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Barley is one of the most important cereal crops in the world, and its value as a food is constantly being revealed, so the research into and the use of barley germplasm are very important for global food security. Although a large number of barley germplasm samples have been collected globally, their specific genetic compositions are not well understood, and in many cases their origins are even disputed. In this study, 183 barley germplasm samples from the Shanghai Agricultural Gene Bank were genotyped using genotyping-by-sequencing (GBS) technology, SNPs were identified and their genetic parameters were estimated, principal component analysis (PCA) was preformed, and the phylogenetic tree and population structure of the samples were also analyzed. In addition, a genome-wide association study (GWAS) was carried out for the hulled/naked grain trait, and a KASP marker was developed using an associated SNP. The results showed that a total of 181,906 SNPs were identified, and these barley germplasm samples could be roughly divided into three categories according to the phylogenetic analysis, which was generally consistent with the classification of the traits of row type and hulled/naked grain. Population structure analysis showed that the whole barley population could be divided into four sub-populations (SPs), the main difference from previous classifications being that the two-rowed and the hulled genotypes were sub-divided into two SPs. The GWAS analysis of the hulled/naked trait showed that many associated loci were unrelated to the Nud/nud locus, indicating that there might be new loci controlling the trait. A KASP marker was developed for one exon-type SNP on chromosome 7. Genotyping based on the KASP assay was consistent with that based on SNPs, indicating that the gene of this locus might be associated with the hulled/naked trait. The above work not only lays a good foundation for the future utilization of this barley germplasm population but it provides new loci and candidate genes for the hulled/naked trait. [ABSTRACT FROM AUTHOR]

Gelatin-based hydrogels with excellent mechanical properties and conductivities are desirable, but their fabrication is challenging. In this work, an innovative approach for the preparation of gelatin-based conductive hydrogels is presented that improves the mechanical and conductive properties of hydrogels by integrating Z–Gln–Gly into gelatin polymers via enzymatic crosslinking. In these hydrogels (Gel–TG–ZQG), dynamic π–π stacking interactions are created by the introduction of carbobenzoxy groups, which can increase the elasticity and toughness of the hydrogel and improve the conductivity sensitivity by forming effective electronic pathways. Moreover, the mechanical properties and conductivity of the obtained hydrogel can be controlled by tuning the molar ratio of Z–Gln–Gly to the primary amino groups in gelatin. The hydrogel with the optimal mechanical properties (Gel–TG–ZQG (0.25)) exhibits a high storage modulus, compressive strength, tensile strength, and elongation at break of 7.8 MPa at 10 °C, 0.15 MPa at 80% strain, 0.343 MPa, and 218.30%, respectively. The obtained Gel–TG–ZQG (0.25) strain sensor exhibits a short response/recovery time (260.37 ms/130.02 ms) and high sensitivity (0.138 kPa−1) in small pressure ranges (0–2.3 kPa). The Gel–TG–ZQG (0.25) hydrogel-based sensors can detect full-range human activities, such as swallowing, fist clenching, knee bending and finger pressing, with high sensitivity and stability, yielding highly reproducible and repeatable sensor responses. Additionally, the Gel–TG–ZQG hydrogels are noncytotoxic. All the results demonstrate that the Gel–TG–ZQG hydrogel has potential as a biosensor for wearable devices and health-monitoring systems. [ABSTRACT FROM AUTHOR]

Laryngopharyngeal reflux disease (LPRD) is a condition characterized by the regurgitation of stomach and duodenal contents into the laryngopharynx, with variable and non-specific symptoms. Therefore, developing an accurate symptom scale for different regions is essential. Notably, the symptoms of "dryness and burning sensation in the laryngopharynx or mouth" are prevalent among the Chinese population but are often omitted from conventional symptom assessment scales, such as the Reflux Symptom Index (RSI) and Reflux Symptom Score-12 (RSS-12) scales. To address this gap, our study incorporated the symptoms into the RSI and RSS-12 scales, developing the RSI-10/RSS-13 scales. Afterward, we assessed the role of the new scale's reliability (Cronbach's α and test–retest reliability), construct validity (confirmatory factor analysis and confirmatory factor analysis), and diagnostic efficiency. Our study encompassed 479 participants (average = 39.5 ± 13.4 years, 242 female) and 91 (average = 34.01 ± 13.50 years, 44 female) completed 24 h MII-pH monitoring. The Cronbach's α values of 0.80 and 0.82 for the RSI-10 and RSS-13 scales, respectively. RSI-10 and RSS-13 exhibited strong test–retest reliability (ICCs = 0.82–0.96) and diagnostic efficacy (AUC = 0.84–0.85). Furthermore, the factor analysis identified the RSS-13 and its three sub-scales (ear-nose-throat, digestive tract, respiratory tract) exhibited good to excellent structural validity (χ2/df = 1.95, P < 0.01; CFI = 0.95, RMSEA = 0.06, SRMR = 0.05). The AUC optimal thresholds for the RSI-10 and RSS-13 in the Chinese population were 13 and 36, respectively. Besides, the inclusion of the new item significantly improved the diagnostic efficiency of the RSI scale (P = 0.04), suggesting that RSI-10 holds promise as a more effective screening tool for LPRD, and global validation is needed to demonstrate the impact of this new symptom on the diagnosis of LPRD. [ABSTRACT FROM AUTHOR]

More than 100 tidal disruption events (TDEs) have been detected at multiple bands, which can be viewed as extreme laboratories to investigate the accretion physics and gravity in the immediate vicinity of massive black holes. Future transient surveys are expected to detect several tens of thousands of TDEs, among which a small fraction may be strongly gravitationally lensed by intervening galaxies. In this paper, we statistically estimate the detection rate of lensed TDEs, with dependence on the limiting magnitude of the transient all-sky surveys searching for them. We find that the requisite limiting magnitude for an all-sky transient survey to observe at least 1 yr−1 is ≳21.3, 21.2, and 21.5 mag in the u, g, and z bands, respectively. If the limiting magnitude of the all-sky survey can reach ∼25–26 mag in the u, g, and z bands, the detection rate can be up to about several tens to hundreds per year. The discovery and identification of the first image of the lensed TDE can be taken as an early warning of the second and other subsequent images, which may enable detailed monitoring of the pre-peak photometry and spectroscopy evolution of the TDE. The additional early-stage information may help to constrain the dynamical and radiation processes involved in the TDEs. [ABSTRACT FROM AUTHOR]

As the global human population continues to increase, the use of saline–alkali land for food production is an important consideration for food security. In addition to breeding or cultivating salt-tolerant crop varieties, microorganisms are increasingly being evaluated for their ability to improve plant salt tolerance. Barley is one of the most important and salt-tolerant cereal crops and is a model system for investigating the roles of microorganisms in improving plant salt tolerance. However, a comprehensive review of the mechanisms by which microorganisms improve barley salt tolerance remains lacking. In this review, the mechanisms of barley salt tolerance improvement by microorganisms are summarized, along with a discussion of existing problems in current research and areas of future research directions. In particular, with the development of sequencing technology and the great reduction of prices, the use of omics can not only comprehensively evaluate the role of microorganisms but also evaluate the impact of the microbiome on plants, which will provide us with many opportunities and challenges in this research area. [ABSTRACT FROM AUTHOR]

The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving. [ABSTRACT FROM AUTHOR]

In friction stir welding (FSW) of dissimilar Al/Mg alloys, inevitable formation of intermetallic compounds (IMCs) has deleterious effect on the performance of joints. Although ultrasonic vibration enhanced FSW (UVeFSW) was invented to suppress the IMCs formation, the mitigating mechanism is not elucidated. In this work, the measured maximum temperature in weld nugget zone (WNZ) confirmed that in butt FSW of Al/Mg alloys the IMCs are generated by diffusion of solid atoms. The acoustic softening and antifriction effects were taken into account for computation of temperature, strain rate and dislocation density which are main factors affecting the inter-diffusion coefficients of Mg and Al atoms. An atom-diffusion based model was developed, and the formation and growth of the IMCs were quantitatively analyzed in FSW/UVeFSW of Al/Mg alloys. It was found that as the checking point flows toward to the tool, Al 12 Mg 17 layer is first formed at the bonding interface, and then Al 3 Mg 2 is formed at the Al side and grows rapidly. Under the conditions used in this study, the predicted values of IMCs thickness at the WNZ center in FSW and UVeFSW are 3.9 µm and 1.8 µm, respectively. The calculated IMCs thicknesses at the selected locations on the transverse cross-section and horizontal cross-section agree well with the measured ones. Ultrasonic induced dislocation-annihilation and associated decreasing of dislocation density in UVeFSW cause declining of the atomic diffusion coefficients, so that the formation and growth of IMCs are inhibited with ultrasonic action. • The measured temperature confirmed that in butt FSW the IMCs are generated by diffusion of solid atoms. • The IMCs thickness are quantitatively analyzed in FSW/UVeFSW of Al/Mg alloys. • The calculated IMCs thicknesses at selected locations on different cross-sections agree well with the measured ones. • The reason why ultrasonic vibration has an inhibition influence on IMCs thickness is explained. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel kinetic modeling strategy combining the moments method with an improved multi-objective mayfly algorithm is proposed. • The improved multi-objective mayfly algorithm is proposed. • Industrial-scale propylene polymerization model based on the proposed method is established. • The proposed algorithm presents the state-of-art ability on prediction results of the PR and the MI. • This proposed algorithm has a good prospect for the PP industrial application. Polypropylene (PP) is a synthetic resin with exceptional physical and chemical properties, making it suitable for various household and industrial applications. However, as the demand for PP continues to rise, the polypropylene industry faces challenges in improving its quality and efficiency. Developing an accurate mathematical model that describes the continuous polymerization process is crucial for enhancing PP production, as it is useful for controlling product quality and improving production efficiency. While previous studies have focused on modeling propylene polymerization using laboratory-scale and pilot-scale reactors, these models do not effectively apply to industrial-scale processes due to the complexity of the industrial environment. Therefore, a novel kinetic modeling method combining the modeling of the kinetic mechanism of the polymerization reaction with an intelligent optimization algorithm for the industrial-scale continuous gas-phase fluidized bed propylene polymerization process is proposed. To establish the kinetic mechanism model, the improved moments (MOM) method is utilized to derive a set of mass balance equations that account for various fundamental processes such as catalyst activation, chain initiation, chain growth, chain transfer to hydrogen, chain transfer to monomers, chain transfer to co-catalysts, and spontaneous deactivation. However, kinetic parameters serve as the link between the theoretical model and the actual production process, so the kinetic parameters estimation problem need to be transformed into a multi-objective problem by minimizing the least squares difference between the measurement data of the actual industry process and the predicted result of the kinetic model. Therefore, an improved multi-objective mayfly algorithm (IMOMA) is introduced to estimate the polymerization kinetic parameters, which incorporates the simulated binary crossover (SBX) operator and the polynomial mutation (PM) operator into the basic multi-objective mayfly algorithm (MOMA). Finally, by fitting industrial measurement data of the melt index (MI) and the polymerization rate (PR) of an actual polypropylene industrial case study, the proposed algorithm accurately determines the polymerization kinetic parameters. The results demonstrate that the MOM-IMOMA modeling approach outperforms other methods and holds significant promise for industrial-scale polypropylene production. [ABSTRACT FROM AUTHOR]

Background: Our study aims to delineate the miRSNP–microRNA–gene–pathway interactions in the context of hypertrophic scars (HS) and keloids. Materials and Methods: We performed a computational biology study involving differential expression analysis to identify genes and their mRNAs in HS and keloid tissues compared to normal skin, identifying key hub genes and enriching their functional roles, comprehensively analyzing microRNA‐target genes and related signaling pathways through bioinformatics, identifying MiRSNPs, and constructing a pathway‐based network to illustrate miRSNP‐miRNA‐gene‐signaling pathway interactions. Results: Our results revealed a total of 429 hub genes, with a strong enrichment in signaling pathways related to proteoglycans in cancer, focal adhesion, TGF‐β, PI3K/Akt, and EGFR tyrosine kinase inhibitor resistance. Particularly noteworthy was the substantial crosstalk between the focal adhesion and PI3K/Akt signaling pathways, making them more susceptible to regulation by microRNAs. We also identified specific miRNAs, including miRNA‐1279, miRNA‐429, and miRNA‐302e, which harbored multiple SNP loci, with miRSNPs rs188493331 and rs78979933 exerting control over a significant number of miRNA target genes. Furthermore, we observed that miRSNP rs188493331 shared a location with microRNA302e, microRNA202a‐3p, and microRNA20b‐5p, and these three microRNAs collectively targeted the gene LAMA3, which is integral to the focal adhesion signaling pathway. Conclusions: The study successfully unveils the complex interactions between miRSNPs, miRNAs, genes, and signaling pathways, shedding light on the genetic factors contributing to HS and keloid formation. [ABSTRACT FROM AUTHOR]

IRAK4 is a critical mediator in NF-κB-regulated inflammatory signaling and has emerged as a promising therapeutic target for the treatment of autoimmune diseases; however, none of its inhibitors have received FDA approval. In this study, we identified a novel small-molecule IRAK4 kinase inhibitor, DW18134, with an IC50 value of 11.2 nM. DW18134 dose-dependently inhibited the phosphorylation of IRAK4 and IKK in primary peritoneal macrophages and RAW264.7 cells, inhibiting the secretion of TNF-α and IL-6 in both cell lines. The in vivo study demonstrated the efficacy of DW18134, significantly attenuating behavioral scores in an LPS-induced peritonitis model. Mechanistically, DW18134 reduced serum TNF-α and IL-6 levels and attenuated inflammatory tissue injury. By directly blocking IRAK4 activation, DW18134 diminished liver macrophage infiltration and the expression of related inflammatory cytokines in peritonitis mice. Additionally, in the DSS-induced colitis model, DW18134 significantly reduced the disease activity index (DAI) and normalized food and water intake and body weight. Furthermore, DW18134 restored intestinal damage and reduced inflammatory cytokine expression in mice by blocking the IRAK4 signaling pathway. Notably, DW18134 protected DSS-threatened intestinal barrier function by upregulating tight junction gene expression. In conclusion, our findings reported a novel IRAK4 inhibitor, DW18134, as a promising candidate for treating inflammatory diseases, including peritonitis and IBD. [ABSTRACT FROM AUTHOR]

• Vibro-electric coupling model of the VEH under square wave excitation is established. • The parameter adjusting priority of the VEH system is proposed. • The maximum output power of the proposed system is 2.7 W. • The proposed VEH is suitable for multi-vibration scenarios. Harvesting energy from surrounding vibrations is an effective method for powering various low-power electronic devices. The energy output performance of vibration energy harvesters under some special excitations remains to be investigated. In this paper, a vibration energy harvester (VEH) system based on a rack and pinion mechanism and a bevel gear set is proposed, and a detailed electromechanical coupling modeling of the system under general excitation and square wave excitation is carried out. The energy output characteristics of the proposed system under square wave excitation are explored through simulation and experiment. When the energy output is the target, the priority of adjusting the constituent parameters in the equivalent mass parameter, equivalent damping parameter and equivalent stiffness parameter of the system can be obtained based on the simulation results. The experimental results under periodic square wave excitation force show that the maximum output power of the proposed system is 2.7 W at an amplitude of 4 kN and a frequency of 3 Hz. This is a 55 % increase in the maximum power compared to the sinusoidal excitation under the same conditions. It is demonstrated that the proposed system has great potential to power various low-power electronic devices. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Isolated microspore culture is a useful biotechnological technique applied in modern plant breeding programs as it can produce doubled haploid (DH) plants and accelerate the development of new varieties. Furthermore, as a single-cell culture technique, the isolated microspore culture provides an excellent platform for studying microspore embryogenesis. However, the reports on isolated microspore culture are rather limited in rice due to the low callus induction rate, poor regeneration capability, and high genotypic dependency. The present study developed an effective isolated microspore culture protocol for high-frequency androgenesis in four japonica rice genotypes. Several factors affecting the isolated microspore culture were studied to evaluate their effects on callus induction and plantlet regeneration. Results: Low-temperature pre-treatment at 4 ℃ for 10–15 days could effectively promote microspore embryogenesis in japonica rice. A simple and efficient method was proposed for identifying the microspore developmental stage. The anthers in yellow-green florets located on the second type of primary branch on the rice panicle were found to be the optimal stage for isolated microspore culture. The most effective induction media for callus induction were IM2 and IM3, depending on the genotype. The optimal concentration of 2, 4-D in the medium for callus induction was 1 mg/L. Callus induction was negatively affected by a high concentration of KT over 1.5 mg/L. The differentiation medium suitable for japonica rice microspore callus comprised 1/2 MS, 2 mg/L 6-BA, 0.5 mg/L NAA, 30 g/L sucrose, and 6 g/L agar. The regeneration frequency of the four genotypes ranged from 61–211 green plantlets per 100 mg calli, with Chongxiangjing showing the highest regeneration frequency. Conclusions: This study presented an efficient protocol for improved callus induction and green plantlet regeneration in japonica rice via isolated microspore culture, which could provide valuable support for rice breeding and genetic research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel multiscale variational autoencoder regressor is proposed. • The multiscale CNN module can extract multiscale information. • Establishing prediction models for ethylene and propylene production for energy structure optimization. • The prediction accuracy of the proposed model on ethylene and propylene data can reach around 95% and 96%, respectively. • The proposed model can effectively increase propylene and ethylene production and reduce carbon emissions. As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy. [ABSTRACT FROM AUTHOR]

The inner liners of reusable regeneratively-cooled thrust chambers endure significant thermal and mechanical loads, resulting in doghouse deformation and even cracks during cyclic operations. To correctly reveal the failure mechanism of the thrust chamber inner liner, this study investigates the material properties for CuZr alloy and develops a temperature-dependent material model spanning from cryogenic to elevated temperatures. Firstly, we experimentally obtain the thermal and mechanical properties of the material in the temperature range from 77 K to 900 K. The experimental data reveal that the material properties vary considerably with temperature. Then, to precisely characterize the mechanical behavior of CuZr alloy under cyclic loading across a wide temperature range, a material model that combines Voce nonlinear isotropic hardening model with Chaboche nonlinear kinematic hardening model is proposed, and temperature-dependent parameters of this model are calibrated and verified. Finally, the developed material model is applied to perform the thermal-structural analysis on a reusable thrust chamber, with the aim of investigating its failure mechanism. The results demonstrate the capability of the developed model to accurately replicate the doghouse deformation of the inner liner and quantitatively capture the ratcheting effect. [ABSTRACT FROM AUTHOR]

Background: To compare the clinical characteristics and prognoses of patients with isolated regional lymph node recurrent nasopharyngeal carcinoma (irrNPC) who underwent surgery or re-irradiation treatment. Methods: We retrospectively reviewed 124 irrNPC patients who underwent initial radiotherapy between January 2010 and December 2020. The staging of regional lymph node recurrence was as follows: 75.8% for rN1, 14.5% for rN2, and 9.7% for rN3. Fifty-five patients underwent regional lymph node surgery (Surgery group), and sixty-nine patients received salvage radiotherapy with or without chemotherapy (Re-irradiation group). The survival rate was compared using Kaplan‒Meier analysis and evaluated by the log-rank test. Cox proportional hazard models were used to analyze prognostic factors. Results: The median follow-up time was 70 months, the 5-year overall survival (OS) was 74%, and the median survival time was 60.8 months. There were no significant differences in 5-year OS (75.6% vs. 72.4%, P = 0.973), regional recurrence-free survival (RRFS, 62.7% vs. 71.1%, P = 0.330) or distant metastasis-free survival (DMFS, 4.2% vs.78.7%, P = 0.677) between the Surgery group and Re-irradiation group. Multivariate analysis revealed age at recurrence, radiologic extra-nodal extension (rENE) status, and recurrent lymph node (rN) classification as independent prognostic factors for OS. The rENE status was an independent prognostic factor for DMFS. Subgroup analysis of the Surgery group revealed that the rN3 classification was an adverse prognostic factor for OS. Age at recurrence ≥ 50 years, GTV-N dose, and induction chemotherapy were found to be independent prognostic factors for OS, RRFS, and DMFS, respectively, in the Re-irradiation group. Conclusions: For NPC patients with isolated regional lymph node recurrence after initial radiotherapy, those who underwent surgery had survival prognosis similar to those who underwent re-radiotherapy with or without chemotherapy. A prospective study is needed to validate these findings. [ABSTRACT FROM AUTHOR]

Highlights: The innovation of this work mainly lies in the auto-regulation of reactive oxygen species (ROS) balance effective by combination of ROS antibacterial and ROS scavenging anti-inflammatory functions with a core–shell nanoplatform (CeO2@ZIF-8/Au), which can not only achieve high antibacterial efficiency, but also promote wound healing, and provide a new idea for the nano-catalytic system in the field of wound healing of bacterial infection. Reactive oxygen species (ROS) plays important roles in living organisms. While ROS is a double-edged sword, which can eliminate drug-resistant bacteria, but excessive levels can cause oxidative damage to cells. A core–shell nanozyme, CeO2@ZIF-8/Au, has been crafted, spontaneously activating both ROS generating and scavenging functions, achieving the multi-faceted functions of eliminating bacteria, reducing inflammation, and promoting wound healing. The Au Nanoparticles (NPs) on the shell exhibit high-efficiency peroxidase-like activity, producing ROS to kill bacteria. Meanwhile, the encapsulation of CeO2 core within ZIF-8 provides a seal for temporarily limiting the superoxide dismutase and catalase-like activities of CeO2 nanoparticles. Subsequently, as the ZIF-8 structure decomposes in the acidic microenvironment, the CeO2 core is gradually released, exerting its ROS scavenging activity to eliminate excess ROS produced by the Au NPs. These two functions automatically and continuously regulate the balance of ROS levels, ultimately achieving the function of killing bacteria, reducing inflammation, and promoting wound healing. Such innovative ROS spontaneous regulators hold immense potential for revolutionizing the field of antibacterial agents and therapies. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a chronic joint disease characterized by synovitis and joint cartilage destruction. The severity of OA is highly associated with the imbalance between M1 and M2 synovial macrophages. In this study, a novel strategy is designed to modulate macrophage polarization by reducing intracellular reactive oxygen species (ROS) levels and regulating mitochondrial function. A ROS‐responsive polymer is synthesized to self‐assemble with astaxanthin and autophagy activator rapamycin to form nanoparticles (NP@PolyRHAPM). In vitro experiments show that NP@PolyRHAPM significantly reduced intracellular ROS levels. Furthermore, NP@PolyRHAPM restored mitochondrial membrane potential, increased glutathione (GSH) levels, and promoted intracellular autophagy, hence successfully repolarizing M1 macrophages into the M2 phenotype. This repolarization enhanced chondrocyte proliferation and vitality while inhibiting apoptosis. In vivo experiments utilizing an anterior cruciate ligament transection (ACLT)‐induced OA mouse model revealed the anti‐inflammatory and cartilage‐protective effects of NP@PolyRHAPM, effectively mitigating OA progression. Consequently, the findings suggest that intra‐articular delivery of ROS‐responsive nanocarrier systems holds significant promise as a potential and effective therapeutic strategy for OA treatment. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) has been fully considered in natural language generation (NLG) tasks. A KG can help models generate controllable text and achieve better performance. However, most existing related approaches still lack explainability and scalability in large‐scale knowledge reasoning. In this work, we propose a novel CogNLG framework for KG‐to‐text generation tasks. Our CogNLG is implemented based on the dual‐process theory in cognitive science. It consists of two systems: one system acts as the analytic system for knowledge extraction, and another is the perceptual system for text generation by using existing knowledge. During text generation, CogNLG provides a visible and explainable reasoning path. Our framework shows excellent performance on all datasets and achieves a BLEU score of 36.7, which increases by 6.7 compared to the best competitor. [ABSTRACT FROM AUTHOR]

Short answer scoring is a significant task in natural language processing. On datasets comprising numerous explicit or implicit symbols and quantization entities, the existing approaches continue to perform poorly. Additionally, the majority of relevant datasets contain few-shot samples, reducing model efficacy in low-resource scenarios. To solve the above issues, we propose a Multi-level Semantic Inference Model (M-Sim), which obtains features at multiple scales to fully consider the explicit or implicit entity information contained in the data. We then design a prompt-based data augmentation to construct the simulated datasets, which effectively enhance model performance in low-resource scenarios. Our M-Sim outperforms the best competitor models by an average of 1.48 percent in the F1 score. The data augmentation significantly increases all approaches' performance by an average of 0.036 in correlation coefficient scores. • M-Sim improves Chinese few-shot SAS with multi-scale similarity calculation. • New prompt-based data augmentation enhances SAS models low-resource performance. • New Chinese SAS dataset released to promote further study. [ABSTRACT FROM AUTHOR]

• Novel graph attention integrating spatio-temporal features is proposed. • The fusion module can adaptively control spatio-temporal interaction features. • A propylene production prediction model for energy structure optimization is built. • The proposed model increases the propylene production by about 7.6 %. • The proposed model reduces carbon emissions by about 532.0115 tons. Propylene is essential to the national economy. However, the propylene production process data have complex spatiotemporal, nonlinear and dynamic features, and the consideration of spatiotemporal effects can predict the propylene production more accurately. Therefore, a novel graph attention network (GAT) based long short-term memory network (LSTM)(GAT-LSTM) is proposed for the production prediction and energy structure optimization of propylene production processes. The GAT extracts spatial features to obtain correlations between variables, while the improved LSTM model extracts potential temporal-dependent features. The collected spatiotemporal interaction features are fused by the spatiotemporal fusion module and the adaptive control is realized. Finally, a fully connected layer maps spatiotemporal interaction features to the target domain for obtaining the output. Experimental results show that the GAT-LSTM outperforms the Conv-LSTM, the LSTM, the GAT, the Attribute-relevant distributed variational autoencoder and the Attention-LSTM. Additionally, the GAT-LSTM can provide the optimal raw material scheme for actual propylene production processes. [ABSTRACT FROM AUTHOR]