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Extracellular vesicles (EVs), products released by cells in multiple biological activities, are currently widely accepted as functional particles and intercellular communicators. From the orthodox perspective, EVs derived from apoptotic cells (apoEVs) are responsible for cell debris clearance, while recent studies have demonstrated that apoEVs participate in tissue regeneration. However, the underlying mechanisms and particular functions in tissue regeneration promotion of apoEVs remain ambiguous. Some molecules active during apoptosis also function in tissue regeneration triggered by apoptosis, such as caspases. ApoEVs are generated in the process of apoptosis, carrying cell contents to manifest biological effects and possess biomarkers to target phagocytes. The regenerative effect of apoEVs might be due to their abilities to facilitate cell proliferation and regulate inflammation. Such regenerative effect has been observed in various tissues, including skin, bone, cardiovascular system, and kidneys. Engineered apoEVs are produced to amplify the biological benefits of apoEVs, rendering them optional for drug delivery. Meanwhile, challenges exist in thorough mechanistic exploration and standardization of production. In this review, we discussed the link between apoptosis and regeneration, current comprehension of the origination and investigation strategies of apoEVs, and mechanisms in tissue regeneration of apoEVs and their applications. Challenges and prospects are also addressed here.

ObjectiveTo investigate the contamination, molecular typing characteristics and antibiotic resistance of Cronobacter in infants formula food in He'nan.MethodsA total of 227 commercially available infants formula food were collected, and Cronobacter was isolated and identified according to GB 4789.40—2016. Pulse field gel electrophoresis (PFGE) and the broth microdilution method were used to confirm the molecular type and antibiotic resistance. 16S rRNA and multilocus sequence typing (MLST) were used to identify the species and analyze the phylogenetic evolution of Cronobacter.Results13 Cronobacter strains isolated from 227 infants formula food were identified as C.sakazakii, C.turicensis and C.dublinensis. The antibiotic susceptibility result of 13 Cronobacter strains showed that the resistance rate of cefazolin reached 84.6% (11/13). 13 strains of Cronobacter were divided into 7 clusters and 6 ST types. Majority of the strains with the same PFGE type also had the same MLST type of which ST1 was the dominant type.ConclusionThe main contamination of Cronobacter in infants formula food was caused by C.sakazakii with diverse biochemical phenotypes and genotypes. The molecular phylogenetic tree of MLST well interpreted the genetic relationship between different kind of species.

[Introduction] Aiming at the current problems of the continuous update of the security requirements and low efficiency and uncontrollable effects of security rectification on-site operations for the power monitoring system, and the status quo of large area off-grid operation of some secondary equipment without effective rectification methods, secondary equipment centralized security operation and maintenance and key technologies of active perception, diagnosis and early warning are studied. [Method] Compliant grid-connected devices were unified deployed in substations, the technologies of deep belief nets multi-class support vector machine intrusion detection and adaptive probabilistic marking scheme IP traceback were used to filter recording data, active percept and retrospective diagnosis invaded threats, the risk early warning and comprehensive assessment technology based on combining with threat and adversarial analysis were adopted to carry out impact assessment and rating, and the existing recording master station system was used to expand the security operation and maintenance management business to form centralized operation and maintenance management model. [Result] Practical results show that this method can achieve the goals that the security system of substation secondary equipment is centralized managed and efficiency of operation and maintenance is higher. [Conclusion] The proposed method is effective and feasible to solve the problem of on-site security rectification of secondary equipment combined with the Guangxi power grid operation case.

BackgroundThe calculation error of the stacked pulse amplitude generated by traditional pulse shaping methods leads to distortion in the X-ray fluorescence spectrum; thus, it is difficult to accurately analyze the spectrum measured in a high-stacking rate background.PurposeThis study aims to propose a transformer model based on deep learning for the pulse amplitude estimation of radiation measurements using high-performance silicon drift detectors.MethodsFirstly, multi-head attention was applied to the transformer model, and an encoder-decoder structure with embedded positional encoding was employed to estimate the amplitude of stacked pulses. Then, a predefined mathematical model was used to simulate the pulse signal output by the detector for model training, and Gaussian noise corresponding to thermal noise and shot noise was added to the signal to simulate real nuclear pulses. Finally, experimental verifications were carried out on powdered iron ore samples and powdered rock samples, and relative error, corresponding to the accuracy of pulse amplitude estimation, was used as a model performance evaluation indicator.Results & ConclusionsExperimental verification results show that the average relative error obtained for eight offline pulse sequences of powdered iron ore samples and powdered rock samples is 0.89%, which means that the model can accurately estimate the amplitude of stacked pulses.

[Introduction] Aiming at the problems of business concentration, fault decision delay and high data transmission overhead for power grid dispatching control center, a fast and intelligent information processing method for fault recorder master station based on edge computing was proposed. [Method] Based on the edge computing design principle and overall framework of fault recording information processing, we used DataSocket asynchronous parallel real-time communication and Hadoop distributed storage scheme to ensure stable transmission, fast storage and retrieval for data, adopted improved SSDA algorithm to sift out the fault information to analysis quickly, and submitted the fault analysis results electronically. [Result] The experimental results show that this method has good application effects in mass data processing, rapid fault diagnosis, auxiliary decision support, etc. It can alleviate the pressure of information processing of dispatching control center and shorten the time delay of fault decision-making. [Conclusion] The proposed method is effective and feasible to handle massive fault recording information reliably combined with the Guangxi power grid operation case.

Artificial intelligence (AI) is an important enabling technology for promoting global digital development, and it is leading a new round of technological revolution and industrial transformation. Promoting AI core software and hardware technologies and industry is strategically significant for the national development, industrial upgrading, and productivity improvement in China. In this study, we summarize the development status of AI core hardware and software in China and abroad from the aspects of technology, industry, and policy, and analyze the problems faced by China’s development. Subsequently, we present the development ideas of China’s AI hardware and software technology and industry, propose the strategic objectives for 2025 and 2035, and summarize the key tasks for future development from the aspects of AI core hardware, AI core software, and AI-related basic technology. To provide references for the sustainable development of AI core hardware and software in China, we suggest that AI core software and hardware technologies should be included in the national top-level planning for science and technology innovation to acquire increased scientific research investment; AI open source platforms should be constructed and demonstrated; the research and development of AI key generic technologies should be promoted to achieve collaborative innovation; the AI industrial base should be reengineered to upgrade the industrial chain; and the AI innovative talent training system should be improved.

Two different morphological ZnO materials with hierarchical micro/nano structures were fabricated through solvothermal method and oxalate method, respectively. Their photocatalytic performances were evaluated by using the degradation of methylene blue (MB) solution as model reaction. The results show that the product via solvothermal method exhibits flower-like micro-spherical appearance assembled by many nano-flakes, while its counterpart derived from oxalate strategy shows micro-rod like morphology constructed by many nanoparticles. ZnO fabricated by oxalate method has much better photocatalytic activity towards MB degradation, whose reaction rate constant is 7.65 times as high as that one obtained by solvothermal method. The identification results for active radicals verify that ·OH and ·O2- are both formed in the two samples when they are irradiated under ultraviolet light. However, due to their diverse band structures, there is difference in number of active radicals produced in the reaction system, which can further lead to different photocatalytic efficiency for MB degradation. As for oxalate-derived ZnO, greater number of ·OH and ·O2- can be formed under the irradiation of ultraviolet light. Meanwhile, ·OH with strong oxidation capacity plays a dominant role in the photocatalytic reaction system. As a result, it possesses more excellent property in degradation of MB than that of ZnO originated from solvothermal method.

【Background】 Drought could significantly impact hydrological processes and water resources, and understanding frequency and uncertainty of drought occurrence is hence imperative to water resources planning. The copula method has emerged as an improved multivariate analysis over the univariate analysis for quantitative analysis of drought. 【Objective】 This paper is to present the results of the frequency and severity of droughts in Songhua river basin calculated using the copula multivariate method. 【Method】 The analysis was based on archived data, and duration and severity of the pooled daily drought events were identified using the variable threshold level method. The drought index variables were fitted by six common distribution functions respectively, and they were then evaluated using the Kolmogorov Smirnov test method. Optimal model selected from three Copula functions was used to establish a two-dimensional joint distribution for the drought index variables, from which we calculated the joint distribution probability as well as the return period. The uncertainty of the drought was analyzed using the Monte Carlo method. 【Result】 With the pooling criteria set to be pc=0.1 and tc=5, the average drought duration was 81~105 days in the basin. The lognormal distribution can adequately describe the marginal distribution of the drought duration in Dalai, Fuyu and Harbin stations in the basin, despite the differences in optimal distribution of the drought severity between them. The Frank copula was the best model for the two-dimensional joint distribution of the drought, and the return periods of the droughts were less than 20 years. When the return period was 20 years, the maximum design values for Dalai, Fuyu, Harbin and Jiamusi hydrological stations were most uncertain. 【Conclusion】 The copula multivariate model can adequately describe the joint distribution of the drought index variables in Songhua river basin, and its application should consider drought uncertainty.

As one of the most promising nanofiller, carbon nanotube has attracted more and more attention due to its extraordinary stiffness and strength. While it is not feasible to obtain the effects of carbon nanotubes on the mechanical properties of composites by solely relying on experimental methods, cleaving and analysing the influence of various parameters of carbon nanotubes on the mechanical properties of composites through numerical simulation methods has become a tendency. Based on the morphology of carbon nanotubes, the nano-meso scale model was proposed. Then three numerical simulation methods were classified depending on the differences between the way of modelling and the objects of discussing. In the end, the influencing law of carbon nanotubes on the mechanical properties of composites was reviewed from two aspects(intrinsic properties of carbon nanotubes, content and distribution of carbon nanotubes), which is expected to provide support to the credibility of results from numerical simulation method. In addition, due to the variety of numerical simulation methods and the diversity of carbon nanotube variables, the numerical simulation research of carbon nanotubes reinforced FRP composites still has great potential.

Brain edema is one of the major causes of fatality and disability associated with injury and neurosurgical procedures. The goal of this study was to evaluate the effect of ulinastatin (UTI), a protease inhibitor, on astrocytes in a rat model of traumatic brain injury (TBI).

This study was aimed to explore the composition and diurnal variation analyses of floral scent emission from Rosa rugosa Thunb. and Tulipa gesneriana L. The floral scent from the fresh flower were collected at different time points (9:00, 12:00, 15:00, 18:00, and 21:00) using dynamic headspace collection and were analyzed using autothermal desorber-gas chromatography/mass spectrometry (ATD-GC/MS). The results showed that a total of 62 volatile flavor compounds were detected from Rosa rugosa Thunb and a total of 70 volatile flavor compounds were detected from Tulipa gesneriana L. They were identified with eight functional categories: alcohols, fatty hydrocarbons, terpenes, aldehydes, ketones, esters, and other substances. The total release amount first decreased, and then increased with time, and arrived at the lowest at 15:00. The release amounts of different categories present distinct change patterns. Among the components, phenylethyl alcohol, citronellol, methylene chloride, hexane, and acetone showed relatively higher release amounts and were thought as the main components in floral scent of Rosa rugosa Thunb. Alpha-Farnesene, ethanol, pentadecane, beta-ocimene, longifolene, caryophyllene, and acetone showed relatively higher release amounts and were thought as the main components in floral scent of Tulipa gesneriana L. Research of roses and tulips in aromatic in the garden provides a theoretical basis and research and improvement of the aroma components of aroma.

Background Contrast-induced acute kidney injury (CIAKI) is the third leading cause of acute renal failure in hospitalized patients. This study was aimed to investigate whether atorvastatin could upregulate the expression of hydrogen sulfide (H2S) and hence protect against CIAKI. Methods We treated male rats and NRK-52E cells by iopromide to establish in vivo and in vitro models of CIAKI. Pretreatment with atorvastatin was given in CIAKI rats to investigate its effect on CIAKI. We collected serum and urine samples to detect renal function. We obtained kidney tissue for histological analysis and detection of protein concentration. We tested the serum concentration of H2S and renal expression of two H2S synthetases [cystathionine γ-lyase (CSE) and cystathionine-β synthase (CBS)]. NaHS was pretreated in NRK-52E cells to testify its underlying effect on contrast-induced injury. Results Atorvastatin significantly ameliorated renal dysfunction and morphological changes in CIAKI rats, as well as inflammation, apoptosis, and excessive oxidative stress. Atorvastatin also markedly increased the serum concentration of H2S and renal expression of CSE and CBS. Moreover, pretreatment with NaHS in NRK-52E cells considerably attenuated contrast-induced cell death and inflammation. Conclusion Atorvastatin protects against CIAKI via upregulation of endogenous hydrogen sulfide.

BackgroundLittle is known about how the residential distance to the coast is associated with incident myocardial infarction (MI) and which mechanisms may explain the association. We aim to explore this association using data from a prospective, population-based cohort with unprecedented sample size, and broad geographical coverage.MethodsIn this study, 377,340 participants from the UK Biobank were included.ResultsIt was shown that 4,059 MI occurred during a median 8.0 years follow-up. Using group (64 km) were both associated with a higher risk of incident MI (HR 1.12, 95% CI 1.04–1.21 and HR 1.09, 95% CI 1.01–1.18, respectively). HR for offshore region (64 km) was larger in subgroup in urban area (HR 1.12, 95% CI 1.03–1.22, pinteraction = 0.065) and in subgroup of high nitrogen dioxide (NO2) air pollution (HR 1.29, 95% CI 1.11–1.50, pinteraction = 0.021).ConclusionWe found a U-shaped association between residential distance to the coast and incident MI, and the association was modified by physical activity, population density, and air pollution.

A highly efficient deep fully convolutional neural network (DFCN) for image quality assessment (IQA) is designed in this paper. The DFCN consists of two branches, one scoring local patches and the other estimating the weights of local patches to enhance quality prediction. Then, the DFCN outputs quality score of the whole image with aggregate weighted average pooling. There are no fully connected layers in the DFCN, resulting in far fewer parameters. In addition, the network model utilizes multiscale images as inputs to enrich the extracted distortion information. Furthermore, the parameters of the model are optimized in two steps to reduce the requirement for computing power and the risk of overfitting. The parameters of the shared layers and the quality module are optimized firstly, and then, the parameters of the weight module are optimized with the designed loss function. The extensive experimental results show that the proposed DFCN outperforms other competing IQA methods and has strong generalization ability.

This paper discloses an efficient one-pot protocol to convert easily accessible 3-nitropyridines to 3-acetamidopyridin-2-yl 4-methylbenzenesulfonate derivatives which are core structures of many pharmaceutical molecules. The strategy successfully combined a three-step reaction in one pot via progressively adding different reactants at rt. The reaction displays good functional group tolerance and regioselectivity. Structurally diversified 3-nitropyridine could be time-efficiently (3.5 h) derivatized to various functional 2-O,3-N-pyridines which are apt for further elaborations. The transformation was amenable to gram-scale synthesis.

[Introduction] To fill the shortcomings of traditional on-line power quality monitoring system, such as high deployment cost, large maintenance workload, low coverage and single function, which are highlighted under the higher requirement of information and intellectualization for the rapid development of UHV grid and new energy grid-connected technology. [Method] A method of identifying, extracting and calculating power quality characteristic parameters based on fault recorder data mining was proposed, and a system architecture of non-intrusive power quality monitoring, evaluation and decision support for wide area power grid using existing recorders and dispatching data network was designed. [Result] The results of pilot application show that the power quality indicators monitored by the system are comprehensive, the acquisition accuracy is better than the standard requirements, and the accuracy of event recognition and diagnosis is high. It has obvious advantages in station coverage, capacity of monitoring points, file upload, early warning and information delivery. [Conclusion] The design of the system is feasible and can extend and supplement the existing power quality online monitoring system in wide area power grid with a small input.

[Introduction] With the development of cloud computing, big data, artificial intelligence, 5G, and ubiquitous Internet of Things, the data center for the power industry is to improve the planning and leading of the data center of the power industry and the full life cycle digital service capability of the system integration. Scholars and builders provide an academic exchange platform. The 2nd China Power Industry Data Center Summit Forum was held in Guangzhou on May 9, 2019. [Method] The conference was co-sponsored by the Data Center Working Group of the Information Engineering Professional Committee of the China Engineering Construction Standardization Association and the China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., and hosted by the China Institute of Intelligent Information. [Result] This forum will focus on the construction of China′s power industry data center, technology leapfrog development, industry innovation and integration; in the environment of the fourth industrial revolution led by digital, network and intelligent, co-operate with electricity Cross-border development and technology integration with data centers. [Conclusion] The conference is of great significance to promote the development of data centers in China′s power industry.

In order to provide the accurate real-time knowledge of future channel state information for the adaptive transmission of narrowband land mobile satellite (LMS) communication systems, an adaptive long-range prediction (ALRP) method, suitable for a three-state LMS channel model at S-band, is proposed. The very slow shadowing conditions' variations of satellite propagation channel can be described as a three-state Gilbert-Elliot channel model based on Markov chain. The channel state of future long-range is predicted based on weighting prediction while the parameters of linear auto-regression model are updated using an iterative adaptive tracking algorithm. The future channel fading series are predicted by the latest observations within current channel state. Simulation results show that the proposed method can be used to predict the future long-range channel state and fading series of three-state Gilbert-Elliot channel model accurately; and it is observed from the mean square error results that the prediction performance of the ALRP method is much more accurate than that of long-range prediction method. Moreover, the method has advantage of real-time and low-complexity which will make the adaptive transmission techniques feasible for LMS channel models. [ABSTRACT FROM AUTHOR]

The lattice reduction aided Tomlinson-Harashima precoding (LRA-THP) is computationally intensive. In order to reduce the computational complexity, a low-complexity group precoding algorithm is proposed for multi-beam satellite systems based on an existing group precoding algorithm. Users are split into groups according to the distance between beams in the proposed algorithm. Then, preprocessing matrix is obtained by maximizing the successive signal-to-leakage-plus-noise ratio (SSLNR) to suppress the inter-group interference. The LRA-THP is applied in each group. The characteristic of the multi-beam satellite channel is exploited to reduce the dimension of the preprocessing matrix which results in computational complexity reduction. Simulation results show that the complexity of the proposed algorithm is much less than that of the LRA-THP. Furthermore, the proposed algorithm reduces the complexity over the existing group precoding algorithm by 24% while limiting the performance loss to 0.1 dB. In addition, the proposed algorithm can provide a flexible tradeoff between the performance and complexity. [ABSTRACT FROM AUTHOR]

Considering the narrowband two-state land mobile satellite channel model with variable model parameter at S-Band, an adaptive long-range prediction method is proposed based on weighting prediction. Firstly, a two-state Markov Gilbert-Elliot channel model with an ability of describing shadowing conditions of satellite communication downlink is established. And then, the future long-range channel state is predicted by weighting prediction, and the coefficients of linear auto-regression model are updated by iterative adaptive tracking method using the least mean square algorithm. Finally, the future channel fading series are predicted. Simulation results show that the proposed method not only can be used to predict the future long-range channel states and fading series accurately, but also improve prediction performance compared with the long-range prediction method. Moreover, this method has ability of real-time and low-complexity and can be used in the adaptive transmission performance analysis of narrowband LMS communication systems. [ABSTRACT FROM AUTHOR]

In view of the problem of limited usage scenarios as well as inaccuracy of state duration probability density function (SDPDF) in existing land mobile satellite (LMS) channel model, we propose a narrowband LMS channel model which is capable of describing much more operational scenarios accurately and effectively. The state sequence is achieved by using Semi-Markov chain which approximates the SDPDF as a lognormal distribution. Within each state the carrier-to-noise ratio (C/N) time series is generated and defined as a Loo distribution by combining with propagation parameters. Doppler frequency shift is applied in slow fading components to increase parameter database and extend usage scenarios. The state probabilities, the first-order statistical characterization of C/N time series, and the required margins of 90% signal availability are investigated and compared with measurement results under different scenarios conditions. Simulation results show that the proposed model is effective for the representation of shadowing conditions on much more scenarios. In addition, it has a good applicability for different elevations and driving directions in different environments, and can be used in system performance analysis. [ABSTRACT FROM AUTHOR]

Asphaltene deposition is a common phenomenon during CO2 flooding in ultralow permeability reservoirs. The deposited asphaltene occupies the pore volume and decreases permeability, resulting in serious formation damage and pore well productivity. It is urgent to investigate the asphaltene deposition mechanisms, adverse effects, and preventive measures. However, few asphaltene deposition investigations have been systematically conducted by now. In this research, the asphaltene precipitation mechanisms and adverse effects were comprehensively investigated by using experimental and numerical methods. To study the effects of pressure, asphaltene content, and temperature on asphaltene precipitation qualitatively and quantitatively, the microscope visible detection experiment and the PVT cell static experiment were firstly conducted. The adverse effects on porosity and permeability resulted from asphaltene deposition were also studied by the core flooding experiment. Secondly, simulation models of asphaltene precipitation and deposition were developed and validated by experimental data. Finally, a case study from Changqing oil field was presented to analyze the asphaltene deposition characteristic and preventive measures. The experimental results showed that the asphaltene precipitation increases with the increased pressure before reaching the minimum miscible pressure (MMP) and gets the peak value around the MMP, while decreases slowly. The asphaltene precipitation increases with the increased temperature and asphaltene content. The variation trend of adverse effects on porosity and permeability resulted from asphaltene deposition is similar to that of asphaltene precipitation under the influence of pressure, asphaltene content, and temperature. The case study shows that the water-altering-gas (WAG) with high injection rate suffers more serious asphaltene deposition compared with the WAG with low injection rate, for the asphaltene precipitation increases as the increased pressure before reaching the MMP. The CO2 continuous injection with high injection rate is the worst choice, for low sweep efficiency and the most severe formation damage. Thus, the WAG with optimal injection rate was proposed to maintain well productivity and to reduce formation damage resulted from asphaltene deposition during developing ultralow permeability reservoirs.

A high-throughput screening technology for single-cell by fluorescence-activated droplet sorting (FADS) has been applied to improve industrial enzymes and production strains, which usually require fluorophore-modified substrates to measure enzyme activity in microfluidic droplets. However, the difficulty of developing fluorophore-modified substrates limits their further application. Here, we employed the intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) within microfluidic droplet sorting for screening Bacillus amyloliquefaciens variants with high glutaminase production. A rapid microplate assay based on the biosensor was developed to quantify glutaminase activity over 700-fold faster than high-performance liquid chromatography (HPLC). The novel FADS workflow was established subsequently for high-throughput screening of glutaminase-producing strains and achieved an enrichment ratio of 56 times. Furthermore, a 105 whole-genome mutant library was sorted, obtaining a clone that over 47 % increase in glutaminase production. Compared with the microtiter plate screening system, the biosensor-based microfluidic platform can give a 5 × 102 screening rate enhancement and decrease reagent consumption by 1.8 × 104 times. This strategy based on genetically encoded fluorescent biosensors will accelerate the application of FADS to the directed evolution of industrial enzymes and cell factories. • Combining the genetically encoded biosensor with droplet microfluidic technology. • Genetically encoded biosensor was used for rapid quantification of glutaminase activity. • A novel workflow achieved high-throughput screening at single-cell resolution. • One round of screening yielded a strain with a 47% increase in glutaminase production. [ABSTRACT FROM AUTHOR]

RNA binding proteins (RBPs) play a key role in genome regulation. Here we report the post-transcript regulation of IGF2BP3, which belongs to the insulin-like growth factor 2 mRNA binding protein family. We used iRIP-seq and RNA-seq to analyze the transcript regulation and alternative splicing on IGF2BP3 treated with overexpression cells and control. Overexpressed IGF2BP3 has broadly increased genes expression which involved in G-protein coupled receptor signaling pathway, positive regulation of cell proliferation, and signal transduction. IGF2BP3 regulated alternative splicing of multiple genes mainly clustered at response to hypoxia, negative regulation of transcription, and embryonic development. This study first provides alternative splicing analysis on transcription level of IGF2BP3 regulation, which laid the foundation for later research on IGF2BP3 critical functions.

Fast capacity estimation for retired batteries is necessary when batteries are recycled for echelon utilization. Here, a fast capacity estimation method is proposed for retired LiFePO4 battery. First, a full survey of battery pack and cells degradation after a long period of service is studied. Then the filtered ICA is used to study degradation variation phenomenon of retired batteries, the relationship between IC curve feature and remaining capacity was studied. Finally, a fast capacity estimation using incremental capacity and Gaussian process regression is proposed. Our results show high efficiency and accuracy of the proposed method.