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Germline stem cells (GSCs) are a group of unique adult stem cells in gonads that act as important transmitters for genetic information. Donor GSCs have been used to produce offspring by transplantation in fisheries. In this study, we successfully isolated and enriched GSCs from the ovary, ovotestis, and testis of Monopterus albus, one of the most important breeding freshwater fishes in China. Transcriptome comparison assay suggests that a distinct molecular signature exists in each type of GSC, and that different signaling activities are required for the maintenance of distinct GSCs. Functional analysis shows that fGSCs can successfully colonize and contribute to the germline cell lineage of a host zebrafish gonad after transplantation. Finally, we describe a simple feeder-free method for the isolation and enrichment of GSCs that can contribute to the germline cell lineage of zebrafish embryos and generate the germline chimeras after transplantation.

Rock bolts play an important supporting role in the construction of slopes, deep foundation pits and tunnels. As such, it is especially necessary to assess bolt anchorage quality. This paper proposes an identification model for bolt anchorage defects based on an Elman neural network (ElmanNN) optimised using an improved chicken swarm optimisation (CSO) algorithm and the frequency response function. First, the principal components of the frequency response functions of different anchorage bolts are used as the input within the model. Next, the weights and thresholds of the ElmanNN are optimised using an improved CSO algorithm based on chaotic disturbance and elite opposition-based learning. Finally, the model is used to identify bolt anchorage defects. The experimental results show that the model has a higher identification accuracy and faster convergence rate than other neural network models.Publisher Note: Following the original publication of this paper, a change was made to identify the corresponding authors on page 588. Full details can be found in the correction notice given after page 597 of the PDF for this article.

The authors have built a 2 kW/28.5 kJ superconducting flywheel energy storage system (SFESS) with a radial-type high-temperature superconducting bearing (HTSB). Its 3D dynamic electromagnetic behaviours were investigated based on the H -method, showing the non-uniform electromagnetic force due to unevenly distributed induced current may cause the superconducting magnet stator of the HTSB cracks. As the SFESS discharges, the amplitude and frequency of the discharge voltage decrease with speed, dropping by 55 and 58%, respectively, from the beginning phase (BP) to the middle phase (MP), by 41 and 37% from the MP to end phase. The decay rates of the discharge voltage and its fluctuation are both increased compared with the no-load operation. During the BP to MP, the output power is higher, the power quality is better and the harmonics are less. The amplitude and frequency of radial vibration are proportional to speed, larger than that during the no-load operation in the same case, and in the BP about 1.8 and 8.8% amplitude in the x and y directions, respectively. Its two norm is increasing by 2.6%, probably leading to the friction between the shaft and protective bearings, gradually becoming greater with speed.

As climate warming is widespread over the world, the Tibetan Plateau is particularly sensitive to climate warming impacts owing to its high elevations and complex topography, receiving worldwide attention for its significant feedbacks through affecting the atmosphere circulation, and thus the Asia climate system even to global. In this study, the new metric “total atmospheric energy”, is considered to investigate the association of thermal anomalies on the Tibetan Plateau with haze events in China. The total atmospheric energy (TPE) of the Tibetan Plateau, including sensible heat energy, potential energy, kinetic energy, and latent heat energy, was calculated using US National Center for Environmental Prediction (NCEP) meteorological reanalysis data from 1980 to 2016 to characterize the atmospheric thermal forcing of the large topography of the Tibetan Plateau. Results show that TPE has displayed periods of increase levels in recent decades, and inter-annual variations of TPE are significantly positively correlated with winter haze days over northern China but are negatively associated with haze events in southern China. Further diagnostic analysis and simulated results using the Weather Research and Forecasting (WRF)-Chem model indicate that the anomalous increase of TPE leads to enhanced thermal stability of the lower atmosphere, a weakened East Asian winter monsoonal wind in northern China, and increased near-surface wind speed in southern China, which are conductive to positive PM2.5 anomalies in northern China and negative PM2.5 anomalies in southern China. This implies that meteorological changes induced by TPE anomalies may play an important role in wintertime haze pollution over different regions of eastern China. Understanding the climate-related TPE modulating the favourable meteorological conditions for winter haze pollution over Eastern China could contribute to long-term planning for air pollution control.

We collected ambient PM10 and PM2.5 samples from six sites in Tian’jin, China, from February to March 2016 and then analyzed their chemical compositions and identified the emission sources using the positive matrix factorization model. The mean concentrations of the PM10 and PM2.5 were 98 and 71 µg m–3, respectively, with a mean PM2.5/PM10 ratio of 0.67. The average concentrations of the combined SO42–, NO3–, and NH4+ were 19.9–23.4 µg m–3, accounting for 72.4–77.1% of the total measured ions. The concentrations and percentages were significantly higher for NO3– and OC than for other species. The SO42–/NO3– ratio showed a decreasing tendency as the PM10 and PM2.5 concentrations increased, implying a strong influence from mobile sources. The mean OC/EC ratios for PM10 and PM2.5 were 3.1 and 3.2, respectively, with small spatial differences. The most abundant elements were crustal, accounting for 73.2–84.2% of the total detected elemental mass, and mainly enriched in the PM10. The optimal number of factors for PM2.5 and PM10 was selected via PMF analysis: the decrease in the Q/Qexcept values of these two fractions lessened when choosing six instead of five factors, indicating that five factors may be optimal. All the factors were mapped in bootstrap (BS) for 100% of the runs, and no swaps occurred with the displacement of factor elements (DISP) for five factors. Secondary inorganic aerosol, coal combustion, crustal dust, vehicle exhaust, and biomass burning contributed 28–30%, 20–21%, 18–21%, 17–20%, and 4%, respectively. Secondary inorganic aerosol displayed less spatial heterogeneity than the other sources in its contributions. Backward trajectory and PSCF analysis showed that air masses affecting Tian’jin mainly originated in the northwest during the heating period, and northeastern He’nan, southwestern Shan’dong, Bei’jing, and Tian’jin itself were major potential source areas.

In the past few decades, quantum computation has become increasingly attractive due to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes. In this paper, we present a quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms than previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with $$p+q$$ p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double-precision and 128-bit extended-precision. Usually, we use nearest-neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point data in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the quantum scaling circuits of 3-D floating-point data are designed.

Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

Health concerns and financial losses caused by mushroom poisoning have been reported worldwide.

The terrain effect on atmospheric environment is poorly understood in particular for the polluted region with underlying complex topography. Therefore, this study targeted the Sichuan Basin (SCB), a deep basin with severe PM

Aiming at the problem of low precision of actuator fault identification under closed loop control of aircraft, a fault diagnosis algorithm was proposed, which extracted fault feature by ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA), classified by AdaBoost and adaptive support vector machine (AdaBoost-ASVM). Firstly, in terms of feature extraction, the fault signal was decomposed into intrinsic mode functions (IMFs) of different frequency range by EEMD to avoid the modal mixing phenomenon in empirical mode decomposition (EMD). Secondly, the features of IMF were calculated from multiple dimensions. Thirdly, those high-dimensional features were analyzed by PCA to extract the main features that contain fault information. AdaBoost-ASVM algorithm was proposed to solve the problem of low accuracy of traditional multi-class SVM. Multiple SVM based classifiers with weak learning ability were constructed into a strong classifier through ensemble learning. Adaptive parameter optimization was carried out for each SVM based classifier. Simulation results shows that, compared with traditional multi-class SVM algorithm and some other machine learning algorithms, proposed method improves the accuracy of fault diagnosis and provides an effective and feasible method for the identification of aircraft actuator fault state under closed-loop control system.

As an important issue in atmospheric environment, the contributions of anthropogenic emissions and meteorological conditions to air pollution have been few assessed over the receptor region in regional transport of air pollutants. In this study on observations of environment and meteorology over 2015–2019, the Kolmogorov–Zurbenko (KZ) filter was performed to decompose the PM2.5 variations into multi-time scale components over the Twain-Hu Basin (THB), a receptor region in regional transport of air pollutants in central China, where the short-term, seasonal and long-term components accounted for respectively 47.5 %, 41.4 % and 3.7 % to daily PM2.5 changes. The short-term and seasonal components dominated the day-to-day PM2.5 variations with long-term component determining the change trend of PM2.5 concentrations over recent years. The emission- and meteorology-related long-term PM2.5 components over the THB were identified. The meteorological contribution to PM2.5 declining trend presented the distinct spatial pattern over the THB with northern positive rates up to 61.92 % and southern negative rates down to −24.93 %. The opposite effects of meteorology on PM2.5 pollution could accelerate and offset the effects of emission reductions in the northern and southern THB, which is attributed to the upwind diffusion and downward accumulation of air pollutants over the receptor region in regional PM2.5 transport. It is noteworthy that the increasing conversion efficiencies of SO2 and NO2 to sulfate and nitrate for secondary PM2.5 could offset the effects of PM2.5 emission reduction on air pollution in the THB during recent years, revealing the enhancing contribution of gaseous precursor emissions to PM2.5 concentrations with controlling anthropogenic emissions of PM2.5 and the gaseous precursors over the receptor region in regional transport of air pollutants.

To analyse relationship between tumour necrosis factor-α (TNF-α) (G308A) polymorphism and Vascular Parkinson (VP) complicated with pulmonary infection.Analytical study.Zhuji people's Hospital, China, from February 2017 to April 2021.Eighty five patients with VP complicated with pulmonary infection were included in the observation group. Eighty five patients with VP were included in the comparison group. Genotypic analysis of TNF-α (G308A) and serum TNF-α were detected.TNF-α (G308A) polymorphism in both the observation group and control group conformed to the genetic balance rule by the Hardy-Weinberg test. The frequency of each gene satisfied the genetic balance (p=0.175, and p=0.184, respectively). Genotype distribution and alleles of TNF-α (G308A) between the observation group and the control group were different (both p0.001). Compared with control group, serum TNF-α level in the observation group was higher (p0.001). In the observation group, serum TNF-α level in cases with GA+AA genotype (carrying A allele) was higher than that in cases with GG genotype (non A allele) (p0.001).TNF-α (G308A) polymorphism is associated with VP complicated with pulmonary infection. VP Patients with TNF-α (G308A) AA genotype and an allele may have a high risk of pulmonary infection.Tumor necrosis factor-α (TNF-α), Polymorphism, Vascular Parkinson (VP), Pulmonary infection.

The Sichuan Basin (SCB), to the east of the Tibetan Plateau (TP), experiences severe ozone (O

MicroRNAs (miRNA) are a kind of endogenous non-coding small molecule RNA, which are widely found in plants, animals, and viruses. miRNA can regulate the post-transcriptional cleavage of target message RNA or inhibit its translation process, and plays an important regulatory role in gene expression. Further studies have shown that miRNAs also play an important role in tumorigenesis and host-pathogen interactions. Given the important contribution of miRNAs in gene regulation and biological functions, rapid and accurate detection of miRNAs has become increasingly significant. Lateral flow assay is a detection method derived to achieve rapid detection in the field. It overcomes the problems of cumbersome, time-consuming, and high cost in traditional detection methods, and provides a good platform for accurate, sensitive, and immediate detection of target analytes. With the advantages convenient, cost-saving, and repaid result readout, the lateral flow test strips are now a recognized point-of-care testing (POCT) device and has been widely used in medical diagnosis, agriculture, food, and environmental safety. For the first time, we summarized the research progress of miRNA detection based on lateral flow methods, focusing on the strategies used in the lateral flow assays for miRNA detection and introducing some main experimental operation details, as well as providing an outlook on the future development direction of this field, aiming to provide a general guidance for colleagues preparing to conduct related research.

By using the multi-source data of meteorology over recent decades, this study discovered a summertime “hollow wet pool” in the troposphere with a center of high water vapor over Asian water tower (AWT) on the Tibetan Plateau (TP), where is featured by a vertical transport “window” in the troposphere. The water vapor transport in the upper troposphere extends from the vertical transport window over the TP with the significant connections among the Arctic, Antarctic and TP regions, highlighting an effect of TP’s vertical transport window of tropospheric vapor in the “hollow wet pool” on global change. The vertical transport window was built by the AWT’s thermal forcing in associated with the dynamic effect of the TP’s “hollow heat island”. Our study improve the understanding on the vapor transport over the TP with an important implication to global change.

The operating conditions of wastewater treatment process is time-varying and nonlinear. In the study of biochemical reaction process, there are coupling relations and size differences among variables of non-single controlled objects. In order to solve the multi-variable control problem, this paper use the Model Predictive Control (MPC) based on neural network. A number of control factors are considered in the design process and by constructing the cost function, the controller is developed in the expected direction. In addition, at the actual working condition change, the single working condition change is very difficult to maintain for a long time. In this paper, a multi-condition identification model predictive control is established by using T-S model. The scheme can make the controller more fit with the change of state. In order to verify the effectiveness of the designed controller and guarantee the data authenticity, the multi-state condition of this paper is constructed by using the real data provided by BSM1.The experimental results show that the designed controller is more stable at single condition and multi-condition, and the control accuracy has been improved obviously. The advantages of the controller are also illustrated by the evaluation indexes of IAE (Integral Absolute Error) and ISE (Integral Square Error).

Precise characterization and targeting of host cell transcriptional machinery hijacked by SARS-CoV-2 remains challenging. To identify therapeutically targetable mechanisms that are critical for SARS-CoV-2 infection, here we elucidated the Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2. The analysis revealed coordinated inactivation of MR-proteins linked to regulatory programs potentiating efficiency of viral replication (detrimental host MR-signature) and activation of MR-proteins governing innate immune response programs (beneficial MR-signature). To identify MR-inverting compounds capable of rescuing activity of inactivated host MR-proteins, with-out adversely affecting the beneficial MR-signature, we developed the ViroTreat algorithm. Overall, >80% of drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 infection, without affecting cell viability. ViroTreat is fully generalizable and can be extended to identify drugs targeting the host cell-based MR signatures induced by virtually any pathogen.

With the application of electronic detonators, millisecond blasting is regarded as a significant promising approach to improve the rock fragmentation in deep rock blasting. Thus, it is necessary to investigate the fracturing mechanisms of short-delay blasting. In this work, a rectangle model with two circle boreholes is modeled as a particles assembly based on the discrete element method to simulate the shock wave interactions induced by millisecond blasting. The rectangle model has a size of 12 × 6 m (L × W) and two blast holes have the same diameter of 12 cm. The shock waves are simplified as time-varying forces applied at the particles of walls of the two boreholes. Among a series of numerical tests in this study, the spacing between two adjacent boreholes and delay time of millisecond blasting are considered as two primary variables, and the decoupling charge with a coefficient of 1.5 is taken into account in each case. The results show that stress superposition is not a key factor for improving rock fragmentation (tensile stress interactions rather than compressive stress superposition could affect the generation of cracks), whereas collision actions from isolated particles or particles with weakened constraints play a crucial role in creating the fracture network. The delay time has an influence on causing cracks in rock blasting, however, whether it works heavily depends on the distance between the two holes.

In this paper, we propose a novel method of computing Hilbert transform based on the mechanical quadrature method. Experiments show that the method outperforms the library function ‘hilbert’ in Matlab when the values of functions being handled are very large or approach to infinity, that is a problem we have to deal with when we compute, for instance, outer functions. As an application, we use the method to obtain the unwinding series of signals, which is a positive frequency decomposition of signals and is dependent of extraction of outer functions involving computation of Hilbert transform. The experimental results show better stability. Then, we give the transient time frequency distribution of the unwinding series of signals. Finally, experiments of noisy signals are given, and the results show that the introduced method can well resist some disturbance.

This paper reports on the fabrication of marking units of controllable size ranging from 300 to 500 μm equipped with anti-reflective micro-structures using defocused femtosecond laser on titanium alloy TC4. The results show that the range of diameters of marking units (laser ablation-based craters) goes through three stages with the increase of the pulse number. The craters are too shallow for the first stage and too deep for the third stage to meet the criterion of depth, so the second stage turns out to be the optimal stage of parameter selection, where the diameter and depth of marking units are within a desired range with modification of laser energy and defocusing amount. Besides meeting the marking requirements of the size and morphology, the anti-reflective micro-structures with great color difference are formed on the surface of marking units, which contributes to the high recognition rate. Compared with the conventional marking methods, this method has a great recognition rate without recast layer and micro-cracks. Therefore, the femtosecond laser-based processing would provide a new marking technology with high efficiency and quality.

The high temperature deformation behaviour of medium Mn steel (0.2C7Mn) at deformation temperature of 950°C~1150°C, strain rate of 0.001s-1~1s-1 and reduction of 60% was conducted by Gleeble-3500 thermal-mechanical simulator. According to the stress-strain curve, combined with the deformation structure, the effects of different deformation conditions on dynamic recrystallization (DRX) were analyzed. The flow stress model and DRX fraction model of the test steel were established by using the Arrhenious constitutive equation relation and Z parameter. The hot deformation activation energy of the steel was 382426J/mol. Finally, the comparison between the predicted curve and the experimental curve showed that the predicted results of the calculation were consistent with the experimental results, which proved the reliability of the model.

Feedforward neural network (FNN) is an information processing system that simulates human brain function to a certain extent by referring to the structure of a biological neural network and the working mechanism of biological neurons. As the most important part, the architecture of FNN essentially influences its application performance. This paper proposed a structure self-adaptive algorithm for FNN (SSAFNN) based on biological mechanism. First, self-adaptive neuron growing and pruning indexes are proposed based on the idea of biological neuron grow factor and neuron competition, respectively. The FNN structure is dynamically adjusted according to the growing and pruning indexes of hidden neurons. Second, the connect weights of FNN are automatically adjusted during the self-organizing process and trained by gradient descent method during the learning process. Third, the theoretical analysis is given that this proposed algorithm not only optimized the network structure but also ensured the convergence and performance of FNN. The proposed SSAFNN is tested on the benchmark problems in the field of classification and prediction and is applied in the engineering problems of anchor bolt non-destructive testing and wastewater effluent ammonia nitrogen predicting. The experiment results reveal the good performance and potentiality of SSAFNN in industrial applications.

Isothermal compression deformation tests of medium-Mn (0.15C-7Mn in wt%) steels (15Mn7) at elevated temperatures were carried out on a Gleeble-3500 thermal mechanical simulator. Hot deformation behavior of the steels was examined with a constant strain rates of 0.001, 0.01, 0.1 or 1 1/s between 1150 oC and 900 oC. The effects of deformation strain rate (0.001~1 1/s) and temperature on compression behavior of the steels was studied. It shows that the deformation conditions have significant influence on stress-strain curves of the steel. The curve shows obvious dynamic recrystallization style at high temperature, and low strain rate or dynamic recovery or work-hardening style at low temperature and high strain rate. The deformation resistance decreases with deformation temperature increasing and strain rate decreasing, and the peak stress and peak strain both decrease, which demonstrates that dynamic recrystallization is more easily to occur at high deformation temperature and low strain rate. The austenite hot deformation equation was established as following. And the corresponding hot deformation activation energy Qdef for the steel is 320.984 KJ/mol. Z0.15c−7Mn = e˙exp(320984/RT) = A[sinh(ασ)]n = 1.15 × 1010[sinh(0.0156σp)]4.01

POGZ, which encodes a multi-domain transcription factor, has been found frequently mutated in neurodevelopmental disorders, particularly autism spectrum disorder (ASD) and intellectual disability (ID). However, little is known about its functions in embryonic stem cells (ESCs) and in transcriptional regulation. Here, we show that POGZ plays key roles in the maintenance of ESCs by association with the SWI-SNF (BAF) chromatin remodeler complex and heterochromatin protein 1 (HP1) proteins. Loss of POGZ induces differentiation of ESCs, likely by up-regulation of primitive endoderm and mesoderm lineage genes and by down-regulation of pluripotency-related and cell cycle genes. Genome-wide binding analysis shows that POGZ is primarily localized to gene promoter and enhancer regions where POGZ is required to maintain an open chromatin. Regulation of chromatin under control of POGZ depends on esBAF complex. Furthermore, there is an extensive overlap of POGZ and OCT4 peaks genome-wide, and both factors interact with each other. We propose that POGZ is an important pluripotency-associated factor, and its absence causes failure to maintain a proper ESC-specific chromatin state and transcriptional circuitry, which eventually leads to loss of ESC phenotype. Our work provides important insights into the roles of POGZ in the maintenance of ESC identity as well as regulation of transcription, which will be useful for understanding the etiology of neurodevelopmental disorders by POGZ mutation.

Voltage Source Converter-based High Voltage Direct Current systems are particularly prone to have IGBT valve short-circuit, AC single phase grounding fault and other types of serious fault. In this paper, for the five most common serious VSC faults of VSC-HVDC, a method to both protect against and identify these kinds of faults that can block a faulty converter valve-arm is presented. Firstly, a mathematical model for three-phase AC currents incorporating converter valve-arm protection is developed. Then, a fault identification method based on a binary tree for fault types and their location is proposed, which uses AC currents over two cycles after blocking as characteristic signals. Fault identification thresholds for distinguishing the type of fault are also provided. Finally, the proposed method is verified using PSCAD simulation. The results show that the method can not only identify the fault type reliably, but also accurately determine its location. The method can be used effectively for serious fault identification across a range of VSC-HVDC converter applications.

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins—whose aberrant activities govern the reprogramed state of infected-coronavirus cells—presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013&ndash, 2019 in Nanjing, an urban area in East China, this study investigated the influences of PM2.5 and RH on atmospheric visibility changes over recent years. The visibility had significantly negative correlations with the PM2.5 concentrations and RH changes. The nonlinear relationships existed between PM2.5 concentrations and visibility, as well as between RH and visibility, with the inflection points in the atmospheric visibility changes. The PM2.5 inflection concentrations were 81.0 &mu, g m&minus, 3, 76.0 &mu, 3, 49.0 &mu, 3, and 33.0 &mu, 3, respectively, for the RH ranges of RH &lt, 60%, 60% &le, RH &lt, 80%, 80% &le, 90%, and RH &ge, 90%, indicating that the improvement of visibility with reducing PM2.5 concentrations could be more difficult under the humid meteorological condition. The visibility changes were most sensitive to PM2.5 concentrations in the RH range of 60&ndash, 80% in this urban area of East China. The relative contributions of natural factor RH and anthropogenic factor PM2.5 to variations of wintertime atmospheric visibility were quantified with 54.3% and 45.7%, respectively, revealing an important role of natural factor RH in the change of atmospheric visibility in the urban area of East Asian monsoon region.