Expansion within the CYP71D subfamily drives the heterocyclization of tanshinones synthesis in Salvia miltiorrhiza

Tanshinones are the bioactive nor-diterpenoid constituents of the Chinese medicinal herb Danshen (Salvia miltiorrhiza). These groups of chemicals have the characteristic furan D-ring, which differentiates them from the phenolic abietane-type diterpenoids frequently found in the Lamiaceae family. However, how the 14,16-epoxy is formed has not been elucidated. Here, we report an improved genome assembly of Danshen using a highly homozygous genotype. We identify a cytochrome P450 (CYP71D) tandem gene array through gene expansion analysis. We show that CYP71D373 and CYP71D375 catalyze hydroxylation at carbon-16 (C16) and 14,16-ether (hetero)cyclization to form the D-ring, whereas CYP71D411 catalyzes upstream hydroxylation at C20. In addition, we discover a large biosynthetic gene cluster associated with tanshinone production. Collinearity analysis indicates a more specific origin of tanshinones in Salvia genus. It illustrates the evolutionary origin of abietane-type diterpenoids and those with a furan D-ring in Lamiaceae

Formation and Soft Magnetic Properties of Co-Fe-Si-B-Nb Bulk Glassy Alloys

Soft ferromagnetic bulk glassy alloys in Co-Fe-Si-B base system were formed in the diameter range up to 1 mm at the composition of (Co 0.705 Fe 0.045 Si 0.1 B 0.15 ) 96 Nb 4 by copper mold casting. Since no bulk glass formation has been obtained in the Co-Fe-Si-B system, the addition of 4%Nb is very effective for the increase in the glass-forming ability. The effectiveness was interpreted by satisfaction of the three component rules for formation of bulk glassy alloys. The bulk glassy alloys exhibit the glass transition before crystallization. The glass transition temperature (T g ), the supercooled liquid region defined by the difference between T g and crystallization temperature (T x ), ∆T x (= T x − T g ) and the reduced glass transition temperature (T g /T l ) are 823 K, 37 K and 0.61, respectively. The bulk glassy alloys also exhibit soft magnetic properties with saturation magnetization (I s ) of about 0.60 T and low coercive force (H c ) below 3 A/m. The synthesis of the Co-based bulk glassy alloy rods with glass transition and good soft magnetic properties is important for future development as a new type of soft magnetic bulk material

Soft Magnetic Properties of Nanocystalline Fe-Si-B-Nb-Cu Rod Alloys Obtained by Crystallization of Cast Amorphous Phase

An amorphous alloy rod of 0.5 mm in diameter was produced for an Fe 72.5 Si 10 B 12.5 Nb 4 Cu 1 alloy by copper mold casting, though the maximum diameter of 1.5 mm for (Fe 0.75 Si 0.1 B 0.15 ) 96 Nb 4 alloy decreased by the addition of 1 at%Cu and the decrease in B content. The amorphous alloy rod crystallizes through multi-stage exothermic reactions. The first exothermic peak is due to the precipitation of nanoscale bcc-Fe phase and the following exothermic peaks result from the transition of bcc-Fe+amorphous → bcc-Fe+Fe 23 B 6 +Fe 2 B+Fe 3 Si+Fe 2 Nb. The bcc-Fe phase has a particle size of about 10 nm and its volume fraction is approximately 70% after annealing for 300 s at 883 K. The alloy rod consisting of bcc-Fe and amorphous phases exhibits good soft magnetic properties, i.e., high saturated magnetic flux density of 1.21 T, low coercive force of 1.8 A/m and high initial permeability of 32000. The good soft magnetic properties for the nanocrystalline Fe 72.5 Si 10 B 12.5 Nb 4 Cu 1 alloy in a rod form of 0.5 mm in diameter are encouraging for future development as a new type of nanocrystalline soft magnetic bulk material

LSTM combined with BIM technology in the management of small and medium-sized span highway concrete beam bridges

To better carry out maintenance management for medium and small span highway concrete beam bridges, this bridge is taken as the research object. Building information modeling technology is applied to its maintenance management. The corresponding management system is constructed. Through this system, the distribution of diseases and other information can be viewed. Long short-term memory neural network is used to build the corresponding prediction model to predict the technical condition score of bridge components. The results show that the prediction error is small. The minimum error rate in component technical condition scoring prediction is 0.00 %. In the full bridge technology scoring prediction, the minimum scoring error is 2.8 points, and the maximum scoring error is 6 points. The minimum average error rate of this model in predicting component technical condition scores is 0.15 %. Except for individual components, the average error rate of all other components is less than 5 %. The accuracy of the prediction model is 95 %. The performance is superior to linear regression models. The average error rate in the abutment is 7.45 % lower than the linear regression model. This method can effectively predict the degradation of small and medium-sized span highway concrete beam bridges and achieve three-dimensional visualization of diseases