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[Introduction] Aiming at the problems of relatively single perspective, inadequate quantitative and global analysis of existing research on hydropower absorption, this paper systematically studies the measures of Yunnan's surplus hydropower absorption, quantitatively analyses the situation of water releasing in the southern region in the future. [Method] This paper put forward the direction of Yunnan's surplus hydropower absorption, and defined the transmission capacity and margin of the west-to-east power transmission channel on this basis. [Result] Based on the principle of overall coordination, this paper purposefully puts forward two kinds of clean hydropower absorption measures, which are optimal utilization of existing channels and improvement of transmission capacity of Guangdong and Guangxi section. [Conclusion] This is the first time to achieve the coordination and unification of planning and dispatching operation links, and further improve the utilization efficiency and transmission benefit of the west-to-east power transmission in southern region.

ABSTRACT Understanding interactions within the gut microbiome and its stability are of critical importance for deciphering ecological issues within the gut ecosystem. Recent studies indicate that long-term instability of gut microbiota is associated with human diseases, and recovery of stability is helpful in the return to health. However, much less is known about such topics in fish, which encompass nearly half of all vertebrate diversity. Here, we examined the assembly and succession of gut microbiota in more than 550 zebrafish, and evaluated the variations of microbial interactions and stability across fish development from larva to adult using molecular ecological network analysis. We found that microbial interactions and stability in the fish gut ecosystem generally increased with host development. This could be attributed to the development of the zebrafish immune system, the increasing amount of space available for microbial colonization within the gut, and the greater stability of nutrients available for the colonized microbiota in adult zebrafish. Moreover, the potential keystone taxa, even those with relatively low abundances, played important roles in affecting the microbial interactions and stability. These findings indicate that regulating rare keystone taxa in adult fish may have great potential in gut microbial management to maintain gut ecosystem stability, which could also provide references for managing gut microbiota in humans and other animals. IMPORTANCE Understanding gut microbial stability and the underlying mechanisms is an important but largely ignored ecological issue in vertebrate fish. Here, using a zebrafish model and network analysis of the gut microbiota we found that microbial interactions and stability in the gut ecosystem increase with fish development. This finding has important implications for microbial management to maintain gut homeostasis and provide better gut ecosystem services for the host. First, future studies should always consider using fish of different age groups to gain a full understanding of gut microbial networks. Second, management of the keystone taxa, even those that are only present at a low abundance, during the adult stage may be a viable pathway to maintain gut ecosystem stability. This study greatly expands our current knowledge regarding gut ecosystem stability in terms of ecological networks affected by fish development, and also highlights potential directions for gut microbial management in humans and other animals.

Seasonal variations of environmental factors generally result in considerable changes in microbiomes, yet we still lack a clear understanding of how microbial communities maintain their ecological stability. Here, we analyze a long-term (nine years) high-throughput sequencing dataset by network analysis to illustrate how the microbial stability varies among seasons in lake ecosystems. The results showed that the microbial networks were distinctly different among seasons, and the most complex network in autumn exhibited the highest stability. We found that the strong connecting structures among microbes (large modules) were significantly (P

The mechanical interactions among integrated cellular structures in soft tissues dictate the mechanical behaviors and morphogenetic deformations observed in living organisms. However, replicating these multifaceted attributes in synthetic soft materials remains a challenge. In this work, we develop a smart hydrogel system featuring engineered stiff cellular patterns that induce strain-driven heterogeneous subdomains within the hydrogel film. These subdomains arise from the distinct mechanical responses of the pattern and film domains under applied mechanical forces. Unlike previous studies that incorporate reinforced inclusions into soft matrices to tailor material properties, our method manipulates the localization, integration, and interaction of these subdomain building blocks within the soft film. This enables extensive tuning of both local and global behaviors. Notably, we introduce a subdomain-interface mechanism that allows for the concurrent customization and decoupling of mechanical properties and shape transformations within a single material system-an achievement rarely accomplished with current synthetic soft materials. Additionally, our use of in-situ imaging characterizations, including full-field strain mapping via digital imaging correlation and reciprocal-space patterns through fast Fourier transform analysis of real-space pattern domains, provides rapid real-time monitoring tools to uncover the underlying principles governing tailored multiscale heterogeneities and intricate behaviors.

Microbial fuel cells utilize exoelectrogenic microorganisms to directly convert organic matter into electricity, offering a compelling approach for simultaneous power generation and wastewater treatment. However, conventional microbial fuel cells typically require thick biofilms for sufficient metabolic electron production rate, which inevitably compromises mass and charge transport, posing a fundamental tradeoff that limits the achievable power density (

The performance of electrocatalysts is critical for renewable energy technologies. While the electrocatalytic activity can be modulated through structural and compositional engineering following the Sabatier principle, the insufficiently explored catalyst-electrolyte interface is promising to promote microkinetic processes such as physisorption and desorption. By combining experimental designs and molecular dynamics simulations with explicit solvent in high accuracy, we demonstrated that dimethylformamide can work as an effective surface molecular pump to facilitate the entrapment of oxygen and outflux of water. Dimethylformamide disrupts the interfacial network of hydrogen bonds, leading to enhanced activity of the oxygen reduction reaction by a factor of 2 to 3. This strategy works generally for platinum-alloy catalysts, and we introduce an optimal model PtCuNi catalyst with an unprecedented specific activity of 21.8 ± 2.1 mA/cm2 at 0.9 V versus the reversible hydrogen electrode, nearly double the previous record, and an ultrahigh mass activity of 10.7 ± 1.1 A/mgPt.

C18H20O6, monoclinic, C2/c (no. 15), a = 27.191(3) Å, b = 8.7442(3) Å, c = 20.861(2) Å, β = 136.337(18)°, V = 3424.5(9) Å3, Z = 8, Rgt(F) = 0.0553, wRref(F2) = 0.1625, T = 294.7(4) K.

This study investigated the anti-fibrotic effects of reversine and Chinese medicine Xiang–Sha–Liu–Jun–Zi decoction (XSLJZD) on thioacetamide (TAA)-induced hepatic injury. Sprague-Dawley rats were intraperitoneally administered with TAA, then injected with reversine intraperitoneally, and/or orally provided with XSLJZD. TAA resulted in liver injury with increases in the liver index and levels of serum aspartate aminotransferase (AST) and alanine aminotransferase. Reversine alleviated the liver index and AST level and improved TAA-induced pathological changes but decreased TAA-induced collagen deposition, and α-smooth muscle actin and transforming growth factor-β1 expression. Reversine also modulated the mRNA levels of inflammatory cytokines, such as RelA, interleukin (IL)-17A, IL-22, IL-1β, IL-6, NLR family pyrin domain containing 3, platelet-derived growth factor, and monocyte chemoattractant protein, and suppressed nuclear factor (NF)-κB (p65) phosphorylation and caspase 1 activation. Meanwhile, XSLJZD protected TAA-injured liver without increasing fibrosis and enhanced the regulating effect of reversine on RelA, IL-17A, IL-1β, and MCP-1 cytokines. In conclusion, reversine ameliorates liver injury and inhibits inflammation reaction by regulating NF-κB, and XSLJZD protects the liver through its synergistic effect with reversine on regulating inflammatory cytokines.

Objective To evaluate the effects of IR-61 on the development of pulmonary fibrosis and on the proliferation and migration of lung fibroblasts in rats. Methods Forty male SD rats (6 to 8 weeks old) were randomized equally into normal control group, IR-61 group, bleomycin group and bleomycin+IR-61 group. In the latter 2 groups, rat models of pulmonary fibrosis were established by intratracheal infusion of bleomycin (2.5 mg/kg). In the 2 groups with IR-61 treatment, IR-61 was intraperitoneally injected at the dose of 2.0 mg/kg 30 min following intratracheal bleomycin infusion and was then administered twice a week. At 10 d after bleomycin treatment, 3 rats from each group were selected for IR-61 injection (2 mg/kg) via the tail vein to observe the accumulation of IR-61 in each organ at 24 h using near infrared fluorescence imaging (NIR). After 28 d of treatment, the rats were euthanized and the lung tissue was collected for HE and Masson staining. RT-PCR and Western blotting were performed to detect the changes of the expression of the genes related with lung fibroblast transdifferentiation and proliferation (including Collagen-1, Collagen-3, α-SMA, fibronectin, FOXM1, CCNB1, CDC25b and AURKB) in human lung fibroblasts (HFL1) and primary rat lung fibroblasts in response to IR-61 and transforming growth factor-β1 (TGF-β1) treatments; the migration of the cells was assessed using cell scratch test. Results NIR imaging showed that IR-61 could target the injured lung tissue induced by bleomycin. Morphological observation showed that compared with bleomycin-treated rats, the rats with IR-61 treatment presented with better structural integrity of the lungs with less obvious lung collapse, fewer fibrotic nodules and a significantly milder of pulmonary fibrosis (0.103 3±0.014 53 vs 0.460 0±0.037 86, P < 0.05). In HFL1 cells and primary rat lung fibroblasts, IR-61 pretreatment prior to TGF-β1 stimulation resulted in significantly lowered mRNA and protein expression of collagen-1, collagen-3, α-SMA, and fibronectin as well as the proliferation-specific genes FOXM1, CCNB1, CDC25b and AURKB (P < 0.05). Cell scratch test showed that TGF-β1 treatment significantly increased the cell migration activity, which was effectively suppressed by IR-61 treatment. Conclusion IR-61 ameliorates bleomycin-induced pulmonary fibrosis in rats by targeting the injured lung tissue and inhibiting the transdifferentiation, proliferation and migration of lung fibroblasts

Long non-coding RNAs (lncRNAs) play important roles in response to various biotic and abiotic stresses. So far, systematic identification and characterization of lncRNAs have been reported in a few model plant species and major crops, but their roles in abiotic stress response have not yet been reported in common wild rice (Oryza rufipogon). Dongxiang wild rice (DXWR) possesses a high degree of drought resistance and has been well recognized as a precious genetic resource for drought resistant rice breeding. We presented the reference catalog of 1 655 novel lncRNA transcripts in DXWR using strand-specific RNA sequencing and bioinformatics approaches. Meanwhile, a total of 1 092 lncRNAs were determined as differentially expressed lncRNAs under drought stress. Quantitative real-time PCR results exhibited a high concordance with RNA sequencing data, which confirmed that the expression patterns of lncRNAs based on RNA sequencing were highly reliable. Furthermore, 8 711 transcripts were predicted as target genes of the differentially expressed lncRNAs. Functional annotation analysis based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases showed that the target genes were significantly enriched in cellular and metabolic processes, cell part, binding and plant hormone signal transduction, as well as many other terms related to abiotic stress resistance. These results expanded our understanding of lncRNA biology and provided candidate regulators for genetic improvement of drought resistance in rice cultivars. Keywords: Dongxiang wild rice, Drought stress, Long non-coding RNA, Systematic characterization

Understanding the microbial community assembly is an essential topic in microbial ecology. Coastal wetlands are an important blue carbon sink, where microbes play a key role in biogeochemical cycling of nutrients and energy transformation. However, the drivers controlling the distribution patterns and assembly of bacterial and archaeal communities in coastal wetland are unclear. Here we examined the diversity, co-occurrence network, assembly processes and environmental drivers of bacterial and archaeal communities from inshore to offshore sediments by the sequencing of 16S rRNA gene amplicons. The value of α- and β-diversity of bacterial and archaeal communities generally did not change significantly (P > 0.05) between offshore sites, but changed significantly (P < 0.05) among inshore sites. Sediment pH and salinity showed significant effects on the diversity and keystone taxa of bacterial and archaeal communities. The bacterial and archaeal co-occurrence networks were inextricably linked with pH and salinity to formed the large network nodes, suggesting that they were the key factors to drive the prokaryotic community. We also identified that heterogeneous and homogeneous selection drove the bacterial and archaeal community assembly, while the two selections became weaker from offshore sites to inshore sites, suggesting that deterministic processes were more important in offshore sites. Overall, these results suggested that the environmental filtering of pH and salinity jointly governed the assembly of prokaryotic community in offshore sediments. This study advances our understanding of microbial community assembly in coastal wetland ecosystems.

In this era of scientific and technological progress, digital media has made great contributions to the world and modern art and design works. The Internet has helped many artists make their work more visible and accessible to audiences around the world. Advanced technology has also enabled artists to transform and manipulate their work, thus becoming an important artistic medium. This paper summarizes the characteristics of interactive technology and new media, and analyzes the development and application of new media interactive art in experience.

Nuclear magnetic resonance (NMR) spectroscopy has found wide applications in fields of chemistry, biology, medicine, materials, etc, benefitting from its features of non-invasion, high resolution and high-throughput information. Recently, a high level of interest has reemerged in the development of pure shift NMR techniques which aim for significantly improving spectral resolution in overlapped regions. According to studies on new techniques and related applications by our group, herein we briefly review how pure shift technique is developed, and what would be next challenges in its development and extensive applications.

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

ABSTRACT Understanding mechanisms governing the resistance and resilience of microbial communities is essential for predicting their ecological responses to environmental disturbances. Although we have a good understanding of such issues for soil and lake ecosystems, how ecological resistance and resilience regulate the microbiota in the fish gut ecosystem remains unclear. Using the zebrafish model, we clarified the potential mechanisms governing the gut microbiota after exposure to silver nanoparticles (AgNPs). Here, we explored the ecological resistance and resilience of gut microbiota in zebrafish exposed to different concentrations of AgNPs (i.e., 10, 33 and 100 μg/liter) for 15, 45, 75 days. The high-throughput sequencing analysis of the 16S rRNA gene showed that AgNP exposure significantly reduced the α-diversity of gut microbiota and resulted in obvious dynamics of community composition and structure. However, the rebound of zebrafish gut microbiota was pushed toward an alternative state after 15 days of AgNP exposure. We found that homogeneous selection was a more prevalent contributor in driving gut community recovery after AgNP exposure. The resilience and resistance of gut microbiota responses to AgNP disturbance might be mainly determined by the predominant keystone taxa such as Acinetobacter and Gemmata. This study not only expanded our understanding of fish gut microbiota’s responses to pollutants but also provided new insights into maintaining host-microbiome stability during environmental perturbations. IMPORTANCE Understanding the ecological mechanisms governing the resistance and resilience of microbial communities is a key issue to predict their responses to environmental disturbances. Using the zebrafish model, we wanted to clarify the potential mechanisms governing the resistance and resilience of gut microbiota after exposure to silver nanoparticles (AgNPs). We found that AgNP contamination significantly reduced the α-diversity of gut microbiota and resulted in obvious changes in community composition. The resilience and resistance of gut microbiota to AgNPs might be associated with the predominant keystone taxa (e.g., Acinetobacter and Gemmata). This study greatly expanded our understanding of how fish gut microbiota responds to environmental perturbations and maintains stability.

With the dramatic increase of energy demand and the continuous increase of power system operation pressure, higher requirements are put forward for the development of power grid planning and optimization operation. It is important for the refinement of distribution network planning to deeply extract the characteristics of user load. First, the process of load characteristic analysis method from the user level to the industry level is proposed, which achieves the division of electricity consumption patterns of various industries, thus building a panoramic portrait of industry electricity consumption behavior. Then, by expanding the information filled in by traditional customers, the feature vector of each user is extracted, and the users' industry electricity consumption patterns are used as the label. Therefore, a method for identifying the electricity consumption pattern of the customer based on the BB-stacking model fusion framework is proposed, which yields the preliminary forecast results of customer load based on the actual load accounting results of the customers. Finally, comparative simulations with different methods verify the effectiveness of the proposed algorithm, which can provide prominent guidance for the actual distribution network planning work.

In view of the low accuracy and slow speed of goat-face recognition in real breeding environments, dairy goats were taken as the research objects, and video frames were used as the data sources. An improved YOLOv4 goat-face-recognition model was proposed to improve the detection accuracy; the original backbone network was replaced by a lightweight GhostNet feature extraction network. The pyramid network of the model was improved to a channel management mechanism with a spatial pyramid structure. The path aggregation network of the model was improved into a fusion network with residual structure in the form of double parameters, in order to improve the model’s ability to detect fine-grained features and distinguish differences between similar faces. The transfer learning pre-training weight loading method was adopted, and the detection speed, the model weight, and the mean average precision (mAP) were used as the main evaluation indicators of the network model. A total of 2522 images from 30 dairy goats were augmented, and the training set, validation set, and test set were divided according to 7:1:2. The test results of the improved YOLOv4 model showed that the mAP reached 96.7%, and the average frame rate reached 28 frames/s in the frontal face detection. Compared with the traditional YOLOv4, the mAP improved by 2.1%, and the average frame rate improved by 2 frames/s. The new model can effectively extract the facial features of dairy goats, which improves the detection accuracy and speed. In terms of profile face detection, the average detection accuracy of the improved YOLOv4 goat-face-recognition network can reach 78%. Compared with the traditional YOLOv4 model, the mAP increased by 7%, which effectively demonstrated the improved profile recognition accuracy of the model. In addition, the improved model is conducive to improving the recognition accuracy of the facial poses of goats from different angles, and provides a technical basis and reference for establishing a goat-face-recognition model in complex situations.

Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.

The existing FFT-WT algorithm and FFT-BP algorithm have advantages only for the detection of certain specific harmonics in photovoltaic systems. In this paper, the FFT-WT algorithm is improved, and a DFFT-WT algorithm is proposed. The FFT-BP algorithm is improved and an improved FFT-BP model is proposed. On the basis of this, the two are combined, the threshold interval is introduced to limit the search scope of the neural network iteration, and a DFFT-WT-BP(Double FFT-WT-BP) detection algorithm is proposed. The simulation results show that the algorithm can detect complex harmonic signals composed of multiple harmonics in the actual photovoltaic network inversion system, and is more accurate and practical.

With the rapid development of anti-stealth technology and the iterative renewal of firepower destruction weapons, the battlefield survivability of modern weapons and equipment has been severely tested. In the modern warfare of over-the-horizon, radar detection technology is currently the most widely used method; therefore, reducing the radar echo signal is the most important factor to improve the stealth ability of weapon equipment. Generally, stealth technology is divided into structural stealth and material stealth. Reasonable shape structure design can reduce the radar cross section (RCS) value of a weapon equipment. However, due to the high cost of shape structure and the easy reduction of the comprehensive performance of equipment, there are many limitations in application. Because the stealth technology of materials is relatively simple and the design difficulty is relatively low, the research and application of stealth materials are popular and have been well explored. Although the traditional absorbing materials have the advantages of low cost, good flexibility, and easy processing, they also have the defects of high density, narrow working frequency band, and limited strength. Core-shell microwave absorber is a composite multiphase structure with a spherical particle as the core and one or more layers of heterogeneous materials coated on the outer surface. Because of its unique structure and excellent performance, it is a promising material to solve the existing problems. On the basis of reviewing the working principle of microwave absorbing materials, the advantages of core-shell structure materials in the field of microwave absorbing were discussed. This paper mainly introduced the research progress of different types of core-shell structure microwave absorbing materials explored in recent years as well as summarized and commented on their preparation methods, structure, and microwave absorbing properties; these materials are mainly based on ferrite, magnetic metal powder and its oxide, ceramic, conductive polymer, and carbon series materials. Finally, the development trend of core-shell structure mircrowave absorbing materials was predicted, including multi-layer core-shell structure, yolk-shell stucture and special structure combined with other structures, whisch can provide reference for futher study of core-shell shtucture composite absorbing materials.

Copper-doped Bi2Se3 (Cu x Bi2Se3) is of considerable interest for tailoring its electronic properties and inducing exotic charge correlations while retaining the unique Dirac surface states. However, the copper dopants in Cu x Bi2Se3 display complex electronic behaviors and may function as either electron donors or acceptors depending on their concentration and atomic sites within the Bi2Se3 crystal lattice. Thus, a precise understanding and control of the doping concentration and sites is of both fundamental and practical significance. Herein, we report a solution-based one-pot synthesis of Cu x Bi2Se3 nanoplates with systematically tunable Cu doping concentrations and doping sites. Our studies reveal a gradual evolution from intercalative sites to substitutional sites with increasing Cu concentrations. The Cu atoms at intercalative sites function as electron donors while those at the substitutional sites function as electron acceptors, producing distinct effects on the electronic properties of the resulting materials. We further show that Cu0.18Bi2Se3 exhibits superconducting behavior, which is not present in Bi2Se3, highlighting the essential role of Cu doping in tailoring exotic quantum properties. This study establishes an efficient methodology for precise synthesis of Cu x Bi2Se3 with tailored doping concentrations, doping sites, and electronic properties.

Heterogeneous nanocatalysts play a crucial role in both the chemical and energy industries. Despite substantial advancements in theoretical, computational and experimental studies, identifying their active sites remains a major challenge. Here we utilize atomic electron tomography to determine the three-dimensional atomic structure of PtNi and Mo-doped PtNi nanocatalysts for the electrochemical oxygen reduction reaction. We then employ the experimental atomic structures as input to first-principles-trained machine learning to identify the active sites of the nanocatalysts. Through the analysis of the structure–activity relationships, we formulate an equation termed the local environment descriptor, which balances the strain and ligand effects to provide physical and chemical insights into active sites in the oxygen reduction reaction. The ability to determine the three-dimensional atomic structure and chemical composition of realistic nanoparticles, combined with machine learning, could transform our fundamental understanding of the active sites of catalysts and guide the rational design of optimal nanocatalysts. (Figure presented.)

Electrified solid-liquid interfaces (ESLIs) play a key role in various electrochemical processes relevant to energy1-5, biology6 and geochemistry7. The electron and mass transport at the electrified interfaces may result in structural modifications that markedly influence the reaction pathways. For example, electrocatalyst surface restructuring during reactions can substantially affect the catalysis mechanisms and reaction products1-3. Despite its importance, direct probing the atomic dynamics of solid-liquid interfaces under electric biasing is challenging owing to the nature of being buried in liquid electrolytes and the limited spatial resolution of current techniques for in situ imaging through liquids. Here, with our development of advanced polymer electrochemical liquid cells for transmission electron microscopy (TEM), we are able to directly monitor the atomic dynamics of ESLIs during copper (Cu)-catalysed CO2 electroreduction reactions (CO2ERs). Our observation reveals a fluctuating liquid-like amorphous interphase. It undergoes reversible crystalline-amorphous structural transformations and flows along the electrified Cu surface, thus mediating the crystalline Cu surface restructuring and mass loss through the interphase layer. The combination of real-time observation and theoretical calculations unveils an amorphization-mediated restructuring mechanism resulting from charge-activated surface reactions with the electrolyte. Our results open many opportunities to explore the atomic dynamics and its impact in broad systems involving ESLIs by taking advantage of the in situ imaging capability.

Human milk oligosaccharides (HMOs) are a diverse class of carbohydrates which support the health and development of infants. The vast health benefits of HMOs have made them a commercial target for microbial production; however, producing the approximately 200 structurally diverse HMOs at scale has proved difficult. Here we produce a diversity of HMOs by leveraging the robust carbohydrate anabolism of plants. This diversity includes high-value and complex HMOs, such as lacto-N-fucopentaose I. HMOs produced in transgenic plants provided strong bifidogenic properties, indicating their ability to serve as a prebiotic supplement with potential applications in adult and infant health. Technoeconomic analyses demonstrate that producing HMOs in plants provides a path to the large-scale production of specific HMOs at lower prices than microbial production platforms. Our work demonstrates the promise in leveraging plants for the low-cost and sustainable production of HMOs.

Current harmonics and power loss caused by traditional two-level inverters working under high-frequency switching need to be solved urgently. Multi-level inverters do not require a boost converter stage and can output multiple different voltage levels with a small THD of the output voltage.A single-phase asymmetrical five-level transformerless PV inverter topology is proposed, consisting of a basic H-bridge and a T-bridge. The T-bridge is located between the H-bridge and the DC voltage-dividing capacitor. The power tube and diode consist of parallel bridges.Several working states of the new topology are analyzed in detail. The corresponding PWM modulation strategy is given. The simulation verifies the effectiveness of the topology and modulation strategy.