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ABSTRACT: Lactococcus lactis, widely used in the food fermentation industry, has developed various ways to regulate acid adaptation in the process of evolution. Investigation into how peptidoglycan (PG) senses and responds to acid stress is an expanding field. Here, we addressed the regulation of murT-gatD genes, which are responsible for the amidation of PG d-Glu. We found that lactic acid stress reduced murT-gatD expression, and overexpressing these genes notably decreased acid tolerance of L. lactis NZ9000, possibly due to a reduction in the negative charge of PG, thereby facilitating the influx of extracellular protons into the cell. Subsequently, by using a combination of DNA pulldown assay and electrophoretic mobility shift assay, we identified a novel MarR family regulator, RmaH, as an activator of murT-gatD transcription. Further MEME motif prediction, electrophoretic mobility shift assay verification, and fluorescent protein reporter assay showed that RmaH directly bound to the DNA motif 5′-KGVAWWTTTTGCT-3′ located in the upstream region of murT-gatD. Beyond the mechanistic investigation of RmaH activation of murT-gatD, this study provides new insight into how PG modification is regulated and responds to lactic acid stress.

The travel time of rock compressional waves is an essential parameter used for estimating important rock properties, such as porosity, permeability, and lithology. Current methods, like wireline logging tests, provide broad measurements but lack finer resolution. Laboratory-based rock core measurements offer higher resolution but are resource-intensive. Conventionally, wireline logging and rock core measurements have been used independently. This study introduces a novel approach that integrates both data sources. The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data. By combining machine learning with random field theory, the method allows for probabilistic predictions in regions with sparse data sampling. In this framework, 12 parameters from wireline tests are used to predict trends in rock core data. The residuals are modeled using random field theory. The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual. By utilizing unconditional and conditional random field theories, this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates. This integrated approach optimizes the use of existing core and logging data. Its applicability is confirmed in an oil project in West China.

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters, making the slope reliability assessment closer to the engineering reality. However, multi-source information (including test data, monitored data, field observation and slope survival records) is rarely used in current probabilistic back-analysis. Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved. In this paper, a framework by integrating a modified Bayesian Updating with Subset simulation (mBUS) method with adaptive Conditional Sampling (aCS) algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction. Within this framework, the high-dimensional probabilistic back-analysis problem can be easily tackled, and the multi-source information (e.g. monitored pressure heads and slope survival records) can be fully used in the back-analysis. A real Taoyuan landslide case in Taiwan, China is investigated to illustrate the effectiveness and performance of the established framework. The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations. Furthermore, the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12, 2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City, Taiwan, China.

To reinforce compatibility with the thermophysical and mechanical properties of SiC/Al composites for electronic packaging to improve the stability and reduce fatigue failure of electronic integrated devices, a novel 3D SiC reinforced framework with interpenetrated plate-like α-SiC grains was synthesized. A small amount of doped α-SiC was seeded to induce the transformation of β-SiC to plate-like α-SiC at 2,300 °C, forming a high-purity α-SiC strongly bonded framework. Vacuum/gas pressure infiltration of Al alloy was subsequently used to manufacture the 3D interpenetrated network structure SiC/Al (SiC3D/Al) composite. Characterization results showed that 15% (in mass) seeds provided the composite with the optimal comprehensive performance, including a low coefficient of thermal expansion (CTE) of 5.54 × 10−6 K−1, a high thermal conductivity (λ) of 239.08 W·m−1·K−1, the highest flexural strength of 326.84 MPa, and a low thermal deformation parameter (TDP) of 0.023. High-purity plate-like grains enhanced the purity of the framework promoting a significant improvement in λ. As the seed content increased to 20% (in mass), both CTE and λ reached optimal values of 5.22 × 10−6 K−1 and 243.14 W·m−1·K−1, but the mechanical properties declined by 10.30%. The synergistic effect of the well-bonded interface and the high-purity 3D SiC framework balanced excellent mechanical properties and multiple thermal functions.

Top grafting is an efficient and practical technique for the renewal and rejuvenation of citrus trees in old orchards. However, root death after top grafting restricts plant growth and canopy reconstruction. Thus, applications of rooting promotion substances before citrus top grafting may increase the amount and activity of roots, thereby enhancing top-grafted plant performance. To test this assumption, four rooting promotion substances, i.e., rooting promotion powder, biochar, organic fertilizer, and potassium fulvic acid, were applied before top grafting, and the effects on biometric and physiological parameters were analyzed after top grafting. The results showed that the application of all rooting promotion substances before top grafting has a positive effect on growth and mineral nutrient acquisition, as well as on foliar C and N assimilates and the activity of anti-oxidative enzymes of top-grafted plants. Rooting promotion powder and biochar had the best effect on top-grafted tree performance in the short term. In conclusion, pre-grafting root promotion reduced root damage, enhanced nutrient acquisition, and improved the physiological performance of top-grafted plants. Therefore, this approach can play a crucial role in accelerating canopy reconstruction in old citrus orchards and in improving citrus plant development.

Citrus is the largest fruit crop around the world, while high nitrogen (N) application in citrus orchards is widespread in many countries, which results not only in yield, quality and environmental issues but also slows down the establishment of citrus canopies in newly cultivated orchards. Thus, the objective of this study was to investigate the physiological inhibitory mechanism of excessive N application on the growth of citrus seedlings. A pot experiment with the citrus variety Orah (Orah/Citrus junos) at four N fertilization rates (0, 50, 100, and 400 mg N/kg dry soil, denoted as N0, N50, N100, and N400, respectively) was performed to evaluate the changes of root morphology, biomass, N accumulation, enzyme activities, and so on. The results showed that the N400 application significantly reduced the total biomass (from 14.24 to 6.95 g/Plant), N accumulation (from 0.65 to 0.33 g/Plant) and N use efficiency (92.69%) in citrus seedlings when compared to the N100 treatment. The partial least squares pathway model further showed that the decline of biomass and N accumulation by high N application were largely attributed to the reduction of root growth through direct and indirect effects (the goodness of fit under the model was 0.733.) rather than just soil N transformation and activity of root N uptake. These results are useful to optimize N management through a synergistic N absorption and utilization by citrus seedlings.

This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning (ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields (GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations (SOFs) estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods (extra trees (ETs), gradient boosting (GB), extreme gradient boosting (XGBoost), random forest (RF), general regression neural network (GRNN) and k-nearest neighbors (KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method (GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.

Numerical simulation is a powerful technique for slope stability assessment and landslide hazard investigation. However, the physicomechanical parameters of the simulation results are susceptible to uncertainty. Displacement back-analysis is considered an effective method for the prediction of the geomechanical parameters of numerical models; therefore, it can be used to deal with the parameter uncertainty problem. In this study, to improve the interpretability of the back-analysis model, an analytical function relationship between slope displacements and physicomechanical parameters was established using geographically weighted regression. By combining the least-squares and linear-algebra algorithms, a displacement back-analysis method based on geographically weighted regression (DBA-GWR) was developed; in particular, the multi-objective displacement back-analysis was represented as an analytical problem. The developed method was subsequently used for a slope of the Guiwu Expressway in Guangxi, China. Simulation experiments and GNSS real-data experiments demonstrated that the GWR could achieve high-precision deformation modelling in the spatial domain with model-fitting precision in the order of mm. Compared with state-of-the-art methods, the precision of the simulated displacement with the proposed method was significantly improved, and equivalent physicomechanical parameters with higher accuracy were obtained. Based on the corrected numerical model, the most severely deformed profiles were forward-analysed, and the simulated deformation and distribution patterns were found to be in good agreement with the field investigation results. This approach is significant for the determination of geomechanical parameters and the accurate assessment of slope safety using monitoring data.

Abstract Increasing evidence has revealed a close association between intestinal bacterial community and hosts health. However, it is unclear whether and what extend Baimang disease alters the intestinal microbiota in mud crab (Scylla paramamosain). Here, we conducted intestinal contents Illumina sequencing of healthy and Baimang diseased mud crab (S. paramamosain) to understand bacterial community variations among health status. In addition, bacterial functional predication was used to investigate whether and how the bacteria variations further change their functions? The phyla of Proteobacteria, Fusobacteria, Cyanobacteria, Tenericutes, Firmicutes, Bacteroidetes, and Spirochaetae constituted over 96.44% of the total intestinal bacteria, with being the dominant taxa. The 7 most significantly different orders, including the increased four orders of Clostridiales, Entomoplasmatales, Bacteroidales, and Mycoplasmatales and the decreased three orders of Vibrionales, Campylobacterales, and Fusobacteriales, accounted for 61.14% dissimilarity, probably being the indicator taxa of Baimang disease. Accordingly, 12 Kyoto Encyclopedia of Genes and Genomes orthologies in level 3 shifted significantly at the diseased crabs. Especially, bacterial secretion system, secretion system, lipopolysaccharide biosynthesis proteins and Vibrio cholerae pathogenic cycle, being related to bacterial virulence, were reduced. In addition, the reduced butanoate metabolism, and induced methane metabolism and one carbon pool by folate were important metabolic processes of probiotic, such as Bacteroides spp. and Clostridium spp., with playing critical roles in host health. This study suggests that Baimang disease coupled altered the intestinal bacterial communities and functions, providing timely information for further analysis the influencing mechanism of Baimang disease in mud crab (S. paramamosain).

BackgroundPigmented villonodular synovitis (PVNS) is a rare condition that involves benign proliferation of the synovial tissue and is characterized by severe joint destruction and high recurrence even after surgical resection. However, poor understanding of the pathogenesis limits its effective therapy.MethodIn this study, gene expression profiles of six patients with PVNS, 11 patients with osteoarthritis (OA), nine patients with rheumatoid arthritis (RA) (E-MTAB-6141), and three healthy subjects (GSE143514) were analyzed using integrating RNA sequencing (RNA-seq) and microarray to investigate the PVNS transcriptome. Gene ontology, string, and cytoscape were used to determine the gene functional enrichment. Cell functional molecules were detected using flow cytometry or immunohistochemical test to identify the cell subset and function. CD14+ cells were isolated and induced to osteoclast to evaluate the monocyte/macrophage function.ResultsThe most obvious local manifestations of PVNS were inflammation, including increased immune cells infiltration and cytokine secretion, and tumor phenotypes. High proportion of inflammatory cells, including T cells, natural killer (NK) cells, NKT cells, and B cells were recruited from the blood. Th17 and monocytes, especially classical monocytes but not nonclassical monocytes, increased in PVNS synovium. An obvious increase in osteoclastogenesis and macrophage activation was observed locally. Elevated expression of MMP9, SIGLEC 15, and RANK were observed in myeloid cell of PVNS than OA. When compared with RA, osteoclast differentiation and myeloid cell activation are PVNS-specific characters, whereas T cell activation is shared by PVNS and RA.ConclusionThe transcriptional expression characteristics of PVNS showed increased immune response, cell migration, and osteoclastogenesis. Osteoclast differentiation is only observed in PVNS but not RA, whereas T-cell activation is common in inflammatory arthritis.

Abstract This paper analyses the decorative polychrome painting of architectural members (caihua) that has survived in the Hall of Mental Cultivation (Yangxindian) in the Forbidden City, Beijing. Beginning with the Yongzheng emperor, the Hall of Mental Cultivation was the residence and centre of daily governance for Chinese emperors. Combining information gained through on-site surveys and scaled drawings executed during a recent conservation project with evidence from historical records and other sources, this study finds that the remaining decorative polychrome painting can be dated to four different historical phases: the reign of the Jiajing emperor; the late Ming and early Qing dynasties; the reign of the Qianlong emperor; and the late Qing Dynasty and beginning of the Republic of China. It reveals that the designs of decorative polychrome painting on architectural members are closely related to changes in the functions of the buildings and imperial use. As part of an investigative conservation project, the study aims to provide a reliable basis for the conservation and repair work in the next step.

Aiming at the removal of refractory organic pollutants in aqueous solution, self-assembled nano-Fe3C embedded in reduced graphene oxide (nano-Fe3C@RGO) aerogel was prepared by hydrothermal synthesis and high temperature treatment, and characterized by SEM, HRTEM, pore size distribution, XRD, XPS and FTIR. The results showed that the aerogel was porous, and most of the Fe3C particles were less than 100 nm in size. They were evenly dispersed and embedded in the RGO aerogel. Furthermore, the mapping images confirmed that the elements of carbon, nitrogen and iron were homogeneously distributed. Moreover, the specific surface area of the aerogel was up to 324.770 m2/g and most of the pore sizes were between 5 and 10 nm. The formation of nano-Fe3C was identified by XRD pattern and HRTEM. Analysis of an XPS spectrum indicates that the nano-Fe3C was embedded in the graphene layer. The aerogel contained a large number of functional groups, including –NH2, –NH and –C=O, etc., which greatly strengthened the adsorption of organics. Finally, the Fenton-like catalytic degradation properties of the self-assembled nano-Fe3C@RGO aerogel were investigated by testing the removal of methyl orange from the aqueous solution. The results showed that the value of Ct/C0 decreased to 0.050 after 240 min, suggesting a high degradation rate was obtained. Meanwhile, the chemical reaction was verified in accordance with the first-order kinetic model, and the higher temperature was beneficial to the catalytic degradation. At the same time, methyl orange was degraded into small molecules by the hydroxyl and superoxide radicals generated during the reactions. Therefore, the self-assembled nano-Fe3C@RGO aerogel, as a novel Fenton-like catalyst, introduces a new approach in the field of treatment of refractory organic wastewater.

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

In this study, to decrease the evaluation uncertainty of the fatigue characteristics of cement-treated aggregate base materials under different test conditions, unconfined compressive, indirect tensile, flexural tensile strength tests and fatigue tests of these base materials with different cement content and at different curing times were carried out. The Weibull distribution was employed to analyze fatigue test results. The standardization model of fatigue characteristics for cement-treated aggregate base materials under different stress states was established. Based on the interval analysis theory, the fatigue characteristic model under different stress states was established using interval parameters. Results revealed that the curing time and cement content considerably affect the strength and fatigue characteristics of cement-treated aggregate base materials, and with increasing cement content and curing time, the fatigue resistance of cement-treated aggregate base materials can be improved. Clear differences between the fitting parameters a and b of the S-N fatigue equation of cement-treated aggregate base materials under different stress states were observed, which can be eliminated by using the analysis method based on the Weibull distribution and the standardization model, and a unified expression for the cement-treated aggregate base materials under different test conditions was realized. A Standardization model of fatigue characteristics based on the interval analysis new method could solve several problems such as inadequate sampling representation, low precision, and insufficient stability of test equipment; thus, the errors caused by materials, structures, the environment, and loads can be reduced, making the fatigue life interval more reasonable and scientific compared to the point numerical fatigue life. Regression parameters a and be were in intervals [9.0, 10.6] and [9.9, 11.3], respectively, and parameters a and b were similar, which improve the test accuracy and reduce the data error.