Effects of ankle isokinetic training on muscle strength and balance amongst older women with mild Parkinson's disease: A randomised trial.

BACKGROUND: Patients with Parkinson's disease frequently experience problems with balance and gait. OBJECTIVE: This study examined the influences of regular ankle isokinetic muscle strength training on muscle strength and balance ability amongst elderly women with mild Parkinson's disease. METHODS: A total of 55 elderly women with mild Parkinson's disease were randomly divided into the experimental group (n= 28) and the control group (n= 27) for 16 weeks. The experimental group was trained for 16 weeks by using isokinetic muscle strength training. RESULTS: After 16 weeks of training in the experimental group, the peak torque (PT) of ankle (average of the left and right sides) dorsiflexion, plantarflexion, eversion and inversion, and the standing balance index revealed that the left and right stability and overall stability indexes significantly increased by 25.9%, 43.5%, 62.7%, 82.8%, 37.1% and 32.2%, respectively (p< 0.05). The experimental group exhibited significantly increased PT of ankle dorsiflexion, plantarflexion, eversion and inversion. Moreover, the left and right stability and overall stability indexes were significantly improved compared with those of the control group (p< 0.05). This study found a linear regression relationship between the PT of ankle inversion and overall stability index. CONCLUSION: This study determined that ankle isokinetic muscle strength training improved ankle dorsiflexion, plantarflexion, eversion and inversion muscle strength, and balance ability amongst elderly women with mild Parkinson's disease. However, it did not improve stability in the anterior and posterior directions. Ankle inversion muscle strength is more likely to affect overall balance.

Hierarchical Co2 P/CoS2 @C@MoS2 Composites with Hollow Cavity and Multiple Phases Toward Wideband Electromagnetic Wave Absorption.

The synergistic effect of hollow cavities and multiple hetero-interfaces displays huge advantages in achieving lightweight and high-efficient electromagnetic wave absorption, but still confronts huge challenges. Herein, hierarchical Co2 P/CoS2 @C@MoS2 composites via the self-sacrificed strategy and a subsequent hydrothermal method have been successfully synthesized. Specifically, ZIF-67 cores first act as the structural template to form core-shell ZIF-67@poly(cyclotriphosphazene-co-4,4'-sulfonyldiphenol) (ZIF-67@PZS) composites, which are converted into hollow Co2 P@C shells with micro-mesoporous characteristics because of the gradient structural stabilities and preferred coordination ability. The deposition of hierarchical MoS2 results in phase transition (Co2 P→Co2 P/CoS2 ), yielding the formation of hierarchical Co2 P/CoS2 @C@MoS2 composites with hollow cavities and multiple hetero-interfaces. Benefiting from the cooperative advantages of hollow structure, extra N,P,S-doped sources, lattice defects/vacancies, diverse incoherent interfaces, and hierarchical configurations, the composites deliver superior electromagnetic wave capability (-56.6 dB) and wideband absorption bandwidth (8.96 GHz) with 20 wt.% filler loading. This study provides a reliable and facile strategy for the precise construction of superior electromagnetic wave absorbents with efficient absorption attenuation.

Hierarchical Fe-Co@TiO2 with Incoherent Heterointerfaces and Gradient Magnetic Domains for Electromagnetic Wave Absorption.

Induced polarization response and integrated magnetic resonance show prosperous advantages in boosting electromagnetic wave absorption but still face huge challenges in revealing the intrinsic mechanism. In this work, we propose a self-confined strategy to construct hierarchical Fe-Co@TiO2 microrods with numerous incoherent heterointerfaces and gradient magnetic domains. The results demonstrate that the use of polyvinylpyrrolidone (PVP) coating is crucial for the subsequent deposition of Co-zeolitic imidazolate frameworks (ZIF-67), the distance of ordered arranged metal ions manipulates the size of magnetic domains, and the pyrolysis of PVP layers restricts the eutectic process of Fe-Co alloys to some extent. As a result, these introduced lattice defects, oxygen vacancies, and incoherent heterointerfaces inevitably generate a strong polarization response, and the regulated gradient magnetic domains realize integrated magnetic resonance, including macroscopic magnetic coupling, long-range magnetic diffraction, and nanoscale magnetic bridge connection, and both of the intrinsic mechanisms in dissipating electromagnetic energy are quantitatively clarified by Lorentz off-axis electron holography. Owing to the cooperative merits, the Fe-Co@TiO2 absorbents exhibit enhanced absorption intensity and strong absorption bandwidth. This study inspires us to develop a generalized strategy for manipulating the size of magnetic domains, and the integrated magnetic resonance theory provides a versatile methodology in clarifying magnetic loss mechanism.

Synthesis of quinazolinones via Cp*Co(III)-catalyzed C-H functionalization of primary amides with oxadiazolones.

Multi-heteroatom heterocycle synthesis through direct C-H bond activation is methodologically appealing but synthetically challenging. An efficient double C-N bond formation sequence to prepare quinazolinones utilizing primary amides and oxadiazolones in a catalytic redox-neutral [CoCp*(CO)I2]/AgSbF6 system, where oxadiazolone could function as an internal oxidant to maintain the catalytic cycle, is reported. Amide-directed C-H bond activation and oxadiazolone decarboxylation are key to the success of this traceless, atom- and step-economic, and cascade approach for the construction of the quinazolinone skeleton.

Does the establishment of nature reserves increase rural residents' income? : Empirical evidence from China based on PSM-DID.

Clarifying the impact of nature reserves on rural residents' income is of great significance to the organic integration of environmental protection and rural residents' income improvement. Based on the panel data of 600 counties from nine provinces in China, ranging from 2010 to 2019, this study employs the quasi-experimental method of Difference in Difference and Propensity Score Matching (PSM-DID) to examine how rural resident's income is influenced by the establishment of nature reserves. It is found that (1) Nature reserves, with a positive impact on rural resident's income, bring more earnings to the rural residents. (2) The impact varies with the heterogeneity of natural reserve types. That is, national and natural parks can increase rural residents' income, while the ones with wildlife or remote desert ecosystem play a limited role. (3) The increased income is mainly originated from the development of tourism, the improvement of facilities and the enhancement of ecosystem services. On this basis, multiple methods are proposed to promote rural residents' income and livelihood security of local communities. Policy recommendations are brought forth as well, including the focus on guaranteeing the livelihood security of rural residents nearby nature reserves where wildlife and remote desert ecosystem are protected.

Construction of Binary Quantum Error-Correcting Codes from Orthogonal Array.

By using difference schemes, orthogonal partitions and a replacement method, some new methods to construct pure quantum error-correcting codes are provided from orthogonal arrays. As an application of these methods, we construct several infinite series of quantum error-correcting codes including some optimal ones. Compared with the existing binary quantum codes, more new codes can be constructed, which have a lower number of terms (i.e., the number of computational basis states) for each of their basis states.

Study on the heterogeneity of China's agricultural economic growth in the context of temperature shocks.

Under the background of the new development concept, compared with the absolute impacts, the relative impacts of climate change on agricultural growth deserve more attention. Based on the data from China for years 1991 and 2018, this paper uses historical fluctuations in temperature within cities to identify the heterogeneous effects on aggregate agricultural outcomes during farming and fallow periods. The results show that: first, as temperature rises reduce the economic growth rate of each agricultural sector, and the areas that are relatively vulnerable (i.e., areas where disposable income of farm households is below the sample mean) are more significantly affected by the negative impact of temperature rise; second, the impact of temperature rise on agricultural economic growth is mainly concentrated in the farming period, while the marginal damage of temperature rise is on a decreasing trend; third, the heterogeneous impact of temperature rise on agricultural economic growth during the agricultural fallow period is also not negligible. At the same time, its impact on agricultural economy is still in the primary stage, that is, its marginal damage tends to increase with the increase in temperature fluctuation. These results inform identifying the climate's role in agricultural development and provide a theoretical and operational perspective for further optimizing the adaptive policy systems. With wide coverage of adaptive technology, we should pay more attention to the even distribution of technological dividends and continuously improve the coping ability of vulnerable groups.

Tumor organoids: applications in cancer modeling and potentials in precision medicine.

Cancer is a top-ranked life-threatening disease with intratumor heterogeneity. Tumor heterogeneity is associated with metastasis, relapse, and therapy resistance. These factors contribute to treatment failure and an unfavorable prognosis. Personalized tumor models faithfully capturing the tumor heterogeneity of individual patients are urgently needed for precision medicine. Advances in stem cell culture have given rise to powerful organoid technology for the generation of in vitro three-dimensional tissues that have been shown to more accurately recapitulate the structures, specific functions, molecular characteristics, genomic alterations, expression profiles, and tumor microenvironment of primary tumors. Tumoroids in vitro serve as an important component of the pipeline for the discovery of potential therapeutic targets and the identification of novel compounds. In this review, we will summarize recent advances in tumoroid cultures as an excellent tool for accurate cancer modeling. Additionally, vascularization and immune microenvironment modeling based on organoid technology will also be described. Furthermore, we will summarize the great potential of tumor organoids in predicting the therapeutic response, investigating resistance-related mechanisms, optimizing treatment strategies, and exploring potential therapies. In addition, the bottlenecks and challenges of current tumoroids will also be discussed in this review.

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes.

Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe-disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.

Size-Dependent Oxidation-Induced Phase Engineering for MOFs Derivatives Via Spatial Confinement Strategy Toward Enhanced Microwave Absorption.

Precisely reducing the size of metal-organic frameworks (MOFs) derivatives is an effective strategy to manipulate their phase engineering owing to size-dependent oxidation; however, the underlying relationship between the size of derivatives and phase engineering has not been clarified so far. Herein, a spatial confined growth strategy is proposed to encapsulate small-size MOFs derivatives into hollow carbon nanocages. It realizes that the hollow cavity shows a significant spatial confinement effect on the size of confined MOFs crystals and subsequently affects the dielectric polarization due to the phase hybridization with tunable coherent interfaces and heterojunctions owing to size-dependent oxidation motion, yielding to satisfied microwave attenuation with an optimal reflection loss of -50.6 dB and effective bandwidth of 6.6 GHz. Meanwhile, the effect of phase hybridization on dielectric polarization is deeply visualized, and the simulated calculation and electron holograms demonstrate that dielectric polarization is shown to be dominant dissipation mechanism in determining microwave absorption. This spatial confined growth strategy provides a versatile methodology for manipulating the size of MOFs derivatives and the understanding of size-dependent oxidation-induced phase hybridization offers a precise inspiration in optimizing dielectric polarization and microwave attenuation in theory.

Mechanisms of Senescence-Related NKG2D Ligands Release and Immune Escape Induced by Chemotherapy in Neuroblastoma Cells.

Chemotherapy-induced senescence promotes immunocyte aggregation in the tumor microenvironment by upregulating the surface expression of activating ligands in cancer cells. However, these senescent tumor cells cannot be completely cleared and can induce tumor recurrence. Previous studiesshowed that soluble natural killer (NK) group 2D (NKG2D) ligands impair the recognition of multiple immune cells. In this study, we established an in vitro senescence model using neuroblastoma cells subjected to low-dose Chemotherapeutic drug doxorubicin or the Aurora A inhibitor MLN8237. The results showed that different neuroblastoma cell lines showed increased secretion of the NKG2D ligand MHC class I polypeptide-related sequence A/B (MICA/B) following proteolysis after treatment, with MICA/B subsequently recruited to exosomes to downregulate NKG2D expression in NK cells. Interestingly, disintegrin and metalloproteinase domain-containing 10 (ADAM10) was upregulated in senescent tumor cells, and combined treatment with the ADAM10 inhibitor GI254023X and chemotherapeutic drugs inhibited MICA/B secretion and enhanced recognition and killing by NK cells. Additionally, we found that expression of the long noncoding RNA MALAT1 was significantly increased in senescent neuroblastoma cells, and that MALAT1 served as a sponge for microRNA (miR)-92a-3p to counteract miR-92a-3p-mediated repression of ADAM10 levels. Furthermore, administration of a MALAT1 inhibitor or an miR-92a-3p mimic reduced the MICA/B shedding and enhanced recognition and killing by NK cells. These results confirmed that low-dose chemotherapy induces senescence in neuroblastoma cells, and that senescent tumor cells promote the shedding of the NKG2D ligand MICA/B through the MALAT1/miR-92a/ADAM10 axis, thereby contributing to the formation of a suppressive immune microenvironment and promoting immune escape.

Multi-Omics Approach Reveals the Potential Core Vaccine Targets for the Emerging Foodborne Pathogen Campylobacter jejuni.

Campylobacter jejuni is a leading cause of bacterial gastroenteritis in humans around the world. The emergence of bacterial resistance is becoming more serious; therefore, development of new vaccines is considered to be an alternative strategy against drug-resistant pathogen. In this study, we investigated the pangenome of 173 C. jejuni strains and analyzed the phylogenesis and the virulence factor genes. In order to acquire a high-quality pangenome, genomic relatedness was firstly performed with average nucleotide identity (ANI) analyses, and an open pangenome of 8,041 gene families was obtained with the correct taxonomy genomes. Subsequently, the virulence property of the core genome was analyzed and 145 core virulence factor (VF) genes were obtained. Upon functional genomics and immunological analyses, five core VF proteins with high antigenicity were selected as potential core vaccine targets for humans. Furthermore, functional annotations indicated that these proteins are involved in important molecular functions and biological processes, such as adhesion, regulation, and secretion. In addition, transcriptome analysis in human cells and pig intestinal loop proved that these vaccine target genes are important in the virulence of C. jejuni in different hosts. Comprehensive pangenome and relevant animal experiments will facilitate discovering the potential core vaccine targets with improved efficiency in reverse vaccinology. Likewise, this study provided some insights into the genetic polymorphism and phylogeny of C. jejuni and discovered potential vaccine candidates for humans. Prospective development of new vaccines using the targets will be an alternative to the use of antibiotics and prevent the development of multidrug-resistant C. jejuni in humans and even other animals.

Detection of soil physical properties of reclaimed land in open-pit mining area: feasibility of application of ground penetrating radar.

In order to obtain the typical soil physical properties of reclaimed land more quickly and accurately, the South Dump of the China Coal's Antaibao Open-Pit Mine in Pingshuo was focussed on in this paper, and ground penetrating radar (GPR) technology was utilized to detect the soil physical properties of reclaimed land in the mining area. The soil profile sampling and GPR detection methods were used to acquire the data. The gravel content of surface soil was analyzed by counting the number of isolated gravel signals in GPR images. The change of effective soil thickness was analyzed by establishing the fitting relationship between calibration depth and GPR image depth. The Topp's model was validated by comparing its inversion with the measured soil volumetric water content. And the Topp's model was further validated by the soil volumetric water content obtained from the Topp's model and which obtained from the wave velocity inversion. The results are as follows: (1) Based on the number of isolated gravel signals in GPR images, we could qualitatively analyze the gravel content of surface soil reclaimed in the mining area. As the number of isolated gravel signals was greater, the gravel content of the surface soil was higher. (2) Using the known relative permittivity, electromagnetic wave velocity, and soil volumetric water content, the calibration depth and the reflection depth of the target (calibrator) of the GPR images were segmented, and the correlation between them is high. The fitting relationship (R2) of each segment was higher than 0.940, and the average value of the five-segment R2 was 0.966, which indicated more accurate detection of the effective soil thickness of the reclaimed land in the mining area. (3) GPR could be used to detect the soil volumetric water content of reclaimed land in mining area, and Topp's model was used for calculation of soil volumetric water content. The average deviation rate between the values from Topp's model and the measured values was 12%, and the average absolute value of the difference was 2%. In summary, the benefits of using GPR to detect soil physical properties of reclaimed land in mining area are as follows: (1) GPR can be used to detect soil layer thickness and surface gravel content faster and more accurately. (2) Topp model can also be used to calculate the soil moisture content of non-natural deposits such as reclaimed land in mining area.

Environmental change and fishermen's income: is there a poverty trap : Evidence from China's coastal areas.

The unequal cost and benefit led by environmental changes may potentially set China's rural areas a "Poverty Trap." Therefore, clarifying the relationship between environmental changes and rural income distribution is of great significance to realize the organic integration of environmental improvement and poverty governance. Based on the panel data of China's coastal areas, this paper explores the mutual influence between environmental changes and fishermen's income distribution, thus testing the hypothesis of the poverty-environment trap. The results show that environmental degradation has a significant negative impact on fishermen's income. To be specific, compared with the middle- and high-income groups, the impact of environmental degradation on people with less income is more noticeable; as for the low-income groups represented by fishermen, the marginal effects of their income reduction on environmental degradation are more prominent; continuous decrease of their income together with environmental deterioration will form a vicious circle, bringing the risk of falling into the poverty-environment trap. In the follow-up environmental governance, authorities need to impose targeted measures and adopt tax or subsidy policies that are inclusive and preferential, so as to address the income gaps between fishermen and further relative poverty.

Determination of quasi-primary odors by endpoint detection.

It is known that there are no primary odors that can represent any other odors with their combination. Here, we propose an alternative approach: "quasi" primary odors. This approach comprises the following condition and method: (1) within a collected dataset and (2) by the machine learning-based endpoint detection. The quasi-primary odors are selected from the odors included in a collected odor dataset according to the endpoint score. While it is limited within the given dataset, the combination of such quasi-primary odors with certain ratios can reproduce any other odor in the dataset. To visually demonstrate this approach, the three quasi-primary odors having top three high endpoint scores are assigned to the vertices of a chromaticity triangle with red, green, and blue. Then, the other odors in the dataset are projected onto the chromaticity triangle to have their unique colors. The number of quasi-primary odors is not limited to three but can be set to an arbitrary number. With this approach, one can first find "extreme" odors (i.e., quasi-primary odors) in a given odor dataset, and then, reproduce any other odor in the dataset or even synthesize a new arbitrary odor by combining such quasi-primary odors with certain ratios.

Development of a Copper-Based Metal Organic Electrode for Nitrite Sensing.

BACKGROUND: Nitrite is naturally present in vegetables and added to processed meats to enhance their color and prolong their shelf life. It is of concern because it reacts to form nitrosamines, which have been linked to cancer. OBJECTIVE: To develop a quick, reliable, and inexpensive method for quantifying nitrite in foods. METHOD: A copper-based metal organic framework (Cu-MOF)/gold-platinum alloy nanoparticle(Au@Pt)-modified glassy carbon electrode (GCE) was developed via a simple wet chemical synthesis followed by electrochemical deposition of gold-platinum alloy nanoparticles onto the surface of a GCE. Morphological characterization and component analysis of the prepared nanomaterials were carried out by Fourier transform infrared spectroscopy and scanning electron microscopy. Cyclic voltammetry and electrochemical impedance spectroscopy were used to study the electrochemical behavior of the fabricated electrodes. RESULTS: The quantitative and specific detection of nitrite was obtained by the amperometric i-t method. At a pH of 7, temperature of 25°C, and ionic strength of 0.4 M, the electrode exhibited a linear range of 0.001-12.2 mM nitrite with a low detection limit of 72 nM (S/N = 3). CONCLUSIONS: The Cu-MOF/Au@Pt/GCE exhibited good repeatability, reproducibility, stability, and selectivity to provide a capable analysis method for food samples. HIGHLIGHTS: A Cu-MOF with a large surface area and high porosity was developed to provide an electrode with many active sites. The Au@Pt alloy nanoparticle improved the electrocatalytic activity toward nitrite. The synergistic action between the Cu-MOF and Au@Pt alloy nanoparticle enhanced the electrochemical performance of the sensor.

Effect of high-density lipoprotein on penile erection: A cross-sectional study.

Previous studies have shown that elevated levels of high-density lipoprotein (HDL) could inhibit penile erection, but the relationship between HDL and the erection of the penile tip or base has not been extensively researched. We investigated the effects of HDL on erection of the penile tip and base through a cross-sectional study of 113 patients with erectile dysfunction, using a cut-off score of ≤21 on the International Index of Erectile Function-5. The following patient data were collected: nocturnal penile tumescence; blood pressure; platelet count; platelet distribution width; mean platelet volume; plateletcrit; and levels of serum glucose, total cholesterol, triglyceride, HDL, and low-density lipoprotein. Univariate and multivariate analyses were used to assess the association between HDL levels and the erection of the penile tip and base. We confirmed that HDL had a beneficial effect on penile erectile function. We also found that when the HDL level exceeded the normal range, the change in HDL had a significant effect on the penile base. In addition, our study did not find any relationship between platelet parameters and erection of the penile tip or penile base.

Protein-Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network.

Protein-protein interaction (PPI) not only plays a critical role in cell life activities, but also plays an important role in discovering the mechanism of biological activity, protein function, and disease states. Developing computational methods is of great significance for PPIs prediction since experimental methods are time-consuming and laborious. In this paper, we proposed a PPI prediction algorithm called GRNN-PPI only using the amino acid sequence information based on general regression neural network and two feature extraction methods. Specifically, we designed a new feature extraction method named Mutation Spectral Radius (MSR) to extract evolutionary information by the BLOSUM62 matrix. Meanwhile, we integrated another feature extraction method, autocorrelation description, which can completely extract information on physicochemical properties and protein sequences. The principal component analysis was applied to eliminate noise, and the general regression neural network was adopted as a classifier. The prediction accuracy of the yeast, human, and Helicobacter pylori1 (H. pylori1) data sets were 97.47%, 99.63%, and 99.97%, respectively. In addition, we also conducted experiments on two important PPI networks and six independent data sets. All results were significantly higher than some state-of-the-art methods used for comparison, showing that our method is feasible and robust.

MDAKRLS: Predicting human microbe-disease association based on Kronecker regularized least squares and similarities.

BACKGROUND: Microbes are closely related to human health and diseases. Identification of disease-related microbes is of great significance for revealing the pathological mechanism of human diseases and understanding the interaction mechanisms between microbes and humans, which is also useful for the prevention, diagnosis and treatment of human diseases. Considering the known disease-related microbes are still insufficient, it is necessary to develop effective computational methods and reduce the time and cost of biological experiments. METHODS: In this work, we developed a novel computational method called MDAKRLS to discover potential microbe-disease associations (MDAs) based on the Kronecker regularized least squares. Specifically, we introduced the Hamming interaction profile similarity to measure the similarities of microbes and diseases besides Gaussian interaction profile kernel similarity. In addition, we introduced the Kronecker product to construct two kinds of Kronecker similarities between microbe-disease pairs. Then, we designed the Kronecker regularized least squares with different Kronecker similarities to obtain prediction scores, respectively, and calculated the final prediction scores by integrating the contributions of different similarities. RESULTS: The AUCs value of global leave-one-out cross-validation and 5-fold cross-validation achieved by MDAKRLS were 0.9327 and 0.9023 ± 0.0015, which were significantly higher than five state-of-the-art methods used for comparison. Comparison results demonstrate that MDAKRLS has faster computing speed under two kinds of frameworks. In addition, case studies of inflammatory bowel disease (IBD) and asthma further showed 19 (IBD), 19 (asthma) of the top 20 prediction disease-related microbes could be verified by previously published biological or medical literature. CONCLUSIONS: All the evaluation results adequately demonstrated that MDAKRLS has an effective and reliable prediction performance. It may be a useful tool to seek disease-related new microbes and help biomedical researchers to carry out follow-up studies.

Substantial Nitrogen Oxide Pollution Is Embodied in the Bilateral Trade between China and the European Union.

Against the backdrop of globalization and trade facilitation, the products consumed by a country are more and more relying on the importation of those products from other countries. Therefore, the pollutant emissions of products associated are transferred from consuming countries to exporting countries, which significantly changes the spatial distribution of global pollutant emissions. The objective of this research is to analyse the embodied nitrogen oxide (NOx) emissions in the trading process between China and the European Union (EU) and to further trace the interindustry and intercountry transfer paths. This study constructs a multiregional input-output (MRIO) model based on the latest EORA global supply chain database. The MRIO model quantitatively analyses the total NOx emissions from the production and consumption ends of China and the EU from 1995 to 2014. Important findings are derived from the empirical results as follows. (1) In 2014, China's production end emissions were 1824.38 kilotons higher than those of the consumption end. By contrast, the situation in the EU was the opposite, i.e., production end emissions were 1711.97 kilotons lower than those of the consumption end. (2) In the trade between China and the EU, the EU is a net importer of embodied NOx, and China is a net exporter of embodied NOx. In 2014, 2.55% of China's domestic NOx emissions were transferred to the EU in China-EU trade, accounting for 2.75% of China's domestic consumption demand. (3) In 2014, Electricity, Gas and Water (397.75 kilotons), Transport (343.55 kilotons), Petroleum, Chemical and non-metallic Products (95.9 kilotons), Metal Products (49.88 kilotons), Textiles and Apparel (26.19 kilotons), are among the industries with the most embodied NOx emissions from China's net exports during its two-way trade with the EU. (4) In the bilateral trade between the EU and China, many countries are in the state of embodied NOx net import. The top three net importers in 2014 were Germany (169.24 kilotons), Britain (128.11 kilotons), France (103.21 kilotons).