Search

The coking process hinders the long-term stable production of ethylene, so the rational design of the catalyst structure to achieve efficient in situ coke removal is the focus of research. Herein, a synthesis method for the controllable preparation of the perovskite molecular structure (A2B2O6, R-P, ABO3) was proposed, and the relationship between the perovskite structure and coke removal performance was revealed. DFT and experimental results jointly proved that the O 2p-band of the double perovskite shifts upward to the Fermi level, thus promoting the formation of free radical species. Double perovskite mainly promoted the contact reaction between the adsorbed hydroxyl radical and graphite carbon interface and also strengthened the gas–solid reaction between the free hydroxyl radical and graphite carbon, thus improving the conversion rate of coke. In the presence of double perovskite LaKSr0.17Fe1.5O6, the maximum coke conversion rate is 100%. This method provides a theoretical basis for the synthesis of heterogeneous catalysts.

• The introduction of hard and brittle borides into the B2-type RHEAs realizes a multifold increase in plasticity without sacrificing strength. • In the vicinity of borides, the occurrence of chemical order-disorder transition improves the mobility of dislocation, thereby facilitating the plastic deformability. • The existence of stable borides prevents grain coarsening at elevated temperatures by Zener pinning. Refractory high-entropy alloys (RHEAs) exhibit remarkable strengths at elevated temperatures and are hence extremely promising candidates for high-temperature structural materials. However, the RHEAs with ordered superlattice structures generally suffer from poor room-temperature plasticity, which severely hampers their widespread applications. Here, we discovered that the introduction of multicomponent ceramic nanoparticles (MCNPs) into the RHEAs makes the problem alleviative and realizes a multifold increase in plasticity without sacrificing strength. The detailed characterizations show that the improvement originates from the chemical ordering-disordering transition near MCNPs in the B2-ordered RHEAs. This transition promotes the formation of local disordered regions where the mobility of dislocations is significantly enhanced. These regions wrap around MCNPs to form a unique heterogeneous structure, which suppresses the premature microcracks by the boosted dislocation mobility. Simultaneously, the existence of stable MCNPs prevents grain coarsening at elevated temperatures by Zener pinning. These novel alloy-design ideas shed new insights into developing RHEAs with an outstanding combination of strength and plasticity. [Display omitted] [ABSTRACT FROM AUTHOR]

Shaped charge liner (SCL) has been extensively applied in oil recovery and defense industries. Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse, high-speed jet stretching, and penetration. This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function. The strategy combines experimentally validated finite element method with machine learning (FEM-ML). We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning. This index effectively evaluates the jet penetration performance. Furthermore, a multi-model fusion based on a machine learning optimization method, called XGBOOST-MFO, is put forward to optimize SCL structure over a large input space. The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy. Finally, this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners. Therefore, the strategy can provide helpful guidance for the engineering design of SCL.

The ineffective digitization of core English course resources, due to limited autonomy, fragmentation, and inadequate management, has prompted the development of digital teacher libraries using multimedia and Internet technologies. Fuzzy neural networks (FNNs), combining fuzzy and neural network controls, have emerged as a promising approach for mathematical modeling. This paper presents an FNN-based model for sharing digital English educational resources and proposes an effective guidance mechanism for sharing digital science education resources. Experimental results show that the FNN outperforms the Apriori algorithm in training sample error by 0.02069 and reduces running time by 0.0034 seconds on average. This indicates the FNN's superior approximation capabilities with appropriate initial fuzzy rules. Consequently, the FNN-based model enhances the value and quality of educational resources, expands the capacity of information resource libraries, and effectively mitigates the creation of low-quality resources.

Face recognition has increasingly become the predominant biometric recognition technology for identity verification, propelled by advancements in deep learning technology. This study introduces a lightweight face detection and recognition method optimised for mobile devices with limited computational resources using an improved MobileFaceNet framework. Initially, the approach refines the network structure, elevating face detection efficiency through median filtering and a minimal bounding box constraint strategy grounded in the multitask convolutional neural network (MTCNN). Subsequently, to address the challenges of multi-pose in real-world scenarios of face detection, the method employs Affine Transformation for facial angle rotation and centre point adjustment, thus achieving accurate pose correction in facial images. The study presents a lightweight face recognition network model based on MobileFaceNet in its final phase. It improves the model by optimising the loss function and learning rate and reducing convolutional layers by integrating depthwise separable convolution. In addition, regarding the privacy security of face recognition information, it proposes a face information encryption scheme built on a fully homomorphic encryption algorithm. Experiments on prevalent face databases demonstrate that this model is better in recognition accuracy and network performance.

Polymorphic phase transition in metallic materials under high pressure is a critical aspect of dynamic properties and has been attracting a great interest. Despite the extensive researches have been made on understanding of this phase transition in traditional single-principal element alloys, little is known about the phase transition in recently emergent multi-principal medium and high entropy alloys, especially compressed under high strain rates. In this work, based on molecular dynamic simulations, three impact loading strategies with distinct loading paths, such as single-shock, double-shock and ramp-wave loading are carried out on the single crystalline CoCrNi medium-entropy alloy (MEA) to investigate the phase transition under high strain-rate compression. Careful characterizations show that the phase transition of CoCrNi MEA is loading-path dependent, as evidenced by the significant differences in macroscopic pressure evolution and microscopic structural phase transition among the samples under various thermodynamic paths. An intriguing pressure “overshoot” is found and demonstrated as the characteristic of the critical structural phase transition from face-centered cubic (FCC) structure to hexagonal-close-packed (HCP) structure mediated by body-centered cubic (BCC) like clusters. We show that such loading-path dependence is attributed to the strain rate and temperature rise in the loading process, which control the evolution of microstructure and deformation field. The inherent correlation between the atomistic process of phase transition and loading strategies results in polymorphic phase transition under high strain rates. These findings shed new light on the nature of impact phase transition of multi-principal alloys.

Photovoltaic (PV) power generation technology is an effective approach for alleviating the current energy crisis. However, PV modules are prone to hot-spot faults and shorter service lives because of harsh long-term operating conditions. Therefore, in this study, we propose a bi-branch collaborative training algorithm based on knowledge distillation for PV hot-spot detection systems, focusing on the accuracy and computational efficiency of the algorithm. The proposed approach uses a knowledge distillation model based on a “teacher and student” collaborative training structure to improve detection accuracy while ensuring inference speed. The anchor-free YOLOX detection algorithm is used as the student network to reduce the number of network parameters. This includes a decoupled prediction head designed to improve detection performance. The teacher network first uses a HorNet-based cross-stage partial network (CSPHN)-based backbone network to enhance feature representation ability by capturing higher order spatial interactions. Second, a bi-branch multilevel feature adaptive fusion (BiMAF) module is designed to fuse multiscale features from both global and local perspectives in a parallel manner. Third, a shift contextual transformer (SCT)-based prediction head is adopted to enhance the long-range interaction between cross-scale features, thereby significantly improving the accuracy of the detection algorithm in dense scenes. Finally, comparative experiments are conducted on 13 classical detection algorithms to verify the effectiveness of the proposed detection network. The experimental results demonstrated that the proposed system can achieve fast and accurate detection of multiscale hot-spot faults under various harsh conditions, achieving an AP50 metric of 82.2%.

Metagenomics is an unobtrusive science linking microbial genes to biological functions or environmental states. Classifying microbial genes into their functional repertoire is an important task in the downstream analysis of Metagenomic studies. The task involves Machine Learning (ML) based supervised methods to achieve good classification performance. Random Forest (RF) has been applied rigorously to microbial gene abundance profiles, mapping them to functional phenotypes. The current research targets tuning RF by the evolutionary ancestry of microbial phylogeny, developing a Phylogeny-RF model for functional classification of metagenomes. This method facilitates capturing the effects of phylogenetic relatedness in an ML classifier itself rather than just applying a supervised classifier over the raw abundances of microbial genes. The idea is rooted in the fact that closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together, or one of these could be dropped from the analysis, to improve the ML process. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware methods of MetaPhyl and PhILR, using three real-world 16S rRNA metagenomic datasets. It has been observed that the proposed method not only achieved significantly better performance than the traditional RF model but also performed better than the other phylogeny-driven benchmarks (p < 0.05). For example, Phylogeny-RF attained a highest AUC of 0.949 and Kappa of 0.891 over soil microbiomes in comparison to other benchmarks.

The composite oxides of Fe2O3-LaFeO3provide sufficient lattice oxygen for the chemical looping conversion of methane, which is beneficial to the generation of CO2with the concentration greater than 99% by regulation of lattice oxygen conversion.

Electrocatalytic nitric oxide reduction (eNORR) to ammonia (NH3) provides an environmental route to alleviate NO pollution and yield great-value chemicals. The evolution of eNORR has been primarily hindered, however, by the poor reaction kinetics and low solubility of the NO in aqueous electrolytes. Herein, we have rationally designed a cobalt-based composite with a heterostructure as a highly efficient eNORR catalyst. In addition, by integrating boron to modulate the electronic structure, the catalyst CoB/Co@C delivered a significant NH3yield of 315.4 μmol h–1cm–2for eNORR and an outstanding power density of 3.68 mW cm–2in a Zn-NO battery. The excellent electrochemical performance of CoB/Co@C is attributed to the enrichment of NO by cobalt and boron dual-site adsorption and fast charge-transfer kinetics. It is demonstrated that the boron is pivotal in the enhancement of NO, the suppression of hydrogen evolution, and Co oxidation to boost eNORR performance.

Softmax regression, which is also called multinomial logistic regression, is widely used in various fields for modeling the relationship between covariates and categorical responses with multiple levels. The increasing volumes of data bring new challenges for parameter estimation in softmax regression, and the optimal subsampling method is an effective way to solve them. However, optimal subsampling with replacement requires to access all the sampling probabilities simultaneously to draw a subsample, and the resultant subsample could contain duplicate observations. In this paper, the authors consider Poisson subsampling for its higher estimation accuracy and applicability in the scenario that the data exceed the memory limit. The authors derive the asymptotic properties of the general Poisson subsampling estimator and obtain optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under both A- and L- optimality criteria. The optimal subsampling probabilities contain unknown quantities from the full dataset, so the authors suggest an approximately optimal Poisson subsampling algorithm which contains two sampling steps, with the first step as a pilot phase. The authors demonstrate the performance of our optimal Poisson subsampling algorithm through numerical simulations and real data examples.

The Chinese government has committed to achieving a carbon emission peak by 2030 and carbon neutrality by 2060. The Yellow River Basin is an important economic region and ecological protection basin tying nine provinces in Eastern, Central, and Western China. The heterogeneity of economies, industrial structures, resource endowment, locations, social environments, and other aspects of the various regions in the Yellow River Basin make this region important for achieving the government’s carbon commitment. In this study, 54 cities in the upper, middle, and lower reaches of the Yellow River Basin were chosen as research areas. A model that described the coupling coordination between the regions’ economic growth and carbon neutrality based on the GDP, carbon emission, and carbon sequestration data was presented. The spatial patterns and trends of economic growth, carbon neutrality and coupling coordination between the two for various regions of the Yellow River Basin from year 2001 to 2021 were analyzed empirically using exploratory spatial data analysis method. The results showed that, during the analyzed period, the economic growth of the Yellow River Basin transitioned from high-speed growth to low-speed, and the annual growth rate of carbon neutrality index slowed down gradually then increased slightly. The coupling coordination generally trended toward a better direction. In recent years, the coupling coordination in the lower reaches of the Yellow River Basin was better than that in the upper and middle reaches (mainly Inner Mongolia and some cities in Ningxia and Gansu province). The spatial heterogeneity will provide a theoretical basis for the overall planning of basin’s ecological priorities and low-carbon development, and is of great importance for achieving the goal of carbon neutrality.

Layered double hydroxides (LDHs) are perceived as a hopeful capacitive deionization (CDI) faradic electrode for Cl–insertion due to its tunable composition, excellent anion exchange capacity, and fast redox activity. Nevertheless, the self-stacking and inferior electrical conductivity of the two-dimensional structure of LDH lead to unsatisfactory CDI performance. Herein, the three-dimensional (3D) hollow nanocage structure of CoNi-layered double hydroxide/carbon composites is well designed as a CDI anode by cation etching of the pre-carbonized ZIF-67 template. C/CoNi-LDH has a unique 3D hollow nanocage structure and abundant pore features, which can effectively suppress the self-stacking of LDH sheets and facilitate the transport of ions. Moreover, the introduced amorphous carbon layer can act as a conductive network. When employed as the CDI anode, C/CoNi-LDH exhibited a high Cl–removal capacity of 60.88 mg g–1and a fast Cl–removal rate of 18.09 mg g–1min–1at 1.4 V in 1000 mg L–1NaCl solution. The mechanism of the Cl–intercalation pseudo-capacitance reaction of C/CoNi-LDH is revealed by electrochemical kinetic analysis and ex situ characterization. This study provides vital guidance for the design of high-performance electrodes for CDI.

Posttranslational modifications (PTMs) of proteins, the major mechanism of protein function regulation, play important roles in regulating a variety of cellular physiological and pathological processes. Although the classical PTMs, such as phosphorylation, acetylation, ubiquitination and methylation, have been well studied, the emergence of many new modifications, such as succinylation, hydroxybutyrylation, and lactylation, introduces a new layer to protein regulation, leaving much more to be explored and wide application prospects. In this review, we will provide a broad overview of the significant roles of PTMs in regulating human cancer hallmarks through selecting a diverse set of examples, and update the current advances in the therapeutic implications of these PTMs in human cancer.

The introduction of superconductivity to the Dirac surface states of a topological insulator leads to a topological superconductor, which may support topological quantum computing through Majorana zero modes1,2. The development of a scalable material platform is key to the realization of topological quantum computing3,4. Here we report on the growth and properties of high-quality (Bi,Sb)2Te3/graphene/gallium heterostructures. Our synthetic approach enables atomically sharp layers at both hetero-interfaces, which in turn promotes proximity-induced superconductivity that originates in the gallium film. A lithography-free, van der Waals tunnel junction is developed to perform transport tunnelling spectroscopy. We find a robust, proximity-induced superconducting gap formed in the Dirac surface states in 5–10 quintuple-layer (Bi,Sb)2Te3/graphene/gallium heterostructures. The presence of a single Abrikosov vortex, where the Majorana zero modes are expected to reside, manifests in discrete conductance changes. The present material platform opens up opportunities for understanding and harnessing the application potential of topological superconductivity.

Protein–protein interactions (PPIs) control many essential biological pathways which are often misregulated in disease. As such, selective PPI modulators are desirable to unravel complex functions of PPIs and thus expand the repertoire of therapeutic targets. However, the large size and relative flatness of PPI interfaces make them challenging molecular targets for conventional drug modalities, rendering most PPIs “undruggable”. Therefore, there is a growing need to discover innovative molecules that are able to modulate crucial PPIs. Peptides are ideal candidates to deliver such therapeutics attributed to their ability to closely mimic structural features of protein interfaces. However, their inherently poor proteolysis resistance and cell permeability inevitably hamper their biomedical applications. The introduction of a constraint (i.e., peptide cyclization) to stabilize peptides' secondary structure is a promising strategy to address this problem as witnessed by the rapid development of cyclic peptide drugs in the past two decades. Here, we comprehensively review the recent progress on stabilized cyclic peptides in targeting challenging PPIs. Technological advancements and emerging chemical approaches for stabilizing active peptide conformations are categorized in terms of α-helix stapling, β-hairpin mimetics and macrocyclization. To discover potent and selective ligands, cyclic peptide library technologies were updated based on genetic, biochemical or synthetic methodologies. Moreover, several advances to improve the permeability and oral bioavailability of biologically active cyclic peptides enable the de novodevelopment of cyclic peptide ligands with pharmacological properties. In summary, the development of cyclic peptide-based PPI modulators carries tremendous promise for the next generation of therapeutic agents to target historically “intractable” PPI systems.

The control of harmful cyanobacterial blooms has been becoming a global challenge. The development of eco-friendly algicides with strong specificity is urgently needed. The photosynthetic apparatus is a promising target site for algicides to minimize the possible harmful effects on animals and humans. In this study, biologically derived 2-hydroxychalcone efficiently inhibited the growth of bloom-forming M. aeruginosaby selectively interfering with photosynthesis. 2-Hydroxychalcone targeting Photosystem II (PSII) inhibited electron transfer between the primary and secondary electron acceptors (QAand QB) and the binding of plastoquinone (PQ) molecules to the QBbinding pocket at the acceptor side of PSII, as revealed by polyphasic chlorophyll (Chl) a fluorescence induction and QA–reoxidation kinetics. Molecular docking for 2-hydroxychalcone to D1 protein and the proteomic responses of M. aeruginosasuggested that 2-hydroxychalcone formed a stable monodentate ligand with the nonheme iron in D1 protein, provoking significant modulation of PSII proteins. The unique binding mode of 2-hydroxychalcone with PSII differentiated it from classical PSII inhibitors. Furthermore, 2-hydroxychalcone down-regulated the expression of microcystin (MC) synthesis-related genes to restrain MC synthesis and release. These results indicated the potential application of 2-hydroxychalcone as an algicide or a template scaffold for designing novel derivatives with superior algicidal activity.

Poria cocos, also known as Jade Ling and Songbai taro, is a dry fungus core for Wolfiporia cocos, which is parasitic on the roots of pine trees. The ancients called it “medicine of four seasons” because of its extensive effect and ability to be combined with many medicines. Pachymic acid (PA) is one of the main biological compounds of Poria cocos. Research has shown that PA has various pharmacological properties, including anti-inflammatory and antioxidant. PA has recently attracted much attention due to its anticancer properties. Researchers have found that PA showed anticancer activity by regulating apoptosis and the cell cycle in vitro and in vivo. Using PA with anticancer drugs, radiotherapy, and biomaterials could also improve the sensitivity of cancer cells and delay the progression of cancer. The purpose of this review was to summarize the anticancer mechanism of PA by referencing the published documents. A review of the collected data indicated that PA had the potential to be developed into an effective anticancer agent.

Changes in stock markets impact the optimal policies for governments, businesses, and even individual households. Causal networks summarizing pairwise directed dependencies between stocks can be used to characterize these changes, as well as to identify influential stocks whose variations have unusually strong effects on the movements of other stocks. In this paper, we apply Granger causality tests in sliding windows to build directed causal networks representing the interactions of stock markets in the world's second largest economy, China, during recent economic, trade and epidemiological crises. We propose identifying influential stocks by using distance correlation and surrogate tests, rather than linear Pearson correlation and asymptotic distributions, to quantify the strength of general nonlinear dependencies between network centrality and financial indicators. We find that, at the onset of a crisis, influential stocks gain their position by themselves being attentive to the stock market. As the crisis progresses, the stock market steadily adjusts to give a stronger leadership role to influential stocks at the expense of non-influential stocks. Interestingly, the industries to which influential stocks belong are closely related to the nature of crisis events. Finally, we show that industries which contained influential stocks represented better investments. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text.

Three [Fe2S2–Agx]-hydrogenase active-site-containing coordination polymers (CPs), {[Fe2S2–Ag1](4-cpmt)2(CO)6(ClO4–)}n(1), {[Fe2S2–Ag2](4-cpmt)2(CO)6(OTf–)2(benzene)}n(2), and {[Fe2S2–Ag2](3-cpmt)2(CO)6(ClO4–)2}n(3), were obtained by a direct synthesis method from ligands [FeFe](4-cpmt)2(CO)6[L1; 4-cpmt = (4-cyanophenyl)methanethiolate] and [FeFe](3-cpmt)2(CO)6[L2; 3-cpmt = (3-cyanophenyl)methanethiolate] with silver salts. 1–3represent the first examples of [FeFe]-hydrogenase-based CPs. It was worth noting that the Ag–S bonding between the Ag centers and S atoms of a [Fe2S2] cluster produced a novel [Fe2S2–Agx] (x= 1 or 2) catalytic site in all three polymers. The results of photochemical H2generation experiments indicated that 2and 3containing [Fe2S2–Ag2] active sites showed obviously improved catalytic performances compared with ligands L1and L2and [Fe2S2–Ag1]-containing 1. This work provides a pioneering strategy for the direct synthesis of [Fe2S2]-based CPs or metal–organic frameworks.

There are limited options for patients who develop liver metastasis from colorectal cancer (CRC), the leading cause of cancer-related mortality worldwide. Emerging evidence has provided insights into iron deficiency and excess in CRC. Ferroptosis is an iron-dependent form of programmed cell death characterized by aberrant iron and lipid metabolism, which play crucial roles in tumorigenesis, tumor progression, and treatment options. A better understanding of the underlying molecular mechanism of ferroptosis has shed light on the current findings of ferroptosis-based nanodrug targeting strategies, such as driving ferroptosis in tumor cells and the tumor microenvironment, emerging combination therapy and against multidrug resistance. Furthermore, this review highlights the challenge and perspective of a ferroptosis-driven nanodrug delivery system for CRC-targeted therapy.

Two novel uranium-containing selenotungstates Na3[H19(UO2)2(μ2O)(Se2W14O52)2]·41H2O (U2) and (NH4)10[H4(SeO)2(UO2)2(H2O)2(H2Se2W14O52)(Se2W14O52)]·66H2O (Se2U2) based on the {Se2W14O52} unit were successfully prepared and fully characterized. To our knowledge, the uranium is firstly introduced into the selenotungstates. Moreover, it is notable that U2exhibits excellent Lewis acid-base catalytic activities in the condensation cyclization of sulfonyl hydrazides with diketones to synthesize polysubstituted pyrazoles. All the desired products were obtained in moderate to good yields (up to 99%).

Anthracycline-based chemotherapy resistance represents a major challenge in diffuse large B-cell lymphoma (DLBCL). MiRNA and gene expression profiles (n= 47) were determined to uncover potential chemoresistance mechanisms and therapeutic approaches. An independent correlation between high expression of miRNA-363-3p and chemoresistance was observed and validated in a larger cohort (n= 106). MiRNA-363-3p was shown to reduce doxorubicin-induced apoptosis and tumor shrinkage in in vitro and in vivo experiments by ectopic expression and CRISPR/Cas9-mediated knockout in DLBCL cell lines. DNA methylation was found to participate in transcriptional regulation of miRNA-363-3p. Further investigation revealed that dual specificity phosphatase 10 (DUSP10) is a target of miRNA-363-3p and its suppression promotes the phosphorylation of c-Jun N-terminal kinase (JNK). The miRNA-363-3p/DUSP10/JNK axis was predominantly associated with negative regulation of homologous recombination (HR) and DNA repair pathways. Ectopic expression of miRNA-363-3p more effectively repaired doxorubicin-induced double-strand break (DSB) while enhancing non-homologous end joining repair and reducing HR repair. Targeting JNK and poly (ADP-ribose) polymerase 1 significantly inhibited doxorubicin-induced DSB repair, increased doxorubicin-induced cell apoptosis and tumor shrinkage, and improved the survival of tumor-bearing mice. In conclusion, the miRNA-363-3p/DUSP10/JNK axis is a novel chemoresistance mechanism in DLBCL that may be reversed by targeted therapy.

Hyperuricemia seriously impacts our health and life quality. Probiotics play a vital role in ameliorating hyperuricemia through decrease urate production by purine substance assimilation. However, most research screened probiotics based on the assimilation of purine nucleoside, ignoring nucleotides. Nucleosides are derived from nucleotides. We hypothesized that lactic acid bacteria with the ability of nucleotides assimilation might be better to reduce intestinal absorption of dietary purine substances. In this study, a total of 235 strains of lactic acid bacteria were isolated from Chinese traditional fermented foods and evaluated for their purine nucleotides assimilation ability. A new potential candidate Levilactobacillus (L.) brevisgrx821 isolated from naturally fermented soy whey Suanjiangshui, with a strong ability to degrade purine nucleotides and purine nucleosides, effectively reduced the serum levels of UA and creatinine in hyperuricemia mice by 20.76% and 14.38%. Based on the evaluation of antibiotic susceptibility, production of nitrite and biogenic amines, and hemolytic activity, L. brevisgrx821 can be considered safe. L. brevisgrx821 showed good gastrointestinal tolerance in vitroand cow milk fermentation characteristics, and could be a promising candidate of dietary supplements for prevention of hyperuricemia. This study suggests a novel way screening potential probiotic candidate for hyperuricemia, which has great guiding significance in the dealing with hyperuricemia.

Electrochemical deionization (EDI) is hopefully the next generation of water treatment technology. Bismuth (Bi) is a promising anode material for EDI, due to its high capacity and selectivity toward Cl–, but the large volume expansion and severe pulverization aggressively attenuated the EDI cycling performance of Bi electrodes. Herein, carbon-layer-encapsulated nano-Bi composites (Bi@C) were prepared by a simple pyrolysis method using a Bi-based metal–organic framework as a precursor. Bi nanoparticles are uniformly coated within the carbon layer, in which the Bi–O–C bond enhances the interaction between Bi and C. Such a structure effectively relieves the stress caused by volume expansion by the encapsulation effect of the carbon layer. Moreover, the introduction of a carbon skeleton provides a conductive network. As a consequence, the Bi@C composite delivered excellent electrochemical performance with a capacity of 537.6 F g–1at 1 mV s–1. The Cl–removal capacity was up to 133.5 mg g–1at 20 mA g–1in 500 mg L–1NaCl solution. After 100 cycles, the Bi@C electrode still maintains 71.8% of its initial capacity, which is much higher than the 26.3% of the pure Bi electrode. This study provides a promising strategy for improving EDI electrode materials.

Nitrogen fertilization has been shown to effectively increase soybean yield at the early flowering stage (R1). However, the rapid effect of nitrogen enrichment on soybean nitrogen assimilation and distribution during R1 stage lacked reports. Continuous high nitrogen, low nitrogen, and enriched nitrogen during the R1 stage treatments were used on two different potential soybean yield cultivars (Liaodou11 and Liaodou14) at R1. The soybean nitrogen content, distribution, and assimilation were rapidly measured after nitrogen enrichment at the R1 stage. After nitrogen enrichment for 12 h, the NR activities in two cultivars’ leaves increased 75% and 46%, respectively. Nitrogen enrichment increased the nitrate reductase and nitrite reductase activities by upregulating their gene expression. In contrast, the glutamate dehydrogenase and glutamate synthase activities decreased rapidly, which suggested that the ammonium assimilation was inhibited. And the suppression of ammonium assimilation could regulate the absorption and assimilation of nitrates. The enriched nitrogen at the beginning of the flowering stage promoted nitrogen absorption and assimilation, increasing the nitrogen content in the soybean plant. The high yield cultivar Liaodou14 absorbed nitrogen faster than the common yield cultivar Liaodou11. Nitrogen enrichment at the R1 stage could improve nitrogen supply capacity by regulating nitrate and ammonia assimilation capacity.

This study was to analyse the application of electrospinning (EP) nanofibres in the rehabilitation of fracture of anterior cruciate ligament (ACL). The patients in groups A, B, and C were treated with exercise rehabilitation, ozone therapy combined with rehabilitation exercises, and EP nanofibre stents for anterior cruciate ligament reconstruction. It was found that the water contact angle of the dissolved and blended nanofibres decreased as the specific gravity (SG) of PCL increased; and the elongation rate and mechanical properties of nanofibre materials increased as the tensile strength increased. The scores of ligament injury for patients in the control group (78.66 ± 6.24) and the experimental group (89.65 ± 5.89) were much higher than those in the blank group (62.1 ± 6.02), showing statistical differences (P < 0.05). It showed that the nanofibre stent was successfully prepared, which had good mechanical properties and hydrophilicity.

We propose a new paradigm for describing complex networks in terms of the spectrum of the adjacency matrix and its submatrices. We show that a variety of basic node information, such as degree, clique, and subgraph centrality, can be calculated analytically. Moreover, we find that energy of spectrum series can uncover randomness and complexity of network structure. Interestingly, it presents an universal linear growth pattern with the growth of networks. Furthermore, the spectrum series of synthetic and real networks present clearly self-similarity characteristics for which the associated scaling exponents remain constant. Our work reveals that spectrum series representation will provide an alternative perspective for studying and understanding structure and function of complex networks rather than connectivity. [ABSTRACT FROM AUTHOR]

Spallation, rupture under impulsive tensile loading, is a dynamic failure process involving the collective evolution and accumulation of enormous microdamage in solids. In contrast to traditional alloys, the spallation mechanism in medium entropy alloys, the recently emerged multiprinciple and chemically disordered alloys, is poorly understood. Here we conduct molecular dynamics simulations and first principle calculations to investigate the effects of impact velocities and the local chemical order on spallation microvoid nucleation in a CrCoNi medium entropy alloy under shock wave loading. As the impact velocity increases, the microvoid nucleation site exhibits a transition from the grain boundaries to the grains to release redundant imposed energy. During the intragranular nucleation process, microvoids nucleate in the poor-Cr region with a large local nonaffine deformation, which is attributed to the weak metallic bonds in this position with sparse free electrons. For intergranular nucleation, a Franke-like dislocation source forms through the dislocation reaction, leading to enormous dislocations piling up in a narrow twin stripe, which markedly increases the local stored energy and promotes microvoid nucleation. These results shed light on the mechanism of spallation in chemically complexed medium entropy alloys.

The contamination of aquatic ecosystems has resulted in significant health and environmental ramifications. The removal of pollutants from wastewater poses a significant challenge for water purification, requiring innovative and efficient technologies to ensure the protection of human health and the environment. Recently, capacitive deionization (CDI) has garnered significant attention as a cost-effective and user-friendly water purification process. The development of high-performance electrode materials plays a pivotal role in CDI systems, serving as the primary approach to achieve enhanced adsorption capacity for contaminant ions and expedited ion removal rates. This study employs specifically designed organic framework structures, which are subjected to pyrolysis to facilitate the integration of cobalt nanoparticles within a carbon matrix, resulting in enhanced exposure to active sites. Revolving around its distinctive exposed structure, it exhibits exceptional electrochemical performance alongside remarkable ion removal capabilities. The adsorption capacity of anions, specifically chloride ions (500 ppm), reached 152 mg∙g−1. Additionally, the removal efficiency for low concentrations of cations such as lead ions (40 ppm) and chromium ions (20 ppm) was found to be 91.83% and 60.82%, respectively. Therefore, this study presents a novel approach for the elimination of ions from wastewater.

Two 3d-4f-5d heterometallic cluster-containing polyoxometalates, formulated as Na22{(SbW9O33)4[La3W6MO18(H2O)8(CH3COO)4]2}·nH2O (abbreviated as La6M2, M = Co/Mn) were synthesized and structurally characterized. Single-crystal X-ray diffraction analyses reveal that the polyanions of La6Co2and La6Mn2consist of the uncommon 3d-4f-5d clusters {La6W12Co2} and {La6W12Mn2}, which are encapsulated by four trilacunary Keggin tungstoantimonates to form the parallelogram-shaped title compounds. Additionally, the polyanions can be extended into a two-dimensional (2D) frame by the linkage of peripheral Na+ions. The inner space of the 2D layer was filled with water molecules and thus an H-bonded network was formed, which is expected to exhibit a fascinating proton conductivity. The study of water-assisted proton conduction demonstrated that La6Co2and La6Mn2were temperature- and humidity-dependent proton conductors, respectively, and the proton conductivities could reach 1.3 × 10−2and 2.3 × 10−2S/cm at 65 °C and 90% RH conditions.

Chemical reactions that modify the compositions of nanoparticles are important for optoelectronic and catalytic applications. Understanding how they occur, and the unique features that can be produced as a result, is an important prerequisite to designing intricate nanostructures with complex morphologies. Here, we report the conversion of α-chalcocite copper sulfide nanorods into weissite copper telluride nanorods through anion exchange. By examining the elemental composition, morphology, and crystallinity post exchange, it was found that the tellurium ions replaced sulfur ions to generate weissite in a way that maintained the (pseudo-)hexagonally close-packed sublattice as well as the morphology and crystallinity. Unusually, the anion exchange proceeded without inducing voids in the product nanoparticles. Such voids, produced through the Kirkendall effect, are commonly observed during nanocrystal anion exchange reactions, yet can be important to avoid minimizing defects. The lack of void formation was explained by the balancing of inward and outward anion diffusion offered by nanoscopic pathways that formed within the nanorods at the early stages of the anion exchange reaction. The presence of these exchange-facilitating faults results in an unusual multistep process altering the locations at which copper telluride regions emerged during partial exchange. Three different anion movement regimes resulted in three distinct geometries. Initially, a near-isotropic exchange produced a copper sulfide/copper telluride core–shell structure. As the exchange progressed, the defect-mediated movement resulted in irregularly shaped copper sulfide domains within copper telluride. Phase segregation then led to a unique double-core copper sulfide/copper telluride heterostructure. This work offers insights into the mechanism behind anion exchange, highlights the design capabilities emergent from defective materials, and creates new opportunities for rational synthesis of complex nanoheterostructures.

In order to simplify the workload and find a reliable method for screening soybeans with different phosphorus (P) efficiencies, 47 soybean varieties were screened from 90 varieties according to yield under normal phosphorus, and 10 indicators including root phenotype, phosphorus utilization efficiency at the seedling stage and yield were measured. Through single-index cluster analysis, the performance value and relative value of the above-mentioned indexes under low-phosphorus conditions were analyzed, and then, the combined indexes were analyzed by principal components method. The membership function method and the cluster analysis method was used to calculate and analyze the comprehensive score value. Three indexes of root P utilization efficiency (X1), relative value of root P utilization efficiency (X2), and root volume (X3) under low P at the seedling stage were selected as the most consistent with the yield screening results under low P condition, and the mathematical model of the comprehensive score value was obtained: D ​= ​1.218X1+ 0.320X2+ 0.007X3-0.664(P ​= ​0.000, R2 ​= ​1.000). The comprehensive score can screen and identify the P efficiency of soybeans with fewer indexes in the early growth stages, which provides a more rapid and reliable mathematical model for screening and identifying a larger number of germplasm resources for P efficiency.

There are many factors which influence the absolute open flow potential (AOFP) of gas well. One of them is the angle between maximum principal stress direction and natural fracture strike in gas reservoir. In order to find out how the angle influences the AOFP of gas well. A lot of data related to gas well productivity of 14 wells located in gas reservoir T were collected and collated. Influential intensity of each factor on the AOFP before and after reservoir modification was investigated through grey relation analysis method. Results indicated that the AOFP of gas well before and after reservoir modification was governed by 10 factors. The five central factors influencing the initial AOFP are natural fracture density, porosity, permeability, elevation of geological top surface, and gas saturation, respectively. The five central factors influencing the AOFP of hydraulically fractured gas well are porosity, gas saturation, elevation of geological top surface, minimum principal stress, and permeability, respectively. Angle between maximum principal stress direction and natural fracture strike was not the central factor influencing gas well productivity. Reservoir modification can greatly improve gas well productivity in fractured tight sandstone reservoir. Natural fracture density was the strongest influencing factor of the initial AOFP. Minimum principal stress was one of the central factors influencing the AOFP of hydraulically fractured gas well. Research results can be used to guide well deployment and gas productivity investment projects of fractured tight sandstone reservoir.

Aiming at the problem of long response time of the traditional power line operation and maintenance monitoring system, a kind of power line safety operation and maintenance monitoring system based on cloud computing is proposed. The secure access platform is used to connect the mobile terminal and the mobile application server, and the data is stored by means of flash external. The signal transmission of the sensor and the analogue-to-digital converter is realised by the signal amplification operation circuit. The user allocation of the user interface is carried out by the HASH algorithm to realise the safety operation and maintenance monitoring of power lines. Compared with the three existing methods, the results show that the maximum response time of the system is 2.79 s, and the time is the shortest, indicating that the system is more suitable for the operation and maintenance monitoring of power lines.

The recent discovery of comammox Nitrospiraas complete nitrifiers has fundamentally renewed perceptions of nitrogen cycling in natural and engineered systems, yet little is known about the environmental controls on these newly recognized bacteria. Based on improved phylogenetic resolution through successful assembly of ten novel genomes (71–96% completeness), we provided the first biogeographic patterns for planktonic and benthic comammox Nitrospirain the Yangtze River over a 6030 km continuum. Our study revealed the widespread distributions and relative abundance of comammox Nitrospirain this large freshwater system, constituting 30 and 46% of ammonia-oxidizing prokaryotes (AOPs) and displaying 30.4- and 17.9-fold greater abundances than canonical Nitrospirarepresentatives in water and sediments, respectively. Comammox Nitrospiracontributed more to nitrifier abundances (34–87% of AOPs) in typical oligotrophic environments with a higher pH and lower temperature, particularly in the plateau (clade B), mountain and foothill (clade A) areas of the upper reach. The dominant position of planktonic comammox Nitrospirawas replaced by canonical Nitrospirasublineages I/II and ammonia-oxidizing bacteria from the plateau to downstream plain due to environmental selection, while the dissimilarity of benthic comammox Nitrospirawas moderately associated with geographic distance. A substantial decrease (83%) in benthic comammox Nitrospiraabundance occurred immediately downstream of the Three Gorges Dam, consistent with a similarly considerable decrease in overall sediment bacterial taxa. Together, this study highlights the previously unrecognized dominance of comammox Nitrospirain major river systems and underlines the importance of revisiting the distributions of and controls on nitrification processes within global freshwater environments.

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

The present study aimed to investigate the expression levels and clinical significance of the calcineurin B homologous protein 2 (CHP2) in non–small cell lung cancer (NSCLC), and to study its effects on biological characteristics of NSCLC cells. Tumor and adjacent samples were collected from 196 NSCLC patients. Western blot analysis was used to detect the expression levels of the CHP2 in 8 pairs of NSCLC fresh tissues and 4 NSCLC cell lines. Immunohistochemical analysis was used to detect the expression of the CHP2 in 188 additional pairs of NSCLC wax block tissues. The data indicated that the expression levels of the CHP2 in the paraffin and fresh tissues of NSCLC were significantly higher than those of the adjacent tissues. According to the histo-score, univariate and multivariate analysis indicated that a high expression level of CHP2 was an important factor affecting the 5-year survival rate of NSCLC patients. After knocking down the expression of CHP2 in NSCLC cell lines, the proliferative, migratory, and invasive activities of NSCLC-CHP2 cells were decreased which were assessed by Western blotting, Cell Counting Kit-8, and transwell and wound-healing assays. In conclusion, the data demonstrated that CHP2 was highly expressed in NSCLC and that it could promote the development of NSCLC, suggesting its potential application for the therapy of NSCLC.

When regulating a pipe network according to user demand, hydraulic balance and power consumption are crucial factors for a multi-source looped-pipe network applying distributed variable-speed pumps compared to the conventional central circulating pump system. In this paper, the influence of the fill point on power consumption and hydraulic balance of the multi-source looped-pipe network was studied. A mathematical model for electricity energy consumption analysis was built and calculated for a large sized looped-pipe network with multiple heat sources and distributed variable-speed pumps. The hydraulic calculation models of each single element, such as pipe, distribution pump, valve, replenishment pump, heat source and substation, were built. A case located in Dezhou city, China was analyzed. The results showed that: the maximum power saving (39.2%) could be achieved when each heat source had its own fill point, but the heat sources would not meet their design flows; to meet the design flows of all the heat sources, only one fill point should be necessarily located near the heat source with the lowest flow rate to get the expected hydraulic stability and energy saving.

To investigate the dynamic stereochemical inversion behavior of pillar[5]arenes (P[5]s) in more detail, we synthesized a series of novel rim-differentiated P[5]s with various substituents and examined their rapid rotations by variable-temperature NMR (203–298 K). These studies revealed for the first time the barrier of “methyl-through-the-annulus” rotation (ΔG‡= 47.4 kJ·mol–1in acetone) and indicated that for rim-differentiated P[5]s with two types of alkyl substituents, the smaller rim typically determines the rate of rotation. However, substituents with terminal C═C or C≡C bonds give rise to lower inversion barriers, presumably as a result of attractive π–π interactions in the transition state. Finally, data on a rim-differentiated penta-methyl-penta-propargyl P[5] exhibited the complexity of the overall inversion dynamics.

Suspended matter and organic contaminants would prevent short-wavelength UV/visible light from penetrating deeper into real wastewater. This has hindered the practical use of photocatalysts in wastewater remediation. Construction of near-infrared light responsive photocatalysts can partially overcome this issue, due to the larger penetration depth of near-infrared light into the waste media. However, the development of near-infrared responsive photocatalysts is constrained by low photoconversion efficiency. In this work, we developed an approach to enhance the performance of NIR-responsive photocatalysis of a lanthanide-doped upconversion nanoparticle–TiO2hybrid system through coupling the surface adhesion capability of heavy lanthanides with a NIR to UV/vis light upconversion process. We demonstrated that a Gd3+-rich nanoparticle surface can enhance the adhesion of organic compounds on the heterogeneous catalysts, resulting in more complete cycloreversion of organic chromophores. We systematically investigated the photooxidation pathways of the upconversion nanoparticle–TiO2hybrid catalysts in degradation of rhodamine B through liquid chromatography-mass spectrometry analysis. The results reveal that Gd3+ions on the catalyst surface could facilitate the complete mineralization of the organic molecules to achieve a total organic carbon removal efficiency of around 70%, which is three times higher than that of the conventional upconversion nanoparticle–TiO2systems. The results offer new opportunities for near-infrared light driven photocatalysis in water remediation, which is potentially applicable for large quantity deep wastewater treatment.

To realize the resource and high-value utilization, a new approach, named bagasse lignin (BL) used to produce aryl oxygen-containing compounds by catalytic pyrolysis over perovskite, was proposed. LaTi0.2Fe0.8O3(LTF) samples prepared by the sol-gel method (SG) and the solid-state reaction method (SS) were characterized. The catalytic action on BL pyrolysis was performed by the test of TG-DTG and the evaluation of the fixed bed micro-reactor, the components and contents of the products were determined. The results show that LTF samples have cubic perovskite phase, LTF prepared by SG (LTF-SG) is porous with larger specific surface area than LTF prepared by SS (LTF-SS). During the pyrolysis of BL, the addition of LTF lowers the pyrolysis temperature and the activation energy, the contents of CO2and CO in gaseous products reduce by 4.6%–8.0% and 30.7%–34.3%, respectively, the total content of aryl oxygen-containing compounds (including phenolics, guaiacols, syringols and phenylates) in liquid products increases from 62 wt% to more than 72 wt%, and LTF-SG shows better catalytic performance. LTF samples have nice phase and catalytic stabilities for BL pyrolysis after five successive redox cycles.

Capacitive deionization (CDI) by Ag@C electrodes is a promising desalination technology for chlorine removal because there is no waste flow and no additional chemicals used during the removal and regeneration/cleaning processes. However, its efficacy and capacity are significantly limited by the low stability of Ag/C composites during the electrochemical process. In this study, we synthesized a new highly stable structure of Ag@C composites (named as Ag/N-CNP) through reductive adsorption of Ag+by a polyaromatic amine followed by carbonization. We used in situRaman spectroscopy, for the first time, to monitor in real-time the Cl−electrosorption/desorption process of Ag/N-CNP electrodes, which revealed that the stability of Ag@C electrodes was mainly controlled by the dissolution of Ag. The newly synthesized Ag–Nxstructure stabilized Ag nanoparticles and uniformly distributed Ag species on the carbon matrix, which resulted in a high affinity for Cl−and significantly improved cyclic dechlorination performance. Results showed that the electrosorption capacity of the newly synthesized Ag/N-CNP electrodes could reach 75.3 mg g−1, substantially higher than that of traditional carbon electrodes. Moreover, such a capacity had almost no loss (only 1.4% loss) after 50 continuous treatment cycles and could still retain 76% after 100 cycles, demonstrating a highly reliable and cost-effective desalination performance significantly superior to that of previously reported Ag@C electrodes.

To date, no studies have investigated the expression of Doublecortin (DCX) in pituitary adenomas or evaluated the clinical value of DCX in the diagnosis of pituitary adenomas. This study aims to determine the expression levels of DCX in pituitary adenomas and to investigate its role in the staging of this condition.

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of “Bovine,” exposure of “Statistical Analysis or Machine Learning,” and outcome of “Methane Emissions”. The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.This systematic review outlines the strengths and limitations of traditional dairy cattle methane emission prediction approaches, as well as highlights the promising potential of machine learning in methane emission prediction. Special consideration is also given to the prediction of dairy cattle methane emissions on commercial farms.This review provides a comprehensive overview of the different modeling approaches taken in the prediction of dairy cattle methane emissions.Mechanistic models, which mathematically simulate the methane production process of the dairy cattle rumen, are both accurate and adaptable, yet their necessary input data is difficult to obtain and if imprecise, can produce misinformative results.Empirical models, which statistically quantify the relationships between methane emissions and production factors, are a more accessible alternative to mechanistic models, yet their accessible structure limits them to the same range of data on which they were originally developed.Machine learning models, which are based on a particular learning pattern, can be trained to identify trends in methane production and use these lessons to make accurate predictions. Their application in the prediction of dairy cattle methane emissions remains scarce, yet those that have been show promising potential.Commercially deployable models can utilize any of the previous approaches, as long as the traits they use are obtainable in a commercial farm setting. Those developed favor the use of milk fatty acids, yet the variation in their results needs to be consolidated before robust predictions of methane emissions on commercial farms can be achieved.

•An inverted MCLP-CC model was proposed for rural settlement reconstruction.•A rank-based method was employed to integrate multiple public service facilities.•The accessibility, as assessed by the MH3SFCA, was improved by a maximum of 44.21%.•The Gini and RS indices were reduced by up to 19.53% and 21.09%, respectively.•Accessibility and equity were enhanced to achieve SDGs in rural Changyuan, China.

Phaeodactylum tricornutumhave garnered significant attention for its ability to accumulate fucoxanthin (Fx) and eicosapentaenoic acid. However, its limited biomass in autotrophic culture becomes a restrictive factor for the large-scale production of Fx. Herein, 6-benzylaminopurine and 5-aminolevulinic acid at 1.0 mg/L, were proved conducive for promoting the Fx and biomass accumulation. Under the combined effect of the two, Fx and peaked at 36.01 mg/L after 10 d of cultivation, at which point biomass was 1.50 g/L and Fx content was 24.01 mg/g. Meanwhile, EPA production was elevated to 22.73 mg/L. Such combined effect was also found in regulating the transcriptional levels of Fx biosynthesis-related genes by the two plant hormones. This study demonstrates that Fx and biomass can be enhanced easily and efficiently under the combined effect of these two plant hormones, enabling massive Fx production in the autotrophic system.

Cellular senescence has emerged as an important contributor to aging and age-related diseases. Non-ionizing radiation(NIR), including ultraviolet radiation and electromagnetic fields, has been increasingly recognized as a key inducer of premature senescence. In this review, we discuss the molecular mechanisms of non-ionizing radiation-induced cellular senescence and its effects on aging and age-related diseases. We also summarize the modulation strategies for non-ionizing radiation-induced cellular senescence. A better understanding of the complex relationship between non-ionizing radiation, cellular senescence and age-related pathology may lead to interventions to ameliorate radiation damage and delay aging. Further research is still needed to elucidate the precise mechanisms, dose-response effects, and to develop protective strategies against radiation-induced senescence.