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Abstract Purpose This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for clinical treatment. Method The study population was collected from the national claims database in China. A total of 4,532 patients aged 4–18 who began risperidone therapy for mania between September 2013 and October 2019 were identified. The data were randomly divided into two datasets: training (80%) and testing (20%). Five regularly used machine learning methods were employed, in addition to the SuperLearner (SL) algorithm, to develop prediction models for the continuation of atypical antipsychotic therapy. The area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI) was utilized. Results In terms of discrimination and robustness in predicting risperidone treatment continuation, the generalized linear model (GLM) performed the best (AUC: 0.823, 95% CI: 0.792–0.854, intercept near 0, slope close to 1.0). The SL model (AUC: 0.823, 95% CI: 0.791–0.853, intercept near 0, slope close to 1.0) also exhibited significant performance. Furthermore, the present findings emphasize the significance of several unique clinical and socioeconomic variables, such as the frequency of emergency room visits for nonmental health disorders. Conclusions The GLM and SL models provided accurate predictions regarding risperidone treatment continuation in children and adolescents with episodes of mania and hypomania. Consequently, applying prediction models in atypical antipsychotic medicine may aid in evidence-based decision-making.

Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R2 = 0.87, p

Currently, hyperspectral remote sensing technology used for vegetation monitoring mainly uses empirical and semi-empirical statistical methods to calculate heavy metal content. Combining physical models and machine learning algorithms is an effective method for estimating biochemical vegetation parameters without much ground measurement data. However, a deep extreme learning machine (DELM) has a faster training speed and better generalization ability. By introducing the Aquila Optimizer (AO) algorithm, the training process of the DELM method can be accelerated. This study combined the PROSAIL model, chlorophyll concentration information, and vegetation SO2 purification ability, and comprehensively applied physical and empirical models to analyze the optical characteristics of the urban forest SO2 purification rate and other factors. A coupled model (PROSAIL + AO + DELM) was constructed to simulate urban forests canopy SO2 purification ability and was then applied to remote sensing inversion. These results indicated that the Syringa oblate Lindl. (S. oblate) and Ulmus pumila “Jinye” (U. pumila) had moderate SO2 purification capacities, whereas that of Prunus cerasifera var. atropurpurea Jack. (P. cerasifera) were low. The SO2 purification rate was highly sensitive in the green, red, and red-edge spectral ranges. In the SO2 purification rate estimation model constructed by the subset (Corresponding to Sentinel-2 band), NDI, DI, and RVI indices, the PROSAIL + AO + DELM model had the best performance, with R2, root mean square error (RMSE), and relative percent deviation (RPD) of 0.73, 0.056, 1.61, and 0.68, 0.096, and 1.06 for T2 (low concentration) and T3 (high concentration) treatments, respectively. The PROSAIL + AO + DELM model was extended to multispectral images (Sentinel-2), where the results of the NDI model inversion were the closest to those of field monitoring. These results indicate that the urban forest SO2 purification rate model constructed in this study has the potential to be applied on a large scale. This study addresses the research gap regarding rapid, non-destructive, and low-input evaluation of plant purification capacity and achieves the non-destructive detection of plant purification capacity from point to point and from static to dynamic. This enables efficient, non-destructive, and cost-effective evaluation of air purification levels, and provides a basis for predicting air purification in large regions in later stages.

To improve the extraction accuracy, the Savitzky-Golay filter is used to filter the noisy Brillouin spectra. Based on the obtained relatively accurate Brillouin frequency shift, the symmetric part of the Brillouin spectrum is selected according to the raw Brillouin spectrum. Based on the selected spectrum, the Brillouin frequency shift is extracted by the parabolic fitting. Since the proposed method can obtain the Brillouin spectrum with a high degree of symmetry and more calculation time, the improved parabolic fitting method can significantly improve the accuracy of the extracted Brillouin frequency shift in real-time demonstration. This paper considers the original parabolic fitting method, the fitting method based on the pseudo-Voigt model and the improved parabolic fitting method to calculate the Brillouin frequency shift along the optical fiber. The results show that the calculation time of the modified parabola fitting method is longer than that of the original parabola fitting method, but only 1/37~1/28 of that of the fitting method based on the pseudo-Voigt model. The accuracy of the Brillouin frequency shift extracted by the former is similar to that of the latter. The proposed method is effective to realize fast sensing of temperature and strain along the optical fiber.

Effective treatment of water pollution is an economic and social requirement globally. Humic acid (HA) is a popular mitigator for such waters. However, the combined effect of HA and restorative plants on cadmium (Cd) remediation is not well understood. Therefore, we experimented on Cd remediation using HA along with vetiver grass and HA-vetiver grass. We observed that vetiver grass effectively removed Cd at 15～30 mg/L. The accumulation capacity of the root was significantly higher than the shoots (P organelle > soluble substance (F1 > F2 > F3). The plant’s accumulation capacity against 25 mg/L Cd was higher than for other treatments. The root accumulation capacity was much higher (702.3 mg/L) than those without added HA. However, upon adding 200 and 250 mg/L HA, the phytoremediation of Cd in the root and shoot significantly reduced (P

Grassland ecosystem dominates in the Mongolian Plateau, mostly located in its arid and semi-arid regions. Although the ecosystem is an important source for agriculture, it is also a fragile system ecologically. This system is one of the most sensitive areas to global climate change. Precipitation (PPT) and soil moisture (SM) are important water sources in the grassland ecosystem, and their changes would greatly affect vegetation growth. This paper generates the precipitation use efficiency (PUE) and soil water use efficiency (SWUE) of Mongolian Plateau grassland based on multi-source remote sensing data to investigate the spatio-temporal distribution pattern and identify the driving factors. Results showed four main findings. Firstly, two water use efficiency (WUE) indicators show a generally increasing trend from 2000 to 2018, with average PUE and SWUE 1.07 gC·m−2·mm−1 and 1.03 gC/kg·H2O, respectively. They have similar spatial distribution patterns, consistent with the available water resources, decreasing from northeast to southwest. However, grassland vegetation growth is more sensitive to soil moisture than precipitation, and the dynamic change of SWUE is smoother and more significant than PUE. Secondly, due to the higher species richness, better vegetation biological characteristics and less severe growth environment, meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Thirdly, PUE and SWUE are relatively low in extremely arid and humid regions. In areas with relatively moderate water conditions (PPT in 148–360 mm, SM in 0.14–0.35 cm3/cm3), two indicators increase with the abundance of moisture conditions and reach the maximum. Fourthly, there is a positive linear relationship between PUE (SWUE) with precipitation and a unimodal correlation between PUE (SWUE) with temperature across the entire grassland. However, a varying correlation exists in different grassland ecosystems, especially meadow grassland. Together with analyzing past and future trends, this study provides strong evidence to reflect the impact of global climate change and the management and protection of the grassland ecosystems in arid and semi-arid regions.

Coronavirus disease 2019 (COVID-19) has crucially influenced anthropogenic activities, which in turn impacts upon the environment. In this study, we investigated the variations on aerosol optical depth (AOD) at 550 nm over the Bohai Sea and Yellow Sea during the COVID-19 lockdown (from February to March in 2020) of China mainland based on Moderate-resolution Imaging Spectroradiometer (MODIS) observation by comparing with historical AOD records (2011–2019). Our results show that with the lockdown implementation, the decade-low AOD levels are achieved in February and March 2020 (0.39 ± 0.18 and 0.37 ± 0.19, respectively), which are 22% and 28% (p

Hulunbuir, Otindag, Horqin and Mu Us are the four greater sandy lands in China. They are typical ecologically fragile areas with limited ability to resist interference and are susceptible to changes in the external environment. With climate change in recent decades, these areas have experienced severe land desertification and decreased groundwater levels. The ecological environment is at risk of further deterioration, and continuous vegetation restoration and management should be adapted to the local climate and maintain the water balance with natural precipitation. This paper combines multi-source satellite observations to explore the relationship between vegetation growth and water supply. The results show four main points. (1) Although the vegetation growth in Otindag and Horqin had degraded before 2010, it improved after 2010. The vegetation growth and soil moisture (SM) of the four sandy lands both generally increased from 2003 to 2016. The SM of the Horqin increased significantly (84% anomaly > 1.4 × 10−2 cm3/cm3). Other than the Hulunbuir, the other sandy lands show a downward trend in terrestrial water storage (TWS), especially the Mu Us declined the most (97% anomaly

With the increasing popularity of online retail, many manufacturers have opened up their own online marketplaces, which has accelerated competition in the retail market. This paper considers a two-echelon supply chain consisting of a manufacturer and a retailer, where the retailer and the manufacturer sell the same product on their respective online platforms. This sales model creates channel conflicts in the supply chain. In order to explore the impact of this kind of conflict and sales efforts on the supply chain, we constructed consumers' utility functions when they buy products in different channels as well as a supply chain profit model where there is channel competition and sales efforts. The results show that the severity of channel conflicts affects consumers' choice of purchasing channels, and excessive channel conflicts will cause consumers to lose interest in shopping, channel conflicts will also make retailers and manufacturers more competitive. In addition, sales efforts can increase channel conflicts. Numerical analysis shows that although channel conflicts weaken the overall profit of the supply chain, it is more beneficial to manufacturers. Finally, we analyze the impact of channel conflicts and sales efforts on consumer surplus. We generate results to provide guidance for the operations of two-echelon supply chains.

The estimation of water clarity in coastal and inland waters from satellite images is limited by significant uncertainties attributable to the optical complexity of these waters. To address this issue, we propose an improved semi-analytical model for retrieving the Secchi disk depth (ZSD) based on the mechanistic model (Lee model) developed by Lee et al. (2015). In the Lee model, a quasi-analytical algorithm (QAA) was employed to estimate the total absorption coefficient (a(λ)) and total backscattering coefficient (bb(λ)) to retrieve the diffuse attenuation coefficient (Kd(λ)). ZSD was then estimated by remote sensing reflectance (Rrs) and Kd. In this study, two improvements were made to the Lee model based on the band specifications of the Sentinel-2 Multispectral Instrument (MSI). First, the reference wavelength (λ0) was shifted from 560 nm and 665 nm to the longer red edge band 705 nm, and the modified empirical formula for estimating a(λ0) was applied to improve ZSD retrieval. Second, waters were classified into different optical water types (OWTs), and the optimal ZSD algorithm was identified for each OWT to further improve the accuracy of ZSD estimation. The proposed model (denoted as QAA_OWT) was calibrated and evaluated with in situ data (N = 399) collected from waters with various optical properties including a lake (Daihai Lake) dominated by colored dissolved organic matter (CDOM), a macrophytic lake (Ulansuhai Lake), and coastal water (the Bohai Sea) dominated by suspended sediments. The validation showed that QAA_OWT improved the accuracy of ZSD retrieval (0.18–5.0 m), with the averaged percentage difference (MAPD) decreasing from 31.9% to 24.9% and the root mean square deviation (RMSD) decreasing from 0.38 m to 0.32 m. The model was further applied to satellite images from the Sentinel-2 Multispectral Instrument (MSI) and independently assessed based on in situ-satellite match-ups (N = 31). The results verified the reliability of the model (MAPD = 31.8%, RMSD = 0.16 m). Time series MSI-derived ZSD maps clearly characterized the spatio-temporal patterns of the water clarity of Daihai Lake. Such maps are of significance in the accurate evaluation of the effectiveness of pollution control measures and aquatic ecosystem protection for lakes with low water quality. Sensitivity analysis revealed the robustness and insensitivity of the proposed model to input noise. This model could be applicable to many satellite sensors with spectral bands similar to those of Sentinel-2 MSI. Furthermore, it can be used as an effective tool for highly accurate remote sensing retrieval of ZSD in optically complex coastal and inland waters.

Background: Myocardial infarction (MI) resulting from acute coronary ischemia may cause significant morbidity and mortality, and microRNAs play a vital role in this pathophysiology. Limonin (LIM) is a natural medicine from citrus fruit that protects organs against ischemic diseases, but the candidate genes and pathways associated with cardioprotection are unknown. Methods: MI was induced by ligating the left anterior descending coronary in male Sprague-Dawley rats. LIM was orally administered for 7 days after the induction of MI. Subsequently, the hearts were collected to examine significant changes in microRNAs and mRNAs among the control (CON), MI, and LIM + MI groups. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks were used to identify the biological functions and signaling pathways of differentially expressed mRNAs. Candidate genes were validated by RT-qPCR. Results: Compared to the CON group, MI caused significant changes in the expression of 26 microRNAs and 1979 mRNAs. The bioinformatics analysis showed that inflammation, apoptosis, and oxidation were enriched in GO terms, while RAP1, PI3K/AKT, RAS, and cGMP-PKG were enriched in KEGG pathways. In addition, compared to the MI group, LIM induced significant changes in the expression of 4 microRNAs and 173 mRNAs. The differentially expressed mRNAs were related to collagen biosynthesis, the immune response, extrinsic apoptosis, and tight junctions. One microRNA (rno-miR-10a-5p) and 2 mRNAs (IGLON5 and LMX1A) were differentially expressed among the CON, MI, and LIM + MI groups. Conclusions: Our results suggest that the rno-miR-10a-5p-IGLON5/LMX1A axis may be a candidate pathway and promising target through which LIM alleviates MI-induced cardiac dysfunction.

Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.

The presence of a large amount of fine particles and muddy ore during the heap leaching process leads the occurrence of low leaching rate. Herein, agglomeration experiments using low-grade secondary copper sulfide ore were conducted to enhance the poor heap permeability and low leaching rate caused by the presence of a large amount of fine particles during the heap leaching process. The optimum binder, agglomeration technology, and agglomeration method were selected after investigating the bonding effects of different binders on mineral particles. The effect of single factor, including the binder mass fraction, acid quality, and bulk of water spraying on agglomeration experiments were conducted before the orthogonal experiment. The key factors that have a considerable effect on agglomeration were identified through the orthogonal experiment. According to the experimental results, the order of bonding effect of different granulation binders is as follows: SFS-2 > SFS-3 > cement > hemihydrate gypsum > SFS-1 > SFS-0 > sodium silicate > cationic polyacrylamide. The effect of agglomeration is the best when SFS-2 is selected as a binder, the acid quality is measured as 25 kg·t-1, and the mass fraction of spraying water is 30% during the agglomeration process. The wet strength and compressive strength reaches up to 94.62% and 417.44 N, respectively, after drying. The acid leaching time of agglomerations is maintained for more than 25 d, during which the shape of agglomerations remains unchanged and is without obvious fracture. According to the orthogonal experiment, the factors affecting the agglomeration in the descending order are as follows: binder mass fraction, acid quality, and bulk of water spraying. The bacterial inoculation experiment in the presence of binder was conducted, but it shows no considerable effect of binder on the bacterial community. The bacterial number of experiment in the presence of binder reaches 8.79×107 mL-1, while that in the absence of binder is 8.86×107 mL-1. The leaching experiments results show that the copper leaching rate increases by 12.74% after agglomeration because agglomeration increases the porosity between the minerals and improves the contact between leaching solution and minerals.

In order to ensure the full melting of the thermoplastic composites (TPC) prepreg in the process of automated fiber placement (AFP) and offer the potential to realize the in-situ consolidation for TPC, it is necessary to control the temperature of the prepreg in AFP. Aiming at the processing speed will be changed within the larger scope, an infrared heating technology with fast response rate and efficient was proposed. Based on the analysis of the heat transfer process between the infrared heat source and the layers, the control equation of dynamic temperature was put forward and the matching relationship between the radiation intensity and laying speed was established. Based on the AFP platform, the infrared heating system which adopts the feedforward control method was constructed and the corresponding control strategy was formulated according to the dynamic thermostatic control equation to realize the heating temperature of prepreg controlled accurately. The experiment results show that the infrared heating thermostatic control system can meet the requirement of dynamic thermostatic control in the condition of variable speed and the compressive strength and the interlaminar shear strength for AFP component are closed to the moulded component.

Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila ‘Jinye’ (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation.

The non-isothermal crystallization kinetics of glass fiber reinforced polypropylene for automated fiber placement was studied by differential scanning calorimetry (DSC) and Avrami equation. To investigate the influence of cooling rate and cooling time on non-isothermal crystallization behavior of matrix material and solve the maximum processing speed at different cooling speed, the non-isothermal crystallization kinetics model and the heat transfer model were derived and the cooling conditions in the process of automated fiber placement were set up. The result shows that the crystallinity of the polypropylene decreases along with the increase of cooling speed. The initial and final crystallization temperature are both moving in the direction of low temperature with the increase of cooling speed. The relative crystallinity is closed to the S type curve along with the change of temperature. The compress strength and the interlaminar shear strength are all increasing with the increase of the crystallinity, however, the impact strength is decreasing along with the increase of the crystallinity which is opposite to the compress strength and the interlaminar shear strength.

The Automated Fiber Placement (AFP) of thermoplastic composite (TPC) realizes in-situ crystallization layer by layer and offers the potential to increase production rate and significantly reduces manufacturing cost when processing of large parts. Desired quality of composite parts depends intensively on the thermal history of the AFP, the heater moving with the AFP head and the case of layer continuously being added on bottom prepreg were simulated by the ANSYS software to obtain the temperature field distribution in the laminate. At the same time, a real-time temperature measurement system based on WinCC flexible was established, aiming at acquiring and memorizing the temperature during the experiments. The results show that the temperature curves of each layer appear several peaks and the peaks are gradually increasing with the increase of the temperature of hot gas, the higher the temperature of hot gas is, the bigger the peak temperature difference is and the smaller the difference between thermocouple and simulation is. The finite element simulation results are verified to be correct by comparing the peak temperature of the experimental results with the finite element simulation results. The results show that peaks are gradually decreasing with the increase of the speed of roll. In order to meet the requirements of the component, the maximum speed is 1.2m/min when the temperature of hot gas is 600℃.

Largely due to the heterogeneity of environmental parameters and the logistical difficulty of moving photosynthetic equipment in the paddy fields, effective measurement of lowland rice photosynthesis is still a challenge. In this study, we showed that measuring detached rice leaves in the laboratory can not effectively represent the parameters measured in situ. We further described a new indoor facility, high-efficiency all-weather photosynthetic measurement system (HAPS), and the associated measurement protocol to enable whole-weather measurement of photosynthetic parameters of rice grown in the paddy fields. Using HAPS, we can conduct photosynthetic measurements with a time span much longer than that appropriate for the outdoor measurements. Comparative study shows that photosynthetic parameters obtained with the new protocol can effectively represent the parameters in the fields. There was much less standard deviation for measurements using HAPS compared to the outdoor measurements, no matter for technical replications of each recording or for biological replications of each leaf position. This new facility and protocol enables rice photosynthetic physiology studies to be less tough but more efficient, and provides a potential option for large scale studies of rice leaf photosynthesis.

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

It is known that high-risk human papillomavirus infection is the main etiological factor in cervical carcinogenesis. However, human papillomavirus screening is not sufficient for early diagnosis. In this study, we aimed to identify potential biomarkers common to cervical carcinoma and human papillomavirus infection by proteomics for human papillomavirus–based early diagnosis and prognosis. To this end, we collected 76 cases of fresh cervical tissues and 116 cases of paraffin-embedded tissue slices, diagnosed as cervical squamous cell carcinoma, cervical intraepithelial neoplasia II–III, or normal cervix from ethnic Uighur and Han women. Human papillomavirus infection by eight oncogenic human papillomavirus types was detected in tissue DNA samples using a quantitative polymerase chain reaction. The protein profile of cervical specimens from human papillomavirus 16–positive squamous cell carcinoma and human papillomavirus–negative normal controls was analyzed by proteomics and bioinformatics. The expression of candidate proteins was further determined by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. We identified 67 proteins that were differentially expressed in human papillomavirus 16–positive squamous cell carcinoma compared to normal cervix. The quantitative reverse transcriptase-polymerase chain reaction analysis verified the upregulation of ASAH1 , PCBP2 , DDX5 , MCM5 , TAGLN2 , hnRNPA1 , ENO1 , TYPH , CYC , and MCM4 in squamous cell carcinoma compared to normal cervix ( p