The PSD-95/Dlg-A/ZO-1 (PDZ) domain is highly expanded, diversified, and well distributed across metazoa where it assembles diverse signaling components by virtue of interactions with other proteins in a sequence-specific manner. In contrast, in the microbial world they are reported to be involved in protein quality control during stress response. The distribution, functions, and origins of PDZ domain-containing proteins in the prokaryotic organisms remain largely unexplored. We analyzed 7,852 PDZ domain-containing proteins in 1,474 microbial genomes in this context. PDZ domain-containing proteins from planctomycetes, myxobacteria, and other eubacteria occupying terrestrial and aquatic niches are found to be in multiple copies within their genomes. Over 93% of the 7,852 PDZ domain-containing proteins were classified into 12 families including six novel families based on additional structural and functional domains present in these proteins. The higher PDZ domain encoding capacity of the investigated organisms was observed to be associated with adaptation to the ecological niche where multicellular life might have originated and flourished. Predicted subcellular localization of PDZ domain-containing proteins and their genomic context argue in favor of crucial roles in translation and membrane remodeling during stress response. Based on rigorous sequence, structure, and phylogenetic analyses, we propose that the highly diverse PDZ domain of the uncharacterized Fe–S oxidoreductase superfamily, exclusively found in gladobacteria and several anaerobes and acetogens, might represent the most ancient form among all the existing PDZ domains. Proteins displaying both signaling and protein–protein interaction domains are prevalent in eukaryotic signal transduction systems . This doMetazoan PDZ domains referred to as the canonical PDZ domains typically comprise 80–100 amino acid residues harboring a highly conserved fold . The typThe origin, diversity, and functions of prokaryotic and fungal PDZ domains are largely unknown. The genome-wide analysis of nonmetazoan PDZ domains dates back to 1997, wherein these domains were shown to occur in bacteria (in abundance), plants, and fungi . HoweverIn the current study, we have identified, analyzed, and classified a complete repertoire of the PDZ domains in 1,474 prokaryotic and fungal genomes using cutting-edge remote homology detection techniques. We found that genomes of several eubacteria encode more than 15 PDZ domain-coding genes. These bacterial species exhibit features of complex traits and few also form multicellular communities. We have classified 93% of these proteins into 12 families based on conserved domain architecture, of which eight families are reported here for the first time, providing a glimpse of their ancestral history and functional clues. Furthermore, the genomic context analysis connects these genes to protein synthesis. This work bridges the ever-increasing gap between prokaryotic/fungal and metazoan PDZ domain studies and provides insights into the role of PDZ domains in the evolution of multicellularity.ftp://ftp.ncbi.nih.gov/genomes/, last accessed November 2012  obtained from the National Center for Biotechnology Information (NCBI) for completely sequenced prokaryotic and fungal species using ber 2012 . Out of  package . The incb server . The datb server .clustalo program was used to construct a multiple sequence alignment (MSA) with default settings for each group, which were manually analyzed to exclude highly divergent sequences  with levels of support shown as posterior probabilities. The 3D structures were modeled using the Phyre2 web server  analyzed genomes  with 9,9ectively a. Our reectively .Buchnera aphidicola. Buchnera aphidicola is reported to have shed all the genes encoding cell-surface components such as lipopolysaccharides and phospholipids; therefore, the absence of PDZ domain in the organism is congruent with the context , mainly belonging to cell-wall lacking mollicute and candidate phytoplasma species, of which many are adapted to a parasitic lifestyle . The los context . The arcspitalis . These oP-value 2.2e-16 . Collectively, our results indicate the wide occurrence of PDZ domains in prokaryotes and fungi. Their absence in certain species could have evolutionarily linkage to the loss of cell envelope components.Gram-negative and Gram-positive bacteria differ in their outer envelope architecture. Gram-negative bacteria possess a double membrane with periplasmic space between them compared with the single membrane in Gram-positive species . If the P-value 2.1e-05 , the noncanonical form abundant in prokaryotes; tail-specific protease (Tsp); EpsC C-terminal domain-like; MTH1368 C-terminal domain-like; and interleukin 16 domain-like ; and PDZ_1, found in few members of fungi and archaea  in bacteria, which might have contributed toward expansion and diversity in metazoan counterparts. Corroborating this hypothesis, we have identified several canonical PDZ domains in numerous bacteria . We successfully classified 93%  out of total 7,852 proteins into 12 families  in the UniProt database and found similar sequences in five archaeal and many metazoan species including the early branch point’s demosponge, sea anemone, and in human. However, we were unable to detect similar sequences in fungi and ecdysozoa members—worm and fly. This suggests the possible horizontal transfer of eubacterial PDZ domains to metazoa along with DUF3340 (IRBP). Phylogenetic analysis discussed in subsequent sections confirms the recent divergence of the PDZ domains of Ctp family.The canonical PDZ domain was detected based on Pfam as well as Superfamily HMM in 309 (out of 316) DUF3340 domain-containing family proteins in 298 species  online. E.coli (S-adenosyl-l-methionine (SAM) domain is observed in 156 uncharacterized proteins in 148 eubacterial species. These proteins are referred to as the Fe–S oxidoreductases due to the highly conserved iron–sulfur binding motif and radical SAM domain. The PDZ domain occurs at the N-terminus of radical SAM domain (with a CxxxCxxC conserved motif) in these proteins  and is well characterized in the E.coli . As the proteins  online.Synechocystis PCC 6803 and Geobacter sulfurreducens PCA was used to predict the 3D structure using the Phyre2 protein modeling web server. The 61% N-terminus amino acids of both sequences modeled with more than 90% confidence level, which includes PDZ and radical SAM domain. The predicted structures were compared with the known ligand-bound PDZ domain structure of the HtrA2 protein isolated from Mycobacterium tuberculosis  cluster to cleave SAM reductively and produce a radical  . The radrculosis  , and Fe–S oxidoreductase family is conserved in phylogenetically diverse species (24 (RpoE) factor upon its activation by the HtrA family protein DegS in response to damaged outer membrane proteins in E.coli (RseP mostly encode proteins responsible for outer membrane biogenesis and translation. These include bamA (OMP biogenesis) (cdsA (lipid biosynthetic pathway), uppS  , rpsB , and frr (ribosome recycling factor upon translation termination)  during entry into the stationary phase, suggesting that σ24 can respond to internal signals as well as stress signals originating in the cell envelope  , cdsA (lnthesis) , dxr (upination)  (fig. 6aat 42 °C . Mutatiove 41 °C . The upsgulation  online. envelope . We proparvation . These ogpsA (encodes a glycerol-3-phosphate dehydrogenase enzyme) and engA  from B.subtilis, respectively, along with the well-characterized HtrA, Ctp, RseP, and GspC family members. The 3D structures of PDZ domains of representative members shown in Burkholderia pseudomallei exhibits best matches to known PDZ domain structures (PDB id 1K32 and 1FC6) of the Ctp protease family members. This clearly supports the recent divergence of aspartyl proteases from Ctp evident in the phylogenetic analysis shown in Desulfovibrio vulgaris, which is placed at the base of HtrA family protein in the phylogenetic tree reveals best match with the HtrA family structures (PDB id 2P3W and 2Z9I). Furthermore, the RMSD values yielded from the structural comparison of predicted and known structures show that the PDZ domain of the AP family has 0.922 RMSD with the canonical metazoan PDZ domain of 1PDR, whereas 2.658 with the eubacterial and 1.701 with the metazoan noncanonical form  was presumably to bind the iron available in large quantities in the early earth atmosphere. The ability of PDZ domains to recognize the unfolded proteins, which are potential substrates for the cellular proteolysis, is likely to have facilitated during their evolution along with the variety of protease domains. The HtrA protease family is encoded by almost all investigated genomes , whereas it was previously reported in eukaryotes including plants and animals , suggestcal form  online. cal form  online. cal form e. Taken 24 response. It would be worthwhile to experimentally explore how PDZ proteins modulate translation efficiency and remodel membranes during stress, in addition to their probable role in tRNA and rRNA modifications.In search of their putative functions, we analyzed the genomic neighborhood of all protein-coding genes. Conserved neighborhoods were observed for three families, of which the ATP-dependent lon proteases  and Fe–S oxidoreductases are often placed with genes coding for proteins involved in translation and membrane biogenesis, indicating their functions in these processes. RseP intramembrane metalloproteases are already well characterized at experimental levels and are also placed with genes involved in the above-mentioned functions. Such consistent chromosomal proximity of the PDZ domain-coding genes reinforces their roles in translation and membrane biogenesis. If the PDZ domains are indeed involved in translation and membrane biogenesis, the coupling of translation and transcription in prokaryotes might explain the depletion of noncanonical PDZ domains from eukaryotes wherein these processes are spatiotemporally isolated and such domains might not have provided functional advantages. The membranes are the first line of defense for eubacteria against fluctuations in the extracellular milieu. The PDZ domain-containing proteins sense unfolded proteins during various stresses and activate the specialized σSaccharomyces cerevisiae also has a second PDZ domain encoding gene, NAS2/YIL007C (UniProt id P40555), which was missed in the earlier genome-scale study in 1997  is intriguing because both species exhibit many metazoan/eukaryotic like features, that are unusually not found in eubacteria. For instance, planctomycetes have unique internal membrane structures, which could serve as a primitive form of the nuclear membrane and its extension to the endoplasmic reticulum observed in extant eukaryotic cells. On the other hand, myxobacterial species have been used as a model for studying evolution of multicellularity because they form fruiting bodies and have developed mechanisms of coordinated behavior of cells. Furthermore, Ser/Thr/Tyr protein kinases are reported in both species, which are a major class of regulatory and signaling proteins in eukaryotes . The exp in 1997 . NAS2 wa in 1997  and showt plants . In our t plants  online. In summary, though we provide evidence toward the presence of metazoan-like canonical PDZ domains (in Ctp protease family) in eubacteria, their depletion or absence in archaea and fungi hinders logical explanation for the transition to metazoan phylogeny. Based on the analysis presented here, we argue that archaea presumably never possessed the canonical PDZ domains. A bacterial endosymbiont might have contributed these domains to the eukaryotic phylogeny, wherein they might be lost recurrently only in fungi and ecdysozoa. Acquisition of HtrA family genes in eukaryotic organisms from the α-proteobacterial endosymbiont is a firm example of this possibility. Moreover, phylogenetic analysis confirms the recent divergence of the PDZ domains of the Ctp family, which might have contributed the canonical PDZ form to the metazoan phylogeny. Three PDZ domain-containing genes identified in fungi offer an interesting starting point for further investigation into the relationship between eubacterial, fungal, and metazoan PDZ domains, along with the Ctp family proteases.Based on the sequence, structure and phylogenetic analyses presented, we conclude that the PDZ domains of the HtrA family proteins presumably exist across the three domains of life . A large fraction of the PDZ domain-containing proteins remain uncharacterized to date, and this is the first report on their classification into different families. We conclude that the PDZ domains coevolved with the protease domains and are likely to provide functional context to them. Phylogenetic analysis confirms the PDZ domains of the Fe–S oxidoreductases as an ancestral form, whereas the Ctp family domains have diverged recently. Collectively, our comprehensive genomic analysis provides insights into the origin of the PDZ domains and their functional divergence during the evolution.Genome Biology and Evolution online.Supplementary DataClick here for additional data file.


The alignment between the boundaries of protein domains and the boundaries of exons could provide evidence for the evolution of proteins via domain shuffling, but literature in the field has so far struggled to conclusively show this. Here, on larger data sets than previously possible, we do finally show that this phenomenon is indisputably found widely across the eukaryotic tree. In contrast, the alignment between exons and the boundaries of intrinsically disordered regions of proteins is not a general property of eukaryotes. Most interesting of all is the discovery that domain–exon alignment is much more common in recently evolved protein sequences than older ones. In 1978, Walter Gilbert asked: ‘Why genes in pieces?’ . Early e2P2 databases  were excluded from the analysis as they contained so few multi-exon transcripts.The loci of exons for all transcripts of 91 eukaryotic genomes were extracted from the Ensembl database  . Genomesatabases ,16. The To determine if exon boundaries align with domain boundaries, a similar method to that used by Liu and Grigoriev was used . A windoUsing SUPERFAMILY’s ancestral reconstructions of domain content, novel domain architectures that are most likely to have been created at each genome’s node in the species tree were identified . ProteinTo test the significance of the difference in observed and expected numbers of exon boundaries aligning to domain boundaries (and similarly for disordered regions), a chi-square test was used in line with previous analyses . To dete2P2 disorder assignment. Expected frequencies were calculated from the null hypothesis of exon boundaries being randomly distributed within a protein sequence.For 88 eukaryote genomes, we counted the number of exon boundaries that, when mapped to protein sequence positions, are within one residue of the start or end of a SUPERFAMILY structural domain assignment or a Dx-axis. The x-axis is the ratio between the observed and expected number of exon boundaries that align to domain assignments. In genomes where the ratio is statistically significant for that genome alone, points are shown in red or green. The genomes with the greatest domain–exon alignment are all chordates, though as this is a large grouping there is large range, with exons aligning to domains between 1.5x and 3x as often as is expected by chance in most of these genomes. In addition, a significant correspondence between exon boundaries and domain boundaries is observed throughout the plants, nematodes and arthropods in this study, as well as some fungi and protists. Those genomes that do not display a significant alignment between domains and exons typically have comparatively few multi-exon transcripts. Taken together, it is clear that the alignment between exon boundaries and domain boundaries is a general property observed in eukaryotic genomes.Domain–exon alignment occurs more than expected in 87 of 88 genomes in our study. Figure y-axis, which shows the alignment of exons to regions of predicted disorder. Though the boundaries of disordered regions do align more than expected in certain genomes (above the 1.0 line), they align less than expected in many others (below the 1.0 line). Domain and disorder boundary ratios are not independent, as seen by the points clustering on a diagonal line . This is not surprising as disordered regions can share a boundary with a structural domain. Importantly, the regions of predicted disorder do not necessarily reflect conserved protein sequence. It may be that such conserved disorder—sometimes termed disordered domains . For example, in the case of the human genome, there are 304 proteins identified as containing novel domain architectures. Exons in these proteins align to the boundaries of domains more than 3.2x as often as expected by chance, compared to 1.9x as often in all other proteins in the human genome. The relative difference between new and old proteins appears fairly consistent in the different taxonomic groups.Returning to exons in structured domains and considering their evolution, we found that boundaries correlate more often in recently evolved proteins than in older proteins. For each genome, we examined those proteins that have undergone a domain re-arrangement since the last ancestor common to another sequenced genome . These In summary, we have shown that the boundaries of exons align with the boundaries of domains more than expected by chance and that this effect is stronger in recently evolved proteins. This is found to be a consistent property of eukaryotic genomes and provides strong evidence that exon shuffling has played some role in the evolution of novel domain architectures throughout eukarya. The alignment of exon boundaries with boundaries of disordered regions is, in contrast, variable and inconsistent. It remains to be seen whether conserved regions of intrinsic disorder display a similar relationship with exon boundaries as globular domains.http://supfam.mrc-lmb.cam.ac.uk/exonsIt is now clear, although previously suspected, that genes-in-pieces facilitates the modular evolution of the proteome, as well as affording the diversity and complexity obtained through alternative splicing. Nonetheless, we wish to highlight a more specific question: why domains in pieces? Many domains are encoded over multiple exons, yet the reuse of parts of a domain through shuffling and splicing ought not to be evolutionarily beneficial if domains act as units of protein sequence, structure or evolution. As a starting point for exploring this deeper question, we provide an interactive website for visualizing the locations of splice junctions on structured domains. This can be found at http://supfam.mrc-lmb.cam.ac.uk/exons.The interactive website for visualizing the locations of splice junctions on structured domains is available at


Antigen binding to B-cell antigen receptors (BCRs) followed by signaling initiates the humoral immune response. The signaling is intimately coupled to nanoclustering of BCRs and their sorting to specific membrane domains, a process that is ruled by interactions between the BCR transmembrane domain and lipids. While the structure of the extracellular domains of BCRs has been resolved, little is known about the configuration of the constituting four immunoglobulin domains spanning the membrane. Here, we modeled the structure of the transmembrane (TM) domain of the IgM B-cell receptor using self-assembly coarse-grained molecular dynamics simulations. The obtained quaternary structure was validated against available experimental data and atomistic simulations. The IgM-BCR-TM domain configuration shows a 1:1 stoichiometry between the homodimeric membrane-bound domain of IgM (mIgM) and a Ig-α/Ig-β heterodimer. The mIgM homodimer is based on an asymmetric association of two mIgM domains. We show that a specific site of the Ig-α/Ig-β heterodimer is responsible for the association of IgM-BCRs with lipid rafts. Our results further suggest that this site is blocked in small-sized IgM-BCR clusters. The BCR TM structure provides a molecular basis for the previously suggested dissociation activation model of B-cell receptors. Self-assembly molecular dynamics simulations at the coarse-grained scale here proved as a versatile tool in the study of receptor complexes. As one of the main parts of the adaptive immune sytem, B cells play a key role in the protection against pathogens. Defects during B-cell development and selection may lead to resistance against healthy tissue resulting in autoimmunity, malignancy, or allergy . B cellsThe membrane-anchorage of mIgs, which are tetramers consisting of two identical heavy (μ) and two identical light chains, is granted only by the C-terminal ends of both heavy chains. In case of mIgM, the C-terminal parts can be further divided into three domains, namely the extracellular membrane-proximal domain, followed by a transmembrane domain (TMD) and a cytoplasmic domain , 3. The The assembly of the mIgM-TMD with Ig-α/Ig-β-TMD was shown to be crucial for surface expression and overcoming of endoplasmatic reticulum retention , 9. LateSo far there is no structural information available about the molecular architecture of the transmembrane domain of IgM-BCR . NeverthSeveral studies and models couple the activation mechanism of BCRs and their supraorganisation on the cell surface: The cross-linking model states that cross-linking of separated, inactive receptors, e.g., with the assistance of a bivalent antigen, leads to receptor activation. This model could well explain the discovery that only bivalent but not monovalent antigen binding fragments are able to trigger BCR-signaling –23. HoweThe membrane composition was suggested to play a key role in BCR activation. Both IgD- and IgM-BCRs were reported to form oligomers or nanoclusters of different sizes , 25. Howin silico study of the dimerization of the G protein coupled receptor CXCR4, which revealed a cholesterol-dependent dimerization site that could be blocked by cholesterol  simulations were employed to study the spontaneous association of the TM domains of mIgM and of Ig-α/Ig-β, accompanied by atomistic-scale MD simulations addressing the stability of obtained quarternary structures. This approach was shown before to yield excellent results for the dimerization and oligomerization of TM peptides –33 but aWe report a BCR-TMD configuration that is in agreement with the available experimental data. The Ig-α/Ig-β lipid interface is shown to drive the association of IgM-BCR-TMD to lipid raft-like domains. Shielding of this interface upon IgM-BCR oligomerization is suggested to suspend BCR lipid raft association; In turn, BCR cluster opening upon activation would shift the preferred BCR-TMD environment toward the disordered membrane phase.The transmembrane domain of the B-cell receptor consists of four TM α-helices: the mIgM TMD homodimer and one α-helix of Ig-α and Ig-β, respectively. Association of the BCR-TMD was addressed in separate self-assembly MD simulations of the mIgM TMD monomers to a homodimer, of the Ig-α and Ig-β helices to a heterodimer, and finally of the pre-assembled mIgM TMD homodimer and the Ig-α/Ig-β heterodimer to the full IgM-BCR-TMD  scheme , combineInput structures for self-association simulations of isolated TM α-helices were based on the sequences of the individual IgM-BCR components Table . PyMOL  was usedIn simulations targeting the spontaneous self-assembly of two transmembrane domains, the TM helices/domains were embedded at a center of mass distance of 5 nm and random in-plane rotations in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membrane  membrane, in order to assure for a comparable membrane thickness between models at coarse-grained (CG) and atomistic resolution .Atomistic simulation production runs of 500 ns length, three replicas for each system, using Gromacs 5  for prot−5 bar−1. Lennard-Jones interactions and short-range electrostatic interactions were taken into account until a cut-off of 1 nm, while the PME method was used for long-range electrostatics beyond the cutoff. The production runs were carried out with a timestep of 2 fs. Bonds to hydrogen atoms were constrained by LINCS  and for helical dimers in the formation of the full BCR TMD (mIgM TMD homodimer and the Ig-α/Ig-β heterodimer) the interaction energy (sum of Lennard-Jones and Coulomb interactions) between two monomers was set to −200 kJ/mol. This cutoff value was chosen from visual inspection of the compactness of the related complexes. For oligomerization of BCR TM complexes, the cutoff was increased to −800 kJ/mol. Here, in order to exclude less compact complexes, an additional cutoff criterium was employed for the buried surface area (BSA) between two BCR complexes .Protein-protein binding interfaces were assessed by analysis of the average minimum distances between all interchain residue pairs during the last 50 ns of simulation time of all CG-simulations belonging to specific binding modes and visualized in contact maps. To that end, all simulation frames from the last 50 ns of those simulations showing a compact dimer at the end of the simulation were assigned to the different dimer configuration labels  using a watershed transform  as descrModeling of the TM domain of IgM-BCR  initially separated by 5 nm. During the spontaneous self-assembly, μ-1 and μ-2 dimerized in 102 of 104 simulations within 5 μs of simulation time Figure , 31, 34 Binding mode BM-A. BM-A describes a symmetric, right handed homodimer of the μ TM domains. The monomers are tilted by ≈ 40o relative to each other, with a tilt angle of ≈20o between the membrane normal and each μ-chain. The dimerization interface comprises the central part of the TM-C sites (conserved) of each monomer, whereas the TM-S sites (specific) are turned away from the interaction site and remain freely accessible —and of the Ig-α/Ig-β heterodimer (BM-α) configuration Table . SimilarOnly the asymmetrical BM-B conformer, i.e., the tetramer configurations BM-B-1 and BM-B-2 allow for the reported 1:1 stoichiometry of mIgM and Ig-α/Ig-β , 60. In Several experimental studies showed that mutation of mIgM-Tyr463 and mIgM-Ser464 to valines (YS/VV) results in uncoupling of mIgM from Ig-α/Ig-β , 61, 62.backmapping scheme .All studied TM dimers were stable on the studied timescale with root mean square deviations (RMSD) of approximately 3 – 4 Å Figure . A compaFor the full mIg-BCR TM helical tetramers, the configurations based on the asymmetric mIgM TM dimer (BM-B conformer) were stable with RMSD values between 3Å and 4Å. In contrast, the tetramers based on the symmetric mIgM TM homodimer  were found to be overall less stable on the 500 ns timescale with RMSD values of 4−6 Å Figure . OverallThe association of the mIg-BCR TM complex to differently ordered membrane domains was addressed by placing the BCR transmembrane domain into a membrane consisting of a three component lipid mixture composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC)/1,2-di--sn-glycero-3-phosphocholine (DIPC)/cholesterol. This mixture is based on a three component lipid mixture of Risselada and Marrink  that conWithin hundreds of nanoseconds of simulation time, two distinguishable lipid-phases emerged: a DPPC-rich region with a high amount of cholesterol . In turn, BCR clusters were experimentally shown to not associate with lipid rafts , 66. TheHowever, the dimer structures allow for the construction of higher order oligomers. For example, a symmetric BCR tetramer built based on the BM-Y binding mode  bound to Ig-α/Ig-β (see Figure The asymmetric TM-C/TM-S mIgM TM dimer Figure  is in coOligomer models for the BCR TMD provide cues for the mechanisms underlying the observed translocation of BCRs upon activation from non-raft to lipid raft domains , 69: pasIn summary, we suggest a structural model for the transmembrane domain of IgM-BCR that is in line with the available experimental data. Monomer and oligomer structures and their differing membrane domain association provide a molecular view on the dissociation activation model, which states that activation-induced BCR cluster opening leads to a transition of single, active BCRs from fluid membranes to lipid-raft like domains . SimilarMF and KP performed MD simulations and analysis. RB designed and supervised the study. All wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Surface and underground stretched deformation is one of the most important physical measurement quantities for geological-disaster monitoring. In this study, a parallel helical sensing cable (PHSC) based on the time–domain reflectometry (TDR) technique is proposed and used to monitor large ground stretched deformation. First, the PHSC structure and manufacturing process are introduced, and then, distributed capacitance, distributed inductance, and characteristic impedance were derived based on the proposed stretched-structure model. Next, the relationship between characteristic impedance and stretched deformation was found, and the principle of distributed deformation measurement based on the TDR technique and PHSC characteristic impedance was analyzed in detail. The function of the stretched deformation and characteristic impedance was obtained by curve fitting based on the theoretically calculated results. A laboratory calibration test was carried out by the designed tensile test platform. The results of multi-point positioning and the amount of stretched deformation are presented by the tensile test platform, multi-point positioning measurement absolute errors were less than 0.01 m, and the amount of stretched deformation measurement absolute errors were less than 3 mm, respectively. The measured results are in good agreement with the theoretically calculated results, which verify the correctness of theoretical derivation and show that a PHSC is very suitable for the distributed measurement of the ground stretched deformation.  In recent years, natural geological disasters, such as earthquakes, landslides, and mudslides, have occurred frequently in earthquake-stricken areas, which are serious threats to human life, safety, and economic security. Ground deformation is one of the most important physical measurement quantities for monitoring and predicting geological disasters. Sensing technology is mainly used to monitor ground deformation, including draw-wire displacement sensor technology ,2, globaThe draw-wire displacement sensor is a two-point measurement, which can accurately measure the relative displacement between two points, but it is difficult to reflect the whole stretched deformation using this method. GPS technology can monitor three-dimensional displacement information by point distribution measurement . HoweverHere, a parallel helical sensing cable (PHSC), as a distributed sensor based on the TDR technique, is proposed to achieve the point positioning and deformation size measurement for a large-surface stretched deformation measurement. Compared with distributed optical fiber sensing technique, a PHSC can achieve precise point positioning of ground deformation; in addition, it can realize the transverse tensile measurement and enlarge the measurement range. In this study, we propose using a PHSC to realize the precise point positioning of the stretched deformation of the ground and the large deformation measurement of a landslide. First, the structure of a PHSC and the manufacturing process are introduced. Then, the relationship between characteristic impedance and stretched deformation is derived based on the proposed physical model. The principle of distributed deformation measurement based on TDR and PHSC characteristic impedance is also analyzed in detail. A laboratory calibration test and physical model test were carried out by the stretched test platform, and the procedure for these laboratory tests and the related results are presented here.2 mm2, and the equivalent diameter (De) is about 0.5060 mm, obtained from the cross-sectional area.A PHSC used as a sensing component for the distributed measurement of ground deformation is typically buried under the ground. All the parameters of a PHSC, including the characteristic impedance and pitch, are basically fixed if there is no geological deformation. The characteristic impedance is changed when the PHSC is stretched, and the change of the characteristic impedance accurately reflects the stretched deformation. The characteristic impedance and the position of the deformation can be measured by the tensile test platform, the relationship between stretched deformation and the characteristic impedance can be found, and the determination position and the amount of stretched deformation of the surface and the underground deformation can also be determined . The relDUTZ is the impedance of the tested devices, Z0 is the characteristic impedance of the TDR measurement system, which is 50 Ω, incidentV is the incident step signal voltage value, reflectedV is the reflected signal voltage value, and measuredV is the measured voltage value, which is equal to (incidentV + reflectedV). The TDR technique is usually used for local ground deformation monitoring. TDR instruments are composed of a step signal generator with an 80E04 TDR module and a high-speed sampling oscilloscope of Tektronix DSA8300 . The measurement method is shown in ρ is written as follows:The reflection coefficient Z of the PHSC can be obtained by the following [Therefore, the characteristic impedance ollowing ,28:(2)Z=Z. The position of the characteristic impedance change is expressed as follows:Llocation is the reflected position from the beginning of the PHSC, ν is the transmission speed of the step signal in the PHSC, and T is the time difference between the reflection point and the input point measured by the TDR [When the PHSC is stretched, the stretched deformation becomes larger, which leads to a change in  the TDR .L is the unit length conductance and C is unit length capacitance. The units of L and C are H/m and F/m, respectively.If the resistance of a 100 m length is less than 10 Ω for the four types of PHSC, then the influence of the distributed resistance can be neglected . For loss) of the PHSC, the cross-section model of the PHSC in the stretched region is shown in 0 and B0 are the two parallel copper wires in the center of the stretched region. There are (2N + 1) turns of spiral wires in the stretched region. N turns are in the left region, and another N turns are in the right region. The charge densities of two parallel copper wires A and B are −η and +η, respectively. The radius of the two wires are r, the fixed distance of the two parallel copper wires is d, and the pitch of spiral transmission line is (d + s). The diameter of the central silica gel strip is D, which is a structural constant, and the angle between the wire and the vertical direction is θ. Assuming that there exists a point P at the middle of B0 and AR1, based on the Biot–Savart law [E shown in x-axis direction at point P for a finite wire ε is relative permittivity and the Lp is the length of the wire x is the distance from point P to origin O.In order to obtain the relationship between the distributed parameters and the stretched deformation –(7d) are brought into Equation (8), and the potential difference K = 3. Then, for 22AWG PHSC , the initial value of s is equal to d, the curves of ζAL, ζBL and (ζBL− ζAL) with s are shown in s becomes larger, and all the results increase, but the rate of increase slows down.In order to simplify the calculation and guarantee the calculation precision, set 0 and AR1 is the ratio of the charge density to the voltage, which is given byBased on the Gauss theorem, it is known that the capacitance per unit length between Bs, but the charge density is a constant. Additionally, the distribution capacitance becomes smaller and the distribution inductance becomes larger. Then, the distributed capacitance C is obtained asAccording to Equation (11), the electric field strength becomes larger with the increase of μ,ε) in the medium are given byThe constitutive parameters , (12), and (14), the characteristic impedance ained as (15)Z=μεZ is only related to the physical size of r, d, s, D, and θ. The relative dielectric constant ε is 5.5, and the relative permeability μ is 1. The distribution capacitance C and inductance L of 22AWG PHSC  with the s are shown in C becomes smaller with the increase of s, but the distribution inductance L becomes larger. It can be seen from Equation (15) that Z of four types of PHSC are shown in De increases. When the PHSC is stretched, s becomes larger. Z gradually increases with the increase of s, but the rate of increase slows down. Z at the center point of the PHSC is a maximum when s is a fixed value. Because the deformation region is divided into the stretched deformation region, gradual region, and a normal region, Z will be decreased from the maximum at the center point of the stretched region to the minimum of the normal region.The characteristic impedance a, b, and c are the fitting coefficients.In application, the tensile deformation should be obtained based on the measured characteristic impedance of the PHSC. However, considering that Equation (15) is very complex, the inverse function is not easy to obtain. A simple inverse function is easily obtained by curve fitting based on the theoretically calculated results shown in In order to verify the theoretical derivation correctness and realize the distributed deformation measurement of the ground, it is necessary to establish a PHSC tensile test platform and carry out a stretching experiment. The established tensile test platform shown in s is calculated according to Equation (16). The amount of the stretched deformation is a product of (s–d), and the parallel wire turns in the stretched region. For many stretched regions, the positions can be found, and the acquired waveforms are analyzed, respectively. The software interface for the distributed measurement of deformation based on the TDR technique is shown in r = 0.3099 mm, d = 1.6 mm, D = 4.6 mm, θ = 30°, ε = 5.5) was selected for the stretched deformation experiment, and the stretching center point was selected to be 2, 3.5, and 5 m from the beginning of the PHSC, respectively. The tensile region was set to 140 mm, and the amount of the deformation was set to 20 mm. A photograph of the multi-point positioning measurement experiment is shown in To determine the distributed measurement of the ground stretched deformation by using PHSC, the first step is to locate the position of the stretched region. In many cases, multiple positions are simultaneously stretched, while ground deformations occur in many places. Therefore, multiple stretched points should be located and calculated. A 6.6 m AWG22 PHSC  was selected for the tensile deformation experiment, and the stretching center point was selected to be 2 and 4 m from the start point of the test PHSC, respectively. The stretched region was set to 200 mm, the initial value of s was 1.6 mm, and there were 62.5 turns in the tensile region. The maximum value of the characteristic impedance was measured by the PHSC tensile test platform. A set of measured results were selected and are listed in A 6.6 m AWG24 PHSC , and the stretching center point was selected to be a position 2.5 and 4.5 m from the starting end of the PHSC, respectively. The stretched region was set to 200 mm, the initial value of s was 1.6 mm, and the turns of the PHSC in the tensile region were 200/3.2, which is 62.5. The amount of the stretched deformation was increased from 0 to 50 mm with an initial step with a 5 mm increase. The incremental step of s is 0.080 mm from the initial value. The sampling characteristic impedance curves cut from the host computer software interface are shown in The inverse function can be obtained by curve fitting based on the theoretically calculated results shown in (1)Ground deformation monitoring based on the TDR technique and proposed PHSC is a very feasible technique, and the sensing parallel spiral transmission line implantation method is quite effective.(2)A distribution parameter calculation model of PHSC is proposed and used to derive the distributed capacitance, distributed inductance, and characteristic impedance. The distribution capacitance becomes smaller with the increase of stretched s, and distribution inductance becomes larger. The relationship between the stretched deformation and the characteristic impedance of PHSC can be found based on the model and is shown here as Equation (15). It can be seen from Equation (15) that the impedance increases when the cable is stretched.(3)The derived characteristic impedance is related to the physical size of the equivalent radius of the two wires, the fixed distance between the two parallel copper wires, the pitch of the spiral transmission line, the diameter of the central silica gel strip, and the angle between the wire and the vertical direction. The characteristic impedance decreases with the value of the equivalent radius of the two wires.(4)The characteristic impedance at the stretched center point is a maximum when s is a fixed value. The deformation region is divided into the stretched deformation region, the gradual region, and a normal region. The characteristic impedance decreases from the maximum at the center point of stretched region to the minimum of the normal region.(5)In application, tensile deformation can be obtained based on the measured characteristic impedance of the PHSC. The function of the stretched deformation and characteristic impedance can be obtained by curve fitting based on the theoretically calculated results. According to the function, the stretched deformation measurement is carried out.(6)A measurement experiment platform, including a stretching device, TDR measurement system, and host computer software, was established to carry out the multi-point positioning measurement and the amount of stretched deformation measurement at different positions. The theoretically calculated results are in good agreement with the experiment results, which show that the PHSC can measure the rock–soil large deformation and verify the accuracy of the theoretical derivation.This research describes the mechanism of using a PHSC for the distributed measurement of ground deformation. The following conclusions have been drawn based on the theoretical analysis and experimental results.In future work, the model for the characteristic impedance and stretched deformation should be further optimized to improve the measurement accuracy. The parallel helical sensing cable should be applied to monitor the distributed measurement of ground deformation.


The molecular functions of TMEM132 genes remain poorly understood and under-investigated despite their mutations associated with non-syndromic hearing loss, panic disorder and cancer. Here we show the full domain architecture of human TMEM132 family proteins solved using in-depth sequence and structural analysis. We reveal them to be five previously unappreciated cell adhesion molecules whose domain architecture has an early holozoan origin prior to the emergence of choanoflagellates and metazoa. The extra-cellular portions of TMEM132 proteins contain five conserved domains including three tandem immunoglobulin domains, and a cohesin domain homologue, the first such domain found in animals. These findings strongly predict a cellular adhesion function for TMEM132 family, connecting the extracellular medium with the intracellular actin cytoskeleton.Bioinformatics online. Many genes remain experimentally under-investigated not because they are functionally less important but because their discovery came relatively late (TMEM132E (Arg420Gln) was confirmed using a zebrafish model to cause autosomal-recessive nonsyndromic hearing loss  are maintained by a variety of transmembrane proteins that signal and physically link between the cytoskeletons of adjacent cells. Many such proteins  contain one or more immunoglobulin (IG) superfamily domains . Here weCaenorhabditis elegans) and hexapods (Drosophila melanogaster). As input for our analysis we used a full-length multiple sequence alignment generated with T-Coffee (Leptospira interrogans (PDB ID: 2mh4), for example, with E-values of 0.031, 0.018 and 0.022, respectively. Moreover in all three HHpred results, in support of the first match, the next most statistically significant matches corresponded to additional members of the immunoglobulin superfamily    . CohesinThe conserved region (corresponding to amino acids 127–239 of human TMEM132A) preceding the cohesin domain in TMEM132 is also evident in choanoflagellate and sponge proteins  . Strikin−4) in the general population (Eleven proteins are currently known to contain missense mutations within IG domains associated with 23 different disorders , has beepulation , the latTheir ancient ancestry and their associations with neurological disease suggest that TMEM132 genes have been undeserving of their relative obscurity. Our identification of these proteins as CNS-expressed IG domain superfamily adhesion molecules now places them in a more appropriate perspective as a putative key connection between the extracellular matrix and the actin-based cell cytoskeleton, with major roles in regulating changes in neuronal cell morphology, motility and migration. These findings should precipitate more detailed experimental and structural characterization of the TMEM132 family, and assist in formulating hypotheses concerning the cellular mechanisms by which sequence variants in these genes contribute to neurological disease.This work has been supported by the Medical Research Council UK.Conflict of Interest: none declared.Supplementary DataClick here for additional data file.


To explore the regional spread of endemic pathogens, investigations are required both at within and between population levels. The bovine viral diarrhoea virus (BVDV) is such a pathogen, spreading among cattle herds mainly due to trade movements and neighbourhood contacts, and causing an endemic disease with economic consequences. To assess the contribution of both transmission routes on BVDV regional and local spread, we developed an original epidemiological model combining data-driven and mechanistic approaches, accounting for heterogeneous within-herd dynamics, animal movements and neighbourhood contacts. Extensive simulations were performed over 9 years in an endemic context in a French region with high cattle density. The most uncertain model parameters were calibrated on summary statistics of epidemiological data, highlighting that neighbourhood contacts and within-herd transmission should be high. We showed that neighbourhood contacts and trade movements complementarily contribute to BVDV spread on a regional scale in endemically infected and densely populated areas, leading to intense fade-out/colonization events: neighbourhood contacts generate the vast majority of outbreaks (72%) but mostly in low immunity herds and correlated to a rather short presence of persistently infected animals (P); trade movements generate fewer infections but could affect herds with higher immunity and generate a prolonged presence of P. Both movements and neighbourhood contacts should be considered when designing control or eradication strategies for densely populated region.The online version of this article (10.1186/s13567-019-0647-x) contains supplementary material, which is available to authorized users. In managed metapopulations such as farming systems, endemic pathogens commonly spread at local and regional levels through different routes of transmission. Trade movements are primarily the consequence of constant renewal of the breeding stock to sustain productivity and herd size –3 and coAssessing the contributions of different transmission mechanisms to pathogen spread provides policymakers with valuable information on how to allocate limited disease control resources more effectively over the course of a control program. In this context, modelling has been proven as an efficient approach to investigate the spread mechanisms of pathogens and to assess the impact of control strategies , based on summarized epidemiological data and cattle detention data, we assessed herd infection probability and highlighted the characteristics of infected herds according to the transmission route involved.We developed a discrete time stochastic compartmental model describing within-herd demographics calibrated on herd-specific detention data and BVDV vertical and horizontal transmission dynamics. The within-herd modules are coupled at a regional between-herd scale through discrete trade movements represented explicitly from historical cattle movement data, as well as through neighbourhood contacts modelled as an infection pressure from infected herds located within a given radius around each exposed herd Figure . The modWe modelled over the period 1/1/2005–31/12/2013 a metapopulation composed of 12 750 dairy cattle herds located in Brittany  is a comprehensive database describing individual cattle life trajectories from birth to death. Cattle are defined by their national identification number, their breed and sex, and their date of birth, which enables all of the demographic parameters (birth rates and age-specific outgoing rates other than trade movements) to be estimated per herd per year Table .Table 1In addition, the FNCD contains animal movements, with its date, the source and destination holdings , as well as the causes and dates of entry into  and exit from  each holding. Pregnancy status was not available, neither the filial relationship between calves and dams. For a descriptive analysis of the national dataset, see , 46. OnlHerd locations were required to calculate distances between herds and to define neighbourhood contacts see “” sectionIn the period 2000–2003, before the implementation of the regional control strategy for BVD in Brittany, the Animal Health Services (AHS) followed twice a year serological status of herds using ELISA tests on collected milk. Herds were considered positive if their seroprevalence was above 10% . AHS proThe within-herd infection dynamics was adapted from a previously published model fully described in . In brieg and health state X in herd h was updated  and five health states  were considered. Demography included birth, culling, and aging processes. Culling was performed before aging, using a Binominal distribution. The number of animals  the herd seroprevalence, calculated as the proportion of herds with more than 10% of R animals, and (ii) the yearly test-based herd incidence, defined as the annual number of herds detected as newly infected. In both cases, the outputs were defined similarly as in epidemiological data to enable comparison. For herd incidence, a regular sampling scheme was implemented in the model, using the proportion of animals in health state R as a proxy for test result and with tests implemented every 6 months. Test result was assumed to be negative for herds having less than 10% of R animals, positive otherwise. A simulated herd was considered as detected as newly infected if it was negatively tested at two consecutive sampling points immediately before two consecutive positive tests. The number of newly detected herds was aggregated per year.t − 1 and at least 1 P or T animal at time t); (iii) the annual probability of a herd to be infected (Prob_being_inf), calculated as the proportion of runs in which the herd was infected over a year; (iv) the annual probability of a herd to be newly infected (Prob_getting_inf), calculated as the proportion of runs in which the herd was newly infected that year. As spontaneous fade-out is frequent for this disease, we also assessed (v) the herd re-infection probability, defined as the probability of a herd being newly infected at least twice over the 9-year period, and (vi) the proportion of presence time of at least one P animal over the 9 years in a specific herd.To assess the contribution of the two transmission routes to BVDV regional spread, six model outputs were generated. At each time step over 9 years, we recorded: (i) the number of infected herds in the metapopulation ; (ii) the number of newly infected herds .At each time step, herd specific state variables were updated thanks to three processes: aging, infection, and trade movements.Parameters values used in simulations are given in Table th–75th percentiles = 3–8, median = 6), 26 , and 58 , respectively for 2, 4 and 6 km radius. Model outputs analysed were the mean herd seroprevalence, the mean proportion of infected herds, and the mean yearly test-based herd incidence over time. Sensitivity indices for the dynamic outputs, either at each time step or on their summaries over the whole trajectory, were calculated using the R package {multisensi} : (i) the within-herd transmission rates of T animals within age groups, mentclass2pt{minimmentclass2pt{minimFirst, three scenarios were compared  at the scale of Brittany: (1) transmission was possible by neighbourhood contacts only; (2) transmission was possible by animal trade movements only; (3) both transmission routes were possible. For each scenario, we looked at the number of infected and of newly infected herds over time. We carried out 100 runs for each scenario.Prob_being_inf) could be discriminated by their number of neighbours and of animals purchased, and if these herds had a specific spatial distribution in Brittany . Then, we characterized the annual probability of getting infected (Prob_getting_inf) per causal transmission route using the Random Forest method (R package {randomForest}). Potential explanatory variables were per herd: the number of herds in the neighbourhood (nNB), the annual number of animals purchased (InStrength), the annual number of animals younger than 1 year of age purchased by the herd (InStrength_Less1yr), the annual number of source herds (InDegree), the annual number of animals purchased by neighbouring herds (InStrengthNB), the annual number of animals younger than 1 year of age purchased by neighbouring herds (InStrengthNB_less1yr), the proportion of cows aged 24 to 36 months (%24_36) in the herd, the number of females younger than 1 year of age (Size1yr) and the herd size (SizeTot). Furthermore, assuming herd re-infection probability was barely correlated to its probability to be infected (Prob_being_inf), we investigated the four possible patterns of successive infections: both by neighbourhood contacts, first by neighbourhood contacts and second by trade movements, both by trade movements, and first by trade movements and second by neighbourhood contacts. Analysing these pairs enabled to assess if herds were randomly infected with respect to the transmission route, or if they were more exposed to one or the other route  of the distribution of the probability of being infected , the herd prevalence reached more than half of the 12 750 simulated herds Figure A. The ocProb_being_inf of 0.42 and 0.69 corresponding to the 5th and 95th percentiles, respectively; Figure Ninety percent of the simulated herds had quite a high annual probability to have a detected infected status  for 90% of them. The re-infection probability grew linearly with respect to the infection probability, with a slight saturating effect Figure A. Among The causal routes of two successive within-herd infections were not associated at random, with higher probability of having similar causal routes than expected, i.e. to be more exposed to one or the other route throughout time. Indeed, when the previous within-herd infection was caused by neighbourhood contacts , the latter one was caused by the same route with probability 0.96 , whereas the probability for a herd to be infected by neighbourhood contacts  was 0.93 . Herds only infected by neighbourhood contacts over all runs  had on average 27 neighbours  and purchased 0.1 animals per year . Only 10 herds were infected exclusively by trade movements and they were all but one isolated herds.Most herds predominantly infected by neighbourhood contacts had at least one persistently infected animal less than half the simulation period Figure A. HowevePredictions from a new multi-scale epidemiological model highlighted that neighbourhood contacts and trade movements complementarily contribute to BVDV spread on a regional scale in endemically infected and densely populated areas, leading to intense fade-out/colonization events: neighbourhood contacts generate the vast majority of outbreaks (72%) but mostly in low immunity herds and cause a rather short presence of persistently infected animals (P); trade movements generate fewer infections but could affect herds with higher immunity and generate a prolonged presence of P Figure A. InfectThe simulations showed that even herds introducing yearly very few animals could be reached by the infection, in agreement with field observations in France . The movEscherichia coli O157:H7 [Trade movements have been recognized as an important, if not the main, transmission route in managed metapopulations, spreading several pathogens responsible for infectious diseases such as Johne’s disease , tubercu O157:H7 , and foo O157:H7 . Althoug O157:H7 ), it sti O157:H7  for Q fe O157:H7 . However O157:H7 . NeighboUnsurprisingly, the number of neighbours and the number of purchased animals  appearedThe proposed model includes all of the movements occurring between farms, but we neglected the intermediary stays in markets and assembly centres, which indeed have been shown to be quite frequent in the French cattle trade system . NeverthCurrent approximation for neighbourhood contacts assumes that all persistently infected animals from a given neighbourhood have an influence on all herds in this neighbourhood. Such an assumption might lead to over-estimate the influence of neighbourhood contacts for some herds and under-estimate it for some others. However, a more accurate representation of neighbourhood contacts would require a precise description of land use for pasture purpose, which was not available for the whole Brittany and which might be difficult to collect. For instance, the geographical coordinates of a farm usually point to the location of the “registered” housing unit, which does not necessarily correspond to pasture locations. Indeed, the pastures of a farm could be anywhere within a few kilometres around the geolocation of the farm and are not necessarily contiguous. Precise boundaries might not be obtainable due to various difficulties including privacy concerns. Moreover, data on pasture use are not of sufficient precision, being aggregated over all livestock species . In the The model accuracy might be improved once more information would be available, for instance through a longitudinal regional epidemiological follow-up, that could be used to better infer parameters such as between-neighbour transmission rate for P animals  Characteristics of the trade network. (B) Comparison of model outputs from simulations based on real vs. random herd coordinates. (C) Newborn health states. (D) Sensitivity analysis. (E) Probability of herds to be more exposed to one route than the other. (F) Descriptive sensitivity analysis and comparison with summarized epidemiological observed data. (G) Herd infection probabilities. (H) Explanation of the IncMSE in Random Forest analysis.


Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.  As one of the key components of rotating machines, the working condition of rolling bearing is critical to the safe running of the machines. Effective fault diagnosis, which aims to identify early faults and prevents system failure, could increase the safety and reliability of machinery. In the past years, a large number of intelligent fault diagnosis methods have been proposed, such as support vector machine (SVM), artificial neural network (ANN) and deep learning approaches . RecentlHowever, most deep learning methods only work well under the assumption that enough labeled training data is available, and training and test data are drawn from the same distribution . When thUnsupervised domain adaptation (UDA) aims at transferring of knowledge from a labeled source domain to an unlabeled target domain, and a lot of research has been carried out on this issue. Early domain adaptation approaches try to map the data into shared feature space and minimize the distance between the feature distributions of different domains. Correlation distances  and maxiRecently, adversarial based adaptation approaches ,13 have Existing work mainly concentrates on minimizing the global distribution discrepancy when learning shared feature representation across different domains. Even if the global distribution has been successfully aligned, however, the samples with the same label from different domains could still be mapped far from each other in the feature space. Some approaches have been proposed to alleviate this class-level misalignment problem . One kinAnother approach of class-level alignment aims to further minimize the distance between the same classes from different domains which is called semantic domain alignment. Xie et al.  proposedTo reduce the burden of computing and updating the class center in semantic domain alignment, in this paper, we present a two-level alignment approach for unsupervised domain adaptation of bearing fault diagnosis. Specifically, in domain-level alignment, we utilize Wasserstein distance to minimize the distribution discrepancy of both domains in the latent space. In class-level alignment, inspired by , we impoWe propose a novel and effective unsupervised domain adaptation approach for bearing fault diagnosis. Data-level and class-level alignment between the source domain and target domain are both considered.We propose to use triplet loss to achieve better intra-class compactness and inter-class separability for samples from both domains simultaneously.Extensive experiments are performed to validate the efficacy of the proposed method. In addition to transfer learning between different working conditions on CWRU dataset and Paderborn dataset, we also validate the transfer learning tasks between different sensor locations on CWRU dataset.The main contributions of this study are as follows:The remainder of this paper is organized as follows. The background of unsupervised domain adaptation, Wasserstein distance, and deep metric learning are discussed in In this section, unsupervised domain adaptation for fault diagnosis, Wasserstein distance and deep metric learning are introduced.Unsupervised domain adaptation aims to alleviate the domain shift problem by aligning the distribution between the labeled source domain and the unlabeled target domain. A common approach for domain adaptation is to map the data into a shared feature space and then employ some distance measurement to minimize the distance between the feature distributions of different domains. Maximum mean discrepancy (MMD) ,25 measuFor fault diagnosis tasks, Lu et al.  firstly In contrast, adversarial learning is also widely used in domain adaptation. Many recent UDA approaches leverage deep neural networks with the adversarial training strategy, which allows the learning of feature representations to be simultaneously discriminative for the labeled source domain data and indistinguishable between source and target domains. In , Ganin eIn the research of adversarial based domain adaptation for bearing diagnosis, Han et al.  employedRecently, inspired by WGAN , the WasShen et al.  proposedAlthough the distributions of source and target domains could be aligned using domain adaptation methods aforementioned, the samples from different domains could still be misaligned at class level, since they mainly concentrate on minimizing the global distribution discrepancy when learning shared feature representation . Even ifTo alleviate the class-level misalignment problem, deep metric learning is commonly used to make the feature more discriminative, so as to reduce the possibility of misclassifying the samples far from their corresponding class centers . Deep meZhang et al.  introducTriplet loss was initially introduced in face recognition tasks in order to learn a metric or an embedding space that makes the instances from the same category closer to each other than those from different categories . As showIn unsupervised domain adaptation, we have labeled dataset We introduce an unsupervised domain adaptation method to jointly align the distributions between the source domain and target domain in both data-level and class-level. Wasserstein distance is used to minimize the distribution discrepancy at data-level, and triplet loss is utilized to further align the distribution at class-level. The framework of the proposed method is illustrated in E, classifier C and domain critic D. After adversarial training between feature extractor E and the others, domain alignment could be achieved, and domain invariant representation could be obtained.To align the distribution globally using Wasserstein distance, three components are involved in this stage, namely feature extractor Given an instance If the parameterized family of domain critic functions are all 1-Lipschitz, then we can approximate the empirical Wasserstein distance by maximizing the domain critic loss D is as follows:ρ is the balancing coefficient. When optimizing Equation (3) under constrain of 1-Lipschitz, a common approach is to enforce gradient penalty D, we optimize the feature extractor E and classifier C during the adversarial training. The optimization goal of E is to minimize the Wasserstein distance with respect to parameter D fixed:After training the domain critic C with the parameter C is defined as:The classifier λ is the coefficient that controls the balance between discriminative and transferable feature learning and ρ should be set to 0 when optimizing the minimum operator.To sum up, the objective function in the global alignment is as Equation (8), where Given 2 mini-batches of samples from the source domain and target domain, we compose triplet training samples using online hard negative mining strategy . We firsλ1 and λ2 are balancing coefficients.The objective function in the class-level alignment is as followThe algorithm details of the proposed method are described in We evaluate our method on CWRU rolling bearings dataset and Paderborn dataset under different loads. Additional, unsupervised domain adaptation between different sensor locations is performed on CWRU dataset. The testbeds of CWRU and Paderborn are shown in The detail of the network architecture is shown in Wasserstein distance guided representation learning (WDGRL) proposed by Shen et al. . WassersDeep convolutional transfer learning network (DCTLN) proposed by Lei et al. . Both adTransfer component analysis (TCA) proposed by Pan et al. .CNN: The neural network trained on labeled data from the source domain is used to classify the target domain directly without domain adaptation.DeepCoral proposed by Sun et al. . Mean anDeep domain confusion (DDC) proposed by Tzeng et al. . DDC useDeep adaptation network (DAN) proposed by Long et al. . FeatureTo validate the performance of the proposed method, we compare our method with the Wasserstein distance-based adversarial domain adaptation approach (WDGRL) and deep learning-based domain adaptation methods. To be fair, the neural network used in our method and the compared deep learning methods are kept the same.https://github.com/schwxd/TLADA.The reported experimental results are averaged by 10 trials to reduce the effect of randomness, and the mean values are provided. All the experiments are implemented using Pytorch and were running on NVIDIA GTX 2060 GPU. The source code is available at In case 1, we use the public fault bearing dataset provided by Case Western Reserve University (CWRU) Bearing Data Center to evaluate the proposed method. In this study, the vibration signals recorded at 12,000 samples/second (Hz) for the drive-end bearings and fan-end bearings are used. For experiments between different working conditions of drive end, healthy condition and three fault categories  with three different fault depth  are used, which make the experiments 10-category classification tasks. For experiments between different sensor locations, the samples are collected from healthy condition and three fault categories with two different fault depth , but we ignore the variance of working conditions and fault depth in this task, which make the experiments 4-category classification tasks .We mainly fellow the experimental setup in  where eaThe dataset consists of four working conditions with different motor load and rotating speed, i.e., Load0 = 0 hp/1797 rpm, Load1 = 1 hp/1772 rpm, Load2 = 2 hp/1750 rpm and Load3 = 3 hp/1730 rpm. When transferring from different load conditions, all 4 load conditions are used to perform 12 transfer scenarios. When transferring from different sensor locations, 2 transfer scenarios (DE -> FE and FE -> DE) are performed. When transferring from dataset A to dataset B, all samples of A and half samples of B are used for training, and the models are tested on another half samples of B. The description of CWRU dataset in use is shown in α1 = 1 × 10−4 and α2 = 1 × 10−4. The gradient penalty ρ is 10. Coefficient λ1 and λ2 are 1.0 and 0.2, respectively. For MMD  based methods, the coefficient between classification loss and MMD  loss is chosen from {0.01, 0.1, 1.0, 10, 100}. For Wasserstein based methods (WDGRL and TLADA), the coefficient λ1 is chosen from {0.1, 0.2, 0.5, 0.8, 1.0, 2.0}.The hyperparameters used in the experiment are as follows. Learning rates of classifier and domain critic are To better understand the effect of class-level alignment in domain adaptation, we compare the domain-invariant representations between WDGRL and TLADA via t-SNE in In case 2, we evaluate our method on Paderborn University bearing dataset . The datIn this study, we adopt 5 healthy bearing sets and 10 real damaged bearing sets to evaluate our method. Since 5 of damaged bearings are in the inner race and 5 are in the outer race, the experiment is a 3-way classification task. The healthy bearing sets mainly differ in the operating hours, as shown in To validate our method in the setting of unsupervised domain adaptation, we compose 6 transfer learning tasks between 3 working conditions. The vibration data is also preprocessed under the sliding window mechanism as case 1. Since the sample rate is 64 kHz and the rotational speed is 1500 rpm, the frame length of each sample is set to 5120 to cover 2 cycles of bearing running. Each class has 4000 training samples and 1000 test samples. Fast fourier transform (FFT) is applied to each sample to obtain the frequency spectrum and the first half of the spectrum is kept. Hence, the feature dimension of each sample is 2560. The description of Paderborn dataset in use is shown in 1 = 10−3 and α2 = 10−3. The gradient penalty ρ is 10. Coefficient λ1 and λ2 is 1.0 and 0.1, respectively.The hyperparameters used in case 2 experiment are as follow: Learning rates of classifier and domain critic are αThe visualization results produced by t-SNE method are shown in To further inspect the effects of triplet loss in unsupervised domain alignment, other two variants of TLADA are built for comparison: one imposes triplet loss only on data samples from source domain called TLADA-S, and another one imposes triplet loss only on data samples from target domain called TLADA-T. We perform the experiments on four tasks: ‘DE -> FE’ and ‘FE -> DE’ on CWRU dataset, ‘PB -> PC’ and ‘PC -> PB’ on Paderborn dataset. The results are shown in We investigate the parameter sensitivity of threshold when assigning pseudo-labels. We can observe that the for task ‘PB -> PC’, TLADA accuracy is not much affected by variants of threshold since the majority of samples have been correctly classified. For task ‘FE-DE’, the accuracy of TLADA first increases and then decreases as the threshold varies. When the threshold is too low, more samples with falsely pseudo-labels will be involved in triplet loss, thus have a negative impact on the accuracy. When the threshold is too high, fewer samples are involved in triplet loss and class-level alignment will not work. It is suggested to use a moderate threshold value for hard transfer tasks.O(N3/C) per epoch, where N is the number of samples and C is the number of classes. In our method, we use two approaches to reduce the complexity of computation. One approach involves selecting samples using online hard triplets within each mini-batch. For example, in the experiment of Paderborn dataset, we observed that about 1000~2000 triplets are selected in each minibatch with batch size of 256, thus only a small portion of samples are selected for training. Second approach is imposing triplet loss only when the training of Wasserstein distance and the classifier have been stabilized, since the selection of triplet samples of target domain depends on the pseudo-labels. At the beginning of model training, the model is not discriminative enough and the predicted labels have low confident. In the experiment we found that the triplet loss could be enabled during the last 20 epochs of training, which not only aligned the distribution at class-level, but also not bring heavy burden of computation. The time cost of each method are listed in The triplet loss comes with run-time complexity In this paper, we propose a triplet loss guided adversarial domain adaptation method for bearing fault diagnosis. We match distribution at domain-level using Wasserstein distance, as well as class-level using pseudo-labeling and triplet loss. We use triplet loss to guide the feature extractor to preserve class information for target samples in aligning domains. Experiments on two different bearing diagnosis scenarios verify the efficacy of our proposed approach.In the future, we plan to eliminate the effect of falsely pseudo-labels in the target domain.


The human transcription elongation factor DSIF is highly conserved throughout all kingdoms of life and plays multiple roles during transcription. DSIF is a heterodimer, consisting of Spt4 and Spt5 that interacts with RNA polymerase II (RNAP II). DSIF binds to the elongation complex and induces promoter-proximal pausing of RNAP II. Human Spt5 consists of a NusG N-terminal (NGN) domain motif, which is followed by several KOW domains. We determined the solution structures of the human Spt5 KOW4 and the C-terminal domain by nuclear magnetic resonance spectroscopy. In addition to the typical KOW fold, the solution structure of KOW4 revealed an N-terminal four-stranded β-sheet, previously designated as the KOW3-KOW4 linker. In solution, the C-terminus of Spt5 consists of two β-barrel folds typical for KOW domains, designated KOW6 and KOW7. We also analysed the nucleic acid and RNAP II binding properties of the KOW domains. KOW4 variants interacted with nucleic acids, preferentially single stranded RNA, whereas no nucleic acid binding could be detected for KOW6-7. Weak binding of KOW4 to the RNAP II stalk, which is comprised of Rpb4/7, was also detected, consistent with transient interactions between Spt5 and these RNAP II subunits. An important and widespread regulatory step is promoter proximal transcriptional pausing, which introduces an early block to RNAP II elongation after 20 to 70 transcribed bases. Participation of the general transcription factor DRB  sensitivity inducing factor (DSIF) in this transcription rate limiting step is required for normal RNA synthesisSaccharomyces cerevisiae5. The hSpt5 subunit contains a region that is homologous to the N-terminal domain of the bacterial transcription factor NusG (NGN). NusG/Spt5 proteins are conserved in all three kingdoms of life6. Bacterial NusG and the archaeal Spt5 proteins are composed of the NGN-domain and a flexibly-connected C-terminal Kyrpides-Ouzounis-Woese (KOW) domain. Eukaryotic Spt5 proteins harbour several KOW domain copies whose functions have not been studied in full detail8. The function of the NGN is conserved in bacteria, archaea, and eukaryotes. In eukaryotes, the NGN domain binds to the Rpb1 and Rpb2 subunits of RNAP II forming a processivity clamp10. It thereby locks the nucleic acids with the RNAP in a closed and pause-resistant state12. Spt4, which is not present in bacteria, interacts with Spt5 via the NGN-domain.Human DSIF is a heterodimer composed of a 14 kDa (hSpt4) and a 120 kDa (hSpt5) subunit, the human homologues of Spt4 and Spt5 of 13. Whereas KOW1-5 domains have been detected in all analysed eukaryotes, the region downstream of the CTRs can only be found in metazoan and plant Spt515.Additionally, eukaryotic Spt5 includes an acidic region at its N-terminus and carries two C-terminal repeat (CTR) regions that can be phosphorylated by the positive transcription elongation factor pTEFb. Phosphorylated DSIF activates RNAP II elongation10. hSpt5 is known to interact with RNAP II not only via the NGN but also through the region harbouring the KOW motifs. The KOW1 domain is flexible and binds between the clamp and the RNA exit tunnel of RNAP II9. Crosslinks and protein mass finger prints demonstrated an interaction between the C-terminal region of hSPT5 and Rpb1. The position of DSIF lies over the active centre cleft in the clamp domain of RNAP II9.Although the structure of the mammalian RNAP II elongation complex has been determined, the precise functions of the different KOW domains of DSIF as well as that of the N-terminal acidic regions remain elusiveSaccharomyces cerevisiae show that the Spt5 KOW4 interacts with RNAP II subunits Rpb4 and Rpb716. In archaea, RpoE and RpoF are homologous to Rpb7 and Rpb4, respectively, and have similar functions18. Rpb4 is suggested to function mainly in mRNA synthesis19. Rpb4/7 is thought to have an additional non-transcriptional role in transcription-coupled DNA repair mechanisms. The Rpb4/7 heterodimer can shuttle between nucleus and cytoplasm bound to mRNA and prevent it from degradation, implicating a role in mRNA export and translation. Furthermore, the RNA-binding protein Nrd1, involved in 3′ end formation of small nucleolar and nuclear RNAs during transcriptional termination, appears to interact with Rpb721.A cryo-electron microscopy (EM) structure of the mammalian RNAP II/DSIF complex and cross-linking experiments with Spt4/5 from 10. Moreover, the C-terminal region of hSpt5 also includes a tandem KOW domain, designated KOW6-7, which is positioned near the exiting RNA, suggesting a function in recruiting factors for RNA capping and in 3′ RNA processing23.The cryo-EM analysis of the RNAP II/DSIF complex and X-ray crystallography revealed that the hSpt5 KOW4 requires a structural element at its N-terminus, KOWx-4, for stabilityin vitro interaction studies with nucleic acids and Rpb4/7. Fluorescence and NMR-based titrations showed that only KOW4 variants bind to nucleic acids with micromolar to nanomolar dissociation constants and interact weakly with Rpb4/7. For KOW6-7 no binding to nucleic acids or Rpb4/7 could be detected.To explore in depth the functions of KOW4 and KOW6-7, we determined the solution structures of KOW4 (S522-G647) and KOW6-7 (G961-A1087) of hSpt5 by solution nuclear magnetic resonance (NMR) spectroscopy and performed 24. In addition, we determined the solution structure of the C-terminal KOW6-7 using a construct spanning G961 to A1087  with the strand order found in typical KOW domains. However, in KOW domains a fifth β-strand at the C-terminus adds to the β-sheet by pairing with the first strand, thereby forming the barrel-like domain. As the fifth β-strand is absent the region comprising only β-strands 1–4 should not be regarded as a typical KOW domain. It is required to stabilize the KOW4 domain and is connected to it by a cationic linker spanning K578 to F583.The solution structure of KOW4 (S522-G647) shows the β-barrel fold typical for KOW domains (β-strands 7–11) that is covered by a β-hairpin (β-strands 5–6) called lid and stabilized N-terminally by a tilted, convex β-sheet (β-strands 1–4) Fig. . This N-2 in size. The interface mainly involves residues W536, L539, V547, V549, V551, M563 and L561 in the connection domain as well as F618, F621, F623, M635 and V637 in the KOW4 domain. In addition, the interface may be stabilized by a salt bridge between R552 and E631  at 14 T shows values in the range of 0.7–0.8 for residues, except for the region N581-V585 and the termini. The hetNOE provides information about the motion of individual N-H bond vectors on the sub-ns timescale, significantly faster than the overall rotational tumbling. The values obtained are characteristic for restricted dynamics on the timescale of molecular tumbling for folded proteins. The hetNOE decreases towards the termini, demonstrating the increased flexibility on this timescale as is typical for unstructured termini. The cationic linker region shows slightly reduced values for the hetNOE indicating an enhanced flexibility on the sub-ns timescale. The transverse and longitudinal relaxation rates of 15N spins are very similar throughout the folded region of the protein, and the narrow distribution of the R1/R2 ratios suggests uniform overall tumbling of the protein  construct. The fold of the KOW4 (S522-G647) domain deviates from the ideal sphere-like shape suggesting that the overall tumbling cannot be described by an isotropic rotation with a single correlation time. Determination of the rotational diffusion tensor based on the 15N relaxation rates requires a structural model and is a suitable method to validate the determined structure. The overall tumbling of KOW4 (S522-G647) can be well described by a prolate axial symmetric rotational diffusion tensor   that are surrounded by polar residues. In particular, one lysine residue and one arginine residue  in KOW7 may form salt bridges with aspartates (D978 and D983) from the linker/KOW6 side  is also larger than predicted previously-7) Fig. , is in gide Fig. . This ma15N-based spin relaxation experiments to characterize the domain movement of KOW6-7 (G961-A1087). 15N relaxation rates were obtained for 112 non-overlapping amide resonances. The hetNOE experiments show the typical values around 0.7–0.8 for the folded part, demonstrating the highly restricted flexibility for the N-H bond vectors of this region. The flexibility on the sub-ns timescale increases towards the termini. For the region linking both domains no increased dynamical behaviour on the sub-ns timescale is observed. Similarly to KOW4 (S522-G647), the narrow, monomodal distribution of R1/R2 ratios suggests that the two KOW domains of KOW6-7 (G961-A1087) interact and the protein moves as one entity  we performed ity Fig. . Analysites Fig.  of 79 retes Fig. . SimilarAn overlay of all available hSpt5 KOW domain structures discloses that they all share the typical KOW domain β-barrel fold Fig. . The firIn summary, both KOW4 (S522-G647) and KOW6-7 (G961-A1087) exhibit intramolecular domain-domain interactions and enlarge the structure of the basic KOW fold compared to other KOW domains of hSpt5. The extended structure might be important to present a larger binding surface for additional molecular interactions.28. Thus, we investigated the affinity of several KOW4-5 variants  has similar sequence-independent micromolar range affinities for DNA and RNA, with some preference for single-stranded substrates , the presence of KOW5 in the construct KOW4-5 (G531-G754) did not further enhance RNA binding. These data suggest a substantial contribution of the linker region G648-G705 between KOW4 and KOW5, but not of KOW5 itself, to RNA binding  Fig. , or, in ing Fig. . These r10, we could not detect nucleic acid binding for the corresponding KOW6-7 (G961-A1087) construct, indicating that it probably interacts exclusively with protein factors necessary for RNA elongation and/or RNA processing during transcription termination -based titration experiments of 15N-labelled KOW4 (S522-G647) with ssRNA . Moreover, most binding site residues carry a polar, positively charged, or aromatic side chain Fig. . This is1H, 15N] HSQC spectra of the two 15N labelled proteins to identify the signals corresponding to the KOW4-KOW5 linker , which harbours the KOW4-KOW5 linker region, were higher than of KOW4 (S522-G647), we used an overlay of the 2D  HSQC spectrum shows chemical shift changes of the same signals that were already affected upon addition of Rpb4/7 to KOW4 (G531-L705)  Fig. . No addiWe postulate that in a transcription initiation complex, the KOW4 domain of DSIF is loosely associated with the Rpb4/7 stalk of RNAP II. However, during elongation, when the transcribed RNA leaves the exit channel, the KOW4 domain and the KOW4-5 linker associate with the exiting RNA, thus stabilizing the elongation complex. Once the transcript is finished, Spt5 no longer binds to Rpb4/7. Without this stabilization, Rpb4/7 is able to dissociate from RNAP II and leave the nucleus together with the RNA, preventing its degradation. Thus Spt5, like NusG, could play a role in coupling transcription with translation, at least indirectly.10. Comparison of the RNA binding affinities of KOW4 (S522-G647) lacking the linker and KOW4 (G531-L705) which includes the linker, further indicated that the linker between KOW4 and 5 contributes substantially to RNA binding , the dimer interface of KIN17 harbours an Arg and a Lys residue . In addition, the protein also comprises an extended loop between β-strands 1 and 2. In contrast to hSpt5 KOW6-7 (G961-A1087), human KIN17 binds RNA. The negatively charged groove of the domain interface of KIN17 might constitute an additional surface for interaction with other proteins.Interestingly, KOW6-7 (G961-A1087) exhibits a similar spatial arrangement as has been determined for the CTD of human KIN17, which harbours two SH3-like domains (PDB: 2CKK). KIN17 is a 45 kDa DNA and RNA binding protein that plays an important role in nuclear metabolism10. Thus, no function for KOW6-7 could be determined. Since we find no direct interaction between KOW6-7 and nucleic acids, we propose that KOW6-7 might recruit other factors, for example proteins that play a role in RNA elongation, termination and processing, possibly via the positively charged amino acids flanking the groove  and the hydrophobic amino acids located within .In the cryo-EM structure of the RNAP II/DSIF complex, no density was observed beyond the KOW5 domainvia a tobacco etch virus (TEV) protease cleavage site located between GB1 and the KOW domain. Gene expression in lysogeny broth or in M9 medium for 15N and 13C labelling was performed in Escherichia coli strain BL21 (DE3)  as described30. After induction with 100 µM isopropyl-thiogalactoside (IPTG) the temperature was reduced to 20 °C and protein overexpression was performed overnight. Proteins were purified via Ni-affinity chromatography  and TEV cleavage followed by a second Ni-affinity chromatography which allowed the removal of the 6xHis-GB1 tag. The free KOW proteins were collected in the flow-through and purified further via anion exchange chromatography using a QXL column . All constructs were flash-frozen with liquid nitrogen and stored at −80 °C.The genes coding for KOW4 variants (S522-G647), (G531-L705) and (G531-G754) and KOW6-7 (G961-A1087) of hSpt5 were amplified by PCR using cDNA plasmid pOTB7 huSUPT5H  as a template. 5′ and 3′ primers harbouring NcoI and BamH I restriction sites, respectively were used to clone the PCR fragments into the vector pET-GB1a . The proteins expressed were thus fused to the C-terminus of the B1 domain of streptococcal protein G (GB1) and could be released E. coli were cloned in tandem into the expression vector pET15b. Rpb4 harboured a sequence coding for an N-terminal 6His tag. E. coli BL21 (DE3) cells  transformed with the expression plasmid were grown to an optical density at 600 nm of 0.7–0.9 at 37 °C in LB or M9 medium containing 100 µg/ml ampicillin. Gene expression was induced with 1 mM IPTG for 4 h at 37 °C. The cells were then harvested by centrifugation. Cell lysis was performed as described for the KOW constructs in 50 mM sodium phosphate (pH 6.8), 500 mM NaCl, 1 mM dithiothreitol (DTT). The heterodimer was purified via Ni-affinity chromatography , followed by anion exchange chromatography  after dialysis against 20 mM Tris/HCl pH 6.8, 20 mM NaCl, 1 mM DTT). The protein complex was eluted with an NaCl step gradient. Rpb4/7 containing fractions were combined, dialyzed against 20 mM Tris/HCl pH 6.8, 20 mM NaCl, 1 mM DTT, concentrated by ultrafiltration, and stored at −80 °C after flash-freezing with liquid nitrogen.Genes adapted for KD values31. All binding curves were measured in triplicates.Fluorescence anisotropy measurements were performed at 25 °C on a Synergy 2 microplate reader (biotek) equipped with black, sterile 96-well microtiter plates. The single stranded (ss) or double stranded (ds) DNAs or ssRNA were labelled with 6-FAM at the 5′ ends and contained the following sequences: ssDNA1: 6FAM-CTTATTGAATTA; ssDNA2: 6FAM-GAAAATTGGGTAAG; ssRNA: 6FAM-GGCGGUAGCGUG . For dsDNA, the corresponding complementary strands without label were hybridized to ssDNA1 and 2 in fluorescence buffer  at a molar ratio of 1:1.2 (labelled:unlabelled strand) by heating the sample for 3 min at 95 °C, followed by a 10 min incubation step at 34 °C, or 40 °C for dsDNA1, or dsDNA2, respectively. Titrations were performed with individual samples, each containing 25 nM (for KOW4 (G531-L705), and KOW4 (G531-G754)) or 50 nM for KOW4 (G522-G647) and KOW6-7 (G961-A1087) of nucleic acid substrate and increasing amounts of protein in a total volume of 100 μl. The anisotropy of each sample was measured 6 times. Data were analysed by plotting the anisotropy value corrected for the value of the free nucleic acid vs. the protein concentration in the sample. The curves in Fig. 33 were conducted for backbone and sidechain resonance assignments at 298 K. 15N- and 13C-edited 3D NOESY experiments were recorded with mixing times of 120 ms at 298 K, in a buffer containing 20 mM sodium phosphate, pH 6.4, 20 mM NaCl, and 0.5 mM DTT.All NMR experiments were conducted on Bruker Avance 600 MHz, 700 MHz, 900 MHz and 1000 MHz spectrometers, the latter three equipped with cryogenically cooled probes. Standard double and triple resonance experiments1H, 15N residual dipolar couplings (RDCs) were determined for KOW6-7 (G961-A1087) by in-phase/anti-phase (IPAP) experiments34 using a sample containing 10 mg/ml Pf1 phages 35. Determination of RDCs for KOW4 (S522-G647) were not successful using either Pf1 phages or mixtures of hexa-ethylene glycol monododecyl ether (C6E12), hexanol and water36 due to sample instability.15N longitudinal (R1) and transverse (R2) relaxation rates together with the {1H}15N steady state NOE were determined using standard methods37 at 600.2 MHz (KOW4 (S522-G647)) or 700.2 MHz (KOW6-7 (G961-A1087)) 1H frequency at a calibrated temperature of 298 K.For characterization of overall and internal motions, R1 and R2 relaxation rates were determined by fitting a mono-exponential curve to the signal intensities using the CURVEFIT program . Rotational diffusion tensor analysis was done using the program tensor238. The error of relaxation rates was set to 5% to reflect potential systematic errors due to pulse imperfections, different temperatures in R1 and R2 experiments due to different radio frequency heating, and potential structural noise in the structural models. Residues with {1H},15N steady state values below 0.7 and residues with enhanced R2 rates due to chemical exchange were not included in the analysis. The different models for the rotational diffusion tensor were accepted or rejected based on the χ2 statistics using 500 Monte Carlo simulations.15N-edited NOESY and 13C-edited NOESY spectra. NOESY cross peaks were classified according to their relative intensities and converted to distance restraints with upper limits of 3.0 Å (strong), 4.0 Å (medium), 5.0 Å (weak), and 6.0 Å (very weak). For ambiguous distance restraints the r−6 summation over all assigned possibilities defined the upper limit. Hydrogen bonds were included for backbone amide protons in regular secondary structure, when the amide proton did not show a water exchange cross peak in the 15N-edited NOESY spectrum.Distance restraints for structure calculation were derived from 39 using a three-step simulated annealing protocol with floating assignment of prochiral groups including a conformational database potential. The 14 (KOW4) and 20 (KOW6-7) structures showing the lowest values of the target function excluding the database potential were further analysed with X-PLOR39, PyMOL, and PROCHECK 3.5.440.Structure calculations were performed with the program XPLOR-NIH 1.2.141.Structure alignments were performed using WinCoot’s secondary structure matching algorithm. Sequence alignment of the hSpt5 KOW motifs was performed manually, based on the KOW consensus sequence provided by PfamThe structure coordinates of hSpt5 KOW4 (S522-G647) and KOW6-7 (G961-A1087) were deposited in the Protein Data Bank under the accession codes 6EQY and 6ER0, respectively. Chemical shift assignments were deposited in the BioMagResBank accession numbers 34184 and 34185, respectively.Supplementary Information


We introduce the Embryology domain of TheScientificWorld and outline the scope and aims. We argue for an interdisciplinary approach to problems in develop-mental biology. Three areas are identified as being of particular relevance to this domain: evolutionary developmental biology, teratology, and descriptive or experimental embryology.