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Abstract Resistive memory devices feature drastic conductance change and fast switching dynamics. Particularly, nonvolatile bipolar switching events (set and reset) can be regarded as a unique nonlinear activation function characteristic of a hysteretic loop. Upon simultaneous activation of multiple rows in a crosspoint array, state change of one device may contribute to the conditional switching of others, suggesting an interactive network existing in the circuit. Here, we prove that a passive resistive switching circuit is essentially an attractor network, where the binary memory devices are artificial neurons while the pairwise voltage differences define an anti-symmetric weight matrix. An energy function is successfully constructed for this network, showing that every switching in the circuit would decrease the energy. Due to the nonvolatile hysteretic function, the energy change for bit flip in this network is thresholded, which is different from the classic Hopfield network. It allows more stable states stored in the circuit, thus representing a highly compact and efficient solution for associative memory. Network dynamics (towards stable states) and their modulations by external voltages have been demonstrated in experiment by 3-neuron and 4-neuron circuits.

Abstract The α-tubulin subtype, Tubulin α-1b chain (TUBA1B), has been shown to influence immune cell infiltration, cancer growth, and survival across various malignancies. However, a comprehensive study has not yet been undertaken examining the immunological and predictive effects of TUBA1B in a pan-carcinoma context. Using data from TCGA, GEO, and other databases, we analyzed TUBA1B expression across various carcinoma types using transcriptional profiling, prognostic implications, genetic and epigenetic alterations, methylation patterns, and immunological significance. To validate our findings, we conducted Western blot analysis to assess TUBA1B protein levels in matched breast cancer tissue samples and performed CCK-8 proliferation assay, flow cytometry, transwell invasion, and migration assays to comprehensively examine the functional impact of TUBA1B on breast cancer cells. Our pan-cancer analysis found TUBA1B upregulation across most tumor types, with varying expression patterns in distinct immune and molecular subtypes. High TUBA1B expression was an independent risk factor and associated with poor prognoses in several cancers, including BRCA, KICH, LGG, LUAD, and MESO. TUBA1B also demonstrates moderate to high diagnostic accuracy in most tumor types. Increased m6A methylation levels were observed in the TUBA1B gene, while its promoter region displayed low methylation levels. TUBA1B's expression impacted some cancers by elevating tumor mutation burden, microsatellite instability, neoantigen formation, immune cell infiltration, and the modulation of immune checkpoints. Functional enrichment analysis highlights TUBA1B’s involvement in important cellular processes such as the cell cycle, p53 signaling, cell senescence, programmed cell death, and the regulation of immune-related pathways. Moreover, our study reveals higher TUBA1B protein expression in breast cancer tissues compared to adjacent tissues. In vitro experiments confirm that TUBA1B deletion reduces breast cancer cell proliferation, invasion, and migration while increasing apoptosis. In conclusion, our study suggests that TUBA1B could potentially serve as a diagnostic marker for predicting cancer immunological profiles and survival outcomes and shed light on the expression and role of TUBA1B in breast cancer, providing a solid foundation for considering it as a promising therapeutic target for breast cancer patient treatment.

Abstract Background The present study aimed to investigate the expression level, biological function, and underlying mechanism of transmembrane protein 176B (TMEM176B) in gastric cancer (GC). Methods TMEM176B expression was detected by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB). The function of TMEM176B was determined by various in vitro assays including colony formation, 5-ethynyl-2ʹ-deoxyuridine (EdU), Transwell, and flow cytometry. Bioinformatics techniques were then used to elucidate the signaling pathways associated with TMEM176B activity. Tumor formation experiments were conducted on nude mice for in vivo validation of the preceding findings. TMEM176B expression was cross-referenced to clinicopathological parameters and survival outcomes. Results It was observed that TMEM176B was overexpressed in GC cells and tissues. Targeted TMEM176B abrogation inhibited colony formation, proliferation, migration, and invasion but promoted apoptosis in GC cell lines while TMEM176B overexpression had the opposite effects. Subsequent experimental validation disclosed an association between TMEM176B and the phosphatidylinositol 3-carboxykinase (PI3K)-protein kinase B (Akt)-mammalian target of rapamycin (mTOR) signaling axis. Moreover, TMEM176B affects GC cancer progression by regulating asparagine synthetase (ASNS). The in vivo assays confirmed that TMEM176B is oncogenic and the clinical data revealed a connection between TMEM176B expression and the clinicopathological determinants of GC. Conclusion The foregoing results suggest that TMEM176B significantly promotes the development of gastric cancer and is an independent prognostic factor of it.

Abstract Objective The research intended to probe the connection between the risk of stroke and serum vitamin D levels. Methods Three electronic databases (Cochrane Library, EMBASE, PubMed) were searched according to the subject terms from inception until July 29, 2022, and retrieved researches were screened on the basis of inclusion and exclusion criteria. Two investigators conducted the quality assessment and data extraction. Using Stata 16.0 software, a meta‐analysis was conducted on the extracted data. Findings In total, 27 studies with 45,302 participants were included. Among these studies, 20 focused on stroke risk, while 7 examined stroke prognosis. According to the meta‐analysis findings, it was observed that a higher stroke risk is connected to reduced levels of serum vitamin D. This association was reflected in a combined relative risk (RR) of 1 .28 (95% confidence interval (CI): 1.15–1.42) and a worse prognosis after stroke (RR = 2.95, 95% CI: 1.90–4.60). Additional analysis indicated that no apparent relationship between a decrease in vitamin D and the probability of experiencing a hemorrhagic stroke was found. The RR found was 1.93 (95% CI: 0.95–3.95). On the other hand, it was observed that a reduction in serum vitamin D levels was linked to an elevated likelihood of developing an ischemic stroke. The RR identified was 1.72 (95% CI: 1.78–2.03). Moreover, a lower level of vitamin D in the bloodstream was associated with a more unfavorable prognosis for individuals who suffered from a stroke. The RR for this correlation was 2.95 (95% CI: 1.90–4.60). However, further research is required to confirm the above‐mentioned findings. Conclusion In conclusion, lower concentration vitamin D was found to be related to an increased risk of stroke, which could mainly be reflected in ischemic stroke patients but not in patients with hemorrhagic stroke. A lower serum vitamin D level was correlative with the poor prognosis of stroke.

This paper discusses the existence of positive radial symmetric solutions of the nonlinear biharmonic equation ▵2u=f(u,▵u) on an annular domain Ω in RN with the Navier boundary conditions u|∂Ω=0 and ▵u|∂Ω=0, where f:R+×R−→R+ is a continuous function. We present some some inequality conditions of f to obtain the existence results of positive radial symmetric solutions. These inequality conditions allow f(ξ,η) to have superlinear or sublinear growth on ξ,η as |(ξ,η)|→0 and ∞. Our discussion is mainly based on the fixed-point index theory in cones.

The conflict between industrial solid waste treatment and environmental protection in Inner Mongolia is becoming increasingly prominent. Using industrial solid waste such as mineral powder, fly ash and wet calcium carbide slag as raw materials, using the alkali excitation method to prepare geopolymer, and replacing part of the cement for pavement base can effectively absorb industrial solid waste and realize the dual goals of waste utilization and environmental protection. Through mechanical properties tests before and after a freeze–thaw cycle and micro tests such as scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP), the strength variation rule and mechanism of geopolymer-cement stabilized aggregate under freeze–thaw cycles were deeply investigated. The relationship between different porosity indexes and mechanical properties in mercury intrusion porosimetry (MIP) was established by grey relation analysis. The results prove that a mixture with impaired properties after freeze–thaw cycles and the anti-freezing performance of the mixture with 20% geopolymer content are better than that of the mixture with no geopolymer content and 40% geopolymer content. The loss rates of unconfined compressive strength (UCS) after 5, 10 and 20 freeze–thaw cycles were 9.5%, 27.6% and 36.4%, respectively. The appropriate addition of geopolymer can enhance the anti-freezing performance of a stable aggregate. Following freezing and thawing cycles, the unconfined compressive strength (UCS) damage of the mixture is mainly influenced by a rise in total porosity, and the grey correlation degree is 0.75. The increase in more harmful pores and total porosity mainly results in an indirect tensile strength (ITS) loss. The grey correlation degree is 0.91. The compressive rebound modulus (CRM) is not affected by the change in pores but decreases with a rise in the geopolymer dosage.

This paper concerns with the existence of radial solutions of the biharmonic elliptic equation ▵2u=f(|x|,u,|∇u|,▵u) in an annular domain Ω={x∈RN:r1<|x|

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. Although an on-line pointing model had been developed in the past for several large radio telescopes to correct these effects, it is also valuable to explore new ways to correct these effects. Therefore, in order to estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin (DT) is used in this paper. Digital models of the Wuqing 70-m radio telescope (WRT70) are created based on the DT operational framework. Finally, the reliability of the WRT70’s DT system is verified, and the dynamic pointing error of WRT70 (staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which is equal to 4/11 of the beam width for WRT70 operating at 8 GHz. In this paper, it is feasible to estimate the time-varying pointing errors of large radio telescopes at rest under solar radiation by constructing a DT system, but it is not effective to estimate the dynamic pointing errors of large radio telescopes in operation. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes in operation by DT technology in the future.

PolSAR image classification has attracted extensive significant research in recent decades. Aiming at improving PolSAR classification performance with speckle noise, this paper proposes an active complex-valued convolutional-wavelet neural network by incorporating dual-tree complex wavelet transform (DT-CWT) and Markov random field (MRF). In this approach, DT-CWT is introduced into the complex-valued convolutional neural network to suppress the speckle noise of PolSAR images and maintain the structures of learned feature maps. In addition, by applying active learning (AL), we iteratively select the most informative unlabeled training samples of PolSAR datasets. Moreover, MRF is utilized to obtain spatial local correlation information, which has been proven to be effective in improving classification performance. The experimental results on three benchmark PolSAR datasets demonstrate that the proposed method can achieve a significant classification performance gain in terms of its effectiveness and robustness beyond some state-of-the-art deep learning methods.

Light plays an essential role in a variety of physiological processes, including vision, mood, and glucose homeostasis. However, the intricate relationship between light and an animal's feeding behavior has remained elusive. Here, we found that light exposure suppresses food intake, whereas darkness amplifies it in male mice. Interestingly, this phenomenon extends its reach to diurnal male Nile grass rats and healthy humans. We further show that lateral habenula (LHb) neurons in mice respond to light exposure, which in turn activates 5-HT neurons in the dorsal Raphe nucleus (DRN). Activation of the LHb5-HTDRN circuit in mice blunts darkness-induced hyperphagia, while inhibition of the circuit prevents light-induced anorexia. Together, we discovered a lightresponsive neural circuit that relays the environmental light signals to regulate feeding behavior in mice. [ABSTRACT FROM AUTHOR]

Abstract Cu catalyses electrochemical CO2 reduction to valuable multicarbon products but understanding the structure-function relationship has remained elusive due to the active Cu sites being heterogenized and under dynamic re-construction during electrolysis. We herein coordinate Cu with six phenyl-1H-1,2,3-triazole derivatives to form stable coordination polymer catalysts with homogenized, single-site Cu active sites. Electronic structure modelling, X-ray absorption spectroscopy, and ultraviolet–visible spectroscopy show a widely tuneable Cu electronics by modulating the highest occupied molecular orbital energy of ligands. Using CO diffuse reflectance Fourier transform infrared spectroscopy, in-situ Raman spectroscopy, and density functional theory calculations, we find that the binding strength of *CO intermediate is positively correlated to highest occupied molecular orbital energies of the ligands. As a result, we enable a tuning of C–C coupling efficiency—a parameter we define to evaluate the efficiency of C2 production—in a broad range of 0.26 to 0.86. This work establishes a molecular platform that allows for studying structure-function relationships in CO2 electrolysis and devises new catalyst design strategies appliable to other electrocatalysis.

Summary: Background: Highly pathogenic respiratory RNA viruses such as SARS-CoV-2 and its associated syndrome COVID-19 pose a tremendous threat to the global public health. Innate immune responses to SARS-CoV-2 depend mainly upon the NF-κB-mediated inflammation. Identifying unknown host factors driving the NF-κB activation and inflammation is crucial for the development of immune intervention strategies. Methods: Published single-cell RNA sequencing (scRNA-seq) data was used to analyze the differential transcriptome profiles of bronchoalveolar lavage (BAL) cells between healthy individuals (n = 27) and patients with severe COVID-19 (n = 21), as well as the differential transcriptome profiles of peripheral blood mononuclear cells (PBMCs) between healthy individuals (n = 22) and severely ill patients with COVID-19 (n = 45) or influenza (n = 16). Loss-of-function and gain-of-function assays were performed in diverse viruses-infected cells and male mice models to identify the role of TOMM34 in antiviral innate immunity. Findings: TOMM34, together with a list of genes encoding pro-inflammatory cytokines and antiviral immune proteins, was transcriptionally upregulated in circulating monocytes, lung epithelium and innate immune cells from individuals with severe COVID-19 or influenza. Deficiency of TOMM34/Tomm34 significantly impaired the type I interferon responses and NF-κB-mediated inflammation in various human/murine cell lines, murine bone marrow-derived macrophages (BMDMs) and in vivo. Mechanistically, TOMM34 recruits TRAF6 to facilitate the K63-linked polyubiquitination of NEMO upon viral infection, thus promoting the downstream NF-κB activation. Interpretation: In this study, viral induction of TOMM34 is positively correlated with the hyperinflammation in severely ill patients with COVID-19 and influenza. Our findings also highlight the physiological role of TOMM34 in the innate antiviral signallings. Funding: A full list of funding sources can be found in the acknowledgements section.

ObjectiveTo investigate diagnostic approaches for preoperative localization of secondary hyperparathyroidism, as well as to give surgeons with precise parathyroid gland localization and imaging so that surgery can be performed safely.MethodsThe clinical data of 710 patients with secondary hyperparathyroidism who underwent surgery in our center from October 2009 to October 2023 were retrospectively analyzed. The changes in calcium, phosphorus, and parathyroid hormone levels were observed to ascertain the anatomical location and number of parathyroid glands.ResultsAmong the 710 patients, 55 underwent total parathyroidectomy, the others underwent total parathyroidectomy with autotransplantation. In total, 2,658 parathyroid glands were removed, with 43 glands being removed in 35 reoperation cases. The median parathyroid hormone level at 6 months postoperatively was 13.40 (interquartile range, 7.00-29.80) pg/mL. The detection rates of the parathyroid glands before first and repeat surgeries were higher using 99mTc-MIBI SPECT/CT fusion imaging than ultrasound (P

Previous studies have modified rice's resistant starch (RS) content by mutating single and double genes. These mutations include knocking out or reducing the expression of sbe1 or sbe2b genes, as well as overexpressing Wxa. However, the impact of triple mutant sbe2b/sbe1/OE-Wxa on RS contents remained unknown. Here, we constructed a double mutant with sbe2b/RNAi-sbe1, based on IR36ae with sbe2b, and a triple mutant with sbe2b/RNAi-sbe1/OE-Wxa, based on the double mutant. The results showed that the amylose and RS contents gradually increased with an increase in the number of mutated genes. The triple mutant exhibited the highest amylose and RS contents, with 41.92% and 4.63%, respectively, which were 2- and 5-fold higher than those of the wild type, which had 22.19% and 0.86%, respectively. All three mutants altered chain length and starch composition compared to the wild type. However, there was minimal difference observed among the mutants. The Wxa gene contributed to the improvement of 1000-grain weight and seed-setting rate, in addition to the highest amylose and RS contents. Thus, our study offers valuable insight for breeding rice cultivars with a higher RS content and yields.

Abstract The response to programmed death-1 (PD-1) blockade varies in hepatocellular carcinoma (HCC). We utilize a panel of 16 serum factors to show that a circulating level of serum amyloid A (SAA) > 20.0 mg/L has the highest accuracy in predicting anti-PD-1 resistance in HCC. Further experiments show a correlation between peritumoral SAA expression and circulating SAA levels in patients with progressive disease after PD-1 inhibition. In vitro experiments demonstrate that SAA induces neutrophils to express PD-L1 through glycolytic activation via an LDHA/STAT3 pathway and to release oncostatin M, thereby attenuating cytotoxic T cell function. In vivo, genetic or pharmacological inhibition of STAT3 or SAA eliminates neutrophil-mediated immunosuppression and enhances antitumor efficacy of anti-PD-1 treatment. This study indicates that SAA may be a critical inflammatory cytokine implicated in anti-PD-1 resistance in HCC. Targeting SAA-induced PD-L1+ neutrophils through STAT3 or SAA inhibition may present a potential approach for overcoming anti-PD1 resistance.

Due to the mismatch between transmit waveforms and receive filters, Cross-Ambiguity Function (CAF) shaping plays an important role in the design of cognitive radar waveforms and allows more freedom for waveform optimization problem than conventional ambiguity function shaping. A CAF shaping method is proposed for designing phase-shift keying transmit waveforms and receive filters jointly to maximize the output Signal-to-Interference-plus-Noise Ratio (SINR), thereby solving the problem of weaking-moving target detection under strong clutter conditions. The optimization problem is first modeled as a quadratic fractional programming problem under the Constant Modulus (CM) constraint of the transmit waveform. The conjugated gradient method is utilized to solve the minimization problem of the Stiefel manifold space through the introduction of auxiliary variables; furthermore the nonconvex optimization problem is converted into a unimodular quadratic programming problem. An algorithm based on alternately iterative maximization and power method-like iteration is proposed to solve the quadratic optimization problem. Since transmit waveforms are limited by hardware and achieving CM is difficult, the nearest vector method is employed under the constraint of a low peak-to-average power ratio. Finally, the experiments with simulated and real measured data under different parameters reveal that the transmit waveforms and receive filters designed using the proposed method exhibit better SINR performance and faster convergence speed compared with other existing algorithms.

Achieving automatic target recognition in synthetic aperture radar (SAR) imagery is a long-standing difficulty because of the limited training samples and its sensitivity to imaging condition. Active target recognition methods can offer an innovative perspective to improve recognition accuracy compared to their passive counterparts. Although prevailing in the optical imagery area, the active target recognition in SAR image processing remains underexplored. This article proposes an active SAR target recognition framework based on deep reinforcement learning for the first time, where we design a simple view-matching task and model it as a Markov decision process. The proximal policy optimization algorithm is used to help the agent learn how to alter the observing azimuth to seek more discriminative target images for the classifier. Furthermore, the single-view feature extractor is trained with the contrastive learning method to help distinguish the target images under different azimuths, allowing the agent to successfully learn the active data collection policy in the training environment and transfer it to the test environment. Lastly, the effectiveness and advancement of the proposed framework are verified on the SAMPLE dataset. When the training samples for the classifier are very scarce, it could bring around 10% more gain in target recognition rate compared to existing active target recognition frameworks.

Synthetic aperture radar automatic target recognition (SAR ATR) has been widely studied in recent years. Most ATR models are designed based on the traditional closed-set assumption. This type of ATR model can only identify target categories existing in the training set, and it will result in missed detection or misclassification of unseen target categories encountered in battlefield reconnaissance, posing a potential threat. Therefore, it is of great significance to design a model that can simultaneously achieve known class classification and unknown class judgment. In addition, researchers usually use the obtained unknown class data for model relearning to enable it to recognize new categories. However, before this process, it is necessary to manually interpret and annotate the obtained unknown class data, which undoubtedly requires a large time cost and is difficult to meet the timeliness requirements. To solve these problems, we propose a framework that integrates the open-set recognition module and the novel class discovery module. By introducing the K-contrast loss, the open-set recognition module can accurately distinguish unknown class data, classify known class data, and then transfer the known class knowledge through deep clustering for clustering annotation of unknown class data. Extensive experimental results on the MSTAR benchmark dataset demonstrate the effectiveness of the proposed methods.

Molecular-assisted breeding is an effective way to improve targeted agronomic traits. dep1 (dense and erect panicle 1) is a pleiotropic gene that regulates yield, quality, disease resistance, and stress tolerance, traits that are of great value in rice (Oryza sativa L.) breeding. In this study, a colorimetric LAMP (loop-mediated isothermal amplification) assay was developed for the detection of the dep1 allele and tested for the screening and selection of the heavy-panicle hybrid rice elite restorer line SHUHUI498, modified with the allele. InDel (Insertion and Deletion) primers (DEP1_F and DEP1_R) and LAMP primers (F3, B3, FIP, and BIP) for genotyping were designed using the Primer3 Plus (version 3.3.0) and PrimerExplore (version 5) software. Our results showed that both InDel and LAMP markers could be used for accurate genotyping. After incubation at a constant temperature of 65 °C for 60 min with hydroxynaphthol blue (HNB) as a color indicator, the color of the LAMP assay containing the dep1 allele changed to sky blue. The SHUHUI498 rice line that was detected in our LAMP assay displayed phenotypes consistent with the dep1 allele such as having a more compact plant architecture, straight stems and leaves, and a significant increase in the number of effective panicles and spikelets, demonstrating the effectiveness of our method in screening for the dep1 allele in rice breeding.

Abstract Electrosynthesis of acetate from CO offers the prospect of a low-carbon-intensity route to this valuable chemical––but only once sufficient selectivity, reaction rate and stability are realized. It is a high priority to achieve the protonation of the relevant intermediates in a controlled fashion, and to achieve this while suppressing the competing hydrogen evolution reaction (HER) and while steering multicarbon (C2+) products to a single valuable product––an example of which is acetate. Here we report interface engineering to achieve solid/liquid/gas triple-phase interface regulation, and we find that it leads to site-selective protonation of intermediates and the preferential stabilization of the ketene intermediates: this, we find, leads to improved selectivity and energy efficiency toward acetate. Once we further tune the catalyst composition and also optimize for interfacial water management, we achieve a cadmium-copper catalyst that shows an acetate Faradaic efficiency (FE) of 75% with ultralow HER (

Summary: The importance of trained immunity in antitumor immunity has been increasingly recognized, but the underlying metabolic regulation mechanisms remain incompletely understood. In this study, we find that squalene epoxidase (SQLE), a key enzyme in cholesterol synthesis, is required for β-glucan-induced trained immunity in macrophages and ensuing antitumor activity. Unexpectedly, the shunt pathway, but not the classical cholesterol synthesis pathway, catalyzed by SQLE, is required for trained immunity induction. Specifically, 24(S),25-epoxycholesterol (24(S),25-EC), the shunt pathway metabolite, activates liver X receptor and increases chromatin accessibility to evoke innate immune memory. Meanwhile, SQLE-induced reactive oxygen species accumulation stabilizes hypoxia-inducible factor 1α protein for metabolic switching into glycolysis. Hence, our findings identify 24(S),25-EC as a key metabolite for trained immunity and provide important insights into how SQLE regulates trained-immunity-mediated antitumor activity.

The global prevalence of obesity is a pressing health issue, increasing the medical burden and posing significant health risks to humans. The side effects and complications associated with conventional medication and surgery have spurred the search for anti-obesity drugs from plant resources. Previous studies have suggested that Artemisiae argyi Folium (Aiye) water extracts could inhibit pancreatic lipase activities, control body weight increase, and improve the plasma lipids profile. However, the exact components and mechanisms were not precisely understood. Therefore, this research aims to identify the chemical profile of Aiye and provide a comprehensive prediction of its anti-obesity mechanisms. The water extract of Aiye was subjected to LC-MS analysis, which identified 30 phenolics. The anti-obesity mechanisms of these phenolics were then predicted, employing network pharmacology and molecular docking. Among the 30 phenolics, 21 passed the drug-likeness screening and exhibited 486 anti-obesity targets. The enrichment analysis revealed that these phenolics may combat obesity through PI3K-Akt signaling and MAPK, prolactin, and cAMP signaling pathways. Eight phenolics and seven central targets were selected for molecular docking, and 45 out of 56 docking had a binding affinity of less than −5 kcal/mol. This research has indicated the potential therapy targets and signaling pathways of Aiye in combating obesity.

As the cereal-producing region of China’s black soil, there are many agricultural activities, mainly including cultivation, straw processing, and harvesting, in Northeast China. In the process of carrying out these agricultural activities, they inevitably lead to large carbon emissions, among which straw burning and wind erosion are two processes that directly lead to carbon emissions from farmland. In this study, we estimated the carbon emissions of these two processes based on two algorithms: the improved Fire Radiative Power and Community Multiscale Air Quality (FENGSHA) algorithms. The results showed that the carbon emissions from straw burning in Northeast China can reach up to 126,651 Gg in 2017, and those from wind erosion of agricultural land can reach up to 80.45 Gg a year. When compared with the carbon emissions in 2017, the implementation of the Action Plan for Straw Disposal in Northeast China resulted in around a 40% decrease in the carbon emissions from straw burning in 2022. However, the carbon emissions from agricultural land wind erosion increased by about 10%. The seasonal characteristics of both straw burning and farmland wind erosion were obvious, with both being concentrated in the spring. In addition, based on the potential impacts of straw burning on wind erosion, we proposed that a Y-shaped integrated monitoring network should be constructed to monitor both straw burning and wind erosion in Northeast China. Thus, the study of carbon emissions from straw burning and wind erosion in Northeast China is of great importance for energy conservation and emission reduction, and the implementation of a straw burning ban policy, straw recycling and reuse, and a black soil protection policy is recommended.