Its rheological behavior presented characteristics of work-hardening followed by dynamic softening and its flow stress was accurately described by the proposed strain-compensated Arrhenius-type constitutive model. Three-dimensional processing maps were established. The instability was mainly concentrated in regions with high strain rates or low temperatures, with cracking being the main instability. A workable domain was determined as 385–450 °C, 0.001–0.26 s−1, in which dynamic recovery (DRV) and dynamic recrystallization (DRX) occurred. As the temperature rose, the dominant dynamic softening mechanism shifted from DRV to DRX. The DRX mechanisms transformed from continuous dynamic recrystallization (CDRX), discontinuous dynamic recrystallization (DDRX), and particle-stimulated nucleation (PSN) at 350 °C, 0.1 s−1 to CDRX and DDRX at 450 °C, 0.01 s−1, and eventually to DDRX at 450 °C, 0.001 s−1. The eutectic T-Mg32(AlZnCu)49 phase facilitated DRX nucleation and did not trigger instability in the workable domain. This work demonstrates that the workability of as-cast Al-Mg-Zn-Cu alloys with low Zn/Mg ratios is sufficient for hot forming.One of the key issues limiting the application of Al-Mg-Zn-Cu alloys in the automotive industry is forming at a low cost. Isothermal uniaxial compression was accomplished in the range of 300–450 °C, 0.001–10 s Linear fitting was applied to extract the pending parameters by calculating the arithmetic means of the slopes of the fitting curves in β, n1, α, and n were 0.099, 5.704, 0.017, and 4.58, respectively, while Q was 181.6 kJ/mol. lnA was extracted to be 29.5 by calculating the intercept of the fitting curve in Based on Equations (3)–(5), n can be used to speculate on the movement modes of the dislocations or grain boundaries [ε = 0.2, the calculated n were, successively, 5.42, 4.61, 4.30, and 3.99 at 300 °C, 350 °C, 400 °C, and 450 °C. n was close to 5 at 300 °C, according to Refs. [n decreased with increasing T, indicating a decrease in the barrier of dislocation or boundary migration, which is usually associated with the promoted grain boundary sliding, DRX, and inhibited precipitation at high temperatures [The derived constitutive parameters undaries ,19,35. AQ and lnA when ε < 0.2. Accordingly, the strain-compensated constitutive model applicable to ε = 0–0.9 is:To reflect the strain dependence of the constitutive model, the constitutive parameters at other strains were derived and the compensation functions were established using quintic polynomial fitting ,26. As sRMSE), average absolute relative error (AARE), and correlation coefficient (R), were calculated [RMSE and AARE were only 5.93 and 6.3%, respectively, and R reached 99.2%, demonstrating the satisfactory accuracy of the constitutive model.The experimental and predicted flow stress were compared and the deviation between them was calculated. Furthermore, three statistical parameters (Equations (7)–(9)), namely the root mean square error  satisfies:G = m (the strain rate sensitivity) is defined as the partition between G and J [Processing maps were constructed based on the dynamic material model, which treats metal processing as an irreversible power dissipation system containing source, storage, and consumption units ,43. Duri G and J :(11)m=dJAssuming that the processing follows the power law :(12)σ=KεK as a material-dependent coefficient. J is modified to:With J reaches the theoretical maximum value of Jmax = P/2 in ideal plastic flow (m = 1), then the power dissipation coefficient is defined as the proportion of J to Jmax [If  to Jmax :(14)η=J/On the other hand, the flow instability is identified by the criteria ,43:(15)ξξ indicates flow instabilities, which are usually manifested as crack, flow localization, adiabatic shear band, and void, etc. [A negative id, etc. .η values (in percent) are labeled on the contour lines and the unsafe zones are rendered in grey. The η generally increased with an increasing temperature and decreasing η < 0.25. There were two unsafe domains at a 0.1 strain, with corresponding deformation parameters of 395–433 °C, 0.47–10 s−1 and 300–383 °C, 0.001–0.34 s−1, respectively. The two unsafe domains coalesced when the strain reached 0.3 and gradually encroached on the entire scope of η (0.25–0.36), which is the range where DRV and even DRX may occur [η 0.25–0., which iay occur .−1 (domain B) always belongs to the safe zone. In domain B, the η exceeded 0.37 under all the strains, indicating that the deformation energy storage was consumed efficiently by microstructural changes, most likely DRX [−1.In contrast, the range of 385–450 °C and 0.001–0.26 skely DRX . Therefo−1. The metallographic photographs demonstrate that the cracks originated from the bulge regions, and dense voids can also be observed near these cracks. A similar cracking situation was also observed for the samples deformed in domain A , in which ρ = 2.6 g/cm3 and pC = 1.24 J/(g·K) represent the density and specific heat of the alloy at 450 °C. As a result, ∆T reached 26.3 °C, implying that the actual temperature of the sample during the deformation approached the incipient melting point of the eutectic T phase [At the crack tips , as the temperature increased, the diffusion coefficient increased sharply [−1, decreasing to 45% of that before the deformation. It was shown that dislocation-assisted diffusion promoted the dissolution of the eutectic T phase [ sharply . When th T phase . At a hi T phase .Q was always higher than the lattice diffusion activation energy of aluminum (142 kJ/mol), implying that the deformation was hindered by supplementary impediments, apart from high-temperature diffusion [The illustration in iffusion ,30,48. Tiffusion . These fiffusion .−1. As illustrated in the IPF maps in The softening mechanisms were affected by temperature. Generally, Al alloys have a high stacking-fault energy (SFE) and are prone to DRV during hot deformation . MeanwhiSome original grains were segmented into substructures by the LAGBs, and HAGBs were detectable within the original grains a–c. As s−1, the density of the LAGBs decreased continuously and the misorientation gradient also gradually eased. The proportion of microstructures in deformed and DRV states decreased, while that of DRX increased from 17.2% to 65.3%  The rheological behavior presented characteristics of work hardening, followed by DRV/DRX, which was accurately described by the proposed strain-compensated Arrhenius-type constitutive model.−1.(2) A 3D processing map was established. Instability was mainly concentrated in regions with high strain rates or low temperatures, mainly occurring in the form of cracking. One workable domain was identified as 385–450 °C, 0.001 to 0.26 s−1, and eventually to DDRX at 450 °C, 0.001 s−1.(3) As the temperature rose, the dominant dynamic softening mechanism changed from DRV to DRX. The DRX mechanisms transformed from CDRX, DDRX, and PSN at 350 °C to CDRX and DDRX at 450 °C, 0.01 s32(AlZnCu)49 phase facilitated DRX nucleation and did not trigger instability in the workable domain.(4) The coarse eutectic T-Mg(5) The as-cast Al-5Mg-3Zn-1Cu alloy had a satisfactory hot workability.


Pseudomonas aeruginosa PAO1 strain, a bacterium that forms a ridged filament with a dimerization of its flagellin outer domains, motility is categorically dependent on these flagellin outer domains. Moreover, a comprehensive network of intermolecular interactions connecting the inner domains to the outer domains, the outer domains to one another, and the outer domains back to the inner domain filament core, is required for motility. This inter-domain connectivity confers PAO1 flagella with increased stability, essential for its motility in viscous environments. Additionally, we find that such ridged flagellar filaments are not unique to Pseudomonas but are, instead, present throughout diverse bacterial phyla.In its simplest form, bacterial flagellar filaments are composed of flagellin proteins with just two helical inner domains, which together comprise the filament core. Although this minimal filament is sufficient to provide motility in many flagellated bacteria, most bacteria produce flagella composed of flagellin proteins with one or more outer domains arranged in a variety of supramolecular architectures radiating from the inner core. Flagellin outer domains are known to be involved in adhesion, proteolysis and immune evasion but have not been thought to be required for motility. Here we show that in the  Pseudomonas aeruginosa use flagella to move freely through the environment, adhere to surfaces and infect the host. The most prominent component of the flagellum is a hollow filament made of thousands of copies of the protein flagellin, or FliC. The inner part of the filament, the core, is structurally the same in all flagellated bacteria, while surface-exposed flagellin domains differ significantly, and their exact role is not fully understood. Here we present a new type of filament organization in which outer domains establish extensive contacts with the core and dimerize along the axis resulting in a ridged filament type. By studying the effects of mutations on bacterial swimming, we found that outer domains of the ridged filaments of Pseudomonas are indispensable for swimming, unlike the outer domains of Salmonella. Certain contacts between outer domains themselves and outer domains and the core allow bacteria to swim efficiently through porous or viscous environments. We found that this type of filament organization is present in many other bacterial species not closely related to P. aeruginosa, suggesting that this type of filament architecture could be advantageous in colonization.Opportunistic bacteria like  Pseudomonas aeruginosa, to many, as in Escherichia coli and Salmonella enterica serovar Typhimurium [The bacterial flagellum is a complex and dynamic nanomachine that propels bacteria through liquids. In pathogenic species, motility provided by flagella is critical for host colonization and infection, and flagellar filaments are recognized as important virulence factors involved in adherence, toxin delivery, biofilm formation and activation of innate immunity –8. The nhimurium . Each flhimurium ,11. TorqThe flagellar filament, a tubular structure composed of thousands of copies of the protein flagellin (FliC in many bacteria), exhibits helical symmetry in its straight non-motile forms  and is sSalmonella has been studied as the archetypal model of bacterial flagella for decades. The first flagellin protein structure was from that of S. Typhimurium [S. Typhimurium flagellar filaments, the outer domains are splayed outward from the filament core such that no extensive interactions are made between the D2 and D3 domains of neighboring subunits along the protofilament [S. Typhimurium outer domains are tolerated without abolishing motility [Bacillus subtilis and Kurthia sp. [The flagellar system of himurium , revealihimurium ,19. In Sfilament ,20. Thismotility . Recentlmotility –24. In sthia sp. ,26. A reSalmonella mutants that were flagellated but immotile due to the fact that mutations of flagellin in the D1 domains locked it in one of the two conformations—having all left- (L) or all right- (R) handed protofilaments, resulting in straight filaments [B. subtilis, P. aeruginosa and Campylobacter jejuni [P. aeruginosa strain PAO1 revealed that the overall shape of this filament was ridged, unlike the splayed geometry of the S. Typhimurium filament [P. aeruginosa PAO1 supercoiled filament without imposing helical symmetry. Based on the resulting model, we identified three interfaces that the D2 and D3 outer domains form with one another as well as with the D1 inner domain. Combining genetic tools, phase-contrast light and electron microscopy and swimming motility assays, we showed that these interfaces form a connected network of inter-domain interfaces that are critical not only for the structural integrity of the filament, but also for motility of the bacterium. Using bioinformatic tools, we found that ridged flagellar filaments similar to that in P. aeruginosa PAO1 are common throughout the bacterial kingdom and that such interconnected filament architectures may provide an advantage to bacteria in generating the thrust needed to swim through liquids of high viscosity.Until recently, technical challenges limited structural studies to straight flagellar filaments that exhibit helical symmetry. Some of the first filament structures were derived from ilaments . Equivalr jejuni ,25, and r jejuni , which aS. Typhimurium is inserted into D2 and they form two separated domains, a different topology of the secondary structural elements in PAO1 FliC results in the D2 and D3 domains folding into a single bilobal structure with two distinct surfaces, the concave surface facing the filament core, and the convex surface exposed to the outside. Starting from the N-terminus, the first β-strand is incorporated into the D3 moiety, before the backbone turns downward, forming one half of D2  and consist of residues in helix α2, and loop regions 249–253 and 266–269. The second interface is formed between D3 and the β hairpin on the D1 domain of that same neighboring FliC subunit (D30-D1+11 interface). Finally, the third interface arises from D2 also forming polar contacts with its own D1 domain (D20-D10 interface).An outer domain in the PAO1 filament forms three interfaces . The firfliC mutant genes on a plasmid that we used for P. aeruginosa PAO1-ΔfliC strain complementation. We then observed the effects of mutations on filament formation and tested swimming motility in an agar-based swimming assay by comparing their motile spread to that of the P. aeruginosa PAO1-ΔfliC strain complemented with the wild type gene ; again, the formation of filaments was not affected   .fliC genes were not a result of substantial FliC protein structural differences, aggregation or instability.Regardless of how the above-described mutations affected motility, all resulted in the formation of flagellar filaments, suggesting that each FliC mutant was expressed, exported and properly folded by the bacterium. We confirmed this by Western blot analysis showing that the amount of intracellular FliC and FliC in the culture media was comparable among complemented strains . The difP. aeruginosa contain FliC of either type A or type B and their structures differ significantly. We used the available crystal structure of the type A FliC from the P. aeruginosa PAK strain to model the PAK filament by superposing D0 and D1 domains of type A FliC to the corresponding domains in our PAO1 filament structure. Our model suggests that the outer domains of the PAK filament in each protofilament are farther apart than those in PAO1 and do not engage in direct subunit-to-subunit interactions  led to a decrease in spread of only 15% compared to the knockout strains complemented with wild type fliC, comparable to the observed decrease in swimming speed (PAO1D1D2PAK) resulted in nearly complete abolition of swimming motility, as indicated by a decrease of motile spread of 95% compared to the wild type. This was in sharp contrast to the almost wild type swimming speed of D0PAO1D1D2PAK in liquid  is exposed to the channel lumen. The other three non-conservative substitutions, as well as the majority of conservative substitutions are located on the side of Helix 5 forming contacts with other inner core helices. This suggests that while changes in lateral contacts of the inner core are tolerable, the specific sequence lining the lumen could be important for transport of FliC through the channel.The C-terminal helix forms part of the inner core of the filament, lining the central channel, making it the only structural part of the filament in contact with the incoming FliC molecule that is being transported to the filament tip . Of the P. aeruginosa PAO1 is markedly different from that of S. Typhimurium. In Salmonella, outer domains D2 and D3 are clearly separated from each other and from neighboring outer domains and do not interact [C. jejuni filament shows a flagellin with three distinct outer domains, with domains D2 and D3 having similar topology to that of D2 and D3 of PAO1  travel on their way to encountering the FliD filament cap, folding and thereby extending the flagellar filament from its distal end. They are also required for the supercoiling that generates thrust in what would otherwise simply be a spinning rod. Since growing a filament that subsequently generates thrust is the minimum requirement for swimming motility, it should come as no surprise that some motile bacteria, such as S. Typhimurium [P. aeruginosa PAO1 [Sinorhizobium meliloti (24), and sheathed, as in enterohemorrhagic E. coli O157:H7 (EHEC) [Most flagellated bacteria, however, express flagellins with one or more additional outer domains. Moreover, these outer domains are highly divergent in sequence and, as an increasing number of structural studies have demonstrated, in domain conformation and overall supramolecular architecture. While these remain difficult to fully categorize, some of the observed architectures include splayed, as in himurium ,20, ridgosa PAO1 , screw-lmeliloti , and shemeliloti , and sheP. aeruginosa PAO1 as a model system of ridged flagellar filaments, we dissected interactions involving its D2 and D3 outer domains and their effects on motility. As a consequence of the architecture of these ridged filaments, these outer domains create a network of interfaces along the protofilament from the inner core, through the outer domains and back to the inner core. By measuring motility of P. aeruginosa PAO1 mutant filaments, we found that each one these interfaces—between D10 and D20, connecting the inner core to the outer domains; between D30 and D2+11, bridging the outer domains along the protofilament; and between D30 and D2+11, reconnecting the outer domains to the inner core—is required for motility. Together, these interfaces create a network of interactions upon which motility depends.Here we show that FliC outer domain interactions: (1) can involve more extensive networked interactions than just intermolecular contacts in outer domain interfaces; (2) provide a motility advantage to bacteria in viscous versus fluid environments; and (3) are more widespread throughout the bacterial kingdom than previously appreciated. Using To form this unique outer domain network the flagellin proteins must adopt three slightly different conformational states. This contrasts starkly with the outer domain dimerization described previously in bacterial flagellins where the outer domain conformations involve a 180° rotation of the domains creating a dimer interface with D1 symmetry . The conP. aeruginosa PAO1 with PAO1/PAK chimeric filaments. Unlike the ridged filaments in P. aeruginosa PAO1, P. aeruginosa PAK exhibits splayed filaments in which the outer domains do not make contacts with one another along the protofilament. When we measured motility in liquid, wild type PAO1 and PAK, as well as all chimeras that produced flagella, were similarly motile. Conversely, when challenged with viscous liquid media or a gel-like soft agar environment, wild type PAO1 was substantially more motile than wild type PAK. Of those chimeras that produced flagella, only that which included the D1, D2 and D3 domains from PAO1 –thereby reconstituting the entire network of interactions between the inner core and outer domains in wild type PAO1 –was similarly motile to wild type PAO1.To demonstrate that FliC outer domains conferred a motility advantage to bacteria in viscous environments, we complemented P. aeruginosa PAO1. Our computational predictions show that not only are ridged filaments found extensively throughout Pseudomonas species, at least 46 species outside of the Pseudomonas genus have similarly folded outer domains. While sequence alignment reveals no significant identity between these outer domains, the structures of these outer domains have been conserved. Due to the laborious method by which we identified these similarly-folded FliC proteins, which relied on visual inspection of individual AlphaFold-predicted structures, this undoubtedly represents an undercount; ridged flagellar filaments may be even more widespread throughout bacteria than we have found here. It is also reasonable to believe that the total number of different outer domain structures is smaller than suggested by sequence diversity of outer domains and that filaments could be classified into just a handful of architectures, minimally including: naked , splayed , ridged , screw-like  and sheathed  filaments. Different architectures could reflect the specific needs of distinct groups of bacterial species.We found that ridged flagellar filaments, and consequently the networked interactions between the inner core and outer domains, are not unique to C. jejuni, and consequently in other species, could be in the functional properties of the flagellum. The flagellar motor causes rotation of the filament, creating thrust and enabling bacteria to swim forward. The motor consists of two distinct molecular assemblies, the membrane-embedded rotor, and a torque-generating stator. Stators are composed of a variable number of stator units, with each unit being a complex of the proteins MotA and MotB. In Salmonella, the stator consists of 11 subunits. Conversely, the C. jejuni motor has up to 17 stator units [Salmonella. C. jejuni also swims faster in moderately viscous fluid than in low viscosities [P. aeruginosa, a recent cryo-electron tomography analysis showed that its motor is wider than the motor of S. Typhimurium and includes additional prominent densities adjacent to the P- and L-rings made of the MotY homolog [Vibrio alginolyticus Na+-driven motor. Additionally, P. aeruginosa has two types of stator units, MotA/MotB for swimming in low viscosity, and MotC/MotD for swimming in higher viscosity [P. aeruginosa motor would result in generation of greater maximum torque, allowing bacteria to swim at higher velocities through more viscous media [C. jejuni outer domains do not. In C. jejuni the singular outer domain conformation along the 11-start protofilaments might allow for greater stability due to their weakened protofilament interface in domain D1 [The explanation for the existence of ridged filaments in PAO1 and or units . This trcosities . While n homolog , a proteiscosity . All theus media . Intriguomain D1 . The resP. aeruginosa of 50 μm/s is half the speed of C. jejuni, it is still twice that of S. Typhimurium [P. aeruginosa and C. jejuni have polar flagella. Due to the higher force and load to which their filament is subjected, it is reasonable to expect a much tighter packing to preserve its structural integrity. As such, outer domains with extensive inter-domain interactions could stabilize the filament while swimming in the viscous environment. The ridged outer domains might also provide a stability to PAO1 and C. jejuni flagellar filaments, as both species wrap their flagella around their cell body and appear to undergo limited polymorphic transitions [Pseudomonas species, S. Typhimurium is peritrichously flagellated, and its individual filaments bundle during straight forward swimming. A looser outer domain structure could serve as a mechanism for easier bundling and unbundling when the change of direction is needed.Although the maximum swimming speed of himurium . Both P.nsitions ,45. The fliC from P. aeruginosa PAO1 between residues 178 and 395 containing D2 and D3 domains (FliCD2D3) was cloned into a pGEX-5x-2 plasmid with the GST tag on N-terminus followed by a TEV protease recognition site. The plasmid was introduced into BL21(DE3) cells. For the native protein expression, cells were grown in LB medium and the expression was induced with 1 mM IPTG, after which the cells were grown for another 3 hours at 37°C. Selenomethionine-labeled (SeMet) FliCD2D3 was expressed for 6 hours after the induction at 37°C in M9 medium containing the amino acids lysine (100 mg/L), phenylalanine (100 mg/L), threonine (100 mg/L), isoleucine (50 mg/L) and valine (50 mg/L) to suppress methionine synthesis, and 60 mg/L of selenomethionine. The cells were harvested, resuspended in PBS and sonicated. The soluble fraction of the lysate was applied to the Glutathione Sepharose beads , washed and eluted with PBS containing 25 mM of reduced glutathione. GST tag was removed by overnight incubation with TEV protease followed by affinity chromatography using Ni-NTA beads to remove TEV protease, and size-exclusion chromatography . SeMet-FliCD2D3 crystallized in 1.9 M ammonium sulfate and 0.1 M sodium Citrate pH 5.5, while native FliCD2D3 crystallized in 2 M ammonium sulfate and 0.1 M Tris pH 7.5.The sequence of Data sets were collected on the BL12-2 beamline of the Stanford Synchrotron Radiation Lightsource (SSRL) equipped with the Pilatus 6M detector (Dectris). For the selenomethionine-labeled FliC, we collected 3 data sets at peak (λ = 0.97929 Å), high-energy remote (λ = 0.9116 Å) and inflection (λ = 0.9795 Å) wavelengths. Data were indexed, integrated and scaled using the XDS program package . The MADP. aeruginosa PAO1 was grown in LB medium at 370 C until OD600 = 1.5, after which cells were harvested and resuspend in 30 mL of cold PBS. Filaments were sheared off using a Waring commercial blender for 1 minute, followed by centrifugation at 12000xg for 10 minutes. Supernatant was collected and concentrated to the final volume of 1 mL. For EM grid preparation, 3.5 μL aliquots of flagellar filaments were applied to Lacey carbon grids and were subsequently plunge frozen using a Vitrobot Mark IV plunge freezers. The blotting time was 3 seconds and the blot forces ranged from 3–6. Imaging was done on a Fisher Scientific Glacios equipped with a Falcon IV direct electron detector with a pixel size of 0.92 Å and a total dose per movie of 50 e-/Å2. Images were then processed using cryoSPARC [ryoSPARC . InitialryoSPARC ,28. InitA composite model of the PAO1 flagellin was fit into the cryo-EM density map, and the outer domains where adjusted to fit the density using UCSF Chimera  and CootΔfliC strains P. aeruginosa PAO1 were obtained from the Manoil Lab at the University of Washington, while P. aeruginosa PAK strains were kindly provided by Joanna Goldberg from Emory University. Different constructs of fliC genes were cloned into the pBBR1 plasmid, which was then transformed into the knockout strains by electroporation [Wild-type and poration .Pseudomonas aeruginosa strains were performed as described in [0C for approximately 20 hours. The area of each bacterial swim circle was quantified using the software ImageJ [www.graphpad.com).Swimming motility assays of ribed in . Plates e ImageJ . Ten rephttps://doi.org/10.15139/S3/FXGXYY).Bacteria were grown in Luria Broth liquid culture overnight at 37°C and immobilized on a Formvar grid (Electron Microscopy Sciences) for 1 min 30 sec and the samples were negatively stained with 0.5% (w/v) Phosphotungstic acid (PTA) and imaged using a FEI Talos 120 KV electron microscope. Filament lengths were calculated using ImageJ program , and thehttps://doi.org/10.15139/S3/KGXDRA).Cells from overnight culture were diluted into the LB medium to an OD600 of 0.05 and incubated for 30 minutes at 37°C. For swimming in viscous media, cells were diluted into the LB medium supplemented with 5, 10 or 15% Ficoll 400. Cells were observed and recorded in a phase-contrast mode on Lionhear FX automated microscope (Biotek) using a wet mount technique. The recordings were analyzed using the manual tracking plugin in the image processing package Fiji . AverageE. coli BL21(DE3) cells, grown in LB medium at 370 C until OD600 = 0.6. After induction with 1 mM IPTG, cells were grown for another 4 hours at 370 C. Clarified lysate was applied to NiNTA resin (Qiagen) and eluted with 500 mM imidazole in PBS buffer, followed by size-exclusion chromatography using Superdex 200 Increase 10/300 GL column (Cytiva). For DSF analysis, 5 μM of protein in PBS was mixed with SYPRO Orange dye (ThermoFisher) to a final concentration of 25X. The experiment was performed on a real-time thermal cycler for a temperature range between 250 and 900 C.Genes of the wild type FliC and deletion mutants were subcloned into pET28-a vector with C-terminal polyhistidine tag. Plasmids were transformed into 0 C until reaching final OD600 = 1. For each mutant 1 mL samples were taken, cells harvested at 4000xg for 5 minutes. Leftover media was precipitated using acetone and resuspended in 100 μL of PBS. After SDS-PAGE, proteins were transferred on a nitrocellulose membrane using iBlot 2 (Invitrogen). Membrane was blocked using 5% milk, incubated with PAO1 or PAK FliC antisera [PAO1D1-3St, D0StD1PAO1D2/3St and D0D1StD2/3PAO1 was incubated with primary S. Typhimurium flagellin FliC Detection antibody (clone X5A12) (InvivoGen), followed by HRP-conjugate secondary anti-Mouse antibody.—Membranes were visualized using ChemiDoc MP (Bio-Rad).Cells were grown in LB medium at 37S1 FigD2D3 2Fo—Fc electron density map. (B) Comparison of D2 and D3 domains of P. aeruginosa PAO1 and S. Typhimurium. (C) Topology map of FliCD2D3. blue—domain D2; red—domain D3; yellow–β-strand shared between D2 and D3 domains.(A) Stereo image of a portion of the 2Fo—Fc electron density map of native FliC(PNG)Click here for additional data file.S2 FigThe general workflow for structural determination performed in cryoSPARC is shown here. It is important to note that the helical reconstruction was rather unusual in that an 8 Å starting D0/D1 map was required for a good final density map. For more detailed information please consult the methods section.(PNG)Click here for additional data file.S3 FigA. Local resolution estimate for the PAO1 flagellar filament. Left image shows an axial view of the filament. The middle image shows the surface of the filament. The right image shows the core of the filament. B. Fourier shell correlation (FSC) curves for the PAO1 filament reconstruction. FSC used in the curves is the gold standard 0.143 map:map FSC (GFSC).(PNG)Click here for additional data file.S4 FigCampylobacter jejuni. (B) Power spectrum of the PAO1 flagellar filament. As shown previously [C) Helical net showing the arrangement of the different PAO1 flagellin conformations along the filament assuming a perfectly straight flagellum with helical symmetry for domains D0 and D1. Each dot represents a subunit and the colors correspond to the conformations shown in (A) Power spectrum of a standard monomeric bacterial flagellar filament from eviously , the num(PNG)Click here for additional data file.S5 Fig(PNG)Click here for additional data file.S6 Fig(A) Alanine mutants. (B) Deletion mutants. Negative-staining EM images presented are the images of mutants not included in (PNG)Click here for additional data file.S7 FigDifferent shades of green and gray represent different domains and corresponding residues.(PNG)Click here for additional data file.S8 Fig(A) Anti-FliC western blot showing the presence of FliC in cells and in the media. (+) positive control, purified recombinant FliC from P. aeruginosa PAO1. (B) Growth curves for the subset of tested mutants.(PNG)Click here for additional data file.S9 Fig(A) Size-exclusion chromatogram for recombinantly purified wild type FliC and 7 deletion mutants. (B) Fluorescence emission and the first derivative obtained from differential scanning fluorimetry (DSF) experiment.(PNG)Click here for additional data file.S10 FigSalmonella Typhimurium. Expected FliC band in red square. Variation in sizes is due to different molecular weight of three flagellins: PAK-FliC– 40 kDa, PAO1-FliC– 49 kDa, St-FliC– 52 kDa.(A) PAO1-PAK FliC chimeras. (B) PAO1-S. Typhimurium chimeras. (C)–cells; M—media; St–(PNG)Click here for additional data file.S11 Fig(PNG)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.


Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods. It has Therefore, unsupervised domain adaptation (UDA) can be highly desired, which seeks to transfer knowledge from a labeled source domain to an unlabeled target domain that is different but related . There iIn recent years, self-training has been shown to be a powerful tool for UDA ; in factTo tackle the above issues, we introduce a generative self-training (GST) framework for UDA medical image synthesis that employs continuous value prediction and regression objectives. A practical variational Bayes learning scheme is proposed to gauge the epistemic and aleatoric uncertainties in self-training UDA in a unified manner. The supervision of pseudo-labels on target domain data is explicitly regularized by a reliability mask, which is a learnable variable and is determined by the quantified uncertainty. An optimization strategy is then used to alternate between pseudo-label refinement and reliability mask generation, while also retraining the reliability regularized translator network for adaptation. In our prior conference version , we consTo address the aforementioned difficulties, in the present work, we build on our prior conference version  in the fThe main contributions are summarized as follows:To our knowledge, this is the first attempt to jointly utilize both paired source domain data and unpaired target domain data for deep UDA in medical image modality translation scenarios .We propose a novel GST framework which extends the self-training to a generative UDA task, i.e., image modality translation. It is based on adaptive control of the reliability of pseudo-label with a practical Bayesian reliability mask. Furthermore, we systematically study both the aleatoric and epistemic uncertainties in GST UDA.We introduce a self-attention continuous reliability mask that achieves fine-grained pseudo-label control with well-quantified uncertainty, guided by self-learned attention.Our proposed GST framework is applied to cross-scanner/center and inter-subject tagged-to-cine tongue MR UDA translation, as well as T1-to-FA brain MR UDA translation tasks. These tasks have significant potential to reduce the acquisition time and costs of extra cine/diffusion MR scans.Our framework is evaluated using both quantitative and qualitative measures, and the results demonstrate its validity and superiority over conventional adversarial UDA approaches.2.2.1.Image modality translation is a critical pre-processing step in medical image analysis for filling in incomplete modalities or reducing scanning costs . For exa2.2.There are three main categories of related approaches that have been explored to address domain shift in medical image synthesis . The fir2.3.Early attempts at UDA focused on minimizing a prescribed discrepancy measurement—e.g., maximum mean discrepancy (MMD)  or batchThe recent work  has show2.4.Self-training was originally developed for semi-supervised learning  and has 2.5.aleatoric uncertainty accounts for label noise inherent in the dataset and can be modeled by introducing a prior distribution over the model weights to capture the degree of variability of these weights given some data. Second, epistemic uncertainty captures the freedom of the model parameters and can be minimized with sufficient data during training. Bayesian deep learning has advanced uncertainty estimation in recent years  and 2). Intuitively, step 1) is equivalent to performing simultaneous pseudo-label generation and reliability-based selection. To solve step 2), we use a conventional gradient descent algorithm. Additionally, we linearly increase the meta portion parameter We define a round of self-training as the sequential execution of steps 3.4.In the previous section, we described the binary uncertainty selection scheme, which is widely used in discriminative self-training . The priWe propose constructing a continuous reliability mask 3.5.In addition to our use of a continuous mask, we propose to emphasize anatomical structures rather than the background region in MR data. Notably, the zero or small In this way, the background region is not emphasized during training. We adopt a conventional 2D encoder-decoder structure for Pseudo-label and attentive reliability mask generation. With the current Translatorand attention moduleretraining. Fix The overall training protocol is outlined in Algorithm 1. During testing, only the trained translator 4.−3 throughout all experiments. In each iteration, the batch size was set to 16 slices in both the source and target domains.To demonstrate the effectiveness of our framework, we evaluated it on two translation tasks, including cross-scanner/center tagged-to-cine tongue MR image UDA translation and T1-to-FA brain MR image UDA translation. We followed the test time UDA setting as in We denote our proposed framework as AC:GST, which uses the self-attention continuous reliability mask, and BM:GST, which uses the binary reliability mask as in . For ourSimilar to For a fair comparison, we used the UNet-based translator in Pix2Pix  as the be domain . Note th4.1.Tagged MR images have been widely used to quantify tissue deformation in moving organs, including the heart  and the As the source domain, we acquired a total of 1,768 paired tagged and cine tongue MR slices from a cohort of 10 healthy subjects at UMB. The data was acquired using a segmented gradient-echo sequence on a Siemens 3.0T TIM Trio system with a 12-channel head coil and a 4-channel neck coil . The imaFor the target domain, i.e., cross-scanner target domain 1, we collected a total of 1,014 paired tagged and cine tongue MR slices from a cohort of 5 different healthy subjects at UMB. We used a Siemens 3.0T Prisma scanner with a 64-channel head and neck coil, and acquired the images using the same sequence as in the source domain . Due to In In the ablation study, the results in −4, and 4.70 × 10−6, respectively.We expect that the synthesized images would have realistic and structurally consistent textures as compared with their corresponding ground truth images. To quantitatively assess our framework, we adopted well-known evaluation metrics including structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), mean L1 error, and inception score (IS) . Table 1We empirically set weights The multiplier s, as in . HoweverAs shown in 4.2.To further demonstrate the generality of our framework in handling larger domain gaps, we collected 120 tagged 2D MR slices from one subject at Massachusetts General Hospital (MGH) as the cross-center target domain. The MRI scanning was performed using a Siemens Skyra 3T scanner with a 64-channel head and neck coil, employing a gradient recalled echo sequence. The MRI acquisition parameters included a 128×128 field of view, 12 slices of 6 mm thickness acquired at 10 equally spaced time points, 86 ms apart, synchronized to the subject’s verbal response. The in-plane resolution was 2 mm×2 mm, and the tag spacing was 12 mm. Due to differences in soft tissue contrast, as well as imaging parameters such as tag spacing and field of view, there were notable discrepancies between the data collected at MGH and UMB. As there were no paired cine MR images available as ground truth in the target domain, we set In Of note, in the MGH dataset, we do not have paired ground truth data. Therefore, we provide quantitative comparisons using the inception score (IS), which does not require paired labels . As show4.3.Diffusion MRI is a non-invasive imaging method that enables the measurement of water molecule diffusion in tissue, allowing for the quantification of microstructural tissue properties. Scalar measures of diffusion computed from diffusion MRI, such as fractional anisotropy (FA) and mean diffusivity, have become widely used. However, collecting high-quality diffusion MRI data can be challenging and time-consuming. Moreover, diffusion MRI can be affected by various artifacts, such as motion and susceptibility artifacts, which can hinder subsequent analyses . Therefo2, which was acquired using a 3T scanner with a maximum gradient strength of 70–100mT/m for diffusion MRI. As the target domain, we used data from a total of ten adult subjects (20–59 years old) from the MGH HCP database3 acquired using the customized MGH Siemens 3T Connectome scanner, which has a maximum gradient strength of 300mT/m. Prior to synthesis, each T1-weighted MRI was affinely registered to its corresponding b0 image using the cross-correlation similarity metric with the ANTs toolkit  from the WUMinn HCP database toolkit . Each su toolkit  with the toolkit , and the−4, respectively, indicating statistical significance. When comparing AC:GST with JGLA, we obtained p-values of 0.0037, 0.0016, 2.65×10−4, and 5.49 × 10−5 for the L1, SSIM, PSNR, and IS metrics, respectively, also indicating statistical significance. The ablation study results are shown in We provide qualitative comparisons against the adversarial UDA methods GAUDA  and ADDA5.In this work, we proposed a novel generative self-training framework with adaptively learned attention for two unsupervised domain adaptive medical image translation tasks. By equipping the self-training framework with a unified uncertainty quantification scheme for both epistemic and aleatoric uncertainties in a UDA setting, we were able to adaptively control the generative pseudo-label supervision according to the reliability of the pseudo-labels. This was the first attempt at achieving the UDA of tagged-to-cine and structure-to-diffusion  image translation. Our experimental results clearly showed that, when quantitatively and qualitatively assessed, our framework yielded superior translation performance over popular adversarial UDA methods. The synthesized cine MR or FA images with test time UDA could be utilized to delineate the tongue and to observe surface motion or quantify micro-structural tissue properties, without the need for additional acquisition costs and time .This work aimed to achieve adaptation with limited unpaired target data and fast convergence in the test-time UDA setting . We wereOur framework has notable features as follows. First, instead of setting pseudo-labels as a variable with one-hot or all zero vectors as in conventional self-training methods , our GSTA few important components have not been fully investigated in the present work. First, although we applied our framework to MR image translation tasks, the idea of the variable reliability mask and the continuous reliability mask may also be helpful for discriminative self-training UDA with more accurate uncertainty measurement than the maximum softmax probability. Second, it is worth noting that our investigation only focused on the scenario, where the source and target domain models share the same backbone, which is a common approach in UDA. However, different backbones in the source and target domain can also be used, by initializing the target model with knowledge distillation . Third,


The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species. The density of mitotic figures, i.e., dividing cells, is a key component for prognostication and part of many grading systems . Usually, the number of mitotic figures in a standardized region of interest (ROI), i.e., the mitotic count (MC), is incorporated into the grade by introducing multiple thresholds, e.g., for low, medium and high mitotic activity1. However, the identification of mitotic figures is subject to high intra- and inter-rater variability, resulting in low reproducibility of the MC10. Besides object-level differences, selection of the ROI with the assumed highest mitotic count in the entire histological section(s), as requested by the guidelines12, is prone to significant inter-rater differences10. Consequently, the computerized identification of mitotic figures in digitized whole slide images (WSIs) is a relevant topic of ongoing scientific interest, after previous attempts with classical image analysis using special stains13.Predicting the biological tumor behavior using histopathology is a central requirement for the identification of therapeutic options and the planning of tailored therapy. For this, micrometer-thin sections of tissue are produced from a formalin-fixed and paraffin-embedded tissue block and subsequently stained with histochemical dyes ) that highlight morphological patterns of the tumor. Several histological patterns are evaluated in a standardized manner and combined to tumor-type specific grading systems14. The development of deep learning-based algorithms was primarily supported by the availability of open datasets, such as the challenge datasets of the MITOS 2012 and 2014 challenges16, the AMIDA13 challenge17, and the TUPAC16 challenge19. All of these challenge datasets used human breast carcinoma images and have since been complemented by two datasets covering two canine tumor types (breast carcinoma and mast cell tumors)21 annotated on the complete WSI. Besides their significant merit in the field of mitosis detection, these existing datasets are mostly limited to single image domains, i.e., they include only a single imaging device (whole slide image scanner), lab environment , species, or tissue/tumor type. The only exception in this regard is the TUPAC16 data set, which included two scanners and three labs. As was recently shown, deep learning methods for mitotic figure detection can severely degrade in the presence of domain shifts23. This limits the use of deep learning-based mitotic figure detectors for a wide application in tumor research and in clinical workflows.Especially since the advent of deep learning, automatized approaches have reached or even exceeded the performance of human experts and have shown a high potential to improve this prognostic task24. By combining numerous domains in a single dataset  the dataset benefits from homogeneous selection/inclusion criteria, and a consistent annotation process, that allows for a high comparability of algorithms across domains.This motivated the inception and conduction of the 2021 MItosis DOmain Generalization (MIDOG) challenge, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). The objective of this challenge was to tackle the domain shift caused by the digitization device on histopathology images of human breast carcinoma. In 2022, the succeeding MIDOG 2022 challenge extended the previous dataset with a specific focus on different tumor types from humans and dogs, while ensuring high domain variability from several imaging devices and lab environments25 provided 354 annotated images across five different cancer types. We extend this dataset by providing images and/or annotations for another 149 cases on two additional tumor types  ), which is the routine approach27, and has therefore been used for this and previous mitotic figure datasets18. Due to differing scan resolutions between the different scanners, the resulting image size varies mildly between the domains. The original image formats use varying lossy compression settings as in the default settings in the respective manufacturers’ software. The selected region was cropped and exported in the TIFF format using lossless compression for each case.After digitization, a pathologist (C.A.B.) selected a region of interest within each WSI spanning exactly 2 mm20. The seven domains were annotated in separate workflows, meaning that all images of one domain were processed at the same time. A pathologist (C.A.B.) screened all images of one domain twice in the H&E stained sections using the screening mode of the software SlideRunner28 and annotated each mitotic figure as well as structures with similar morphology (hard negatives) with the respective class label. All structures of interest were marked by a circular annotation with a radius of 50 pixels with the center coordinate in the approximate center of the structures. The hard negative class was solely provided as discriminative annotation toward the mitotic figure class and non-exhaustively annotated with the objective of reaching an object count in the same order of magnitude as the mitotic figure class per tumor type.The annotations were created according to previously established standards21. To detect these missed candidates, the initial labels from the screening process were used to train a deep learning model  using three-fold cross-validation. The model was applied to the images of the respective validation fold to find candidates for mitotic figures that were not part of the initial screening process. This process was performed on each domain independently. Low detection thresholds were used to guarantee a low number of false negative detection results. Another benefit of the low detection threshold was the creation of a high proportion of false positives, which reduced the risk of a confirmation bias for the annotators.Regardless of the rigorous screening by the pathologist, it can be expected that some mitotic figures were overlooked21. Classification of mitotic figures against hard negatives was done according to the current guidelines26.Under the assumption that this rigorous annotation process resulted in a low rate of missed mitotic figures, we then aimed to find a multi-expert consensus. All annotated mitotic figure candidates from the manual annotations and the algorithmic detections were cropped as 128 × 128 pixel-sized patches (png format), which were named according to the label identification number, i.e., blinded to the assigned class label. These patches were sent to a second pathologist (R.K.) who was asked to assign a class label (mitotic figure or hard negative). Labels with an agreement between the two pathologists were directly incorporated in the ground truth database and patches with a disagreed label were sent to a third pathologist (T.A.D) for final decision. This multi-expert label process was conducted to improve the quality of the final labels. The three involved pathologists had a high level of experience with mitotic figure annotation through involvement in diagnostic service and development of previous datasets14, with no clear advantage for one strategy over the other, and consequently, the simpler, single-stage approach was selected for this evaluation. For this, we stratified a 20% test set of each of the 10 domains summarized in Table For technical validation of the dataset, we trained an object detection network for the task of mitotic figure detection. Mitotic figure detection was successfully performed using single- and multi-stage detectors30. Afterwards, we conducted a leave-one-out training, where we trained the model on all tumor types but one, and again evaluated the models across the test sets of all tumor types. Finally, we trained the object detector on the complete training set of 392 images/cases. For each experiment, we performed a stratified 5-fold cross-validation  and averaged the performance results.We first performed a single-domain training, where we trained the object detector on the training subset of each tumor type and evaluated the model across the test sets of all tumor types. To limit the number of experiments and have sufficient support for the respective classes in each domain, we combined subsets a-c of domain 1 and subsets a and b of domain 6, which were the same tumor types, resulting in seven experiments. We used this strategy also to show the potential of the dataset to investigate generalizability across tumor types rather than scanners as these questions may be investigated by focusing on the MIDOG2021 dataset31 customized for the task of cell detection on microscopic images29. We trained the network with image patches sized 512 × 512 pixels, extracted at the highest magnification level. During each epoch, we sampled 1000 training and 250 validation patches uniformly across all images of the stratified subsets. We followed a guided sampling strategy to account for the rare mitotic figure events: If no mitotic figure was present on the slide, patches were sampled randomly across the 2 mm2 image. For the remaining slides, 50% of the patches were sampled randomly and 50% of the patches were sampled from a 512-pixel radius around mitotic figure annotations. Hard negatives were disregarded during training, i.e., the classification task of detected objects was designed as a two-class problem (mitotic figure vs. background). The models were trained with a batch size of 12 and a discriminative32 learning rate in an interval of . We trained the models for 200 epochs after which we observed convergence of the validation loss and used the validation set to retrospectively select the model with the highest average precision (AP) for mitotic figure detection. The models were optimized with the standard RetinaNet loss as the sum of the bounding box regression loss (smooth L1 loss) and the instance classification loss . During model training, we used the standard augmentations provided by the Fastai v133 framework, including random flipping, affine transformations, and random lightning and contrast change. For each model, the input patches were z-score normalized using the mean and standard deviation of all tissue-containing areas of the respective training images.For all experiments, we used the RetinaNet architecture1 score across all test WSIs of each tumor type.For inference on the test set, we used a sliding-window approach with a 10% overlap and removed duplicate detections using non-maximum suppression (NMS). We then evaluated the detected mitotic figure candidates against the ground truth annotations and computed the mean F2 cropout images are provided on figshare34 for public non-restricted access. Annotations are provided in two formats: (1) The annotations for each object together with the class (mitotic figure/non-mitotic figure) of the expert consensus as JSON file, and (2) An SQLite database in the format used by the open source WSI viewer SlideRunner28. We extended the MS COCO format to include also the individual labels by each of the experts, which can be found in the labels field of each annotation. Additionally, we provide a datasets_xvalidation.csv file, which summarizes the slide-level train/test split used for the results presented in this work in the figshare repository34.The 2 mmThe following section provides an overview of the presented dataset including the distribution of mitotic figure and non-mitotic figure annotations across all tumor types included in the database. Furthermore, we elaborate on the inter-annotator concordance for the task of mitotic figure annotation.The respective tumor domains show a strong visual representation shift. As seen in Fig. 2 area  was a difficult task for this domain in general. As shown in Tables 1 score value also did not originate from a suboptimal threshold setting, but stems from a general low recognition performance on the domain. The off-diagonal elements of the domain matrix in Fig. 1 score compared to in-domain, which highlights the inherent domain shift of the presented dataset. The visualization shows that some models, e.g. the model trained on the canine soft tissue sarcoma domain, generalize comparably well, while other models, e.g., the model trained on the canine cutaneous mast cell tumor domain, encounter difficulties for many tumor type domains. Interestingly, for the human neuroendocrine tumor domain the model trained on human melanoma domain slightly outperformed the in-domain model. This, again, could be explained by the low total number of mitotic figures seen when training on the human neuroendocrine tumor samples, which might have hindered robust training. The human melanoma column of the domain matrix in Fig. Figure Of note, four out of six models scored worst on lymphosarcoma in the cross-domain performance  from the samples included in the dataset presented in this work. In particular, the canine cutaneous mast cell tumor cases were from a different lab (VMU Vienna) and scanned with a different scanner (3DHistech Pannoramic Scan II), and the melanoma cases were digitized using a different scanner (3DHistech Pannoramic Scan II).To test the domain generalization of the trained models, we applied them to the test set of the MIDOG 2022 challenge1 score of 0.71 achieved by the canine soft tissue sarcoma model when being applied to human bladder carcinoma. The most difficult tumor doamin was the human astrocytoma, where no single-domain model achieved an F1 score higher than 0.48 . As with the neuroendocrine tumor, we expect this to be explained by class imbalance generally lower MC counts for this domain.The domain matrix in Fig. 1 score of 0.62. This further highlights the particular domain shift between lymphosarcoma and other solid tumors of our evaluation in their visual representation, as also immanent from the smaller average cell size. The last row of the domain matrix in Fig. 1 score for the model trained on all seven tumor types, which shows a comparably good generalization performance across all tumor types except for canine hemangiosarcoma. This validates the general assumption that high variability of training data increases the domain generalization capability of neural networks.The feline lymphosarcoma was also a challenging domain for most single-domain models except for the canine lymphosarcoma model, which achieved an F1 score for the leave-one-out models when being applied to the MIDOG 2022 test set. The results show that the models trained on six domains overall show a good generalization across the unseen tumor types except for human astrocytoma, where all models faced difficulties and scored a maximum F1 score of 0.47. Furthermore, the results show that when not including canine lymphosarcoma in the training database, the model performance considerably declined on the feline lymphosarcoma, which could again be explained by the varying average cell size of lymphosarcomas. Finally, the results indicate that the domain shift between different animal species may be negligible for the task of mitotic figure detection as the models did not show a significant performance drop on feline tumor types compared to human and canine tumor types, which constituted the training database.Figure Overall, the results presented in the technical validation highlight the domain shift inherent in the multi-domain dataset for mitotic figure detection presented in this work. Furthermore, they show that neural networks can achieve a certain level of domain generalization if they are trained on a diverse dataset, thus highlighting the need for open-source datasets that cover a wide range of domains. By covering multiple species, tumor types, pathology laboratories, and whole slide scanning systems we intended to cover this domain diversity from as many aspects as possible, which allows for validating the domain generalization capability of developed algorithms in multiple domain shift settings.14. As both under- and overestimation of mitotic figures can directly influence the tumor grade, precision and recall are equally important for MC assessment, which has motivated us to use the F1 score for algorithm evaluation. In a collaborative setting between algorithm and pathologist, lower detection thresholds might be favorable as discarding false positive mitotic figure detections might be easier than detecting missed candidates on the WSI.Previous studies have shown that individual pathologists follow different precision-recall trade-offs during MC assessment37 for WSI processing, Fastai v133 for network training, Hydra38 for model configuration, and Weights & Biases39 for experiment tracking.To facilitate the use, we provide a Python Jupyter notebook to download all data automatically. All code examples are based on OpenSlide


Mutations in the multidomain protein Leucine-rich-repeat kinase 2 (LRRK2) have been identified as a genetic risk factor for both sporadic and familial Parkinson’s disease (PD). LRRK2 has two enzymatic domains: a RocCOR tandem with GTPase activity and a kinase domain. In addition, LRRK2 has three N-terminal domains: ARM (Armadillo repeat), ANK (Ankyrin repeat), and LRR (Leucine-rich-repeat), and a C-terminal WD40 domain, all of which are involved in mediating protein–protein interactions (PPIs) and regulation of the LRRK2 catalytic core. The PD-related mutations have been found in nearly all LRRK2 domains, and most of them have increased kinase activity and/or decreased GTPase activity. The complex activation mechanism of LRRK2 includes at least intramolecular regulation, dimerization, and membrane recruitment. In this review, we highlight the recent developments in the structural characterization of LRRK2 and discuss these developments from the perspective of the LRRK2 activation mechanism, the pathological role of the PD mutants, and therapeutic targeting. SNCA), glucocerebrosidase (GBA), and Leucine-rich-repeat kinase 2 (LRRK2) [LRRK2 is a commonly mutated gene that emerges in both sporadic and familial PD [Parkinson’s disease (PD), the second most common progressive neurodegenerative disease, affects more than 7 million people worldwide, and it is estimated that this number will be doubled by 2040 . Besides (LRRK2) . LRRK2 iilial PD .LRRK2 is a large 286 KDa protein that comprises seven domains . The thrRCKW) and flLRRK2 have been reported by Cryo-EM [Structural studies on LRRK2 have been ongoing for almost two decades. However, the expression of the separate LRRK2 domains and full-length protein has been a major challenge for a long time. So far, only the crystal structures of the human LRRK2 Roc and WD40 domain have been resolved ,17. In a Cryo-EM ,24,25. I Cryo-EM . Another Cryo-EM ,28.In this review, we summarize the current structural knowledge of LRRK2 in the context of the complex activation mechanism of LRRK2 and discuss the potential mechanism of the disease-linked mutations.The LRRK2 kinase domain is considered the main output of the protein. It can phosphorylate a group of Rab GTPase family members to regulate several cellular pathways . In addiRCKW and flLRRK2 Cryo-EM structures were published in 2019 [RCKW and flLRRK2 structures, while in the flLRRK2 structure, the DYGψ motif forms an unusual helix  and Src (Proto-oncogene tyrosine-protein kinase), the conserved Regulator spine (R-spine) and Catalytic spine (C-spine) were identified . The C-sormation . The DYGThe LRRK2 RocCOR domain has GTPase hydrolysis activity and, in addition, regulates LRRK2 dimerization . As disc2+ and nucleotide binding. The G4-G5 motifs are important for guanine specificity, and are only partly conserved in LRRK2 [C. tepidum (Ct) Roco has revealed instead that the dimerization is mainly mediated by the C-terminal of the COR domain (CORB) [CtRoco structure showed that in addition to the tight CORB interaction, the Roc domain is also involved in the dimeric interaction by Roc–Roc, Roc–CORA , and Roc–CORB interactions. These dimeric interfaces are mainly formed by the P-loop, switch II, and a highly conserved Roc dimerization loop between β4 and α3 [CtRoco structures and a monomeric MbRocCORA of Methanosarcina barkeri (Mb) Roco2 structure show there are rearrangements of the P-loop, switch II, and CORA domain, suggesting that they might play an important role during the Roco G-protein cycle [CtRoco proteins revealed a monomer–dimer transition during the G-protein cycle [The first LRRK2 Roc crystal structure reported by Deng et al. in 2008 showed that the GDP-bound Roc domain has a typical G domain fold with five α-helix and six β-strands ,45. The in LRRK2 ,47,48. Tin LRRK2 . Howeverin LRRK2 . Biochemn (CORB) . The ful4 and α3 . An overin cycle  (Figure in cycle ,50. The RCKW, flLRRK2, and predicted AlphaFold2 RocCOR structures all overlap well, with the exception of some differences observed for the Switch II in the LRRK2RCKW structure (RCKW single-particle and microtubule-associated structures revealed a WD40–WD40 domain interface [CtRoco is conserved in LRRK2, need to be determined.The LRRK2tructure B. All thtructure C. The COnterface ,26. How The LRRK2 N-terminal ARM, ANK, LRR, and C-terminal WD40 domains are tandem repeat domains that most likely are involved in PPIs with upstream and downstream regulators ,52,53. RFor the ANK domain, it was initially predicted that it consists of seven repeats from aa679-902, while a later study proposed it consists of five repeats from aa690 to aa860 ,55. HoweCtRoco protein, this linker between the LRR and Roc domain is around 40 residues in length and includes a PLxxPPPE motif that is conserved in prokaryotic Roco proteins [CtRoco was shown to be important for the stability of the Roc–COR construct protein, and the crystal structure showed direct interaction of the linker with the Roc domain switch II motif [The LRR domain of flLRRK2 is folded into a 14 + 1 repeat horse-shaped structure . The betproteins . This liII motif ,32. HoweRCKW EM structure [RCKW, flLRRK2, and AlphaFold2 structures also show that the C-terminal helix of the WD40 domain directly interacts with the CORB and kinase domain, suggesting that the WD40 domain might regulate the enzymatic activity of LRRK2 [The crystal structure of the dimeric WD40 domain published by Zhang et al. in 2019 revealed a typical seven blade ring-like structure, each consisting of four anti-parallel β-strands  Figure . The WD4tructure ,26. Inteof LRRK2  (Figure RCKW protein [From biochemical and cellular data, it is clear that LRRK2 cycles between a monomeric cytosolic state that has low kinase activity and a high active dimeric membrane bound state  Figure . In the  protein . The two protein ,22,23,24 protein .RCKW structure revealed that the microtubule binding is mediated by two patches within the Roc domain [Taken together, this shows that all LRRK2 domains are involved in the activation, phosphorylation, and/or regulation of the cellular localization. However, LRRK2 inactivation, monomerization, and dissociation from the membrane still need to be determined. In the presence of ATP-competitive inhibitors, LRRK2 accumulates in the form of well-ordered filaments on microtubules, where, in turn, it acts as a roadblock for both actin and dynein movement . The mutc domain . Howeverc domain . TherefoThe most common PD mutation, G2019S, is located within the kinase domain and has an increased kinase activity  Figure . HoweverThe N2081D mutation is a risk factor for both CD and PD . This muSeveral PD mutations have been identified in the RocCOR tandem domain . Most ofThe G2385R PD mutation is mainly found within the Chinese Han and Korean populations ,76,77. IMany rare PD variants have been identified in the LRRK2 N-terminus , but theAltogether, these studies show that, although almost all mutants result in increased kinase activity and reduced GTPase activity, the various mutations affect different steps in the activation mechanism.Recent developments in structural biology have provided key insights into the LRRK2 activation mechanism and have helped explain the mechanistic effect of important PD LRRK2 mutations. These structures also have revealed that various interfaces on LRRK2 are important for intermolecular and intramolecular interactions, dimerization, and activation. Importantly, these interfaces are potential targets to block LRRK2 activity ,89. Ther


Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks. Recently, methods based on domain generalization have yielded promising results. However, due to distribution discrepancies between various domains, the differences in the feature space related to the domain considerably hinder the generalization of features from unfamiliar domains. In this work, we propose a multi-domain feature alignment framework (MADG) that addresses poor generalization when multiple source domains are distributed in the scattered feature space. Specifically, an adversarial learning process is designed to narrow the differences between domains, achieving the effect of aligning the features of multiple sources, thus resulting in multi-domain alignment. Moreover, to further improve the effectiveness of our proposed framework, we incorporate multi-directional triplet loss to achieve a higher degree of separation in the feature space between fake and real faces. To evaluate the performance of our method, we conducted extensive experiments on several public datasets. The results demonstrate that our proposed approach outperforms current state-of-the-art methods, thereby validating its effectiveness in face anti-spoofing. With the extensive use of deep learning in computer vision, face recognition (FR) ,2 technoDespite the notable achievements of previous face anti-spoofing (FAS) methods in intra-domain testing, their performance significantly deteriorates in cross-domain testing. This is primarily due to the introduction of bias resulting from the distinct characteristics of domains and the inability to address such bias by considering their internal relationships. Consequently, the generalization effect of the model on the novel domain is insufficient. To mitigate this limitation, recent studies have utilized unsupervised-learning-based domain adaptation (DA) techniques to eliminate the domain bias between source and target domains. Nevertheless, the target domain usually denotes an unseen domain for the source domain, and acquiring an adequate amount of target domain data for training in real-world scenarios is not typically feasible.In response to the weakness of a model’s generalization in the unseen domain, several studies related to domain generalization (DG) have been proposed. Conventional DG  proposedBased on the aforementioned considerations and an analysis of the feature space of the source domain, we found that differences in features between multiple source domains significantly interfere with the generalization effectiveness of face anti-spoofing (FAS) in an unseen feature space. To address this issue, we introduce a novel framework for domain generalization in this work, named the Multi-domain Feature Alignment Domain Generalization (MADG) framework. Specifically, the proposed framework utilizes feature generators to produce real and fake face features in each domain, which are subsequently aligned using a multi-domain feature alignment method that employs a loss function based on minimized margin discrepancy disparity between multiple source domains. To optimize this loss function, we use multiple adversarial learning processes. The feature alignment process tackles the challenges associated with generalization, while the employment of multi-directional triplet mining strengthens class boundaries to further enhance the classification performance.We propose a novel source-domain-alignment domain generalization algorithm that utilizes margin disparity discrepancy and adversarial learning. This approach is designed to improve the generalization performance of the FAS model significantly.In the context of multi-domain problems, we devise two alignment strategies and modularize the alignment process. The experimental findings demonstrate that multi-domain alignment surpasses cross-domain alignment in terms of generalization performance, rendering it more advantageous in multi-domain scenarios.We combine the new algorithm with multi-directional triple mining and analyze the source domain feature space. This results in a novel multi-domain feature alignment framework (MADG), which improves the classification accuracy of the FAS model.Extensive experiments and comparisons have been conducted, demonstrating that our proposed approach achieves state-of-the-art performance on most protocols.The present work makes four primary contributions:The rest of this article is structured into four main sections. In the section second, we provide a detailed introduction to the related work, including mainstream research on face anti-spoofing, domain generalization, and multi-domain learning. We also discuss some works that have provided inspiration for this study. In the third section, we present the proposed method, which includes a comprehensive introduction to the new alignment method and domain generalization framework developed in this study. The fourth section details the experiments conducted to evaluate the proposed method and presents a thorough analysis of the experimental results. Finally, in the fifth section, we provide a summary of our work and give an outlook for future research directions in this area.There exist two principal categories of conventional face anti-spoofing (FAS) techniques: appearance-based and temporal-based methods. Appearance-based methods involve the extraction of hand-crafted features for classification, such as local binary patterns (LBPs) ,10 and sRecently, deep neural networks, specifically convolutional neural networks (CNNs), have gained widespread adoption in computer vision tasks and have been extensively applied to FAS. Yang et al.  were theIn conclusion, traditional FAS methods include appearance-based and temporal-based methods, which extract hand-crafted features and mine temporal cues, respectively. However, deep neural networks, especially CNNs, have achieved state-of-the-art performance in FAS by combining the appearance and temporal cues, using techniques such as reverse decomposition and frame-level FAS for improved biometric authentication security.The use of multiple datasets has recently sparked research interest in multi-domain processing. In particular, the research community has amassed several large-scale FAS datasets with rich annotations ,29,30,31Domain adaptation (DA) and domain generalization (DG) are two fundamental methods used in FAS research. While the DG method mines the relationships among multiple domains, the DA method aims to adapt the model to a target domain. In this work, we propose a novel domain generalization method that introduces a new loss function inspired by the work of Motiian et al. , which eThe proposed method aims to enhance the generalization ability of face anti-spoofing (FAS) by improving feature space generalization. Generalization to unseen domains is challenging, as samples from the target domain are usually not available during training. However, similarities exist between the features of the source domains and the target domain, and our approach aims to identify these similarities by aligning multiple source domains. To this end, we propose a multi-domain feature alignment framework called MADG, as shown in N source domains, each with a set of training samples denoted by Y. In the context of the face anti-spoofing task, the labels correspond to This study considers Pre-alignment Feature Generator and Alignment Strategies. We designed pre-alignment feature generators to transform sample data into features, as follows:The features extracted from mixed samples of real and fake faces are denoted as Cross-domain Alignment Module. To enhance the generalization ability of the feature space for the face anti-spoofing task, it is crucial to reduce the presence of domain-relevant cues in the extracted features. Aligning the features of multiple domains into a common feature space effectively reduces deviations from any specific domain, leading to better performance on unseen domains. This insight is akin to feature alignment techniques employed in domain adaptation.S1 and S2, and leverages the cross-domain alignment module to achieve this goal. MDD has the following key property:f and D denotes the margin disparity discrepancy and f and M denotes the adversarial learning function.With the aim of reducing the presence of domain-relevant cues and improving generalization performance, we propose a cross-domain feature alignment module to align the features of different domains. There is typically a substantial gap between the features of different domains, characterized by domain-relevant clues, which can be quantified using measures such as the margin disparity discrepancy (MDD) proposed by Zhang et al. . By progg et al.  is a domMulti-domain Alignment Loss. A multi-domain alignment loss was designed by combining all the obtained cross-domain diverging divergence values m, denoted as:In the context of different domains’ feature spaces, the disparity between real and fake faces is often less pronounced within domains than across them. This is due to the relatively strong similarities between attack samples corresponding to each real sample, which can arise from variations in data collection techniques and attack methods. Our framework aims to learn a concise feature space for the same class. Nonetheless, scattering real faces in a feature space is often easier. To overcome this challenge, we partition the feature spaces of real and fake faces and seek to compress all real faces into a compact feature space. To this end, we utilize a multi-directional triplet loss proposed by SSDG . This loAs shown in The weighting factors To assess the effectiveness of the proposed approach, we conducted experiments on four publicly available face anti-spoofing datasets; namely, Idiap Replay-Attack  . The bottleneck and head layers consist of fully connected layers, as do the adversarial head layers. During backpropagation, the GRL layer inverts the gradients, enabling the network to learn domain-invariant features.The optimization of the framework was performed using stochastic gradient descent (SGD) with a momentum of x by feeding them into the model for classification. The classification outcome is denoted as l and obtained via G represents the trained model, and F refers to the feature generator utilized during training. The classification results on the testing set were used to evaluate the model’s generalization ability, which is a crucial factor for machine learning models. Furthermore, the testing results can be utilized to compare the performance of our proposed method with state-of-the-art methods currently available.During the testing phase, the performance of the trained model was assessed on new test samples MADG, which includes multi-domain feature alignment loss , MADG-M (ours), and MADG-S (ours) on these 12 task protocols are shown in Analysis of Limited Source Domain Experiments. The impact of training set diversity and data quantity on the generalization performance of the proposed framework is demonstrated in To provide a comprehensive comparison of the generalization effects of the two alignment strategies implemented in the MADG-M and MADG-S frameworks, we conducted experiments on 12 limited source domain task protocols and 4 leave-one-out task protocols. To ensure a fair evaluation, we compared all of these protocols with the baseline method. The results of the limited source domain experiments are presented in Based on the experimental results presented in Our proposed method, as shown in To assess the effectiveness of our proposed method in improving classification performance, we compared it with the SSDG method , a one-sThe SSDG method is considered a strong baseline approach and outperforms most state-of-the-art methods, as demonstrated in We employed t-SNE visualization, a powerful technique for visualizing high-dimensional data in a low-dimensional space, to analyze the feature space. The scatter plot on the right illustrates the feature space learned by MADG, showing that the distribution of feature vectors is more compact and well-separated, indicating that the proposed method is effective in learning discriminative features for face anti-spoofing. In contrast, the plot on the left represents the visualization result of the ablation study without the key component of the multi-domain feature alignment module, To conduct a comprehensive analysis of our model’s performance, we examined misclassified sample frames, as presented in The left box of In contrast, the misclassified real faces in our test involved many individuals, as shown in the right-hand box below. This suggests that our model is less effective in classifying real faces than attacking faces, indicating a certain level of overfitting. This may be for several reasons, including the planar graph architecture of the real face input network with the same dimension as the attack face, the presence of artificial actions in real face videos, such as expressions and motion, and the weakening of sample authenticity due to ornaments and lighting on the faces. Examining classification errors can provide insights for future research.We designed a set of experiments to evaluate the effectiveness of the proposed multi-domain feature alignment framework (MADG). Firstly, we performed an ablation study to assess the contribution of each component to the framework’s classification performance, and our results demonstrate that each component played a significant role in improving performance. Furthermore, we examined the impact of varying weights on the multi-domain alignment process and found that the alignment process of the O&I domain had the greatest interference with the generalization performance in the O&M&I to C protocol. This observation implies that cross-domain alignment between two domains has a significant impact on the overall alignment process due to differences in the feature space. The effectiveness of the alignment module was also demonstrated through comparison. Additionally, we evaluated the performance of MADG on limited source domains and found that the generalization performance of two-source domains was generally inferior to that of three-source domains, indicating the potential of MADG to enhance the classification performance in multi-domain scenarios. Our comparison of different alignment strategies revealed that at least one alignment strategy outperformed the baseline method in most task protocols. Finally, we validated the superiority of MADG by comparing it with the existing SOTA method.In this paper we presented a novel approach to improve the generalization capability of face anti-spoofing by utilizing a multi-domain feature alignment domain generalization framework (MADG). To the best of our knowledge, this is the first work to introduce margin discrepancy disparity in domain generalization. Our approach modularizes the alignment algorithm and proposes two multi-domain alignment strategies to enhance the performance of multi-domain alignment. We combine multi-directional triplet loss with the multi-domain alignment module, enabling the effective separation of real and attack face distributions in the feature space. Our method outperforms previous approaches that focus on aligning the entire source domain without considering the distribution characteristics of real and attack faces or only extracting attack face features. We conducted a comprehensive set of experiments on four public databases to validate the effectiveness of our method, and the results demonstrate its superior performance over state-of-the-art approaches.However, our work also has some limitations that require further improvements in the future. Although our method has made significant progress in the study of feature alignment, it only makes a limited attempt and only involves the exploration of multi-domain problems. Recent studies have shown that the introduction of auxiliary supervision information can effectively address the challenges posed by multi-domain problems. Therefore, future work can be conducted around multi-domain problems and the integration of auxiliary supervision information to further enhance the generalization ability of face anti-spoofing. Overall, our study provides a promising direction for future research on face anti-spoofing.


Thermotoga maritima reverse gyrase this latch can be designed into a minimal β-bulge loop that has natural precedence in Thermosipho africanus reverse gyrase. The properties and functionalities of different latch domains across reverse gyrases are discussed.The latch domain in reverse gyrases mediates the cooperation of the helicase and topoisomerase domains. In  Thermotoga maritima reverse gyrase with such a β-bulge loop as a minimal latch is reported. It is shown that the β-bulge loop supports ATP-dependent DNA supercoiling of reverse gyrase without engaging in specific interactions with the topoisomerase domain. When only a small latch or no latch is present, a helix in the nearby helicase domain of T. maritima reverse gyrase partially unfolds. Comparison of the sequences and predicted structures of latch regions in other reverse gyrases shows that neither sequence nor structure are decisive factors for latch functionality; instead, the decisive factors are likely to be electrostatics and plain steric bulk.Reverse gyrase is the only topoisomerase that introduces positive supercoils into DNA in an ATP-dependent reaction. Positive DNA supercoiling becomes possible through the functional cooperation of the N-terminal helicase domain of reverse gyrase with its C-terminal type IA topoisomerase domain. This cooperation is mediated by a reverse-gyrase-specific insertion into the helicase domain termed the ‘latch’. The latch consists of a globular domain inserted at the top of a β-bulge loop that connects this globular part to the helicase domain. While the globular domain shows little conservation in sequence and length and is dispensable for DNA supercoiling, the β-bulge loop is required for supercoiling activity. It has previously been shown that the β-bulge loop constitutes a minimal latch that couples ATP-dependent processes in the helicase domain to DNA processing by the topoisomerase domain. Here, the crystal structure of  Their overall shape resembles a padlock, with the helicase and topoisomerase domains loosely connected by a ‘latch’ (see below). The helicase domain consists of two RecA-like domains (termed H1 and H2) that carry degenerate versions of conserved sequence motifs typical of the superfamily 2 helicase family .The general architecture of reverse gyrase has become evident from crystal structures of the enzymes from T. maritima and A. fulgidus, the latch consists of a globular domain inserted at the tip of a short two-stranded antiparallel β-sheet emerging from the H2 domain , lacking amino acids 395–455 and fused to a C-terminal TEV-cleavable His6 tag, was produced recombinantly in Escherichia coli Rosetta (DE3) and purified as described previously  followed the same protocol.2.2.M) crystallized from 20% PEG 3350, 0.2 M KNO3 pH 6.9, while rgyr_Y851F (at 165 µM) crystallized from 0.1 M HEPES–NaOH pH 7.0, 30% Jeffamine ED-2001. The rgyr_minlatch crystals were directly vitrified by hyperquenching . The rgyr_minlatch data were collected from a single crystal. For rgyr_Y851F, three data sets were merged to arrive at the final statistics reported in Table 14ddt. High-resolution limits for the data were selected based on I/σ(I) ≥ 1 and CC1/2 ≥ 0.3 in the outer shell. Phases were generated by molecular replacement with Phaser  models . PyMOL was also used to superimpose coordinates and render figures. Surface electrostatic potentials were calculated with APBS  and subtracting the number of Glu/Asp residues. Pairwise alignment and identity/similarity assignment was performed using the Smith–Waterman algorithm as implemented in EMBOSS , reported little effect of this mutation on the local structure. The corresponding cleavage-deficient variant of reverse gyrase has also been used  and 0.56 Å (PDB entry 4ddt), respectively. The smaller r.m.s.d. when comparing rgyr_Y851F with PDB entry 4ddt may stem from the fact that these crystals are isomorphous (both belong to space group C2 with similar unit-cell dimensions). There are also few conformational differences between wild-type reverse gyrase and the Y851F variant around the catalytic site . The Tyr/Phe residue at position 851 stacks on Pro660 and is further encased by the side chains of Asp668, Phe844, His852 and Arg853. A hydrogen bond present in the wild-type structures (PDB entries 4ddu and 4ddt) between the Tyr851 hydroxyl and Arg853 guanidinium groups is lost upon the Tyr851Phe change . In all structures the guanidinium group of Arg853 remains fixed through a charged hydrogen bond to Asp670, but the aliphatic part of Arg853 can adopt slightly different rotamers. These minor structural differences are thus likely to be a result of inherent domain mobility in reverse gyrase. In summary, the Y851F variant adopts the same overall structure as wild-type reverse gyrase, and together with the two wild-type T. maritima reverse gyrase crystal structures (PDB entries 4ddu and 4ddt) adds an independent data point for comparison with the structures and models of rgyr_minlatch and other reverse gyrases (see below).Cleavage-deficient variants of topoisomerases in which the catalytic tyrosine is replaced by a phenylalanine are widely used in topoisomerase research. Crystal structures of two type IA topoisomerases, te Fig. 1b. The Tge Fig. 1b. In al3.2.T. maritima reverse gyrase is dispensable for positive supercoiling of DNA , revealing a padlock shape. Superposition of all residues except the latch region in rgyr_minlatch results in r.m.s.d. values of 0.94 Å (PDB entry 4ddu), 0.98 Å (PDB entry 4ddt) and 1.0 Å (rgyr_Y851F). Equally small r.m.s.d. values are observed for the individual helicase domains (0.94–0.98 Å) and topoisomerase domains (0.52–0.67 Å), demonstrating that deletion of the globular domain of the T. maritima reverse gyrase latch has no significant effect on the reverse gyrase structure outside the latch.It has been shown that the globular part (residues 395–455) of the latch domain of FSLEELIIPD-400 emanates from the H2 subdomain and retains its short β-sheet, i.e. the first and last four residues of the sequence. The β-sheet is topped by a loop of six residues of sequence FSLEEL generated by deletion of the region 395–455  and the carbonyl group of residue i + 5 , is not present, however . The twist is a result of Phe391, the first residue in the β-bulge loop, rotating its side chain into a void that is normally occupied by Phe401. Phe401 is one of the few residues in the latch that engages in direct contacts with the T3 region of the topoisomerase domain. Deletion of the globular domain entails the removal of Phe401, apparently prompting Phe391 to fill the void and maintain van der Waals interactions with the methylene groups of Glu850 in the T3 region of the topoisomerase domain. Notably, the latch domains in T. maritima and A. fulgidus reverse gyrases form only few contacts with their topoisomerase domains, and the inter-domain interfaces are small and rather shallow. Both aspects argue in favor of a transient interaction of latch and topoisomerase domains, in accord with the proposed unlatching and opening of the topoisomerase domain during supercoiling  in subdomain H2  compared with wild-type reverse gyrase, with the hydroxyl groups of Tyr364 shifted apart by 21 Å . Although it appears to be conceivable that this conformational difference may be due to the nearby β-bulge loop, this is not the case: the same conformation of this α-helix has been observed in two independent crystal structures of the T. maritima helicase domain lacking the entire latch domain . These structures show the same main-chain trace as in rgyr_minlatch, although the rotamers of the Tyr364, Tyr365 and Arg370 side chains are different. These side-chain conformations are not imposed by crystal-packing effects. Hence, this conformational difference could be due to the reduced mass or volume of the latch in rgyr_minlatch compared with authentic reverse gyrases. For most of the ∼108 sequences in the UNIREF90 database  models have now been predicted . Of these models, T. africanus reverse gyrase is particularly interesting since it has a natural minimal latch.Outside the latch region, a notable difference between rgyr_minlatch and other H2 Fig. 3. This re Å Fig. 3a. Altho3.3.T. africanus is an example of a naturally occurring reverse gyrase that contains a small latch of sequence PKFRIEKEDLILPD with the same number of residues as rgyr_minlatch. We had previously hypothesized that this region forms a β-hairpin that acts as a minimal latch, similar to the β-bulge loop in T. maritima reverse gyrase  the Phe(391)-to-Ile(388) change, (ii) the bulge in T. maritima reverse gyrase, which is a β-hairpin in the T. africanus enzyme, and (iii) a tilt of the T. africanus latch away from the topoisomerase domain. The bulge, the larger side chain and the closer proximity of the latch to the topoisomerase domain in T. maritima reverse gyrase jointly enable an interaction between the latch and the topoisomerase domain. All of these features are absent in the T. africanus AF2 model: the corresponding side chain is smaller, the bulge is absent and the loop is tilted away from the topoisomerase domain.There are three crucial differences in the minimal latch regions between the crystal structure of T. maritima rgyr_minlatch the β-bulge structure was predicted to be in a conformation similar to T. africanus reverse gyrase, with no interaction of Phe391 with the T3 domain . In a phylogenetic tree generated from all 184 complete sequences, reverse gyrases are clearly grouped according to the lengths of their latch regions . The phylogenetic tree places reverse gyrases into three main clades rooted at the origin. Clade 1 includes the majority of all reverse gyrases analyzed, all of them except one with long latches, supporting an evolutionary relationship of reverse gyrases with long latch regions. Clades 2 and 3 comprise reverse gyrases with latches of intermediate lengths. Rooted at (or at least near) the origin, the latch regions of intermediate lengths might stem from a common evolutionary ancestor. Interestingly, the reverse gyrases from T. maritima and A. fulgidus are located in evolutionarily distant clades 2 and 3, respectively, which may rationalize the different structures of the globular domains of their latches despite their similar lengths  and superimposed the corresponding AF2 models on the H2 subdomain of T. maritima reverse gyrase , raising the possibility that these insertions in the two helicase domains communicate with each other during supercoiling. Even evolutionarily very distant reverse gyrase sequences are predicted to fold into such an L-shaped latch .Next, we selected representatives of each cluster  are predicted to have an overall positive charge, 13 are electroneutral and 57 are slightly negatively charged . Thus, latch regions might depend on other mechanisms or other protein regions for inter-domain communication and/or DNA binding. It is possible that the minimal function of the latch is to act as a small, steric block. Additionally, larger patches of positive electrostatic potential and specific interactions with the topoisomerase domain and with other regions of the enzyme enable varying degrees of coupling throughout the catalytic cycle.Since the latch interacts with DNA during the catalytic cycle  in a different space group (P6122) for reverse gyrase with a minimal latch was collected that is congruent with all salient features of the data described in the paper. The refined coordinates and structure factors have been deposited with the PDB with ID 8ofb.reverse gyrase with a minimal latch, 7fsePDB reference: Y851F mutant, 7fsfPDB reference: 10.1107/S2059798323002565/rr5231sup1.pdfSupplementary Figures. DOI: