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The spread of rumors has an important impact on the production and life of human society. Moreover, in the process of rumor propagation, individuals with different educational levels show different degrees of trust and ability to spread rumors. Therefore, a new rumor propagation model was established, which considers the influence of education level on rumor propagation. Initially, the basic reproduction number of the model was calculated. Then, we analyzed the existence and stability of the rumor equilibrium point. Next, based on the principle of Pontryagin's maximum value, we obtained a control strategy, which effectively reduced the spread of rumors. Numerical simulations verified the results of theoretical analysis. The results showed that the higher the education level of the population, the slower the spread of rumors to a certain extent, but it could not prevent the spread of rumors. In addition, through the support of the government and the propaganda of the official media, strengthening education can improve people's education level to a certain extent, and then minimize the speed of rumor propagation.

Abstract Knowledge diffusion in regional innovation ecosystems is an important factor that influences the regional innovation efficiency. In regional innovation ecosystems under digital empowerment, the knowledge diffusion enables the optimal allocation of innovation resources and promotes the sustainable development and ecological evolution of regional innovation ecosystems. In this paper, a SEIR (Susceptible–Exposed–Infected–Recovered) model is proposed for knowledge diffusion in regional innovation ecosystems under digitization. The basic reproduction number of the proposed model is calculated and its stability is validated. Finally, the expressions for the optimal control system and the optimal control parameters are presented. According to the research conclusions of this paper, the knowledge-diffusion ability of innovation agents in an innovation ecosystem affects the knowledge diffusion in a system; the contact rate between innovation agents affects the efficiency of knowledge diffusion in the system and the structure of the system; the digital transmission ability of innovation agents affects the breadth of knowledge diffusion in the system; and the self-learning ability of innovation agents affects the efficiency of knowledge diffusion in the system.The digital technologies help heterogeneous innovation agents in regional innovation ecosystems to break down the knowledge silos. At the same time, the digital technologies enhance the ability of innovation agents to absorb and learn knowledge in regional innovation ecosystems under digitization, thereby increasing the infection rate of knowledge diffusion in such systems.These conclusions extend the theoretical boundaries of innovation ecosystems and knowledge diffusion and offer management implications for enterprises and governments in decision-making.

Currently, most of the wells in X Oilfield are self-flowing wells. In order to adjust the production system of oil wells in time according to the production requirements of oilfields, it is necessary to predict the ceasing–flowing time. Therefore, how to accurately predict the ceasing–flowing time is the main problem faced by the self-flowing well. As the conventional prediction methods only consider the influence of a single variable, the prediction results are not ideal. Combining the production prediction based on the long short-term memory (LSTM) neural network and the inflow and outflow dynamic curves, this study proposes a comprehensive method for predicting the ceasing–flowing time of a flowing well by considering multiple factors. Using the minimum wellhead pressure prediction method, the changes in bottom hole flowing pressure and reservoir pressure are also considered. The practical application results in X Oilfield show that the calculated and predicted results are highly consistent with the actual production data, verifying the reliability of this method. This study can provide a reference for the prediction of oil well ceasing–flowing in other oilfields.

Abstract Cuproptosis is an emerging cell death pathway that depends on the intracellular Cu ions. Elesclomol (ES) as an efficient Cu ionophore can specifically transport Cu into mitochondria and trigger cuproptosis. However, ES can be rapidly removed and metabolized during intravenous administration, leading to a short half‐life and limited tumor accumulation, which hampers its clinical application. Here, the study develops a reactive oxygen species (ROS)‐responsive polymer (PCP) based on cinnamaldehyde (CA) and polyethylene glycol (PEG) to encapsulate ES‐Cu compound (EC), forming ECPCP. ECPCP significantly prolongs the systemic circulation of EC and enhances its tumor accumulation. After cellular internalization, the PCP coating stimulatingly dissociates exposing to the high‐level ROS, and releases ES and Cu, thereby triggering cell death via cuproptosis. Meanwhile, Cu2+‐stimulated Fenton‐like reaction together with CA‐stimulated ROS production simultaneously breaks the redox homeostasis, which compensates for the insufficient oxidative stress treated with ES alone, in turn inducing immunogenic cell death of tumor cells, achieving simultaneous cuproptosis and immunotherapy. Furthermore, the excessive ROS accelerates the stimuli‐dissociation of ECPCP, forming a positive feedback therapy loop against tumor self‐alleviation. Therefore, ECPCP as a nanoplatform for cuproptosis and immunotherapy improves the dual antitumor mechanism of ES and provides a potential optimization for ES clinical application.

Neuroscientists have recognized the importance of astrocytes in regulating neurological function and their influence on the release of glial transmitters. Few studies, however, have focused on the effects of general anesthetic agents on neuroglia or astrocytes. Astrocytes can also be an important target of general anesthetic agents as they exert not only sedative, analgesic, and amnesic effects but also mediate general anesthetic-induced neurotoxicity and postoperative cognitive dysfunction. Here, we analyzed recent advances in understanding the mechanism of general anesthetic agents on astrocytes, and found that exposure to general anesthetic agents will destroy the morphology and proliferation of astrocytes, in addition to acting on the receptors on their surface, which not only affect Ca2+ signaling, inhibit the release of brain-derived neurotrophic factor and lactate from astrocytes, but are even involved in the regulation of the pro- and anti-inflammatory processes of astrocytes. These would obviously affect the communication between astrocytes as well as between astrocytes and neighboring neurons, other neuroglia, and vascular cells. In this review, we summarize how general anesthetic agents act on neurons via astrocytes, and explore potential mechanisms of action of general anesthetic agents on the nervous system. We hope that this review will provide a new direction for mitigating the neurotoxicity of general anesthetic agents.

Variations in the tumor genome can result in allelic changes compared to the reference profile of its homogenous body source on genetic markers. This brings a challenge to source identification of tumor samples, such as clinically collected pathological paraffin-embedded tissue and sections. In this study, a probabilistic model was developed for calculating likelihood ratio (LR) to tackle this issue, which utilizes short tandem repeat (STR) genotyping data. The core of the model is to consider tumor tissue as a mixture of normal and tumor cells and introduce the incidence of STR variants (φ) and the percentage of normal cells (Mxn) as a priori parameters when performing calculations. The relationship between LR values and φ or Mxn was also investigated. Analysis of tumor samples and reference blood samples from 17 colorectal cancer patients showed that all samples had Log10(LR) values greater than 1014. In the non-contributor test, 99.9% of the quartiles had Log10(LR) values less than 0. When the defense’s hypothesis took into account the possibility that the tumor samples came from the patient’s relatives, LR greater than 0 was still obtained. Furthermore, this study revealed that LR values increased with decreasing φ and increasing Mxn. Finally, LR interval value was provided for each tumor sample by considering the confidence interval of Mxn. The probabilistic model proposed in this paper could deal with the possibility of tumor allele variability and offers an evaluation of the strength of evidence for determining tumor origin in clinical practice and forensic identification.

High sensitivity and selectivity and short response and recovery times are important for practical conductive polymer gas sensors. However, poor stability, poor selectivity, and long response times significantly limit the applicability of single-phase conducting polymers, such as polypyrrole (PPy). In this study, PPy/MoS2 composite films were prepared via chemical polymerization and mechanical blending, and flexible thin-film resistive NO2 sensors consisting of copper heating, fluorene polyester insulating, and PPy/MoS2 sensing layers with a silver fork finger electrode were fabricated on a flexible polyimide substrate using a flexible electronic printer. The PPy/MoS2 composite films were characterized using X-ray diffraction, Fourier-transform infrared spectroscopy, and field-emission scanning electron microscopy. A home-built gas sensing test platform was built to determine the resistance changes in the composite thin-film sensor with temperature and gas concentration. The PPy/MoS2 sensor exhibited better sensitivity, selectivity, and stability than a pure PPy sensor. Its response to 50 ppm NO2 was 38% at 150 °C, i.e., 26% higher than that of the pure PPy sensor, and its selectivity and stability were also higher. The greater sensitivity was attributed to p–n heterojunction formation after MoS2 doping and more gas adsorption sites. Thus, PPy/MoS2 composite film sensors have good application prospects.

Information dissemination has a significant impact on social development. This paper considers that there are many stochastic factors in the social system, which will result in the phenomena of information cross-dissemination and variation. The dual-system stochastic susceptible-infectious-mutant-recovered model of information cross-dissemination and variation is derived from this problem. Afterward, the existence of the global positive solution is demonstrated, sufficient conditions for the disappearance of information and its stationary distribution are calculated, and the optimal control strategy for the stochastic model is proposed. The numerical simulation supports the results of the theoretical analysis and is compared to the parameter variation of the deterministic model. The results demonstrate that cross-dissemination of information can result in information variation and diffusion. Meanwhile, white noise has a positive effect on information dissemination, which can be improved by adjusting the perturbation parameters.

Introduction Focal segmental glomerulosclerosis (FSGS) is a common glomerulopathy with unclear mechanism. The demand for FSGS clinical diagnostic biomarkers has not yet been met. Circular RNA (circRNA) is a novel non-coding RNA with multiple functions, but its diagnostic value for FSGS remains unexplored. This study aimed to identify circRNAs that could aid in early clinical diagnosis and to investigate their mechanisms in podocyte injury. Methods The signature of plasma circRNAs for FSGS was identified by circRNA microarray. The existence of circRNAs was confirmed by qRT-PCR, RNase R assay, and DNA sequencing. Plasma levels of circRNAs were evaluated by qRT-PCR. The diagnostic value was appraised by receiver operating characteristic curve. The circRNA-miRNA-mRNA network was built with Cytoscape 7.3.2. Statistically significant differences were calculated by the Mann-Whitney U-test. Results A total of 493 circRNAs (165 upregulated, 328 downregulated) were differentially expressed in the plasma of FSGS patients (n = 3) and normal controls (n = 3). Eight candidate circRNAs were demonstrated to be circular and stable transcripts. Among them, hsa_circ_0001230 and hsa_circ_0023879 were significantly upregulated in FSGS patients (n = 29) compared to normal controls (n = 51). The areas under the curve value of hsa_circ_0001230 and hsa_circ_0023879 were 0.668 and 0.753, respectively, while that of two-circRNAs panel was 0.763. The RNA pull-down analysis revealed that hsa_circ_0001230 and hsa_circ_0023879 could sponge hsa-miR-106a. Additionally, hsa_circ_0001230 and hsa_circ_0023879 positively regulated hsa-miR-106a target genes phosphatase and tensin homolog (PTEN) and Bcl-2-like protein 11 (BCL2L11) in podocytes. Conclusion Hsa_circ_0001230 and hsa_circ_0023879 are novel blood biomarkers for FSGS. They may regulate podocyte apoptosis by competitively binding to hsa-miR-106a.

Prostate specific antigen (PSA), as a biomarker, plays important roles in early diagnosis of male prostate diseases, especially prostate cancer. In this study, a novel electrochemical probe polymer of poly(ferriporphyrin-co-acrylamide) was synthesized, by further combined with reduced graphene oxide and gold nanoparticles, and oriented immobilization of PSA antibody, a label-free electrochemical immunosensor was obtained, and then successfully applied in the determination of PSA in 10 % non-antigen serum system. The prepared biosensor expressed high selectivity toward PSA, with a LOD of 0.001 ng mL−1, a linear range of 0.01–110 ng mL−1, and a sensitivity of 15.78 µA mL ng−1. What’s more, it showed good stability, well repeatability and high selectivity in the real PSA sample detection. This work provided certain references for the design of the new electrochemical probes and the detection of tumor markers.

The effects of general anesthetic agents (GAAs) on microglia and their potential neurotoxicity have attracted the attention of neuroscientists. Microglia play important roles in the inflammatory process and in neuromodulation of the central nervous system. Microglia-mediated neuroinflammation is a key mechanism of neurocognitive dysfunction during the perioperative period. Microglial activation by GAAs induces antiinflammatory and pro-inflammatory effects in microglia, suggesting that GAAs play a dual role in the mechanism of postoperative cognitive dysfunction. Understanding of the mechanisms by which GAAs regulate microglia may help to reduce the incidence of postoperative adverse effects. Here, we review the actions of GAAs on microglia and the consequent changes in microglial function. We summarize clinical and animal studies associating microglia with general anesthesia and describe how GAAs interact with neurons via microglia to further explore the mechanisms of action of GAAs in the nervous system. [ABSTRACT FROM AUTHOR]

BackgroundThe main functional gastrointestinal disorders (FGIDs) include functional dyspepsia (FD) and irritable bowel syndrome (IBS), which often present overlapping symptoms with gastroesophageal reflux disease (GERD), posing a challenge for clinical diagnosis and treatment. The gut microbiota is closely associated with FGIDs and GERD, although the causal relationship has not been fully elucidated. Therefore, we aimed to investigate the potential causal relationship using bidirectional two-sample Mendelian randomization (MR) analysis.Materials and methodsThe genetic data of the 211 gut microbiota were obtained from the MiBioGen consortium (N = 14,306, from phylum to genus level) and species level of gut microbiota were acquired from the Dutch Microbiome Project (N = 7,738). For FD and IBS, we utilized the FinnGen consortium, whereas, for GERD data analysis, we obtained the IEU OpenGWAS project. The inverse-variance weighted (IVW) method was used as the primary method to calculate causal effect values. Sensitivity analyses were also performed to confirm the robustness of the primary findings of the MR analyses. Moreover, a reverse MR analysis was conducted to assess the likelihood of reverse causality.ResultsCombining the results of the preliminary and sensitivity analyses, we identified that 8 gut microbial taxa were associated with FD. Genus Lachnospiraceae NK4A136 group (p = 3.63 × 10−3) and genus Terrisporobacter (p = 1.13 × 10−3) were strongly associated with FD. At the same time, we found that 8 gut microbial taxa were associated with IBS. Family Prevotellaceae (p = 2.44 × 10−3) and species Clostridium leptum (p = 7.68 × 10−3) display a robust correlation with IBS. In addition, 5 gut microbial taxa were associated with GERD using the IVW approach. In the reverse MR analysis, 2 gut microbial taxa were found to be associated with FD, 5 gut microbial taxa were found to be associated with IBS, and 21 gut microbial taxa were found to be associated with GERD.ConclusionThe study reveals the potential causal effects of specific microbial taxa on FD, IBS, and GERD and may offer novel insights into the diagnosis and treatment of these conditions.

The occurrence of benign prostate hyperplasia (BPH) was related to disrupted sex steroid hormones, and metformin (Met) had a clinical response to sex steroid hormone-related gynaecological disease. However, whether Met exerts an antiproliferative effect on BPH via sex steroid hormones remains unclear. Here, our clinical study showed that along with prostatic epithelial cell (PEC) proliferation, sex steroid hormones were dysregulated in the serum and prostate of BPH patients. As the major contributor to dysregulated sex steroid hormones, elevated dihydrotestosterone (DHT) had a significant positive relationship with the clinical characteristics of BPH patients. Activation of adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) by Met restored dysregulated sex steroid hormone homeostasis and exerted antiproliferative effects against DHT-induced proliferation by inhibiting the formation of androgen receptor (AR)-mediated Yes-associated protein (YAP1)-TEA domain transcription factor (TEAD4) heterodimers. Met’s anti-proliferative effects were blocked by AMPK inhibitor or YAP1 overexpression in DHT-cultured BPH-1 cells. Our findings indicated that Met would be a promising clinical therapeutic approach for BPH by inhibiting dysregulated steroid hormone-induced PEC proliferation.

Depression is a common mental health disorder, and in recent years, the incidence of various forms of depression has been on the rise. Most medications for depression are highly dependency-inducing and can lead to relapse upon discontinuation. Therefore, novel treatment modalities and therapeutic targets are urgently required. Traditional Chinese medicine (TCM) offers advantages in the treatment of depression owing to its multi-target, multi-dimensional approach that addresses the root cause of depression by regulating organ functions and balancing Yin and Yang, with minimal side effects. Cynaroside (CNS), an extract from the traditional Chinese herb honeysuckle, is a flavonoid compound with antioxidant properties. In this study, network pharmacology identified 44 potential targets of CNS associated with depression and several highly correlated inflammatory signaling pathways. CNS alleviated LPS-induced M1 polarization and the release of inflammatory factors in BV-2 cells. Transcriptomic analysis and validation revealed that CNS reduced inflammatory polarization, lipid peroxidation, and ferroptosis via the IRF1/SLC7A11/GPX4 signaling pathway. In vivo experiments showed that CNS treatment had effects similar to those of fluoxetine (FLX). It effectively ameliorated anxiety-, despair-, and anhedonia-like states in chronic unpredictable mild stress (CUMS)-induced mice and reduced microglial activation in the hippocampus. Thus, we conclude that CNS exerts its therapeutic effect on depression by inhibiting microglial cells from polarizing into the M1 phenotype and reducing inflammation and ferroptosis levels. This study provides further evidence that CNS is a potential antidepressant, offering new avenues for the treatment of depression.

Quantum variational algorithms (VQAs) are highly promising to realize quantum advantages on near-term quantum devices. Existing VQAs based on a manually fixed quantum Ansatz are computationally inefficient due to noise and the limited coupling maps of these devices. Previous work considers various quantum architecture search (QAS) algorithms to autodesign a quantum Ansatz based on specific questions to improve the performance of VQAs. Compared to manual design, autodesign can more efficiently explore the large space of a possible Ansatz and achieve better performance. However, two main challenges in utilizing QAS to design quantum circuits efficiently are the tremendous amount of space required for candidate quantum circuits, and the disconnection between quantum devices and autodesign in terms of qubit mapping and quantum noise. To address these issues, we propose an adaptive diversity-based quantum Ansatz search algorithm to efficiently generate the optimal quantum circuit based on device qubit mapping and noise. By considering the diversity among different candidate circuits and adaptively adding circuit depths, our approach only needs to focus on a small optimization space at each iteration step. In addition, the synchronization of optimizing circuit structure and aligning qubit mapping enables us to generate quantum circuits while avoiding additional mapping overhead. We evaluate the performance of our algorithm on simulators and real quantum devices for quantum eigenvalue problems and classification tasks. Results demonstrate that quantum circuits generated by our method outperform both a fixed hardware-efficient Ansatz and randomly generated quantum circuits in terms of final performance and resource-saving. Our algorithm provides a flexible way to efficiently generate excellent quantum circuits for significantly improving the performances of VQAs on near-term quantum devices.

Identifying the interactions of land use functions (LUFs) is of great significance for alleviating the contradiction between human and land, and promoting sustainable use of land resources. However, few studies concerned the interactions of LUFs in urban agglomerations of ecologically fragile areas in China at a fine scale. In this study, we constructed a quantitative and visualized evaluation system of LUFs that conforms to three primary functions, ten sub-functions, and nineteen indicators of Lanzhou-Xining urban agglomeration (LXUA) in the upper reaches of the Yellow River basin based on the production-living- ecological functions. Then, the comprehensive evaluation method, hot spot analysis, and geographic weighted regression (GWR) model were used to identify the interactions among LUFs and influencing factors of LXUA from 2000 to 2020 at the county and grid scales. The results show that the LXUA is dominated by ecological function (EF), and EF and living function (LF) showing a “U” shaped change feature, while production function (PF) show an inverted “U” shaped change feature. Meanwhile, the cold spots and hot spots of PF and EF present the spatial characteristic of “overall dispersion and local aggregation”, while the LF has no cold spots, and the hot spots present the spatial characteristic of “point-axis”. Moreover, the PF and LF are in a synergistic relationship at two scales, with complementarity in space, while the EF and PF and the PF and LF are both in a trade-offs relationship, with overlap in space. Finally, socioeconomic development factors have remarkable impact on LUFs at the county scale, while LUFs at the grid scale is the comprehensive result of natural conditions, socioeconomic factors, accessibility and political factors. The results can provide references for the LXUA to differentiated design the land use policy, and provide empirical case for other ecologically fragile areas to alleviate the trade-offs of LUFs.

Quantum mechanics has withstood every experimental test thus far. However, it relies on ad-hoc postulates which require experimental verification. Over the past decade there has been a great deal of research testing these postulates, with numerous tests of Born’s rule for determining probabilities and the complex nature of the Hilbert space being carried out. Although these tests are yet to reveal any significant deviation from textbook quantum theory, it remains important to conduct such tests in different configurations and using different quantum states. Here we perform the first such test using coherent states of light in a three-arm interferometer combined with homodyne detection. Our proposed configuration requires additional assumptions, but importantly allows us to use quantum states which exist in a larger Hilbert space compared to previous tests. For testing Born’s rule, we find that the third order interference is bounded to be $\kappa = 0.002\pm0.004$ and for testing whether quantum mechanics is complex or not we find a Peres parameter of $F = 1.0000 \pm 0.0003$ ( F = 1 corresponds to the expected complex quantum mechanics). We also design and implement a test of Glauber’s theory of optical coherence.

Classification is one of the main applications of supervised learning. Recent advancements in developing quantum computers have opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely, bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilizing shallow circuits to solve complex classification problems.