Search

Abstract Macrophage metabolism dysregulation, which is exacerbated by persistent stimulation in infectious and inflammatory diseases, such as diabetic infectious bone defects (DIBD), eventually leads to the failure of bone repair. Here, we have developed an injectable, macrophage-modulated GAPDH-Silence drug delivery system. This microsphere comprises chondroitin sulfate methacrylate (CM) and methacrylated gelatin (GM), while the dimethyl fumarate (DMF)-loaded liposome (D-lip) is encapsulated within the microsphere (CM@GM), named D-lip/CM@GM. Triggered by the over-expressed collagenase in DIBD, the microspheres degrade and release the encapsulated D-lip. D-lip could modulate metabolism by inhibiting GAPDH, which suppresses the over-activation of glycolysis, thus preventing the inflammatory response of macrophages in vitro. While beneficial for macrophages, D-lip/CM@GM is harmful to bacteria. GAPDH, while crucial for glycolysis of staphylococcal species (S. aureus), can be effectively countered by D-lip/CM@GM. We are utilizing existing drugs in innovative ways to target central metabolism for effective eradication of bacteria. In the DIBD model, our results confirmed that the D-lip/CM@GM enhanced bacteria clearance and reprogrammed dysregulated metabolism, thereby significantly improving bone regeneration. In conclusion, this GAPDH-Silence microsphere system may provide a viable strategy to promote diabetic infection bone regeneration.

Abstract Background As the global population continues to age, social realities such as advanced age, disability and living alone are coming to the fore, and the demand for medical care and health services for the elderly is increasing dramatically, especially in geriatrics. Given the important role geriatric nurses play in the diagnosis and treatment of diseases and rehabilitation of elderly patients, and due to the uniqueness and complexity of geriatric work, this requires geriatric nurses not only to have the competencies that are available in general nursing, but also to ensure that they have sufficient geriatric core competencies in order to effectively meet the needs of the patients and accelerate their recovery. Although previous studies have investigated the core competencies of nursing staff, there has been little research on geriatric nurses’ core geriatric nursing competencies and their predictors. The aim of this study was to investigate the current status of the geriatric nursing competency inventory (GNCI) among geriatric nurses using latent profiling, to identify potential subgroups and their population characteristics, and to explore the factors that influence the potential subgroups. Methods From January to March 2024, 1,313 geriatric nurses in Hefei City were selected by stratified cluster sampling method and surveyed with general information questionnaire, geriatric nursing competency inventory, and occupational coping self-efficacy scale for nurses(OCSE-N). Potential subgroups of GNCI differences among geriatric nurses were identified by latent profile analysis (LPA). Multiple logistic regression analyses were used to explore the factors influencing the GNCI of geriatric nurses with different latent profiles. Results Geriatric nurses’ OCSE-N was positively correlated with GNCI, and the GNCI score was 123.06(41.60), which indicated that geriatric nurses’ GNCI was at an intermediate level. The OCSE-N score was 35.44(7.34), which was at a relatively high level. There was heterogeneity in the GNCI of geriatric nurses, which was classified into three subgroups i.e., Low-competency group, Medium-competency group, High-competency group. The results of multiple logistic regression analyses showed that OCSE-N, title, whether or not they attended geriatric nurse specialist training, and specialist nurse status were predictors of GNCI among geriatric nurses (P

Abstract When traditional statistical quality control protocols, represented by the Westgard protocol were applied to infectious disease serology, the rejection limits were questioned because of the high rejection probability. We first define the probability of false rejection (Pfr) and error detection (Ped) for infectious disease serology. QC data in 6 months were collected and the Pfr of each rule in the Westgard protocol and Rilibak protocol was evaluated. Then, as improvements, we chose different rules for negative and positive QC data to constitute an asymmetric protocol, furthermore, while reagent lot changes, the mean value of QC protocol is reset with the first 15 QC results of new lot reagent. QC materials and Standard Reference Materials were tested synchronously in the next 6 months, to verify whether the Pfr and Ped of the asymmetric protocol could meet the requirement. Protocol 1 exhibited the higher level of rejection rate among the two protocols, especially after reagent lot changes; Pfr below the lower control limit (LCL) was 1.39–21.78 times higher than the upper control limit (UCL); false rejections were more likely to occur in negative QC data, with Pfr-total of 27–65%. The asymmetric protocol can significantly reduce the proportion of analytes with Pfr by over 20%. Systematic error due to reagent lot changes and random error due to routine QC data variation were considered potential factors for excessive Pfr. Asymmetric QC protocol that can reduce Pfr by different control limits for negative and positive QC data.

Abstract Objectives Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is extensively employed for the identification of filamentous fungi on MALDI Biotyper (Bruker Daltonics) and Vitek MS (biomerieux), but the performance of fungi identification on new EXS2600 (Zybio) is still unknow. Our study aims to evaluate the new EXS2600 system's (Zybio) ability to rapidly identify filamentous fungi and determine its effect on turnaround time (TAT) in our laboratory. Methods We tested 117 filamentous fungi using two pretreatment methods: the formic acid sandwich (FA-sandwich) and a commercial mold extraction kit (MEK, Zybio). All isolates were confirmed via sequence analysis. Laboratory data were extracted from our laboratory information system over two 9-month periods: pre-EXS (April to December 2022) and post-EXS (April to December 2023), respectively. Results The total correct identification (at the species, genus, or complex/group level) rate of fungi was high, FA-sandwich (95.73%, 112/117), followed by MEK (94.02%, 110/117). Excluding 6 isolates not in the database, species-level identification accuracy was 92.79% (103/111) for FA-sandwich and 91.89% (102/111) for MEK; genus-level accuracy was 97.29% (108/111) and 96.39% (107/111), respectively. Both methods attained a 100% correct identification rate for Aspergillus, Lichtheimia, Rhizopus Mucor and Talaromyces species, and were able to differentiate between Fusarium verticillioides and Fusarium proliferatum within the Fusarium fujikuroi species complex. Notably, high confidence was observed in the species-level identification of uncommon fungi such as Trichothecium roseum and Geotrichum candidum. The TAT for all positive cultures decreased from pre EXS2600 to post (108.379 VS 102.438, P

Abstract The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.

Abstract Rammed earth is a kind of cleaning material, widely used in all kinds of buildings in the world. The Great Wall of ancient China is a typical world cultural site built from rammed earth. The rammed earth Great Wall of Shanhaiguan is close to Bohai Bay, which has suffered from long-term erosion by rain, causing a series of problems such as soil loss, collapse and gully flushing. The protection materials of the rammed earth site have always puzzled scholars. However, during the rainy season, it was found that some of the walls at Xiaowan Gouge and Nantuzhuang Gouge in the Shanhaiguan Great Wall had unwashed traces, the soil surface of the walls was intact, and the anti-erosion ability of the walls was significantly higher than that of other places. In order to explore the reasons for its strong anti-erosion ability in the natural state of rammed earth wall, guide the protection of rammed earth Great Wall, and carry out different experimental tests to explore its anti-erosion reasons and internal mechanisms. Firstly, the characteristics of rammed soil were understood through the composition test of rammed soil, and the indoor and outdoor erosion test was carried out to determine that the anti-erosion reason was the protection of gray-green soil crust. The property and composition of soil crust were determined through the immersion test and genome sequencing. Finally, the protection mechanism of soil crust was analyzed by scanning electron microscopy.

Abstract In the pursuit of cost reduction and enhanced construction efficiency in the mechanical connection of reinforcing bars within precast assembled concrete structures, a novel-reinforcing bar connection joint is introduced. This innovative solution entails the utilization of 40Cr alloy steel tubing and the implementation of a hot forging high-precision rolling process to fabricate a series of new rebar joint specimens. Through unidirectional tensile testing, the failure modes of the specimens and the variations in load–displacement curves were meticulously analyzed. The investigation delved into the intricacies of the joint connection mechanism, scrutinizing the influence of three pivotal structural design parameters—namely the embedded length of the rebar, the sleeve thickness, and the clamp thickness—on the ultimate bearing capacity of the joint. The findings indicate that an embedded rebar length ranging from 1.5 to 1.75 times the nominal rebar diameter ensures a tensile strength ratio of the joint to the standard rebar tensile strength not less than 1.1. Notably, the sleeve thickness emerged as a significant factor impacting connection performance, while the clamp thickness exhibited negligible effects on specimen performance. Furthermore, an augmented internal thread angle was found to enhance shear resistance. The optimal design parameters encompass an outer sleeve diameter of 78 mm, a large end clamp diameter of 65 mm, and an internal thread angle of 60°. The proposed single taper sleeve locking-type joint, characterized by a rational structural design, aligns with the stipulations of Grade I joints outlined in the industry standard JG/J 107–2016. This novel joint design is well-suited for the mechanical connection of large-diameter rebars, offering a promising avenue for advancing construction practices in precast assembled concrete structures.

With the increasing demand for the refined management of residential loads, the study of the non-invasive load monitoring (NILM) technologies has attracted much attention in recent years. This paper proposes a novel method of residential load identification based on load feature matrix and improved neural networks. Firstly, it constructs a unified scale bitmap format gray image consisted of multiple load feature matrix including: V-I characteristic curve, 1–16 harmonic currents, 1-cycle steady-state current waveform, maximum and minimum current values, active and reactive power. Secondly, it adopts a convolutional layer to extract image features and performs further feature extraction through a convolutional block attention module (CBAM). Thirdly, the feature matrix is converted and input to a bidirectional long short-term memory (BiLSTM) for training and identification. Furthermore, the identification results are optimized with dynamic time warping (DTW). The effectiveness of the proposed method is verified by the commonly used PLAID database.

IntroductionObesity, a pervasive global epidemic, has heightened susceptibility to chronic ailments and diminished the overall life expectancy on a global scale. Probiotics and inulin (IN) have been documented to mitigate obesity by exerting an influence on the composition of the gut microbiota. Whether heat-killed Bifidobacterium longum BBMN68 (MN68) and IN have an anti-obesity effect remains to be investigated.MethodsIn this study, Wistar rats were fed a high-fat diet (HFD), and orally administered heat-killed MN68 (2 × 1011 CFU/kg) and/or inulin (0.25 kg/kg) for 12 weeks. Histological analysis, serology analysis and 16S rRNA gene sequencing were performed.ResultsHeat-killed MN68 + IN treatment showed an enhanced effect on preventing weight gain, diminishing fat accumulation, and regulating lipid metabolism, compared to either heat-killed MN68 treatment or inulin treatment. Gut microbiota results showed that heat-killed MN68 + IN treatment significantly increased the relative abundance of Bacteroidota, Oscillospira, Intestinimonas, Christensenella, and Candidatus_Stoquefichus, and reduced the relative abundance of Enterococcus. Furthermore, heat-killed MN68 + IN significantly increased the SCFA levels, which were correlated with changes in the gut microbiota.DiscussionThis research provides support for the application of heat-killed MN68 and IN in the treatment of obesity, and highlights the combination of heat-killed BBMN68 and IN as functional food ingredients.

This paper intends to provide key insights to the manufacturing industrial park designers for selecting the typical days of electric load and planning the resources for energy-producing infrastructure. First, a hybrid time-series model of energy-consuming equipment based on the autoregressive integral moving average model (ARIMA) and temporal convolutional network (TCN) is generated. According to this model, the energy consumption (EC) curve of large equipment in the industrial park can be depicted. Moreover, the present study designed a TLSM-IPML (typical load stratification method for industrial parks with manufacturing load) algorithm based on the typical day-selected method. The data clustering method is utilized to analyze the energy usage characteristics. Furthermore, an energy usage-based planning model is proposed, network constraints are considered, and a multi-optional method is designed to solve the problem. Finally, case studies validate the superior performance of TLSM-IPML in analyzing the characteristics of energy consumption and planning the model in reducing MES (manufacturing industrial factory integrated energy system) economic costs.

Abstract Three‐dimensional (3D) current collectors (CCs) have emerged as an effective strategy to inhibit dendrites and ensure the safety of lithium (Li) metal anodes. However, existing 3D CCs are generally too heavy (typically tens of mg cm−2) or too thick (tens to hundreds of micrometers), making large‐scale production and further application challenging. Additionally, the use of single‐component 3D CCs, whether electrically active or inert, only exhibits limited effects on stabilizing Li anodes. Here, we present a scalable screen‐printing technique for the synthesis of ultralight (~0.4 mg cm−2) and ultrathin (~0.54 μm) SiO2 grids on Cu foil to regulate both the vertical electric field and Li‐ion concentration by forming an electrically active/inert dual‐function architecture. This technology breaks the limitations of traditional 3D CCs in material/fabrication costs, weight, thickness and especially, scalability for large‐scale fabrication. By using this dual‐function architecture, our Cu@SiO2‐grid CCs (~8.31 mg cm−2), which are even lighter than the original Cu‐foil CCs (~8.85 mg cm−2), realize an ultra‐smooth anode surface without Li dendrites, and thus leads to an ultra‐long cyclic life of over 1500 h at 1 mA cm−2. The assembled Li metal batteries demonstrate excellent capacity retention of ~80% over 400 cycles at 1 C and ~ 76% over 250 cycles at 5 C, which highlight the promising 3D CCs for practical applications.

Ti6Al4V lattice structures (LSs) have great potentials in medical implants due to their excellent biocompatibility and high strength-to-weight ratio. This paper investigates the manufacturability and performance of unique Ti6Al4V heterogeneous glass sponge (GS) LSs fabricated via laser powder bed fusion (LPBF). Compared with the commonly used LSs, including Cross, body-centered cubic (BCC), Diamond and octet-truss (OCT), Ti6Al4V GS LSs exhibit the strongest compressive strength and energy absorption ability of 58 MPa and 23.84 J/cm3, respectively. Finite element (FE) models are established to evaluate the macroscopic deformation, microscopic stress and strain evolution in the solid struts of five different LSs. Local plastic stresses are found to generate near the nodes of Cross, BCC, Diamond and OCT LSs, thus forming plastic hinges, while the GS LSs can evenly transfer local plastic stresses to the grid-like rods of each layer, eventually showing a uniform stress distribution. Moreover, all the LSs are featured with satisfactory biocompatibility concerning cytotoxicity, cell proliferation, and cell attachment. The results indicate that the Ti6Al4V GS LSs can address the conflict between lightness and strength simultaneously, thus achieving excellent energy absorption performance, compressive properties and great biocompatibility, endowing it with tremendous application potential in the field of medical implants.

How to precisely modulate the morphology and distribution of precipitated phases to have long-term antibacterial activity and outstanding strength-ductility has slowed the overall development and engineering applications of Cu-bearing biomedical titanium alloys. For the first time, the electron beam powder bed fusion (EBPBF) was employed to design titanium alloys with a completely solid solution, and containing fine Ti2Cu precipitates in both uniform and layered structures. The mechanical properties of the designed alloy with the layered (α-Ti and Ti2Cu) structure are superior to the other structures, especially with an outstanding compressive yield strength of 1221.9 MPa. Simultaneously, the wear resistance of the heterogeneous structures containing Ti2Cu precipitates was significantly improved, with a specific wear rate only half of that of the EBPBF-fabricated Ti6Al4V alloy. The compact arrangement of Ti2Cu phases created a large number of interfaces conducive to the formation of corrosion channels, which provided the capacity of continuous Cu2+ release. This work comprehensively analyzes the effects of heterogeneous structures on enhancing the sustained antibacterial capacity and optimizing the mechanical properties of a Cu-containing titanium alloy, laying a good foundation for their application in clinical and implantable devices.

It is well known that common orthopedic implant titanium materials have a higher Young's modulus (100+GPa) than bone. Stress shielding leaves the neighboring bone unstressed for long periods of time and bone resorption occurs around the implant, which triggers implant failure. Here, we developed a novel Ti–Mo–Zr alloy with a customizable Young's modulus by using in situ alloying and laser additive manufacturing (AM) methods. Electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) were used to elucidate the distribution of submicron Mo particles in the materials, and selected laser melting (SLM) was used to customize the spatial concentration of Mo elements in novel alloys in a predictable way. By regulating the concentration and distribution of unmelted Mo particles, it is possible to tailor the grain growth angle and phases of the alloy. This results in excellent comprehensive mechanical properties for the alloys with a customizable Young's modulus ranging from 59.3 to 83.6 GPa, tensile strength from 564 to 1045 MPa, and a fracture strain of up to 13.1%. Orthopedic implants with a customizable Young's modulus can be adapted to bone tissues in different body parts, providing endless possibilities for personalized surgical solutions for patients. The economic potential for such materials in smart medical is enormous.

Abstract The fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy‐intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy‐intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank‐Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance‐constrained theory of a reserve coefficient to describe time‐variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118‐bus system show that the energy‐intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power.

Abstract Background Understanding how plants and pathogens regulate each other's gene expression during their interactions is key to revealing the mechanisms of disease resistance and controlling the development of pathogens. Despite extensive studies on the molecular and genetic basis of plant immunity against pathogens, the influence of pitaya immunity on N. dimidiatum metabolism to restrict pathogen growth is poorly understood, and how N. dimidiatum breaks through pitaya defenses. In this study, we used the RNA-seq method to assess the expression profiles of pitaya and N. dimidiatum at 4 time periods after interactions to capture the early effects of N. dimidiatum on pitaya processes. Results The study defined the establishment of an effective method for analyzing transcriptome interactions between pitaya and N. dimidiatum and to obtain global expression profiles. We identified gene expression clusters in both the host pitaya and the pathogen N. dimidiatum. The analysis showed that numerous differentially expressed genes (DEGs) involved in the recognition and defense of pitaya against N. dimidiatum, as well as N. dimidiatum’s evasion of recognition and inhibition of pitaya. The major functional groups identified by GO and KEGG enrichment were responsible for plant and pathogen recognition, phytohormone signaling (such as salicylic acid, abscisic acid). Furthermore, the gene expression of 13 candidate genes involved in phytopathogen recognition, phytohormone receptors, and the plant resistance gene (PG), as well as 7 effector genes of N. dimidiatum, including glycoside hydrolases, pectinase, and putative genes, were validated by qPCR. By focusing on gene expression changes during interactions between pitaya and N. dimidiatum, we were able to observe the infection of N. dimidiatum and its effects on the expression of various defense components and host immune receptors. Conclusion Our data show that various regulators of the immune response are modified during interactions between pitaya and N. dimidiatum. Furthermore, the activation and repression of these genes are temporally coordinated. These findings provide a framework for better understanding the pathogenicity of N. dimidiatum and its role as an opportunistic pathogen. This offers the potential for a more effective defense against N. dimidiatum.

Strawberry, considered to be a model for non-climacteric fruits, has traditionally been viewed as having a less pronounced reliance on ethylene in fruit development. However, the inherent tissue heterogeneity within strawberry fruit, coupled with variable ethylene production levels, suggests a potential role for ethylene in the maturation of the true fruit, the achenes, dispersed on the fleshy receptacle. This intricate process is likely to exert ripple effects on the subsequent growth and ripening of the receptacle. To comprehensively unravel the functions of ethylene in strawberry fruit, there is a need for ethylene detection sensors, ethylene response reporter transgenic plants, and genetically engineered mutants achieved through genome editing, encompassing both biosynthesis and signaling mutants.

δ-ferrite usually plays an important role in the mechanical properties of martensitic stainless steel. However, there are few studies on the effect of δ-ferrite on hydrogen embrittlement (HE) in martensitic steel. In this study, the H-trapping of δ-ferrite and the effect on the HE in martensitic stainless steel is investigated. The hydrogen distribution characteristics were observed by atomic force microscope scanning (AFM), and the related results show that the δ-ferrite, martensite and δ-ferrite/matrix (F/M) interface can act as hydrogen traps. The martensite and F/M interface has the higher hydrogen-trapping capability, hydrogen diffuses in δ-ferrite is faster than that of martensite. During deformation, Electron Back Scatter Diffraction (EBSD) observation shown that the exist of δ-ferrite can release the stress concentration caused by the poor deformation ability of martensitic grains, and further improve the resistance of HE. The results provide valuable insights into the behavior of δ-ferrite in the HE.

Adapting virtual power plants (VPPs) to the development of new power systems has gradually become a trend. Meanwhile, the economical and effective use of demand-side resources in VPPs has become a research hotspot in the field of new energy. This paper proposes a low-carbon operation strategy with progressive demand response (DR). A DR model id established by evaluating the scheduling potential of low-carbon and flexible loads and the DR transfer of different users motivated by incentive mechanisms. Because the changing carbon price in carbon trading also affects the response transfer amount, it is beneficial to reduce the operating cost of the energy system by using consumers’ surplus value according to consumer psychology to guide energy users to participate in DR. Finally, wind, light, user load data and VPP internal unit output are optimized in rolling mode over multiple time scales, and the optimal adjustment results of the previous day are taken as the basis. At the same time, the output deviation penalty function is added to further reduce the system cost. The nonlinear model of DR cost is transformed into a mixed integer linear model, which is modeled on MATLAB software and solved by a CPLEX optimization solver. The case analysis shows that the progressive DR strategy may increase the total operating cost of the system, but reduce the cost of the carbon emission. The proposed model and method can not only improve the feasibility and effectiveness of market supervision strategy, but also provides a useful reference for operators and demand-side users in energy systems to choose strategies.

Abstract Automatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.

Titanium carbide (TiC), tantalum carbide (TaC) and zirconium carbide (ZrC) in form of particles are widely added in W to improve its mechanical and anti-irradiation properties. However, TiC, TaC and ZrC particles are decomposed to Ti-C-O, Ta-C-O and Zr-C-O in W, respectively. To understand the micro-mechanisms, we carry out systematical simulations and find that the presence of impurities such as O, N, and P has great influence on the bond length and bond strength of Ti-C, Ta-C and Zr-C in W. The bond lengths of Ti-C, Ta-C and Zr-C are generally increased when O meets Ti-C, Ta-C and Zr-C. The bond strength of O to Ti-C, Ta-C and Zr-C is much larger than that of C to Ti-O, Ta-O and Zr-O. In contrast, N has little effect on the Ti-C bond, but has great influence on the Ta-C bond. P significantly increases Ta-C bond length, while its influence on Zr-C bond is negligible. The kinetics calculations elucidate that the diffusion barriers of C away from TiO are significantly increased by about 1 eV. It is very difficult for C to escape from TiO and ZrO, and it is also extremely difficult for O to escape from TiC, TaC and ZrC. This may be the reason why Ti-C-O, Ta-C-O, and Zr-C-O particles are found in W when TiC TaC and ZrC compounds are dispersed in W.

PurposeCamptodactyly, clasped thumbs, and windblown hands are distinctive features of distal arthrogryposis (DA). Current therapeutic interventions often yield suboptimal effects, predisposing patients to relapses and complications. This study explicates a corrective approach involving a progressive extension brace for the management of DA and evaluates its clinical outcomes.MethodsBetween 2015 and 2023, progressive extension braces were used in 32 DA patients, with an average follow-up of 4.8 years. Patients were stratified by age into four groups: 0–1, 1–3, 3–7, and above 7 years. The correction of camptodactyly was assessed based on the total active movement (TAM) of metacarpophalangeal joints (MPJ) and proximal interphalangeal joints (PIPJ), as well as the extensor lag of PIPJ. Clasped thumb correction was evaluated by measuring the thumb-to-index finger metacarpal angle (M1M2 angle) and the degree of deviation at the first MPJ (M1P1 angle). The quality of life for the children was measured using PedsQL 4.0, while parental satisfaction was gauged using the FACE questionnaire.ResultsEarlier intervention with a progressive extension brace yielded superior corrective results. Infants aged 0–1 year and toddlers aged 1–3 years achieved average TAM scores of 152° and 126° after correction; however, patients older than 3 years experienced a significant decrease in TAM with the same treatment. Infants and toddlers with DA showed improvement in the average extensor lag from 46° to 6°. The M1M2 angle increased from an average of 38° to 65°, with the M1P1 angle decreasing from an average of 43° to 5°. After the treatment, average PedsQL scores of 94.7 (parent-reported) and 89.3 (child-reported) were achieved. Among the 32 parents, 24 expressed high satisfaction, 5 expressed moderate satisfaction, and 3 expressed fair satisfaction.ConclusionThe early, progressive, and consistent use of an extension brace significantly improved joint mobility and corrected camptodactyly and clasped thumbs. It can be an effective approach to addressing hand deformities in patients with DA.

Morphology of bivalves, a kind of quantitative character, is closely connected with the ecological and physiological behaviors of bivalves, including growth, survival, metabolism and reproduction. In this study, a multivariate statistical method was used to analyze seven indices for shell shape evaluation of Ruditaps philippinarum (R. philippinarum). Polymorphism of R. philippinarum shell shape across seven geographical populations from the southern and northern regions of China was revealed, and decisive effect of shell shape in determining the trait of weight was discussed. In total, three types of R. philippinarum shell shape were observed: globular, flat, and lateral-view round. The Laizhou (LZ) population from the north of China displayed an approximately globular shell shape, with a shell-width-to-shell-length ratio of 0.5496 and an ellipse index ventral view (EIV) value of 0.9543. The Lianjiang (LJ) population from the south of China displayed a flat shell shape, with a shell-width-to-shell-length ratio of 0.4044, an ellipse index lateral view (EIL) value of 1.2518, and an EIV value of 0.7063. The Zhangzidao (ZZD) population from the north of China displayed a lateral view round shell shape, with a shell-width-to-shell-length ratio of 0.7476 and a circle index lateral view (CIL) value of 0.7195. According to the results of principal component analysis (PCA), discriminant analysis and cluster analysis on shell shape parameters, LJ population and LZ population could be grouped separately. However, despite the long geographical distance, Rongcheng (RC) population was highly similar with ZZD population in terms of shell shape traits, implying that these two populations might be more closely related. Lowest degree of divergence in shell shape was observed in Beihai (BH) population and Yingkou (YK) population, both of which belonged to Zuidong (ZD) population. Weight of globular Manila clams was significantly higher than that of flat clams (P < 0.05). This study not only lays a foundation for conservation of R. philippinarum germplasm resources, but also highlights the importance of considering shell morphology in the breeding and culture of R. philippinarum.

Hereditary hemochromatosis (HH) is a disease characterized by disordered iron metabolism. It often involves mutations of the HFE gene, which encodes the homeostatic iron regulator protein (HFE), as well as mutations affecting hepcidin antimicrobial peptide, hemojuvelin, or transferrin receptor 2. Historically, HH has been observed primarily in European and European diaspora populations, while classical HH is rare in Asian populations, including in China. In this article, we report a rare case of HH in a Chinese man that could be attributed to a heterozygous C282Y/H63D HFE mutation. Based on clinical examination, liver biopsy, and genetic testing results, the patient was diagnosed with HH. Clinical signs and symptoms and serum iron-related test results were recorded for a period of two years after the patient began treatment. Over this observation period, the patient was subjected to 25 phlebotomies (accounting for a total blood loss of 10.2 L). His serum ferritin levels decreased from 1550 μg/L to 454 μg/L, his serum iron concentration decreased from 40 μmol/L to 24.6 μmol/L, and his transferrin saturation decreased from 97.5% to 55.1%. Early diagnosis is essential for patients with HH to obtain good outcomes. Regular phlebotomy after diagnosis can improve HH symptoms and delay HH disease progression.

Near real-time spatial prediction of earthquake-induced landslides (EQILs) can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake; thus, EQIL prediction is very crucial to the 72-hour ‘golden window’ for survivors. This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau, a famous seismically-active zone, and proposes a novel interpretable self-supervised learning (ISeL) method for the near real-time spatial prediction of EQILs. This new method innovatively introduces swap noise at the unsupervised mechanism, which can improve the generalization performance and transferability of the model, and can effectively reduce false alarm and improve accuracy through supervised fine-tuning. An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution. Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods. Furthermore, according to the interpretable module in the ISeL method, the critical controlling and triggering factors are revealed. The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

Abstract A method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one‐sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.

The removal of nitrogen trifluoride (NF3) is of significant importance in atmospheric chemistry, as NF3 is an important anthropogenic greenhouse gas. However, the radical species OH and O(1D) in atmospheric conditions are nonreactive towards NF3. It is necessary to explore possible ways to remove NF3 in atmosphere. Therefore, the participation of water molecules in the reaction of NF3 with OH was discussed, as water is abundant in the atmosphere and can form very stable complexes due to its ability to act as both a hydrogen bond donor and acceptor. Systemic DFT calculations carried out at the CBS-QB3 and ωB97XD/aug-cc-pVTZ level of theory suggest that water molecules could affect the NF3 + OH reaction as well. The energy barrier of the SN2 mechanism was decreased by 8.52 kcal/mol and 10.58 kcal/mol with the assistance of H2O and (H2O)2, respectively. Moreover, the presence of (H2O)2 not only reduced the energy barrier of the reaction, but also changed the product channels, i.e., formation of NF2O + (H2O)2-HF instead of NF2OH + (H2O)2-F. Therefore, the removal of NF3 by reaction with OH is possible in the presence of water molecules. The results presented in this study should provide useful information on the atmospheric chemistry of NF3.

Background: Patients with unprotected left main (UPLM) disease who underwent percutaneous coronary intervention (PCI) were found to have inconsistent results compared to those treated with coronary artery bypass grafting (CABG). Methods: We identified and enrolled randomized controlled trials (RCTs) and observational studies (OSs) comparing PCI versus CABG for UPLM disease. A meta-analysis was performed using Stata 17.0. The primary endpoints were major adverse cardiovascular and cerebrovascular events (MACCEs). Additionally, all-cause death, cardiac death, myocardial infarction (MI), stroke, target vessel revascularization (TVR), and stent thrombosis (ST) were included as secondary endpoints. The odds ratios and 95% confidence intervals (CIs) were calculated. Sensitivity analyses were implemented if I2 > 50% or p < 0.01. Publication bias analysis was conducted if more than 10 studies were included. Results: A total of 5 RCTs and 18 OSs involving 35,409 patients were included. The CABG strategy had a significantly lower incidence of MACCEs, primarily due to TVR. A significantly lower stroke rate was observed with the PCI strategy, as well as a significantly lower all-cause death, cardiac death, MI, and ST rate compared with the CABG strategy. Conclusions: MACCE rates were significantly lower in patients who underwent CABG, primarily due to TVR, but stroke rates were higher. RCTs with different study types need further investigation to confirm the most effective strategy.

The drain pipe wrapped in steel wire mesh serves a dual purpose of drainage and reinforcement in tailings pond projects. The self-filtering layer that develops upstream of the steel wire mesh influences the reinforcement characteristics of the drainage pipe. This study first conducts interfacial shearing experiments to explore the impact of the self-filtering layer on the shearing properties between tailings and the steel wire mesh. An exponential interface constitutive model is then proposed to delineate the shear stress–displacement relationship. Finally, through finite element simulations, the study assesses the effect of the self-filtering layer on the load-bearing behavior of the drain pipe, considering the interactive dynamics between the tailings and the steel wire mesh. The results reveal that the interfacial shear strength, across varying median particle sizes of the self-filtering layer, adheres to Mohr–Coulomb strength theory. Specifically, as the median particle size of the self-filtering layer increases, interfacial cohesion diminishes while the friction coefficient rises. The initial shear stiffness demonstrates a linear increase with the median particle size. With the presence of the self-filtering layer, the pull-out resistance of the drainage pipe can be enhanced by up to 26%. Moreover, the self-filtering layer significantly affects the distribution of negative skin friction. This research enhances the safety assessment of tailings ponds by providing crucial insights and solutions, emphasizing the influence of the self-filtering layer on the bearing behavior of the drain pipe.

Automatic recognition of numerous coseismic landslides after a violent earthquake is crucial for emergency rescue and post-disaster reconstruction. Currently, deep learning techniques have achieved state-of-the-art performance in coseismic landslide recognition. However, Convolutional Neural Networks (CNNs) often lose detailed information during downsampling and cannot adequately learn changeable shapes, colors, and sizes of coseismic landslides. In addition, complicated backgrounds, e.g., bare slopes and dry riverbeds, are easily misidentified as coseismic landslides. Focusing on the above difficulties, this work proposes a Gated Dual-Stream Convolutional Neural Network (GDSNet) for landslide recognition, which contains two branches and a feature fusion module. One branch, called as CPSConv, can extract detailed landslide information of shapes, sizes, spectra, and textures and guarantee the accurate identification of small landslides. Another branch utilizes a gated convolution strategy to adjust feature weights and importance and to enhance landslide features and suppress background features. The feature aggregation and fusion module fuses the features from two branches to effectively improve the recognition accuracy of coseismic landslides. The GDSNet is applied to the landslide recognition of four earthquakes by model training and testing. In test dataset, compared with 9 state-of-the-art models, the mIoU, F1, Kappa coefficient values by the GDSNet are improved by at least 12.30%, 8.12%, and 16.25%, respectively. The recognition accuracy of small landslides is improved by 1.08%-37.68% than the other 9 deep learning models.

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Background and aimInvasion of the central nervous system by Treponema pallidum can occur at any stage of syphilis. In the event that T. pallidum is not cleared promptly, certain individuals may experience progression to neurosyphilis, which manifests as cognitive and behavioral abnormalities, limb paralysis, and potentially fatal outcomes. Early identification or prevention of neurosyphilis is therefore crucial. The aim of this paper is to conduct a critical and narrative review of the latest information focusing exclusively to the pathogenesis and clinical management of neurosyphilis.MethodologyTo compile this review, we have conducted electronic literature searches from the PubMed database relating to neurosyphilis. Priority was given to studies published from the past 10 years (from 2013 to 2023) and other studies if they were of significant importance (from 1985 to 2012), including whole genome sequencing results, cell structure of T. pallidum, history of genotyping, and other related topics. These studies are classic or reflect a developmental process.ResultsNeurosyphilis has garnered global attention, yet susceptibility to and the pathogenesis of this condition remain under investigation. Cerebrospinal fluid examination plays an important role in the diagnosis of neurosyphilis, but lacks the gold standard. Intravenous aqueous crystalline penicillin G continues to be the recommended therapeutic approach for neurosyphilis. Considering its sustained prominence, it is imperative to develop novel public health tactics in order to manage the resurgence of neurosyphilis.ConclusionThis review gives an updated narrative description of neurosyphilis with special emphasis on its pathogenesis, susceptibility, diagnosis, treatment, and prevention.

Peripheral nerve injury can lead to progressive muscle atrophy and poor motor function recovery, which is a difficult point of treatment, and the mechanism needs to be further explored. In previous studies, we found that miR-142a-3p was significantly upregulated and persistently highly expressed in denervated mouse skeletal muscle. Here, we show that overexpression of miR-142a-3p inhibited the growth and differentiation of C2C12 myoblast, while knockdown of miR-142a-3p had a promoting effect. In vitro, knockdown of miR-142a-3p in denervated mouse skeletal muscle effectively increased proliferating muscle satellite cells and ameliorated muscle atrophy. Mechanistically, the myoregulator Mef2a was proved to be an important downstream target of miR-142a-3p, and miR-142a-3p regulates skeletal muscle differentiation and regeneration by inhibiting the expression of Mef2a. The co-knockdown of Mef2a and miR-142a-3p effectively alleviated or offset the biological effects of miR-142a-3p knockdown. In conclusion, our data revealed that miR-142a-3p regulates neurogenic skeletal muscle atrophy by targeting Mef2a.

Although the use of heterosis in maize breeding has increased crop productivity, the genetic causes underlying heterosis for nitrogen (N) use efficiency (NUE) have been insufficiently investigated. In this study, five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations (ILs) consisting of recombinant inbred lines (RIL) and advanced backcross (ABL) populations, derived from crossing Ye478 with Wu312. Both populations were crossed with P178 to construct two testcross populations. IL populations, their testcross populations, and the midparent heterosis (MPH) for NUE were investigated. Kernel weight, kernel number, and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations. Based on a high-density linkage map, 138 quantitative trait loci (QTL) were mapped, each explaining 5.6%–38.8% of genetic variation. There were 52, 34 and 52 QTL for IL populations, MPH, and testcross populations, respectively. The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci. A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL. Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy. Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.

Autologous nerve grafting serves is considered the gold standard treatment for peripheral nerve defects; however, limited availability and donor area destruction restrict its widespread clinical application. Although the performance of allogeneic decellularized nerve implants has been explored, challenges such as insufficient human donors have been a major drawback to its clinical use. Tissue-engineered neural regeneration materials have been developed over the years, and researchers have explored strategies to mimic the peripheral neural microenvironment during the design of nerve catheter grafts, namely the extracellular matrix (ECM), which includes mechanical, physical, and biochemical signals that support nerve regeneration. In this study, polycaprolactone/silk fibroin (PCL/SF)-aligned electrospun material was modified with ECM derived from human umbilical cord mesenchymal stem cells (hUMSCs), and a dual-bionic nerve regeneration material was successfully fabricated. The results indicated that the developed biomimetic material had excellent biological properties, providing sufficient anchorage for Schwann cells and subsequent axon regeneration and angiogenesis processes. Moreover, the dual-bionic material exerted a similar effect to that of autologous nerve transplantation in bridging peripheral nerve defects in rats. In conclusion, this study provides a new concept for designing neural regeneration materials, and the prepared dual-bionic repair materials have excellent auxiliary regenerative ability and further preclinical testing is warranted to evaluate its clinical application potential.

Abstract Background Various data have been obtained on the relationship between body mass index (BMI) and C-reactive protein (CRP) and periodontitis. The aim of this study was to determine whether CRP/BMI are associated with periodontitis using data from the National Health and Nutrition Examination Survey (NHANES) database. Methods A cross-sectional analysis of data from 3602 participants in the 2009–2010 NHANES cycle was performed. The definition of periodontitis was used to divide participants into four groups according to the criteria of Eke. Correlations between CRP/BMI and periodontitis were tested for statistical significance by means of descriptive statistics, multivariate regression, and subgroup-stratified analyses, with and without adjustments for confounders (such as age and sex). Results There were no statistically significant differences (p > 0.05) regarding BMI and the development of periodontitis. After adjustment for age, sex, race, marital status, annual family income, alcohol consumption, hypertension, smoking, chronic pulmonary disease, cardiovascular disease, diabetes, flossing, and arthritis, CRP correlated significantly with the development of periodontitis in the subgroups stratified by obesity, with an odds ratio (OR) of 1.2 (95% CI, 1.0 to 1.5). Conclusion Through data analysis, we found an association between CRP levels and periodontitis prevalence in the American population, although this association was only present in the obese population. While there are several hypotheses about the underlying mechanism, further studies are needed to validate these findings.

Abstract Background Heme oxygenase 1 (HO-1) has an influential but insufficiently investigated effect on ferroptosis, which is a novel form of programmed cell death and may play an effect on nonalcoholic steatohepatitis (NASH). However, the understanding of the mechanism is limited. Herein, our study aimed to explore the mechanism and role of HO-1 in NASH ferroptosis. Methods Hepatocyte conditional HO-1 knockout (HO-1HEPKO) C57BL/6J mice were established and fed a high-fat diet (HFD). Additionally, wild-type mice were fed either a normal diet or a HFD. Hepatic steatosis, inflammation, fibrosis, lipid peroxidation, and iron overload were assessed. AML12 and HepG2 cells were used to investigate the underlying mechanisms in vitro. Finally, liver sections from NASH patients were used to clinically validate the histopathology of ferroptosis. Results In mice, HFD caused lipid accumulation, inflammation, fibrosis, and lipid peroxidation, which were aggravated by HO-1HEPKO. In line with the in vivo results, HO-1 knockdown upregulated reactive oxygen species accumulation, lipid peroxidation, and iron overload in AML12 and HepG2 cells. Additionally, HO-1 knockdown reduced the GSH and SOD levels, which was in contrast to HO-1 overexpression in vitro. Furthermore, the present study revealed that the NF-κB signaling pathway was associated with ferroptosis in NASH models. Likewise, these findings were consistent with the liver histopathology results of NASH patients. Conclusion The current study showed that HO-1 could alleviate NASH progression by mediating ferroptosis.

The current study involved the preparation of a number of MnOx/Sep catalysts using the impregnation (MnOx/Sep-I), hydrothermal (MnOx/Sep-H), and precipitation (MnOx/Sep-P) methods. The MnOx/Sep catalysts that were produced were examined for their ability to catalytically oxidize formaldehyde (HCHO). Through the use of several technologies, including N2 adsorption–desorption, XRD, FTIR, TEM, H2-TPR, O2-TPD, CO2-TPD, and XPS, the function of MnOx in HCHO elimination was examined. The MnOx/Sep-H combination was shown to have superior catalytic activities, outstanding cycle stability, and long-term activity. It was also able to perform complete HCHO conversion at 85 °C with a high GHSV of 6000 mL/(g·h) and 50% humidity. Large specific surface area and pore size, a widely dispersed active component, a high percentage of Mn3+ species, and lattice oxygen concentration all suggested a potential reaction route for HCHO oxidation. This research produced a low-cost, highly effective catalyst for HCHO purification in indoor or industrial air environments.

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future.

Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize–rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

Zr-based metallic glasses (MGs) are promising materials for mold manufacturing due to their unique mechanical and chemical properties. However, the high hardness of metallic glasses and their tendency to crystallize at high temperatures make it challenging to fabricate precise and smooth microscale structures on metallic glasses. This limitation hampers the development of metallic glasses as molds. Jet electrochemical machining (jet-ECM) is a non-contact subtractive manufacturing technology that utilizes a high-speed electrolyte to partially remove material from workpieces, making it highly suitable for processing difficult-to-machine materials. Nevertheless, few studies have explored microgroove structures on Zr-based MGs using sodium nitrate electrolytes by jet-ECM. Therefore, this paper advocates the utilization of the jet-ECM technique to fabricate precise and smooth microgroove structures using a sodium nitrate electrolyte. The electrochemical characteristics were studied in sodium nitrate solution. Then, the effects of the applied voltages and nozzle travel rates on machining performance were investigated. Finally, micro-helical and micro-S structures with high geometric dimensional consistency and low surface roughness were successfully fabricated, with widths and depths measuring 433.7 ± 2.4 µm and 101.4 ± 1.6 µm, respectively. Their surface roughness was determined to be 0.118 ± 0.002 µm. Compared to non-aqueous-based methods for jet-ECM of Zr-based MGs, the depth of the microgrooves was increased from 20 μm to 101 μm. Furthermore, the processed microstructures had no uneven edges in the peripheral areas and no visible flow marks on the bottom.

Titanium alloys have many excellent characteristics, and they are widely used in aerospace, biomedicine, and precision engineering. Meanwhile, titanium alloys are difficult to machine and passivate readily. Electrochemical grinding (ECG) is an ideal technology for the efficient–precise machining of titanium alloys. In the ECG process of titanium alloys, the common approach of applying high voltage and active electrolytes to achieve high efficiency of material removal will lead to serious stray corrosion, and the time utilized for the subsequent finishing will be extended greatly. Therefore, the application of ECG in the field of high efficiency and precision machining of titanium alloys is limited. In order to address the aforementioned issues, the present study proposed an efficient–precise continuous ECG (E-P-C-ECG) process for Ti–6Al–4V applying high-pulsed voltage with an optimized duty cycle and low DC voltage in the efficient ECG stage and precise ECG stage, respectively, without changing the grinding wheel. According to the result of the passivation properties tests, the ideal electrolyte was selected. Optimization of the process parameters was implemented experimentally to improve the processing efficiency and precision of ECG of Ti–6Al–4V. Utilizing the process advantages of the proposed process, a thin-walled structure of Ti–6Al–4V was obtained with high efficiency and precision. Compared to the conventional mechanical grinding process, the compressive residual stress of the machined surface and the processing time were reduced by 90.5% and 63.3% respectively, and both the surface roughness and tool wear were obviously improved.

Abstract As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO2-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.

Abstract Understanding the molecular mechanisms that regulate grain yield is important for improving agricultural productivity. Protein ubiquitination controls various aspects of plant growth but lacks understanding on how E2-E3 enzyme pairs impact grain yield in major crops. Here, we identified a RING-type E3 ligase SGD1 and its E2 partner SiUBC32 responsible for grain yield control in Setaria italica. The conserved role of SGD1 was observed in wheat, maize, and rice. Furthermore, SGD1 ubiquitinates the brassinosteroid receptor BRI1, stabilizing it and promoting plant growth. Overexpression of an elite SGD1 haplotype improved grain yield by about 12.8% per plant, and promote complex biological processes such as protein processing in endoplasmic reticulum, stress responses, photosystem stabilization, and nitrogen metabolism. Our research not only identifies the SiUBC32-SGD1-BRI1 genetic module that contributes to grain yield improvement but also provides a strategy for exploring key genes controlling important traits in Poaceae crops using the Setaria model system.

Abstract N6-Methyladenosine (m6A) modification is one of the most abundant post-transcriptional modifications that can mediate autophagy in various pathological processes. However, the functional role of m6A in autophagy regulation is not well-documented during Vibrio splendidus infection of Apostichopus japonicus. In this study, the inhibition of m6A level by knockdown of methyltransferase-like 3 (AjMETTL3) significantly decreased V. splendidus-induced coelomocyte autophagy and led to an increase in the intracellular V. splendidus burden. In this condition, Unc-51-like kinase 1 (AjULK) displayed the highest differential expression of m6A level. Moreover, knockdown of AjULK can reverse the V. splendidus-mediated autophagy in the condition of AjMETTL3 overexpression. Furthermore, knockdown of AjMETTL3 did not change the AjULK mRNA transcript levels but instead decreased protein levels. Additionally, YTH domain-containing family protein (AjYTHDF) was identified as a reader protein of AjULK and promoted AjULK expression in an m6A-dependent manner. Furthermore, the AjYTHDF-mediated AjULK expression depended on its interaction with translation elongation factor 1-alpha (AjEEF-1α). Altogether, our findings suggest that m6A is involved in resisting V. splendidus infection via facilitating coelomocyte autophagy in AjULK-AjYTHDF/AjEEF-1α-dependent manner, which provides a theoretical basis for disease prevention and therapy in A. japonicus.

Abstract Objective Osteosarcoma is the most malignant and common primary bone tumor with a high rate of recurrence that mainly occurs in children and young adults. Therefore, it is vital to facilitate the development of novel effective therapeutic means and improve the overall prognosis of osteosarcoma patients via a deeper understanding of the mechanisms of chemoresistance in osteosarcoma progression. Methods In this research, the relationship between ITGB3 and the clinical characteristics of patients was detected through analysis of publicly available clinical datasets. The expression of ITGB3 was analysis in collected human osteosarcoma tissues. In addition, the potential functions of ITGB3 in the cisplatin resistance of osteosarcoma cells were investigated in vitro and in tumor xenotransplantation. Finally, the molecular mechanism of ITGB3 in the progression and recurrence of osteosarcoma were explored via transcriptome analysis. Results ITGB3 was identified as a potential regulator of tumorigenicity and cisplatin resistance in relapsed osteosarcoma. Furthermore, the decreased osteosarcoma cell proliferation and migration ability in ITGB3 knockout osteosarcoma cells were related to increased apoptosis and slowing cell cycle progression. In addition, ITGB3 had a positive correlation with cisplatin resistance in cells and tumor xenografts in mice. Accordingly, ITGB3 performed the functions of proliferation and cisplatin resistance in osteosarcoma through the MAPK and VEGF signaling pathways. Conclusion Our results will contribute to a better understanding of the function and mechanism of ITGB3 in osteosarcoma cisplatin resistance and provide a novel therapeutic target to decrease cisplatin resistance and tumor recurrence in osteosarcoma patients.

Abstract Background Both N6-methyladenosine (m6A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m6A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m6A-related ferroptosis genes in lung adenocarcinoma. Methods Lung adenocarcinoma sample data were downloaded from the University of California Santa Cruz Xena and Gene Expression Omnibus databases. Spearman’s correlation analysis was used to screen for m6A-related ferroptosis genes. Univariate Cox regression, Kaplan–Meier, and Lasso analyses were conducted to identify prognostic m6A-related ferroptosis genes, and stepwise regression was used to construct a prognostic gene signature. The predictive value of the gene signature was assessed using a multivariate Cox analysis. In the validation cohort, survival analysis was performed to verify gene signature stability. The training cohort was divided into high- and low-risk groups according to the median risk score to assess differences between the two groups in terms of gene set variation analysis, somatic mutations, and tumor immune infiltration cells. Results Six m6A-related ferroptosis genes were used to construct a gene signature in the training cohort and a multivariate Cox analysis was conducted to determine the independent prognostic value of these genes in lung adenocarcinoma. In the validation cohort, Kaplan–Meier and receiver operating characteristic analyses confirmed the strong predictive power of this signature for the prognosis of lung adenocarcinoma. Gene set variation analysis showed that the low-risk group was mainly related to immunity, and the high-risk group was mainly related to DNA replication. Somatic mutation analysis revealed that the TP53 gene had the highest mutation rate in the high-risk group. Tumor immune infiltration cell analysis showed that the low-risk group had higher levels of resting CD4 memory T cells and lower levels of M0 macrophages. Conclusion Our study identified a novel m6A-related ferroptosis-associated six-gene signature (comprising SLC2A1, HERPUD1, EIF2S1, ACSL3, NCOA4, and CISD1) for predicting lung adenocarcinoma prognosis, yielding a useful prognostic biomarker and potential therapeutic target.

ABSTRACT Neoscytalidium dimidiatum (class Dothideomycetes) is a fungus responsible for canker disease in pitaya stems and fruits, leading to significant economic losses. However, little is known about the pathogenesis, family evolution, and genetic variants of this species. In this study, we report a high-quality genome sequence of N. dimidiatum based on the Nanopore sequencing technology platform for sequencing and Hi-C assembly technology for genome assembly. The genome contains 12 chromosomes (2n = 2× = 12; diploid), with a sequencing depth of 186.1×, encoding 12,349 proteins. Molecular phylogenetic analysis showed that N. dimidiatum is evolutionarily close to Botryosphaeria dothidea. Compared to other fungi, the N. dimidiatum genome contains many carbohydrate-active enzymes and secondary metabolites. Additionally, we predict that N. dimidiatum contains 121 candidate effectors that may play important roles in infection and colonization, promoting pathogenicity in pitaya. Nine of these effectors were confirmed to contain signal peptides and inhibit BAX/INF1-induced necrosis in Nicotiana benthamiana, demonstrating their importance during infection. Finally, we also confirm that N. dimidiatum does not form an appressorium or infection thread but instead infects pitaya via open stomata. In conclusion, the results provide a foundation for future research on N. dimidiatum and the control of pitaya canker. IMPORTANCE Pitaya canker is a significant disease in the pitaya industry in China, causing significant economic losses. Therefore, systematic research on Neoscytalidium dimidiatum, the fungus implicated in pitaya canker, is essential for comprehending the pathogenesis of this disease and developing effective control strategies. We applied comparative genomics to reveal the genetic evolution, metabolic diversity, environmental adaptation, and pathogenicity of N. dimidiatum, providing ideal targets for studies of pathogenesis and molecular targets for fungicide development. Moreover, the systematic study of the N. dimidiatum growth cycle, morphological characteristics, and molecular phylogenetic analysis can promote a comprehensive understanding of its genetic basis.