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Selective encryption technology is a data security protection method that can balance encryption security and efficiency. Currently, there is ongoing research on applying this technology to vector geographic data. However, existing algorithms often use random selection methods for choosing objects to be encrypted, which results in lower security. To address this problem, we proposed a selective encryption algorithm for vector geographic data based on feature point grouping strategy. The study includes four steps: (1) Based on the user-input encryption ratio, calculate the feature point grouping thresholds for each element in the data. (2) Extract feature point sets for each element based on the grouping threshold. (3) Sort and group the feature point sets, reducing the smallest encryption unit to group objects within individual elements. (4) To reduce encryption costs and enhance the algorithm's resistance to attacks, perform frequency domain coefficient encryption and spatial domain coordinate value encryption in a stepwise manner on the group objects, ultimately producing the encryption results. The experiment showed that: (1) The decrypted encrypted data maintains consistency with the original data coordinates, achieving lossless decryption. (2) The correlation between encrypted data and the original data is significantly disrupted, resulting in a high level of randomness and thereby ensuring the algorithm's robust security. (3) The key space is substantial and highly sensitive, capable of withstanding brute force attacks. (4) The algorithm significantly improves encryption efficiency. (5) The algorithm displays resilience against deletion attacks and noise attacks. In conclusions, the proposed method enhances encryption efficiency while upholding a high level of security, thus it is an efficient selective encryption algorithm for vector geographic data.

The domestication of ducks represents a pivotal evolutionary shift from the unguided propagation of wild species to deliberate human-mediated selection, culminating in distinct behavioural, morphological, and physiological traits that differentiate domesticated ducks from their wild counterparts. This transition has yielded breeds with traits fine-tuned to specific economic roles, such as egg production, meat yield, or dual-purpose functionality. Duck domestication plays a significant role in poultry production globally, meeting the growing demand for eggs and meat in various regions. Here, we focus on the Jinding Duck (JDD), a breed renowned for its prolific egg-laying traits. Employing whole-genome resequencing data from 325 individuals across five Chinese indigenous duck breeds, we aimed to dissect the unique population structure and assess the genetic diversity within the JDD cohort. The findings reveal the distinct genetic heritage of JDD, diverged from other domesticated breeds, and show a relative paucity of genetic diversity. A salient discovery was a 200 kb genomic interval containing three genes (NCF2, SMG7, and ARPC5) with almost exclusive haplotypes, which were inherited from Anas platyrhynchos or Anas zonorhyncha, impacting the morphological attributes of JDD. The study highlights a c.28G>A non−synonymous mutation in the first exon of the LAMC1 gene, which is potentially influencing feather morphology in JDD. Our findings suggest that unique blue eggshell colouration in JDD is likely attributable to variations within the promoter element of the ABCG2 gene, distinguishing it from other breeds. Moreover, the MAP7 and FHL1 genes emerge as significant factors in the laying performance of JDD. These genetic insights are not only crucial for improving the JDD breed but also provide valuable information that could be applied to duck breeding programmes worldwide, helping enhance productivity and meet international demands for high-efficiency poultry breeds.

Cloud storage has become essential in managing and retrieving extensive volumes of data, providing economical alternatives and adaptability for effective storage environment. However, in light of the rapid expansion of comprehensive datasets in cloud storage, the preservation of security has emerged as a matter of utmost importance for large data sets. Encryption has become a crucial mechanism for protecting confidential large data sets from unauthorized individuals. Encryption is necessary for safeguarding sensitive data by transforming it into indecipherable code so prevent unauthorized entry, and the encryption and decryption process is done at the end-user and cloud server. In the present situation, searchable symmetric encryption assumes a pivotal function by facilitating safe data retrieval while concurrently upholding the principle of secrecy. This research presents the Searchable Encryption Scheme in Cloud Storage Environment (SES-CSE), which offers a resilient solution for tackling the obstacles related to data security and retrieval efficiency for large data sets. The SES-CSE framework effectively incorporates encryption techniques inside a robust search engine, establishing a reliable framework for large data sets protection with Okapi BM25. The approach exhibits significant performance benefits, as shown by an encryption time of 14.85 ms, decryption time of 10.06 ms, memory consumption of 77.87 MB, and search times of 13.5 ms. The SES-CSE model demonstrates remarkable retrieval accuracies of 98.41%, 98.57%, and 97.51% throughout the training, testing, and validation phases. The results underscore the usefulness and security of SES-CSE as a solution for cloud storage, improving both the secrecy of data and the efficiency of retrieval in large-scale settings. This paper is part of special issue AI-DRIVEN SECURE COMMUNICATION IN MASSIVE IOT FOR 5G AND BEYOND.

Forest is an important component of ecosystem. To estimate forest above-ground biomass (AGB) accurately, this paper proposed an individual tree AGB estimation method based on fractal geometry and individual tree volume. In this study, fractal parameters, such as fractal dimension and intercept were first calculated. And then, a fast tree volume estimation method based on point clouds voxelization was proposed. By combining fractal parameters, tree volume and specific wood density together, an individual tree AGB estimation method was developed. The datasets of three different tree species with harvest referenced AGB values were used for evaluating the performance of the developed model. Experimental results showed that the coefficient of determination (R2) of the developed model was 0.853. Compared with other four traditional allometric models, the proposed model performs the best no matter which accuracy indicator was adopted.

SUMMARY: In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM2.5, PM10, and total suspended particulates emissions from U.S. broiler operations using inputs of daily mean ambient temperature, relative humidity, and live animal weight. The EPA developed these models using datasets collected between 2005 and 2007. It is important to note that over 15 yr have passed since the original data were collected, and broiler genetics, nutrition, and management have improved considerably. These models, if adopted, could have substantial impact on the U.S. broiler industry regarding air emission management and regulation. The objectives of this work were to 1) evaluate the draft EPA emissions model robustness and practicality, and 2) assess the estimated annual emissions for broiler operations in representative U.S. climates using the draft EPA models. The draft EPA emissions models and coefficients were coded and checked against example calculations provided in the draft EPA report, with discrepancies noted. The draft EPA models with log transforms were found to be overly complicated for deployment by most laypersons intending to estimate daily annual emissions from a house or a site with multiple houses. Substantive, fundamental model challenges were uncovered due to narrow model input ranges. For example, lower bird inventory (

Objective: The focus of this study was the six-transmembrane epithelial antigen of the prostate 4 (STEAP4) gene, on the basis of the cloned goat STEAP4 gene sequence. Its molecular and expression characteristics were analyzed, and its influence on the differentiation of goat subcutaneous adipocytes was explored through overexpression. Method: Reverse-transcription PCR (RT-PCR) was used to clone the goat STEAP4 sequence, and online tools were used to analyze the molecular characteristic. Real-time quantitative PCR (qPCR) was used to detect the expression level of STEAP4 in goat tissues and subcutaneous adipocyte differentiation. Liposome transfection, BODIPY, Oil Red O staining, and qPCR were used to explore the effect of overexpression of STEAP4 on adipocyte differentiation. Results: The cloned goat STEAP4 gene sequence was 1388 bp, and the complete coding sequence (CDS) region was 1197 bp, which encoded a total of 398 amino acids. Compared with the predicted sequence (XM_005679300.3), there were three base mutations in the CDS region of goat STEAP4, A188G, T281C, and A507G. Among them, A507G changed the amino acid at position 170 from Ile to Val. Analysis of the physical and chemical properties of the protein showed that STEAP4 was a stable hydrophilic basic protein. STEAP4 gene expression level was highest in goat liver tissue (P), followed by lung and back subcutaneous adipose tissue. STEAP4 showed different expression levels in goat subcutaneous adipocytes at different times during the induction of differentiation. The expression in the late stage of differentiation was higher than that before differentiation and lowest at 12 h (P). Overexpression of STEAP4 promoted the accumulation of intracellular lipid droplets; C/EBPβ (CCAAT enhancer binding protein) was extremely significantly up-regulated (P), and aP2 (fatty acid binding protein) was significantly up-regulated (P). Conclusion: Overexpression of STEAP4 could promote the differentiation of goat subcutaneous preadipocytes. This study lays the foundation for an in-depth study of the role of STEAP4 in goat lipid deposition.

Understanding the patterns of atmospheric phosphorus (P) deposition is essential for assessing the global P biogeochemical cycle. Atmospheric P is an essential source of P in agricultural activities as well as eutrophication in waters; however, the information on P deposition is paid relatively less attention, especially in the anthropogenic influencing region. Therefore, this study chose a typical urban–rural transition as a representative case to monitor the dry and wet P depositions for 2 years. The results showed that the fluxes of atmospheric total P deposition ranged from 0.50 to 1.06 kg P hm−2 yr−1, and the primary form was atmospheric dry P deposition (76.1 %, 0.76–0.84 kg P hm−2 yr−1). Moreover, it was found that the monthly variations of P deposition were strongly correlated with meteorological factors, including precipitation, temperature and relative humidity. However, the fluxes of dry P deposition and total P deposition were more affected by land use, which increased with the agro-facility, town and paddy field areas but decreased with the forest and country road areas. These findings suggested that dry P deposition was the primary form of total P deposition, and P deposition could be affected both by meteorological factors and land-use types. Thus, proper management of land use may help mitigate the pollution caused by P deposition.

Accurate global terrestrial evapotranspiration (ET) estimation is essential to better understand Earth's energy and water cycles. Although several global ET products exist, recent studies indicate that ET estimates exhibit high uncertainty. With the increasing trend of extreme climate hazards (e.g., droughts and heat waves), accurate ET estimation under extreme conditions remains challenging. To overcome these challenges, we used 3 h and 0.25∘ Global Land Data Assimilation System (GLDAS) datasets (net radiation, land surface temperature (LST), and air temperature) and a three-temperature (3T) model, without resistance and parameter calibration, in global terrestrial ET product development. The results demonstrated that the 3T model-based ET product agreed well with both global eddy covariance (EC) observations at daily (root mean square error (RMSE) = 1.1 mm d−1, N=294 058) and monthly (RMSE = 24.9 mm month−1, N=9632) scales and basin-scale water balance observations (RMSE = 116.0 mm yr−1, N=34). The 3T model-based global terrestrial ET product was comparable to other common ET products, i.e., MOD16, P-LSH, PML, GLEAM, GLDAS, and Fluxcom, retrieved from various models, but the 3T model performed better under extreme weather conditions in croplands than did the GLDAS, attaining 9.0 %–20 % RMSE reduction. The proposed daily and 0.25∘ ET product covering the period of 2001–2020 could provide periodic and large-scale information to support water-cycle-related studies. The dataset is freely available at the Science Data Bank (https://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022).

Landslide dams are caused when landslide materials block rivers. After the occurrence of large-scale landslides, it is necessary to conduct a large-scale investigation of barrier lakes and a rapid risk assessment. Remote sensing is an important means to achieve this goal. However, at present, remote sensing is only used for the monitoring and extraction of hydrological parameters, without predicting the potential hazard of the landslide dam. The key parameters of the barrier dam, such as the dam height and the maximum volume, still need to be obtained based on a field investigation, which is time consuming. Our research proposes a procedure that is able to calculate the height of the landslide dam and the maximum volume of the barrier lake using a single remote-sensing image and a pre-landslide DEM. The procedure includes four modules: (a) determining the elevation of the lake level, (b) determining the elevation of the bottom of the dam, (c) calculating the highest height of the dam and (d) predicting the lowest crest height of the dam and the maximum volume. Finally, a sensitivity analysis of the parameters used during the procedure and an analysis of the influence of the image resolution is carried out. This procedure is mainly demonstrated through the Baige landslide dam and the Hongshiyan landslide dam. A single remote-sensing image and a pre-landslide DEM are used to predict the height of each dam and the key parameters of the dam break, which are in good agreement with the measured data. This procedure can effectively support quick decision making regarding hazard mitigation.

The 3K planetary gear system is a basic planetary transmission structure with many advantages over the 2K-H planetary gear system. However, the vibration characteristics will be more complicated due to the increase of central gears meshing with each planet gear simultaneously. In this paper, a lumped-parameter model for a 3K-II planetary gear set was developed to simulate the dynamic response. The time-varying stiffness of each meshing pair for different gear tooth root crack faults is calculated via the finite element method. By considering the effect of time-varying transmission paths, the transverse synthetic vibrations are obtained. Subsequently, the feasibilities of transverse synthetic vibration signals and output torsional vibration signals as reference for fault diagnosis are analyzed by studying the time-domain and frequency-domain characteristics of these two vibration signals. The results indicate that both the transverse synthetic vibration signals and output torsional vibration signals can be used for fault identification and localization of the 3K-II planetary gear train, and yet they both have their limitations. Some results of this paper are available as references for the fault diagnosis of 3K planetary gear trains.

Understanding the air temperature distribution, ammonia (NH3) and carbon dioxide (CO2) levels in poultry housing systems are crucial to poultry health, welfare, and productivity. In this study, 4 Intelligent Portable Monitoring Units and 7 temperature sensors were installed inside and between the cages and above 2 minimum ventilation fans of a commercial stacked-deck cage laying hen house in the Midwest United States (425,000 laying hens) to continuously monitor the interior environment over a 6-month period. During cold conditions (March 12th–May 22nd), there was a variation noted, with barn center temperatures consistently being highest in the longitudinal and lateral direction (P < 0.001) and the top floor deck warmer than the bottom floor (P < 0.05). During hotter conditions (May 23rd–July 26th), the interior thermal environment was more uniform than during the winter, resulting in a difference only in the longitudinal direction. The daily CO2 and NH3 concentrations were 400 to 4,981 ppm and 0 to 42.3 ppm among the 4 sampling locations, respectively. Both CO2 and NH3 decreased linearly with increasing outside temperatures. The mean NH3 and CO2 concentrations varied with sampling locations and with the outside temperatures (P < 0.001). For CO2, the minimum ventilation sidewall had lower values than those measured in the barn’s center (P < 0.05) during cold weather, while the barn center and the manure room sidewall consistently measured the highest concentrations during warmer weather (P < 0.05). For NH3, the tunnel ventilation inlet end consistently had the lowest daily concentrations, whereas the in-cage and manure drying tunnel sidewall locations measured the highest concentrations (P < 0.001). Higher NH3 and CO2 concentrations were recorded within the cage than in the cage aisle (P < 0.05). The highest NH3 concentration of 42 ppm was recorded above the minimum exhaust fan adjacent to the manure drying tunnel, which indicated that higher pressure (back pressure) in the manure drying tunnel allowed air leakage back into the production area through nonoperating sidewall fan shutters.

Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was colocated with a reference instrument, the Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, at the Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine-learning algorithms using random search techniques for the best model architectures. The two machine-learning algorithms are XGBoost and a feedforward neural network (NN). Field evaluation showed that the Pearson correlation (r) between the low-cost sensor and the SHARP instrument was 0.78. The Fligner and Killeen (F–K) test indicated a statistically significant difference between the variances of the PM2.5 values by the low-cost sensor and the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F–K test using the test dataset showed that the variances of the PM2.5 values by the NN, XGBoost, and the reference method were not statistically significantly different. From this study, we conclude that a feedforward NN is a promising method to address the poor performance of low-cost sensors for PM2.5 monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.

The O2(a1Δg) emission near 1.27 µm is well-suited for remote sensing of global wind and temperature in near-space by limb-viewing observations to its bright signal and extended altitude coverage. However, vibrational–rotational emission lines of the OH dayglow produced by the hydrogen–ozone reaction (H+O3→OH•+O2) overlap the infrared atmospheric band emission (a1Δg→X3Σg) of O2. The main goal of this paper is to discuss the effect of OH emission on the wind and temperature measurements derived from the 1.27 µm O2 dayglow limb-viewing observations. The O2 dayglow and OH dayglow spectrum over the spectral region and altitude range of interest is calculated by using the line-by-line radiative transfer model and the most recent photochemical model. The method of four-point sampling of the interferogram and sample results of measurement simulations are provided for both O2 dayglow and OH dayglow. It is apparent from the simulations that the presence of OH dayglow as an interfering species decreases the wind and temperature accuracy at all altitudes, but this effect can be reduced considerably by improving OH dayglow knowledge.

In this paper, we report in situ observations by the Cluster spacecraft of energetic ions scattered into the loss cone during the inbound pass from the plasma sheet into the plasmasphere. During the inbound pass of the plasma sheet, Cluster observed the isotropy ratio of energetic ions to gradually decrease from unity and the isotropic boundary extended to lower L value for higher-energy ions, implying that the field line curvature scattering mechanism is responsible for the scattered ions into the loss cone from the plasma sheet. In the outer boundary of a plasmasphere plume, Cluster 3 observed the increase of the isotropy ratio of energetic ions accompanied by enhancements of Pc2 waves with frequencies between the He+ ion gyrofrequency and O+ ion gyrofrequency estimated in the equatorial plane. Those Pc2 waves were left-hand circularly polarized and identified as electromagnetic ion cyclotron (EMIC) waves. Using the observed parameters, the calculations of the pitch angle diffusion coefficients for ring current protons demonstrate that EMIC waves could be responsible for the ions scattering and loss-cone filling. Our observations provide in situ evidence of energetic ion loss in the plasma sheet and the plasmasphere plume. Our results suggest that energetic ions scattering into the loss cone in the central plasma sheet and the outer boundary of the plasmaspheric plume are attributed to the field line curvature scattering mechanism and EMIC wave scattering mechanism, respectively.

In photonic integrated networks on chip (NoCs), microrings are commonly used for adding or dropping a single optical signal to be switched in the NoC. This paper demonstrates the feasibility of adding or dropping two optical signals at the same wavelength in the same microring of NoCs with bus and ring topology. More specifically, the same microring can be used to support simultaneous bidirectional transmissions of two signals to be coupled in the NoC topology, leading to two different configurations, called shared source-microring and shared destination-microring. Spectral characterization shows good agreement between simulations and measurements taken on silicon-based integrated NoC. Bit-error-rate (BER) measurements indicate that the shared sourcemicroring configuration performs better, achieving a penalty as low as 1.5 dB for a BER of 10-9 at 10 Gb/s in the bus NoC. A higher penalty in the ring NoC for both configurations is due to higher crosstalk in the interconnecting ring.

Drivers of soil respiration (Rs) in farmland ecosystem have already been widely studied. However, the relationship between Rs and soil fauna (Fs), hydrothermic factor in the triple intercropping system remains poorly known. An LI-6400 XT portable photosynthesis measurement system equipped with a soil respiratory chamber was adopted in the experimental field. Straw mulching treatment increased soil respiration rate but ridge tillage treatment did not have a consistent effect. A regression analysis of the relationship revealed that exponential equation fitted well the relationship between Rs and soil temperature at 10 cm soil depth. However, the relationship between Rs and soil moisture was best confirmed by a parabolic function. The common dominant groups of Fs in wheat, corn, and soybean farmland were Collembola and Acarina, while Diptera emerged in the two rear crops farmlands. Compared to the control, ridge tillage reduced the number of Fs, but straw mulching increased it and improved the index of soil fauna diversity. In conclusion, the higher was the amount of animals being active above soil surface, the stronger was the soil respiration.

The present study investigated the oxidation kinetics of biodiesel stabilized with pyrogallol conducted at 110- 150°C using the PetroOXY method. It was demonstrated that the induction period increased with the increasing concentration and decreased with increasing temperature. The natural logarithm of antioxidant concentration varied linearly with respect to induction period, and the consumption of pyrogallol during the induction period followed the pseudo-first-order reaction kinetics over the temperature range studied. An increasing rate of consumption could be observed as temperature increased. Based on the Arrhenius equation, the activation energy for pyrogallol consumption could be calculated, which is different from the apparent activation energy for inhibited oxidation process. The introduction of pyrogallol into biodiesel could possibly lead to a change in the mechanism of oxidation process, due to the possible participation of antioxidant molecules in side reactions other than the main reaction of chain termination.

The existent gum of five diesel samples under five different partial pressure of oxygen (PPO) (21%, 15%, 10%, 5%, 1%) was investigated by means of storage test method. The formation rate of existent gum (K) was established by the least square method. The influence of diesel composition and partial pressure of oxygen on K is examined. Multiple linear regression method and least square method were carried out to establish two models to predict the existent gum formation rate. Results indicate that existent gum increases linearly with the storage time during oxidation reaction. The reaction of olefin and oxygen is the main one of forming existent gum. The relationship between K and PPO is power function, mass fraction of olefin has a linear effect on K. F test results proved the correctness of the diesel oxidation model, which can be used to predict the diesel oxidation.

Significant QTL for carcass and meat quality traits on Sus scrofa chromosome 7 (SSC7) were detected in various Meishan derived resource populations, especially on q1.1-q1.4 region. In order to confirm and narrow the QTL in this region, seven single-nucleotide polymorphisms (SNPs) and one insertion or deletion located in eight genes (BTNL1, SLC39A7, COL21A1, PPARD, GLP1R, MDFI, GNMT, and PLA2G7) were included for linkage mapping in a Large White × Meishan resource population, as well as two flanking microsatellite markers (SW2155 and SW352). Ten chromosome-wise significant QTL and two suggestive QTL were found. QTL affecting carcass weight and dressing percentage were mapped within the interval BTNL1 and SLC39A7. QTL for skin weight and percentage, bone weight and percentage in carcass were located between the interval PPARD and GLP1R. QTL for fat weight and percentage in carcass were detected between GNMT and PLA2G7 genes, while QTL for loin muscle width was found between GLP1R and MDFI. The results of this study will help to facilitate identifying the causative molecular genetic variation in this region.

Khuskia oryzae being as a contaminating fungus of jet fuel results in many damage. We have developed and valuated a repaid and accurate method for detection of Khuskia oryzae by loop- mediated isothermal amplification (LAMP) combined with a lateral flow dipstick (LFD).The result could be visualized by LFD within 10 min and did not rely on expensive experiment instrumentation. The detection limit of DNA of Khuskia oryzae by LAMP-LFD was 230fg/ul which was 10-fold more sensitive than LAMP and PCR methods.Khuskia oryzae could be specifically detected by LAMP-LFD, and no positive result could be observed when another 5 control genomic DNA were used. The whole process of detection by LAMP-LFD was 80 min which saved 90min compared to PCR method. The LAMP-LFD assay developed by this study was used for field detection and successfully detected Khuskia oryzae from the jet fuel samples. The LAMP combined with LFD makes the detection of Khuskia oryzae to be more simple, repaid and accurate, therefore, it will be a new tool for field detection.

TCF21 is a basic helix–loop–helix transcription factor that has recently been implicated as contributing to susceptibility to coronary heart disease based on genome wide association studies. In order to identify transcriptionally regulated target genes in a major disease relevant cell type, we performed siRNA knockdown of TCF21 in in vitro cultured human coronary artery smooth muscle cells and compared the transcriptome of siTCF21 versus siCONTROL treated cells. The raw (FASTQ) as well as processed (BED) data from 3 technical replicates per treatment has been deposited with Gene Expression Omnibus (GSE44461).

Asthma is characteristic with chronic airway inflammation and remodeling. Azithromycin (AZM), the 15-member macrolide, is known to present an anti-inflammatory effect and is increasingly being used in the treatment of chronic inflammatory pulmonary diseases. We hypothesize that low-dose azithromycin can inhibit allergen-induced airway remodeling except allergic airway inflammation in rat model. Male SD rats underwent intraperitoneal ovalbumin sensitization on days 1 and 6 followed by an intranasal challenge on day 7–13. On day 14, airway inflammation and remodeling were assessed by quantifying leukocytes in the airway, expression of multiple inflammatory mediators in BALF, histological examination in lung and TGF-β1 mRNA and protein levels by qRT-PCR, immunohistochemistry and Western blotting. Treatment with low-dose azithromycin at the dose of 25 mg/kg significantly reduced ovalbumin-dependent airway inflammation, including accumulation of neutrophils, lymphocytes and eosinophils, secretion of IL-2, IL-4, IL-13 and TNF-α. Moreover, airway remodeling was significantly abrogated by azithromycin in this model. The mucus cell hyperplasia, thickening of the peribronchial smooth muscle layer, secretion of ET-1, IL-2, IL-4, IL-13 and TNF-α, and increasing mRNA and protein expressions of TGF-β1 in lung tissue were all significantly decreased in azithromycin-treated rats. These findings demonstrate the protective effect of low-dose azithromycin on allergic airway remodeling in rat and suggest low-dose azithromycin may have beneficial effects in treating allergic airway inflammation.