Search

Abstract Background The psychological situation of high school students during adolescence is not promising, and the most obvious manifestation is the lack of subjective well-being (SWB). This network analysis presents a model of the interaction and correlation between different items of SWB, identifying the most central items for high school students. Methods Through offline and online surveys, 4,378 questionnaires were sent out and finally 4,282 Chinese high school students were available. The response rate was 97.807%. The study used the eLASSO method to estimate the network structure and centrality measures. This algorithm used the EBIC to select the best neighbor factor for each node. Results The average age for high school students was 16.320 years old and the average SWB score was 76.680. The distribution of SWB between male and female students was significant different (P

ObjectiveTo investigate the associations of Insulin-like growth factor-II (IGF2) gene, Insulin-like growth factor-II receptor (IGF2R) gene and Insulin-like growth factor-II binding protein 2 (IGF2BP2) gene polymorphisms with the susceptibility to gestational diabetes mellitus (GDM) in Chinese population.MethodsA total of 1703 pregnant women (835 GDM and 868 Non-GDM) were recruited in this case-control study. All participants underwent prenatal 75 g oral glucose tolerance test (OGTT) examinations during 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. Genotyping of candidate SNPs (IGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs1374910, rs11705701, rs6777038, rs16860234, rs7651090) was performed on Sequenom MassARRAY platform. Logistic regression analysis was conducted to investigate the associations between candidate SNPs and risk of GDM. In addition, multifactor dimensionality reduction (MDR) method was applied to explore the effects of gene-gene interactions on GDM risk.ResultsThere were significant distribution differences between GDM group and non-GDM group in age, pre-pregnancy BMI, education level and family history of diabetes (P < 0.05). After adjusted for age, pre-pregnancy BMI, education level and family history of diabetes, there were no significant associations of the candidate SNPs polymorphisms and GDM risk (P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the candidate SNPs (P > 0.05). However, the fasting blood glucose (FBG) levels of rs6777038 CT carriers were significantly lower than TT carriers (4.69±0.69 vs. 5.03±1.57 mmol/L, P < 0.01), and the OGTT-2h levels of rs6777038 CC and CT genotype carriers were significantly lower than TT genotype carriers (8.10±1.91 and 8.08±1.87 vs. 8.99±2.90 mmol/L, P < 0.01).ConclusionsIGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs11705701, rs6777038, rs16860234, rs7651090 polymorphisms were not significantly associated with GDM risk in Wuhan, China. Further lager multicenter researches are needed to confirm these results.

Introduction To identify the association of the cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 (CDKAL1) gene polymorphism with gestational diabetes mellitus (GDM) in the Chinese population.Research design and methods This case-control study enrolled 835 pregnant women with GDM and 870 pregnant women without diabetes who underwent antenatal examination during 24 to 28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. Trained nurses collected their clinical information and blood samples. CDKAL1 gene rs10440833, rs10946398, rs4712523, rs4712524, rs7754840, rs7756992 and rs9465871 loci were genotyped by Agena MassARRAY system. SPSS V.26.0 software and online SHesis were used to analyze the relationship between CDKAL1 gene polymorphism and GDM susceptibility.Results After being adjusted for maternal age, prepregnancy body mass index (BMI), parity and family history of type 2 diabetes mellitus (T2DM), CDKAL1 gene rs10440833 (AA vs TT, OR=1.631, 95% CI 1.192 to 2.232), rs10946398 (CC vs AA, OR=1.400, 95% CI 1.028 to 1.905), rs4712523 (GG vs AA, OR=1.409, 95% CI 1.038 to 1.913), rs4712524 (GG vs AA, OR=1.418, 95% CI 1.043 to 1.929) and rs7754840 (CC vs GG, OR=1.407, 95% CI 1.036 to 1.911) polymorphisms were all associated with the increased risk of GDM. In addition, there was a powerful linkage disequilibrium (LD) among rs10946398, rs4712523, rs4712524 and rs7754840 (D′>0.900, r2>0.900). And there were significant differences in haplotype CGGC (OR=1.207, 95% CI 1.050 to 1.387) and AAAG (OR=0.829, 95% CI 0.721 o 0.952, p=0.008) between the GDM group and the control group.Conclusions rs10440833, rs10946398, rs4712523, rs4712524 and rs7754840 of CDKAL1 gene are associated with GDM susceptibility in central Chinese population.

ObjectiveTo investigate the correlation between transcription factor 7-like 2 (TCF7L2) gene polymorphisms and gestational diabetes mellitus (GDM) risk in the central Chinese population.MethodsThis case–control study examined the association of seven TCF7L2 gene single-nucleotide polymorphisms (SNPs) (rs11196218, rs4506565, rs7895340, rs7901695, rs11196205, rs12243326, and rs290487) with GDM risk in the central Chinese population (843 GDM and 877 controls). The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test, Fisher’s exact test, and logistic regression were performed.ResultsDifferences in age, pre-pregnant body mass index (BMI), and family history of type 2 diabetes mellitus (T2DM) between the case and control groups were significant (p < 0.05). Compared with the wild-type genotype, pregnant women with genotypes of rs4506565-AT (OR = 1.89, 95%CI: 1.18–3.02), rs7895340 GA (OR = 1.93, 95%CI: 1.06–3.54), rs7901695-TC (OR = 1.79, 95%CI: 1.11–2.88), and rs11196205-GC (OR = 2.15, 95%CI: 1.16–3.98) had a significantly higher risk of GDM, adjusted by age, pre-pregnant BMI, and family history of T2DM. Functional annotation showed that all these four SNPs fell in the functional elements of human pancreatic islets. Further cumulative effects analysis concluded that when participants carried all these four risk genotypes, the risk of GDM was 3.51 times (OR = 3.51, 95%CI: 1.38–8.90) than that of those without any risk genotypes.ConclusionsThe findings of this study suggested that rs4506565, rs7895340, rs7901695, and rs11196205 were the genetic susceptibility SNPs of GDM in the central Chinese population. Further studies are needed to validate our findings and clarify the underlying mechanisms.

Abstract Background Gestational diabetes mellitus (GDM) increased risk of perinatal complications for both the women and the fetuses. The association between the vitamin D receptor (VDR) gene polymorphism and GDM has not been thoroughly investigated in Chinese pregnant women. Therefore, we aimed to determine whether VDR gene single nucleotide polymorphisms (SNPs) rs154410, rs7975232, rs731236, rs2228570 and rs739837 contribute to GDM risk in Wuhan, China. Moreover, we aimed to explore their combined effects on the risk of GDM. Methods Pregnant women who had prenatal examinations at 24 to 28 weeks’ gestation in our hospital from January 15, 2018 to March 31, 2019 were included in this case-control study. After exclusion, a total of 1684 pregnant women (826 GDM patients and 858 non-diabetic controls) were recruited. The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of candidate SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test and logistic regression were performed to the data with SPSS Software to evaluate differences in genotype distribution and associations with GDM risk. Multifactor dimensionality reduction method was used to explore the gene-gene interactions on the risk of GDM. Results Differences in age, pre-pregnancy BMI, family history of diabetes and previous history of GDM between the case and control groups were statistically significant (P 0.05). There were no significant differences at genotype distributions of the examined VDR gene SNPs (P > 0.05). After adjusting by age, pre-pregnancy BMI, family history of diabetes, the results of logistic regression analysis showed no associations of the five SNPs with GDM in all the four genotype models(P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the five examined VDR gene SNPs. Conclusions The VDR gene SNPs rs154410, rs7975232, rs731236, rs2228570 and rs739837 showed neither significant associations nor gene-gene interactions with GDM in Wuhan, China.

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R2), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy.

The 30 m resolution Landsat data have been used for high resolution aerosol optical depth (AOD) retrieval based on radiative transfer models. In this paper, a Landsat-8 AOD retrieval algorithm is proposed based on the deep neural network (DNN). A total of 6390 samples were obtained for model training and validation by collocating 8 years of Landsat-8 top of atmosphere (TOA) data and aerosol robotic network (AERONET) AOD data acquired from 329 AERONET stations over 30°W–160°E and 60°N–60°S. The Google Earth Engine (GEE) cloud-computing platform is used for the collocation to avoid a large download volume of Landsat data. Seventeen predictor variables were used to estimate AOD at 500 nm, including the seven bands TOA reflectance, two bands TOA brightness (BT), solar and viewing zenith and azimuth angles, scattering angle, digital elevation model (DEM), and the meteorological reanalysis total columnar water vapor and ozone concentration. The leave-one-station-out cross-validation showed that the estimated AOD agreed well with AERONET AOD with a correlation coefficient of 0.83, root-mean-square error of 0.15, and approximately 61% AOD retrievals within 0.05 + 20% of the AERONET AOD. Theoretical comparisons with the physical-based methods and the adaptation of the developed DNN method to Sentinel-2 TOA data with a different spectral band configuration are discussed.

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.

In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM2.5 concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (R2) between the measured and predicted PM2.5 concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 µg/m3. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient R2 of the measured and predicted results was 0.784, and the RMSE was 20.104 µg/m3, which is only 8 µg/m3 higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM2.5 concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.

The authors wish to make the following corrections to this paper [...]

Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM2.5 monitoring system, the ground-based PM2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific Particle Swarm Extinction Mass Conversion Algorithm (SPSEMCA) using remotely sensed data is introduced. Ground-level observed PM2.5, planetary boundary layer height (PBLH) and relative humidity (RH) reanalyzed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and aerosol optical depth (AOD), fine-mode fraction (FMF), particle size distribution, and refractive indices from AERONET (Aerosol Robotic Network) of the Beijing area in 2015 were used to establish this algorithm, and the same datasets for 2016 were used to test the performance of the SPSEMCA. The SPSEMCA involves four steps to obtain PM2.5 values from AOD datasets, and every step has certain advantages: (I) In the particle correction, we use η2.5 (the extinction fraction caused by particles with a diameter less than 2.5 μm) to make an accurate assimilation of AOD2.5, which is contributed to by the specific particle swarm PM2.5. (II) In the vertical correction, we compare the performance of PBLHc retrieved by satellite Lidar CALIPSO data and PBLHe reanalysis by ECMWF. Then, PBLHc is used to make a systematic correction for PBLHe. (III) For extinction to volume conversion, the relative humidity and the FMF are used together to assimilate the AVEC (averaged volume extinction coefficient, μm2/μm3). (IV) PM2.5 measured by ground-based air quality stations are used as the dry mass concentration when calculating the AMV (averaged mass volume, cm3/g) in humidity correction, that will avoid the uncertainties derived from the estimation of the particulate matter density ρ. (V) Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km × 1 km AOD was used to retrieve high resolution PM2.5, and a LookUP Table-based Spectral Deconvolution Algorithm (LUT-SDA) FMF was used to avoid the large uncertainties caused by the MODIS FMF product. The validation of PM2.5 from the SPSEMCA algorithm to the AERONET observation data and MODIS monitoring data achieved acceptable results, R = 0.70, RMSE (root mean square error) = 58.75 μg/m3 for AERONET data, R = 0.75, RMSE = 43.38 μg/m3 for MODIS data, respectively. Furthermore, the trend of the temporal and spatial distribution of Beijing was revealed.

The European Space Agency’s (ESA’s) Aerosol Climate Change Initiative (CCI) project intends to exploit the robust, long-term, global aerosol optical thickness (AOT) dataset from Europe’s satellite observations. Newly released Swansea University (SU) aerosol products include AATSR retrieval and synergy between AATSR and MERIS with a spatial resolution of 10 km. In this study, both AATSR retrieval (SU/AATSR) and AATSR/MERIS synergy retrieval (SU/synergy) products are validated globally using Aerosol Robotic Network (AERONET) observations for March, June, September, and December 2008, as suggested by the Aerosol-CCI project. The analysis includes the impacts of cloud screening, surface parameterization, and aerosol type selections for two products under different surface and atmospheric conditions. The comparison between SU/AATSR and SU/synergy shows very accurate and consistent global patterns. The global evaluation using AERONET shows that the SU/AATSR product exhibits slightly better agreement with AERONET than the SU/synergy product. SU/synergy retrieval overestimates AOT for all surface and aerosol conditions. SU/AATSR data is much more stable and has better quality; it slightly underestimates fine-mode dominated and absorbing AOTs yet slightly overestimates coarse-mode dominated and non-absorbing AOTs.

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

Based on the multi-routing protocols and the reed-solomon error-correcting coding scheme,a data transmission method was presented,in which,round-encryption and round-signature were not needed. Data packets were all encoded by RS codes and cut into pieces before sending in different paths. After receive all the pieces of a data packet,the destina-tion node could judge whether a malicious modification had been occurred,if so,the malicious node would be found out or be located in a malicious-node-group by a detecting process. Then by fully using the information gotten from detecting process,a reputation mechanism was proposed to resist the modification attacks. When implement it,the Bayesian ap-proach was used to evaluate a node’s reputation. The simulation results show that it can react to the attacks more quickly and accurately.

The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up-Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.

The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

Airborne dust aerosols impact negatively the climate, ecosystems, air quality, and human health. To mitigate these impacts, it is crucial to identify their three-dimensional spatiotemporal distribution, transport pathways and driving factors. In this study, the three-dimensional spatiotemporal variations and distribution of dust aerosols in China from 2007 to 2021 were first analyzed using multiple dust datasets, including Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dust aerosol optical depth (DAOD) data, ultraviolet aerosol index (UVAI) data from the Ozone Monitoring Instrument (OMI), and the Vertical Feature Mask (VFM) product of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Also, the transport pathways and potential source regions for dust haze in principle provincial capital cities of West and North China in spring were identified using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Potential Source Contribution Function (PSCF). Additionally, DAOD variations over different land cover types and the impacts of meteorological driving factors were discussed with geographic detectors. Results indicate that: (1) The multi-year average of DAOD in China from 2007 to 2021 was 0.076. A mutation in annual average DAOD occurred between 2010 and 2011, with an insignificant increasing trend between 2007-2010, a downward trend between 2011-2021 and a significant downward trend between 2014-2017. (2) The Taklamakan Desert exhibited the highest DAOD for the entire China during spring throughout the years, with DAOD values ranging from 0.4 to 0.6 and UVAI values exceeding 2.0. The highest frequency of dust occurrence in the northwest and northern regions is at an altitude of 2-4 km in spring and summer, and of 0-2 km in autumn and winter, while it is at an altitude of 4-6 km for the Qinghai-Tibet region. (3) The dust transport routes for the provincial capital cities can be primarily divided into: western, northwestern, northern, southwestern, and local. Northwest cities are notably affected by dust from surrounding deserts. Dust originating from the Qaidam Basin and the Hexi Corridor can be carried further downwind to inland cities, such as Xining and Lanzhou. A dust backflow was found in Beijing and Tianjin. Moreover, the discussion revealed that barren and cropland had the highest DAOD and additionally precipitation, evaporation, and soil moisture were identified as the strongest driving factors affecting dust aerosol variations. The combination effect of precipitation and temperature had the highest explanatory power, ranging from 0.72-0.84, followed by 0.75-0.81 for precipitation and U10m wind speed and 0.67-0.75 for temperature and evaporation. [ABSTRACT FROM AUTHOR]

AIM: To investigate the alterations in corneal aberration and relative refractive power following the reduction of back optic zone diameters(BOZD)of orthokeratology lenses.METHODS: Myopic children aged 8-12 years, deemed suitable and willing to wear orthokeratology lenses, were randomly allocated to wear lenses with a 6.0 mm BOZD or a 5.0 mm BOZD. Data collection included changes in higher-order aberrations, relative refractive power and the treatment zone diameter of the two groups after wearing lenses for 1 d, 1 wk, 1, and 3 mo. The correlation of increase in corneal higher-order aberrations with refractive power was analyzed.RESULTS: The increases in total higher-order aberrations, spherical aberrations and coma aberrations varied over time following lens wear(all P0.05). A significant difference was observed in the increment of spherical aberrations in the 5 mm range between the two groups of patients, which varied over time(Ftime=40.179, Ptime

Abstract Objectives Emerging data have shown that local treatment could provide clinical benefit for non-small cell lung cancer (NSCLC) patients with oligometastasis. Liver metastases have the worst prognosis in advanced NSCLC, but the genomic characteristics of liver oligometastasis remain unclear. The aim of our study was to elucidate the molecular features of liver oligometastatic NSCLC. Methods Paired liver metastatic tissue samples and peripheral blood from 32 liver oligometastatic NSCLC patients were concurrently collected for comprehensive genomic analysis using next-generation sequencing. Results A total of 206 mutated genes in 32 patients were detected, with a median of 4 mutations per sample. The most frequent alterations (> 10%) in liver oligometastasis were TP53 (72%), EGFR (50%), RB1 (19%) and SMARCA4 (12%). The co-occurrence rate of TP53 and RB1 in our cohort was significantly higher than that in the TCGA-LUAD cohort. Age, APOBEC, homologous recombination deficiency (HRD) and deficient mismatch repair (dMMR) established the mutational signature of liver oligometastatic NSCLC. The median tumor mutation burden (TMB) was 4.8 mutations/Mb. A total of 78.12% patients harbored at least one potentially actionable molecular alteration that may guide further targeted therapy according to the OncoKB evidence. Conclusions Our study comprehensively delineated the genomic characteristics of liver oligometastatic NSCLC - such findings were helpful to better understand the distinct clinic-biological features of oligometastasis and optimize personalized treatment of this population.

Abstract Integrating the Internet of Things (IoT) in smart grids has revolutionized the energy sector, enabling real-time data collection and efficient energy distribution. However, this integration also introduces significant security challenges, particularly data encryption. Traditional encryption algorithms used in IoT are vulnerable to various attacks, and the advent of quantum computing exacerbates these vulnerabilities. To address the above challenges, this paper proposes a novel encryption mechanism, the Quantum-Resistant Hybrid Encryption for IoT (QRHE-IoT), designed to enhance the security of communications in IoT-enabled smart grids. QRHE-IoT combines the strengths of symmetric and asymmetric encryption algorithms and incorporates quantum-resistant algorithms to provide robust security. This paper explains the QRHE-IoT mechanism, its theoretical basis, and how it addresses the security challenges in smart grid communications. It also presents the results of tests conducted to evaluate the effectiveness of QRHE-IoT in a simulated smart grid environment. The proposed QRHE-IoT mechanism presents a promising solution to these challenges, offering robust security for smart grid communications in the face of emerging threats, including quantum computing.

［Objective］ To reduce the impact of elevated lines in urban rapid rail transit on environmental noise, multiple noise-reduction measures such as near-track sound barriers and ballast bed sound-absorbing panels are adopted. On-site measurements and analyses should combine with on-site operating conditions to verify the effectiveness of trackside noise reduction measures in reducing environmental noise. ［Method］ In the case study of the already operating Line S1 of Wenzhou Urban Rapid Rail Transit, the environmental noise in the viaduct section of this line is measured on site. The noise reduction effects of near-track sound barriers and ballast bed sound-absorbing panels for the environmental noise under different train running speeds are compared and analyzed, and the influence characteristics of different noise reduction measures on the viaduct section noise are explored. ［Result & Conclusion］ The near-track sound barrier reduces the maximum instantaneous A-weighted time-domain sound pressure level (hereinafter abbreviated as “A-weighted level”) on the viaduct inner and outer sides by approximately 20 to 21 dB(A). At the position 7.5 meters away from the track center line and 7.5 meters above the rail surface, the A-weighted level in the 500 Hz and 800-1 000 Hz frequency bands are respectively reduced by 6 to 11 dB(A) and 17 to 21 dB(A). The ballast bed sound-absorbing panels can further reduce the trackside noise on and outside the viaduct. Compared with the working conditions where only the near-track sound barriers are installed, when using the sound-absorbing panels, the maximum instantaneous A-weighted level on the viaduct is further reduced by 14 to 17 dB(A) with the same train running speed, and the A-weighted level is reduced by 7 to 8 dB(A) and 5 to 7 dB(A) respectively for the noise in the 500 Hz and 800 - 1 000 Hz frequency bands at the same measuring point outside the viaduct.

Abstract Background Performing spinal anesthesia in elderly patients with spine degeneration is challenging for novice practitioners. This stratified randomized controlled trial aims to compare the effectiveness of mixed reality-assisted spinal puncture (MRasp) with that of landmark-guided spinal puncture (LGsp) performed by novice practitioners in elderly patients. Methods This prospective, single-center, stratified, blocked, parallel randomized controlled trial will include 168 patients (aged ≥ 65 years) scheduled for elective surgery involving spinal anesthesia. All spinal punctures will be performed by anesthesiology interns and residents trained at Huadong Hospital. Patients will be randomly assigned to the MRasp group (n = 84) or the LGsp group (n = 84). Based on each intern/resident’s experience in spinal puncture, participants will be stratified into three clusters: the primary group, intermediate group, and advanced group. The primary outcome will be the comparison of the rate of successful first-attempt needle insertion between the MRasp group and the LGsp group. Secondary outcomes will include the number of needle insertion attempts, the number of redirection attempts, the number of passes, the rate of successful first needle pass, the spinal puncture time, the total procedure time, and the incidence of perioperative complications. A stratified subgroup analysis will also be conducted for interns/residents at different experience levels. Discussion The findings from this trial establish the effectiveness of MRasp by novice practitioners in elderly patients. This trial may provide experimental evidence for exploring an effective visualization technology to assist in spinal puncture. Trial registration Chinese Clinical Trials Registry ChiCTR2300075291. Registered on August 31, 2023. https://www.chictr.org.cn/bin/project/edit?pid=189622 .

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by massive neuronal loss in the brain. Both cortical glutamatergic neurons and basal forebrain cholinergic neurons (BFCNs) in the AD brain are selectively vulnerable. The degeneration and dysfunction of these two subtypes of neurons are closely associated with the cognitive decline of AD patients. The determination of cellular and molecular mechanisms involved in AD pathogenesis, especially in the early stage, will largely facilitate the understanding of this disease and the development of proper intervention strategies. However, due to the inaccessibility of living neurons in the brains of patients, it remains unclear how cortical glutamatergic neurons and BFCNs respond to pathological stress in the early stage of AD. In this study, we established in vitro differentiation systems that can efficiently differentiate patient-derived iPSCs into BFCNs. We found that AD-BFCNs secreted less Aβ peptide than cortical glutamatergic neurons did, even though the Aβ42/Aβ40 ratio was comparable to that of cortical glutamatergic neurons. To further mimic the neurotoxic niche in AD brain, we treated iPSC-derived neurons with Aβ42 oligomer (AβO). BFCNs are less sensitive to AβO induced tau phosphorylation and expression than cortical glutamatergic neurons. However, AβO could trigger apoptosis in both AD-cortical glutamatergic neurons and AD-BFCNs. In addition, AD iPSC-derived BFCNs and cortical glutamatergic neurons exhibited distinct electrophysiological firing patterns and elicited different responses to AβO treatment. These observations revealed that subtype-specific neurons display distinct neuropathological changes during the progression of AD, which might help to understand AD pathogenesis at the cellular level.