The contrasting dynamics between Mauna Loa and Kīlauea have been studied over the last 100 years from multiple viewpoints. The fact that dynamic changes of one volcano trigger a dynamic response of the other volcano indicates a connection may exist. Petrological works show a direct relationship between the magmatic systems of these two volcanoes is not possible. We analysed DInSAR data and GPS measurements of ground deformation patterns associated with the activity of Mauna Loa and Kīlauea volcanoes. The DInSAR SBAS dataset spans the interval between 2003 and 2010, and was acquired along ascending and descending orbits of the ENVISAT (ESA) satellite under different look angles. Of the 10 tracks that cover the Big Island (Hawai‘i), 4 cover both volcanic edifices. Using GPS measurements, we computed the areal strain on 15 triplets of stations for Kīlauea volcano and 11 for Mauna Loa volcano. DInSAR data was analysed by applying Independent Component Analysis (ICA) to decompose the time-varying ground deformation pattern of both volcanoes. The results revealed anticorrelated ground deformation behaviour of the main calderas of Mauna Loa and Kīlauea, meaning that the opposite response is seen in the ground deformation of one volcano with respect to the other. At the same time, Kīlauea exhibits a more complex pattern, with an additional component, which appears not to be correlated with the dynamics of Mauna Loa. The GPS areal strain time series support these findings. To corroborate and help interpret the results, we performed inverse modelling of the observed ground deformation pattern using analytical source models. The results indicate that the ground deformation of Mauna Loa is associated with a dike-shaped source located at 6.2 km depth. In comparison, the anticorrelated ground deformation of Kīlauea is associated with a volumetric source at 1.2 km depth. This excludes a hydraulic connection as a possible mechanism to explain the anticorrelated behaviour; instead, we postulate a stress-transfer mechanism. To support this hypothesis, we performed a 3D numerical modelling of stress and strain fields in the study area, determining the elastic interaction of each source over the others. The most relevant finding is that the Mauna Loa shallow plumbing system can affect the shallowest magmatic reservoir of Kīlauea, while the opposite scenario is unlikely. Conversely, the second independent component observed at Kīlauea is associated to a sill-shaped source located at a depth of 3.5 km, which is less affected by this interaction process. Furthermore, we computed the GPS areal strain time series around Mauna Loa and Kīlauea calderas, comparing them with the results provided by ICA. Subsequently, we present inverse modelling of ground deformation sources, which provides constraints for conceptual models of the shallow feeding system of Mauna Loa and Kīlauea. Conclusively, we realized a 3D numerical modelling of the stress and strain fields produced by the inflation/deflation of the individual ground deformation sources to better understand the mechanism of their interaction. The details of data processing and modelling are described in the Methods section.The interaction processes between the two most active Hawaiian volcanoes are still controversial, and despite multiple studies carried out over more than a century, an unambiguous model has yet to be identified. In order to provide new insights to this discussion, we compared the ground deformation patterns in both volcanoes using DInSAR SBAS and Global Positioning System (GPS) datasets. In this work, we processed 10 tracks of ENVISAT ASAR satellite images from 2003-2010, together with available GPS data from 15 stations located around the two summit calderas of Mauna Loa and Kīlauea. We applied the Independent Component Analysis (ICA) to the DInSAR SBAS ground deformation data to reveal relationships between the spatio-temporal patterns of the ground deformation of the two volcanoes. ICA is widely used Data Mining technique, which allows detecting, separating and characterizing hidden patterns into a spatio-temporal dataset2. The summit area of Mauna Loa is composed of a large summit caldera (Moku’āweoweo) and two elongated ridges: the Northeast Rift Zone (NERZ) and the Southwest Rift Zone (SWRZ); , the Southwest Rift Zone (SWRZ), a large summit caldera, and the Halema’uma’u crater within it2 . The Halema‘uma‘u reservoir is the summit storage located between 1 and 2 km depth below the main caldera. The Keanakāko‘i is considered a temporal storage, with magma inputs occurring intermittently. The South Caldera reservoir, mentioned by Poland et al.3 is located at a depth of 3–5 km below the Halema‘uma‘u caldera and is considered the principal storage of magma at Kīlauea. Both Rift Zones are a set of fractures and vents with directions toward the East and Southwest of the main caldera. A full catalogue of dike intrusions over Kīlauea edifice can be found in Montgomery–Brown et al.24.Poland et al.25. Volcanic activity at Pu’u ’Ō’ō-Kūpa’ianahā from 1983 to 2001 was characterised by dominant deflation, followed by a new inflation episode starting in 2001 when Kīlauea experienced a new uplift phase. Six months later, similar behaviour at Mauna Loa volcano was observed13. The enhancement of volcanic activity in this period was caused by an increase in the magma supply from the mantle13.In the last three decades, some relevant deformation episodes took place at Kīlauea and were linked to the Pu’u ’Ō’ō-Kūpa’ianahā eruption (1983–2018)17. In 2005, magma accumulation in ERZ led to summit inflation and an increase in the output of 13. In the same year, a major collapse of the lava delta occurred15, and one year later, an uplift episode along the southern part of the Kīlauea summit caldera was registered15. The ground deformation pattern of Kīlauea during 2003–2007 was dominated by inflation along the ERZ and the summit crater17.The sudden inflation that started in 2003 at Kīlauea mainly affected the summit caldera, but was recorded as far away as 50 km from the summit and lasted until 200717. It caused rapid deflation of the Kīlauea summit area due to magma withdrawal to eruptive vents located along the ERZ, about 8 km away17. The first episode of the FD07 eruption lasted for two days. On 21st July of 2007, another eruptive episode began along the ERZ, with vents located about 19 km away from the summit16. ERZ vent activity was then continuous, while the summit crater of Halema’uma’u showed increased volcanic gas emission levels until an explosion of Kīlauea’s main summit crater on 19th March 200817. After the FD07 episode until 2008, the ground deformation pattern of Kīlauea was characterised by deflation, and summit seismicity returned to background values17.In this work, we consider only the magmatic intrusions that occurred in the summit area of the volcanoes and only during the time interval considered in this study. A major episode of volcanic unrest occurred between 2003-2010, beginning on 17th June 2007 . The event entailed changes in volcanic activity and formation of new eruptive vents26. Rhodes and Hart27 confirm that the chemical composition of lavas at Kīlauea and Mauna Loa are different, indicating the magmatic feeding systems are independent . However, geophysical studies seem to indicate the opposite: Klein11 first noticed the anticorrelation between the two volcanoes, emphasising that an increase in activity at Kīlauea often corresponds to a decrease in Mauna Loa dynamism. Miklius and Cervelli12 captured the opposite behaviour in the ground deformation patterns of the volcanoes: at the beginning of the high-volume effusive episode in Kīlauea, inflation of Mauna Loa was observed (May 2002). Shirzaei et al.28 studied the coupling behaviour of both volcanoes between 2003 and 2008. The authors postulate that the causative source of the interaction between the two volcanoes is related to deep-seated mantle surges. Despite being the subject of many studies, the nature and the mechanism of the interaction between the feeding systems of the two volcanoes and their level of interconnection are still contentious and remain unclear.The interaction between the two most active volcanoes of Hawai‘i Island has been discussed for over 100 yearsThe analysis of the observed ground deformation, reported in the first column of Fig. mentclass2pt{minimmentclass2pt{minimmentclass2pt{minim29.Also, let us remark that, as shown by Fig. The second component of the ground deformation pattern . Furthermore, the presence of another source is evidenced by the ICA2 component beneath Kīlauea alone. In order to better understand the physical mechanisms responsible for the observed ground deformation patterns, we performed inverse modelling of the three detected sources and used Akaike Information Criterion (AIC)31 source located at 1.2 km depth  in the Fig. 3. This work has provided evidence for two sources associated with ICA1 and showing an anticorrelated temporal relationship between the Mauna Loa and Kīlauea  of decomposition. We again emphasise the effectiveness of ICA in separating the contribution of individual sources, significantly reducing the intrinsic ambiguity in geodetic inverse problems.The findings of the inverse modelling are consistent with previous studies. Poland et al.pth Fig. . Accordi5. Our findings suggest a dike-like geometrical structure as a primary source during the interval considered . In contrast, ICA2 shows a ground deformation source affecting only Kīlauea volcano. The rapid deflation of Kīlauea from 2007 to 2009 Fig.  has a soThe GPS strain area results support anticorrelated behaviour of these two volcanoes. The 2009 strain area reduction in Mauna Loa and the opposite increment of the strain area in Kīlauea indicates that while Mauna Loa was undergoing a deflation, Kīlauea was experiencing an inflation episode.27. On the other hand, the existence of a second component, related to Kīlauea alone, suggests a more complex configuration of the plumbing system of Kīlauea, characterised by multiple reservoirs.Finally, based on the inverse modelling results, we maintain that the pattern associated with the anticorrelated component ICA1 points to a very shallow source beneath Kīlauea. This makes it unlikely that there is a direct connection between the magmatic systems of the two volcanoes in the deeper crust. A direct connection at shallow depths would provide a further source of ground deformation, which has never been observed. Furthermore, it would contradict petrological and geochemical evidenceThe spatial configuration suggests a possible explanation for the observed anticorrelation between the ground deformation sources related to ICA1  technique that provided a Line-Of-Sight (LOS) time series for coherent pixels of the SAR images. SBAS algorithms allow the production of a deformation time series for each coherent pixel34. For data processing, we employed the Grid Processing On-Demand (G-POD) platform of ESA that applies the SBAS algorithm and can process large volumes of DInSAR data35. Tracks covering only one of the two volcanoes were discarded from the analysis  across Hawai’i Island. Specifically, C-band  were associated with a clear and meaningful signal, while the third component (ICA3) was mainly composed of noise; hence it was not considered further. In Table 33: the Mogi point source31, the spheroidal source38, the closed pipe39 and the rectangular crack32. In all cases, we performed the modelling assuming a Poisson’s ratio of 0.25 within a half-space. We selected the best model for each ICA and each volcano following the Akaike Information Criterion (AIC)30. We performed a non-linear inversion of each track for all previous source models for each ICA and each volcano. The inverse method relies on a non-linear optimisation of a misfit function using the Nelder-Mead simplex algorithm40. The misfit function is defined as the sum of the squared residuals between the observed and synthetic data. The synthetic data, computed using the aforementioned analytical models, were projected along the LOS corresponding to each track. The final models resulted from averaging the results obtained for each track. We obtained a source model for Mauna Loa, corresponding to ICA1, and two models for Kīlauea, corresponding to ICA1 and ICA2, respectively.To model the observed ground deformation, we tested four analytical source modelshttp://geodesy.unr.edu)41.GPS stations up to 10 km from the summit calderas of the two volcanoes were selected (Fig. 45. For this purpose, we computed the area for triplets of GPS stations covering more than half of the summit calderas (see Figs. We used horizontal components to compare results obtained through the ICA decomposition of DInSAR SBAS data with GPS time series, since vertical components have a higher signal/noise ratio. The areal strain time series was computed since it does not require the assumption of a reference point on the island. The areal strain is a geodetic method widely applied in active volcanic areas and has been described by many authors46. The boundary conditions of the domain were chosen to be fixed on the bottom and lateral sides. The computational domain was built as tetrahedral mesh elements with dimensions ranging between 150 and 3500 m. As explained in the “Discussion” section, we applied an overpressure to each source, calculating the corresponding perturbation of the stress and strain fields.To validate the stress-transfer model, we realized a finite-element tridimensional elastic modelling using the software COMSOL Multiphysics ®. The model includes the topography of Hawaii Island and the sources of the ground deformation determined by non-linear inversion. The size of the computational domain was 100x100x70 Supplementary Information.


These PPD-treated HBMGs clearly display improved strength and compressive plasticity. On the other hand, the mechanical properties of BMGs with low Poisson’s ratios  become worse due to a few shear bands and micro-cracks in extremely small plastic deformation zones. Additionally, for the PPD-treated HBMGs with similar high Poisson’s ratios, the Zr56Co28Al16 BMG exhibits much larger plasticity than the Zr46Cu46Al8 BMG. This phenomenon is mainly due to more defective icosahedral clusters in the Zr56Co28Al16 BMG, which can serve as nucleation sites for shear transformation zones (STZs) during subsequent deformation. The present study may provide a basis for understanding the plastic deformation mechanism of BMGs.Pre-plastic deformation (PPD) treatments on bulk metallic glasses (BMGs) have previously been shown to be helpful in producing multiple shear bands. In this work, the applicability of the PPD approach on BMGs with different Poisson’s ratios was validated based on experimental and simulation observations. It was found that for BMGs with high Poisson’s ratios (HBMGs, e.g., Zr In past decades, bulk metallic glasses (BMGs) have attracted widespread interest due to their outstanding mechanical properties ,3,4,5,6,63.78Cu14.72Ni10Al10Nb1.5 BMG. So far, most research objects are Zr-based alloy systems because these BMGs are more ductile [Furthermore, the pre-plastic deformation (PPD) method is frequently used to induce a certain amount of multiple shear bands in a glassy matrix and thus enhance mechanical properties, which involves the first and third strategies due to the micro-scale in length and nanoscale in width of induced shear bands together with structural heterogeneity brought by the rejuvenation via PPD treatments. Until now, improved mechanical properties in Zr-, ZrCu-, and Ti-based BMGs have been achieved through PPD methods  ,20. Most ductile ,30,31. H46Cu46Al8, Zr56Co28Al16, and Fe48Cr15Mo14Y2C15B6 BMGs were selected to test the applicability of the PPD method on the mechanical properties of BMGs with high and low Poisson’s ratios . Previous results have demonstrated that their corresponding Poisson’s ratios are ~0.366, ~0.367, and ~0. 309, respectively [In addition, previous observations have demonstrated that the shape, number, and distribution of the pre-existing shear bands induced by PPD treatments, as well as the activated microstructural heterogeneity ) in the plastic deformation zone, play a vital role in the enhancement of mechanical properties of BMGs ,26,27,28ectively ,32. The 56Co28Al16, Zr46Cu46Al8, and Fe48Cr15Mo14Y2C15B6 (at.%) were fabricated by arc melting pure elements (>99.9%) under Ti-gettered argon atmosphere, respectively. To ensure chemical homogeneity, the ingots were flipped and remelted at least three times during melting. The rods with a diameter of 2 mm were obtained using suction casting. The glassy structures were examined using X-ray diffraction , while the chemical composition distributions were observed by scanning electron microscopy  equipped with energy-dispersive X-ray spectrometry . The subsequent pre-deformation was conducted using an electronic tensile testing machine  at an initial strain rate of 5 × 10−4 s−1. Ingots with nominal compositions of the Zr−4 s−1. The dimension of the specimens for compression was Φ2 mm × 4 mm. The shear bands induced by PPD treatments were observed, and the surface morphologies after fracture were also investigated using SEM. Furthermore, Young’s modulus was measured using a nanoindentation device . During nanoindentation, a maximum force of 50 mN was achieved at a loading rate of 100 mN/min with a duration time of 10 s. Furthermore, the atomic structural features of the Zr56Co28Al16 BMG were observed by the ab initio molecular dynamics simulations using the Vienna ab initio simulation package (VASP) based on the density functional theory (DFT). The detailed simulation processing can be found in Reference [Uniaxial compression tests were conducted using an electronic tensile testing machine under a strain rate of 2.5 × 10eference .56Co28Al16, Zr46Cu46Al8, and Fe48Cr15Mo14Y2C15B6 BMGs before and after PPD treatments. Only typical broad diffraction peaks are observed without any obvious crystalline diffractions.56Co28Al16, Zr46Cu46Al8, and Fe48Cr15Mo14Y2C15B6 BMGs. The corresponding average chemical compositions of the first two BMGs were measured as Zr56.2Co28.0Al15.8 and Zr46.6Cu45.3Al8.1, respectively. Since it is impossible to measure elements with small atomic numbers accurately by EDS, the average composition of Fe-based BMG was determined to be Fe50.2Cr15.3Mo20.7Y3.3C15.3 BMGs without element B. These observations confirm their homogeneous amorphous structure. 56Co28Al16, Zr46Cu46Al8, and Fe48Cr15Mo14Y2C15B6 BMGs after PPD treatments. It can be found that a pair of plastic deformation regions (green dotted circles) appears on almost parallel sides of both Zr56Co28Al16 and Zr46Cu46Al8 BMGs  [G) and bulk modulus (B) may be responsible for good plasticity of BMGs [Compared to the Fes (STZs) ,37,38,39s (STZs) , i.e., as (STZs) ,41,42, as (STZs) . The pres (STZs) ,45,46. M of BMGs . The cri of BMGs . Thus, t3 [3 [gT)  d, which 56Co28Al16 HBMG exhibits better mechanical properties than the as-cast Zr46Cu46Al8 in the as-cast state and after PPD treatments, respectively, even though both Poisson’s ratios and STZ volumes are at the same level size. It is noted that the STZs are not structural defects in BMGs, but an event defined in a local volume that is also strongly influenced by local atomic arrangements [46Cu46Al8 and Zr56Co28Al16 HBMGs should be crucial in determining the multiplication of shear bands and ultimately manipulating plastic deformation behaviors. Fang et al. simulated the atomic configuration of the as-cast Cu46Zr46Al8 HBMG by the ab initio molecular dynamics simulations and investigated its local atomic packings by virtue of the Voronoi tessellation method [However, the Zrngements . The shengements ,62. Thern method . It was n method . Most pon method . Therefon method .56Co28Al16 HBMG, the ab initio MD calculation was also performed, whose polyhedral distributions are shown in 46Zr46Al8 HBMG, most polyhedra in the Zr-centered architectures are large Kasper polyhedra with coordination numbers (46Zr46Al8 BMG, the Co- and Al-centered architectures in the as-cast Zr56Co28Al16 BMG contains more defective icosahedral clusters, which should provide more nucleation sites for STZs during deformation. Therefore, even though PPD treatments generate similar shear bands in both Zr46Cu46Al8 and Zr56Co28Al16 HBMGs before the final compression test, more multiple shear bands should be induced in the PPD-treated Zr56Co28Al16 BMG, leading to larger plasticity of the Zr56Co28Al16 HBMG.In order to illustrate the local atomic packings of the as-cast Zr numbers b. In the numbers c. Compar46Cu46Al8, Zr56Co28Al16, and Fe48Cr15Mo14Y2C15B6 BMGs, were chosen to verify the impact of the pre-plastic deformation on the generation of multiple shear bands in BMGs. According to the XRD and EDS observations, all the samples are still amorphous, and no nanocrystallization appears after the pre-plastic deformation via the current sideloading method. For the Zr46Cu46Al8 and Zr56Co28Al16 BMGs with high Poisson’s ratios, a pair of larger plastic deformation zones can be induced at loading positions after pre-plastic deformation. Many long semi-circular shear bands appear within both plastic deformation zones, while some dense wavy shear bands emerge between these long shear bands. The dominant reason is that the resultant force changes continuously when the pre-plastic deformation proceeds. For the Fe48Cr15Mo14Y2C15B6 BMG with a high Poisson’s ratio, only very small plastic deformation zones are caused after the pre-plastic deformation. Within these small zones, only a few semi-circular shear bands appear, accompanied by several micro-cracks.In this work, three types of BMGs with different Poisson’s ratios, i.e., Zr48Cr15Mo14Y2C15B6 BMG with a high Poisson’s ratio after the pre-plastic deformation exhibits worse mechanical properties than the as-cast state, which should be attributed to the relatively small STZ volume because of a low Poisson’s ratio. Additionally, despite the Poisson’s ratios of the Zr56Co28Al16 and Zr46Cu46Al8 BMGs being similar, the improvement of mechanical properties of the Zr56Co28Al16 BMG is more evident than those of the Zr46Cu46Al8 BMG. Molecular dynamics simulation observations indicate that the Co- and Al-centered architectures in the as-cast Zr56Co28Al16 BMG contain more defective icosahedral clusters, which can provide more nucleation sites for STZs during deformation and lead to better mechanical properties.Then the room-temperature compression tests were conducted on these pre-deformed BMG rods to measure their mechanical properties. It was observed that those BMGs with high Poisson’s ratios show higher strength and larger plasticity after pre-plastic deformation. However, the Fe


Z-type hexagonal ferrites have recently received attention for their room-temperature magnetoelectric (ME), which is activated when the temperature at which the transverse-conical spin-state transitions to a ferrimagnetic state is increased. The changes in the magnetic domain structure at the transition have been well-documented; however, they are still not understood in detail. In the present study, Lorentz transmission electron microscopy (TEM) analysis combined with an in situ heating experiment was conducted to demonstrate the shift in magnetic domain structure during the transition from the transverse-conical spin arrangement to a ferrimagnetic spin order. The dynamics of the magnetic domain structure changes with the increasing temperature were acquired in real-time. At 490 K, the magnetization transition from the transverse-conical spin state to the ferromagnetic state was demonstrated. Cross-tie domain walls formed during the magnetic transition process. The increased effect of the demagnetizing field applied to the 180° magnetic domains was caused by a lower magnetocrystalline anisotropy (MCA) at the easy axis of magnetization. Hexagonal ferrites (hexaferrites) are a group of ferrites with a hexagonal crystal structure. Hexaferrites have a wide range of applications, and thus, they constitute up to 50% of the total magnetic materials manufactured globally, at over 300,000 tons per year . In addi0.5Sr2.5Co2Fe24O41) were grown from a Na2O-Fe2O3 flux. The chemicals for the hexaferrite  were melted at 1420 °C in a Pt crucible, followed by heat treatment at 900 °C under O2 flow for 8 days to remove oxygen vacancies [α radiation at 40 kV and 30 mA . Needle-shaped (0.3 mm × 3 mm × 0.3 mm) single-crystal hexaferrite samples were used to measure the magnetic properties. The temperature and magnetic field were controlled using a physical property measurement system . In all measurements, a magnetic field was applied along the + ion milling  with a liquid nitrogen-cooled cold stage. Cross-sectional samples were observed using a TEM  with an acceleration voltage of 200 kV. Lorentz TEM images were acquired under magnetic-field-free conditions, through the free-lens control function in the TEM. An in situ TEM heating stage  was used to heat the sample. A heating rate of 100 °C/min was used to reach the target temperature. Magnetic domain structures were reconstructed using the transport of intensity equation (TIE). The TIE method [Z-type hexaferrite single crystals  were investigated. The dynamics of the magnetic domain structure changes with the increasing temperature were acquired in real-time. The magnetization transition from the transverse-conical spin state to the ferromagnetic state at 490 K was demonstrated by the combined use of Lorentz TEM and in situ heating techniques. During the magnetic transition process, cross-tie domain walls were developed. The reduced MCA at the easy axis of magnetization resulted in an increase in the effect of the demagnetizing field applied to the 180° magnetic domains.The temperature-dependent changes in the magnetic domain structure of Z-type hexaferrite single crystals (Ba


The NLRP3 inflammasome is a critical component of innate immunity that defends the host from microbial infections. However, its aberrant activation contributes to the pathogenesis of several inflammatory diseases. Activation of the NLRP3 inflammasome induces the secretion of proinflammatory cytokines IL-1β and IL-18 and pyroptotic cell death. NLRP3 contains a leucine-rich repeat (LRR) domain at its C terminus. Although posttranslational modifications in this LRR domain have been shown to regulate NLRP3 inflammasome activation, the role of the entire LRR domain in NLRP3 inflammasome activation remains controversial. Here, we generated mouse macrophages that express an endogenous NLRP3 mutant lacking the LRR domain. Deletion of the LRR domain diminished NLRP3 inflammasome activation in macrophages. Furthermore, using NLRP3-deficient macrophages that are reconstituted with NLRP3 mutants lacking the LRR domain, we found that deletion of the LRR domain inhibited NLRP3 inflammasome activation. Mechanistically, deletion of the LRR domain inhibited NLRP3 self-association, oligomerization, and interaction with the essential regulator NEK7. Our results demonstrate a critical role for the LRR domain in NLRP3 inflammasome activation. Inflammasomes are cytoplasmic multiprotein complexes that form in response to microbial infection or cell damage . In moste.g., nigericin), and particulate matter , has been shown to activate the NLRP3 inflammasome , a central NAIP, CIITA, HET-E, and TP1 (NACHT) domain, and a C-terminal leucine-rich repeat (LRR) domain . NLRP3 i pathway , 34. How pathway , 36.In this study, we have used mouse knock-in (KI) macrophages that express an endogenous NLRP3 mutant lacking the LRR domain and NLRP3-deficient macrophages that are reconstituted with NLRP3 mutants lacking the LRR domain. Our results demonstrate that the LRR domain is required for NLRP3 inflammasome activation. Furthermore, our results show that the LRR domain mediates NLRP3 self-association, oligomerization, and interaction with its essential regulator NEK7.A) (Nlrp3C721Stop/C721Stop) in which a stop codon (TGA) has successfully replaced the cysteine (C721) codon (TGC) at the coding region of mouse Nlrp3 gene . These KI macrophages are expected to express an NLRP3 mutant that consists of the first 720 amino acid residues and lacks the entire LRR domain.Previous studies showed conflicting results on the role of the LRR domain in NLRP3 inflammasome activation. Notably, most of these studies overexpressed NLRP3 mutants lacking the LRR domain by transfection or viral infection or replaced the LRR domain of the endogenous NLRP3 protein with a large LacZ protein, which may interfere with NLRP3 function , 35, 36.Fig. 1Nlrp3−/− mice were included as controls in our experiments. The full-length NLRP3 (∼118 kDa) was detected in wildtype macrophages at the resting state (C). As expected, all three KI macrophage clones expressed the short version of NLRP3 protein (∼79 kDa), further confirming our sequencing results (C). Of note, no protein band appeared in the lane for Nlrp3−/− macrophages, indicating the specificity of our antibody (C). Importantly, the detected protein level of this NLRP3 mutant from each clone was markedly lower (less than 50%) when compared with that of full-length NLRP3 expressed in wildtype macrophages (C). Additionally, the mRNA levels of this NLRP3 mutant in KI macrophages were also lower than those of the full-length NLRP3 in wildtype macrophages (A). However, the expression levels of other NLRP3 inflammasome components, including caspase-1, NEK7, and ASC, were comparable between wildtype and KI macrophages (C). Next, we assessed NLRP3 inflammasome activation in these macrophages under the conditions of lipopolysaccharide (LPS) priming or LPS priming plus nigericin treatment. Previous studies have suggested that the LRR domain keeps an inactive conformation of NLRP3 through intramolecular interaction (D and E). Nigericin, a classic NLRP3 stimulus, induced NLRP3 inflammasome activation in wildtype macrophages, as revealed by caspase-1 activation, GSDMD cleavage, and IL-1β secretion (D and E). In contrast, KI macrophages, similar to NLRP3-deficient macrophages, were defective in NLRP3 inflammasome activation after LPS priming plus nigericin treatment (D and E). The secretion of TNF-α, an inflammasome-independent cytokine, was comparable among these macrophages (F). Collectively, these results indicate that the LRR domain is required for nigericin-induced NLRP3 inflammasome activation in macrophages.We assessed the protein expression of endogenous NLRP3 protein from both wildtype (parent cells) and KI macrophages by Western blot. An antibody that recognized an epitope in the N-terminal pyrin domain of NLRP3 protein (Cryo-2) was used to detect both full-length and truncation mutant NLRP3 from these macrophages. In addition, macrophages derived from ng state C. As exp results C. Of notantibody C. Importrophages C. Additirophages A. Howeverophages C. Next, eraction . Similareraction , D and Eecretion , D and Ereatment , D and Erophages F. Collece.g., ATP, nigericin, Nano-SiO2) or independent stimuli  (F). Macrophages were stimulated with ATP, nigericin, Nano-SiO2, or imiquimod after LPS priming. KI macrophages showed markedly reduced caspase-1 activation, GSDMD cleavage, and IL-1β secretion when compared to wildtype macrophages (A and B). In contrast, KI macrophages had comparable AIM2 inflammasome activation induced by poly(dA:dT) or NLRC4 inflammasome activation by Salmonella when compared to wildtype macrophages (A and B). Inflammasome activation induces the formation of ASC aggregates called ASC specks, which are composed of ASC oligomers in the cytosol (C and D). However, KI macrophages had comparable ASC speck formation in response to Salmonella as compared to wildtype macrophages (C and D). Biochemical analysis revealed that KI macrophages had much less ASC oligomerization as compared to wildtype cells after treatment of ATP or nigericin, although they showed a comparable level of ASC oligomerization in response to Salmonella (E). To exclude whether other nonspecific factors besides this LRR domain deletion in endogenous NLRP3 attributed to the defect of NLRP3 inflammasome activation observed in KI macrophages, we expressed C-terminal HA-tagged NLRP3 full-length [NLRP3(FL)], LRR deletion mutant [NLRP3(1–720)], or the LRR domain [NLRP3(730–1033)] in KI macrophages by a lentiviral vector. Macrophage clones that expressed higher levels of the LRR deletion mutant protein than those of full-length NLRP3, indicated by anti-HA immunoblots, were selected for assessing nigericin-induced NLRP3 inflammasome activation (F). Expression of full-length NLRP3 restored NLRP3 inflammasome activation, indicated by caspase-1 activation and IL-1β secretion, in KI macrophages, while both the LRR deletion mutant and LRR domain alone failed to rescue NLRP3 inflammasome activation (F and G). Taken together, these results indicated that the LRR domain is required for NLRP3 inflammasome activation in macrophages.NLRP3 inflammasome activation is induced by potassium efflux-dependent (iquimod) , 12. Giviquimod) F. Macroprophages , A and Brophages , A and B cytosol . Both AT cytosol , C and Drophages , C and Dlmonella E. To exctivation F. Exprestivation , F and GA). In contrast to the previous study in which macrophages were primed with LPS for 11 h in the presence of doxycycline, we primed reconstituted macrophages with LPS for 4 h before stimulation with ATP, nigericin, nano-SiO2, or imiquimod (A and B). The levels of TNF-α secretion were comparable between macrophages reconstituted with wildtype or mutant NLRP3 under the same conditions . Accordingly, macrophages reconstituted with NLRP3 mutant 1–686 or 1–720 had less ASC oligomerization in response to ATP or nigericin when compared to macrophages with full-length NLRP3 . Furthermore, NLRP3-deficient macrophages that expressed an untagged NLRP3 mutant 1–720 through a constitutive or doxycycline-inducible system also failed to restore caspase-1 activation after nigericin stimulation (B and C). Taken together, these results further indicated that the LRR domain is also essential for NLRP3 inflammasome activation in reconstituted NLRP3-deficient macrophages.Several LRR domain-lacking mutants of NLRP3, including NLRP3 1–686 and 1–720, have been reported to fully restored inflammasome activation in mouse NLRP3-deficient macrophages . As showFig. 3miquimod . Consistmulation , B and CA). In macrophages, after stimulation of NLRP3 stimuli, NLRP3 forms high-molecular-mass complexes, as indicated by protein native gels . Accordingly, in 2-D native gels, these high-molecular-mass forms of NLRP3 proteins were markedly reduced in macrophages reconstituted with NLRP3 mutant 1–686 or 1–720 as compared with the full-length NLRP3 . Taken together, our results indicate that the LRR domain mediates NLRP3 self-association and oligomerization.Recent structural studies with purified NLRP3 oligomers have revealed that NLRP3 self-associates through the LRR-LRR interaction , 37. To Fig. 4ive gels . We compA). The NLRP3 mutant 1–720 or the LRR domain alone also failed to pull down NEK7 from HEK 293T cells . According to the Uniprot/Swiss-Prot, there are nine LRRs in the LRR domain of NLRP3. To further define the exact LRR(s) of the LRR domain that is essential for NLRP3-NEK7 interaction, we generated a series of NLRP3 mutants which progressively lack one LRR starting from the C terminus. Co-expression of the Flag-tagged NLRP3 mutant and HA-tagged NEK7 in HEK 293T cells was used to assess the interaction between NEK7 and NLRP3 mutants. All of these NLRP3 mutants that lack one or several LRRs in the LRR domain lost the ability to bind NEK7 . Furthermore, the NLRP3 mutant lacking one LRR (Δ LRR 9) failed to restore nigericin-induced caspase-1 activation in reconstituted NLRP3-deficient macrophages . Collectively, these results indicate that the intact LRR domain is essential for NEK7-NLRP3 interaction and NLRP3 inflammasome activation.Previous studies have shown that the LRR domain in NLRP3 is required for the NEK7-NLRP3 interaction in HEK 293T cells , 18. FurFig. 5NLRP3 activation triggers the assembly of an inflammasome that plays a critical role in innate immunity. In addition to its N-terminal pyrin domain and central NACHT domain, NLRP3 contains an LRR domain at its C terminus. Several posttranslational modifications have been found on the LRR domain to regulate NLRP3 inflammasome activation , 12. In Nlrp3 gene (A) (B and C). This role of the LRR domain in NLRP3 protein stability, although it has not been explored further, has been suggested in previous studies (via the LRR domain and stabilizes the NLRP3 protein (Our results showed that the protein level of NLRP3 1–720 in KI macrophages was much less than that of full-length NLRP3 in wildtype cells. This reduction might result from the nonsense-mediated decay of NLRP3 1–720 mRNA after the introduction of the stop codon in the rp3 gene A 38). H. HNlrp3 gene (A) , B and C studies . In addi protein . Therefo protein . Interes protein , 40. It et al.. Importantly, we found that LRR deletion reduced NLRP3 self-association and oligomerization. Consistent with this observation, NLRP3 mutants with mutations in the LRR domain have been reported to be defective in the formation of a double-ring cage structure of mouse NLRP3 . The constructs for NLRP3-Flag, NLRP3-SFP, and NEK7-HA have been previously described (Anti-Flag (A00187–200), anti-HA (A01244–100), anti-Actin (A00730–100), and protein G resin (L00209) were purchased from GenScript. Anti-NLRP3 (AG-20B-0014-C100) was purchased from Adipogen. Anti-GSDMD (ab209845) and anti-NEK7 (ab133514) were purchased from Abcam. Anti-ASC (67824) was purchased from CST. Anti-mouse caspase-1 was a kind gift from Dr Gabriel Núñez . Ultra-pure LPS (tlrl-pb5lps), Nano-SiO2 (tlrl-sio), and poly(dA:dT)/lyovec (tlrl-patc) were purchased from InvivoGen. Nigericin (481990), doxycycline hydrochloride (10592–13–9), and EDTA-free protease inhibitor cocktail (11873580001) were purchased from Sigma. escribed .Nlrp3−/− mice were generated by murine retrovirus infection as previously described (Mycoplasma contamination.iBMDMs from WT C57BL/6 or escribed . iBMDMs Nlrp3 (targeting sequence: CACGGCAGAAGCTAGAAGTGAGG), tracrRNA (1072533), HDR donor oligos , Cas9 nuclease (1081059), and HDR Enhancer (1081072) were purchased from IDT, and the ribonucleoprotein complexes were prepared according to the manufacturer’s protocol. The ribonucleoprotein complexes were delivered into iBMDMs by electroporation with nucleofector kit V . Cell clones harboring Nlrp3C721Stop homologous alleles were identified by sequencing.Alt-R CRISPR-Cas9 crRNA for mouse 5 per well in IMDM containing 1% (vol/vol) FBS. Cells were primed with 200 ng ml−1 ultrapure LPS for 4 h in serum-free IMDM. After priming, cells were then stimulated with ATP , nigericin , Nano-SiO2 , imiquimod , poly(dA:dT) , or Salmonella . For the doxycycline-inducible system, macrophages were treated with LPS (100 ng ml−1) and doxycycline (1 μg ml−1) for 12 h before stimulation with nigericin . After stimulation, culture supernatants were collected and cells were directly lysed with 150 μl of 2 × Laemmli buffer . Proteins in culture supernatants, cell lysates, or mixtures of cell lysates and supernatants, were separated by SDS-PAGE and transferred onto PVDF membranes by using the Trans-Blot Turbo system (Bio-Rad). Membranes were immunoblotted with anti-caspase-1, anti-GSDMD, or other indicated antibodies. When specified, equal amounts of cell lysates and supernatants were combined for immunoblotting. The immunoblotting images were developed with HyBlot ES high sensitivity autoradiography films  or captured digitally by using ChemiDoc Imaging System (Bio-Rad). The release of cytokines IL-1β and TNF-α into supernatants was determined with ELISA kits  according to the manufacturer’s instructions.iBMDMs were seeded overnight into 12-well plates at a density of 5 × 10−1) for 4 h or left unstimulated. Total RNA was extracted from iBMDMs using E.Z.N.A. Total RNA kit I  according to the manufacturer’s instructions. cDNA was synthesized using a High-Capacity-RNA-to-cDNA kit . Real-time PCR was performed using SYBR Green Master Mix  at a 7500 Real-Time PCR system. The primers sequence was: Nlrp3 forward, CCCTTGGAGACACAGGACTC; Nlrp3 reverse, GAGGCTGCAGTTGTCTAATTCC; GAPDH forward, AGCTTGTCATCAACGGGAAG; GAPDH reverse, TTTGATGTTAGTGGGGTCTCG; The relative expression level of Nlrp3 to GAPDH was calculated using the 2(−ct) method and normalized to the level of unstimulated wildtype iBMDMs.iBMDMs were stimulated with LPS  for 24 h before replacement with the fresh culture medium. After 3 to 4 days, transduced cells were sorted by flow cytometry using GFP as a marker. The expression of reconstituted proteins was determined by immunoblotting with an anti-HA, anti-NLRP3, or anti-Flag antibody. Macrophages expressing comparable levels of GFP were used in bulk for experiments with untagged NLRP3 proteins. For experiments with tagged NLRP3 proteins, cell clones of transduced macrophages that expressed different levels of NLRP3 proteins were selected by serial dilution and western blots.SFP-tagged, HA-tagged, or untagged full-length mouse NLRP3 or its truncation mutant  were cloned into lentiviral plasmid pHIV-EGFP  or pINDUCER21 . Each lentiviral construct was co-transfected with package plasmids pCMV-VSV-G  and pCMV-dR8.2 dvpr  into HEK 293T cells for 96 h. Lentiviruses in the culture supernatants were concentrated by using lenti-X concentrator . iBMDMs were seeded into 6-well plates overnight at a density of 1 × 105 cells per well. Plasmids expressing HA-tagged NEK7, Flag, or SFP-tagged wild-type NLRP3, or its truncation mutant were single- or co-transfected into HEK 293T cells by Lipofectamine LTX  for 16 h. After transfection, cells were washed with ice-cold PBS twice and lysed in ice-cold lysis buffer  suberate (BS3) . The cross-linked pellets were spun down at 6000g for 15 min and dissolved in an SDS sample buffer for subsequent immunoblotting analysis.iBMDMs were seeded overnight in 6-well plates at a density of 1 × 106 cells per well and stimulated as indicated. After stimulation, cells were washed once with cold PBS and then lysed in ice-cold native lysis buffer  glycerol, 0.5% digitonin, 0.5 mM Na3VO4, 1 mM PMSF, 0.5 mM NaF, 1 × EDTA-free Roche protease inhibitor cocktail, pH 7.0) for 15 min on ice. Cell lysates were clarified by centrifugation at 20,000g for 30 min at 4 °C; proteins were separated in 4 to 12% blue native PAGE and then analyzed by Western blot. For the two-dimensional (2D) PAGE, the natively resolved gel slice was loaded into the well of 4 to 12% SDS–PAGE gel after being soaked in 1 × Laemmli buffer as previously described (Blue native gel electrophoresis was performed as previously described . Brieflyescribed .t test or one-way ANOVA with GraphPad 9.0. A p value less than 0.05 was considered statistically significant.Data are represented as mean ± SD. Statistical analysis was performed using unpaired two-tailed Student’s All data generated for this study are included in this article.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


F1 score all exceeding 98% on the test data. In addition, four types of deformation can be further classified with the support of infrastructure base information. The proposed method can be used for directly locating adverse substructure deformations, and is also becoming a promising addition to existing deformation monitoring methods.High-speed railway administrations are particularly concerned about safety and comfort issues, which are sometimes threatened by the differential deformation of substructures. Existing deformation-monitoring techniques are impractical for covering the whole range of a railway line at acceptable costs. Fortunately, the information about differential substructure deformation is contained in the dynamic inspection data of longitudinal level from comprehensive inspections trains. In order to detect potential differential deformations, an identification method, combining digital filtering, a convolutional neural network and infrastructure base information, is proposed. In this method, a low-pass filter is designed to remove short-waveband components of the longitudinal level. Then, a one-dimensional convolutional neural network is constructed to serve as a feature extractor from local longitudinal-level waveforms, and a binary classifier of potential differential deformations in place of the visual judgement of humans with profound expertise. Finally, the infrastructure base information is utilized to further classify the differential deformations into several types, according to the positional distribution of the substructures. The inspection data of four typical high-speed railways are selected to train and test the method. The results show that the convolutional neural network can identify differential substructure-deformations, with the precision, recall, accuracy and  Nowadays, the high-speed railway (abbr. HSR) in China has become the predominant and preferred means of transport, due to the merits of efficiency and comfort. However, the comfortable and safe operation of high-speed vehicles is inevitably dependent on the stability of railway substructures, including subgrade, bridge and tunnel, as depicted in In general, the differential deformation of substructures has several forms of existence. The first is the subgrade differential settlement due to foundation deformation, typically induced by groundwater exploitation , structuUp until now, many monitoring methods have been adopted for HSR substructure deformation. Traditional methods, on the basis of settlement monitoring pile, sedimentation plate, sedimentation cup, and leveling measurement ,12,13,14In spite of the aforementioned monitoring methods, people tend to ignore track dynamic inspection by the comprehensive inspection trains (abbr. CITs) as an auxiliary means of monitoring substructure deformation. Nowadays, in order to guarantee the safe and stable operation of HSR lines, railway administrations and companies around the world utilize CITs to periodically inspect geometric track irregularities, which reflects the unevenness of the rail surface. The track inspection data (abbr. TID) are then compared with predefined regulatory limits to locate adverse track states. More importantly, according to the mapping relationship of deformation between substructures and rail surface ,5,7, theIn light of the above situation, this study aims to fulfil the purpose of the differential-deformation identification of HSR substructures, mainly on the basis of dynamic inspection of longitudinal-level irregularity. Meanwhile, the base information account of railway infrastructures, which contains locations, lengths and types of substructures and curves, is also gathered and utilized. Then, a differential-deformation-identification method is proposed. In the method, a digital filter is designed to remove irrelevant information in the longitudinal level and a delicate convolutional-neural-network (abbr. CNN) is constructed to automatically identify potential substructure deformations. Finally, the locations of identified deformations are matched with the base information account, to determine the exact deformation types.The rest of the paper is organized as follows. The fundamental principle of track dynamic inspection, on-board CITs, and basic information about TID are introduced in CITs are instrumented with the track-dynamic-inspection system, which mainly consists of accelerometers, displacement meters, gyroscopes, acquisition devices and signal processing computers. CITs typically conduct dynamic inspection at a commercial speed, and the major inspection item is geometric track irregularities, including longitudinal level (surface), alignment, gauge, cross-level and twist . As the Z represent the vertical displacement of the vehicle body, Y represent the longitudinal-level irregularity and W represent the relative distance between the rail surface and the vehicle body. In detail, Z can be calculated by the quadratic integral of vertical vehicle-body acceleration, and W is directly measured by the displacement meter. Therefore, longitudinal level Y can be calculated in accordance with the following formula:The inertial reference method builds an inertial reference in the running vehicle body by using accelerometers and gyroscopes, and measures the relative distance between the rail and the vehicle body by using displacement meters. Then, the relative position of the rail surface in the inertial coordinate, i.e., the longitudinal level, can be generated. As depicted in According to the above principle, the track dynamic-inspection system synthesizes several original signals into various track-irregularity parameters, after a series of processes including A/D conversion and filtering. The TID are spatially sequential data and are discretized at 0.25 m increments along the railway track. Moreover, the measurements of longitudinal level are output in three wavebands, i.e., 1.5~42 m, 1.5~70 m and 1.5~120 m, as plotted in In order to identify substructure deformation and determine the deformation type, an identification method combining digital filter, CNN and a base information account, is constructed. The method takes the spatially sequential longitudinal-level inspection data as the input, and outputs the deformation type together with the corresponding position. Each component and the whole structure of the proposed method are detailed as follows.It is generally acknowledged that the differential deformation of railway substructures is mainly influenced by the long-wave components of longitudinal level, and the components with wavebands below 40 m are empirically considered as an interference in the identification of substructure deformation. Thus, a digital filter is designed to remove the irrelevant components of longitudinal level. In detail, a Chebyshev low-pass filter is adopted, with the pass-band cut-off wavelength of 60 m, the stop-band cut-off wavelength of 40 m, the pass-band ripple of 1dB and the stop-band attenuation of 30 dB. Furthermore, zero-phase filtering is performed by processing the longitudinal-level data in both the forward and reverse directions, in accordance with . The ampThe longitudinal-level waveforms activated by various types of differential substructure deformation share similar characteristics of sinusoidal or cosinoidal shapes, typically with large amplitudes and wavelengths ranging from 40 m to above 100 m. Such waveform characteristics in shape are useful in the identification of differential substructure deformations. As the convolutional neural network is advantageous in learning shape properties , it is aX =  and Y =  as the position and amplitude of the longitudinal level, respectively, and N denotes the data capacity. The longitudinal-level data  is segmented in a step-wise manner with a window length of L, which is set as 120 m , according to the upper waveband-limit of longitudinal-level inspection data. The moving step of the window can be randomly set to increase flexibility in the selection of waveform samples. Then the selected waveforms are manually labelled with 1 or 0, representing whether or not a waveform sample corresponds to a differential substructure deformation. Thus, a dataset of waveform and label pairs, i.e., {, ky}, k∈, can be generated. In the dataset, kX = , kY = , kt∈, and ky = 1 or 0.The inspection data of the longitudinal level is composed of amplitudes and corresponding positions. Suppose Y of the longitudinal level is taken as the input of the convolutional neural network. Due to the sequential property of the input data, one-dimensional convolution as well as one-dimensional pooling is adopted in the following layers. Therefore, the proposed structure is named 1DCNN here. Four stacking layers of convolution and pooling are adopted to gradually extract key shape features in the longitudinal-level waveform samples. The extracted features are then put into two stacking fully-connected layers, to generate the most probable labels corresponding to the inputs.The amplitude dCKi,j)  is takenYd+1 is halved, without altering the channel size.In the pooling layer, max-pooling is adopted with kernel size 2 and stride 2. After the pooling operation, the width of feature map kŷ, is classified as positive if the value is above 0.5, indicating that the longitudinal- level waveform is induced by a differential substructure deformation. The specifics of the 1DCNN structure are detailed in In the fully-connected layer, the number of hidden neurons is set as 128, with a drop rate of 0.2. The output is a scalar representing the most probable label with respect to a waveform sample. Furthermore, a predicted label, ŷ, and the actual labels, y, is taken as the loss function, with the L2-norm regularization of model parameters added, to enhance generalization. In addition, a stochastic-gradient-descent algorithm is adopted to train 1DCNN with a learning rate 0.001.The mean square error between the predicted labels, The main function of 1DCNN is to recognize potential differential substructure-deformations through extracting the shape features of local longitudinal-level waveforms and binary classification. However, the exact deformation type of each waveform is still unclear. In this section, we further classify the recognized longitudinal-level waveforms into five types, i.e., vertical curve, transition deformation, pier deformation, tunnel deformation and subgrade deformation, with the aid of the base information account of railway infrastructures. The base information account is available in the database of the permanent way management information system , of China . Moreoveα1 and α2 of neighboring slopes and radius R, the length, S, of the vertical curve can be estimated in accordance with the following formula:From the base information account, the start and end position of bridges, tunnels and slopes can be extracted, thus obtaining the spatial range of different substructures, as well as the central location of transitions and vertical curves. For transitions, the section that covers a length of 200 m around the central point is empirically taken as the transition section. For a vertical curve, based on the gradients SP and EP denote the start and end position, and subscript vc, tr, br, tu, su denote vertical curve, transition, bridge, tunnel and subgrade, respectively. Finally, a dataset of the spatial range, {SP, EP} = {, , , , }, of the aforementioned infrastructures can be generated. For a waveform and predicted label pair {, kŷ}, if kŷ > 0.5, x[kt + L/2] of kX is iteratively compared with all the elements of SP to find the nearest infrastructure type and classify kY into the corresponding deformation type. This process is named position matching, and is detailed in Let In accordance with the above introduction, the flow chart of the differential-deformation-identification method is depicted in X, Y), y}, negative samples of a longitudinal-level waveform are distributed between two adjacent positive samples. An intercepted section of the longitudinal-level waveform and the corresponding labels are depicted in Four railway lines from the China HSR network are selected as the test lines, which are representative of the busiest service routes. The TID of these test lines are all inspected by CITs, and the longitudinal level with a waveband of 1.5~120 m is selected as the data source. For convenience of expression, the test lines are denoted as line 1, line 2, line 3, and line 4 and the corresponding datasets are denoted as data 1, data 2, data 3, data 4, respectively. In the segmentation process, the random moving-step of the window is utilized to extract the most likely waveforms that exactly cover the range of potential substructure deformations and comply well with human visual judgement, without producing as many redundant waveform samples as the point-wise moving step. Due to the difficulties in the in situ verification of substructure deformations, authentic labels are practically unavailable. Manual labelling is adopted here by visually observing waveforms. Three experts with rich expertise are invited for the labelling work, during which process cross-verification is adopted until consensus is reached. Therefore, the trained 1DCNN can be regarded as a predictor of probable differential substructure-deformations, in place of human visual judgement. The manually selected waveforms are labelled with 1 or 0, representing a positive or a negative sample. In order to create a balanced dataset {.F1 score are adopted as the evaluation indices. Their definitions are as follows:TP is true positive, TN is true negative, FP is false positive, and FN is false negative. The evaluation indices of predictions made by 1DCNN on the test data are shown in F1 score of four test-lines exceeding 98%.For the evaluation of the classification ability of 1DCNN, precision, recall, accuracy and The time consumption for prediction on the test data of the four test-lines is 2.46 s, 0.85 s, 0.89 s and 0.89 s, respectively, that is, less than 0.008 s for a waveform sample, on average.FP and FN samples of Line 1 are selected and plotted in In addition, some Trained 1DCNN can then be utilized for prediction. During prediction, a newly inspected TID can be processed in accordance with Recognized substructure deformations need to be further classified into certain deformation types, with the aid of a basic information account. Taking the test data of line 1, for example, among the 150 positive samples there are 54 samples of vertical curves, 44 samples of transition deformation, 34 samples of pier deformation, 8 samples of tunnel deformation and 10 samples of subgrade deformation. Some waveform examples of the above five types are plotted in In accordance with In The longitudinal-level inspection data is helpful in monitoring the deformation state of HSR substructures, with the support of the proposed identification method. Once a suspected differential substructure-deformation is identified, the evolution process of the deformation state, reflected in the longitudinal-level waveforms, can be manifested, as plotted in The differential deformation of high-speed railway substructures is a frequently occurring disease, threatening the safe, stable and comfortable operation of high-speed vehicles. Traditional monitoring techniques are confronted with various difficulties in the effective supervision of such long and vast structures as high-speed railways. In situ monitoring devices such as sedimentation plates and optical sensors are usually placed at particular locations where the deformation is serious, and it is thus economically impossible to cover the whole range of an HSR line. Remote monitoring techniques such as GNSS and InSAR are mainly devoted to large-area deformation monitoring, being incapable of obtaining substructure deformation which covers a spatial range from tens of meters to above a hundred meters. Fortunately, the information of substructure deformation is contained in the longitudinal-level inspection data from regular dynamic inspections by comprehensive inspection trains, and by delving into inspection data, differential substructure deformation can be identified. Moreover, the easily available inspection data covers the whole range of an HSR line, thus greatly reducing the high cost of electronic- or optical-sensor installation.F1 score of the convolutional neural network are 98.2%, 98.3%, 98.2% and 98.2%, respectively, all exceeding 98%. The precision or recall on some test lines even reaches 100%. The specific deformation types can be efficiently classified in accordance with the mapping of data location and actual infrastructure positions. Furthermore, a series of inspection data from different inspection runs can also be analyzed for tracking the development path of deformation. The proposed method is helpful in identifying and locating probable differential substructure-deformations, and is thus valuable for maintenance work. Moreover, it may also be utilized as an affiliation or an alternative to existing deformation-monitoring techniques.Therefore, a differential deformation-identification method based on longitudinal-level irregularity, digital filtering, a convolutional neural network and infrastructure base information is proposed. In detail, a Chebyshev low-pass filter with the cutoff wavelength of 40m is designed to filter out irrelevant components of the longitudinal level. A convolutional neural network, with four stacking layers of convolution and pooling and two fully-connected layers, is then constructed to figure out potential differential substructure-deformations. Finally, infrastructure base information is utilized for the further classification of deformation types, based on a position-mapping algorithm. According to test results in the inspection data of four typical high-speed railway lines in China, the average precision, recall, accuracy and


Upon subunit joining IF2 hydrolyzes GTP and, concomitant with inorganic phosphate (Pi) release, changes conformation facilitating fMet-tRNAifMet accommodation into the P site and transition of the 70 S ribosome initiation complex (70S-IC) to an elongation-competent ribosome. The mechanism by which IF2 separates from initiator tRNA at the end of translation initiation remains elusive. Here, we report cryo-electron microscopy (cryo-EM) structures of the 70S-IC from Pseudomonas aeruginosa bound to compact IF2-GDP and initiator tRNA. Relative to GTP-bound IF2, rotation of the switch 2 α-helix in the G-domain bound to GDP unlocks a cascade of large-domain movements in IF2 that propagate to the distal tRNA-binding domain C2. The C2-domain relocates 35 angstroms away from tRNA, explaining how IF2 makes way for fMet-tRNAifMet accommodation into the P site. Our findings provide the basis by which IF2 gates the ribosome to the elongation phase.During translation initiation, initiation factor 2 (IF2) holds initiator transfer RNA (fMet-tRNA Initiation factor 2 (IF2) guides the ribosome to the elongation phase of protein synthesis. Here, Basu et al. provide structural insights into how compact IF2-GDP makes way for initiator tRNA accommodation into the peptidyl (P) site of the ribosome. In bacteria, this fundamental step in gene expression is regulated by initiation factors 1 (IF1), 2 (IF2), and 3 (IF3). IF2 is the guanosine triphosphatase (GTPase) that guides the entry of the formyl-methionine initiator tRNA (fMet-tRNAifMet) into the peptidyl (P) site of the ribosome. In the small subunit (30 S) initiation complex, IF2-GTP holds fMet-tRNAifMet in a specific orientation and provides anchor points that accelerate joining of the large subunit (50 S), resulting in the formation of the 70 S initiation complex (70S-IC)6. Ribosome-stimulated GTP hydrolysis in IF2 and inorganic phosphate (Pi) release is accompanied by a series of conformational changes in IF2 and the ribosome, leading to the accommodation of fMet-tRNAifMet into the P site and IF2 dissociation from the ribosome16. These events are essential for the maturation of the 70S-IC into an elongation competent ribosome5.Translation initiation is the rate-limiting step of protein biosynthesisEscherichia coli with an N-terminal region of variable length across bacterial species and a conserved C-terminal region that encompasses four domains  appear to remain flexible20. Cryo-electron microscopy (cryo-EM) reconstructions of initiation complexes have shown that GTP-bound IF2 adopts an extended conformation with its β-barrel distal domain C2 interacting with the A76-fMet moiety of initiator tRNA in the p/I state21 and that IF2 induces a rotated conformation of the 30 S subunit in the 70S-IC22. Kinetic studies14 and time-resolved cryo-EM10 revealed that upon GTP hydrolysis by IF2 in the 70S-IC after subunit association, initiator tRNA is released from the C2-domain, accommodates into the P site (p/P state), and the ribosome takes the non-rotated conformation. Single-molecule FRET studies suggest that the nucleotide-bound to the G-domain allosterically modulates the position of the terminal domain C2 relative to that of dI-dIII and initiator tRNA7. Thus, it is presumed that IF2 changes conformation during the last step of translation initiation, facilitating the accommodation of fMet-tRNAifMet into the P site and dissociation of IF2 from the ribosome. However, IF2 bound to the ribosome has only been visualized in the extended conformation interacting with fMet-tRNAifMet positioned in the p/I state, which fails to explain how domains in IF2 rearrange to grant access of initiator tRNA to the P site and commit the ribosome to elongation.Initiation factor IF2 is the largest translation factor in Pseudomonas aeruginosa 70S-IC bound to IF2, mRNA, and initiator fMet-tRNAifMet. The structures reveal the hitherto unseen compact state of IF2-GDP bound to the ribosome. The large conformational changes in IF2 originate from the relatively small-scale rearrangement of the switch 2 (sw2) α-helix in the G-domain and propagate to the distal tRNA-binding domain C2, which collapses onto domain C1 allowing initiator tRNA accommodation into the P site. The structures visualize the interfaces between domains in compact IF2 and the ribosome, providing a detailed view of the IF2-mediated transition of the 70S-IC to a ribosome ready to begin the elongation phase.We report cryo-EM structures of the 19. We hypothesized that the addition of the non-hydrolysable GTP analog β,γ-methyleneguanosine 5’-triphosphate (GDPCP) to the complex could yield at least two populations of the 70S-IC, one bound to IF2-GDPCP and the other to IF2-GDP. Cryo-EM and three-dimensional (3D) classification revealed two main populations of ribosome complexes bound to IF2 and featuring the non-rotated (class average I) and rotated (class average II) 30 S subunit  in the 50 S subunit displays high flexibility in both the non-rotated (class average I) and rotated (class average II) ribosome  . Focused refinement with signal subtraction produced a density map of compact IF2 with a nominal resolution of 3.4 Å, with several regions extending to ~3.0-Å resolution   in the nucleotide-binding pocket of compact IF2 is consistent with bound GDP 3-Val676 and helix H10 in domain C1 form the interface with the sw2-α-helix and H4 in the G-domain  and the ribose O4’ atom of G2459 (G2472) in H89, and no interaction is seen with H91-H92 and uL6  in the SRL  and U362 (U368), respectively, located in the body of the 30 S subunit, in keeping with the importance of A55 during protein synthesis , and of the C-terminal tip of r-protein uL6, together forming one side of the accommodation corridor for the incoming aminoacyl-tRNA Fig. . In thisuL6 Fig. . The carSRL Fig. . This issis Fig. 27. LikewSRL Fig. .2 in structure I-A to ~2409 Å2 in structure I-B, consistent with weaker interactions between ribosome I-B and IF2-GDP primed to dissociate from the 70S-IC15. The absence of interaction between domain II and the 30 S subunit also explains the scattered low-resolution density attributable to domain II, indicating that it becomes highly dynamic in the inter-subunit space in structure I-B , the density of the uL11-region is more scattered than that of the ribosome and IF2, suggesting that the uL11-stalk samples different conformations. Focused 3D variability analysis around extended IF2 and the uL11-stalk revealed structures II-A and II-B with global resolutions of 2.7 Å and 2.6 Å, respectively   was shown to be highly similar to that of the N/G1 domain of ribosome-free T. thermophilus IF222.The N-domain of IF2 is known to promote stable joining of the 30 S and 50 S subunits to form the 70S-ICit Figs. , Fig. 7a35. The high sequence similarity between the N-domain of IF2 in E. coli and P. aeruginosa suggests a similar sub-domain arrangement , of which residues 266 to 312 form the three-helix bundle sub-domain N2 in IF2 not previously included in the E. coli 70S-IC structures21. In the rotated 70S-IC (II-A), the buried surface area by extended IF2-GDPCP is ~5146 Å2, of which ~1173 Å2 is attributed to the N2 sub-domain.It is interesting that the sequence encompassing the N1 sub-domain shares significant similarity with the region of the N-domain proximal to the G-domain (sub-domain N2), suggesting that sub-domain N2 may fold into a similar globular structure with the two repeats N1 and N2 connected by a linker forming the entire N-domain of IF2  with the NMR structure of the E. coli sub-domain N1 (residues 2–50)38 reveals reasonable homology with a root-mean-square-deviation (RMSD) value of 4.9 Å, alignment with the N/G1 domain from the crystal structure of ribosome-free T. thermophilus IF239 shows an almost perfect fit with RMSD of 1 Å 21. Whether the IF2 conformations we observe on the 70S-IC represent on-pathway intermediates is a legitimate concern. Two arguments suggest that the major structures (I-A/B & II-A/B) may nevertheless represent on-pathway, rather than off-pathway intermediates: i) the fact that the dissociation/association of IF2-GDP from/to the ribosome is reversible40, and ii) the similarity of ribosomes II-A/II-B harboring extended IF2-GDPCP with the previous IF2–70S-IC structure obtained under native conditions , which corresponds to the temperature at which P. aeruginosa ribosomes operate but is substantially lower than the optimum temperature for T. thermophilus (~75 °C). Therefore, a reduced flexibility at low temperature of the thermophilic ribosomes could have prevented IF2 from assuming the compact configuration.In the previous low-resolution cryo-EM structure of the 19. The sw2-helix in ribosome-free IF2-GDP is disordered probably because of the absence of interaction with domain C1. In the 70S-IC, the space restraints imposed by the uL11-stalk may facilitate the re-location of domain C1 in IF2-GDP such that it docks against the sw2-helix in the G-domain. The flexibility of helix H8 in IF2 allows it to bend and accommodate the new position of domain C1 under the uL11-stalk, a feature that was not observed in the ribosome-free crystal structures of IF2  and I-B (~2409 Å2), providing the basis by which IF2-GDP eventually dissociates from the 70S-IC. To ensure we detected all conformational changes in IF2 and the ribosome that are present in the cryo-EM dataset reported here, we performed global 3D variability analysis using all ribosome particles, in both the rotated and non-rotated states. This analysis shows the same conformational changes in IF2 and the 70S-IC we describe using focused classification and refinement  containing an N-terminal 6x His-SMT3 tag. Escherichia coli BL21 (DE3) Star (Invitrogen) cells transformed with this construct were grown in the LB medium supplemented with 50 µg/mL kanamycin (Gold Biotechnology) to an absorbance of 0.8 at 600 nm before inducing expression of His-SMT3-PaeIF2 with 0.5 mM isopropylthiol-β-D-galactoside  for 4 h at 37 °C. The cells were collected by centrifugation, flash frozen in liquid nitrogen and stored at –80 °C. To purify IF2, frozen cells were resuspended in lysis buffer at 4 °C  with one EDTA-free protease inhibitor tablet . The resuspended cells were lysed by passing several times through an LM20 high-pressure homogenizer (Microfluidics) operated at 15,000 psi. Cell debris were removed by centrifugation at 16,000 rpm  at 4 °C for 45 min and filtered through a 0.22-μm filter (Millipore). The lysate was then loaded onto a 5 ml HisTrap HP  column and washed with a high salt buffer (lysis buffer with 2 M NaCl) to remove bound nucleic acids and equilibrated back into lysis buffer. IF2 was eluted with a linear gradient of imidazole to 500 mM. Fractions containing His-SMT3-IF2 were pooled and incubated with the Ulp1 protease overnight at 4 °C to remove the SMT3-tag. The proteins were dialyzed into the lysis buffer and IF2 was separated from the SMT3-tag by passing through a reverse HisTrap column. IF2 was further purified by anion exchange on source Q (HR 16/10) and Superdex 200 16/60  chromatography columns. Pure IF2 was concentrated to ~35 mg/ml, flash-frozen and stored at –80 °C.The full-length IF2 sequence was PCR amplified from 42. Briefly, 20 nmoles of purified IF2 or 3 nmoles of GTP or GDP (Millipore Sigma) in 70 µl of buffer  was treated with 0.6 µl of 70% perchloric acid at 22 °C for 2 min to denature the protein. Then, the pH was adjusted by adding 4 µl of 3 M Na-acetate pH 5.1 and the mixture centrifuged to pellet the precipitated protein. The supernatant was diluted 1:1 with HPLC column buffer A  to 150 µl final volume. This sample was injected onto a PROTO 300 C4 HPLC column (10 × 250 mm)  equilibrated in buffer A and eluted with a gradient of 0–50% buffer B containing 30% acetonitrile over 5 column volumes  and reversed-phase chromatography PROTO 300 C4 HPLC (10 × 250 mm)  columns. The deacylated tRNAifMet was then aminoacylated with methionyl-tRNA synthetase, formylated with methionyl-tRNA formyltransferase, and finally purified by reversed-phase HPLC on the C4 column. The 24-mer mRNA, containing a Shine-Dalgarno sequence and the initiation codon in the P site, with the sequence 5’ GGC AAG GAG GUA AAA AUG UUC UAA 3’ (start codon in bold), was chemically synthesized by Integrated DNA Technologies .P. aeruginosa PAO1 as previously reported44 with some modifications. Briefly, the cells were washed and lysed in buffer A  using an Emulsiflex-C3 homogenizer (Avestin Inc.). The lysate was cleared by centrifugation at 18,000 rpm  for 30 min at 4 °C and filter-sterilized through a 0.45 μm filter and stored at –80 °C. To isolate ribosomes, the lysate was layered on a 1.1 M sucrose cushion buffer  and spun at 43,000 rpm  in a Type 45Ti rotor (Beckman) for 20 h at 4 °C. Ribosome pellets were resuspended in 10 mM Hepes pH 7.6, 100 mM NH4Cl, 14.5 mM MgCl2, 0.5 mM EDTA, 6 mM β-mercaptoethanol. Ribosomes were then purified through 10–50% sucrose density gradients in a SW32 rotor (Beckman) at 18,000 rpm  at 4 °C for 19 h. The fractions containing 70 S ribosomes were collected, diluted to adjust Mg2+ concentration to 10 mM and concentrated by centrifugation at 43,000 rpm  at 4 °C. Pure 70 S ribosomes were resuspended and brought to the final buffer 10 mM Hepes pH 7.6, 60 mM NH4Cl, 15 mM KCl, 10 mM MgCl2, flash-frozen in liquid nitrogen and stored at –80 °C.70 S ribosomes were purified from P. aeruginosa 70 S ribosome, 25 μM mRNA, 10 μM fMet-tRNAifMet and incubated in final 70 S buffer  at 37 °C for 15 min. The ternary complex was then incubated with 30 μM IF2 and 2 mM GDPCP (Millipore Sigma) at room temperature for 15 min. The mixture containing 2 μM P. aeruginosa 70S-IC (4 μl) was applied to Quantifoil R2/1 gold 200 mesh grids (Electron Microscopy Sciences) which were pre-cleaned in a Solarus 950 plasma cleaner (Gatan). The grids were plunged-frozen in liquid nitrogen-cooled ethane using a Leica EM GP2 cryo-plunger. Grids were transferred into a Titan Krios G3i electron microscope (ThermoFisher Scientific) operating at 300 keV and equipped with a GIF Quantum LS energy filter (Gatan) and a K3 direct electron detector camera (Gatan). The image stacks (movies) were acquired in super-resolution mode with pixel size of 0.425 Å/pixel. Data collection was done in the EPU software (ThermoFisher Scientific) setup to record movies with 31 fractions with a total accumulated dose of ~31 e-/Å2/movie. A total of 8056 image stacks were collected with a defocus ranging between –0.2 and –2.1 µm. The statistics of data acquisition are summarized in Supplementary Table Translation initiation complex was programmed using 2 μM 45. The image stacks were imported into cryoSPARC and gain-corrected. Image frames (fractions) were motion-corrected with dose-weighting and binned twice by Fourier cropping to the final pixel size of 0.85 Å/pixel. Patch contrast transfer function (CTF) estimation was performed on the motion-corrected micrographs. Based on relative ice thickness, CTF fit, and length and curvature of motion trajectories, 7259 micrographs were selected for further processing  that differ in the position of compact IF2 relative to 70 S and the degree of 30 S rotation. Similar focused 3D variability analysis around extended IF2 and the uL11-stalk on rotated 70 S ribosomes in class average II showed conformational variability of the uL11-stalk, sampling the outward and inward position, and the particles were accordingly classified into structures II-A (outward) and II-B (inward). Particles from structures I-A, I-B, II-A, and II-B were re-extracted to full-size (512 × 512 pixel box) and non-uniform and CTF refinement in cryoSPARC yielded reconstructions with overall resolutions ranging between 2.6 – 2.9 Å  classification. After discarding bad particles, 494,268 particles were selected from 2D classification and used to generate ab-initio volumes with five groups. Two of the five groups containing 417,757 particles generated maps resembling the 70 S ribosome. The remaining three groups did not yield anything meaningful and were considered to contain not specimen-related particles which were discarded from further processing. Using ‘heterogeneous refinement’ in cryoSPARC these particles were further classified into three ribosome class averages. 180,951 particles were non-rotated 70 S (class average I) and 219,664 particles were rotated 70 S (class average II), with both class averages containing IF2 and P-site fMet-tRNA46.For structures I-A and I-B, local refinement with signal subtraction using a soft mask around IF2 was performed, improving the overall resolution of compact IF2 to 3.4 Å (I-A) and 3.6 Å (I-B)  of 0.84  into the respective EM density maps obtained after focused refinement. The final models of compact IF2-GDP have RSCC of 0.82 (I-A) and 0.75 (I-B)  and U81 located in the foot region of the small subunit. The solvent accessible area between IF2 and the ribosome was calculated in PyMOL using the refined IF2 structures  with generated hydrogen atoms in the absence and presence of the ribosomal components within a radius of 30 Å around IF2. The numbering of helices in IF2 is according to Roll-Mecak et al.52.The 30 S subunit rotation was calculated in PyMOL by aligning the 50 S subunit, excluding flexible regions , of the four 70S-IC structures  with that of the non-rotated 45. The major variability component showed the 30 S subunit rotation relative to the 50 S subunit, coupled with IF2 domain rearrangements and fMet-tRNAifMet accommodation from the p/PI state to the p/P position. Particles were sorted into 20 clusters along this 3D conformational trajectory, and based on the extent of the 30 S rotation, clusters were ordered to reflect the transition from the rotated to the non-rotated 70S-IC. Clusters containing ribosomes with similar 30 S rotation were merged into distinct groups followed with non-uniform refinement53, which yielded nine 70S-IC reconstructions with a global resolution ranging between 2.8 and 2.9 Å  bound to compact IF2-GDP and a well-ordered fMet-tRNAifMet accommodated into the P site. To visualize the conformational coupling that appears to exist between the ribosome, IF2 and initiator tRNA within the 70S-IC, the nine EM reconstructions were morphed together into a movie . Movies were made in UCSF ChimeraX 1.254.All figures showing atomic models were generated using PyMOL  or UCSF Chimera 1.14Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Reporting Summary