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The New Enterprise Income Tax Act was passed by the Chinese government in 2008, which reduced the tax rate from 33% to 25% and applied a tax rate of 15% to high-tech businesses as a preferential tax policy. This shows that high-tech businesses are taxed differently from other businesses. The study aims to analyse how the Chinese government's ownership affects the tax avoidance of high-tech enterprises. The analysis's findings are listed below. First off, high-tech corporations engaged in more tax avoidance than other businesses did before to the 2008 tax rate cut. This means that high-tech companies, with large effect of the tax rate cut, have more incentives to minimize their tax burden in the year immediately preceding the tax rate cut compared to general companies. Second, the higher the Chinese government's ownership ratio, the lower the level of tax avoidance. Third, the higher the ownership is, the higher the tax avoidance behaviour becomes in the year just before the tax rate cut. This study provides policy implications pertaining to the tax avoidance behaviour of companies in the case of tax rate cuts and experimental situations in which a differential tax exists.

Test collusion (TC) is a form of cheating in which, examinees operate in groups to alter normal item responses. TC is becoming increasingly common, especially within high-stakes, large-scale examinations. However, research on TC detection methods remains scarce. The present article proposes a new algorithm for TC detection, inspired by variable selection within high-dimensional statistical analysis. The algorithm relies only on item responses and supports different response similarity indices. Simulation and practical studies were conducted to (1) compare the performance of the new algorithm against the recently developed clique detector approach, and (2) verify the performance of the new algorithm in a large-scale test setting.

The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remains an open problem. This paper considers an edge computing system where the server and workers have possibly different computing and communication capabilities and employ quantization before transmitting messages. To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence. Then, we analyze its convergence for an arbitrary step size sequence and specify the convergence results under three commonly adopted step size rules, namely the constant, exponential, and diminishing step size rules. Next, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint, with the focus on the overall implementing process of FL. Specifically, for any given step size sequence under each considered step size rule, we optimize the numbers of global and local iterations and mini-batch size to optimally implement FL for applications with preset step size sequences. We also optimize the step size sequence along with these algorithm parameters to explore the full potential of FL. The resulting optimization problems are challenging non-convex problems with non-differentiable constraint functions. We propose iterative algorithms to obtain KKT points using general inner approximation (GIA) and tricks for solving complementary geometric programming (CGP). Finally, we numerically demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters over existing FL algorithms and reveal the significance of optimally designing general FL algorithms., 33 pages, 9 figures. This work is submitted to IEEE Trans. Wireless Commun. (uner major revision). arXiv admin note: text overlap with arXiv:2110.12987

We created an integrated nano-system (Cuhemin–Au) for sensitizing radiotherapy. Cuhemin–Au upsets redox equilibrium and destroys mitochondria, leading to radiation sensitivity.

The corrosion of materials severely limits the application scenarios of triboelectric nanogenerators (TENGs), especially in laboratories, chemical plants and other fields where leakage of chemically corrosive solutions is common.

Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all model parameters. However, they are no longer effective for partial stragglers as they cannot utilize incomplete computation results from partial stragglers. This paper aims to design a new gradient coding scheme for mitigating partial stragglers in distributed learning. Specifically, we consider a distributed system consisting of one master and N workers, characterized by a general partial straggler model and focuses on solving a general large-scale machine learning problem with L model parameters using gradient coding. First, we propose a coordinate gradient coding scheme with L coding parameters representing L possibly different diversities for the L coordinates, which generates most gradient coding schemes. Then, we consider the minimization of the expected overall runtime and the maximization of the completion probability with respect to the L coding parameters for coordinates, which are challenging discrete optimization problems. To reduce computational complexity, we first transform each to an equivalent but much simpler discrete problem with N\llL variables representing the partition of the L coordinates into N blocks, each with identical redundancy. This indicates an equivalent but more easily implemented block coordinate gradient coding scheme with N coding parameters for blocks. Then, we adopt continuous relaxation to further reduce computational complexity. For the resulting minimization of expected overall runtime, we develop an iterative algorithm of computational complexity O(N^2) to obtain an optimal solution and derive two closed-form approximate solutions both with computational complexity O(N). For the resultant maximization of the completion probability, we develop an iterative algorithm of..., Comment: 17 pages, 7 figures, to appear in IEEE Trans. Signal Process. arXiv admin note: text overlap with arXiv:2109.08933

This study aimed to determine whether the prevalence of thyroid nodules (TNs) increased due to modern lifestyles or other factors, despite the advances in screening and diagnostic tools.This study included 3474 pairs of participants, who were matched by gender and age (±3 years) from two cross-sectional sampling surveys: (1) the program on the iodine nutritional status and related health status of residents in Shanghai in 2009; (2) the thyroid disease screening program for adults in Shanghai between 2017 and 2018. The prevalence of TNs and thyroid diseases in 2009 and 2017-2018 were compared, and the potential risk factors of TNs were detected.The prevalence of TNs in 2009 was 28.9%: 22.5% in males and 34.5% in females. In 2017, this increased to 43.8%: 37.9% in males and 49.1% in females. The prevalence of TNs significantly increased from 2009 to 2017 (odds ratio, 1.486; 95% confidence interval, 1.238-1.786). In addition, female gender, thyroid disease history, and age were the main risk factors for TNs after adjusting for confounders in the logistic regression across the time period.The prevalence of TNs significantly increased across nearly 10 years in Shanghai.

OBJECTIVE The hypoglycemic potential of β-blockers among users of sulfonylureas, drugs that strongly increase the risk of this potentially fatal adverse effect, is not well understood. Our population-based cohort study assessed the potential association between concomitant use of sulfonylureas and β-blockers versus use of sulfonylureas alone and the risk of severe hypoglycemia. RESEARCH DESIGN AND METHODS Using the U.K. Clinical Practice Research Datalink Aurum, we included patients initiating sulfonylureas between 1998 and 2020, excluding those with β-blocker use in the past 6 months. Time-dependent Cox models estimated hazard ratios (HRs) with 95% CIs of severe hypoglycemia (hospitalization with or death resulting from hypoglycemia; ICD-10 codes E16.0, E16.1, and E16.2) associated with current concomitant use of sulfonylureas and β-blockers compared with current sulfonylurea use alone, adjusted for baseline confounders. We also compared current concomitant use of sulfonylureas and non-cardioselective versus cardioselective β-blockers. RESULTS Our cohort included 252,869 initiators of sulfonylureas (mean age 61.3 years; 43% female). Median follow-up was 7.9 years. The crude incidence rate of severe hypoglycemia was 7.8 per 1,000 per year. Concomitant use of sulfonylureas and β-blockers was associated with an increased risk of severe hypoglycemia compared with sulfonylurea use alone (HR 1.53; 95% CI 1.42–1.65). There was no difference in the risk between concomitant use of sulfonylureas and noncardioselective β-blockers and concomitant use of sulfonylureas and cardioselective β-blockers (HR 0.95; 95% CI 0.74–1.24). CONCLUSIONS β-blockers could further increase the risk of severe hypoglycemia when used concurrently with sulfonylureas. β-blocker cardioselectivity did not seem to play a major role in this regard.

Traditional denitrification often produces high operating costs and excessive sludge disposal expenses due to conventional carbon sources. A novel electric-magnetic field (MF) 48 mT with Fe

Dendritic cells (DCs), a kind of specialized immune cells, play key roles in antitumor immune response and promotion of innate and adaptive immune responses. Recently, many strategies have been developed to utilize DCs in cancer therapy, such as delivering antigens and adjuvants to DCs and using scaffold to recruit and activate DCs. Here we outline how different DC subsets influence antitumor immunity, summarize the FDA-approved vaccines and cancer vaccines under clinical trials, discuss the strategies for engineering DCs and noninvasive tracking of DCs to improve antitumor immunotherapy, and reveal the potential of artificial neural networks for the design of DC based vaccines.

To comprehensively compare the efficacy of different antiplatelet therapies for secondary prevention of lacunar stroke (LS).The relevant studies were identified by searching PubMed, EMBASE, Web of Science, and Cochrane Collaboration Database up to May 2022. Cardiovascular and cerebrovascular events were chosen to evaluate the efficacy of antiplatelet therapy for secondary prevention. Loop-specific approach and node-splitting analysis were used to evaluate consistency and inconsistency, respectively. The value of the surface under the cumulative ranking (SUCRA) was calculated and ranked. Funnel-plot symmetry was used to evaluate publication bias. The meta-analysis was performed by using STATA 16.0.Thirteen studies with a total of 33,011 subjects were included in this network meta-analysis. Compared with placebo, aspirin, clopidogrel, cilostazol, ticlopidine, aspirin plus dipyridamole, and aspirin plus clopidogrel were associated with reducing cardiovascular and cerebrovascular events. The SUCRA estimated relative ranking of treatments showed that cilostazol may be the most effective (RR 0.56, 95% CI 0.42-0.74, SUCRA 95.8). No significant inconsistency or publication bias was found in the study.This meta-analysis suggests that cilostazol may be a priority option for secondary prevention of patients with LS. These findings still need further study in the future.

This paper studies a structured compound stochastic program (SP) involving multiple expectations coupled by nonconvex and nonsmooth functions. We present a successive convex programming-based sampling algorithm and establish its subsequential convergence. We describe stationary properties of the limit points for several classes of the compound SP. We further discuss probabilistic stopping rules based on the computable error bound for the algorithm. We present several risk measure minimization problems that can be formulated as such a compound stochastic program; these include generalized deviation optimization problems based on the optimized certainty equivalent and buffered probability of exceedance (bPOE), a distributionally robust bPOE optimization problem, and a multiclass classification problem employing the cost-sensitive error criteria with bPOE.

C33H31BrN4O3, monoclinic, P21/n (no. 14), a = 11.8558(12) Å, b = 12.1159(11) Å, c = 24.014(2) Å, β = 94.812(2)°, V = 3437.3(6) Å3, Z = 4, Rgt (F) = 0.0574, wRref (F 2) = 0.1725, T = 296(2) K.

Saline-alkalisation of the soil environment and microorganism is a global challenge. However, relevant studies on the effects of saline-alkali stress on soil bacterial communities are limited. In this study, we investigated the effects of saline-alkali stress on the carbon source metabolic utilisation of the microbial community, bacterial diversity, and composition in soil using Biolog Ecoplate and 16S rRNA gene amplicon sequencing. Biolog Ecoplate results showed that saline-alkali stress decreased the metabolic activity and functional diversity, and changed the utilisation characteristics of carbon sources in soil microorganisms. Particularly, high level of saline-alkali stress significantly decreased the utilisation of carbohydrates and amino acids carbon sources. The results of 16S rRNA gene amplicon sequencing showed that high level of saline-alkali stress significantly reduced the diversity of soil bacterial communities. In addition, high level of saline-alkali stress significantly decreased the relative abundances of some key bacterial taxa, such as Gemmatimonas, Sphingomonas, and Bradyrhizobium. Furthermore, as saline-alkali content increased, the soil catalase, protease, urease, and sucrase activities also significantly decreased. Collectively, these results provide new insight for studies on the changes in the soil bacterial community and soil enzyme activity under saline-alkali stress.

Pathogenic large inversions are rarely reported on DMD gene due to the lack of effective detection methods. Here we report two DMD pedigrees and proposed a reliable pipeline to define large inversions in DMD patients. In the first pedigree, conventional approaches including multiplex ligation-dependent probe amplification, and whole-exome sequencing by next generation sequencing were failed to detect any pathologic variant. Then an advanced analysis pipeline which consists of RNA-seq, cDNA array capture sequencing, optical mapping, long-read sequencing was built. RNA-seq and cDNA capture sequencing showed a complete absence of transcripts of exons 3-55. Optical mapping identified a 55 Mb pericentric inversion between Xp21 and Xq21. Subsequently, long-read sequencing and Sanger sequencing determined the inversion breakpoints at 32,915,769 and 87,989,324 of the X chromosomes. In the second pedigree, long-read sequencing was directly conducted and Sanger sequencing was performed to verify the mutation. Long-read sequencing and Sanger sequencing found breakpoints at 32,581,576 and 127,797,236 on DMD gene directly. In conclusion, large inversion might be a rare but important mutation type in DMD gene. An effective pipeline was built in detecting large inversion mutations based on long-read sequencing platforms.

Aiming at the problems that the fuel releases a lot of cold energy and the refrigerated containers consume a lot of electricity on large LNG powered container ships, a set of cold energy cascade utilization scheme that mainly uses LNG cold energy for refrigerated containers is designed. The simulation software Aspen HYSYS is used to establish and simulate the process of the ship’s cold energy cascade utilization system under five different working conditions, and the main parameters of the key nodes are obtained, according to the established exergy efficiency model, the exergy efficiency of the main equipment and the whole system is solved. Under the five working conditions, the maximum exergy efficiency of the refrigerated container system before optimization is 24.54%, at this time, the exergy efficiency of the entire cold energy utilization system is 24.86%. With the goal of improving the exergy efficiency of the entire system, using a hierarchical optimization method, the key parameters affecting the exergy efficiency of each cold energy utilization module are analyzed and optimized respectively, and the optimal operating parameters of different cold energy utilization module were determined. So as to realize the optimization of the whole cold energy cascade utilization system. The results show that the exergy efficiency of the optimized LNG cold energy cascade utilization system for ships under five working conditions is improved, under the 65% working condition, the exergy efficiency of the optimized refrigerated container system is 27.69%, and the exergy efficiency of the whole cold energy utilization system is 28.04%, which are increased by 3.15% and 3.18% respectively. Which proves that the LNG cold energy cascade utilization system can realize the effective utilization of LNG cold energy.

Background: Cognitive impairment is common in the elderly population. Exploring patterns of white matter damage at the microstructural level would give important indications for the underlying mechanisms. Objective: To investigate the spatial patterns of white matter microstructure and structural network alternations in relation to different cognition domains Methods: Participants from the community-based Shunyi Study were included to investigate the association between white matter measurements and cognition cross-sectionally, via both global and local analysis. Cognitive functions were assessed using digit span, trail making test (TMT)-A/B, Fuld object Memory, and 12-Word Philadelphia Verbal Learning Test (PVLT). White matter measurements including fractional anisotropy (FA), mean diffusivity (MD), and structural network parameters were calculated based on diffusion tensor imaging. Results: Of the 943 participants included, the mean (SD) age was 55.8 (9.1) years, and the mean (SD) education level was 6.7 (3.2) years. We found the whole set of cognitive measurements was related to diffused white matter microstructural integrity damage and lower global efficiency. Poor executive functions (TMTA/B complete time) were related to lower FA and higher MD predominantly on the anterior white matter skeleton, while verbal memory loss (PVLT test scores) was related to sub-network dysconnectivity in the midline and the right temporal lobe. Conclusion: The anterior brain is dominantly involved in executive dysfunction, while midline and right temporal brain disconnection are more prominent in verbal memory loss. Global and regional disruption of white matter integrity and network connectivity is the anatomical basis of the cognitive impairment in the aging population.

Esophageal cancer is of high importance to occurrence, development, and treatment resistance. As evidenced by recent studies, pathways (e.g., Wnt/β-catenin, AMPK, and Hippo) are critical to the proliferation, differentiation, and self-renewal of esophageal cancer. In addition, the above pathways play a certain role in regulating esophageal cancer and act as potential therapeutic targets. Over the past few years, the function of lipid metabolism in controlling tumor cells and immune cells has aroused extensive attention. It has been reported that there are intricate interactions between lipid metabolism reprogramming between immune and esophageal cancer cells, whereas molecular mechanisms should be studied in depth. Immune cells have been commonly recognized as a vital player in the esophageal cancer microenvironment, having complex crosstalk with cancer cells. It is increasingly evidenced that the function of immune cells in the tumor microenvironment (TME) is significantly correlated with abnormal lipid metabolism. In this review, the latest findings in lipid metabolism reprogramming in TME are summarized, and the above findings are linked to esophageal cancer progression. Aberrant lipid metabolism and associated signaling pathways are likely to serve as a novel strategy to treat esophageal cancer through lipid metabolism reprogramming.

The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable completely positive cone. The major difficulty for implementing a Newton-type method to compute the projection of a given large scale matrix onto the DNN cone lies in the possible failure of the constraint nondegeneracy, a generalization of the linear independence constraint qualification for nonlinear programming. Such a failure results in the singularity of the Jacobian of the nonsmooth equation representing the Karush-Kuhn-Tucker optimality condition that prevents the semismooth Newton-CG method from solving it with a desirable convergence rate. In this paper, we overcome the aforementioned difficulty by solving a sequence of better conditioned nonsmooth equations generated by the augmented Lagrangian method (ALM) instead of solving one above mentioned singular equation. By leveraging on the metric subregularity of the normal cone associated with the positive semidefinite cone, we derive sufficient conditions to ensure the dual quadratic growth condition of the underlying problem, which further leads to the asymptotically superlinear convergence of the proposed ALM. Numerical results on difficult randomly generated instances and from the semidefinite programming library are presented to demonstrate the efficiency of the algorithm for computing the DNN projection to a very high accuracy., Comment: 28 pages, 0 figure

This paper has two main goals: (a) establish several statistical properties—consistency, asymptotic distributions, and convergence rates—of stationary solutions and values of a class of coupled nonconvex and nonsmooth empirical risk-minimization problems and (b) validate these properties by a noisy amplitude-based phase-retrieval problem, the latter being of much topical interest. Derived from available data via sampling, these empirical risk-minimization problems are the computational workhorse of a population risk model that involves the minimization of an expected value of a random functional. When these minimization problems are nonconvex, the computation of their globally optimal solutions is elusive. Together with the fact that the expectation operator cannot be evaluated for general probability distributions, it becomes necessary to justify whether the stationary solutions of the empirical problems are practical approximations of the stationary solution of the population problem. When these two features, general distribution and nonconvexity, are coupled with nondifferentiability that often renders the problems “non-Clarke regular,” the task of the justification becomes challenging. Our work aims to address such a challenge within an algorithm-free setting. The resulting analysis is, therefore, different from much of the analysis in the recent literature that is based on local search algorithms. Furthermore, supplementing the classical global minimizer-centric analysis, our results offer a promising step to close the gap between computational optimization and asymptotic analysis of coupled, nonconvex, nonsmooth statistical estimation problems, expanding the former with statistical properties of the practically obtained solution and providing the latter with a more practical focus pertaining to computational tractability.

Insulin is a peptide well known for its role in regulating glucose metabolism in peripheral tissues. Emerging evidence from human and animal studies indicate the multifactorial role of insulin in the brain, such as neuronal and glial metabolism, glucose regulation, and cognitive processes. Insulin resistance (IR), defined as reduced sensitivity to the action of insulin, has been consistently proposed as an important risk factor for developing neurodegeneration and cognitive impairment. Although the exact mechanism of IR-related cognitive impairment still awaits further elucidation, neuroimaging offers a versatile set of novel contrasts to reveal the subtle cerebral abnormalities in IR. These imaging contrasts, including but not limited to brain volume, white matter (WM) microstructure, neural function and brain metabolism, are expected to unravel the nature of the link between IR, cognitive decline, and brain abnormalities, and their changes over time. This review summarizes the current neuroimaging studies with multiparametric techniques, focusing on the cerebral abnormalities related to IR and therapeutic effects of IR-targeting treatments. According to the results, brain regions associated with IR pathophysiology include the medial temporal lobe, hippocampus, prefrontal lobe, cingulate cortex, precuneus, occipital lobe, and the WM tracts across the globe. Of these, alterations in the temporal lobe are highly reproducible across different imaging modalities. These structures have been known to be vulnerable to Alzheimer's disease (AD) pathology and are critical in cognitive processes such as memory and executive functioning. Comparing to asymptomatic subjects, results are more mixed in patients with metabolic disorders such as type 2 diabetes and obesity, which might be attributed to a multifactorial mechanism. Taken together, neuroimaging, especially MRI, is beneficial to reveal early abnormalities in cerebral structure and function in insulin-resistant brain, providing important evidence to unravel the underlying neuronal substrate that reflects the cognitive decline in IR. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

As films are distributed across the globe, film song translation has become a subject of study, which entails considering multi-modal factors. This paper aims to explore the major dimensions and parameters involved in film song translation. Based on previous research on music and translation, this paper proposes a framework for studying film song translation from verbal, vocal, and visual dimensions. The verbal dimension involves semantic meaning, metaphors, images, mood, and emotion. The vocal dimension includes the number of syllables and musical notes, the length of musical notes, rhyme and parallelism, the rise and fall of the melody, and the segmentation of a line. The visual dimension covers the plot, characters, and background pictures. This paper uses this framework to analyze the Chinese translation of Amazing Grace in the film Forever Young to demonstrate how film song translation can be flexible in tackling verbal, vocal, and visual restrictions and possibilities.

Acute respiratory distress syndrome (ARDS) is a severe disease. Inflammation is the key element implicated in ARDS. Steroid receptor coactivator 3 (SRC3), a coactivator protein for transcription, is involved in regulation of inflammatory response. Here we explored the potential roles of SRC3 in ARDS. We utilized the SRC3 deficient (SRC3

Qufeng Zhitong capsule (QZC) is a well-known Chinese patent medicine that has been widely applied for the clinical treatment of rheumatoid arthritis and other inflammatory diseases. To date, its material basis is still unclear, which has greatly limited its clinical application. In this study, by taking advantage of ultra-high-performance liquid chromatography tandem Q-Exactive Orbitrap high-resolution mass spectrometry, 16 chemical components such as gallic acid, protocatechuic acid, and neochlorogenic acid in QZC were characterized and unambiguously identified based on comparison with the corresponding reference standards. In addition, the correlation between the focused components and their corresponding raw herbs from QZC prescription was investigated. For the first time, the relationship between the components mentioned above and their anti-inflammatory activity was explored via network pharmacology analysis, and a visualized network of “medicinal materials-QZC-compounds-targets-pathways” was established. Based on the brief prediction results of network pharmacological analysis, ultra-performance liquid chromatography coupled with photodiode array detector method was validated in terms of linearity, limit of detection, limit of quantification, precision, repeatability, stability, and recovery test and was successfully employed to determine 16 compounds in 28 batches of QZCs, which confirmed the feasibility and reliability of the established method for the quantitative analysis of 16 compounds in QZC. Considering the content and bioactivity of the tested components, four compounds were recommended as candidate indicators for quality evaluation ultimately. The potential value of this study could not only support a quality evaluation of QZC but also provide a theoretical basis for further in-depth research of QZC in clinical research.

Long non-coding RNAs (lncRNAs) have been confirmed to be involved in epilepsy development. It has been reported that lncRNA ZFAS1 plays a vital regulatory role in epilepsy progression. Therefore, the role and molecular mechanism of ZFAS1 in epilepsy progression deserve further investigation. Mice status epilepticus (SE) model was constructed, and hippocampal neurons were isolated from mice hippocampus tissues. The expression of ZFAS1, miR-15a-5p and oxidative stress responsive 1 (OXSR1) were determined by quantitative real-time PCR. ELISA assay was used to detect the concentrations of inflammation factors. Cell viability and apoptosis were examined by MTT assay, EdU staining and flow cytometry. Western blot analysis was conducted to measure protein levels, and the productions of SOD and MDA were measured to assess cell oxidative stress. Dual-luciferase reporter assay and RIP assay were employed to validate the relationship between miR-15a-5p and ZFAS1 or OXSR1. LncRNA ZFAS1 was highly expressed in SE mice and SE-stimulated hippocampal neurons. Silenced ZFAS1 promoted viability, while inhibited inflammation, apoptosis and oxidative stress in SE-induced hippocampal neurons. MiR-15a-5p could be targeted by ZFAS1, and its inhibitor also reversed the suppressive effect of ZFAS1 knockdown on SE-induced hippocampal neurons injury. In addition, OXSR1 was a target of miR-15a-5p, and its silencing also could relieve SE-induced hippocampal neurons injury. OXSR1 overexpression reversed the inhibition effect of miR-15a-5p on SE-induced hippocampal neurons injury. Moreover, ZFAS1 positively regulated OXSR1 expression by sponging miR-15a-5p, thereby activating the NF-κB pathway. LncRNA ZFAS1 might contribute to the progression of epilepsy by regulating the miR-15a-5p/OXSR1/NF-κB pathway.

Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported.We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method.Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted.The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside RgThis method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.