Search

Abstract The oral cavity contains the second‐largest microbiota in the human body. The cavity's anatomically and physiologically diverse niches facilitate a wide range of symbiotic bacteria living at distinct oral sites. Consequently, the oral microbiota exhibits site specificity, with diverse species, compositions, and structures influenced by specific aspects of their placement. Variations in oral microbiota structure caused by changes in these influencing factors can impact overall health and lead to the development of diseases—not only in the oral cavity but also in organs distal to the mouth—such as cancer, cardiovascular disease, and respiratory disease. Conversely, diseases can exacerbate the imbalance of the oral microbiota, creating a vicious cycle. Understanding the heterogeneity of both the oral microbiome and individual humans is important for investigating the causal links between the oral microbiome and diseases. Additionally, understanding the intricacies of the oral microbiome's composition and regulatory factors will help identify the potential causes of related diseases and develop interventions to prevent and treat illnesses in this domain. Therefore, turning to the extant research in this field, we systematically review the relationship between oral microbiome dynamics and human diseases.

In reinforced concrete structures, the interface between old and new concrete is commonplace in precast splices, repairs, and construction joints, and is vital for structural integrity. While extensive research has been conducted on the mechanical bonding properties of interfaces between new and old concrete, there remains a notable gap in understanding the shear behavior under various interfacial treatments. To investigate the influence of interface preparation methods and surface roughness on the shear performance of new-to-old concrete interface, direct shear tests were conducted on Z-shaped specimens. The surface roughness of the initially cast specimens was measured using 3D laser scanning, and the interface morphology was observed by scanning electron microscopy. A comprehensive analysis of comparison between calculated and experimental shear strength values was conducted on 243 sets of direct shear test data. Results revealed that the surface roughness of the specimens ranged from 0.13–3.69 mm, with voids of 0.5–10 μm width present in the bond interfaces. Two failure modes, namely cohesion and adhesion, were identified in the aggregates on the bond interfaces. Within a specific range, the shear strength of bond interfaces increased with an increase in surface roughness. Additionally, bond interfaces with the same roughness but different surface process methods exhibited varying shear bearing capacities. Furthermore, the formula for calculating the shear strength of bond interfaces based on different surface preparation processes and segmented roughness was developed.

Abstract Tunable plasmonics has shown a wide range of applications in (bio)chemical sensing, displays, actuators, photonic chips, biomedicine, etc. in the last 2 decades. This review focuses on their recent development from the device perspective, which involves the understanding of fundamental principles and specific applications with desired functionality and compatibility. We classify these active plasmonic devices based on the nature of their structures and working environment as colloidal‐based, chip‐based, and colloidal chip‐based to further elaborate their performance and functions. We also highlight recent progress on tunable surface‐enhanced Raman spectroscopy, metasurfaces, and chiroptics to show the emerging applications of tunable plasmonic systems. We conclude this review with a general perception of the research trend in this field and challenges for their practical applications.

Trichloroethylene (TCE) is a common environmental contaminant that can induce occupational dermatitis medicamentosa-like TCE (ODMLT), where the liver damage is the most common complication. The study aims to uncover the underlying mechanism of TCE-sensitization-induced liver damage by targeting specific exosomal microRNAs (miRNAs). Among the enriched serum exosomal miRNAs of ODMLT patients, miR-205–5p had a significant correlation coefficient with the liver function damage indicators. Moreover, retinoic acid receptor-related orphan receptor α (RORα) was identified as a direct target of miR-205–5p via specific binding. Further experiments showed that kupffer cells (KCs) underwent M1 phenotypic and functional changes in liver injury induced by TCE which were alleviated by reducing the expression of miR-205–5p. However, this alleviation was reversed by the RORα antagonist SR1001. In vitro experiments showed that miR-205–5p promoted M1 polarization of macrophages and enhanced the secretion of inflammatory factors by regulating RORα. An increase in RORα reversed the polarization direction of M1-type macrophages and reduced the secretion of proinflammatory factors. In addition, pretreatment of mice with SR1078, a specific RORα agonist, effectively blocked M1 polarization of KCs and reduced the severity of TCE-induced liver injury. Our study uncovers that miR-205–5p regulates KC M1 polarization by targeting RORα in immune liver injury induced by TCE sensitization, providing new insight into the molecular mechanisms and new therapeutic targets for ODMLT.

Abstract Cancer, a prevalent and complex disease, presents a significant challenge to the medical community. It is characterized by irregular cell differentiation, excessive proliferation, uncontrolled growth, invasion of nearby tissues, and spread to distant organs. Its progression involves a complex interplay of several elements and processes. Extracellular vesicles (EVs) serve as critical intermediaries in intercellular communication, transporting critical molecules such as lipids, RNA, membrane, and cytoplasmic proteins between cells. They significantly contribute to the progression, development, and dissemination of primary tumors by facilitating the exchange of information and transmitting signals that regulate tumor growth and metastasis. However, EVs do not have a singular impact on cancer; instead, they play a multifaceted dual role. Under specific circumstances, they can impede tumor growth and influence cancer by delivering oncogenic factors or triggering an immune response. Furthermore, EVs from different sources demonstrate distinct advantages in inhibiting cancer. This research examines the biological characteristics of EVs and their involvement in cancer development to establish a theoretical foundation for better understanding the connection between EVs and cancer. Here, we discuss the potential of EVs from various sources in cancer therapy, as well as the current status and future prospects of engineered EVs in developing more effective cancer treatments.

Abstract Hydrogen energy storage plays an important role in improving the operation efficiency and reliability of power systems with high wind energy penetration. Hydrogen to power (HtP) system is the key link of hydrogen applications. However, the single HtP equipment is limited in power output range and efficiency. Hybrid HtP system is an important scheme to realize the performance complementary. A wider power output range can enrich the application scenarios of hydrogen energy storage and provide flexible and reliable energy scheduling in larger and more complex energy systems. In this study, first, a power system including traditional units, wind power generation and hybrid HtP system is established. Second, a bi‐layer hybrid HtP system optimal configuration model considering planning and operation is constructed. Then, a bi‐layer optimal configuration method based on the improved PSO algorithm is proposed. Finally, the optimization model and its solution method are applied to IEEE RTS‐96. The discussions based on the optimization results show that the hybrid HtP system has significant contributions in optimizing the output cost of traditional units, improving the utilization of wind energy, and reducing load shedding losses.

Abstract Optimizing short‐term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short‐term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN‐GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short‐term load forecasting based on CNN‐GRU‐Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state‐of‐the‐art models, the CNN‐GRU‐Attention model outperforms in different evaluation metrics.

Abstract In-depth comprehension and modulation of the electronic structure of the active metal sites is crucial to enhance their intrinsic activity of electrocatalytic oxygen evolution reaction (OER) toward anion exchange membrane water electrolyzers (AEMWEs). Here, we elaborate a series of amorphous metal oxide catalysts (FeCrOx, CoCrOx and NiCrOx) with high performance AEMWEs by high-valent chromium dopant. We discover that the positive effect of the transition from low to high valence of the Co site on the adsorption energy of the intermediate and the lower oxidation barrier is the key factor for its increased activity by synchrotron radiation in-situ techniques. Particularly, the CoCrOx anode catalyst achieves the high current density of 1.5 A cm−2 at 2.1 V and maintains for over 120 h with attenuation less than 4.9 mV h−1 in AEMWE testing. Such exceptional performance demonstrates a promising prospect for industrial application and providing general guidelines for the design of high-efficiency AEMWEs systems.

This study utilized FLUENT dynamic mesh simulation technology to simulate the temperature field distribution characteristics during the continuous casting (CC) process of 5 mm thick Zr41.2Ti13.8Cu12.5Ni10.0Be22.5 (Vit1) bulk metallic glass (BMG), analyzed and discussed the amorphous forming ability of the Vit1 BMG plate prepared through CC. The results indicate that during the CC process, the temperature gradient and cooling rate of Vit1 BMG plate decrease with increasing distance from the cooling copper block surface and prolonged solidification time. Even at the lowest cooling rate, it still remains significantly higher than the critical cooling rate (Rc) of Vit1 bulk amorphous alloy. The temperature variations recorded by the thermocouple during the alloy melt solidification process are in basic agreement with the simulation data. The experimental test and simulation results show that 5 mm thick Vit1 BMG slab can be prepared theoretically by continuous casting technology. Finally, XRD, DSC and TEM were used to analyze the amorphous formation ability and microstructure of the Vit1 BMG slab.

Summary: Clinical research indicates that SARS-CoV-2 infection is linked to several neurological consequences, and the virus is still spreading despite the availability of vaccinations and antiviral medications. To determine how hosts respond to SARS-CoV-2 infection, we employed LC-MS/MS to perform ubiquitinome and proteome analyses of the brain cortexes from K18-hACE2 mice in the presence and absence of SARS-CoV-2 infection. A total of 8,024 quantifiable proteins and 5,220 quantifiable lysine ubiquitination (Kub) sites in 2023 proteins were found. Glutamatergic synapse, calcium signaling pathway, and long-term potentiation may all play roles in the neurological consequences of SARS-CoV-2 infection. Then, we observed possible interactions between 26 SARS-CoV-2 proteins/E3 ubiquitin-protein ligases/deubiquitinases and several differentially expressed mouse proteins or Kub sites. We present the first description of the brain cortex ubiquitinome in K18-hACE2 mice, laying the groundwork for further investigation into the pathogenic processes and treatment options for neurological dysfunction following SARS-CoV-2 infection.

For receiving-end power systems with multi-infeed HVDCs and large-scale renewable energy generation, the challenges of weak reactive power support and limited anti-interference capacity pose a threat to the power system voltage stability. To enhance the voltage stability under large disturbances, this paper proposes a data-driven method for coordinated emergency control strategy against voltage instability in multi-infeed hybrid AC/DC systems based on the squeeze and excitation (SE) module and convolutional neural network (CNN). Firstly, the SE module and CNN are integrated to intuitively capture correlations among various feature channels and extract key features related to voltage stability levels. Secondly, a voltage stability evaluation method is proposed based on the SE-CNN model, utilizing a dual channel structure to unveil the quantitative relationship between key response characteristics and voltage stability margin. Finally, regarding the control measures containing load shedding and DC modulation, the emergency control sensitivity index is proposed to assess the stability margin improvement effect of various control objects, and the optimal emergency control strategy is formulated to coordinate multiple voltage stability emergency control measures. Case studies were conducted on a typical China local region receiving-end power grid test system with voltage instability problems, and the simulation results verified the effectiveness of the proposed method. © 2017 Elsevier Inc. All rights reserved.

Abstract Photonic and plasmonic hybrid nanostructures are the key solution for integrated nanophotonic circuits with ultracompact size but relative low loss. However, the poor tunability and modulability of conventional waveguides makes them cumbersome for optical multiplexing. Here we make use of two‐dimensional molecular crystal, α‐Sb2O3 as a dielectric waveguide via total internal reflection, which shows polarization‐sensitive modulation of the propagating beams due to its large polarization mode dispersion. Both experiments and simulations are performed to verify such concept. These Sb2O3 nanoflakes can be coupled with plasmonic nanowires to form nanophotonic beam splitters and routers which can be easily modulated by changing the polarization of the incidence. It thus provides a robust, exploitable and tunable platform for on‐chip nanophotonics.

BackgroundDiabetic retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. Compound Qilian tablets (CQLT) is a traditional Chinese medicine (TCM) developed for treating DR, but its mechanism is still unclear. This study explored the mechanism of action of CQLT in treating DR through metabolomics and intestinal microbiota.MethodsHistopathologic examination of the pancreas and retina of Zucker diabetic fatty (ZDF) rats and immunohistochemistry were used to determine the expression levels of retinal nerve damage indicators ionized calcium binding adaptor molecule-1 (Iba-1) and glial fibrillary acidic protein (GFAP). Rat fecal samples were tested by LC-MS metabolomics to search for potential biomarkers and metabolic pathways for CQLT treatment of DR. Characteristic nucleic acid sequences of rat intestinal microbiota from each group were revealed using 16S rDNA technology to explore key microbes and related pathways for CQLT treatment of DR. At the same time, we investigated the effect of CQLT on the gluconeogenic pathway.ResultsAfter CQLT intervention, islet cell status was improved, Iba-1 and GFAP expression were significantly decreased, and abnormal retinal microvascular proliferation and exudation were ameliorated. Metabolomics results showed that CQLT reversed 20 differential metabolites that were abnormally altered in DR rats. Intestinal microbiota analysis showed that treatment with CQLT improved the abundance and diversity of intestinal flora. Functional annotation of metabolites and intestinal flora revealed that glycolysis/gluconeogenesis, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism were the main pathways for CQLT in treating DR. According to the results of correlation analysis, there were significant correlations between Iba-1, GFAP, and intestinal microbiota and metabolites affected by CQLT. In addition, we found that CQLT effectively inhibited the gluconeogenesis process in diabetic mice.ConclusionIn conclusion, CQLT could potentially reshape intestinal microbiota composition and regulate metabolite profiles to protect retinal morphology and function, thereby ameliorating the progression of DR.

Despite the rapid development of the application of tissue engineering techniques in regenerative bone repair, the repair of severe pathologic bone defects, represented by osteoporotic bone defects, is still a huge challenge. Recent studies have identified local delivery of parathyroid hormone relative peptides (PTHrPs) as a novel pathway for bone repair as well, but how to load and locally release PTHrPs for good bone repair is a major obstacle. Here, a biopolymer of PLGA-PLL loaded with PTHrP1 is electrospunned into nanofiber membranes for guided bone regeneration. In vitro experiments show that PLGA-PLL synergized with PTHrP1 significantly promote osteogenic differentiation and angiogenesis, providing a favorable bone regeneration microenvironment. In vivo experiments show that the membrane recruits endogenous stem cells and promotes their ectopic osteogenic differentiation in an osteoporotic environment. Furthermore, the membrane can promote osteogenic differentiation and angiogenesis in situ and improve the local osteoporotic microenvironment, ultimately promoting the repair of bone defects. Therefore, present study provides a promising strategy for local delivery of PTH, which opens a new pathway for the treatment of osteoporotic bone defects.

Background: Sarcoma mainly originate from bone and soft tissue and are highly aggressive malignant tumors. Cell division cycle-related protein 3 (CDCA3) is a protein involved in the regulation of the cell cycle, which is highly expressed in a variety of malignant tumors. However, its role in sarcoma remains unclear. This study aims to investigate the function and potential mechanism of CDCA3 in sarcoma and to elucidate its importance in sarcoma. Methods: We first studied the expression and prognosis of CDCA family members in sarcoma by Oncomine and the Gene Expression Profiling Interactive Analysis (GEPIA). The role of CDCA3 protein in sarcoma was further analyzed by the Cancer Genome Atlas Program (TCGA), the Cancer Cell Lineage Encyclopedia (CCLE), and Linke-dOmics. In addition, immunohistochemistry and Western blot were used to verify the expression of CDCA3 protein in clinical samples as well as sarcoma cell lines (U2OS, SAOS2, MG63, and HOS). Subsequently, in vitro experiments (cloning and scratching experiments) were performed using sh-NC as well as sh-CDCA3 group cells to reveal the biological functions of CDCA3. Results: We found that the CDCA family (CDCA3, CDCA4, and CDCA8) is highly expressed in sarcoma, and the expression level of CDCA3, CDCA4, and CDCA8 negatively correlates with the prognosis of sarcoma patients. CDCA3 mRNA was highly expressed in pan-cancer by CCLE and TCGA database analysis. KEGG analysis showed that CDCA3 was mainly enriched in the cell cycle signaling pathway (It promoted the transition of the cell cycle from the G0/G1 phase to the S phase). In the level of immune infiltration, CDCA3 was negatively correlated with pDC cells, CD8+T cells, and cytotoxic cells. Finally, patients with high CDCA3 expression in sarcoma were analyzed for resistance to NU7441 and others, while sensitive to Fulvestrant and Dihydrorotenone. Furthermore, we demonstrated high expression of CDCA3 protein in sarcoma tissues and cell lines by immunohistochemistry and Western blot experiments. Cloning, EDU, scratching, and migration experiments showed that the knockdown of CDCA3 inhibited the Proliferation and progression of sarcoma cells. Conclusion: These results suggest for the first time that knockdown of CDCA3 may inhibit sarcoma progression. CDCA3 may be an effective target for the treatment of sarcoma.

Currently, with the rapid growth of online media, more people are obtaining information from it. However, traditional hotspot mining algorithms cannot achieve precise and fast control of hot topics. Aiming at the problem of poor accuracy and timeliness in current news media hotspot mining methods, this paper proposes a hotspot mining method based on the co-occurrence word model. First, a new co-occurrence word model based on word weight is proposed. Then, for key phrase extraction, a hotspot mining algorithm based on the co-occurrence word model and improved smooth inverse frequency rank (SIFRANK) is designed. Finally, the Spark computing framework is introduced to improve the computing efficiency. The experimental outcomes expresses that the new word discovery algorithm discovered 16871 and 17921 new words in the Weibo Short News and Weibo Short Text datasets respectively. The heat weight values of the keywords obtained by the improved SIFRANK reaches 0.9356, 0.9991, and 0.6117. In the Covid19 Tweets dataset, the accuracy is 0.6223, the recall is 0.7015, and the F1 value is 0.6605. In the President-elects Tweets dataset, the accuracy is 0.6418, the recall is 0.7162, and the F1 value is 0.6767. After applying the Spark computing framework, the running speed has significantly improved. The text mining news media hotspot mining method based on the co-occurrence word model proposed in this study has improved the accuracy and efficiency of mining hot topics, and has great practical significance.

Abstract Chiral sensing of single molecules is vital for the understanding of chirality and their applications in biomedicine. However, current technologies face severe limitations in achieving single-molecule sensitivity. Here we overcome these limitations by designing a tunable chiral supramolecular plasmonic system made of helical oligoamide sequences (OS) and nanoparticle-on-mirror (NPoM) resonator, which works across the classical and quantum regimes. Our design enhances the chiral sensitivity in the quantum tunnelling regime despite of the reduced local E-field, which is due to the strong Coulomb interactions between the chiral OSs and the achiral NPoMs and the additional enhancement from tunnelling electrons. A minimum of four molecules per single-Au particle can be detected, which allows for the detection of an enantiomeric excess within a monolayer, manifesting great potential for the chiral sensing of single molecules.

This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.

This study compares the effectiveness of different methods for coal thickness identification, aiming to identify the most accurate approach and provide a reference for intelligent coalmine development. Focused on the No. 2 coal seam in a mining area in Shanxi, China, the analysis employs well log-constrained impedance inversion and seismic multi-attribute techniques. The results show that the back propagation (BP) neural network model, as part of the seismic multi-attribute approach, delivers prediction accuracy comparable to the well log-constrained inversion method. Specifically, after applying proper static corrections, a four-layer BP neural network was constructed using four optimized sensitive attributes as the input layer, achieving an error range of 0.11% to 1.36%, compared to 0.03% to 6.59% for the logging-based method. The BP neural network demonstrated strong applicability in complex geological environments. Empirical analysis further validated the BP neural network’s geological reliability and practicality in systematic coal thickness determination.

Abstract Neutrophil extracellular traps (NETs) seriously impede diabetic wound healing. The disruption or scavenging of NETs using deoxyribonuclease (DNase) or cationic nanoparticles has been limited by liberating trapped bacteria, short half‐life, or potential cytotoxicity. In this study, a positive correlation between the NETs level in diabetic wound exudation and the severity of wound inflammation in diabetic patients is established. Novel NETs scavenging bio‐based hydrogel microspheres ‘micro‐cage’, termed mPDA‐PEI@GelMA, is engineered by integrating methylacrylyl gelatin (GelMA) hydrogel microspheres with cationic polyethyleneimine (PEI)‐functionalized mesoporous polydopamine (mPDA). This unique ‘micro‐cage’ construct is designed to non‐contact scavenge of NETs between nanoparticles and the diabetic wound surface, minimizing biological toxicity and ensuring high biosafety. NETs are introduced into ‘micro‐cage’ along with wound exudation, and cationic mPDA‐PEI immobilizes them inside the ‘micro‐cage’ through a strong binding affinity to the cfDNA web structure. The findings demonstrate that mPDA‐PEI@GelMA effectively mitigates pro‐inflammatory responses associated with diabetic wounds by scavenging NETs both in vivo and in vitro. This work introduces a novel nanoparticle non‐contact NETs scavenging strategy to enhance diabetic wound healing processes, with potential benefits in clinical applications.

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method.

Zeotropic refrigerants possess the characteristic of temperature glide during phase-change process, making them suitable for temperature matching between cold and hot sources and reducing total temperature differences. In this manuscript, the applicability of two commonly used refrigerants, zeotropic refrigerant R407C and pure refrigerant R134a in data center cooling systems are investigated. Firstly, the heat transfer and flow characteristics of R407C and R134a are studied to obtain boiling heat transfer empirical correlations. Secondly, a data center cooling model is established, and modeling calculations are conducted based on experimental results. Finally, the influence of refrigerants on the energy saving effect are calculated based on the model. Experimental results demonstrate that R134a exhibits a 48.3% higher boiling heat transfer coefficient compared to R407C at heat flux 16500 W/m2. However, when considering a data center cooling system, using R407C as working fluid shows the advantage of temperature matching as heat transfer area increases. Based on modeling results, at large heat transfer area, the annual cooling load factor using R407C at low mass flow rate is 0.5% lower than that of using R134a at low mass flow rate, and 22.5% lower than that of using R134a at large mass flow rate for typical climate city Harbin.

Abstract The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method.

Abstract Integrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified.

Abstract Background The mechanism of microbiota assembly is one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities. Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interactions, is characterized by universality, specificity, and regulatory power, which therefore may influence the assembly processes of human microbiota. However, the regulating role of QS in microbiota assembly is rarely reported. In this study, we developed an optimized in vitro oral biofilm microbiota assembling (OBMA) model to simulate the time-series assembly of oral biofilm microbiota (OBM), by which to excavate the QS network and its regulating power in the process. Results By using the optimized OBMA model, we were able to restore the assembly process of OBM and generate time-series OBM metagenomes of each day. We discovered a total of 2291 QS protein homologues related to 21 QS pathways. Most of these pathways were newly reported and sequentially enriched during OBM assembling. These QS pathways formed a comprehensive longitudinal QS network that included successively enriched QS hubs, such as Streptococcus, Veillonella-Megasphaera group, and Prevotella-Fusobacteria group, for information delivery. Bidirectional cross-talk among the QS hubs was found to play critical role in the directional turnover of microbiota structure, which in turn, influenced the assembly process. Subsequent QS-interfering experiments accurately predicted and experimentally verified the directional shaping power of the longitudinal QS network in the assembly process. As a result, the QS-interfered OBM exhibited delayed and fragile maturity with prolonged membership of Streptococcus and impeded membership of Prevotella and Fusobacterium. Conclusion Our results revealed an unprecedented longitudinal QS network during OBM assembly and experimentally verified its power in predicting and manipulating the assembling process. Our work provides a new perspective to uncover underlying mechanism in natural complex microbiota assembling and a theoretical basis for ultimately precisely manipulating human microbiota through intervention in the QS network. Video Abstract

Abstract Background Studies on the gut microbiota of animals have largely focused on vertebrates. The transmission modes of commensal intestinal bacteria in mammals have been well studied. However, in gastropods, the relationship between gut microbiota and hosts is still poorly understood. To gain a better understanding of the composition of gut microbes and their transmission routes in gastropods, a large-scale and long-term experiment on the dynamics and transmission modes of gut microbiota was conducted on freshwater snails. Results We analyzed 244 microbial samples from the digestive tracts of freshwater gastropods and identified Proteobacteria and Bacteroidetes as dominant gut microbes. Aeromonas, Cloacibacterium, and Cetobacterium were identified as core microbes in the guts, accounting for over 50% of the total sequences. Furthermore, both core bacteria Aeromonas and Cloacibacterium, were shared among 7 gastropod species and played an important role in determining the gut microbial community types of both wild and cultured gastropods. Analysis of the gut microbiota at the population level, including wild gastropods and their offspring, indicated that a proportion of gut microbes could be consistently vertically transmitted inheritance, while the majority of the gut microbes resulted from horizontal transmission. Comparing cultured snails to their wild counterparts, we observed an increasing trend in the proportion of shared microbes and a decreasing trend in the number of unique microbes among wild gastropods and their offspring reared in a cultured environment. Core gut microbes, Aeromonas and Cloacibacterium, remained persistent and dispersed from wild snails to their offspring across multiple generations. Interestingly, under cultured environments, the gut microbiota in wild gastropods could only be maintained for up to 2 generations before converging with that of cultured snails. The difference observed in gut bacterial metabolism functions was associated with this transition. Our study also demonstrated that the gut microbial compositions in gastropods are influenced by developmental stages and revealed the presence of Aeromonas and Cloacibacterium throughout the life cycle in gastropods. Based on the dynamics of core gut microbes, it may be possible to predict the health status of gastropods during their adaptation to new environments. Additionally, gut microbial metabolic functions were found to be associated with the adaptive evolution of gastropods from wild to cultured environments. Conclusions Our findings provide novel insights into the dynamic processes of gut microbiota colonization in gastropod mollusks and unveil the modes of microbial transmission within their guts. Video Abstract

Abstract Background Gastropoda, the largest class within the phylum Mollusca, houses diverse gut microbiota, and some gastropods serve as intermediate hosts for parasites. Studies have revealed that gut bacteria in gastropods are associated with various biological aspects, such as growth, immunity and host–parasite interactions. Here, we summarize our current knowledge of gastropod gut microbiomes and highlight future research priorities and perspectives. Methods A literature search was undertaken using PubMed, Web of Science and CNKI for the articles on the gut microbiota of gastropods until December 31, 2022. We retrieved a total of 166 articles and identified 73 eligible articles for inclusion in this review based on the inclusion and exclusion criteria. Results Our analysis encompassed freshwater, seawater and land snails, with a specific focus on parasite-transmitting gastropods. We found that most studies on gastropod gut microbiota have primarily utilized 16S rRNA gene sequencing to analyze microbial composition, rather than employing metagenomic, metatranscriptomic, or metabolomic approaches. This comprehensive review provided an overview of the parasites carried by snail species in the context of gut microbiota studies. We presented the gut microbial trends, a comprehensive summary of the diversity and composition, influencing factors, and potential functions of gastropod gut microbiota. Additionally, we discussed the potential applications, research gaps and future perspectives of gut microbiomes in parasite-transmitting gastropods. Furthermore, several strategies for enhancing our comprehension of gut microbiomes in snails were also discussed. Conclusions This review comprehensively summarizes the current knowledge on the composition, potential function, influencing factors, potential applications, limitations, and challenges of gut microbiomes in gastropods, with a specific emphasis on parasite-transmitting gastropods. These findings provide important insights for future studies aiming to understand the potential role of gastropod gut microbiota in controlling snail populations and snail-borne diseases. Graphical abstract

The emergence of distributed energy and prosumers has greatly promoted the reform of the energy market. Blockchain technology provides a very potent solution for distributed energy transactions, however, blockchain technology still develops slowly in the energy field due to the unstable performance of the imperfect system. This paper aims to narrow this gap by the following steps. First, a function-driven integration method is proposed to combine the merit of blockchain distributed ledger and traditional centralized databases. Then a hybrid energy blockchain system architecture is designed, including the blockchain layer, smart contract layer, database layer, and client layer. Further, a peer-to-peer market mechanism considering energy storage arbitrage and fully automated matchmaking is developed on the hybrid blockchain system. The case study proves the effectiveness and safety of the system. The proposed architecture of combined blockchain and database technologies effectively paves the way for constructing a more reasonable and feasible distributed energy trading platform.

Dynamic capacity-increase technologies (DCITs), as a solution to reduce the stress of transmission networks, avoid long-term constructions and update the maximum transmitted capacities based on real-time environment factors and the maximum allowable operation temperatures (MAOTs), which provide flexibility in optimizing the operation modes for power systems. This paper proposes a detailed two-stage stochastic unit commitment model considering the physical processes of heat transfer as well as the uncertainties of renewable energy. The dynamic thermal rating (DTR) is calculated based on the heat balance of transmission lines in the steady state, including radiated and convective heat loss and gain from the solar heat and power flows. To simulate the uncertainties from renewable energy outputs, this paper applies the Latin hypercube sampling method to generate output scenarios in stochastic optimization. Finally, the proposed model is verified on a modified IEEE-24-bus test system. The effectiveness of DCITs is illustrated through the case study.

The global environmental concern has promoted the installed capacity of renewable energy to explode. The uncertainty of the growing renewable energy poses a risk of incorrect forecasting of clearing prices and misled strategies to participants in the deregulated power markets. However, the transmission loss and the correlation among integrated wind power outputs are ignored in the existing research. To improve the forecasting accuracy, this paper presents a Markov Chain Monte Carlo method for stochastic LMP considering the correlation among wind power outputs and transmission loss. The expectation of an LMP with a given precision can be obtained from the proposed method. The case study verifies the validity of the proposed method, demonstrating that the squared Euclidean distance of the selected Archimedean Copula function is limited to 9.3143. Additionally, given the same convergence criteria, the sampling times are reduced by an order of magnitude compared to the Acceptance-Rejection sampling method.

Abstract The carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved. However, the uncertainty of renewable energy makes it hard to obtain an optimal operation policy. First, the detailed model of CCS consisting of bypass venting stacks, solvent storage tanks, absorber, and stripper is described. Then, a low‐carbon economic dispatch model of the power system with CCS is proposed to minimize the total operation and renewable energy abandonment penalty costs. Next, the problem is reformulated as multi‐stage stochastic programming, and the stochastic dual dynamic programming (SDDP) method is applied to relieve the computation burden. To control the probability of stopping the algorithm prematurely, a new stopping criterion based on a two‐sided hypothesis test is proposed. Finally, the effectiveness of the proposed model and the new stopping criterion is demonstrated by case studies on a practical power system.

The natural ventilation model plays a crucial role in greenhouse environmental control. It has been extensively studied by previous researchers, but it is limited to low-altitude areas. This study established a numerical model of single-span plastic greenhouses in high-altitude areas. The model was validated using measured data, showing a good agreement between the measured and simulated values. By setting boundary conditions based on on-site monitoring data, ventilation rates were extracted under different conditions for numerical simulations. Through nonlinear fitting, an empirical formula for natural ventilation rates, with a determination coefficient (R2) of 0.9724, was derived. The formula was validated through an energy balance analysis of indoor air. Different ventilation opening sizes were simulated to derive an empirical formula for natural ventilation rates based on opening size. Building on this, the relationship between plant height and ventilation rate was analyzed. As the dominant factors of natural ventilation change with environmental fluctuations, this study also proposed the threshold wind speed for wind pressure ventilation, thermal pressure ventilation, and coupled ventilation, filling the knowledge gap in relevant ventilation rate calculations. This is the first time that a natural ventilation model of single-span plastic greenhouses in high-altitude areas has been proposed, providing the basis in terms of modeling for the further development of local facility agriculture.

Channel wave seismic activity often occurs with thin and medium-thick coal seams being the main target layer. To address the problem of channel wave applicability detection in extremely thick coal seams, the propagation and identification characteristics of channel waves remain the focus of research. Therefore, this paper takes the in-seam wave exploration of a 27 m extremely thick coal seam as an example and uses the staggered mesh finite difference method to construct a three-dimensional medium model for numerical simulation. An analysis of the physical parameters of coal and rock, along with the dispersion characteristics of channel waves in extra-thick coal seams, is utilized, through the Zoeppritz equation and the total reflection propagation method, to calculate the imaging. We found the following: (1) The dispersion areas and weak dispersion areas along the detection direction are extremely thick coal seams. (2) There are apparent channel waves in extra-thick coal seams, with a waveform similar to body waves; the length of the wave train is shorter than that of the conventional channel wave, and the arrival time can be estimated accurately. The amplitude of the apparent channel wave is affected by the degree of dispersion, with lower attenuation and higher resolution. The characteristic of total reflection in extremely thick coal seams is that the incident angle is equal to the critical angle, and the dispersion characteristics are weak. (3) The channel waves with weak dispersion characteristics in extra-thick coal seams are mainly Love-type waves.

Abstract The occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.

Abstract The continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations.

Abstract Background Secondary bacterial infections and pneumonia are major mortality causes of respiratory viruses, and the disruption of the upper respiratory tract (URT) microbiota is a crucial component of this process. However, whether this URT dysbiosis associates with the viral species (in other words, is viral type-specific) is unclear. Results Here, we recruited 735 outpatients with upper respiratory symptoms, identified the infectious virus types in 349 participants using multiplex RT-PCR, and profiled their upper respiratory microbiome using the 16S ribosomal RNA gene and metagenomic gene sequencing. Microbial and viral data were subsequently used as inputs for multivariate analysis aimed at revealing viral type-specific disruption of the upper respiratory microbiota. We found that the oropharyngeal microbiota shaped by influenza A virus (FluA), influenza B virus (FluB), respiratory syncytial virus (RSV), and human rhinovirus (HRV) infections exhibited three distinct patterns of dysbiosis, and Veillonella was identified as a prominent biomarker for any type of respiratory viral infections. Influenza virus infections are significantly correlated with increased oropharynx microbiota diversity and enrichment of functional metabolic pathways such as L-arginine biosynthesis and tetracycline resistance gene tetW. We used the GRiD algorithm and found the predicted growth rate of common respiratory pathogens was increased upon influenza virus infection, while commensal bacteria, such as Streptococcus infantis and Streptococcus mitis, may act as a colonization resistance to the overgrowth of these pathogens. Conclusions We found that respiratory viral infections are linked with viral type-specific disruption of the upper respiratory microbiota, particularly, influenza infections uniquely associated with increased microbial diversity and growth rates of specific pathogens in URT. These findings are essential for clarifying the differences and dynamics of respiratory microbiota in healthy participants and acute respiratory viral infections, which contribute to elucidating the pathogenesis of viral-host-bacterial interactions to provide insights into future studies on effective prevention and treatment of respiratory tract infections. Video Abstract

The progress of deep learning technology has made image classification an important application field. Image style classification is a complex task involving the recognition of the whole picture, including the recognition of salient features and detailed features. This study is based on the ResNet algorithm and has improved its Resnet 50 version with excellent performance. In the model architecture, we introduce blur pool operation and replace the traditional Relu function with Celu activation function. In addition, the triplet attention mechanism was integrated to further enhance the model performance. Through a series of experiments, it is found that the improved ResNet50 model has the highest classification accuracy of 80.6% on large-scale image data sets, which is 11.7% higher than the traditional ResNet50 model. In terms of recognition of similar style images, the model incorporating triplet attention demonstrated higher average accuracy (74%) and recall (82%). This improvement has achieved certain results and has certain technical reference value for various styles of image classification fields.

Abstract Photo‐assisted uranium reduction from uranium mine wastewater is expected to overcome the competition between impurity ions and U(VI) in the traditional process. Here, B‐TiO2@Co2P‐X S‐scheme heterojunction with metal‐oxygen‐hydrogen (M‐O‐H) is developed insitu modification for photo‐assisted U(VI) (hexavalent uranium) reduction. Relying on the DFT calculation and Hard‐Soft‐Acid‐Base (HSAB) theory, the introduction of metal‐oxygen‐hydrogen (M‐O‐H, hard base) metallic bonds in the B‐TiO2@Co2P‐X is found to enhance the hydrophilicity and the capture capability for uranyl ion (hard acid). Accordingly, B‐TiO2@Co2P‐500 hybrid nanosheets exhibit excellent U(VI) reduction ability (>98%) in the presence of competing ions. By self‐consistent energy band calculations and in‐situ KPFM spectral analysis, the formation of the internal electric field between B‐TiO2 and Co2P at the heterojunction is proven, offering a strong driving force and atomic transportation highway for accelerating the S‐scheme charge carriers directed migration and promoting the photocatalytic reduction of uranium. This work provides a valuable route to explore the functionally modified photocatalyst with high‐efficiency photoelectron separation for U(VI) reduction.

Although extensive research on magnetic reconnection, e.g., current sheets, inflows/outflows, and plasma ejections, has been done, the dynamical evolution of newly formed structures during and after the reconnection between two sets of atmospheric structures is rarely studied. Here we investigate five reconnection events based on observations from the Solar Dynamics Observatory and the New Vacuum Solar Telescope. In each event, two independent atmospheric structures are involved. While they meet, a plasma sheet is detected. After exchanging topological connectivity, new structures are formed in the outflow regions. For each new structure, it is composed of a part of one original structure and that of the other one. In this Letter, for the first time, we find that there are two types of evolution patterns of the new structures. The first type is that the new structures move away from the reconnection position as a whole and then undergo a to-and-fro motion (an oscillation). The second is that the new structures display a “throwing whip” movement. We suggest that the evolution patterns are relevant to the topological configuration of the original structures and the position of the reconnection site.

Background: Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) has been reported to play an oncogenic role in a variety of tumors. However, its involvement in gastric cancer (GC) development has not been described. Methods: The cancer genome atlas (TCGA) and the gene expression omnibus database (GEO) databases were used to analyze the expression of MFG-E8 in GC. These findings were further validated using immunohistochemistry (IHC) and western blotting assay (WB). Kaplan-Meier method, univariate logistic regression, and Christopher Cox regression were used to study the relationship between MFG-E8 and clinical pathology. In addition, the potential signaling pathways involved in MFG-E8 and its potential correlation with levels of immune cell infiltration were investigated. Finally, the biological function of MFG-E8 in GC cells was revealed. Results: MFG-E8 was highly expressed in GC patients and cells, and the high level of MFG-E8 was associated with poor overall survival (OS). KEGG analysis indicated that MFG-E8 may play an important role in the cAMP signaling pathway. The expression of MFG-E8 was positively correlated with the infiltration of M2 macrophages. The patients with high MFG-E8 were easy to develop chemotherapy resistance. Furthermore, the knockdown of MFG-E8 significantly inhibited the proliferation and invasion of GC cells. Conclusion: MFG-E8 in GC may serve as a prognostic marker and a potential immunotherapy target for GC.

Existing goaves (e.g., shafts and roadways) in mines represent important hidden dangers during the production of underlying coal seams. In this view, the accurate identification, analysis, and delimitation of the scope of goaves have become important in the 3D seismic exploration of mines. In particular, an accurate identification of the boundary swing position of goaves for 3D seismic data volumes within a certain depth interval is key and difficult at the same time. Here, a wide-band and wide-azimuth observation system was used to obtain high-resolution 3D seismic data. The complex structure of a mine was analyzed, and a seismic double processing system was applied to verify the fine processing effect of a goaf and improve the resolution of the 3D seismic data. Based on the seismic attribute identification characteristics of the goaf structure, we decided to adopt multi-attribute comprehensive identification and data fusion technologies to accurately determine the position of the goaf and of its boundary. Combining this information with the mine roadway engineering layout, we verified the accurateness and correctness of the goaf boundary location. Our study provides a good example of the accurate identification of the 3D seismic data of a roadway goaf.

The genus Ottelia comprises approximately 21 submerged macrophyte species primarily found in tropical Africa and Southeast Asia. The classification of several Ottelia species as vulnerable under the criteria “A2c” in the China Species Red List emphasizes the urgency of establishing a credible taxonomy. The ambiguity in taxonomy and evolutionary history persists, primarily stemming from the absence of a robust phylogeny at the species level. The rapid progress in high-throughput sequencing technology has facilitated the retrieval of complete chloroplast (cp) genome sequences, offering a molecular foundation for conducting phylogenetic analyses. In this study, the entire cp genomes of five samples of four Ottelia species were sequenced. All five Ottelia samples exhibited a circular, quadripartite-structured molecule, with lengths ranging from 156,823 to 162,442 bp. A total of 75–88 simple sequence repeats (SSRs) in the cp genomes of the five Ottelia samples were observed, which could be used for species identification. A preliminary phylogenetic analysis revealed that O. fengshanensis, O. acuminata, and O. guanyangensis clustered with strong support (100 BS). O. acuminata var. jingxiensis was resolved as a sister to O. acuminata var. lunanensis (100 BS), and both were further found to be sisters to O. balansae. The widely distributed O. alismoides exhibited a close phylogenetic relationship as the sister taxon to all the Chinese endemic taxa, supported by robust values. Together, our thorough examination of the entire cp genomes of the five Ottelia samples provides eminent insights into reconstructing their phylogeny. Furthermore, it illuminates the evolutionary dynamics of the cp genome within the Ottelia genus.

BackgroundImmune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome.MethodsWe included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification.ResultsOne WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful.ConclusionWe developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified.

Summary: To achieve its goal of carbon emissions peak and neutrality, China requires synergistic efforts across all sectors. In this study, three scenarios—baseline, policy, and green low-carbon—were developed to explore the pathways for China’s emissions reduction across sectors from 2020 to 2060, and the timing of decoupling economic growth from CO2. The results showed that, under these scenarios, China’s carbon emissions peak in 2030, 2026, and 2025, with strong decoupling time, lagged one year behind peak attainment. The agriculture, forestry, livestock, and fishing (AFH) and mining and quarrying (MQ) sectors would be the first to achieve a carbon peak. Under all three scenarios, all of the other sectors—with the exception of electricity, gas, and water production and supply (EGW)—will achieve a carbon peak by 2030. Therefore, policymakers should set carbon peak goals based on sector characteristics and ensure energy security in the process of achieving carbon neutrality.

Background & Aims: The steatotic grafts have been applied in liver transplantation frequently owing to the high incidence of non-alcoholic fatty liver disease. However, fatty livers are vulnerable to graft injury. Myeloid-derived suppressor cell (MDSC) recruitment during liver graft injury promotes tumour recurrence. Lipid metabolism exerts the immunological influence on MDSCs in tumour progression. Here, we aimed to explore the role and mechanism of inflammasome activation in MDSCs induced by lipid metabolism during fatty liver graft injury and the subsequent effects on tumour recurrence. Methods: MDSC populations and nucleotide-binding oligomerisation domain-like receptor family pyrin domain containing 3 (NLRP3) inflammasome levels were investigated in a clinical cohort and a rat liver transplantation model. The mechanism of NLRP3 activation by specific fatty acids was explored in mouse hepatic ischaemia/reperfusion injury (IRI) with tumour recurrence model and in vitro studies. Results: MDSC populations and NLRP3 levels were increased with higher tumour recurrent rate in patients using steatotic grafts. NLRP3 was upregulated in MDSCs with lipid accumulation post mouse fatty liver IRI. Mechanistically, arachidonic acid was discovered to activate NLRP3 inflammasome in MDSCs through fatty acid transport protein 2 (FATP2), which was identified by screening lipid uptake receptors. The mitochondrial dysfunction with enhanced reactive oxygen species bridged arachidonic acid uptake and NLRP3 activation in MDSCs, which subsequently stimulated CD4+ T cells producing more IL-17 in fatty liver IRI. Blockade of FATP2 inhibited NLRP3 activation in MDSCs, IL-17 production in CD4+ T cells, and the tumour recurrence post fatty liver IRI. Conclusions: During fatty liver graft injury, arachidonic acid activated NLRP3 inflammasome in MDSCs through FATP2, which subsequently stimulated CD4+ T cells producing IL-17 to promote tumour recurrence post transplantation. Impact and implications: The high incidence of non-alcoholic fatty liver disease resulted in the frequent application of steatotic donors in liver transplantation. Our data showed that the patients who underwent liver transplantation using fatty grafts experienced higher tumour recurrence. We found that arachidonic acid activated NLRP3 inflammasome in MDSCs through FATP2 during fatty liver graft injury, which led to more IL-17 secretion of CD4+ T cells and promoted tumour recurrence post transplantation. The inflammasome activation by aberrant fatty acid metabolism in MDSCs bridged the acute-phase fatty liver graft injury and liver tumour recurrence.

Concrete in cold environments may suffer from frost damage, which effects the hydration reaction and early strength development of cement. This study aims to test the early strength development of recycled coarse aggregate concrete in cold region using an assisted heating method. Based on Nurse-Saul and Hansen-Pedersen maturity calculation methods, the early strength of recycled and natural coarse aggregate concrete is fitted and predicted using exponential and logarithmic type functions. The results showed that active heating and thermal insulation can ensure early concrete hydration. The temperature of recycled coarse aggregate concrete was slightly higher than that of natural concrete, while its strength was slightly lower. The early strength development of natural and recycled coarse aggregate concrete correlated more with the logarithmic type function. The prediction error of natural and recycled coarse aggregate concrete is basically within 10% for the first 14 days of compressive strength.

ABSTRACTBacteria synchronize social behaviors via a cell-cell communication and interaction mechanism termed as quorum sensing (QS). QS has been extensively studied in monocultures and proved to be intensively involved in bacterial virulence and infection. Despite the role QS plays in pathogens during laboratory engineered infections has been proved, the potential functions of QS related to pathogenesis in context of microbial consortia remain poorly understood. In this review, we summarize the basic molecular mechanisms of QS, primarily focusing on pathogenic microbes driving gastrointestinal (GI) infections. We further discuss how GI pathogens disequilibrate the homeostasis of the indigenous microbial consortia, rebuild a realm dominated by pathogens, and interact with host under worsening infectious conditions via pathogen-biased QS signaling. Additionally, we present recent applications and main challenges of manipulating QS network in microbial consortia with the goal of better understanding GI bacterial sociality and facilitating novel therapies targeting bacterial infections.

Objective: To assess the efficacy of simvastatin 80 mg/day versus placebo in patients with noninfectious nonanterior uveitis receiving prednisolone ≥ 10 mg/day. Design: Randomized, double-masked, controlled trial. Subjects: Adult patients with noninfectious nonanterior uveitis on oral prednisolone dose of ≥ 10 mg/day. Methods: Patients were randomly assigned at a 1:1 ratio to receive either simvastatin 80 mg/day or placebo. A total of 32 patients were enrolled (16 in each arm), all of whom completed the primary end point, and 21 reached the 2-year visit (secondary end points). Main Outcome Measures: The primary end point was mean reduction in the daily prednisolone dose at 12 months follow-up. Secondary end points were mean reduction in prednisolone dose at 24 months, percent of patients with a reduction in second-line immunomodulatory agents, time to disease relapse, and adverse events. Results: Our results show that simvastatin 80 mg/day did not have a significant corticosteroid-sparing effect at 12 months (estimate: 3.62; 95% confidence interval [CI]: −8.15 to 15.38; P = 0.54). There was no significant difference between the groups with regard to prednisolone dose or change in dose at 12 and 24 months. There was no difference between the 2 groups in percent of patients with reduction in second-line agent by 24 months. Among patients who achieved disease quiescence, the median time to first relapse was longer for those receiving simvastatin (38 weeks, 95% CI: 14–54) than placebo (14 weeks, 95% CI: 12–52), although this was not statistically significant. There was no significant difference in adverse events or serious adverse events between the 2 groups. Conclusions: Simvastatin 80 mg/day did not have an effect on the dose reduction of corticosteroids or conventional immunomodulatory drugs at 1 and 2 years. The results suggest that it may extend the time to disease relapse among those who achieve disease quiescence. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

The COVID-19 pandemic caused by SARS-CoV-2 has emerged as a major global public health concern. In November 2022, Guangzhou experienced a significant outbreak of Omicron. This study presents detailed epidemiological and laboratory data on Omicron infection in a general hospital in Guangzhou between December 1, 2022, and January 31, 2023. Out of the 55,296 individuals tested, 12,346 were found to be positive for Omicron. The highest prevalence of positive cases was observed in the 20 to 39 age group (24.6%), while the lowest was in children aged 0 to 9 years (1.42%). Females had a higher incidence of infection than males, accounting for 56.6% of cases. The peak time of Omicron infection varied across different populations. The viral load was higher in older adults and children infected with Omicron, indicating age-related differences. Spearman’s rank correlation analysis revealed positive correlations between Ct values and laboratory parameters in hospitalized patients with Omicron infection. These parameters included CRP (rs = 0.059, p = 0.009), PT (rs = 0.057, p = 0.009), INR (rs = 0.055, p = 0.013), AST (rs = 0.067, p = 0.002), LDH (rs = 0.078, p = 0.001), and BNP (rs = 0.063, p = 0.014). However, EO (Eosinophil, rs = −0.118, p