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Abstract Background The extract from Metasequoia glyptostroboides Hu et Cheng, a rare and endangered species native to China, exhibits numerous biological and pharmacological activities. The species is recalcitrant to rooting during micropropagation, a challenge that has yet to be resolved. In this study, transcriptomic and hormonal analyses were conducted to appreciate the molecular mechanism of adventitious root (AR) formation in optimized rooting conditions. Results The use of 2/5-strength Woody Plant Medium (WPM) significantly promoted AR formation of M. glyptostroboides shoots while the content of endogenous auxin, cytokinins and gibberellins (GAs) varied at different stages of AR formation. Transcriptomic analysis showed the significant up- or down-regulation of differentially expressed genes (DEGs) associated with plant hormone signal transduction and the phenylpropanoid biosynthesis pathway in response to 2/5-strength WPM. DEGs related to the biosynthesis of indole-3-acetic acid, cytokinins and GAs were identified. Transcript factors involved in 13 families were also revealed. A weighted gene co-expression network analysis indicated a strong correlation between hormones and genes involved in plant hormone signal transduction and the phenylpropanoid biosynthetic pathway. Conclusions These results indicate that the AR-promoting potential of 2/5-strength WPM in M. glyptostroboides was due to complex interactions between hormones and the expression of genes related to plant hormone signal transduction and the phenylpropanoid biosynthetic pathway.

Abstract Understanding the training dynamics of quantum neural networks is a fundamental task in quantum information science with wide impact in physics, chemistry and machine learning. In this work, we show that the late-time training dynamics of quantum neural networks with a quadratic loss function can be described by the generalized Lotka-Volterra equations, leading to a transcritical bifurcation transition in the dynamics. When the targeted value of loss function crosses the minimum achievable value from above to below, the dynamics evolve from a frozen-kernel dynamics to a frozen-error dynamics, showing a duality between the quantum neural tangent kernel and the total error. In both regions, the convergence towards the fixed point is exponential, while at the critical point becomes polynomial. We provide a non-perturbative analytical theory to explain the transition via a restricted Haar ensemble at late time, when the output state approaches the steady state. Via mapping the Hessian to an effective Hamiltonian, we also identify a linearly vanishing gap at the transition point. Compared with the linear loss function, we show that a quadratic loss function within the frozen-error dynamics enables a speedup in the training convergence. The theory findings are verified experimentally on IBM quantum devices.

Abstract Hepatocellular carcinoma (HCC) is one of the leading causes of cancer‐related death worldwide and lacks biomarkers for personalized therapy. Herein, it is reported that MCB1 could be a novel oncofetal protein that is upregulated in the preneoplastic lesions and serum of early HCC patients. Functional studies reveal that MCB1 modulated p53 protein degradation to promote T‐IC generation and drive HCC initiation. Furthermore, the MCB1/p53 axis is shown to determine the responses of hepatoma cells to conventional chemotherapeutics and predict transcatheter arterial chemoembolization (TACE) benefits in patients. Importantly, MCB1 can mediate sorafenib/lenvatinib resistance by downregulating two essential drug targets fibroblast growth factor receptor 1 (FGFR1) and vascular endothelial growth factor receptor 3 (VEGFR3) expression in a proteasome‐dependent manner. Patient‐derived tumor organoids (PDOs), patient‐derived xenografts (PDXs), and patient cohorts analysis suggested that MCB1 levels in HCCs may determine the distinct responses to conventional therapeutics and targeted drugs. Furthermore, treatment of targeted drugs‐resistant HCC with adeno‐associated virus (AAV) targeting MCB1 or a proteasome inhibitor restores targeted drug response, suggesting their clinical significance in HCC combinational therapy. In conclusion, these findings demonstrate that MCB1 could act as a driver for HCC initiation, a contributor to drug resistance, and a biomarker for individualized HCC therapy.

Abstract Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with $${{{\mathcal{O}}}}(\log N)$$ O ( log N ) circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~107 in 1D, ~1015 to ~1020 in 2D, and ~1024 in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science.

Abstract The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

Ningxia is one of the well-known wine producing regions in China. However, the oenological and aroma characteristics of indigenous yeasts remains hidden. The fermentative and oenological properties including stress resistance, hydrogen sulfide, foam production levels; killer phenotype, and flocculation of 89 Ningxia indigenous Saccharomyces cerevisiae isolates and ten commercial yeasts were evaluated. The fermentative and oenological properties of the tested strains varied significantly. They could resist 500 g/L glucose, 300 mg/L SO2, 14% (v/v) ethanol and pH 2.8, and produce more esters. They also produce low levels of ethanol and could conduct fermentations vigorously and at a high rate. Cabernet Sauvignon wines made with NXU 21–24 showed the high intensity of tropical fruit, dry fruit, temperate fruit, and spicy flavor. The floral flavor in NXU 21–102 fermented wine is very intense. The indigenous S. cerevisiae strains of NXU 21–102 and NXU 21–24 exhibited potential use as starter cultures in wine production.

Euryodendron excelsum H. T. Chang, a rare and endangered evergreen tree that is endemic to China. The micropropagation system of this species has been established, but some challenges associated with in vitro rooting remained to be improved. In this study, the in vitro rooting of E. excelsum plantlets were optimized by dark exposure, and the network of gene expression and endogenous hormones levels during dark-induced adventitious root (AR) formation were revealed. AR formation of E. excelsum plantlets were significantly promoted by dark exposure, especially by dark exposure for 15 d. In the stems of E. excelsum plantlets under the treatment of dark exposure for 15 d, lower level of abscisic acid (ABA), gibberellic acid 1 (GA1), isopentenyladenine (IP), isopentenyladenosine (IPA) and zeatin (ZT), as well as higher level of GA7, jasmonic acid (JA) and salicylic acid (SA), promoted the whole course of AR formation. The higher level of trans-zeatin riboside (TZR) and T-zeatin (TZT) promoted the elongation of dark-induced AR, while higher level of indole-3-acetic acid (IAA) stimulated the process of AR primordia formation. Differentially expressed genes (DEGs) involved in hormone biosynthesis, plant hormone signal transduction and phenylpropanoid biosynthesis participated in the regulation of dark-induced AR development. The weighted gene co-expression network (WGCNA) analysis identified five modules that had highly correlation with phytohormone contents, and numerous hub genes associated with carotenoid biosynthesis, tryptophan metabolism, zeatin biosynthesis, alpha-Linolenic acid metabolism, phenylalanine metabolism, plant hormone signal transduction and phenylpropanoid biosynthesis were revealed. Those result will provide technical reference for in vitro rooting of woody species, and promote biological conservation and genetic engineering of rare and endangered species.

Soil salinity, a major environmental stress, restricts agricultural production worldwide. Gynura divaricata (L.) DC. is widely cultivated on tropical islands in China and has both edible and medicinal value. NaCl stress and growth indicators, antioxidant enzyme activity, as well as MDA, proline, and soluble sugar content, were determined. Based on the transcriptomic data of tissue-cultured G. divaricata plantlets grown in control (0 mM NaCl) and salt stress (50 and 200 mM NaCl) conditions, gene expression patterns were examined. KEGG enrichment analysis of differentially expressed genes indicated significant enrichment of plant hormone signaling, MAPK signaling, and starch and sucrose metabolism pathways. These findings allowed key biological pathways and salt stress-responsive genes to be identified, thus providing a molecular basis for breeding salt-tolerant G. divaricata varieties. This transcriptomic analysis revealed a complex tolerance mechanism of G. divaricata in response to NaCl, laying a foundation for screening and cloning key genes related to NaCl tolerance and studying their interactions. These findings would allow the molecular basis of NaCl tolerance to be explored, with the aim of breeding NaCl-tolerant varieties.

Aquaculture contributes significantly to the global economy and has become the key to global food security and nutrition strategies. In order to supply sustainable and equitable aquatic food, studies on aquaculture genomics, genetics and selective breeding are required. Genomic selection (GS) captures diversity and estimates breeding values based on genome-wide distributed markers enhancing aquaculture production efficiency, sustainability, product quality, and profitability. However, the application of GS in aquaculture is still in its initial stage compared with that in plants and livestock. The complex preprocessing, quality control and postprocessing steps, complicated statistical models, fussy file format conversions between various interfaces and frequent switches among different running environments prevent smooth and large-scale applications. In this study, we have developed AquaGS, an open-source Graphic User Interface (GUI) Genomic Selection pipeline offering click-by-click running from inputting raw data for phenotype and genotype to the final mate allocation scheme. AquaGS is a C++ based application that uses QT to create a GUI and integrates the functions needed for the GS process by calling various programs and specific tools in the background, such as C++, Python, R, PLINK and AlphaMate. AquaGS includes phenotype preprocessing, quality control, testing the significance of effects, breeding value predictions, cross-validation and mating allocation scheme generation, integrated from widely-used standalone methods and tools, such as BLUP, GBLUP, SSBLUP, Bayes A, Bayes B, Bayes Cπ, and Bayesian Lasso model. Additionally, AquaGS includes a mating design module based on optimum contribution selection to avoid inbreeding depression and maximize genetic gains. To accommodate various application scenarios, AquaGS offers a flexible, interactive and customized processing pipeline.

Abstract Background Glutamatergic function abnormalities have been implicated in the etiology of treatment-resistant schizophrenia (TRS), and the efficacy of clozapine may be attributed to its impact on the glutamate system. Recently, evidence has emerged suggesting the involvement of immune processes and increased prevalence of antineuronal antibodies in TRS. This current study aimed to investigate the levels of multiple anti-glutamate receptor antibodies in TRS and explore the effects of clozapine on these antibody levels. Methods Enzyme linked immunosorbent assay (ELISA) was used to measure and compare the levels of anti-glutamate receptor antibodies (NMDAR, AMPAR, mGlur3, mGluR5) in clozapine-treated TRS patients (TRS-C, n = 37), clozapine-naïve TRS patients (TRS-NC, n = 39), and non-TRS patients (nTRS, n = 35). Clinical symptom severity was assessed using the Positive and Negative Symptom Scale (PANSS), while cognitive function was evaluated using the MATRICS Consensus Cognitive Battery (MCCB). Result The levels of all four glutamate receptor antibodies in TRS-NC were significantly higher than those in nTRS (p

Abstract System specific neural force fields (NFFs) have gained popularity in computational chemistry. One of the most popular datasets as a bencharmk to develop NFF models is the MD17 dataset and its subsequent extension. These datasets comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampled from direct adiabatic dynamics. However, many chemical reactions involve significant molecular geometrical deformations, for example, bond breaking. Therefore, MD17 is inadequate to represent a chemical reaction. To address this limitation in MD17, we introduce a new dataset, called Extended Excited-state Molecular Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from direct nonadiabatic dynamics, and the energies are computed at both multireference wavefunction theory and density functional theory. We show that the xxMD dataset involves diverse geometries which represent chemical reactions. Assessment of NFF models on xxMD dataset reveals significantly higher predictive errors than those reported for MD17 and its variants. This work underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability.

The generation of k-designs (pseudorandom distributions that emulate the Haar measure up to k moments) with local quantum circuit ensembles is a problem of fundamental importance in quantum information and physics. Despite the extensive understanding of this problem for ordinary random circuits, the crucial situations in which symmetries or conservation laws are in play are known to pose fundamental challenges and remain little understood. Here, we construct explicit local unitary ensembles that can achieve high-order unitary k-designs under transversal continuous symmetry, in the particularly important SU(d) case. Specifically, we define the convolutional quantum alternating (CQA) group generated by 4-local SU(d)-symmetric Hamiltonians as well as associated 4-local SU(d)-symmetric random unitary circuit ensembles and prove that they form and converge to SU(d)-symmetric k-designs, respectively, for all k

At present, research on aerial spraying operations with UAVs mainly focuses on the deposition outcomes of droplets, with insufficient depth in the exploration of the movement process of droplet deposition. The movement characteristics of droplet deposition as the most fundamental factors affecting the effectiveness of pesticide application by UAVs are of great significance for improving droplet deposition. This study takes flat spray nozzles as the research object, uses the Particle Image Velocimetry (PIV) technique to obtain movement data of water droplet deposition under the influence of rotor flow fields, and investigates the variation characteristics of droplet deposition speed under different influencing factors. The results show that the deposition speed and the distribution area of high-speed (>12 m/s) particles increase with the increase of rotor speed, spraying pressure, and nozzle size. When the rotor speed increases from 0 r/min to 1800 r/min, the average increase in maximum droplet deposition speed for nozzle models LU120-02, LU120-03 and LU120-04 is 33.26%, 19.02%, and 7.62%, respectively. The rotor flow field significantly increases the number of high-speed droplets, making the dispersed droplet velocity distribution more concentrated. When the rotor speed is 0, 1000, 1500, and 1800 r/min, the average decay rates of droplet deposition speed are 36.72%, 20.00%, 15.47%, and 13.21%, respectively, indicating that the rotor flow field helps to reduce the decrease in droplet deposition speed, enabling droplets to deposit on the target area at a higher speed, reducing drift risk and evaporation loss. This study’s results are beneficial for revealing the mechanism of droplet deposition movement in aerial spraying by plant protection UAVs, improving the understanding of droplet movement, and providing data support and guidance for precise spraying operations.

Purpose: Current pharmacological treatments for Ulcerative Colitis (UC) have limitations. Therefore, it is important to elucidate any available alternative or complementary treatment, and Chinese herbal medicine shows the potential for such treatment. As a traditional Chinese herbal medicine, Danshen-related preparations have been reported to be beneficial for UC by improving coagulation function and inhibiting inflammatory responses. In spite of this, the credibility and safety of this practice are incomplete. Therefore, in order to investigate whether Danshen preparation (DSP) is effective and safe in the treatment of UC, we conducted a systematic review and meta-analysis.Methods: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database and CQVIP Database were searched for this review.The main observation indexes were the effect of DSP combined with mesalazine or DSP on the effective rate, platelet count (PLT), mean platelet volume (MPV) and C-reactive protein (CRP) of UC. The Cochrane risk of bias tool was used to assess the risk of bias. The selected studies were evaluated for quality and data processing using RevMan5.4 and Stata17.0 software.Results: A total of 37 studies were included. Among them, 26 clinical trials with 2426 patients were included and 11 animal experimental studies involving 208 animals were included. Meta-analysis results showed that compared with mesalazine alone, combined use of DSP can clearly improve the clinical effective rate (RR 0.86%, 95% CI:0.83–0.88, p < 0.00001) of UC. Furthermore it improved blood coagulation function by decreasing serum PLT and increasing MPV levels, and controlled inflammatory responses by reducing serum CRP, TNF-α, IL-6, and IL-8 levels in patients.Conclusion: Combining DSP with mesalazine for UC can enhance clinical efficacy. However, caution should be exercised in interpreting the results of this review due to its flaws, such as allocation concealment and uncertainty resulting from the blinding of the study.Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/myprospero.php, identifier PROSPERO: CRD42022293287

Gene therapy has been extensively investigated and widely used in biomedical fields, such as cancer treatment. However, the most important issues for gene therapy are stability, targeting effect, transfection efficacy, and safety of gene formulation after administration, which seriously limit the further application of gene therapy in clinic. Therefore, gene delivery could be a promising strategy for overcoming these challenges. Two-dimensional (2D) materials are rising nanomaterials with excellent physical and chemical properties, including large specific surface area, easy modification, high conversion efficiency of light, and good biocompatibility, which have achieved promising applications as vehicles for gene delivery in the disease treatment. In this review, we first summarized the research progress of 2D material-based nanosystems for gene delivery to improve the therapeutic efficacy. We discussed that 2D material-based gene delivery nanosystems showed high therapeutic efficacy for many diseases treatment, especially cancer. Furthermore, we also proposed that surface modification of 2D materials might be a promising strategy to prepare multi-functional gene carriers for combination therapy with enhanced treatment efficacy. Finally, the future research progress, challenges, and prospects of 2D material-based nanosystems for gene therapy were discussed and concluded. Conclusively, we believe that 2D material-based nanosystems with good biocompatibility and high transfection efficiency would be potentially used in clinical settings to improve the therapeutic efficacy of gene therapy.

Abstract Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as $${{{{{{{\mathcal{O}}}}}}}}({T}^{2}\times {{{{{{{\rm{polylog}}}}}}}}(n))$$ O ( T 2 × polylog ( n ) ) , where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

Due to the strong unsteadiness of pulse detonation, large flow losses are generated when the detonation wave interacts with the turbine blades, resulting in low turbine efficiency. Considering that the flow losses are dissipated into the gas as heat energy, some of them can be recycled during the expansion process in subsequent stages by the reheat effect, which should be helpful to improve the detonation-driven turbine efficiency. Taking this into account, this paper developed a numerical model of the detonation chamber coupled with a two-stage axial turbine, and a stoichiometric hydrogen-air mixture was used. The improvement in turbine efficiency attributable to the reheat effect was calculated by comparing the average efficiency of the stages with the efficiency of the two-stage turbine. The research indicated that the first stage was critical in suppressing the flow unsteadiness caused by pulse detonation, which stabilized the intake condition of the second stage and consequently allowed much of the flow losses from the first stage to be recycled, so that the efficiency of the two-stage turbine was improved. At a 95% confidence level, the efficiency improvement was stable at 4.5%–5.3%, demonstrating that the reheat effect is significant in improving the efficiency of the detonation-driven turbine.

Abstract Background Several factors can influence the risk of dental caries, among which dietary factors have a significance impact on the occurrence of dental caries. The limitation of current studies is that they only focus on the influence of individual foods on the risk of dental caries. This study use cluster analysis to examine the relationship between dietary patterns and dental caries experience among adolescents aged 12–15. Methods Based on data from the first oral epidemic survey in Shanxi Province, a cross-sectional study was conducted among 11,351 adolescents aged 12–15 in Shanxi Province through oral examination and questionnaires. The questionnaire included the intake frequency of seven types of food. Descriptive statistics, cluster analysis, and multinomial logistic regression were used to analyze the association between dietary patterns and dental caries experience. Results The prevalence rate of caries was 44.57% and the mean DMFT score was 0.98 ± 1.49 in adolescents aged 12–15 in Shanxi Province. The caries rate was higher in females than males (X 2 = 103.59, P

Abstract Background Fractures are the most common orthopedic diseases. It is known that static magnetic fields (SMFs) can contribute to the maintenance of bone health. However, the effect and mechanism of SMFs on fracture is still unclear. This study is aim to investigate the effect of moderate static magnetic fields (MMFs) on bone structure and metabolism during fracture healing. Methods Eight-week-old male C57BL/6J mice were subjected to a unilateral open transverse tibial fracture, and following treatment under geomagnetic field (GMF) or MMF. The micro-computed tomography (Micro-CT) and three-point bending were employed to evaluate the microarchitecture and mechanical properties. Endochondral ossification and bone remodeling were evaluated by bone histomorphometric and serum biochemical assay. In addition, the atomic absorption spectroscopy and ELISA were utilized to examine the influence of MMF exposure on iron metabolism in mice. Results MMF exposure increased bone mineral density (BMD), bone volume per tissue volume (BV/TV), mechanical properties, and proportion of mineralized bone matrix of the callus during fracture healing. MMF exposure reduced the proportion of cartilage in the callus area during fracture healing. Meanwhile, MMF exposure increased the number of osteoblasts in callus on the 14th day, and reduced the number of osteoclasts on the 28th day of fracture healing. Furthermore, MMF exposure increased PINP and OCN levels, and reduced the TRAP-5b and β-CTX levels in serum. It was also observed that MMF exposure reduced the iron content in the liver and callus, as well as serum ferritin levels while elevating the serum hepcidin concentration. Conclusions MMF exposure could accelerate fracture healing via promote the endochondral ossification and bone formation while regulating systemic iron metabolism during fracture healing. This study suggests that MMF may have the potential to become a form of physical therapy for fractures.

Diabetes is a common chronic metabolic disease. Different types of drugs play important roles in controlling diabetes and its complications, but there are some limitations. The glucose-responsive drug delivery system is a novel technology with potential in diabetes treatment. It could automatically release drugs in response to changes in glucose levels in the body to maintain blood glucose within a normal range. The emergence of a glucose-sensitive drug delivery system provides a more intelligent and precise way to treat diabetes. The review is carried out according to the Preferred Reporting Items for Systematic Reviews (PRISMA 2020) guidelines This review focuses on the recent advances in the drugs and different systems of glucose-sensitive drug delivery, including glucose oxidase, phenylboronic acid, Concanavalin A, and other glucose-reactive systems. Furthermore, the glucose-responsive drug delivery system combined with the application applied in hydrogels, microneedles, and nanoparticles is also explored and summarized. The new platforms to sustain the release of anti-diabetic drugs could be desirable for patients. It could lead to increased adherence and glycemic outcomes for the detection and treatment of diabetes. Furthermore, given the limitations of glucose-responsive drug delivery systems, solutions and perspectives are proposed to help the understanding and application of these systems. This review will be helpful for drug discovery and treatment of diabetes from a new perspective.

With the rapid development of chemical engineering and biotechnology, polypeptide, as a promising candidate in the biomedical field, has been thoroughly investigated and extensively used as the drug delivery vehicle for diseases treatment, especially cancer, owing to the high biocompatibility, good biodegradability, versatile constructions, and diverse functions. Engineered polypeptide-based drug delivery system (so-called EPP-DDS) can deliver the cargos to the target site via a specific recognition effect, followed by overcoming the barriers like blood brain barrier (BBB) and releasing them by responding to the microenvironment cues, to improve the therapeutic efficacy and reduce the side-effect. Herein, it's of great importance to conclude and summarize the updates on EPP-DDS developed by chemical engineering methods. In this review, we first summarized the recent updates in the manufacturing of polypeptide and preparation of EPP-DDS based on green biochemical engineering and/or synthetic processes for cancer therapy, including chemotherapy, immunotherapy, photodynamic therapy (PDT), gene therapy, and combination therapy. Then, we surveyed the research progress of inflammation-mediated cancer treatment strategies based on EPP-DDS with high anti-inflammation activity. Finally, we concluded the discovery and green production process of engineered polypeptide, challenges, and perspectives of EPP-DDS. Overall, the EPP-DDS has great potential for cancer therapy in the clinic with improved therapeutic efficacy and reduced adverse effect, which needs the innovation of green biochemical engineering for customized design and production of polypeptides.

Abstract Background Low-grade inflammation and altered inflammatory markers have been observed in treatment-resistant schizophrenia (TRS). Interleukin-6 (IL-6) is one of the pro-inflammatory cytokines linked with TRS and receives increasing attention. Previous studies showed that patients with TRS might have higher IL-6 levels compared with healthy individuals and treatment-responsive patients. Besides, emerging evidence has suggested that there are sex differences in the associations between IL-6 levels and various illnesses, including chronic hepatitis C, metabolic syndrome, etc.; however, there is limited study on TRS. In this present study, we aimed to compare the serum IL-6 levels of TRS and partially responsive schizophrenia (PRS) and explore potential sex differences in the association of TRS and IL-6 levels. Methods The study population consisted of a total of 90 patients with schizophrenia: 64 TRS patients (45.3% males and 54.7% females) and 26 PRS patients (46.2% males and 53.8% females). We measured serum IL-6 levels using enzyme-linked immunosorbent assay (ELISA) and analyzed them separately by gender, controlling for confounders (age, education, medication, body mass index, and PANSS scores) rigorously. Result The results showed that patients with TRS had higher serum IL-6 levels than patients with PRS (p = 0.002). In females, IL-6 levels increased significantly in the TRS group compared with the PRS group (p = 0.005). And a positive correlation tendency was observed between IL-6 levels and PANSS general sub-scores (r = 0.31, p = 0.039), although this correlation was not significant after correcting for multiple comparisons. Whereas, there were no differences in IL-6 levels between the TRS and PRS (p = 0.124) in males. Conclusion Our findings provided evidence supporting the hypothesis that the inflammatory response system (IRS) may play a role in the pathogenesis of TRS in a sex-dependent manner. In addition, sex differences in the immune dysfunction of individuals with schizophrenia cannot be neglected, and inflammation in male and female TRS should be discussed separately.

Abstract Voltage-gated sodium (NaV) channels are critical regulators of neuronal excitability and are targeted by many toxins that directly interact with the pore-forming α subunit, typically via extracellular loops of the voltage-sensing domains, or residues forming part of the pore domain. Excelsatoxin A (ExTxA), a pain-causing knottin peptide from the Australian stinging tree Dendrocnide excelsa, is the first reported plant-derived NaV channel modulating peptide toxin. Here we show that TMEM233, a member of the dispanin family of transmembrane proteins expressed in sensory neurons, is essential for pharmacological activity of ExTxA at NaV channels, and that co-expression of TMEM233 modulates the gating properties of NaV1.7. These findings identify TMEM233 as a previously unknown NaV1.7-interacting protein, position TMEM233 and the dispanins as accessory proteins that are indispensable for toxin-mediated effects on NaV channel gating, and provide important insights into the function of NaV channels in sensory neurons.

With the increasing demand of mobile users for high-speed data transmission services, the problem of insufficient capacity coverage of mobile communication systems in local areas is becoming increasingly prominent.Aiming to eliminate the capacity coverage hole and overcome the complex interference of the network, the resource cell coverage structure was proposed.The coverage structure has the characteristics of elastic scalability and is capable of providing on-demand energy-efficient coverage.Based on the interference management, a basic spatial unit for capacity coverage and energy coverage was formed, and the mutual adaptation of service distribution, energy distribution and coverage structure were realized.Relying on the above coverage structure, a resource cell generation (RCG) method was proposed to enhance capacity coverage.Through dynamically adjusting the coverage structure and providing on-demand network resources, the RCG method could achieve efficient capacity coverage for the uneven service distribution under uniform access point deployment.Simulation and experimental results show that, compared with the traditional static coverage method, the RCG method eliminates the capacity coverage hole, improves the network throughput, and enhances the capacity coverage capability of the network.

Insect toxins with tandem repeats of neurotoxin domains have been found with enhanced receptor avidity. Here, the authors describe a bivalent toxin from remipede venom that targets ryanodine receptors, a rare target for animal venoms.

Gut microbiome (GM) is closely related to the overall health status for the human being. The dysfunction of microbiome can lead to many diseases such as inflammation, cancer and neurodegenerative disease. Therefore, it's of great importance to develop effective strategy to regulate GM. Gut lumen-targeted oral delivery system (GLT-ODS) has been extensively investigated and widely used in food science and engineering in these years, due to the targeted delivery property, controlled release profile, high biocompatibility and enhanced bioavailability of cargos. Herein, we comprehensively summarized the current advances in GLT-ODS for bioactive agent. Specifically, we systematically summarize the GLT-ODS for pre-agents (prebiotics and probiotics) and anti-agents (antibiotics and bacteriophages) according to the type of cargos. Through in-depth discussion of representative researches, we refined the limitations of existing research in carrier material and target selection, and drew a blueprint for the future technological research and development. We believe that GLT-ODS will become a safe, efficient, simple and precise GM management strategy for improvement of health and shine in the development of precision food and pharmaceutical engineering in future.

Abstract Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.

Hemorrhagic stroke (HS) is associated with severe morbidity and high mortality. Identifying the trigger factors for HS is critical for disease prevention. This study aimed to assess the associations between short-term environmental triggers (STETs) and HS risk. We systematically searched six databases for articles published up to September 9, 2022. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using random-effect models to evaluate the associations between STETs and the risk of HS. Heterogeneity was assessed using Cochran Q and I2 tests. A total of 63 studies were included for analysis. Of these, 40 focused on air pollutants and 23 on meteorological factors. Pooling results showed that exposure to particulate matter 2.5 (PM2.5; OR, 1.003 per 10 μg/m3; 95% CI, 1.001–1.007), sulfur dioxide (SO2; OR, 1.022 per 10 ppb; 95% CI, 1.005–1.040), and nitrogen dioxide (NO2; OR, 1.026 per 10 ppb; 95% CI, 1.004–1.047) was associated with an increase in HS risk. Moreover, exposure to PM2.5 (OR, 1.018 per 10 μg/m3; 95% CI, 1.009–1.027) and SO2 (OR, 1.102 per 10 ppb; 95% CI, 1.010–1.204) was positively associated with the risk of intracerebral hemorrhage. In addition, extreme temperature, high pressures, high and low relative humidity were potentially associated with HS risk. Targeted preventive measures to limit the effect of these air pollutants and extreme meteorological factors should be taken to reduce the HS disease burden. Further studies are warranted to verify these findings.

Diabetes mellitus (DM) is a common chronic metabolic disease worldwide. The disturbance of the gut microbiota has a complex influence on the development of DM. Polysaccharides are one type of the most important natural components with anti-diabetic effects. Gut microbiota can participate in the fermentation of polysaccharides, and through this, polysaccharides regulate the gut microbiota and improve DM. This review begins by a summary of the sources, anti-diabetic effects and the gut microbiota regulation functions of natural polysaccharides. Then, the mechanisms of polysaccharides in regulating the gut microbiota to exert anti-diabetic effects and the structure–activity relationship are summarized. It is found that polysaccharides from plants, fungi, and marine organisms show great hypoglycemic activities and the gut microbiota regulation functions. The mechanisms mainly include repairing the gut burrier, reshaping gut microbiota composition, changing the metabolites, regulating anti-inflammatory activity and immune function, and regulating the signal pathways. Structural characteristics of polysaccharides, such as monosaccharide composition, molecular weight, and type of glycosidic linkage, show great influence on the anti-diabetic activity of polysaccharides. This review provides a reference for the exploration and development of the anti-diabetic effects of polysaccharides.

To address the issue of reduced yield in the second season caused by damaged stubbles resulting from being compressed during the harvesting process of the first season’s ratoon rice, a device for rectifying the compressed stubbles was designed. Utilizing the DEM-MBD coupling simulation method, a simulation analysis was conducted to determine the range of key parameters and verify the feasibility of the solution. Using rotational speed, forward speed, and stubble entry angle as experimental factors and stubble rectification rate and second-season yield as evaluation metrics, a three-factor, three-level Box–Behnken response surface field trial was conducted. The theoretically optimal working parameter combination was found to be a forward speed of 1.4 m/s, device rotational speed of 75 rpm, and stubble entry angle of 39°. Under these conditions, three parallel experiments were performed, resulting in a rectification rate of 90.35% in the mechanically harvested and compressed area and a second-season yield of 2202.64 ± 35 kg/hm2. The deviation from the numerical simulation results of parameter optimization was less than 5%. These findings suggest that the designed stubble rectification device for ratoon rice can meet the requirements of stubble rectification during the first-season harvest of ratoon rice. Furthermore, it provides valuable insights for reducing harvest losses in the first season and further improving the level of mechanized harvesting for ratoon rice.

Abstract Background Coronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends. Methods HFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation. Results HFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic. Conclusions The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.

Abstract Background Our previous study explored Amphotericin B (AMB) plus 5-flucytosine (5-FC) combined with fluconazole (FLU) therapy in the induction period, which seemed to be better than the previous AMB + 5-FC antifungal therapy in non-HIV and non-transplant-associated CM. However, based on our clinical finding, the outcomes of some CM patients who received AMB plus 5-FC combined with FLU antifungal therapy were still poor. Therefore, we need to explore new antifungal methods in non-HIV and non-transplant-associated CM during the induction period. Methods Clinical data from 148 patients admitted to the Third Affiliated Hospital of Sun Yat Sen University from January 2011 to December 2020 were collected. These patients were stratified based on antifungal treatment methods in the induction period (group I with AMB + 5-FC + VOR, group II with AMB + 5-FC + FLU, group III with AMB + 5-FC). Results The first hospitalization time of Group I (median: 25 days, IQR: 20–34.5) was significantly shorter than that of Group II (median: 43 days, IQR: 29–62) (p

Abstract Aquaculture is one of the world's fastest‐growing and most traded food industries, but it is under the threat of climate‐related risks represented by global warming, marine heatwave (MHW) events, ocean acidification, and deoxygenation. For the sustainable development of aquaculture, selective breeding may be a viable method to obtain aquatic economic species with greater tolerance to environmental stressors. In this study, we estimated the heritability of heat tolerance trait of Pacific abalone Haliotis discus hannai, performed genome‐wide association studies (GWAS) analysis for heat tolerance to detect single nucleotide polymorphisms (SNPs) and candidate genes, and assessed the potential of genomic selection (GS) in the breeding of abalone industry. A total of 1120 individuals were phenotyped for their heat tolerance and genotyped with 64,788 quality‐controlled SNPs. The heritability of heat tolerance was moderate (0.35–0.42) and the predictive accuracy estimated using BayesB (0.55 ± 0.05) was higher than that using GBLUP (0.40 ± 0.01). A total of 11 genome‐wide significant SNPs and 2 suggestive SNPs were associated with heat tolerance of abalone, and 13 candidate genes were identified, including got2,znfx1,l(2)efl, and lrp5. Based on GWAS results, the prediction accuracy using the top 5K SNPs was higher than that using randomly selected SNPs and higher than that using all SNPs. These results suggest that GS is an efficient approach for improving the heat tolerance of abalone and pave the way for abalone selecting breeding programs in rapidly changing oceans.

We define laziness to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We discuss the difference between laziness and barren plateau in quantum machine learning created by quantum physicists in McClean et al (2018 Nat. Commun. 9 1–6) for the flatness of the loss function landscape during gradient descent. We address a novel theoretical understanding of those two phenomena in light of the theory of neural tangent kernels. For noiseless quantum circuits, without the measurement noise, the loss function landscape is complicated in the overparametrized regime with a large number of trainable variational angles. Instead, around a random starting point in optimization, there are large numbers of local minima that are good enough and could minimize the mean square loss function, where we still have quantum laziness, but we do not have barren plateaus. However, the complicated landscape is not visible within a limited number of iterations, and low precision in quantum control and quantum sensing. Moreover, we look at the effect of noises during optimization by assuming intuitive noise models, and show that variational quantum algorithms are noise-resilient in the overparametrization regime. Our work precisely reformulates the quantum barren plateau statement towards a precision statement and justifies the statement in certain noise models, injects new hope toward near-term variational quantum algorithms, and provides theoretical connections toward classical machine learning. Our paper provides conceptual perspectives about quantum barren plateaus, together with discussions about the gradient descent dynamics in Liu et al (2023 Phys. Rev. Lett. 130 150601).

Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining parsimony and equivariance becomes increasingly challenging. In this paper, we propose using fusion diagrams, a technique widely employed in simulating SU(2)-symmetric quantum many-body problems, to design new spatial equivariant components for neural networks. This results in a diagrammatic approach to constructing novel neural network architectures. When applied to particles within a given local neighborhood, the resulting components, which we term ‘fusion blocks,’ serve as universal approximators of any continuous equivariant function defined on the neighborhood. We incorporate a fusion block into pre-existing equivariant architectures (Cormorant and MACE), leading to improved performance with fewer parameters on a range of challenging chemical problems. Furthermore, we apply group-equivariant neural networks to study non-adiabatic molecular dynamics of stilbene cis-trans isomerization. Our approach, which combines tensor networks with equivariant neural networks, suggests a potentially fruitful direction for designing more expressive equivariant neural networks.

Infrared and visible image fusion aims to integrate complementary information from the same scene images captured by different types of sensors into one image to obtain a fusion image with richer information. Recently, deep learning-based infrared and visible image fusion methods have been widely used. However, it is still a difficult problem how to maintain the edge detail information in the source images more effectively. To address this problem, we propose a novel infrared and visible image fusion method with edge detail implantation. The proposed method no longer improves the performance of edge details in the fused image through making the extracted features contain edge detail information like traditional methods, but by processing source image information and edge detail information separately, and supplementing edge details to the main framework. Technically, we propose a two-branch feature representation framework. One branch is used to directly extract features from the input source image, while the other is utilized to extract features of edge map. The edge detail branch mainly provides edge detail features for the source image input branch, ensuring that the output features contain rich edge detail information. In the fusion of multi-source features, we respectively fuse the source image features and the edge detail features, and use the fusion results of edge details to guide and enhance the fusion results of source image features so that they contain richer edge detail information. A large number of experimental results demonstrate the effectiveness of the proposed method.

Objectives: Rupture of a brain arteriovenous malformation (bAVM) can cause intracranial hemorrhage and severe clinical outcomes. At present, the mechanisms of bAVM-related hemorrhage are poorly understood. This study aimed to summarize the potential genetic risk factors for bAVM-related hemorrhage and appraise the methodological quality of existing genetic studies on bAVM-related hemorrhage using a cross-sectional design.Methods: A systematic literature search was conducted on genetic studies associated with bAVM-related hemorrhage published in PubMed, Embase, Web of Science, China National Knowledge Internet, and Wangfang databases, up to November 2022. Subsequently, a cross-sectional study was performed to describe the potential candidate genetic variants of bAVM associated with risk of hemorrhage and to evaluate the methodological quality of the identified studies using the Newcastle–Ottawa quality assessment scale and Q-genie tool.Results: Of the 1811 records identified in the initial search, nine studies met the filtering criteria and were included. Twelve single nucleotide polymorphisms (SNPs), including IL6 rs1800795, IL17A rs2275913, MMP9 rs9509, VEGFA rs1547651, and EPHB4 rs314353, rs314308, and rs314313, were associated with bAVM-related hemorrhage. However, only 12.5% of the evaluated SNPs showed statistical power> 0.80 (α = 0.05). Methodological quality assessment revealed significant flaws in the designs of the included studies, such as less reliable representativeness of recruited individuals, short follow-up periods in cohort studies, and less comparability between groups of hemorrhagic and non-hemorrhagic patients.Conclusion:IL1B, IL6, IL17A, APOE, MMP9, VEGFA and EPHB4 were potentially associated with bAVM-related hemorrhage. The methodological designs of the analyzed studies required improvement in order to obtain more reliable results. Regional alliances and rare disease banks need to be established to recruit large numbers of bAVM patients (especially familial and extreme-trait cases) in a multicenter, prospective cohort study with an adequate follow-up period. Furthermore, it is important to use advanced sequencing techniques and efficient measures to filter candidate genetic variants.

BackgroundSleep quality among medical staff affects not only their own health but also the health of their patients. This study aimed to investigate the sociodemographic, occupational, and personal factors associated with sleep quality among medical staff in mainland China.MethodsAn online survey was conducted from January 10 to February 5, 2019, involving 3,684 medical staff (female: 84.9%; mean age: 31.6 ± 7.7; age range: 18–72). Sleep quality was measured by the Chinese version of the Pittsburgh Sleep Quality Index (C-PSQI). Sociodemographic, occupational characteristics, and personal lifestyle factors were measured by standard questions. Binary logistic regression analyses were used to determine the factors associated with sleep quality.Results57.9% (95% CI: 56.3–59.5%) of the study population experienced poor sleep quality (C-PSQI > 5). Binary logistic regression showed that poor sleep quality were associated with lower level of education, higher level of hospital care, longer weekly working hours, more than 30 min of cell phone use at bedtime, shift work (OR 1.33, 95% CI[1.12–1.58], P = 0.001), lack of regular naps (OR 1.46, 95% CI[1.26–1.69], P < 0.001) and lack of routine exercise (OR 1.69, 95% CI[1.46–1.97], P < 0.001).ConclusionsPoor sleep quality is highly prevalent among medical staff in mainland China. The findings indicate that appropriate strategies, such as implementing regular breaks, regulating overtime work and vacation interruptions, as well as developing exercise programs, relaxation training, and stress-management programs could help improve the sleep quality of medical staff.

Abstract We propose a new framework for simulating U(k) Yang-Mills theory on a universal quantum computer. This construction uses the orbifold lattice formulation proposed by Kaplan, Katz, and Unsal, who originally applied it to supersymmetric gauge theories. Our proposed approach yields a novel perspective on quantum simulation of quantum field theories, carrying certain advantages over the usual Kogut-Susskind formulation. We discuss the application of our constructions to computing static properties and real-time dynamics of Yang-Mills theories, from glueball measurements to AdS/CFT, making use of a variety of quantum information techniques including qubitization, quantum signal processing, Jordan-Lee-Preskill bounds, and shadow tomography. The generalizations to certain supersymmetric Yang-Mills theories appear to be straightforward, providing a path towards the quantum simulation of quantum gravity via holographic duality.

Abstract Background The aim of this study is to explore the spatial heterogeneity of the retention of PFS in children aged 7–9 years in Shanxi Province, North China and investigate the risk factors associated with PFS retention. Methods In this study, 937 children aged 7–9 years from Shanxi Province, China were randomly selected, all of whom had at least one first permanent tooth sealed with PFS in 2016. The children were surveyed after 12 months (in 2017) and 24 months (in 2018). The Geo-detector model was used to explore the spatial heterogeneity of the retention rate of PFS and analyze the influence and interactions of the ecological factors on PFS. Results 3299 teeth from 937 children were analyzed. The PFS full retention rates after 12 months (in 2017) and 24 months (in 2018) were 81.6% and 75.1%, respectively. The incidence of caries of the first molar was 2.1% after 12 months and 5.4% after 24 months. The spatial heterogeneity of the PFS retention rate after 24 months was significant, which was shown as the retention rate of PFS increased from north to south after 24 months. Remarkably, the natural environmental factors exerted greater influence than the socioeconomic and medical resources factors after 12 months, where the interaction of fluorine in water (FW) had the strongest explanatory power of 52% (P

For thousands of years, terracing has been one of the most important systems for preventing soil erosion, conserving water, and increasing agricultural production. Despite having a long history, the wide-ranging effects and mechanisms of terracing are poorly understood owing to large-scale spatial and temporal distribution patterns and the challenges related to assessing the ecosystem services of terraced landscapes. Thus, our study aims to review comprehensively the effect of terraces by describing the mechanisms behind terraced systems. Terraces provide many ecosystem services, including the reduction of runoff and sediment by over 41.9% and 52%, respectively, and the improvement of grain yields and soil moisture content by 44.8% and 12.9%, respectively. In addition, terracing can also contribute toward the conservation of plant biodiversity on a local scale. However, as terraces age, a number of disadvantages gradually emerge, including interference with water circulation and the development of serious environmental problems caused by poorly designed or mismanaged terraces, where the average runoff and soil loss can be 1–5 times that of well-managed terraces. Although understanding the complexity and multifaceted effects of terracing is vital for terrace construction and management, the negative effects of terracing are often not considered, and existing studies have several shortcomings. Within this context, this paper aims to describe both the benefits and disadvantages of terracing, investigate the gaps in current research, as well as discuss preventive and remedial measures so as to negate the possible bad impacts of terracing.

Bolbostemma paniculatum (Maxim.) Franquet is a unique species in China with a long history of medicinal use, which has the effects of detoxifying, dissolving lumps and dispersing swellings. And it is commonly used to treat many diseases, such as carbuncle and sore, acute mastitis, mammary cancer, scrofula and subcutaneous nodule traditionally. Modern clinical studies have found that B. paniculatum and its compounds can be used for the treatment of a variety of cancers, mastitis, hyperplasia of mammary glands, chronic lymphadenitis, cervical lymph tuberculosis and surgical wart skin diseases, and the curative effect is positive. At present, a variety of Chinese patent medicines containing B. paniculatum have been exploited and marketed in China for the treatment of cancers, breast diseases and flat warts. This review article comprehensively discussed the traditional application, botany, chemical components, pharmacological activities, and quality control of B. paniculatum, put forward some noteworthy issues and suggestions in current studies, and briefly discussed the possible development potential of this plant as well as future research perspectives. 96 compounds have been isolated from B. paniculatum, including triterpenoids, sterols, alkaloids and other components, of which triterpenoid saponins are the main bioactive components. The crude extracts and monomer compounds of B. paniculatum have a wide range of pharmacological activities, such as anti-tumor, antiviral, anti-inflammatory, immunoregulatory, and so on. Moreover, its anti-tumor mechanism involves many aspects, including inhibiting cell proliferation, promoting cell apoptosis, blocking the cell cycle, interfering with cell invasion and metastasis, suppressing angiogenesis, and regulating autophagy. While there is a lack of systematic and in-depth research on its anti-tumor active components and mechanism of action at the moment; and a tight connection between the chemical composition and pharmacological activity of B. paniculatum has also not been established. Besides, a systematic quality determination standard for B. paniculatum should also be built, in order to carry out further research.

The conversion of carbon dioxide (CO2) into high-value added energy fuels and chemicals (CO, formate, C2H4, etc.) through electrochemical reduction (eCO2R) is a promising avenue to sustainable development. However, low selectivity, barren activity and poor stability of the electrodes hinder the large-scale application of eCO2R. Herein, we reported a copper-indium-organic-framework (CuIn-MOF) based high-performance catalyst for eCO2R. Electrochemical measurement results reveal that CuIn-MOF exhibits high Faradaic efficiency (FE) of CO and formate (300 mV, FECO = 78.6% at −0.86 V vs. RHE, FEHCOO− = 48.4% at −1.16 V vs. RHE, respectively) in a broad range of current density (20.1–88.4 mA cm−2) with long-term stability (6 h) for eCO2R in 0.5 M KHCO3 electrolyte solution. Specifically, through anion-regulation engineering, SO42− anion precursor is more beneficial for the formic acid generation than NO3− anion precursor; while for SO42− anion precursor, Cu plays a positive regulating role in eCO2R to CO compared to In. Additionally, the high performance in a home-made eCO2R reactor derives benefit from enhanced intrinsic activity and charge re-distribution can be attributed to the formation of In-doped Cu layer.

Abstract We present a novel framework for simulating matrix models on a quantum computer. Supersymmetric matrix models have natural applications to superstring/M-theory and gravitational physics, in an appropriate limit of parameters. Furthermore, for certain states in the Berenstein-Maldacena-Nastase (BMN) matrix model, several supersymmetric quantum field theories dual to superstring/M-theory can be realized on a quantum device. Our prescription consists of four steps: regularization of the Hilbert space, adiabatic state preparation, simulation of real-time dynamics, and measurements. Regularization is performed for the BMN matrix model with the introduction of energy cut-off via the truncation in the Fock space. We use the Wan-Kim algorithm for fast digital adiabatic state preparation to prepare the low-energy eigenstates of this model as well as thermofield double state. Then, we provide an explicit construction for simulating real-time dynamics utilizing techniques of block-encoding, qubitization, and quantum signal processing. Lastly, we present a set of measurements and experiments that can be carried out on a quantum computer to further our understanding of superstring/M-theory beyond analytic results.

Soil erosion disturbs not only the global carbon cycle, but it is a complex environmental problem. Revegetation could reduce carbon emission by controlling soil erosion and increase carbon sequestration and accumulation by fixing carbon in vegetation and soil. The Grain for Green Program (GFGP) of China is the largest ecological restoration program in the world closely related to land-use/land-cover change (LUCC). Systematically assessing the carbon sequestration benefit of GFGP is crucial for a better understanding of the effects of implementing GFGP and providing reasonable vegetation management. Therefore, we selected the hilly red soil region (HRSR) of southern China as a study area, which is one of the main ecologically vulnerable areas in China. We assessed the carbon sequestration in the GFGP area of the HRSR using the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) model based on land-use/land-cover datasets during 2000–2015. Our results show that implementing GFGP is conducive to the enhancement of carbon sequestration services. Total carbon sequestration showed an increasing trend at a rate of 15.43% from 2000 to 2015. The most significant change mainly happened in Hunan Province with the shortest implementation time. The carbon sequestration of each carbon pool (namely aboveground biomass, belowground biomass, soil, and dead organic matter) also increased slightly. Additionally, for carbon sequestration, unused land converted to forest land is the most effective LUCC. This study can provide scientific support for the management and implementation of GFGP in the HRSR of China.

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in quantum information sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This Roadmap is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

We develop a theoretical framework for S_{n}-equivariant convolutional quantum circuits with SU(d) symmetry, building on and significantly generalizing Jordan’s permutational quantum computing formalism based on Schur-Weyl duality connecting both SU(d) and S_{n} actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow’s statement on the equivalence between SU⁡(d) and S_{n} irrep bases and to establish the S_{n}-equivariant convolutional quantum alternating ansätze (S_{n}-CQA) using Young-Jucys-Murphy elements. We prove that S_{n}-CQA is able to generate any unitary in any given S_{n} irrep sector, which may serve as a universal model for a wide array of quantum machine-learning problems with the presence of SU⁡(d) symmetry. Our method provides another way to prove the universality of the quantum approximate optimization algorithm and verifies that four-local SU⁡(d)-symmetric unitaries are sufficient to build generic SU⁡(d)-symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ansätze to find the ground-state energy of the J_{1}-J_{2} antiferromagnetic Heisenberg model on the rectangular and kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik S_{n} representation theory to quantum physics and machine learning, from which to propose quantum variational ansätze that strongly suggests to be classically intractable tailored towards a specific optimization problem.

Ulcerative colitis (UC) is a chronic systematic inflammation disorder with increasing incidence, unknown pathogenesis, limited drug treatment, and abundant medical expenses. Dietary intake, as a daily indispensable environment factor, is closely related to UC pathogenesis and prevention. The underlying interactions between dietary intake and UC progression are implicated with the modulation of gut microbiome as well as microbial metabolites, suggesting the complex and systematic characteristics of UC. However, the triangular relationships with dietary intake, gut microbiota homeostasis, and UC have not been well summarized so far. Here we review the recent studies of dietary intake on the regulation of gut microbiome homeostasis as well as modulation of UC progression. These findings suggest that varieties in dietary patterns result in the production of diverse microbial fermentation metabolites, which contribute to gut microbiome homeostasis through multiple manipulations including immune modulation, inflammation restriction as well as epithelial barrier maintenance, thus finally determine the fate of UC progression and give implications for functional food development for prevention and treatment of UC patients.

BackgroundThe global public health and socioeconomic impacts of the COVID-19 pandemic have been substantial, rendering herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rates in several months, although vaccine confidence in Japan is the lowest worldwide. ObjectiveWe attempted to find the reasons for rapid COVID-19 vaccination in Japan given its lowest vaccine confidence levels worldwide, through Twitter analysis. MethodsWe downloaded COVID-19–related Japanese tweets from a large-scale public COVID-19 Twitter chatter data set within the timeline of February 1 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister’s Office of Japan. After preprocessing, we applied unigram and bigram token analysis and then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. We then identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions. ResultsWe selected 190,697 vaccine-related tweets after filtering. Through n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top 6 unigrams, tweets with both keywords “reserve” and “venue” showed the largest correlation with daily vaccination cases (r=0.912; P

ObjectiveThis research aims to study the correlation between serum immune factors and post-infectious inflammatory response syndrome (PIIRS) in immunocompetent cryptococcal meningitis (CM), and explore whether serum immune factors could be used to predict the development of PIIRS.MethodsA cohort of 30 patients with PIIRS and 87 patients without PIIRS was selected from 347 CM patients. We analyzed the general clinical information and immunological indexes (cytokines, complement, immunoglobulin, inflammation, related cytological and biochemical indexes). Spearman correlation analysis and principal component analysis were used to explore the effects of the variables on PIIRS. Additionally, the variables were identified by a random forest-based classifier for predicting the development of PIIRS. The clinical value of predictors was verified by survival analysis.ResultsCompared with patients without PIIRS, patients with PIIRS had lower baseline serum interleukin-6 (IL-6, P = 0.006), immunoglobulin M (IgM, P = 0.004), and a higher baseline neutrophil ratio (P