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SUMOylation—the attachment of a small ubiquitin-like modifier (SUMO) to target proteins—plays roles in controlling plant growth, nutrient signalling and stress responses. SUMOylation studies in plants are scarce because identifying SUMOylated proteins and their sites is challenging. To date, only around 80 SUMOylation sites have been identified. Here we introduce lysine-null SUMO1into the Arabidopsissumo1sumo2mutant and establish a two-step lysine-null SUMO enrichment method. We identified a site-specific SUMOylome comprising over 2,200 SUMOylation sites from 1,300 putative acceptors that function in numerous nuclear processes. SUMOylation marks occur on several motifs, differing from the canonical ψKxE motif in distant eukaryotes. Quantitative comparisons demonstrate that SUMOylation predominantly enhances the stability of SUMO1 acceptors. Our study delivers a highly sensitive and efficient method for site-specific SUMOylome studies and provides a comprehensive catalogue of ArabidopsisSUMOylation, serving as a valuable resource with which to further explore how SUMOylation regulates protein function.

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction. Both the quantity and activity of the complex loaded into ZMWs have a critical effect on the efficiency of detection. Herein, a fluorescence evaluation at quenching and accumulation checkpoints was established to assess and optimize single effective complex loading into ZMWs. A primer–template–enzyme ternary complex was designed, and then an evaluation for quantity statistics at the quenching checkpoint and functional activity at the accumulation checkpoint was used to validate the effectiveness of complexes loaded into ZMWs. By optimizing the parameters such as loading time, procedures, and enzyme amount, the single-molecule effective occupancy was increased to 25.48%, achieving 68.86% of the theoretical maximum value (37%) according to Poisson statistics. It is of great significance to provide effective complex-loading validation for improving the sample-loading efficiency of single-molecule assays or sequencing in the future.

The worldwide promotion of electric heating improves the operational flexibility of the power system (PS). However, it also increases the risk of cascading failure due to the bi-directional interaction with the heating system (HS). To prevent such contingencies and facilitate prompt post-outage adjustments, the steady-state security region (SSR) of the integrated energy system (IES) will be a valuable and effective tool. This article derives an accurate SSR model for the heat and electricity IES, composed of a novel combined heat and power (CHP) unit model, the AC power flow model, and the dynamic HS model. An equivalence-based hyperplane method (EHM) is then proposed to solve the SSR model. The EHM treats the SSR as the intersection of the time-invariant and time-varying parts, and each part is described with a set of hyperplanes. On this basis, the sequential SSR (SSSR) is defined for IES to characterize the impact of CHP and HS integration, with which the time-varying process resulting from the thermal dynamics is divided into three periods for investigation. Numerical studies verify the accuracy and efficiency of the SSSR and EHM and demonstrate the influences of the CHP, HS integration, and thermal dynamics on the secure operation of the PS.

The decoupling method, in which different energy vectors are solved alternatively, is widely adopted in the integrated electricity and gas systems (IEGS) analysis. Nevertheless, the bidirectional interaction created by power to gas (P2G) and gas turbines (GT) requires iterative calculations between the electric power system (EPS) and the natural gas network (NGN). Without proper solutions, the iterations will raise predictable concerns about simulation efficiency and convergence. Therefore, this paper investigates the fast and robust simulation in IEGS considering the nonlinearity and dynamics from both the network and device sides. To address the bidirectional interaction, a state decoupling method (SDM) is first proposed as an efficient solver for the dynamic NGN model. Based on this, a sequential united method (SUM) is developed to reduce the computational cost and improve the solver robustness, which decouples the large-scale Jacobian matrix inversion into the sequential solution of multiple small sub-matrixes. Case studies in two systems demonstrate the superiority of the proposed method compared with the decoupling method, indicating a significant improvement of SUM in simulation efficiency and robustness.

With the popularity of online learning in educational settings, knowledge tracing (KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations, we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning. To model the counterfactual world, we conduct counterfactual transformations on students' learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed GraphCA method compared with several state-of-the-art baselines.

Propranolol could repress infantile hemangioma cell growth and induce apoptosis. miR-125b could inhibit cell proliferation in some tumors. However, whether propranolol exerts its proliferation inhibition and apoptosis-promoting effect by regulating the expression of miR-125b needs to be further investigated. In tumor tissue and endothelial cells isolated from infantile hemangioma patients, we found that the expression levels of miR-125b were significantly decreased. In-vitro analysis revealed that propranolol increased the expression of miR-125b in hemangioma cells in a dose-dependent and time-dependent manner. Interestingly, it was observed that regression of miR-125b expression by its inhibitor could abrogate the effect of propranolol on hemangioma cell growth and apoptosis. In addition, our data further identified TFAP4as a direct target of miR-125b. Collectively, our data provided evidence that propranolol may repress infantile hemangioma cell growth and promote apoptosis through upregulating the miR-125b expression, which exerted its suppression of tumor development by targeting TFAP4.

Emerging economies have become new destinations for knowledge sourcing, forcing Multinational Enterprises (MNEs) to reconfigure their global innovation strategies and structure. While foreign subsidiaries located in emerging economies were conventionally viewed as having market or efficiency seeking roles, they have started to evolve towards knowledge-seeking roles. We argue that the conventional wisdom shall be reassessed considering this recent shift. We empirically investigate 129 manufacturing MNE subsidiaries of Fortune 500 companies in China, in terms of their roles and sources of technology. Our results indicate that market and knowledge seeking subsidiaries located in China tend to have a positive impact on the generation of new knowledge, either through locally established MNE R&D laboratories or through collaborations with local firms and scientific institutions.

Data on the occurrence of microplastics and nanoplastics (MP/NPs) in foods have been used to assess the human health risk caused by the consumption of MP/NPs. The reliability of the data, however, remains unclear because of the lack of international standards for the analysis of MP/NPs in foods. Therefore, the data quality needs to be assessed for accurate health risk assessment. This study developed 10 criteria applicable to the quality assessment of data on MP/NPs in foods. Accordingly, the reliability of 71 data records (69 of them only focused on MPs) was assessed by assigning a score of 2 (reliable without restrictions), 1 (reliable but with restrictions), or 0 (unreliable) on each criterion. The results showed that only three data records scored 2 or 1 on all criteria, and six data records scored 0 on as many as six criteria. A total of 58 data records did not include information on positive controls, and 12 data records did not conduct the polymer identification, which could result in the overestimation or underestimation of MP/NPs. Our results also indicated that the data quality of unprocessed foods was more reliable than that of processed foods. Furthermore, we proposed a quality assurance and quality control protocol to investigate MP/NPs in foods. Notably, the characteristics of MP/NPs used in toxicological studies and those existing in foods showed a remarkable discrepancy, causing the uncertainty of health risk assessment. Therefore, both the estimated exposure of MP/NPs and the claimed potential health risks should be treated with caution.

We demonstrate the successful isolation and unambiguous crystallographic characterization of a series of higher carbide cluster metallofullerenes, presenting new insights into the molecular structures and cluster-cage interactions of endohedral metallofullerenes. They are identified as La2C2@C2(41)-C90, La2C2@D3(85)-C92, La2C2@C1(132)-C94, La2C2@C2(157)-C96, La2C2@C1(175)-C98, La2C2@D5(450)-C100, La2C2@Cs(574)-C102, and La2C2@C2(816)-C104. Our experimental results have confirmed that La2C92-106 tend to form carbide cluster metallofullerenes La2C2@C90-104 instead of the dimetallofullerenes La2@C92-106, which is rationalized by considering a synergistic effect of inserting a C2 unit into the fullerene cage to ensure strong metal-cage interactions by partially neutralizing the charges from the metal ions and by fulfilling the coordination requirement of the La3+ ions as much as possible.

Four Newcastle disease virus (NDV) strains were isolated from domestic, commercial geese that showed clinical signs that were believed to be the result of NDV infections. The genetic, antigenic, and pathogenic characteristics of the 4 NDVs were compared with those of NDV strains that were isolated from chickens. The complete genomes of 2 of the NDV strains contained 15,186 nucleotides (nt); the other 2 contained 15,192 nt, and exhibited the typical genomic organization of genotype II NDV and molecular characteristics of VIId NDVs. Phylogenetic analysis confirmed that the genotype II and VIId NDVs that were isolated from geese belonged to the same clusters as the corresponding genotypes of the chicken isolates. A serologic assay demonstrated that the antigenic relatedness among the NDVs was associated with their genotypes, rather than their hosts, and that amino acid substitutions in the F and/or HN proteins may contribute to the antigenic differences among these NDV genotypes. Geese infected with genotype VIId NDVs that were isolated from geese and chickens showed similar pathologic characteristics. NDVs that were isolated from geese did not differ in genetic, serologic, and pathogenic characteristics from those isolated from chickens, indicating that these NDVs were derived from chicken NDVs. Given the significance of geese in NDV epidemiology, effective biosecurity measures should be adopted to prevent the interspecies transmission of NDVs.

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Edge computing and IIoT (Industrial Internet of Things) are two representative application scenarios in 5G (5th Generation) mobile communication technology network. Therefore, this paper studies the resource allocation methods for these two typical scenarios, and proposes an energy-efficient resource allocation method by using DRL algorithm, which enhances the network performance and reduces the network operation cost. Firstly, according to the characteristics of edge computing, taking the connection relationship between base stations, users and the transmission power allocated by base stations to users as decision variables, minimizing the overall energy efficiency as the goal, and taking the needs of mobile users as constraints, a resource allocation model for user quality of service (QoS) guarantee is designed. Secondly, simulation experiments are used to verify that the proposed method has faster training speed and convergence speed, ensures the quality-of-service requirements of mobile users, and realizes intelligent and energy-saving resource allocation. The approximate amounts of steps to convergence under four cases are 500, 350, 250, and 220, respectively, and the values of awards are 21.76, 21.09, 20.38, and 20.25, respectively. Thirdly, aiming at the IIoT environment scenario, this paper proposes a resource allocation method for 5G wide connection low delay services based on asynchronous dominant action evaluation algorithm (A3C). Firstly, according to the characteristics of IIoT environment, taking the connection relationship between base stations and users and the transmission power allocated by base stations to users as decision variables and maximizing energy efficiency while satisfying needs of each user as optimization goal, a resource allocation model for 5G wide connection low delay services is designed. On this basis, an energy-efficient resource allocation method according to A3C is proposed. The hierarchical aggregation clustering algorithm (HAC) is adopted to determine the connection relationship between the base station and users, and the A3C algorithm is adopted to allocate transmission power to users, so as to maximize the overall energy efficiency and ensure the needs of each user.

In this paper, we study the field of rehabilitation walker technology. The passive energy storage gravity support exoskeleton is suitable for patients with lower limb dysfunction or limited function caused by lower limb joint, muscle tissue damage or bone disease. The equipment studied in this paper can assist patients to carry out normal ground walking and up and down stairs, realize patient self-care, slow down the patient's physiological pain, and reduce the economic and energy burden of the patient's family members. As the proportion of the elderly population continues to rise, the physiological decline during the aging process makes the elderly's limbs less flexible, with limb motor dysfunction and lower limb motor dysfunction. Manual laborers have long been engaged in labor with repetitive and long working hours. It is easy to cause occupational chronic musculoskeletal injuries or other occupational diseases by maintaining the same or improper working posture for a long time.

Blind and reckless consumption of the college students has become a common phenomenon. To give a correctly guiding, a decision tree support vector machine (DTSVM) based on particle swarm optimization (PSO) used to evaluate college students consumption behaviour is proposed in this paper. Firstly, the classification standard of college students consumption was derived with the investigation and data analysis. Then, the consumption ability and habits for different students are divided into four categories. Lastly, the targeted consumption index is fed back for the corresponding students. Experiment results show that a classification accuracy of 99.79% can be achieved, which will provide better guidance for the college students' consumption behaviour.

We demonstrate the successful isolation and unambiguous crystallographic characterization of a series of higher carbide cluster metallofullerenes, presenting new insights into the molecular structures and cluster-cage interactions of endohedral metallofullerenes. They are identified as La2C2@C2(41)-C90, La2C2@D3(85)-C92, La2C2@C1(132)-C94, La2C2@C2(157)-C96, La2C2@C1(175)-C98, La2C2@D5(450)-C100, La2C2@Cs(574)-C102, and La2C2@C2(816)-C104. Our experimental results have confirmed that La2C92-106tend to form carbide cluster metallofullerenes La2C2@C90-104instead of the dimetallofullerenes La2@C92-106, which is rationalized by considering a synergistic effect of inserting a C2unit into the fullerene cage to ensure strong metal-cage interactions by partially neutralizing the charges from the metal ions and by fulfilling the coordination requirement of the La3+ions as much as possible.