• Journal of Microbiology and Biotechnology
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• v.31 no.2
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• pp.240-249
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• 2021

Phenotypic plasticity is a rapid response mechanism that enables organisms to acclimate and survive in changing environments. The Chinese mitten crab (Eriocheir sinensis) survives and thrives in different and even introduced habitats, thereby indicating its high phenotypic plasticity. However, the underpinnings of the high plasticity of E. sinensis have not been comprehensively investigated. In this study, we conducted an integrated gut microbiome and muscle metabolome analysis on E. sinensis collected from three different environments, namely, an artificial pond, Yangcheng Lake, and Yangtze River, to uncover the mechanism of its high phenotypic plasticity. Our study presents three divergent gut microbiotas and muscle metabolic profiles that corresponded to the three environments. The composition and diversity of the core gut microbiota (Proteobacteria, Bacteroidetes, Tenericutes, and Firmicutes) varied among the different environments while the metabolites associated with amino acids, fatty acids, and terpene compounds displayed significantly different concentration levels. The results revealed that the gut microbiome community and muscle metabolome were significantly affected by the habitat environments. Our findings indicate the high phenotypic plasticity in terms of gut microbiome and muscle metabolome of E. sinensis when it faces environmental changes, which would also facilitate its acclimation and adaptation to diverse and even introduced environments.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1416-1428
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• 2021

Pebble flow characteristics can be significantly affected by the configuration of pebble bed, especially for HTGR pebble beds. How to achieve a desired uniform flow pattern without stagnation is the top priority for reactor design. Pebbles flows inside some specially designed pebble bed with arc-shaped contraction configurations at the bottom, including both concave-inward and convex-outward shapes are explored based on discrete element method. Flow characteristics including pebble retention, residence-time frequency density, flow uniformity as well as axial velocity are investigated. The results show that the traditionally designed pebble bed with cone-shape bottom is not the most preferred structure with respect to flow pattern for reactor design. By improving the contraction configuration, the flow performance can be significantly enhanced. The flow in the convex-shape configuration featured by uniformity, consistency and less stagnation, is much more desirable for pebble bed design. In contrast, when the shape is from convex-forward to concave-inward, the flow shows more nonuniformity and stagnation in the corner although the average cross-section axial velocity is the largest due to the dominant middle pebbles.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.3
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• pp.145-155
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• 2022

Multidrug resistance of tumors has been a severe obstacle to the success of cancer chemotherapy. The study wants to investigate the reversal effects of imperatorin (IMP) on doxorubicin (DOX) resistance in K562/DOX leukemia cells, A2780/Taxol cells and in NOD/SCID mice, to explore the possible molecular mechanisms. K562/DOX and A2780/Taxol cells were treated with various concentrations of DOX and Taol with or without different concentrations of IMP, respectively. K562/DOX xenograft model was used to assess anti-tumor effect of IMP combined with DOX. MTT assay, Rhodamine 123 efflux assay, RT-PCR, and Western blot analysis were determined in vivo and in vitro. Results showed that IMP significantly enhanced the cytotoxicity of DOX and Taxol toward corresponding resistance cells. In vivo results illustrated both the tumor volume and tumor weight were significantly decreased after 2-week treatment with IMP combined with DOX compared to the DOX alone group. Western blotting and RT-PCR analyses indicated that IMP downregulated the expression of P-gp in K562/DOX xenograft tumors in NOD/SCID mice. We also evaluated glycolysis and glutamine metabolism in K562/DOX cells by measuring glucose consumption and lactate production. The results revealed that IMP could significantly reduce the glucose consumption and lactate production of K562/DOX cells. Furthermore, IMP could also remarkably repress the glutamine consumption, α-KG and ATP production of K562/DOX cells. Thus, IMP may sensitize K562/DOX cells to DOX and enhance the antitumor effect of DOX in K562/DOX xenograft tumors in NOD/SCID mice. IMP may be an adjuvant therapy to mitigate the multidrug resistance in leukemia chemotherapy.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Wind and Structures
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• v.35 no.4
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• pp.269-285
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• 2022

The pressure-mitigating effects of a high-speed train passing through a tunnel with a partially reduced cross-section are investigated via the numerical approach. A compressible, three-dimensional RNG k-ε turbulence model and a hybrid mesh strategy are adopted to reproduce that event, which is validated by the moving model test. Three step-like tunnel forms and two additional transitions at the tunnel junction are proposed and their aerodynamic performance is compared and scrutinized with a constant cross-sectional tunnel as the benchmark. The results show that the tunnel step is unrelated to the pressure mitigation effects since the case of a double-step tunnel has no advantage in comparison to a single-step tunnel, but the excavated volume is an essential matter. The pressure peaks are reduced at different levels along with the increase of the excavated earth volume and the peaks are either fitted with power or logarithmic function relationships. In addition, the Arc and Oblique-transitions have very limited gaps, and their pressure curves are identical to each other, whereas the Rec-transition leads to relatively lower pressure peaks in C_Pmax, C_Pmin, and ΔC_P, with 5.2%, 4.0%, and 4.1% relieved compared with Oblique-transition. This study could provide guidance for the design of the novel railway tunnel.

• Journal of Preventive Medicine and Public Health
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• v.56 no.2
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• pp.128-144
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• 2023

Objectives: The health system reform process is highly political and controversial, and in most cases, it fails to realize its intended goals. This study was conducted to synthesize factors underlying the failure of health system reforms. Methods: In this systematic review and meta-synthesis, we searched 9 international and regional databases to identify qualitative and mixed-methods studies published up to December 2019. Using thematic synthesis, we analyzed the data. We utilized the Standards for Reporting Qualitative Research checklist for quality assessment. Results: After application of the inclusion and exclusion criteria, 40 of 1837 articles were included in the content analysis. The identified factors were organized into 7 main themes and 32 sub-themes. The main themes included: (1) reforms initiators' attitudes and knowledge; (2) weakness of political support; (3) lack of interest group support; (4) insufficient comprehensiveness of the reform; (5) problems related to the implementation of the reform; (6) harmful consequences of reform implementation; and (7) the political, economic, cultural, and social conditions of the society in which the reform takes place. Conclusions: Health system reform is a deep and extensive process, and shortcomings and weaknesses in each step have overcome health reform attempts in many countries. Awareness of these failure factors and appropriate responses to these issues can help policymakers properly plan and implement future reform programs and achieve the ultimate goals of reform: to improve the quantity and quality of health services and the health of society.

• Smart Structures and Systems
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• v.31 no.2
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• pp.131-140
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• 2023

As well-known, the extended Kalman filter (EKF) is a powerful tool for parameter identification with limited measurements. However, traditional EKF is not applicable when the external excitation is unknown. By using least-squares estimation (LSE) for force identification, an EKF with unknown input (EKF-UI) approach was recently proposed by the authors. In this approach, to ensure the influence matrix be of full column rank, the sensors have to be deployed at all the degrees-of-freedom (DOFs) corresponding to the unknown excitation, saying collocated measurements are required. However, it is not easy to guarantee that the sensors can be installed at all these locations. To circumvent this limitation, based on the idea of first-order-holder discretization (FOHD), an improved EKF with unknown input (IEKF-UI) approach is proposed in this study for the simultaneous identification of structural parameters and unknown excitation. By using projection matrix, an improved observation equation is obtained. Few displacement measurements are fused into the observation equation to avoid the so-called low-frequency drift. To avoid the ill-conditioning problem for force identification without collocated measurements, the idea of FOHD is employed. The recursive solution of the structural states and unknown loads is then analytically derived. The effectiveness of the proposed approach is validated via several numerical examples. Results show that the proposed approach is capable of satisfactorily identifying the parameters of linear and nonlinear structures and the unknown excitation applied to them.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1350-1364
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• 2023

2D/1D coupling method is an important neutron transport calculation method due to its high accuracy and relatively low computation cost. However, 2D/1D coupling method may diverge especially in small axial mesh size. To analyze the convergence behavior of 2D/1D coupling method, a Fourier analysis for k-eigenvalue neutron transport problems is implemented. The analysis results present the divergence problem of 2D/1D coupling method in small axial mesh size. Several common attempts are made to solve the divergence problem, which are to increase the number of inner iterations of the 2D or 1D calculation, and two times 1D calculations per outer iteration. However, these attempts only could improve the convergence rate but cannot deal with the divergence problem of 2D/1D coupling method thoroughly. Moreover, the choice of axial solvers, such as DGFEM SN and traditional SN, and its effect on the convergence behavior are also discussed. The results show that the choice of axial solver is a key point for the convergence of 2D/1D method. The DGFEM SN based 2D/1D method could converge within a wide range of optical thickness region, which is superior to that of traditional SN method.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.395-409
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• 2023

Long-span suspension bridges have tunnel anchor systems to maintain stable cables. More investigations are required to determine how closely tunnel excavation beneath the tunnel anchor impacts the stability of the tunnel anchor. In order to investigate the impact of the adjacent tunnel's excavation on the stability of the tunnel anchor, a large-span suspension bridge tunnel anchor is utilised as an example in a three-dimensional numerical simulation approach. In order to explore the deformation control mechanism, orthogonal tests are employed to pinpoint the major impacting elements. The construction of an advanced pipe shed, strengthening the primary support. Moreover, according to the findings the grouting reinforcement of the surrounding rock, have a significant control effect on the settlement of the tunnel vault and plug body. However, reducing the lag distance of the secondary lining does not have such big influence. The greatest way to control tunnel vault settling is to use the grout reinforcement, which increases the bearing capacity and strength of the surrounding rock. This greatly minimizes the size of the tunnel excavation disturbance area. Advanced pipe shed can not only increase the surrounding rock's bearing capacity at the pipe shed, but can also prevent the tunnel vault from connecting with the disturbance area at the bottom of the anchorage tunnel, reduce the range of shear failure area outside the anchorage tunnel, and have the best impact on the plug body's settlement control.