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Ancient buildings carry important information, such as ancient politics, economy, culture, customs. However, with the course of time, ancient buildings are often damaged to different degrees, so the restoration of ancient buildings is of great importance from the historical point of view. There are three commonly used non-contact measurement methods, including UAV-based oblique photogrammetry, terrestrial laser scanning, and close-range photogrammetry. These methods can provide integrated three-dimensional surveys of open spaces, indoor and outdoor surfaces for ancient buildings. Theoretically, the combined use of the three measurement methods can provide 3D (three-dimensional) data support for the protection and repair of ancient buildings. However, data from the three methods need to be fused urgently, because if the image data is not used, it will lead to a lack of real and intuitive texture information, and if only image matching point clouds are used, their accuracy will be lower than that of terrestrial laser scanning point clouds, and it will also lead to a lack of digital expression for components with high indoor historical value of ancient buildings. Therefore, in this paper, a data fusion method is proposed to achieve multi-source and multi-scale 3D data fusion of indoor and outdoor surfaces. It takes the terrestrial laser point cloud as the core, and based on fine component texture features and building outline features, respectively, the ground close-range image matching point cloud and UAV oblique image matching point cloud are registered with the terrestrial laser point cloud. This method unifies the data from three measurements in the point cloud and realizes the high-precision fusion of these three data. Based on the indoor and outdoor 3D full-element point cloud formed by the proposed method, it will constitute a visual point cloud model in producing plans, elevations, sections, orthophotos, and other elements for the study of ancient buildings.

Bacillus licheniformis HDYM-04 was isolated in flax retting water and showed β-mannanase activity. Carbon sources for β-mannanase production, as well as the fermentation conditions and feeding strategy, were optimized in shake flasks. When glucose or konjac powder was used as the carbon source, the β-mannanase activity was 288.13 ± 21.59 U/mL and 696.35 ± 23.47 U/mL at 24 h, respectively, which was approximately 4.4- to 10.68-fold higher than the values obtained with wheat powder. When 0.5% (w/v) glucose and 1% (w/v) konjac powder were added together, maximum enzyme activities of 789.07 ± 25.82 U/mL were obtained, an increase of 13.35% compared to the unoptimized cultures with only 1% (w/v) konjac powder. The enzyme activity decreased in the presence of 1% (w/v) konjac powder, but the highest enzyme activity was 1,533.26 ± 33.74 U/mL, a 1.2-fold increase compared with that in nonoptimized cultures; when 0.5% (w/v) glucose was used, the highest enzyme activity was 966.53 ± 27.84 U/mL, an increase in β-mannanase activity of 38.79% compared with control cultures. In this study, by optimizing fed-batch fermentation conditions, the yield of β-mannanase produced by HDYM-04 was increased, laying the foundation for the industrial application and further research of B. licheniformis HDYM-04.

A herbal prescription in traditional Chinese medicine (TCM) has great complexity, with multiple components and multiple targets, making it extremely challenging to determine its bioactive compounds. Yinchenhao Tang (YCHT) has been extensively used for the treatment of jaundice disease. Although many studies have examined the efficacy and active ingredients of YCHT, there is still a lack of an in-depth systematic analysis of its effective components, mechanisms, and potential targets—especially one based on clinical patients. This study established an innovative strategy for discovering the potential targets and active compounds of YCHT based on an integrated clinical and animal experiment platform. The serum metabolic profiles and constituents of YCHT in vivo were determined by ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q-ToF-MS)-based metabolomics combined with a serum pharmacochemistry method. Moreover, a compound–target–pathway network was constructed and analyzed by network pharmacology and ingenuity pathway analysis (IPA). We found that eight active components could modulate five key targets. These key targets were further verified by enzyme-linked immunosorbent assay (ELISA), which indicated that YCHT exerts therapeutic effects by targeting cholesterol 7α-hydroxylase (CYP7A1), multidrug-resistance-associated protein 2 (ABCC2), multidrug-resistance-associated protein 3 (ABCC3), uridine diphosphate glucuronosyl transferase 1A1 (UGT1A1), and farnesoid X receptor (FXR), and by regulating metabolic pathways including primary bile acid biosynthesis, porphyrin and chlorophyll metabolism, and biliary secretion. Eight main effective compounds were discovered and correlated with the key targets and pathways. In this way, we demonstrate that this integrated strategy can be successfully applied for the effective discovery of the active compounds and therapeutic targets of an herbal prescription.

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2,3-Butanediol (2,3-BD) has been extensively used in chemical syntheses. This study aimed to explore acetic acid as a signaling molecule that activates a quorum sensing (QS) system to promote the p...

In this manuscript, we develop a 3D-printing based microfluidic device for light scattering imaging of single cells. A rapid fabrication method to make microfluidic device that enables 3D-hydrodynamic focusing by utilizing 3D printing technique is proposed. The focusing effect of the microfluidic cytometer was measured and shows the ability to confine the cells to flow near the center stream along the channel. Also, the standard beads with 9.51 μm in diameter were used to test the reliability of the device. The collected light scattering images are in good agreement with simulation results. We suggest that the 3D-printing based light scattering microfluidic device is in principle applicable for fast, label-free detection of single cells.

As the penetration of renewable generation such as wind and solar continues to grow in power systems, system operation calls for curtailment of excess amount of renewable generation. In this regard, an integrated energy system (IES) can be utilized to optimize curtailment. This paper proposes the Electric Heater-Heat Tank Aggregation (EHHTA) as an IES, also an energy coupling device that combines electricity power with heat energy, to deal with redundant wind power curtailment. A combined optimization model was developed in this paper, which incorporates mathematical models of the energy resources. The simulation was carried out based on the practical data in the case study, where the coordinated control of EHHTA and thermal generators from the optimization model was proven to be effective in mitigating wind power curtailment.

Colorectal cancer (CRC) is a highly virulent and malignant disease and always accompanied by metabolic disorders. Currently, there are no effective therapeutic drugs for the treatment of CRC. High-throughput metabolomics approaches have been used to unveil the metabolic pathways related to several diseases. In this study, ultra-performance liquid chromatography/mass spectrometry-based high-throughput metabolomics was used for deciphering the potential preventive mechanism of mirabilite on CRC via the modulation of the associated metabolic disorders; a total of 28 differential biomarkers, including indole acetaldehyde, 5-hydroxyindoleacetic acid, hypoxanthine, retinal, retinal ester, linoleic acid, stearic acid, 6-deoxocastasterone, 2-hydroxybutyric acid and LysoPC, were identified in the APCmin/+ mice. These differential biomarkers are involved in the tryptophan metabolism, glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. Note that these biomarkers and their disturbed metabolic pathways were also regulated by mirabilite. It has been found that the prevention of CRC by mirabilite is mainly associated with tryptophan metabolism; this study shows that high-throughput metabolomics can reveal the perturbed metabolic disorders targeted in the action mechanism of drug treatment.

Data-driven stochastic subspace identification (DATA-SSI) is frequently applied to bridge modal parameter identification because of its high stability and accuracy. However, the existence of abnormal data and noise components may make the identification result of DATA-SSI unreliable. In order to achieve a reliable identification result of the bridge modal parameter, a data inspecting and denoising method based on exploratory data analysis (EDA) and morphological filter (MF) was proposed for DATA-SSI. First, EDA was adopted to inspect the data quality for removing the data measured from malfunctioning sensors. Then, MF along with an automated structural element (SE) size determination technique was adopted to suppress the noise components. At last, DATA-SSI and stabilization diagram were applied to identify and exhibit the bridge modal parameter. A model bridge and a real bridge were used to verify the effectiveness of the proposed method. The comparison of the identification results of the original data and improved data was made. The results show that the identification results obtained with the improved data are more accurate, stable, and reliable.

Saline-alkali environments are widely distributed in China and significantly hinder the development of agriculture. This study characterizes the long-term effects of planting Elaeagnus angustifolia (E. angustifolia) on the physical and chemical properties, enzyme activities and microbial community characteristics of saline-alkali soil in the Songnen Plain (1, 2 and 3 years). The results showed that planting E. angustifolia reduced soil pH and electrical conductivity (EC) and increased soil total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (Nni), total potassium (TK), dissolved organic C (DOC), dissolved organic matter (DOM) and available potassium (AK) content and catalase, urease, polyphenol oxidase, phosphatase, sucrase and cellulase enzyme activities, and the results peaked in the 3 year. High-throughput sequencing showed that the bacterial abundance and diversity were as follows (from high to low) y3 y2 y1 CK. E. angustifolia resulted in an increase in the relative abundance of the dominant bacteria. Proteobacteria and Pseudomonas were the major phylum and genus, respectively. Redundancy analysis showed that changes in the soil microbial community significantly affect the physical and chemical properties of the soil, with Proteobacteria members being the key microorganisms that reduce soil salinity. Network analysis showed that Pseudomonas (Proteobacteria) participated in the synthesis of key soil enzymes. 16S rRNA sequencing predicted that the expression of genes related to carbon (rbcL, acsA, acsB, Pcc and accA) and nitrogen (amoA/B, nxrA, hao, gdh, ureC and nosZ) transformation increased, and Pseudomonas members were key regulators of carbon and nitrogen dynamics. In conclusion, the planting of E. angustifolia could improve the physical and chemical properties of the soil by releasing root exudates into the soil and increasing the diversity and richness of soil microbial communities to improve saline-alkali soil, providing a theoretical basis for improving saline-alkali soil and promoting the sustainable development of modern agriculture.

In this study, the effects of penicillin G (PENG) on the fate of bacterial communities and β-lactamase antibiotic resistance genes (ARGs) during chicken manure composting were assessed, to illustrate the roles of PENG in ARGs behavior. The results showed that the total absolute abundances of 9 ARGs and 4 mobile genetic elements (MGEs) was significantly increased by PENG (P 0.05). Dozens of potential hosts for ARGs were predominantly affiliated with Firmicutes, Proteobacteria, and Actinobacteria. Meanwhile, the higher concentration of PENG significantly increased the abundance of luxI and luxS in quorum sensing (QS) (P 0.05), which enhanced the frequency of inter/intraspecific gene "communication." Redundancy analysis and structural equation modeling further revealed that QS had a strong regulatory role in horizontal gene transfer of ARGs mediated via MGEs. These results provide new insight into the mechanism of ARGs propagation in aerobic composting modified by PENG.

Development of informatization teaching ability of the college English teachers under the network environment is studied in this paper. The technique novelties are focused on two aspects. First, a design-based method is to directly estimate the sample data that well falls into the domain by sampling survey with weighted adjustment to achieve the excellent characteristics of the estimator, this model is optimized by the kernel method. Second, a reliable optimization model is needed to ensure the rationality of data transmission and reception and the quality of data transmission in the sensor network, hence use the kernel model to then represent the efficient framework. The proposed models are applied to the informatization teaching ability enhancement. The experiment results have given the conclusion that the proposed models can improve the overall performance.

Bacteriocins from lactic acid bacteria are natural preservatives that inhibit foodborne pathogenic microorganisms and have recently attracted much attention. Acetic acid metabolism is part of the core of central metabolism. To elucidate the acetic acid stress response of L. paracasei HD1.7, we performed high-throughput RNA-seq to compare gene expression under different concentrations of acetic acid stress. The results showed that with increasing acetic acid stress, the concentration of bacteriocin was increased, its production rate was accelerated, and the number of differentially expressed genes also increased. The continuously upregulated and downregulated KEGG pathways adjusted to the increase in acetic acid stress. The late-stage regulatory system of bacteriocin synthesis was related to ABC transporters (OppABCDF system). Purine and pyrimidine metabolism may provide ATP for ABC transporters. Acetic acid stress reduced glutamate biosynthesis, but arginine biosynthesis increased to attenuate acid stress. In addition, the KEGG enrichment analysis results of different environmental stresses (acetic acid, HCl, citric acid and ethanol stress) indicated that ABC transporters and fatty acid metabolism were the keys to bacteria adapting to environmental stress. This experiment lays the foundation for further research on bacteriocins.

In this study, a combination of network pharmacology and metabolomics was used to explore the mechanism by which mirabilite regulates bile acid metabolism in the treatment of colorectal cancer. The PharmMapper web server was applied to make preliminary predictions for the treatment targets of mirabilite and to predict the interaction between mirabilite and disease targets using Discovery Studio 2.5. Furthermore, the urine metabolic profile was analyzed by the UPLC-Q-TOF-MS technology. The original data were processed by Progenesis QI software and analyzed by multivariate pattern recognition, which allowed us to reveal the metabolic disturbance in colorectal cancer and explain the therapeutic effect of mirabilite. The network pharmacology results showed that mirabilite can act on the disease targets, and the sites of action include amino acid residues Arg-364 and Asp-533, as well as nucleotides TPC-11, DG-112 and DA-113. Based on metabolomics, potential biomarkers were found to lie in the relevant pathways of bile acid metabolism, such as taurine, chenodeoxycholic acid, cholic acid, and deoxycholic acid. The results showed that mirabilite could regulate the distribution of overall metabolic disturbance, and bile acid metabolism was the main targeted pathway. Additionally, we predicted the upstream targets by ingenuity pathway analysis and found that mirabilite played a significant role in regulating the bile acid-related biomarkers, which allowed comprehensive analysis of the effect of mirabilite on colorectal cancer. This study fully explained the role of mirabilite in inhibiting colorectal cancer, which mainly occurs through bile acid metabolism, via the approach of network pharmacology combined with functional metabolomics.

Graphene oxide (GO)-based nanocomposites have attracted increasing attention due to its intriguing mechanical and electrical properties. In this work, we studied the mechanical properties of the GO-Silk nanocomposites focusing on the interactions between GO sheets and silk fibroins using molecular dynamics simulations. We found that the oxygen content in GO sheets significantly influences the mechanical properties of a GO-Silk nanocomposite, because the oxygen plays a crucial role in hydrogen bonds (H-bonds) formation at the GO sheets–silk interface. Water content also plays important roles in the mechanical properties of the composites. Particularly, we find that there is an optimal water content for a best mechanical performance of the nanocomposite. When the water content is low, water molecules can help to form H-bonds between GO sheets and silk, and thus stabilizes the H-bond network at the interface and enhances the mechanical performances of the nanocomposite. However, when the water content is too high, a water layer forms and acts as a lubricant at the interface, which reduces the strength of the composite. This study may provide guidelines for the design of a GO-Silk nanocomposite with superior mechanical performance.

Portable and wearable electronics require much more flexible graphene-based electrode with high fatigue life, which could repeatedly bend, fold, or stretch without sacrificing its mechanical properties and electrical conductivity. Herein, a kind of ultrahigh fatigue resistant graphene-based nanocomposite via tungsten disulfide (WS2) nanosheets is synthesized by introducing a synergistic effect with covalently cross-linking inspired by the orderly layered structure and abundant interfacial interactions of nacre. The fatigue life of resultant graphene-based nanocomposites is more than one million times at the stress level of 270 MPa, and the electrical conductivity can be kept as high as 197.1 S/cm after 1.0 × 105 tensile testing cycles. These outstanding properties are attributed to the synergistic effect from lubrication of WS2 nanosheets for deflecting crack propagation, and covalent bonding between adjacent GO nanosheets for bridging crack, which is verified by the molecular dynamics (MD) simulations. T...

An integrative metabolomics and proteomics approach can provide novel insights in the understanding of biological systems. We have integrated proteome and metabolome data sets for a holistic view of the molecular mechanisms in disease. Using quantitative iTRAQ-LC-MS/MS proteomics coupled with UPLC-Q-TOF-HDMS based metabolomics, we determined the protein and metabolite expression changes in the kidney-yang deficiency syndrome (KYDS) rat model and further investigated the intervention effects of the Jinkui Shenqi Pill (JSP). The VIP-plot of the orthogonal PLS-DA (OPLS-DA) was used for discovering the potential biomarkers to clarify the therapeutic mechanisms of JSP in treating KYDS. The results showed that JSP can alleviate the kidney impairment induced by KYDS. Sixty potential biomarkers, including 5-l-glutamyl-taurine, phenylacetaldehyde, 4,6-dihydroxyquinoline, and xanthurenic acid etc., were definitely up- or down-regulated. The regulatory effect of JSP on the disturbed metabolic pathways was proved by the established metabonomic method. Using pathway analyses, we identified the disturbed metabolic pathways such as taurine and hypotaurine metabolism, pyrimidine metabolism, tyrosine metabolism, tryptophan metabolism, histidine metabolism, steroid hormone biosynthesis, etc. Furthermore, using iTRAQ-based quantitative proteomics analysis, seventeen differential proteins were identified and significantly altered by the JSP treatment. These proteins appear to be involved in Wnt, chemokine, PPAR, and MAPK signaling pathways, etc. Functional pathway analysis revealed that most of the proteins were found to play a key role in the regulation of metabolism pathways. Bioinformatics analysis with the IPA software found that these differentially-expressed moleculars had a strong correlation with the α-adrenergic signaling, FGF signaling, etc. Our data indicate that high-throughput metabolomics and proteomics can provide an insight on the herbal preparations affecting the metabolic disorders using high resolution mass spectrometry.

Natural products are an invaluable source for drug candidates. Currently, plasma metabolome has suggested that compounds present in herbs may exert bioactivity. The present investigation employed global metabolome analysis technology to explore the key target and action mechanism of scoparone, a representative ingredient of Yinchenhao (Artemisia capillaris Thunb.). First, we applied different databases for target prediction and focused on the potential targets of scoparone by network pharmacology, which also theoretically characterizes the effectiveness of scoparone on molecular docking. Among them, we selected the top predictions as the potential and crucial target. Then, non-targeted metabolomics technology based on an advanced UPLC-MS instrument coupled with a robust data processing platform was employed to characterize the metabolic profiling of alcoholic liver disease (ALD) rats. Furthermore, the ingenuity pathway analysis platform was used for metabolic network analysis, which mainly involved multiple-pathways, including tyrosine metabolism, glutathione metabolism, and primary bile acid biosynthesis. Interestingly, as a core biomarker, dopaquinone is directly related with target prediction of tyrosinase and finally resulted in a series of disturbances. Moreover, the prediction also validated the target on a metabolic level. The present investigation demonstrated that global metabolome analysis could provide a novel strategy for deciphering the potential drug targets of natural products.

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy. The Shenqi pill (SQP) is a representative prescription for clinical application in the prevention and treatment of kidney-yang deficiency syndrome (KYDS). However, its effect mechanism is still not clear. This article aims to reveal the intervention effect of SQP on KYDS from the perspective of lipid metabolism. In this study, SQP was used to intervene in the rat model of KYDS, on the foundation of successfully replicating the rat model of KYDS induced by corticosterone. The MetaboAnalyst tool was used for analysis of the serum metabolic profile and pattern recognition of KYDS model, based on UPLC-SYNAPT-G2-Si-HDMS. Finally, twenty-two potential lipid biomarkers related to the KYDS model were characterized, and the effects of SQP on regulating potential lipid markers in serum of KYDS model were analyzed. There were 10 biomarkers and seven metabolic pathways closely related to SQP therapy for KYDS were found. The action mechanism and targets of SQP in treating KYDS were explored based on high-throughput lipidomics. This work could provide valuable data and scientific evidence in subsequent studies for the treatment of KYDS.

High-throughput metabolomics can clarify the underlying molecular mechanism of diseases via the qualitative and quantitative analysis of metabolites. This study used the established Yang Huang syndrome (YHS) mouse model to evaluate the efficacy of geniposide (GEN). Urine metabolic data were quantified by ultraperformance liquid chromatography-tandem mass spectrometry. The non-target screening of the massive biological information dataset was performed, and a total of 33 metabolites, including tyramine glucuronide, aurine, and L-cysteine, were identified relating to YHS. These differential metabolites directly participated in the disturbance of phase I reaction and hydrophilic transformation of bilirubin. Interestingly, they were completely reversed by GEN. While, as the auxiliary technical means, we also focused on the molecular prediction and docking results in network pharmacological and integrated analysis part. We used integrated analysis to communicate the multiple results of metabolomics and network pharmacology. This study is the first to report that GEN indirectly regulates the metabolite "tyramine glucuronide" through its direct effect on the target heme oxygenase 1 in vivo. Meanwhile, heme oxygenase-1, a prediction of network pharmacology, was the confirmed metabolic enzyme of phase I reaction in hepatocytes. Our study indicated that the combination of high-throughput metabolomics and network pharmacology is a robust combination for deciphering the pathogenesis of the traditional Chinese medicine (TCM) syndrome.

This paper focuses on the identification of tension in suspenders equipped with supplemental dampers in through and half-through arch bridges. Although the differential equation governing t...

Stacked electrolysers with titanium bipolar plates are constructed for electrodeposition of polypyrrole electrodes for supercapacitors. The cathode side of the bipolar Ti plates are pre-coated with activated carbon. In this new design, half electrolysis occurs which significantly lowers the deposition voltage. The deposited electrodes are tested in a symmetrical unit cell supercapacitor and an asymmetrical supercapacitor stack. Both devices show excellent energy storage performances and the capacitance values are very close to the design value, suggesting a very high current efficiency during the electrodeposition. The electrolyser stack offers multi-fold benefits for preparation of conducting polymer electrodes, i.e. low energy consumption, facile control of the electrode capacitance and simultaneous preparation of a number of identical electrodes. Therefore, the stacked bipolar electrolyser is a technology advance that offers an engineering solution for mass production of electrodeposited conducting polymer electrodes for supercapacitors.

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers&rsquo, concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

Altered lipid metabolism is an emerging hallmark of cancers. Mirabilite has a therapeutic effect on colorectal cancer (CRC); however, its metabolic mechanism remains unclear. This study aims to explore the potential therapeutic targets of mirabilite protection against colorectal cancer in APCmin/+ mice model. Oral administration of mirabilite was started from the ninth month, while the same dosage of distilled water was given to both the control group and the model group. Based on lipidomics, we collected serum samples of all mice at the 20th week and used a non-targeted method to identify the lipid biomarkers of CRC. Compared with C57BL/6J mice, the metabolic profile of CRC model mice was significantly disturbed, and we identified that 25 lipid-related biomarkers, including linoleic acid, 2-hydroxybutyric acid, 6-deoxocastasterone, hypoxanthine, PC(16:1), PC(18:4), and retinyl acetate, were associated with CRC. According to the abovementioned results, there were six lipid molecules with significant differences that can be used as new targets for handling of CRC through six metabolic pathways, namely, linoleic acid metabolism, retinol metabolism, propanoate metabolism, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids and purine metabolism. Compared with the model group, the metabolic profiles of these disorders tend to recover after treatment. These results indicated that the lipid molecules associated with CRC were regulated by mirabilite. In addition, we identified seven key lipid molecules, of which four had statistical significance. After administration of mirabilite, all disordered metabolic pathways showed different degrees of regulation. In conclusion, high-throughput lipidomics approach revealed mirabilite regulating the altered lipid metabolism as anticancer therapeutics.

In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.

Combining the substructure finite element model updating method with the response surface model updating method, one novelty finite element model updating method of bridge structure is proposed for updating the finite element model of a certain combined cable-stayed suspension model bridge. In light of specific configurations and mechanical features for combined cable-stayed suspension bridge, substructures are partitioned and the updating design parameters which need to be updated are preselected. On the basis of the variance analysis, the updating parameters are determined by the parameter significance test. Samples of updating parameters are obtained by the homogeneous design method, and the corresponding structural responses are obtained by finite element analysis, finally the response surface model for each updating parameter is fitted and verified. The jointed objective function with the linear combination of fitness functions based on the natural frequency and the static displacement are introduced, and experimental data of the static and dynamic testing for the model bridge is adopted to update the design parameters by the genetic algorithm, then the minimization of the discrepancies between the measurements and the finite element model updating results are achieved. The experimental results show that the updated parameters obtained from the proposed method are reasonable and having a clear physical meaning, and the finite element model of bridge structure can be updated effectively by the proposed finite element model updating method.

Traditional Chinese medicine (TCM) expresses its effects through multicomponents and multitargets. The screening and analysis of bioactive components is very important for the drug discovery process and mechanism of TCM. Since most TCMs are orally administered, only the constituents that are successfully assimilated into blood and maintain a considerable concentration level in the target organ have the opportunity to exert curative effects. Serum pharmacochemistry of TCM has been introduced to search for the effective constituents of TCM. It is based on the hypothesis that active compounds could be absorbed into the blood after administration of TCM, and only the absorbed components have the probability of becoming the bioactive constituents. With the help of serum pharmacochemistry, the in vivo bioactive components of Phellodendri amurensis Cortex, Radix Stemonae, Zhi Zhu Wan, and Yin-Chen-Hao-Tang have been observed. With the advantages of short analysis time, high chromatographic resolution, and improved sensitivity, as well as high accuracy of mass values, this approach has been widely accepted and used for the analysis of Chinese herb medicines. This method is usually utilized by coupling with MarkerLynx software, which provides automated peak detection followed by multivariable analysis. A multiple data processing approach (Mdpa) has been developed to handle the acquired data and to search for the discriminating features from biosample sets. The combined high-resolution liquid chromatography mass spectrometry with Mdpa should allow for comprehensive detection and structural interpretation of sample analyses in complex biological matrices.

The aim of this study was to evaluate the use of 4-D echocardiography with inversion mode and spatiotemporal image correlation (IM-STIC) in the detection of normal and abnormal fetal hearts. We retrospectively studied 112 normal fetuses and 16 fetuses with a confirmed diagnosis of congenital heart disease. Two volumes were acquired from each of the fetuses using transverse and sagittal sweeps. Volumes were reconstructed with IM-STIC. In normal fetuses, IM-STIC facilitated visualization of the interior structures of the fetal heart and great vessels. The visualization rates of intended planes obtained from IM-STIC 4D data ranged from 55% to 100%. In 16 fetuses with congenital heart disease, IM-STIC was able to display the cardiac malformations using digital casting. Some of the malformations were suspected during pre-natal 2-D echocardiography, and their pre-natal IM-STIC diagnoses were confirmed by post-natal echocardiography, surgery and/or autopsy. Hence, 4-D IM-STIC allows better visualization of complex congenital heart disease and should be considered a very useful addition to 2-D echocardiography.

Metabolomics is a new approach based on the systematic study of the full complement of metabolites in a biological sample. Extracting biomedical information from large datasets is of considerable complexity. Furthermore, the traditional method of assessing metabolomics data is not only time-consuming but it is often subjective work. Here we used sensitive ultra-performance LC-ESI/Q-TOF high-definition mass spectrometry (UPLC-ESI-Q-TOF-MS) in positive ion mode coupled with a new developed software program TransOmics for widely untargeted metabolomics, which incorporates novel nonlinear alignment, deconvolution, matched filtration, peak detection, and peak matching to characterize metabolites as a case study. The TransOmics method can facilitate prioritization of the data and greatly increase the probability of identifying metabolites related to the phenotype of interest. By this means, 17 urinary differential metabolites were identified (less than 10 min) involving the key metabolic pathways including tyrosine metabolism, glutathione metabolism, phenylalanine metabolism, ascorbate and aldarate metabolism, arginine and proline metabolism, and so forth. Metabolite identification has also been significantly improved, using the correlation peak patterns in contrast to a reference metabolite panel. It can detect and identify metabolites automatically and remove background noise, and also provides a user-friendly graphical interface to apply principal component analyses, correlation analysis and compound statistics. This investigation illustrates that metabolomics combined with the proposed bioinformatic approach (based on TransOmics) is important to elucidate the developing biomarkers and the physiological mechanism of disease, and has opened the door for the development of a new genre of metabolite identification methods.

This work was designed to explore the effective components and targets of herbal medicine AS1350 and its effect on “Kidney-Yang Deficiency Syndrome” (KYDS) based on a chinmedomics strategy which is capable of directly discovering and predicting the effective components, and potential targets, of herbal medicine. Serum samples were analysed by UPLC-MS combined with pattern recognition analysis to identify the biomarkers related to the therapeutic effects. Interestingly, the effectiveness of AS1350 against KYDS was proved by the chinmedomics method and regulated the biomarkers and targeting of metabolic disorders. Some 48 marker metabolites associated with alpha-linolenic acid metabolism, fatty acid metabolism, sphingolipids metabolism, phospholipid metabolism, steroid hormone biosynthesis, and amino acid metabolism were identified. The correlation coefficient between the constituents in vivo and the changes of marker metabolites were calculated by PCMS software and the potential effective constituents of AS1350 were also confirmed. By using chinmedomics technology, the components in AS1350 protecting against KYDS by re-balancing metabolic disorders of fatty acid metabolism, lipid metabolism, steroid hormone biosynthesis, etc. were deduced. These data indicated that the phenotypic characterisations of AS1350 altering the metabolic signatures of KYDS were multi-component, multi-pathway, multi-target, and overall regulation in nature.

The theory and technology of human–machine coordination and natural interaction have a wide range of application prospect in future smart factories. This paper elaborates on the design and implementation of a body-following wheeled robot system based on Kinect, as well as the use of gesture recognition function to enhance the interactive performance. An improved optical flow method is put forward to obtain the direction and speed of the target movement. The smoothing parameters in traditional optical flow are replaced by variables. The new smoothing parameter is related to the local gradient value. Compared with the traditional optical flow method, it can reflect the status of moving objects more clearly, reduce noise and ensure real-time performance, solving the problem of tracking state oscillation caused by the skeleton node drifts when the target is occluded. The experiment on the wheeled robot confirms that the system can accomplish the tracking task in a preferable way.