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Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train. [ABSTRACT FROM AUTHOR]

In order to promote the consumption and application of cassava in the food industry, the cassava–acerola cherry juice beverage was developed using lactic acid bacteria fermentation, which improved the flavor attraction and nutritional value, increased the added value of cassava and acerola cherry. The study investigated the effects of both pure and mixed fermentation using Lactobacillus plantarum (L. plantarum) HNC7 on the chemical compounds and antioxidant activities in cassava–acerola cherry juice. Following 72 h of fermentation, the fruit juice showed increased acidity, a decrease in pH, and higher consumption of soluble solids, and a significant rise in volatile substance content and antioxidant activity (p < 0.05). Notably, the HNC7-YLC92 binary combination demonstrated the most effective fermentation, resulting in a product with uniform color, moderate sourness and sweetness, and a delicate and smooth taste. Overall, the HNC7-YLC92 binary combination, due to its beneficial biological properties, shows great potential as the optimal strain for fermenting this juice. This provides a reference for selecting industrial fermentation conditions and strains, aiming to develop new value-added cassava products and increase their consumption. [ABSTRACT FROM AUTHOR]

Alluvial sedimentary records in the North China Plain are essential in expanding flood history and understanding hazard patterns in the Yellow River basin where inundation risk exists and would probably increase under future global change. A detailed study of the Longwangmiao profile in the lower Yellow River floodplain reveals ancient flooding records over the late Holocene. Slackwater deposits are distinguished by typical sedimentary features and share similarities with those in the upper and middle Yellow River. This indicates that the traditional method can still be applied for paleoflood research beyond the gorge. However, unlike confined bedrock gorges, multiphase flood deposits of slackwater and overbank deposits represent different stages of flood events. These sedimentary assemblages recorded six flooding periods, further confirmed by the analysis of grain size and geochemistry. The profile was broadly subdivided into two flood-poor phases (3.7–6.7 ka, 0.8–1.7 ka) and three flood-rich phases (before 6.7 ka, 1.7–3.7 ka, after 0.8 ka) based on the dating results. Compared with the existing related research in the nearby area, the synchronous deposition cycles of floods and inter-floods are mainly controlled by the hydrodynamic conditions of the old Yellow River course. This study provides an analogue of paleoflood research in the lower Yellow River and similar alluvial plains. It explores the potential of interlinking paleoflood records in the whole Yellow River basin. [ABSTRACT FROM AUTHOR]

The identification of superior haplotypes and haplotype combinations is essential for haplotype-based breeding (HBB), which provides selection targets for genomics-assisted breeding. In this study, genotypes of 42 functional genes in rice were analyzed by targeted capture sequencing in a panel of 180 Indica rice accessions. In total, 69 SNPs/Indels in seven genes were detected to be associated with grain length (GL), grain width (GW), ratio of grain length–width (L/W) and thousand-grain weight (TGW) using candidate gene-based association analysis, including BG1 and GS3 for GL, GW5 for GW, BG1 and GW5 for L/W, and AET1, SNAC1, qTGW3, DHD1 and GW5 for TGW. Furthermore, two haplotypes were identified for each of the seven genes according to these associated SNPs/Indels, and the amount of genetic variation explained by different haplotypes ranged from 3.24% to 27.66%. Additionally, three, three and eight haplotype combinations for GL, L/W and TGW explained 25.38%, 5.5% and 22.49% of the total genetic variation for each trait, respectively. Further analysis showed that Minghui63 had the superior haplotype combination Haplotype Combination 4 (HC4) for TGW. The most interesting finding was that some widely used restorer lines derived from Minghui63 also have the superior haplotype combination HC4, and our breeding varieties and lines using the haplotype-specific marker panel also confirmed that the TGW of the lines was much higher than that of their sister lines without HC4, suggesting that TGW-HC4 is the superior haplotype combination for TGW and can be utilized in rice breeding. [ABSTRACT FROM AUTHOR]

High-speed trains may be subjected to various forms of physical impacts during long-term operation, causing structural damage and endangering driving safety. Therefore, impact damage monitoring remains a daunting challenge for the stable operation of high-speed train structures. The existing methods cannot simultaneously detect the location and severity of impact damage, which poses challenges to structural integrity assessment and preventive maintenance. This article proposes an impact damage monitoring method based on multi-task 2D-CNN. Sensor data fusion is performed using a 2D image processing method to convert a 1D impact damage signal into a 2D grayscale image. The fused grayscale image contains information related to the location and severity of impact damage. A damage detection framework was established using multi-task 2D-CNN for feature extraction, impact location classification, and impact energy quantification. This model can learn the commonalities and characteristics of each task by sharing network structure and parameters and can effectively improve the accuracy of each task. Compared with single-task learning, multi-task learning performs better on the metrics of the impact location task recognizing the impact energy task and reduces the training time by 30.83%. With a reduced number of samples, the performance of multi-task learning is more stable and can still effectively identify the location and severity of impact damage. [ABSTRACT FROM AUTHOR]

Convenient and fast fault diagnosis is the key to improving the service safety and maintenance efficiency of gearboxes. However, the environment and working conditions under complex service conditions are variable, and there is a lack of fault samples in engineering applications. These factors lead to difficulties in intelligent diagnosis methods based on machine learning, while traditional mechanism-based fault diagnosis requires high expertise and long time periods for the manual analysis of data. For the requirements of diagnostic convenience, an automatic fault diagnosis method for gearboxes is proposed in this paper. The method achieves accurate acquisition of rotational speed by constructing a rotational frequency search algorithm. The self-referencing characteristic frequency identification method is proposed to avoid manual signal analysis. On this basis, a framework of anti-interference automatic diagnosis is constructed to realize automatic diagnosis of gear faults. Finally, a gear fault experiment is carried out based on a high-fidelity experimental bench of bogie to verify the effectiveness of the proposed method. The proposed automatic diagnosis method does not rely on a large number of fault samples and avoids the need for diagnosis through professional knowledge, thus saving time for data analysis and promoting the application of fault diagnosis methods. [ABSTRACT FROM AUTHOR]

Early detection of defects inside a rail is of great significance to ensure the safety of rail transit. This work investigated the ability of ultrasonic guided waves (UGWs) to detect internal defects in a rail head. First, the model of UGW propagation in rail, which has an irregular cross-section, was constructed based on the semi-analytical finite element (SAFE) method. Fundamental characteristics, such as wavenumber, phase or group velocity, and wave structure inside the rail, were then calculated. Following modal and vibration energy distribution analysis, a guided wave mode that is sensitive to transverse fissure (TF) defects was selected, and its excitation method was proposed. The effectiveness of the excitation method was confirmed by simulations performed in the ABAQUS software. According to the simulation data, the dispersion curve calculated by using the two-dimensional Fourier fast transform (2D-FFT) coincided well with that of the SAFE method. After that, the sensitivity of the selected mode to internal rail defects was validated and its ability to locate defects was also demonstrated. Finally, the effects of excitation frequency, defect size, and vertical and horizontal defect depth on the reflection waveforms were investigated. [ABSTRACT FROM AUTHOR]

Simple Summary: The homeostasis of intestinal microorganisms has an important effect on the healthy development of the host, as well as for ruminants. However, the study on the intestinal microflora of the Hainan Black Goat, a special breed in Hainan, is not sufficient. Therefore, this study is devoted to the preliminary study of the composition of gut microbiota and the differences between the different intestinal segments of Hainan black goats. According to the results, the large intestine has a greater variety and a greater richness of the intestinal microbiota community than the small intestine. The microbial composition of the cecum and colon is similar. The results of this study contribute to our understanding of the species diversity of gut microbiota in different intestinal segments of Hainan black goats. The intestine of animals is a complex micro-ecosystem containing a large number of microbiomes, which is essential for the host's health development. The Hainan black goat with good resistance and adaptability is a unique species in Hainan, China. These unique physiological characteristics are inseparable from their intestinal microbiota. In this study, high-throughput sequencing was used to investigate bacterial communities in different segments of the intestinal tract of Hainan black goat. The results showed that the indices of Chao1 and ACE in the cecum and colon were significantly greater than those in the ileum (p = 0.007, 0.018). According to PCoA, the intestinal flora composition of the cecum and colon is almost equivalent. In contexts of the phylum, Firmicutes, Bacteroidota, and Pseudomonadota were the dominant phyla in the gut of the Hainan black goat. While in context of the genus, the dominant groups in the gut of black goats mainly include Ruminococcaceae_UCG-005, Bacteroides, Paeniclostridium, Christensenellaceae_R-7_group, Rikenellaceae_RC9_gut_group, and Eubacterium coprostanoligenes _group, Prevotella_1, they have different proportions in different intestinal segments. The gut microbiota of Hainan black goat is mainly Firmicutes, Bacteroidota, and Pseudomonadota. Influenced by the intestinal location where they colonize, the large intestine has a more complex intestinal flora than the small intestine. In contrast, there are only minor differences between the caecum and the colon in the large intestine. [ABSTRACT FROM AUTHOR]

Fermentation technology is of great significance for food preservation; through fermentation, while extending the shelf life of food, it can also improve the sensory quality of food and increase its nutritional value. Compared with natural fermentation, the use of specific microorganisms for fermentation can be used to determine the direction of fermentation. Therefore, in order to better explore the effect of bacterial community structure on the quality of sugarcane juice naturally fermented and inoculated with different lactic acid bacteria in the fermentation process, the most suitable method for sugarcane juice fermentation was found, which provided a theoretical basis for the safe production of fermented sugarcane juice. In this experiment, the sugarcane juice was treated differently and divided into four groups: natural fermentation, fermentation with Lactobacillus HNK10, fermentation with Lactococcus HNK21, and HNK10 + HNK21 compound fermentation. The changes in bacterial community structure of different treatments of sugarcane juice during fermentation were analyzed by high-throughput sequencing technology, and the quality change of different treatments of sugarcane juice during fermentation was analyzed by high-performance liquid chromatography, and the change in its bacteriostatic ability was explored. The results showed that after the sugarcane juice treated with Lactobacillus HNK10 was fermented at 37 °C for 48 h, the final fermented sugarcane juice had a large abundance of lactic acid bacteria and high-quality and strong antibacterial activity. Conclusions: changes in the bacterial community structure during the fermentation of sugarcane juice affect the formation of organic acids and the change of bacteriostatic ability and directly determine the quality and shelf life of fermented sugarcane juice. [ABSTRACT FROM AUTHOR]

To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL. [ABSTRACT FROM AUTHOR]

To effectively utilize banana by-products, we prepared silage with defective bananas using screened lactic acid bacteria (LAB), sucrose, and tannase as additives. Eleven strains of LAB were isolated from the fruits and flowers of defective bananas, all of which were Gram-positive and catalase-negative bacteria that produced lactic acid from glucose. Among these LAB, homofermentative strain CG1 was selected as the most suitable silage additive due to its high lactic acid production and good growth in a low pH environment. Based on its physiological and biochemical properties and 16S rRNA gene sequence analysis, strain CG1 was identified as Lactiplantibacillus plantarum. Defective bananas contain 74.8–76.3% moisture, 7.2–8.2% crude protein, 5.9–6.5% ether extract, and 25.3–27.8% neutral detergent fibre on a dry matter basis. After 45 d of fermentation, the silage of deficient bananas treated with LAB or sucrose alone improved fermentation quality, with significantly (p < 0.05) lower pH and higher lactic acid contents than the control. The combination of LAB and sucrose had a synergistic effect on the fermentation quality of silage. The tannase-treated silage significantly (p < 0.05) decreased the tannin content, while the combination of tannase and LAB in silage not only decreased (p < 0.05) the tannin content, but also improved the fermentation quality. The study confirmed that defective bananas are rich in nutrients, can prepare good quality silage, and have good potential as a feed source for livestock. [ABSTRACT FROM AUTHOR]

The influences of thermal-oxidative aging on the diffusion behaviors of oxygen and cyclohexane in nitrile-butadiene rubber (NBR) at the micro-scale were investigated by molecular dynamics (MD) simulation. The two types of aged rubber models were established on the basis of rubber oxidative chains modified by the introduction of hydroxyl groups and carbonyl groups in rubber chains. The diffusion behaviors of oxygen and cyclohexane in NBR under different conditions were characterized by the fractional free volume (FFV), mean square displacement (MSD), diffusion coefficients, and diffusion trajectory. It turns out that the elevated temperature contributed to the increase in the free volume and diffusion range of oxygen and cyclohexane, while the compressive stress showed the reverse influence. Additionally, the introduction of oxidative polar functional groups (hydroxyl groups and carbonyl groups) in rubber chains lowered the flexibility of the rubber chains and promoted the formation of strong polar interaction, which further inhibits the diffusion of oxygen and cyclohexane. [ABSTRACT FROM AUTHOR]

The effects of oxidative aging on the static and dynamic properties of nitrile rubber at the molecular scale were investigated by molecular dynamics simulation. The aged nitrile rubber models were constructed by introducing hydroxyl groups and carbonyl groups into rubber molecular chains to mimic oxidative aging. The static and dynamic properties of the unaged and aged nitrile rubber under different conditions were evaluated by mean square displacement, self-diffusion coefficients, hydrogen bond, fractional free volume, radial distribution function, cohesive energy density and solubility parameter. The results show that the elevated temperature intensified significantly the mobility of rubber molecular chains and fractional free volume, while the compressive strain displayed the opposite effect resulting in packing and rearrangement of rubber chains. The introduction of hydroxyl groups and carbonyl groups enhanced the polarity, intermolecular interactions, the volume and rigidity of molecular chains, implying weaker mobility of molecular chains as compared to unaged models. The compressive strain and oxidative aging both decreased the fractional free volume, which inhibited gaseous and liquid diffusion into the rubber materials, and slowed down the oxidative aging rate. This study provides insights to better understand the effect of molecular changes due to oxidative aging on the structural and dynamic properties of rubber materials at the molecular level. [ABSTRACT FROM AUTHOR]

Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the time domain is proposed. The method takes the discrepancy of transmissibility of structure response in the time domain before and after damage as the basis of finite element model updating. The damage is located and quantified through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by the finite element model. Taking advantage of the response reconstruction method based on empirical mode decomposition, damage information can be obtained in the absence of prior knowledge on excitation. Moreover, this method directly collects time-domain data for identification without modal identification and frequent time–frequency conversion, which can greatly improve efficiency on the premise of ensuring accuracy. A numerical example is used to demonstrate the overall damage identification method, and the study of measurement noise shows that the method has strong robustness. Finally, the present work investigates the method through a simply supported overhanging beam. The experiments collect the vibration strain signals of the beam via resistance strain gauges. The comparison between identification results and theoretical values shows the effectiveness and accuracy of the method. [ABSTRACT FROM AUTHOR]

Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is of significant interest to develop a general methodology for crack quantification through the optimal design of the excitation waveform and signal-processing methods. This paper presents a new crack size quantification method based on high-resolution Lamb waves. The linear chirp (L-Chirp) signal and Golay complementary code (GCC) signal are used as Lamb wave excitation signals. After dispersion removal, these excitation waveforms, based on pulse compression, can effectively improve the inspection resolution in plate-like structures. A series of simulations of both healthy plates and plates with different crack sizes are performed by Abaqus CAE, using different excitation waveforms. The first wave package of the S0 mode after pulse compression is chosen to extract the damage features. A multivariate regression model is proposed to correlate the damage features to the crack size. The effectiveness of the proposed crack size quantification method is verified by a comparison with tone burst excitation, and the accuracy of the crack size quantification method is verified by validation experiments. [ABSTRACT FROM AUTHOR]

As a complex task, robot sorting has become a research hotspot. In order to enable robots to perform simple, efficient, stable and accurate sorting operations for stacked multi-objects in unstructured scenes, a robot multi-object sorting system is built in this paper. Firstly, the training model of rotating target detection is constructed, and the placement state of five common objects in unstructured scenes is collected as the training set for training. The trained model is used to obtain the position, rotation angle and category of the target object. Then, the instance segmentation model is constructed, and the same data set is made, and the instance segmentation network model is trained. Then, the optimized Mask R-CNN instance segmentation network is used to segment the object surface pixels, and the upper surface point cloud is extracted to calculate the normal vector. Then, the angle obtained by the normal vector of the upper surface and the rotation target detection network is fused with the normal vector to obtain the attitude of the object. At the same time, the grasping order is calculated according to the average depth of the surface. Finally, after the obtained object posture, category and grasping sequence are fused, the performance of the rotating target detection network, the instance segmentation network and the robot sorting system are tested on the established experimental platform. Based on this system, this paper carried out an experiment on the success rate of object capture in a single network and an integrated network. The experimental results show that the multi-object sorting system based on deep learning proposed in this paper can sort stacked objects efficiently, accurately and stably in unstructured scenes. [ABSTRACT FROM AUTHOR]

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S₀ mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions., Competing Interests: The authors declare no conflict of interest.

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification., Competing Interests: The authors declare no conflict of interest.

This paper presents a novel framework for probabilistic crack size quantification using fiber Bragg grating (FBG) sensors. The key idea is to use a high-order extended finite element method (XFEM) together with a transfer (T)-matrix method to analyze the reflection intensity spectra of FBG sensors, for various crack sizes. Compared with the standard FEM, the XFEM offers two superior capabilities: (i) a more accurate representation of fields in the vicinity of the crack tip singularity and (ii) alleviation of the need for costly re-meshing as the crack size changes. Apart from the classical four-term asymptotic enrichment functions in XFEM, we also propose to incorporate higher-order functions, aiming to further improve the accuracy of strain fields upon which the reflection intensity spectra are based. The wavelength of the reflection intensity spectra is extracted as a damage sensitive quantity, and a baseline model with five parameters is established to quantify its correlation with the crack size. In order to test the feasibility of the predictive model, we design FBG sensor-based experiments to detect fatigue crack growth in structures. Furthermore, a Bayesian method is proposed to update the parameters of the baseline model using only a few available experimental data points (wavelength versus crack size) measured by one of the FBG sensors and an optical microscope, respectively. Given the remaining data points of wavelengths, even measured by FBG sensors at different positions, the updated model is shown to give crack size predictions that match well with the experimental observations., Competing Interests: The authors declare no conflict of interest.

Understanding variations of heavy metals in atmospheric particles between different functional areas is significant for pollution control and urban planning in cities. To reveal pollution and spatial distribution of heavy metals in atmospheric particles from different urban functional areas in Shijiazhuang in North China, 43 settled dust samples were collected over the main urban area and heavy metal concentrations were determined in their <63 μm fractions using an ICP-OES. The results suggest that Cr, Mn, Fe, Co, Ni, and V in the dust are not or slightly enriched and their concentrations vary slightly between different sites, implying their natural origins; whereas Cu, Zn, Cd, and Pb are often notably enriched and their concentrations vary significantly between different functional areas, indicating their anthropogenic sources. Integrated pollution indexes (IPIs) of the ten heavy metals are 2.7-13.6 (5.7 ± 2.2), suggesting high or very high pollution levels of most dust. Relatively lower IPIs occur mainly in the administration-education area, the commercial area, and other unclassified sites; while peaks occur mainly in the North Railway Station, the northeastern industrial area, and some sites near heavily trafficked areas, implying the significant influence of intensive industrial (including coal combustion) and traffic activities on atmospheric heavy metal accumulation. These results suggest a clear need of mitigating atmospheric heavy metal pollution via controlling emissions of toxic metals (especially Cd and Pb) from industrial and traffic sources in the city., Competing Interests: The authors declare no conflict of interest.

To examine levels, health risks, sources, and spatial distributions of potentially toxic elements in settled dust over Beijing urban area, 62 samples were collected mostly from residential building outdoor surfaces, and their <63 μm fractions were measured for 12 potentially toxic elements. The results show that V, Cr, Mn, Co, Ni, and Ba in dust are from predominantly natural sources, whereas Cu, Zn, As, Cd, Sb, and Pb mostly originate from anthropogenic sources. Exposure to these elements in dust has significant non-cancer risks to children but insignificant to adults. Cancer risks of Cr, Co, Ni, As, and Cd via inhalation and dermal contact are below the threshold of 10(-6)-10(-4) but As via dust ingestion shows a tolerable risk. The non-cancer risks to children are contributed mainly (75%) by As, Pb, and Sb, and dominantly (92%) via dust ingestion, with relatively higher risks mainly occurring in the eastern and northeastern Beijing urban areas. Although Cd, Zn, and Cu in dust are heavily affected by anthropogenic sources, their health risks are insignificant. Source appointments suggest that coal burning emissions, the dominant source of As, are likely the largest contributors to the health risk, and traffic-related and industrial emissions are also important because they contribute most of the Pb and Sb in dust.

Objective: The aim of this multiple-hospital study was to investigate the prevalence of integrons in multidrug-resistant Acinetobacter baumannii (MDRAB) in Eastern China, and characterize the integron-integrase genes, so as to provide evidence for the management and appropriate antibiotic use of MDRAB infections., Methods: A total of 425 clinical isolates of A. baumannii were collected from 16 tertiary hospitals in 11 cities of four provinces (Fujian, Jiangsu, Zhejiang and Shandong) from January 2009 to June 2012. The susceptibility of A. baumannii isolates to ampicillin/sulbactam, piperacillin/tazobactam, ceftazidime, ceftriaxone, cefepime, aztreonam, meropenem, amikacin, gentamicin, tobramycin, ciprofloxacin, levofloxacin, sulfamethoxazole/trimenthoprim, minocycline and imipenem was tested, and integrons and their gene cassettes were characterized in these isolates using PCR assay. In addition, integron-positive A. baumannii isolates were genotyped using pulsed-field gel electrophoresis (PFGE) assay, and intI1 gene cassette was sequenced., Results: intI1 gene was carried in 69.6% of total A. baumannii isolates, while intI2 and intI3 genes were not detected. The prevalence of resistance to ampicillin/sulbactam, piperacillin/tazobactam, ceftazidime, ceftriaxone, cefepime, aztreonam, imipenem, meropenem, amikacin, gentamicin, tobramycin, ciprofloxacin, levofloxacin and sulfamethoxazole/trimenthoprim was significantly higher in integron-positive A. baumannii isolates than in negative isolates (all p values <0.05), while no significant difference was observed in the prevalence of minocycline resistance (p > 0.05). PFGE assay revealed 27 PFGE genotypes and 4 predominant genotypes, P1, P4, P7 and P19. The PFGE genotype P1 contained 13 extensive-drug resistant and 89 non-extensive-drug resistant A. baumannii isolates, while the genotype P4 contained 34 extensive-drug resistant and 67 non-extensive-drug resistant isolates, appearing a significant antimicrobial resistance pattern (both p values <0.05). Sequencing analysis revealed two gene cassette assays of aacA4-catB8-aadA1 and dfrXII-orfF-aadA2 in MDRAB isolates., Conclusions: The results of this study demonstrate a high prevalence of class 1 integrons in MDRAB in Eastern China, and a greater prevalence of antimicrobial resistance in intI1 gene-positive MDRAB isolates than in negative isolates. Four predominant PFGE genotypes are identified in intI1 gene-positive MDRAB isolates, in which P4 is an epidemic PFGE genotype in Fujian Province, and it has a high proportion of extensive drug resistant A. baumannii. The gene cassette dfrXII-orfF-aadA2 is reported, for the first time, in A. baumannii strains isolated from Fujian Province, Eastern China.

Inflammation and transforming growth factor-β1 (TGF-β1) contribute to the development of peritoneal fibrosis (PF), which is associated with peritoneal dialysis (PD). Astragalus membranaceus (Astragalus) has anti-inflammatory and anti-fibrotic effects in many diseases. The goal of this study was to determine the anti-fibrotic effects of Astragalus on the PF response to PD. A rat model of PD was induced using standard PD fluid, and PF was verified by HE and Masson's staining, as well as through the expression of fibroblast surface protein (FSP) and collagen III. The expression levels of monocyte chemoattractant protein (MCP)-1, F4/80 (macrophage/monocyte marker in rat), TGF-β1 and the downstream proteins phospho-SMAD 2/3 in dialyzed peritoneal tissue treated with or without Astragalus was evaluated using immunohistochemistry analysis. Overall correlations between MCP-1 and TGF-β1 staining were analyzed using both the Spearman and Pearson methods. The results showed that Astragalus could inhibit the recruitment and activation of monocytes/macrophages, thereby reducing the production of TGF-β1 in the dialyzed peritoneal membrane. PF was also significantly decreased following treatment with Astragalus. MCP-1 expression had a strong positive correlation with TGF-β1 sensitivity, suggesting that the anti-fibrotic function of Astragalus was mediated by MCP-1 and the TGF-β1 pathway. Our results indicate that Astragalus could be a useful agent against PD-induced PF.