Effect of bainite layer by LSMCIT on wear resistance of medium-carbon bainite steel at different temperatures

In this work, bainite layer was prepared by Laser surface melting combined with isothermal treatment (LSMCIT) at 250ºC. The microstructures of the samples were analyzed by scanning electron microscopy (SEM), X-ray Diffraction (XRD) and transmission electron microscopy (TEM). Their wear resistances at 20ºC, 100ºC and 200ºC were measured using reciprocating tribometer. After the wear test, the confocal laser scanning microscope and SEM were used to characterize the topography of all abrasion surfaces, and the phase transformations occurred on the contact surfaces were analyzed by XRD. The results show that the microstructure of the LSMCIT sample has been refined to nanoscale. The wear volume reduction ratio of LSMCIT sample is 40.9% at 20ºC. The wear resistances of the samples are decreased with increasing of the temperature, however, the decrease in amplitude of the bainite is relatively small. The harder surface of the LSMCIT sample can provides higher mechanical support, and the white-etching layer on surface are difficult to remove by the reciprocating friction. The wear resistances of the LSMCIT samples at 20ºC, 100ºC and 200ºC are excellent, which shows the wide temperature ranges in wear applications

Controllable Synthesis of Single-Crystalline CdO and Cd(OH)2Nanowires by a Simple Hydrothermal Approach

Single-crystalline Cd(OH)2 or CdO nanowires can be selectively synthesized at 150 °C by a simple hydrothermal method using aqueous Cd(NO3)2 as precursor. The method is biosafe, and compared to the conventional oil-water surfactant approach, more environmental-benign. As revealed by the XRD results, CdO or Cd(OH)2 nanowires can be generated in high purity by varying the time of synthesis. The results of FESEM and HRTEM analysis show that the CdO nanowires are formed in bundles. Over the CdO-nanowire bundles, photoluminescence at ~517 nm attributable to near band-edge emission of CdO was recorded. Based on the experimental results, a possible growth mechanism of the products is proposed

Comprehensive Study in the Inhibitory Effect of Berberine on Gene Transcription, Including TATA Box

Berberine (BBR) is an established natural DNA intercalator with numerous pharmacological functions. However, currently there are neither detailed reports concerning the distribution of this alkaloid in living cells nor reports concerning the relationship between BBR's association with DNA and the function of DNA. Here we report that the distribution of BBR within the nucleus can be observed 30 minutes after drug administration, and that the content of berberine in the nucleus peaks at around 4 µmol, which is twelve hours after drug administration. The spatial conformation of DNA and chromatin was altered immediately after their association with BBR. Moreover, this association can effectively suppress the transcription of DNA in living cell systems and cell-free systems. Electrophoretic mobility shift assays (EMSA) demonstrated further that BBR can inhibit the association between the TATA binding protein (TBP) and the TATA box in the promoter, and this finding was also attained in living cells by chromatin immunoprecipitation (ChIP). Based on results from this study, we hypothesize that berberine can suppress the transcription of DNA in living cell systems, especially suppressing the association between TBP and the TATA box by binding with DNA and, thus, inhibiting TATA box-dependent gene expression in a non-specific way. This novel study has significantly expanded the sphere of knowledge concerning berberine's pharmacological effects, beginning at its paramount initial interaction with the TATA box

Statistical Optimization of Process Variables for Antibiotic Activity of Xenorhabdus bovienii

The production of secondary metabolites with antibiotic properties is a common characteristic to entomopathogenic bacteria Xenorhabdus spp. These metabolites not only have diverse chemical structures but also have a wide range of bioactivities of medicinal and agricultural interests. Culture variables are critical to the production of secondary metabolites of microorganisms. Manipulating culture process variables can promote secondary metabolite biosynthesis and thus facilitate the discovery of novel natural products. This work was conducted to evaluate the effects of five process variables (initial pH, medium volume, rotary speed, temperature, and inoculation volume) on the antibiotic production of Xenorhabdus bovienii YL002 using response surface methodology. A 25–1 factorial central composite design was chosen to determine the combined effects of the five variables, and to design a minimum number of experiments. The experimental and predicted antibiotic activity of X. bovienii YL002 was in close agreement. Statistical analysis of the results showed that initial pH, medium volume, rotary speed and temperature had a significant effect (P<0.05) on the antibiotic production of X. bovienii YL002 at their individual level; medium volume and rotary speed showed a significant effect at a combined level and was most significant at an individual level. The maximum antibiotic activity (287.5 U/mL) was achieved at the initial pH of 8.24, medium volume of 54 mL in 250 mL flask, rotary speed of 208 rpm, temperature of 32.0°C and inoculation volume of 13.8%. After optimization, the antibiotic activity was improved by 23.02% as compared with that of unoptimized conditions

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests