in the community assembly and co-occurrence patterns of both archaea and bacteria in mangrove ecosystem under S. alterniflora invasion. Thereby provides new insights of the plant invasion on mangrove microbial biogeographic distribution and co-occurrence network patterns.
Extensive research has been performed by organizations and academics on models for credit scoring, an important financial management activity. With novel machine learning models continue to be ...proposed, ensemble learning has been introduced into the application of credit scoring, several researches have addressed the supremacy of ensemble learning. In this research, we provide a comparative performance evaluation of ensemble algorithms, i.e., random forest, AdaBoost, XGBoost, LightGBM and Stacking, in terms of accuracy (ACC), area under the curve (AUC), Kolmogorov–Smirnov statistic (KS), Brier score (BS), and model operating time in terms of credit scoring. Moreover, five popular baseline classifiers, i.e., neural network (NN), decision tree (DT), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) are considered to be benchmarks. Experimental findings reveal that the performance of ensemble learning is better than individual learners, except for AdaBoost. In addition, random forest has the best performance in terms of five metrics, XGBoost and LightGBM are close challengers. Among five baseline classifiers, logistic regression outperforms the other classifiers over the most of evaluation metrics. Finally, this study also analyzes reasons for the poor performance of some algorithms and give some suggestions on the choice of credit scoring models for financial institutions.
A soft manipulator usually has infinite joints. The infinite DOFs of a soft manipulator make it impossible to build the mechanical model like traditional rigid manipulator. The dynamic model based on ...circular arcs assumption, proposed by previous literature, does not take torsion into consideration. The introduction of torsion to piecewise constant curvature assumption could improve accuracy for 3-D motion, but it still cannot deal with problems with normal strain and viscidity of soft material, especially when the Young's Modulus is small. In this paper, by combining the geometrically exact Cosserat rod theory and Kelvin model, a new mechanical model for a silicone rubber soft manipulator is proposed. Two vectors, curvature vector and strain vector, are used to depict the bending and torsion effect, and normal strain. Both 2-D and 3-D experiments are performed to verify the mechanical model.
In this paper, an image-based visual servoing control law is proposed for a quadrotor unmanned aerial vehicle using an on-board monocular camera and an inertial measurement unit sensor. Based on the ...perspective projection model, suitable image features are defined on a rotated image plane called virtual image plane, thus a decoupled image feature dynamics is achieved. Then, a translational velocity observer is presented using these image features. The image feature dynamics and quadrotor dynamics are combined to derive a nonlinear controller. The controller is based on backstepping technique to account for the underactuation of the quadrotor. The image-based visual servoing controller only needs three point features, which make it useable in general environment. The closed-loop system is proved globally asymptotic stable by means of Lyapunov analysis. Computer simulations that regulate a quadrotor to a desired position with respect to (w.r.t.) four points lying on a horizontal plane and three points lying on a full rotated slope are conducted separately. Smooth and efficient trajectories are obtained both in virtual image plane and Cartesian space. Finally, experimental tests including pushing and pulling the visual target are conducted to verify the validity and robustness of the proposed controller. The proposed control law regulates the quadrotor to a desired position, defined by desired image, from an unknown initial position, which can be used in monitoring, landing, and other applications.
It is unavoidable for a soft manipulator to interact with environments during some tasks. These interactions may affect the soft manipulator and make the kinematic model different from the one in ...free space, e.g., the soft manipulator's effective length and the target positions might change. In order to apply the soft manipulator to constrained environments, an adaptive visual servo controller based on piecewise-constant curvature kinematic, without knowing the true values of the manipulator's length and the target positions, is proposed in this paper. Experimental results in the free space, constrained environment, and the gravity-influenced environment, demonstrate the convergence of the image errors under the proposed controller.
The shape of soft a manipulator cannot be sensed by the operator directly, when applied to rescue of mine disaster, science exploration, or minimally invasive surgery due to the narrow and closed ...environment. Shape information is sometimes important for the soft manipulator to be controlled. In order to deal with the problem of shape sensing, a shape sensing algorithm and sensor network based on Fiber Bragg Gratings (FBGs) are introduced in this paper. The shape sensing algorithm is based on piecewise constant curvature and torsion assumption, and can translate the curvature and torsion measured by sensor network into global positions and orientations of nodes. Three-dimensional experiments show that the algorithm introduced in this paper can achieve high accuracy for 3-D shapes.
PDF
