At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction ...through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.
To investigate the association between polymorphisms of TNF- α (rs1799724, rs1800629), VEGF (rs3025039) and VEGFR1 (rs 722503) and early onset preeclampsia (EOPE) in Chinese.
A total of 132 EOPE ...patients from January 2016 to December 2018 at the Second Hospital of Tianjin Medical University were selected as the EOPE group, and 156 normal pregnant patients as the Control group. In both groups, 5 ml of peripheral venous blood was obtained after admission. The characteristics of genotype and allele distribution at the four SNPs in the study subjects were examined by matrix-assisted laser desorption ionization time-of-flight mass spectrometric genotyping.
The genotype frequency distribution and allele frequency distribution of rs1799724 were significantly different between the EOPE group and the Control group (P = 0.002，P = 0.003). The T allele was statistically associated with the development of EOPE under a dominant genetic model (P = 0.001). The genotype and allele frequency distributions of rs1800629, rs3025039, and rs 722503 did not differ significantly between the EOPE group and the Control group (P > 0.05). There was no linkage disequilibrium among rs1799724, rs1800629 and rs3025039 loci, the corresponding haploid cannot be formed.
The rs1799724 of TNF- α gene is a genetic susceptibility locus for EOPE and may be a potential predictors of preeclampsia.
The graduate approach applied in China for the economic transition poses the risk of continued government influence on the market. The land reform and the following adjustment in China have ...introduced a seemingly complete market for residential land. However, a widely practiced coalition between the local developmental states and developers might impact residential land leasing in a more hidden way. Taking central Chengdu as the study area, this study takes the enterprise ownership and affiliations as two explanatory factors that impact the land leasing prices and builds an MGWR model to evaluate the premium of political connections for the developers to obtain the land. The result gives a clue to the local protectionism and preference for state-owned enterprises that might exist in land leasing in Chengdu. It is proved in this study that the average purchase price by state-owned enterprises is 8.9% lower than the prices that private enterprises could enjoy, and the average land leasing price by local enterprises is 14.2% lower than that enjoyed by non-local enterprises. The preceding conceptual and empirical discussion in this study advocates for a review and rethinking of the public sector’s intervention in China’s land market. In-depth analyses of the factors that define the land leasing behaviors of the local government are needed.
The saponins, as components of tea seed meal, are undesirable hemolytic components and should be degraded for reducing their hemolytic activity in order to be used in animal feed. In this study, ...β-glucuronidase was verified to be a potent hydrolase of tea seed saponins to reduce their hemolytic activity and a β-glucuronidase-producing Lactobacillus crustorum strain was screened from raw bovine milk. Next, solid-state fermentation with the isolated L. crustorum and a Bacillus subtilis natto strain, which can produce cellulase and hence improve the fermentation performance of tea seed meal, was carried out for detoxification of tea seed meal. The 50% hemolytic dosage (HD50) value of tea seed saponins was increased from 6.69 to 27.43 μg mL-1. The results of LC-MS analysis showed that the percentage of saponin aglycones increased from 30.95 to 84.25% after the fermentation. According to the roles of sugar moieties in hemolytic activity, and the enzymatic hydrolysis characteristics of β-glucuronidase, the degradation of tea seed saponins from glucosides to aglycones may contribute to the reduction of hemolytic activity. Therefore, tea seed meal may be used as animal feed after fermentation with the tested saponin-degrading microbial strains.
Due to the extremely complex regional geological tectonic movements in the Alpide Himalayan seismic belt with the dense population and rapid economic development, the frequent seismic activity has ...caused huge concern. In geology, the research on the spatiotemporal characteristics of seismic activity in different areas at different scales has always been valuable to explore the mechanism of seismic activity for further simulation and prediction of the seismic activity. Using the spatial autocorrelation analysis in this study, we analyzed the spatiotemporal dynamic characteristics of seismic activity in the Alpide Himalayan seismic belt. According to the historical earthquake list (from 1973 to 2017), the data are divided into five periods. Compared with the commonality and diversity of the spatiotemporal characteristics of seismic activity in different periods, it shows that: there is a positive spatial autocorrelation in each period, which proves the spatial aggregation of the seismic activity in the Alpide Himalayan seismic belt. Nevertheless, over time, the spatial aggregation distribution is gradually diminishing from 1973 to 2017; From the Local Spatial Autocorrelation analysis, the spatial aggregation characteristics of seismic activity change with time and have a significant differentiation among the regions. According to the comparison of Local Spatial Autocorrelation characteristics in different periods, seismic activities in the Alpide Himalayan seismic belt were consistent with the periodic characteristics. Assuming the alternating seismic calm period and active period, we could notice that it turned from the active period to the calm period around 2008, which is the most recent turning point between the active and calm periods. At last, the spatio-temporal dynamic characteristics of the seismic activity in the Alpide Himalayan seismic belt is not only consistent with the formation mechanism of seismic activity but also represents the spatial disparity of the geological tectonic movements. It is the feasibility and effectiveness to explore the seismic mechanism based on spatio-temporal dynamic characteristics of the seismic activity.
Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for ...LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of “deeper is better”. However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efficient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic filtered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efficient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.
In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to ...explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest.
With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between ...the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM
), particulate matter with size 10 micrometers or less (PM
), sulfur dioxide (SO
) and nitrogen dioxide (NO
), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM
, while PM
, SO
, and NO
present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.
Landslide detection is crucial for disaster management and prevention. With the advent of multi-channel optical remote sensing technology, detecting landslides have become more accessible and more ...accurate. Although the use of the convolutional neural network (CNN) has significantly increased the accuracy of landslide detection on multi-channel optical remote sensing images, most previous methods using CNN lack the ability to obtain global context information due to the structural limitations of the convolution operation. Motivated by the powerful global modeling capability of the Swin transformer, we propose a new Conv-Trans Dual Network (CTDNet) based on Swin-Unet. First, we propose a dual-stream module (CTDBlock) that combines the advantages of ConvNeXt and Swin transformer, which can establish pixel-level connections and global dependencies from the CNN hierarchy to enhance the ability of the model to extract spatial information. Second, we apply an additional gating module (AGM) to effectively fuse the low-level information extracted by the shallow network and the high-level information extracted by the deep network and minimize the loss of detailed information when propagating. In addition, We conducted extensive subjective and objective comparison and ablation experiments on the Landslide4Sense dataset. Experimental results demonstrate that our proposed CTDNet outperforms other models currently applied in our experiments.
Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by ...CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.
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