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Abstract Objectives To establish an MRI-based radiomics model for predicting the microvascular invasion (MVI) status of cHCC-CCA and to investigate biological processes underlying the radiomics model. Methods The study consisted of a retrospective dataset (82 in the training set, 36 in the validation set) and a prospective dataset (25 patients in the test set) from two hospitals. Based on the training set, logistic regression analyses were employed to develop the clinical-imaging model, while radiomic features were extracted to construct a radiomics model. The diagnosis performance was further validated in the validation and test sets. Prognostic aspects of the radiomics model were investigated using the Kaplan–Meier method and log-rank test. Differential gene expression analysis and gene ontology (GO) analysis were conducted to explore biological processes underlying the radiomics model based on RNA sequencing data. Results One hundred forty-three patients (mean age, 56.4 ± 10.5; 114 men) were enrolled, in which 73 (51.0%) were confirmed as MVI-positive. The radiomics model exhibited good performance in predicting MVI status, with the area under the curve of 0.935, 0.873, and 0.779 in training, validation, and test sets, respectively. Overall survival (OS) was significantly different between the predicted MVI-negative and MVI-positive groups (median OS: 25 vs 18 months, p = 0.008). Radiogenomic analysis revealed associations between the radiomics model and biological processes involved in regulating the immune response. Conclusion A robust MRI-based radiomics model was established for predicting MVI status in cHCC-CCA, in which potential prognostic value and underlying biological processes that regulate immune response were demonstrated. Critical relevance statement MVI is a significant manifestation of tumor invasiveness, and the MR-based radiomics model established in our study will facilitate risk stratification. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights for guiding immunotherapy strategies. Key Points MVI is of prognostic significance in cHCC-CCA, but lacks reliable preoperative assessment. The MRI-based radiomics model predicts MVI status effectively in cHCC-CCA. The MRI-based radiomics model demonstrated prognostic value and underlying biological processes. The radiomics model could guide immunotherapy and risk stratification in cHCC-CCA. Graphical Abstract

Using undercooling technology combined with melt purification of glass and recirculation overheating, different undercoolings were successfully obtained for Cu60Ni38Co2 alloy samples. Applying high speed photography technique, the physical process of undercooled solidification was observed and relationship between undercooling and solidification rate was analyzed. Experimental results indicate the critical undercoolings exist in the structure evolution process. Electron backscatter diffraction (EBSD) analysis was applied to characterize the Cu60Ni38Co2 alloy sample with a maximum undercooling of 255K, revealing grain orientation and the corresponding orientation difference distribution. In addition, transmission electron microscopy (TEM) showed high density dislocation networks. Combining EBSD and TEM data analysis, an important conclusion was drawn: recrystallization is the main factor for grain refinement at high undercooling. This finding is of great significance for a deeper understanding of the structure evolution in the physical process of undercooled alloys.

In an integrated electricity-gas system (IEGS), load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system. The static voltage stability region (SVSR) method is a tool for analyzing the overall static voltage stability in a power system. However, in an IEGS, the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses. Thus, the SVSR method cannot be directly applied to an IEGS. In this paper, the concept of the SVSR is extended to the IEGS-static stability region (SSR) while considering voltage and gas pressure. First, criteria for static gas pressure stability in a natural gas system are proposed, based on the static voltage stability criteria in a power system. Then, the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow (MEF) equations and static voltage and gas pressure stability constraints in the active power injection space of natural gas-fired generator units (NGUs). To determine the IEGS-SSR, a continuation MEF (CMEF) method is employed to trace the boundary point in one specific NGU scheduling direction. A multidimensional hyperplane sampling method is also proposed to sample the NGU scheduling directions evenly. The obtained boundary points are further used to form the IEGS-SSR in three-dimensional (3D) space via a Delaunay triangulation hypersurface fitting method. Finally, the numerical results of typical case studies are presented to demonstrate that the proposed method can effectively form the IEGS-SSR, providing a tool for IEGS online monitoring and dispatching.

Abstract Electrochemical uranium extraction from nuclear wastewater represents an emerging strategy for recycling uranium resources. However, in nuclear fuel production which generates the majority of uranium-containing nuclear wastewater, fluoride ion (F−) co-exists with uranyl (UO2 2+), resulting in the complex species of UO2Fx and thus decreasing extraction efficiency. Herein, we construct Tiδ+-PO4 3− ion pair extraction sites in Ti(OH)PO4 for efficient electrochemical uranium extraction in wastewater from nuclear fuel production. These sites selectively bind with UO2Fx through the combined Ti-F and multiple O-U-O bonds. In the uranium extraction, the uranium species undergo a crystalline transition from U3O7 to K3UO2F5. In real nuclear wastewater, the uranium is electrochemically extracted with a high efficiency of 99.6% and finally purified as uranium oxide powder, corresponding to an extraction capacity of 6829 mg g−1 without saturation. This work paves an efficient way for electrochemical uranium recycling in real wastewater of nuclear production.

Breast cancer has been reported to transcend lung cancer as the most commonly diagnosed cancer in women all over the world. Adipocytes, serving as energy storage and endocrine cells, are the major stromal cells in the breast. Cancer-associated adipocytes (CAAs) are adjacent and dedifferentiated adipocytes located at the invasive front of human breast tumors. Adipocytes can transform into CAA phenotype with morphological and biological changes under the remodeling of breast cancer cells. CAAs play an essential role in breast cancer progression, including remodeling the tumor microenvironment (TME), regulating immunity, and interacting with breast cancer cells. CAAs possess peculiar secretomes and are accordingly capable to promote proliferation, invasiveness, angiogenesis, metastasis, immune escape, and drug resistance of breast cancer cells. There is a complex and coordinated crosstalk among CAAs, immune cells, and breast cancer cells. CAAs can release a variety of cytokines, including IL-6, IL-8, IL-1β, CCL5, CCL2, VEGF, G-CSF, IGF-1, and IGFBP, thereby promoting immune cell recruitment and macrophage polarization, and ultimately stimulating malignant behaviors in breast cancer cells. Here, we aim to provide a comprehensive description of CAA-derived cytokines, including their impact on cancer cell behaviors, immune regulation, breast cancer diagnosis, and treatment. A deeper understanding of CAA performance and interactions with specific TME cell populations will provide better strategies for cancer treatment and breast reconstruction after mastectomy.

Abstract Pneumatic artificial muscles can move continuously under the power support of air pumps, and their flexibility also provides the possibility for applications in complex environments. However, in order to achieve operation in confined spaces, the miniaturization of artificial muscles becomes crucial. Since external attachment devices greatly hinder the miniaturization and use of artificial muscles, we propose a light-driven approach to get rid of these limitations. In this study, we report a miniaturized fiber-reinforced artificial muscle based on mold editing, capable of bending and axial elongation using gas-liquid conversion in visible light. The minimum volume of the artificial muscle prepared using this method was 15.7 mm3 (d = 2 mm, l = 5 mm), which was smaller than those of other fiber-reinforced pneumatic actuators. This research can promote the development of non-tethered pneumatic actuators for rescue and exploration, and create the possibility of miniaturization of actuators.

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

Angelica dahurica var. formosana (ADF), which belongs to the Umbelliferae family, is one of the original plants of herbal raw material Angelicae Dahuricae Radix. ADF roots represent an enormous biomass resource convertible for disease treatment and bioproducts. But, early bolting of ADF resulted in lignification and a decrease in the coumarin content in the root, and roots lignification restricts its coumarin for commercial utility. Although there have been attempts to regulate the synthesis ratio of lignin and coumarin through biotechnology to increase the coumarin content in ADF and further enhance its commercial value, optimizing the biosynthesis of lignin and coumarin remains challenging. Based on gene expression analysis and phylogenetic tree profiling, AdNAC20 as the target for genetic engineering of lignin and coumarin biosynthesis in ADF was selected in this study. Early-bolting ADF had significantly greater degrees of root lignification and lower coumarin contents than that of the normal plants. In this study, overexpression of AdNAC20 gene plants were created using transgenic technology, while independent homozygous transgenic lines with precise site mutation of AdNAC20 were created using CRISPR/Cas9 technology. The overexpressing transgenic ADF plants showed a 9.28% decrease in total coumarin content and a significant 12.28% increase in lignin content, while knockout mutant plants showed a 16.3% increase in total coumarin content and a 33.48% decrease in lignin content. Furthermore, 29,671 differentially expressed genes (DEGs) were obtained by comparative transcriptomics of OE-NAC20, KO-NAC20, and WT of ADF. A schematic diagram of the gene network interacting with AdNAC20 during the early-bolting process of ADF was constructed by DEG analysis. AdNAC20 was predicted to directly regulate the transcription of several genes with SNBE-like motifs in their promoter, such as MYB46, C3H, and CCoAOMT. In this study, AdNAC20 was shown to play a dual pathway function that positively enhanced lignin formation but negatively controlled coumarin formation. And the heterologous expression of the AdNAC20 gene at Arabidopsis thaliana proved that the AdNAC20 gene also plays an important role in the process of bolting and flowering.

With the gradual development of photomedicine, biocompatible optical fiber (BOF) has become more and more important for delivering light energy into deep-tissue in recent years. By this, it can realize light induced medical treatments, such as optogenetics, photothermal cancer treatment, photodynamic therapy, physiological information sensing transmission, etc. In this review, we discuss (i) the characteristics of BOF, (ii) the fabricating methods of BOF, (iii) the application fields of BOF, and (iv) the recent progress and perspectives on the future of BOF. We envision that this review will provide readers with some meaningful guidance on the preparation of BOFs and inspire them to explore more potential applications.

Abstract In this study, three kinds of radical scavenger Chimassorb 944, Tinuvin 622, and Tinuvin 770 are used to suppress the growth of electrical trees in cross‐linked polyethylene (XLPE) with transient superimposed voltage under the temperature gradient. The tree morphology, tree length, accumulated damage, and time to breakdown are used to investigate the effect of radical scavenger on the electrical treeing process. It is found that under the temperature gradient caused by the temperature rise on the high voltage side, only Tinuvin 622 can always suppress the electrical tree as the temperature gradient rises. Under the temperature gradient caused by the temperature rise on the ground side, the three radical scavengers can all suppress the electrical tree. The breakdown of electrical tree exhibits the strong DC polarity dependence. Meanwhile, energy levels of these three radical scavengers are calculated through quantum chemistry, and the results indicate that radical scavengers have greater electron affinity, smaller ionisation energy, and smaller energy gap than XLPE. According to the surface potential decay test results at 60°C, it is found that all three radical scavengers can introduce deep traps. The different performances of radical scavengers under different temperature gradients and voltages are determined by the trap distribution characteristics, the molecular structure and chemical reaction of the additives themselves. It is concluded that Tinuvin 622 has potential for use in high voltage direct current XLPE cable application.

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

Abstract Introduction Lung cancer is the most prevalent cancer with high mortality in China, and it is associated with the dysbiosis of the lung microbiome. This study attempted to screen for specific microorganisms as potential biomarkers for distinguishing benign lung disease from lung cancer. Methods Bronchoalveolar lavage fluid (BALF) sample was selected in the study instead of saliva to avoid contamination with oral microorganisms, and microbial taxonomic and functional differences in BALF samples from patients with lung cancer and those with those from patients with benign lung diseases were performed based on metagenomic next‐generation sequencing, for the first time, so that microorganisms other than bacteria could be included. Results The results showed that the intrasample diversity of malignant samples was different from benign samples, and the microbial differences among malignant samples were smaller, with lower microbial diversity, significantly changed microbial abundance and metabolic functions. Metabolic function analysis revealed amino acid‐related metabolism was more prevalent in benign samples, whereas carbohydrate‐related metabolism was more prevalent in malignant samples. By LEfSe, Metastat and Random Forest analysis, we identified a series of important differential microorganisms. Importantly, the model combining five key genera plus one tumor marker (neuron‐specific enolase) as indicators presented the optimal disease typing performance. Conclusion Thus results suggest the value of these differential microorganisms enriched in tumors in mechanism research and may be potential new targets for lung cancer therapy. More importantly, the biomarkers identified in this study can be conducive to improve the clinical diagnosis of lung cancer and have good application prospects.

Under the global zero-carbon campaign (United Nations Climate Change, 2021), more stochastic renewable generations are being integrated into the system. This trend significantly expands the state space and increases the computational burden for the model-driven reliability assessment. Data-driven approaches are developed to improve efficiency based on artificial intelligence. However, the requirement of large-scale samples limits it in applications. To address that, this paper adopts a self-supervised stage to avoid the high cost of labeling, while ensuring the efficiency and accuracy of reliability assessment. The training process of this method is split into two stages. In the first stage, feature reconstruction and unsupervised learning are used to provide the initial network parameters. Thereafter, the second learning stage can be trained in a task-agnostic way with fewer labels. The results of case study demonstrate the effectiveness of the proposed approach.

This paper presents a new efficient reliability assessment method for multiple energy systems (MES) based on shadow price (SP). The coupling and interaction of multiple energy flows are quantitatively described by the energy hub (EB) model. The shadow price theory can accelerate the calculations of optimal load shedding in the reliability assessment for MES. Large numbers of system states are generated by the fluctuations of renewable energy generation. These numerous system states can be evaluated by the shadow price theory instead of cumbersome optimization computations. The case study shows that the proposed SP approach can greatly improve the efficiency of MES.

With the continuous expansion of renewable energy in the power grid, a massive number of operating states have been added to the power system. The heavy computational task brings new challenges to the efficiency of reliability assessment. In this regard, a stacked-denoising-auto-encoders-based (SDAE) model is proposed to replace the time-consuming optimal power flow (OPF) algorithm in the reliability assessment of power system operation. The system state and the optimal load curtailment are used as training samples, and the corruption process of SDAE is improved to make it more suitable for power systems. Moreover, unsupervised pretraining and supervised fine-tuning are used to get the optimal coding parameters which can establish the nonlinear mapping relationship between the system states and the minimum load curtailment. Case studies are performed on the RTS-79 system considering renewable energy. Results indicate that the proposed method can greatly improve the efficiency of reliability assessment for power system operation.

A rapid rise in the popularity of e-scooters has brought forth an increasing number of e-scooter-related conflicts, crashes, and injuries to pedestrians in many cities. There is a pressing need to understand the factors influencing the severity of injury to pedestrians involved in e-scooter collisions. This paper investigates the dynamics of e-scooter-pedestrian collisions and presents a new method for relating the probability of severe head injury to collision speed in e-scooter-pedestrian collisions. A total of 160 computer simulations representing different collision scenarios have been analyzed. Our results have shown that e-scooter speed is the main determinant of the severity of pedestrian head injury. E-scooter speed ranging from 10 to 15 km/h is found to be critical for pedestrian safety as the probability of severe head injury rises rapidly within this speed range. Moreover, an e-scooter-pedestrian collision is more likely to cause severe head injury to the pedestrian than a bicycle-pedestrian collision within the same speed range. It has also been found that the weight of the e-scooter and the direction of impact do not have a strong influence on the collision metrics, especially on the probability of severe head injury. The study has also investigated the post-collision fall mechanism for different pedestrian profiles and the influence of different impact angles. Finally, some recommendations have been proposed, including a speed limit of not more than 11 km/h for e-scooterist on shared paths where the likelihood of pedestrian and e-scooter conflicts is higher. The recommendations could help authorities develop legislation for safe micro-mobility.

Background Rhizosphere bacteria play important roles in plant growth and secondary metabolite accumulation. Moreover, only with favorable production areas and desirable germplasm can high-yield and high-quality medicinal materials be produced. However, whether origin and germplasm indirectly affect the yield and quality of Angelica dahurica var. formosana through rhizosphere bacterial effects are not known. Methods In this study, a high-throughput sequencing strategy was used to explore the relationship between the rhizosphere bacterial community and the cultivation of A. dahurica var. formosana from different production areas and germplasm for the first time. Results (1) Proteobacteria was the dominant bacterial phylum in the rhizosphere soil of A. dahurica var. formosana, and these bacteria were stable and conserved to a certain extent. (2) High abundance of Proteobacteria was an important rhizospheric indicator of high yield, and high abundance of Firmicutes was an important indicator of high quality. Proteobacteria and Firmicutes might have an important relationship with the yield and quality of A. dahurica var. formosana, respectively. (3) PCoA cluster analysis demonstrated that both production area and germplasm affected the bacterial community structure in the rhizosphere of A. dahurica var. formosana to a certain extent, and production area had the greatest effect. In addition to available potassium, the rhizosphere soil nutrient levels of different production areas strongly affected the bacterial diversity and community. These findings provide a theoretical basis for the exploitation and utilization of rhizosphere microbial resources of A. dahurica var. formosana and offer a novel approach for increasing the yield and quality of this crop.

Reliable planning and operation of power distribution systems are of great significance. In this paper, the impact-increment based state enumeration (IIBSE) method is modified to adapt to the features of distribution systems. With the proposed method, the expectation, probabilistic, and duration reliability indices can be accurately obtained with a lower enumerated order of contingency states. In addition, the time-consuming optimal power flow (OPF) calculation can be replaced by a simple matrix operation for both independent and radial series failure states. Therefore, the accuracy and efficiency of the assessment process are improved comprehensively. The case of RBTS bus 6 system and IEEE 123 node test feeder system are utilized to test the performance of the modified IIBSE. The results show the superiority of the proposed method over Monte Carlo (MC) sampling and state enumeration (SE) methods in distribution systems.

Light penetration and overheating are major issues facing the application of photothermal therapy. Here, the authors develop a temperature responsive hydrogel optical waveguide for controlled delivery of light to deep tumours and demonstrate biocompatibility and temperature responsive phototherapy in vivo

Current endeavours in exoplanet characterization rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of said technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation has become more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in machine learning provide optimization-based variational inference as an alternative approach to perform approximate Bayesian posterior inference. In this investigation we developed a normalizing-flow-based neural network, combined with our newly developed differentiable forward model, Diff - τ , to perform Bayesian inference in the context of atmospheric retrievals. Using examples from real and simulated spectroscopic data, we demonstrate the advantages of our proposed framework: (1) training our neural network does not require a large precomputed training set and can be trained with only a single observation; (2) it produces high-fidelity posterior distributions in excellent agreement with sampling-based retrievals; (3) it requires up to 75% fewer forward model calls to converge to the same result; and (4) this approach allows formal Bayesian model selection. We discuss the computational efficiencies of Diff - τ in relation to TauREx3 's nominal forward model and provide a “lessons learned” account of developing radiative transfer models in differentiable languages. Our proposed framework contributes toward the latest development of neural network–powered atmospheric retrieval. Its flexibility and significant reduction in forward model calls required for convergence holds the potential to be an important addition to the retrieval tool box for large and complex data sets along with sampling-based approaches.

Abstract Power supply reliability (PSR) is a critical factor in the optimal configuration of stand‐alone microgrids. Considering the impact of the failure outage of power generation and energy storage equipment, as well as the uncertainty of renewable energy on PSR, a multi‐objective bi‐level mixed‐integer optimisation model is proposed. Based on Benders decomposition, the model is decoupled into a master problem (MP) of the equipment optimal configuration considering economy and environmental conservation and a sub‐problem (SP) of the PSR check. Considering the load importance hierarchy, two types of slack variables are introduced into the SP to ensure both the reliable power supply of important loads and the PSR of the system. Through interactive iteration of the MP and SP, the complexity of the optimisation model is considerably reduced, improving the solution efficiency while ensuring the rationality of the optimal configuration scheme. Finally, the effectiveness of the proposed model is verified by a typical stand‐alone wind–photovoltaic–diesel–battery microgrid system and analysed in terms of the impact of reliability requirement and energy storage on the configuration scheme and algorithm convergence.

Introduction: This paper proposes a sigmoid-based adaptive inertia control strategy for grid-forming (GFM) inverter to enhance frequency stability.Methods: Firstly, the frequency response characteristics under different disturbances are analyzed theoretically. Then, to solve the problem that fixed inertia leads to a contradiction between the maximum frequency deviation and the setting time, a non-linear inertia regulator is investigated. This non-linear inertia regulator, which is based on an improved sigmoid function, is applied to achieve real-time inertia by the frequency deviation. Moreover, the full-order small-signal model of the system containing the nonlinear inertia regulator is established and thus the stability of the proposed strategy is analyzed.Result and Discussion: The proposed control strategy is implemented without derivative action, which may suffer from high-frequency noises. Finally, the hardware-inthe-loop (HIL) results verify the remarkable performance of the proposed control strategy.

When it comes to grouting in coal mines, polyurethane (PU) is often utilized. However, it is of vital importance to consistently improve the mineral PU, considering the significant amount of environmental deterioration to which it is prone. Laboratory experiments were used to model various coal mine conditions. Additionally, a workable technique for PU strengthening using ultrasonic waves was proposed. Compression tests and scanning electron microscopy (SEM) were used to describe the PU–gangue material’s induration characteristics. The results showed that ultrasound has a positive impact on PU’s mechanical strength. The final strength of the PU was significantly impacted by the size of the coal gangue particles, the amount of dust, and the amount of water. The induration made of gangue and PU with the same mass but differing particle sizes was noticeably different in its compressive strength. The strengthening mechanism showed that the average size of the rigid foam after the ultrasound treatment was smaller, and the ‘honeycomb’-structured space in the inner section was more compact, resulting in the rigid PU foam having a higher compressive strength after ultrasound treatment. Furthermore, the dust content and water content of coal mines need to be controlled within a specific range to ensure the effective use of PU grouting materials.

The basic (region) leucine zippers (bZIPs) are evolutionarily conserved transcription factors widely distributed in eukaryotic organisms. In plants, they are not only involved in growth and development, defense and stress responses and regulation of physiological processes but also play a pivotal role in regulating secondary metabolism. To explore the function related to the bZIP gene family in Stevia rebaudiana Bertoni, we identified 105 SrbZIP genes at the genome-wide level and classified them into 12 subfamilies using bioinformation methods. Three main classes of cis-acting elements were found in the SrbZIP promoter regions, including development-related elements, defense and stress-responsive elements and phytohormone-responsive elements. Through protein–protein interaction network of 105 SrbZIP proteins, SrbZIP proteins were mainly classified into four major categories: ABF2/ABF4/ABI5 (SrbZIP51/SrbZIP38/SrbZIP7), involved in phytohormone signaling, GBF1/GBF3/GBF4 (SrbZIP29/SrbZIP63/SrbZIP60) involved in environmental signaling, AREB3 (SrbZIP88), PAN (SrbZIP12), TGA1 (SrbZIP69), TGA4 (SrbZIP82), TGA7 (SrbZIP31), TGA9 (SrbZIP95), TGA10 (SrbZIP79) and HY5 (SrbZIP96) involved in cryptochrome signaling, and FD (SrbZIP72) promoted flowering. The transcriptomic data showed that SrbZIP genes were differentially expressed in six S. rebaudiana cultivars (‘023’, ‘110’, ‘B1188’, ‘11-14’, ‘GP’ and ‘GX’). Moreover, the expression levels of selected 15 SrbZIP genes in response to light, abiotic stress (low temperature, salt and drought), phytohormones (methyl jasmonate, gibberellic acid and salicylic acid) treatment and in different tissues were analyzed utilizing qRT-PCR. Some SrbZIP genes were further identified to be highly induced by factors affecting glycoside synthesis. Among them, three SrbZIP genes (SrbZIP54, SrbZIP63 and SrbZIP32) were predicted to be related to stress-responsive terpenoid synthesis in S. rebaudiana. The protein–protein interaction network expanded the potential functions of SrbZIP genes. This study firstly provided the comprehensive genome-wide report of the SrbZIP gene family, laying a foundation for further research on the evolution, function and regulatory role of the bZIP gene family in terpenoid synthesis in S. rebaudiana.

The optimal load shedding calculations of an enormous number of contingency states are the most challenging impediments for power system reliability assessment, especially in a large-scale system with multiple types of loads. To deal with that, this paper proposes a fast method that accelerates the calculation of optimal load shedding based on the shadow price. This method calculates the optimal load shedding of systems by shadow-price-based linear functions, rather than the time-consuming OPF algorithms. The hundred percentage criterion is used to look for the matching shadow price of states. Case studies are performed on the RTS-79 system. The results show that the proposed methodology can significantly simplify the solving process and reduce the calculation time.

Mobile energy storage (MES) is a typical flexible resource, which can be used to provide an emergency power supply for the distribution system. However, it is inevitable to consider the complicated coupling relations of mobile energy storage, transportation network, and power grid, which can cause issues of complex modeling and low efficiency. To address that, this paper proposes a mobile energy storage dispatch model to minimize the load curtailment. The framework of rolling optimization is established to update the optimal strategy with the real-time factors, including grid, transportation, and MES. To increase solving efficiency, the model is extended to a mixed integer second-order cone programming model. Finally, the simulation results demonstrate the effectiveness of the proposed method in various scenarios.

The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.

To quickly respond to variations in the state of network load demand, a solution using data-driven techniques to predict optimal power flow (OPF) has emerged in recent years. However, most of the existing methods are highly dependent on large data volumes. This limits their application on the newly established or expanded systems. In this regard, this work proposes a sample-efficient OPF learning method to maximize the utilization of limited samples. By decomposing the OPF task before knowledge distillation, deep learning complexity is reduced. Thereafter, knowledge distillation is used to integrate decoupled tasks and improve accuracy in low-data setups. Unsupervised pre-training is introduced to alleviate the demand for labeled data. Additionally, the focal loss function and teacher annealing strategy are adopted to achieve higher accuracy without extra samples. Numerical tests on different systems corroborate the advanced accuracy and training speed over other training methods, especially on fewer-sample occasions.

Abstract With the increasing distribution network accidents caused by typhoon disasters, risk management in typhoon scenarios is necessary. A risk management framework for power distribution networks (PDNs) undergoing a typhoon disaster is proposed. First, a risk prediction model of the PDN under typhoon conditions based on the Batts model is developed to predict the loss of load risk (LLR) index and economic loss risk (ELR) index of the PDN before the typhoon lands. Then, a vulnerability index of overhead lines, considering the failure frequency of specific overhead lines and their load importance, is introduced for use in a weak link identification model to identify weak links in the PDN. Finally, a two‐time‐step risk relief model based on typhoon forecast information and the identified weak links is developed to allocate PDN relief materials to reduce the LLR and ELR during a typhoon disaster. An IEEE RBTS BUS6 system is employed as a test system to verify the risk management strategy. The vulnerability index proposed by the strategy considers both the importance of load and line failure. The risk management strategy adopts a two‐time‐step model, which reduces the impact of meteorological forecast errors and enables more efficient use of materials. The results show that the proposed framework can take more effective use of relief materials to reduce the loss of PDNs compared with the traditional strategy.

The energy interactions and uncertain factors of integrated energy systems (IES) have brought risks to the reliable energy supply. A large number of states need to be analyzed to obtain a stable reliability value. However, different operating characteristics complicate the optimal energy flow (OEF) model, which brings tremendous computational cost. To address that, a deep-learning-based approach is proposed as an alternative way to solve the OEF problems. This approach constructs the mapping between system state and energy allocation to directly obtain the optimal load curtailment. Thereafter, the deep-learning-based reliability assessment framework for IES is proposed to improve efficiency. Additionally, the Gaussian noise and data-processing strategies are involved to achieve higher accuracy. Compared to the model-based approach, the proposed method increases the reliability assessment efficiency by 6 orders of time. With an accuracy of over 95%, it outperforms other autoencoder and random forest methods. Method accuracy has remained above 90% in various scenarios.

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

Utilizing energy storage systems have been considered as a feasible pathway to achieve carbon neutrality. However, the common battery type for energy storage systems is the cheap lithium iron phosphate battery, which has low output efficiency and is almost impossible to charge in cold areas. Lithium titanate battery has high output efficiency and charge efficiency in cold areas. Meanwhile, the price of a lithium titanate battery is three times that of a lithium iron phosphate battery with the same capacity. To achieves the complementary advantages of lithium iron phosphate battery and lithium titanate battery, this paper proposes the dual battery framework of energy storage systems. Based on this, the operation model of energy storage systems is formulated. Besides, five cases are designed to demonstrate the effectiveness of the proposed method and the results show that the dual battery framework has high output efficiency with a cheaper cost than lithium titanate battery.

Abstract Background The serine protease inhibitor-1 (SERPINE1) rs1799889 single nucleotide polymorphism (SNP) has been constantly associated with diabetes mellitus (DM) and its vascular complications. The aim of this meta-analysis was to evaluate this association with combined evidences. Methods The systematic search was performed for studies published up to March 2021 which assess the associations between SERPINE1 rs1799889 SNP and the risks of DM, diabetic retinopathy (DR), diabetic cardiovascular disease (CVD) and diabetic nephropathy (DN). Only case-control studies were identified, and the linkage between SERPINE1 rs1799889 polymorphism and diabetic vascular risks were evaluated using genetic models. Results 51 comparisons were enrolled. The results revealed a significant association with diabetes risk in overall population (allelic: OR = 1.34, 95 % CI = 1.14–1.57, homozygous: OR = 1.66, 95 % CI = 1.23–2.14, heterozygous: OR = 1.35, 95 % CI = 1.08–1.69, dominant: OR = 1.49, 95 % CI = 1.18–1.88, recessive: OR = 1.30, 95 % CI = 1.06–1.59) as well as in Asian descents (allelic: OR = 1.45, 95 % CI = 1.16–1.82, homozygous: OR = 1.88, 95 % CI = 1.29–2.75, heterozygous: OR = 1.47, 95 % CI = 1.08-2.00, dominant: OR = 1.64, 95 % CI = 1.21–2.24, recessive: OR = 1.46, 95 % CI = 1.09–1.96). A significant association was observed with DR risk (homozygous: OR = 1.25, 95 % CI = 1.01–1.56, recessive: OR = 1.20, 95 % CI = 1.01–1.43) for overall population, as for the European subgroup (homozygous: OR = 1.32, 95 % CI = 1.02–1.72, recessive: OR = 1.38, 95 % CI = 1.11–1.71). A significant association were shown with DN risk for overall population (allelic: OR = 1.48, 95 % CI = 1.15–1.90, homozygous: OR = 1.92, 95 % CI = 1.26–2.95, dominant: OR = 1.41, 95 % CI = 1.01–1.97, recessive: OR = 1.78, 95 % CI = 1.27–2.51) and for Asian subgroup (allelic: OR = 1.70, 95 % CI = 1.17–2.47, homozygous: OR = 2.46, 95 % CI = 1.30–4.66, recessive: OR = 2.24, 95 % CI = 1.40–3.59) after ethnicity stratification. No obvious association was implied with overall diabetic CVD risk in any genetic models, or after ethnicity stratification. Conclusions SERPINE1 rs1799889 4G polymorphism may outstand for serving as a genetic synergistic factor in overall DM and DN populations, positively for individuals with Asian descent. The association of SERPINE1 rs1799889 SNP and DR or diabetic CVD risks was not revealed.

Traditional Chinese medicine (TCM) has accumulated a wealth of ecological wisdom and is regarded as an outstanding cultural and medical resource in China. However, in the context of serious environmental pollution, the potential harm caused by TCM materials (TCMMs) due to toxic heavy metals has seriously affected the sustainable development of TCM. Cadmium (Cd) is an internationally recognized heavy metal contaminant. In this paper, 270 reports on Cd in TCMMs were screened from 1969 publications covering 243 species in 81 families. According to the source of the TCMMs, the data were divided into the following categories: aboveground part, bark, flower and fruit, herb, leaf and rhizome. The temporal dynamics of the Cd content and its correlations with the habitats and categories (parts) of TCMMs were also studied. The results show that 22.05 % of the investigated TCMM samples exceeded the relevant domestic standards (Cd≤0.3 mg/kg), among which the maximum Cd content was 17.75 mg/kg. Myrtaceae and Syzygium aromaticum were the family and species with the highest mean Cd content, respectively. Regarding the source of TCMMs of great concern, the mean Cd content of TCMMs decreased in the order of herb > aboveground part > flower and fruit > leaf > rhizome > bark. In addition, in terms of the spatiotemporal distribution, the spatial distribution of the mean Cd content of TCMMs was significantly higher on the Qinghai-Tibet Plateau, followed by Southwest China. When comparing different times, more serious Cd pollution of TCMMs existed after 2000, and the highest mean Cd content was observed in 2000–2004. In summary, soil acidity must be decreased and the cultivation conditions of Cd-hyperaccumulators such as Ligusticum chuanxiong and Lonicera japonica must be modified, in conjunction with a scientific health risk assessment, to ensure the sustainable development of TCMMs.

Recent advances in nanotechnologies for cancer diagnosis and treatment have received considerable attention worldwide. Nanoparticles are being used to create nanodrugs and probes to diagnose and treat a variety of diseases, including cancer. Nanomedicines have unique advantages, such as increased surface-to-volume ratios, which enable them to interact with, absorb, and deliver small biomolecules to a very specific target, thereby improving the effectiveness of both probes and drugs. Nanoprobe biotechnology also plays an important role in the discovery of novel cancer biomarkers, and nanoprobes have become an important part of early clinical diagnosis of cancer. Various organic and inorganic nanomaterials have been developed as biomolecular carriers for the detection of disease biomarkers. Thus, we designed this review to evaluate the advances in nanoprobe technology in tumor diagnosis.

Melanoma is a cutaneous carcinoma, and its incidence is rapidly increasing with every year. The treatment options for melanoma have been comprehensively studied. Conventional treatment methods (e.g., radiotherapy, chemotherapy and photodynamic therapy) with surgical removal inevitably cause serious complications; moreover, resistance is common. Nanoparticles (NPs) combined with conventional methods are new and promising options to treat melanoma, and many combinations have been achieving good success. Due to their physical and biological features, NPs can help target intended melanoma cells more efficiently with less damage. This creates new hope for a better treatment strategy for melanoma with minimum damage and maximum efficacy.

About 10 major crops basically feed the world. In fact, there are still a large number of plants that have not been fully explored and utilized because they have been ignored by the market and research. The expansion of food sources in various countries plays an important role in maintaining food security and nutrition security in the world. Miwu is the aerial part of the medicinal plant Rhizoma Chuanxiong belonging to a traditional local characteristic food raw material. Its edible value is still little known. Through textual research, component determination, literature survey, field research, and SWOT analysis, this paper has a comprehensive understanding of Miwu’s diet history, chemical components, safety risks, and industrial development status. It is found that Miwu has been eaten for 800 years, is rich in nutrients and active ingredients, and has no acute toxicity. In addition, the current industrial development of Miwu has significant advantages and many challenges. To sum up, Miwu is a potentially underutilized food raw material. This paper also provides countermeasures for the industrialized development of Miwu, which will provide a milestone reference for the future utilization and development of Miwu.

Breast cancer (BC) is the most common malignant tumor in women. There are different risk characteristics and treatment strategies for different subtypes of BC. The tumor microenvironment (TME) is of great significance for understanding the occurrence, development, and metastasis of tumors. The TME plays an important role in all stages of BC metastasis, immune monitoring, immune response avoidance, and drug resistance, and also plays an important role in the diagnosis, prevention, and prognosis of BC. Smart nanosystems have broad development prospect in the regulation of the BC drug delivery based on the response of the TME. In particular, TME-responsive nanoparticles cleverly utilize the abnormal features of BC tissues and cells to achieve targeted transport, stable release, and improved efficacy. We here present a review of the mechanisms underlying the response of the TME to BC to provide potential nanostrategies for future BC treatment.

Cutaneous melanoma is an aggressive cancer type derived from melanocytes and its incidence has rapidly increased worldwide. Despite the vast improvement in therapy, melanoma is still confronted with high invasion, metastasis, and recurrence rate. Recent studies have confirmed that the exosomes are naturally occurring membranous extracellular vesicles with nano-sized lipid bilayers, performing as information messagers within cellular reciprocal action. Exosomes are unquestionably endowed with multifaceted roles in various diseases, including melanoma. Notably, tumor-derived exosomes play a pivotal role in conditioning the tumor microenvironment to promote the growth, metastasis, immune escape, and even drug-resistance of melanoma by transferring carcinogenic nucleic acids and proteins. Clinically, the dynamic expressions of exosomal components and loadings in melanoma patients with different tumor stages confer the clinical application of melanoma exosomes as diagnostic biomarkers. Hence, this review highlights the recent complicated roles and mechanisms of melanoma exosomes, as well as their potential as diagnostic and therapeutic targets in melanoma. The in-depth insights into the properties and behaviors of melanoma exosomes are of great potential to yield attractive therapeutic methods for melanoma.

Melanoma is a malignant tumor arising in melanocytes from the basal layer of the epidermis and is the fifth most commonly diagnosed cancer in the United States. Melanoma is aggressive and easily metastasizes, and the survival rate is low. Nanotechnology-based diagnosis and treatment of melanoma have attracted increasing attention. Importantly, nano drug delivery systems have the advantages of increasing drug solubility, enhancing drug stability, prolonging half-life, optimizing bioavailability, targeting tumors, and minimizing side effects; thus, these systems can facilitate tumor cytotoxicity to achieve effective treatment of melanoma. In this review, we discuss current nanosystems used in the diagnosis and treatment of melanoma, including lipid systems, inorganic nanoparticles, polymeric systems, and natural nanosystems. The excellent characteristics of novel and effective drug delivery systems provide a basis for the broad applications of these systems in the diagnosis and treatment of melanoma, particularly metastatic melanoma.

In this article, the temperature of the needle tip and the ground electrode were changed to obtain different temperature gradients. The harmonic superimposed DC voltage was also simultaneously applied to study the electrical tree initiation, growth and breakdown characteristics in cross-linked polyethylene (XLPE) under the temperature gradient. The tree inception voltage, tree structure, tree length, accumulated damage (AD) and breakdown time were used to analyze the electrical tree degradation process. It was found that the greater the temperature gradient, the easier it is for electrical trees to be initialed which is related to the different charge behaviors affected by the trap distribution indicated by surface potential decay (SPD) results at different temperatures. With the temperature gradient increasing, the electrical tree length increases firstly but then decreases slightly when the temperature of the ground electrode is fixed, but the electrical tree length and AD increase linearly with the temperature gradient increasing when the temperature of the needle tip is fixed. The temperature rise of the ground electrode has a more serious impact on the safety of the insulation compared with the temperature rise of the needle tip. Positive DC bias voltage is more likely to cause damage and breakdown than negative DC bias voltage under the temperature gradient. These results indicate that the temperature gradient and harmonic superimposed DC voltage will promote the electrical growth in cable insulation, so special attention should be paid to the safety of electrical equipment under such special conditions.

We present analysis of the atmospheres of 70 gaseous extrasolar planets via transit spectroscopy with Hubble’s Wide Field Camera 3 (WFC3). For over half of these, we statistically detect spectral modulation that our retrievals attribute to molecular species. Among these, we use Bayesian hierarchical modeling to search for chemical trends with bulk parameters. We use the extracted water abundance to infer the atmospheric metallicity and compare it to the planet’s mass. We also run chemical equilibrium retrievals, fitting for the atmospheric metallicity directly. However, although previous studies have found evidence of a mass–metallicity trend, we find no such relation within our data. For the hotter planets within our sample, we find evidence for thermal dissociation of dihydrogen and water via the H ^− opacity. We suggest that the general lack of trends seen across this population study could be due to (i) the insufficient spectral coverage offered by the Hubble Space Telescope’s WFC3 G141 band, (ii) the lack of a simple trend across the whole population, (iii) the essentially random nature of the target selection for this study, or (iv) a combination of all the above. We set out how we can learn from this vast data set going forward in an attempt to ensure comparative planetology can be undertaken in the future with facilities such as the JWST, Twinkle, and Ariel. We conclude that a wider simultaneous spectral coverage is required as well as a more structured approach to target selection.

We present an atmospheric analysis of LTT 9779 b, a rare planet situated in the hot-Neptune desert, that has been observed with Hubble Space Telescope (HST)/WFC3 with G102 and G141. The combined transmission spectrum, which covers 0.8–1.6 μ m, shows a gradual increase in transit depth with wavelength. Our preferred atmospheric model shows evidence for H _2 O, CO _2 , and FeH with a significance of 3.1 σ , 2.4 σ , and 2.1 σ , respectively. In an attempt to constrain the rate of atmospheric escape for this planet, we search for the 1.083 μ m helium line in the G102 data but find no evidence of excess absorption that would indicate an escaping atmosphere using this tracer. We refine the orbital ephemerides of LTT 9779 b using our HST data and observations from TESS, searching for evidence of orbital decay or apsidal precession, which are not found. The phase-curve observation of LTT 9779 b with JWST NIRISS should provide deeper insights into the atmosphere of this planet and the expected atmospheric escape might be detected with further observations concentrated on other tracers such as Ly α .

Abstract Background Stevia rebaudiana (Bertoni) is considered one of the most valuable plants because of the steviol glycosides (SGs) that can be extracted from its leaves. Glycosyltransferases (GTs), which can transfer sugar moieties from activated sugar donors onto saccharide and nonsaccharide acceptors, are widely distributed in the genome of S. rebaudiana and play important roles in the synthesis of steviol glycosides. Results Six stevia genotypes with significantly different concentrations of SGs were obtained by induction through various mutagenic methods, and the contents of seven glycosides (stevioboside, Reb B, ST, Reb A, Reb F, Reb D and Reb M) in their leaves were considerably different. Then, NGS and single-molecule real-time (SMRT) sequencing were combined to analyse leaf tissue from these six different genotypes to generate a full-length transcriptome of S. rebaudiana. Two phylogenetic trees of glycosyltransferases (SrUGTs) were constructed by the neighbour-joining method and successfully predicted the functions of SrUGTs involved in SG biosynthesis. With further insight into glycosyltransferases (SrUGTs) involved in SG biosynthesis, the weighted gene co-expression network analysis (WGCNA) method was used to characterize the relationships between SrUGTs and SGs, and forty-four potential SrUGTs were finally obtained, including SrUGT85C2, SrUGT74G1, SrUGT76G1 and SrUGT91D2, which have already been reported to be involved in the glucosylation of steviol glycosides, illustrating the reliability of our results. Conclusion Combined with the results obtained by previous studies and those of this work, we systematically characterized glycosyltransferases in S. rebaudiana and forty-four candidate SrUGTs involved in the glycosylation of steviol glucosides were obtained. Moreover, the full-length transcriptome obtained in this study will provide valuable support for further research investigating S. rebaudiana.

Breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) is an uncommon type of T-cell lymphoma. Although with a low incidence, the epidemiological data raised the biosafety and health concerns of breast reconstruction and breast augmentation for BIA-ALCL. Emerging evidence confirms that genetic features, bacterial contamination, chronic inflammation, and textured breast implant are the relevant factors leading to the development of BIA-ALCL. Almost all reported cases with a medical history involve breast implants with a textured surface, which reflects the role of implant surface characteristics in BIA-ALCL. With this review, we expect to highlight the most significant features on etiology, pathogenesis, diagnosis, and therapy of BIA-ALCL, as well as we review the physical characteristics of breast implants and their potential pathogenic effect and hopefully provide a foundation for optimal choice of type of implant with minimal morbidity.