Search

A solid phase extraction-electrospray ionization-ion mobility spectrometry (SPE-ESI-IMS) method was developed to determine six organic acids in cucumber leaves. The multiplexing IMS using correlation technique was adopted to optimize the signal-to-noise ratio (SNR) and resolution. 90% methanol/water was selected to be the solvent, the flow rate was 120 µL·h−1, the stop frequency of ion gate was 5000 Hz, the ion gate opening period was 0.1 ms, the air flow velocity was 2.0 L·min−1, the electrospray voltage was 3.5 kV, and the drift tube temperature was 90 °C. The cucumber leaves were treated with ultrasound in 50% methanol/water. The analytes were subsequently eluted from an SPE cartridge with 0.1 mol·L−1 HCl, and the extract was neutralized to pH 2–3 with ammonia before freeze-drying. SPE-ESI-IMS enabled the effective determination of citric acid, fumaric acid, lactic acid, malic acid, pyruvic acid, and succinic acid based upon the drift time. The limits of detection were from 0.008 µg·mL−1 to 0.2 µg·mL−1, and the recoveries of spiked samples were from 87.00% to 107.20%. The convenient, sensitive, and efficient developed SPE-ESI-IMS method may be applicable for the determination of organic acids in leaves. [ABSTRACT FROM AUTHOR]

Symbolic semantic understanding of staff images is an important technological support to achieve "intelligent score flipping". Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note when the staff is performed. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement the optical staff's low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement the high-level semantic understanding stage. Such an algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory and outputs digital codes for the pitch and duration of the main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results with YOLOv5 show that the precision is 0.989 and that the recall is 0.972. The system's error rate is 0.031, and the omission rate is 0.021. The paper concludes by analyzing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping, and automatic performance. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the effectiveness of network coverage, a directional sensor network coverage optimization algorithm is developed based on the principles of virtual force and particle swarm optimization. Firstly, the article introduces the concept of a segmented virtual negative centroid model. This model revolutionizes the configuration of the virtual negative centroid, thereby enabling a more efficient adjustment of the gravitational forces exerted by the coverage blind areas on the sensor nodes. Therefore, the influence of these blind areas on the improvement of network coverage is significantly amplified. Secondly, taking into account the characteristics of global optimization and the inherent randomness of particle swarm optimization, the algorithm synergistically combines the principles of virtual force and particle swarm optimization. This integration effectively fine-tunes the sensing direction of the sensor nodes, thereby optimizing their overall performance. The algorithm in this study incorporates an adjusted inertia weight strategy and introduces Gaussian disturbance in the local optimization enhancement phase to prevent local optimization, accelerate particle convergence, and facilitate the sensor network's attainment of an optimal distribution for coverage optimization. Simulation experiments were conducted to verify the algorithm's effectiveness. The initial sensor network coverage was 31.04%. After applying the algorithm, the average coverage increased to 80.16%, with a maximum coverage of 84.2%. These results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Lead (Pb) pollution is a serious environmental and health risk and remains a major challenge for China. This study analyzes China's atmospheric Pb emissions from the dual perspectives of production and final demand, by integrating localized emission factors and a Multi-Regional Input – Output model. Our results show that Shandong, Hebei, and Hubei directly contribute over 36% of the national emissions. However, from the final demand perspective, some developed provinces, such as Jiangsu, Guangdong, and Zhejiang, induce a considerable proportion (29%) of the national emissions by relocating emissions to other provinces through inter-provincial trade. Trade-embodied emissions typically flow from interior regions to more affluent coastal regions (e.g. Henan-Jiangsu, Anhui-Jiangsu, Hunan-Guangdong). Considering both production and final demand, we identify different roles for provinces in Pb emission management. Prosperous beneficiary provinces should take more responsibilities by transferring advanced technologies, especially those in industries such as coal dressing, to sacrificial provinces. [ABSTRACT FROM AUTHOR]

China is facing serious water scarcity, and the effects of international trade on its water resources have been widely examined. Processing exports account for nearly half of China's gross exports. Adopting China's multi-regional input–output table that captures processing exports, we enrich the literature on virtual water exports by accounting for trade heterogeneity. The results show that China's virtual water exports show a significant trade heterogeneity. Normal and processing exports are attributed to 86.7% and 13.3% of the Agriculture sector's water use induced by exports respectively. Conversely, normal and processing exports are attributed to 31.8% and 68.3% of the Communications Equipment, Computers sector's water use induced by exports respectively. In addition, a cross-regional compensation is needed to deal with the unequal regional distribution of water uses and economic benefits related to exports. [ABSTRACT FROM AUTHOR]

Parameterization schemes such as soil thermal conductivity (STC) have an important impact on precipitation simulation. The precipitation in the rainy season (April–September) is the main factor affecting aridification in northern China. However, it is unclear how STC affects precipitation simulation during the rainy season. In this study, comparative experiments were conducted using the regional climate model RegCM4.6 coupled with the third-generation land surface model NCAR CLM4.5 to assess the effect of the Johansen and Lu–Ren STC schemes on precipitation. The results show that the STC had a significant effect on the simulation of rainy season precipitation and its variation in northern China. The precipitation variation characteristics simulated by the Lu–Ren scheme were closer than that of the Johansen scheme to the observation. The difference in land surface temperatures (LSTs) simulated by the two STC schemes could be a major cause of the sensitivity in the simulated precipitation. When the local LST increases by 1 K, precipitation decreases by 5–30 mm in most areas of northern China. The numerical experiments revealed that the rise of LST increases the longwave radiation, reduces the surface net radiation, and causes the redistribution of sensible and latent heat flux, forming local water vapor and thermal conditions that are not conducive to precipitation. Moreover, the difference of LST significantly changes the 500-hPa large-scale circulation field, the 700-hPa vapor transportation, and its divergence. The combined action of local heat, water vapor, and large-scale circulation factors reduces the precipitation in the rainy season. On the other side, the variation of the East Asia summer monsoon (EASM) affects the soil water content. In addition, a new STC scheme was added to NCAR CLM4.5, promoting the development of this land surface model. [ABSTRACT FROM AUTHOR]

Background and Purpose: Neuropeptide Y (NPY) has a modulatory role in learning and memory, and is involved in the pathophysiology of neurodegenerative diseases. However, there was no population-based evidence on the relationship between NPY and post-stroke cognitive impairment (PSCI). We aimed to prospectively examine the association between plasma NPY and cognitive impairment among patients with acute ischemic stroke.Methods: On the basis of samples from the China Antihypertensive Trial in Acute Ischemic Stroke, 593 patients with baseline plasma NPY levels were finally included in this study. The study outcome was cognitive impairment (Montreal Cognitive Assessment score < 26) at 3 months after ischemic stroke. Logistic regression models were used to estimate the risk of cognitive impairment.Results: After 3 months of follow-up, 422 participants (71.2 %) experienced cognitive impairment. Multivariable-adjusted odds ratio (95 % confidence interval) for the highest tertile of NPY was 0.58 (0.36-0.92) compared with the lowest tertile. Each 1-SD higher log-NPY was associated with a decreased risk of 20 % (95 % confidence interval 2 %-34 %) for PSCI. The addition of plasma NPY to the basic model with conventional risk factors improved the risk reclassification (continuous net reclassification index was 22.8 %, p = 0.01; integrated discrimination improvement was 0.9 %, p = 0.02) for PSCI.Limitations: We measured plasma NPY only once at baseline and failed to explore the association between NPY changes and PSCI.Conclusions: Elevated plasma NPY levels were associated with a decreased risk of cognitive impairment, suggesting plasma NPY may serve as a predictive factor and potential therapeutic target for PSCI. [ABSTRACT FROM AUTHOR]

Introduction: Ambient pressure electrospray ionisation ion mobility spectrometry coupled to high‐performance liquid chromatography (HPLC) was used to detect alkaloids from different parts of Sophora alopecuroides L. extracts. Multiplexing ion mobility spectrometry (IMS) was used to improve the signal‐to‐noise ratio while maintaining high resolving power for the detecting of eluents from HPLC separation. Material and methods: The alkaloids profile and distribution are demonstrated by retention time‐drift time two‐dimensional spaces, and the contents of five major alkaloids including sophoridine, sophocarpine, cytisine, aloperine, and matrine were determined in the leaf, skin, stem, seed kernel, and seed husk using the HPLC‐IMS method. This method offers extra separation ability to isomers such as matrine and sophocarpine, which can be difficult to distinguish by mass spectrometry. Results: The reduced mobilities for cytisine, sophoridine, sophocarpine, matrine, and aloperine are 0.828, 0.718, 0.731, 0.725, and 0.769 cm2/V/s, respectively. The limits of detection are 0.553, 0.488, 0.479, 0.484, and 0.513 ug/mL. This method adds extra separation ability to HPLC to resolve co‐eluted peaks and provides another qualitative parameter besides HPLC retention time. Ambient pressure electrospray ionization ion mobility spectrometry coupled to high‐performance liquid chromatography (HPLC) was used to detect alkaloids from different parts of Sophora alopecuroides L. extracts. This method adds extra separation ability to HPLC to resolve co‐eluted peaks and provides another qualitative parameter besides HPLC retention time. Five alkaloids were quantitively determined in different parts of Sophora alopecuroides L. with satisfied sensitivity. [ABSTRACT FROM AUTHOR]

Background and purpose: Soluble suppression of tumorigenicity 2 (sST2) might be related to stroke and depression, but the association of sST2 with poststroke depression (PSD) is unclear. The study aimed to prospectively assess the association between plasma sST2 levels and PSD. Methods: A total of 635 acute ischemic stroke patients with sST2 measurements from the China Antihypertensive Trial in Acute Ischemic Stroke were included in this analysis. We used the 24‐item Hamilton Rating Scale for Depression to assess depression at 3 months, and PSD was defined as a score of ≥8. Logistic regression analysis was performed to estimate the risk of PSD associated with sST2, and net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to assess the predictive value of sST2. Results: Two hundred fifty (39.4%) patients developed depression at 3 months after ischemic stroke. Patients with PSD had higher sST2 levels than patients without PSD (172.7 vs. 153.8 pg/ml; p = 0.003). After adjustment for age, sex, education, National Institutes of Health Stroke Scale score, and other covariates, the odds ratio for the highest quartile of sST2 compared with the lowest quartile was 1.84 (95% confidence interval, 1.10–3.08) for PSD. Adding sST2 to a conventional model notably improved risk prediction for PSD (category‐free NRI = 19.34%, 95% confidence interval = 4.39%–34.28%, p = 0.017; IDI = 1.20%, 95% confidence interval = 0.25%–2.15%, p = 0.014). Conclusions: Increased plasma sST2 levels in the acute phase of ischemic stroke were significantly associated with the increased risk of PSD, independently of conventional risk factors. [ABSTRACT FROM AUTHOR]

A fast and convenient high‐performance liquid chromatography‐electrospray ionization‐ion mobility spectrometry method was developed to determine nine representative metabolites in the seedlings of cucumber and wheat. The analytical conditions were obtained by optimizing the parameters of high‐performance liquid chromatography and ion mobility spectrometry. Briefly, acetonitrile–0.1% formic acid solution was selected as the mobile phase for gradient elution at a flow velocity of 0.4 mL/min. Under negative electrospray ionization mode, spray voltage of ion mobility spectrometry was 4.5 kV, and drift tube temperature was set at 90°C. The metabolites from seedling leaves were extracted using 80% acetonitrile as the solvent at 4°C for 12 h. Results showed that under soilless culture conditions, the contents of maltose, citric acid, and p‐hydroxybenzoic acid in the seedlings of cucumber and wheat were reduced by low concentration of itaconic acid, succinic acid, and citric acid. Importantly, this analytical approach demonstrated high sensitivity, good linear response, and high selectivity. The lowest limit of detection was 0.004 μg for p‐hydroxybenzoic acid. Overall, this high‐performance liquid chromatography‐electrospray ionization‐ion mobility spectrometry method is sensitive and efficient for rapid separation and identification of plant metabolites. [ABSTRACT FROM AUTHOR]

Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China; (2) according to daily data analysis, the search index of haze has increased progressively with increased PM 2.5 concentration with a time lag of 0–4 days and the lag time has a declining tendency from 2013 to 2017; (3) according to annual data analysis, the public concern showed a weak correlation with air quality and they showed an opposite temporal trend. However, when the long-term annual trend was removed, the strong positive correlation emerges between the fluctuation parts of the search index of haze and monitoring data of air quality. This indicates the public is more sensitive to the short-term fluctuation of air quality. The results of this paper provide statistical evidence on the evolution of public concern on air quality from 2013 to 2017. This study will help policy makers to better understand the patterns of the public's perception of environmental problems and consequently improve the government's capability to deal with these challenges. [ABSTRACT FROM AUTHOR]

Using China as a case study, this paper explores the integrated regional ecological risk assessment of multiple stressors and multiple receptors on a large spatial scale. The objective is to provide scientific data to support ecological risk identification and prevention. To carry out this assessment, ten natural disasters were chosen as risk sources, and twenty-two ecosystems were chosen as risk receptors. The vulnerability of environment where these ecosystems existed was taken into consideration. Using the software platform GIS, the ecological risk of each disaster was evaluated, the integrated assessment for all disasters was compiled, and the integrated risk of different ecosystems was obtained. All results were shown in assessment maps. The results show that forty-five percent of the ecosystems' areas in China face high or medium ecological risks. This result indicates that the establishment of ecosystem protection and ecological risk prevention mechanisms in China is still a long-term, difficult task, requiring the rational use and conservation of forests, meadows, farmland, wetlands, and other ecosystems alike is of great necessity. The uncertainty analysis of risk assessment using the Monte Carlo Simulation method demonstrated the results to be reliable and credible. [ABSTRACT FROM AUTHOR]

• Data of 599 acute ischemic stroke patients in the CATIS trail were used. • Elevated plasma sTREM2 levels in the short-term phase of ischemic stroke were associated with 3-month cognitive impairment, independently of established risk factors. • Plasma sTREM2 has potential value in predicting cognitive impairment after ischemic stroke. Previous researches have suggested that soluble triggering receptor expressed on myeloid cells 2 (sTREM2) plays a pivotal role in central nervous system pathologies and the development of neurodegenerative disorders. This study aimed to prospectively investigate the association between plasma sTREM2 levels and post-stroke cognitive impairment (PSCI). A sample of 599 consecutive acute ischemic stroke patients with sTREM2 measurements from the China Antihypertensive Trial in Acute Ischemic Stroke were eventually included in this analysis. Cognitive impairment was defined as a score of <25 for Mini-Mental State Examination, measured at 3-month follow up. Binary logistic regression was used to estimate the odds ratios (ORs) of plasma sTREM2 levels on the risk of PSCI. Of the 599 participants (mean age, 60.0 ± 10.4 years; male, 70.5%), 228 (38.1%) patients were diagnosed as PSCI. The risk of PSCI elevated significantly with higher plasma sTREM2 levels (p for trend <0.01). After adjusting for several confounding factors, the ORs for the highest quartile of sTREM2 compared with the lowest quartile was 2.06 (95% confidence interval, 1.20–3.53) for PSCI. Moreover, the addition of sTREM2 to the conventional model with established risk factors significantly improved risk discrimination (C-statistics increased from 0.668 to 0.691, p = 0.02) and reclassification (net reclassification improvement: 32.2%, p < 0.001; integrated discrimination improvement: 1.3%, p = 0.01) for PSCI. Results might be subject to selective bias and potential confounding. The present study demonstrated that elevated level of plasma sTREM2 may be associated with PSCI, and sTREM2 has potential value in predicting PSCI. [ABSTRACT FROM AUTHOR]

To better understand the sources and potential source regions of PM 2.5 , a field study was conducted from January 2011 to November 2011 at a background site, the Yellow River Delta National Nature Reserve (YRDNNR) in the North China Plain. Positive matrix factorisation (PMF) analysis and a potential source contribution function (PSCF) model were used to assess the data, which showed that YRDNNR experienced serious air pollution. Concentrations of PM 2.5 at YRDNNR were 71.2, 92.7, 97.1 and 62.5 μg m − 3 in spring, summer, autumn and winter, respectively, with 66.0% of the daily samples exhibiting higher concentrations of PM 2.5 than the national air quality standard. PM 2.5 mass closure showed remarkable seasonal variations. Sulphate, nitrate and ammonium were the dominant fractions of PM 2.5 in summer (58.0%), whereas PM 2.5 was characterized by a high load of organic aerosols (40.2%) in winter. PMF analysis indicated that secondary sulphate and nitrate (54.3%), biomass burning (15.8%), industry (10.7%), crustal matter (8.3%), vehicles (5.2%) and copper smelting (4.9%) were important sources of PM 2.5 at YRDNNR on an annual average. The source of secondary sulphate and nitrate was probably industrial coal combustion. PSCF analysis indicated a significant regional impact on PM 2.5 at YRDNNR all year round. Local emission may be non-negligible at YRDNNR in summer. The results of the present study provide a scientific basis for the development of PM 2.5 control strategies on a regional scale. [ABSTRACT FROM AUTHOR]