Domain Structure of the Selenocysteine-specific Translation Factor SelB in Prokaryotes

Translation factor SelB is the key component for the specific decoding of UGA codons with selenocysteine at the ribosome. SelB binds selenocysteyl-tRNASec, guanine nucleotides and a secondary structure of the selenoprotein mRNA following the UGA at the 3' side. A comparison of the amino acid sequences of SelB species from E. coli,Desulfomicrobium baculatum, Clostridium thermoaceticum and Haemophilus influenzae showed that the proteins consist of at least four structural domains from which the Nterminal three are well conserved and share homology with elongation factor Tu whereas the C-terminal one is more variable and displays no similarity to any protein known. With the aid of the coordinates of EF-Tu the N-terminal part has been modelled into a 3D structure which exhibits intriguing features concerning its interaction with guanine nucleotides and other components of the translational apparatus. Cloning and expression of fragments of SelB and biochemical analysis of the purified truncated proteins showed that the C-terminal 19 kDa protein fragment is able to specifically bind to the selenoprotein mRNA. SelB, thus, is a translation factor functionally homologous to EF-Tu hooked up to the mRNA with its C-terminal end. The formation by SelB of a quaternary complex in vivo has been proven by overexpression of truncated genes of SelB and by demonstration that fragments comprising the mRNA or the tRNA binding domain inhibit selenocysteine insertion

Condition transfer between prestressed bridges using structural state translation for structural health monitoring

Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible.Thus,estimating the state(condition)of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way.For this purpose,Structural State Translation(SST)has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure.This study uses the SST methodology to translate the state of one bridge(Bridge#1)to a new state based on the knowledge acquired from a structurally dissimilar bridge(Bridge#2).Specifically,the Domain-Generalized Cycle-Generative(DGCG)model is trained in the Domain Generalization learning approach on two distinct data domains obtained from Bridge#1;the bridges have two different conditions:State-H and State-D.Then,the model is used to generalize and transfer the knowledge on Bridge#1 to Bridge#2.In doing so,DGCG translates the state of Bridge#2 to the state that the model has learned after being trained.In one scenario,Bridge#2’s State-H is translated to State-D;in another scenario,Bridge#2’s State-D is translated to State-H.The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence(MMSC),showing that the translated states are remarkably similar to the real ones.For instance,the modes of the translated and real bridge states are similar,with the maximum frequency difference of 1.12%and the minimum correlation of 0.923 in Modal Assurance Criterion values,as well as the minimum of 0.947 in Average MMSC values.In conclusion,this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring(PBSHM).In addition,a critical discussion about the methodology adopted in this study is also offered to address some related concerns.