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A method of producing superstrong yet ductile steels using cheaper and lighter alloying elements is described, based on minimization of the lattice misfit to achieve a maximal dispersion of nanoprecipitates, leading to ultimate precipitation strengthening. Extreme precipitation makes superstrong steel Ultrastrong and yet ductile steels are important materials for the automotive and energy industries, among others. A key subgroup is the maraging steels, martensitic steels that have been aged by extended heat treatment. They acquire their strength from semi-coherent intermetallic precipitates. In this paper, maraging steels are described in which the expensive cobalt and titanium alloying elements are entirely replaced with lightweight and inexpensive aluminium. The resulting precipitates were produced in the steel at high density and with minimal lattice mismatch strain, leading to an impressive combination of very high strength (up to 2.2 gigapascals) and good ductility (about 8.2 per cent). The materials are characterized using a suite of high-resolution techniques, including atom probe tomography, HAADF STEM and synchrotron XRD. Next-generation high-performance structural materials are required for lightweight design strategies and advanced energy applications. Maraging steels, combining a martensite matrix with nanoprecipitates, are a class of high-strength materials with the potential for matching these demands 1 , 2 , 3 . Their outstanding strength originates from semi-coherent precipitates 4 , 5 , which unavoidably exhibit a heterogeneous distribution that creates large coherency strains, which in turn may promote crack initiation under load 6 , 7 , 8 . Here we report a counterintuitive strategy for the design of ultrastrong steel alloys by high-density nanoprecipitation with minimal lattice misfit. We found that these highly dispersed, fully coherent precipitates (that is, the crystal lattice of the precipitates is almost the same as that of the surrounding matrix), showing very low lattice misfit with the matrix and high anti-phase boundary energy, strengthen alloys without sacrificing ductility. Such low lattice misfit (0.03 ± 0.04 per cent) decreases the nucleation barrier for precipitation, thus enabling and stabilizing nanoprecipitates with an extremely high number density (more than 10 24 per cubic metre) and small size (about 2.7 ± 0.2 nanometres). The minimized elastic misfit strain around the particles does not contribute much to the dislocation interaction, which is typically needed for strength increase. Instead, our strengthening mechanism exploits the chemical ordering effect that creates backstresses (the forces opposing deformation) when precipitates are cut by dislocations. We create a class of steels, strengthened by Ni(Al,Fe) precipitates, with a strength of up to 2.2 gigapascals and good ductility (about 8.2 per cent). The chemical composition of the precipitates enables a substantial reduction in cost compared to conventional maraging steels owing to the replacement of the essential but high-cost alloying elements cobalt and titanium with inexpensive and lightweight aluminium. Strengthening of this class of steel alloy is based on minimal lattice misfit to achieve maximal precipitate dispersion and high cutting stress (the stress required for dislocations to cut through coherent precipitates and thus produce plastic deformation), and we envisage that this lattice misfit design concept may be applied to many other metallic alloys.

Tempering reactions in the martensite phase of Fe-13Cr-0.47C (mass pct) stainless steel and its Si- and Mn-added modifications were studied by correlative dilatometry and magnetic measurements. Tempering for 5 minutes was performed at sequentially higher temperatures up to 923 K (650 °C). Classical tempering reactions including the segregation of C atoms at defects, precipitation of M3C and Cr-rich carbides, and austenite decomposition were clearly identified. The formation of M3C carbides was partially and entirely suppressed by Mn and Si additions, respectively. Compared to low-alloy steels, the decomposition of retained austenite in stainless steels was delayed to temperatures above 823 K (550 °C). The latter occurred concurrently with the formation of Cr-rich carbides in the martensite. In addition, non-classical tempering reactions such as the partial dissolution of C clusters at temperatures above 573 K (300 °C) and the short-range diffusion of substitutional elements including Cr and Mn to C clusters and M3C carbides in the temperature range of 673 K to 823 K (400 °C to 550 °C) were identified based on the associated increase in the magnetization.

Fixed-time coordination is critical for networked unmanned aerial vehicle (UAV) systems to accomplish time-sensitive missions such as rapid target encirclement, cooperative search, and emergency response. However, dynamic topology variations, caused by mission reassignment, obstacle avoidance, or communication disruptions, along with model uncertainties and external disturbances, present significant challenges to robust and timely coordination. To address these issues, this paper investigates the fixed-time bipartite containment tracking control problem of uncertain multi-UAV systems under switching communication topologies. A neuroadaptive robust containment tracking controller is developed to guarantee that all follower UAVs converge within a fixed time to the region spanned by multiple dynamic leaders, regardless of initial conditions. To handle unknown nonlinear dynamics, a neuroadaptive estimator is constructed using online parameter adaptation. A topology-dependent multiple Lyapunov function framework is employed to rigorously establish fixed-time convergence under switching topologies. Moreover, an explicit upper bound on the convergence time is analytically derived as a function of system parameters and dwell time constraints. Comparative analysis demonstrates that the proposed method reduces conservativeness in convergence time estimation and enhances robustness against frequent topology changes. Simulation results are provided to validate the effectiveness and advantages of the proposed control scheme.

The current study was performed to assess the effectiveness of detailed operating room quality care on the quality of operating room care and patient satisfaction. A total of 102 patients who underwent surgery in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between October 2020 and April 2022 were recruited and assigned to receive either conventional operating room care (conventional group) or detailed operating room quality care (quality group), with 51 cases in each group. Outcome measures for the evaluation of the detailed quality care included quality of operating room care, safe operation, incidence of errors in instrument preparation, loss of parts, incidence of intraoperative adverse reactions, and patient satisfaction. Patients who received quality care showed higher scores for information acquisition ability, communication ability, standardization of nursing process, and professionalism of nursing service than those who received conventional care (P = .021, .032, .003, .043). Detailed operating room quality care resulted in significantly higher standardization of anesthesia disinfection, promptness of instrument preparation, instrument and equipment management, effectiveness of auxiliary cooperation, and standardization of medical records scores versus conventional care (P = .004, .022, .036, .004, .002). Detailed operating room quality care was associated with a lower incidence of instrument preparation errors, lost parts, and intraoperative adverse reactions than conventional care (P < .05). Patients were more satisfied with quality care (49/51, 96.1%) than with conventional care (39/51, 76.5%) (P = .004). Detailed operating room quality care can significantly improve patient satisfaction, enhance the quality of operating room care and safe operation, and reduce the risk of instrument preparation errors, lost parts, and intraoperative complications in the operating room.

The secondary structures of hepatitis C virus (HCV) RNA and the cellular proteins that bind to them are important for modulating both translation and RNA replication. However, the sets of RNA-binding proteins involved in the regulation of HCV translation, replication and encapsidation remain unknown. Here, we identified RNA binding motif protein 24 (RBM24) as a host factor participated in HCV translation and replication. Knockdown of RBM24 reduced HCV propagation in Huh7.5.1 cells. An enhanced translation and delayed RNA synthesis during the early phase of infection was observed in RBM24 silencing cells. However, both overexpression of RBM24 and recombinant human RBM24 protein suppressed HCV IRES-mediated translation. Further analysis revealed that the assembly of the 80S ribosome on the HCV IRES was interrupted by RBM24 protein through binding to the 5′-UTR. RBM24 could also interact with HCV Core and enhance the interaction of Core and 5′-UTR, which suppresses the expression of HCV. Moreover, RBM24 enhanced the interaction between the 5′- and 3′-UTRs in the HCV genome, which probably explained its requirement in HCV genome replication. Therefore, RBM24 is a novel host factor involved in HCV replication and may function at the switch from translation to replication.

Sodium taurocholate cotransporting polypeptide (NTCP) is a functional receptor for hepatitis B virus (HBV) entry. However, little is known regarding whether NTCP is involved in regulating the postentry steps of the HBV life cycle. Here, we found that NTCP expression upregulated HBV transcription at the postentry step and that the NTCP-targeting entry inhibitor Myrcludex B (MyrB) effectively suppressed HBV transcription both in an HBV in vitro infection system and in mice hydrodynamically injected with an HBV expression plasmid. Mechanistically, NTCP upregulated HBV transcription via farnesoid X receptor α (FxRα)-mediated activation of the HBV EN2/core promoter at the postentry step in a manner that was dependent on the bile acid (BA)-transport function of NTCP, which was blocked by MyrB. Our findings uncover a novel role for NTCP in the HBV life cycle and provide a reference for the use of novel NTCP-targeting entry inhibitors to suppress HBV infection and replication.

The terminal redundancy (TR) sequence of the 3.5-kb hepatitis B virus (HBV) RNA contains sites that govern many crucial functions in the viral life cycle, including polyadenylation, translation, RNA packaging, and DNA synthesis. In the present study, RNA-binding motif protein 24 (RBM24) is shown to be involved in the modulation of HBV replication by targeting the TR of HBV RNA. In HBV-transfected hepatoma cell lines, both knockdown and overexpression of RBM24 led to decreased HBV replication and transcription. Ectopic expression of RBM24 inhibited HBV replication, which was partly restored by knockdown of RBM24, indicating that a proper level of RBM24 was required for HBV replication. The regulation of RBM24 of HBV replication and translation was achieved by the interaction between the RNA-binding domains of RBM24 and both the 5′ and 3′ TR of 3.5-kb RNA. RBM24 interacted with the 5′ TR of HBV pregenomic RNA (pgRNA) to block 80S ribosome assembly on HBV pgRNA and thus inhibited core protein translation, whereas the interaction between RBM24 and the 3′ TR enhanced the stability of HBV RNA. Finally, the regulatory function of RBM24 on HBV replication was further confirmed in a HBV infection model. In conclusion, the present study demonstrates the dual functions of RBM24 by interacting with different TRs of viral RNA and reveals that RBM24 is an important host gene for HBV replication.

Accurate identification of true biological signals from diverse undesirable variations in large-scale transcriptomes is essential for downstream discoveries. Herein, we develop a universal deep neural network, called DeepAdapter, to eliminate various undesirable variations from transcriptomic data. The innovation of our approach lies in automatic learning of the corresponding denoising strategies to adapt to different situations. The data-driven strategies are flexible and highly attuned to the transcriptomic data that requires denoising, yielding significant improvement in reducing undesirable variation originating from batches, sequencing platforms, and bio-samples with varied purity beyond manually designed schemes. Comprehensive evaluations across multiple batches, different RNA measurement technologies and heterogeneous bio-samples demonstrate that DeepAdapter can robustly correct diverse undesirable variations and accurately preserve biological signals. Our findings indicate that DeepAdapter can act as a versatile tool for the comprehensive denoising of the large and heterogeneous transcriptome across a wide variety of application scenarios.Competing Interest StatementThe authors have declared no competing interest.