Review of Calibration Methods for Scheimpflug Camera

The Scheimpflug camera offers a wide range of applications in the field of typical close-range photogrammetry, particle image velocity, and digital image correlation due to the fact that the depth-of-view of Scheimpflug camera can be greatly extended according to the Scheimpflug condition. Yet, the conventional calibration methods are not applicable in this case because the assumptions used by classical calibration methodologies are not valid anymore for cameras undergoing Scheimpflug condition. Therefore, various methods have been investigated to solve the problem over the last few years. However, no comprehensive review exists that provides an insight into recent calibration methods of Scheimpflug cameras. This paper presents a survey of recent calibration methods of Scheimpflug cameras with perspective lens, including the general nonparametric imaging model, and analyzes in detail the advantages and drawbacks of the mainstream calibration models with respect to each other. Real data experiments including calibrations, reconstructions, and measurements are performed to assess the performance of the models. The results reveal that the accuracies of the RMM, PLVM, PCIM, and GNIM are basically equal, while the accuracy of GNIM is slightly lower compared with the other three parametric models. Moreover, the experimental results reveal that the parameters of the tangential distortion are likely coupled with the tilt angle of the sensor in Scheimpflug calibration models. The work of this paper lays the foundation of further research of Scheimpflug cameras

Potent antitumor activity of a bispecific T-cell engager antibody targeting the intracellular antigen KRAS G12V

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is one of the most frequent oncogenes. However, there are limited treatment options due to its intracellular expression. To address this, we developed a novel bispecific T-cell engager (BiTE) antibody targeting HLA-A2/KRAS G12V complex and CD3 (HLA-G12V/CD3 BiTE). We examined its specific binding to tumor cells and T cells, as well as its anti-tumor effects in vivo. HLA-G12V/CD3 BiTE was expressed in Escherichia coli and its binding affinities to CD3 and HLA-A2/KRAS G12V were measured by flow cytometry, along with T-cell activation. In a xenograft pancreatic tumor model, the HLA-G12V/CD3 BiTE's anti-tumor effects were assessed through tumor growth, survival time, and safety. Our results demonstrated specific binding of HLA-G12V/CD3 BiTE to tumor cells with an HLA-A2/KRAS G12V mutation and T cells. The HLA-G12V/CD3 BiTE also activated T-cells in the presence of tumor cells in vitro. HLA-G12V/CD3 BiTE in vivo testing showed delayed tumor growth without severe toxicity to major organs and prolonged mouse survival. This study highlights the potential of constructing BiTEs recognizing an HLA-peptide complex and providing a novel therapy for cancer treatment targeting the intracellular tumor antigen

Revisiting the concentration observations and source apportionment of atmospheric ammonia

While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on whether agricultural or non-agricultural emissions dominate the urban ammonia budget. Identifying the ammonia source by nitrogen isotope helps in designing a mitigation strategy for policymakers, but existing methods have not been well validated. Revisiting the concentration measurements and identifying source apportionment of atmospheric ammonia is thus an essential step towards reducing ammonia emissions

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated

Evaluating the Effects of Renewable Energy Consumption on Carbon Emissions of China&rsquo;s Provinces: Based on Spatial Durbin Model

Renewable energy consumption is considered as the main form of energy consumption in the future. The carbon emissions produced by renewable energy can be approximately ignored, and renewable energy is essential for regional sustainable development. In this study, we used the Durbin model with panel data to explore the spatial dependence between renewable energy consumption the and carbon emissions of China&rsquo;s 30 provinces from 1997 to 2017. The results show that: (1) there is a negative spatial correlation between renewable energy consumption and carbon emissions, and &ldquo;High-Low&rdquo; areas are mainly concentrated in southern provinces in 1997&ndash;2011; (2) the center of gravity of renewable energy consumption moves southwest, which is consistent with the center of gravity of carbon emissions; (3) renewable energy consumption has a significant inhibitory effect on carbon emissions of a local region, but the spatial spillover effect is not significant. Specifically, a 1% increase in renewable energy consumption in a region will reduce carbon emissions by 0.05%. Finally, on the basis of this study, it was proposed to give full play to the advantages of renewable energy in the western region, and further accelerate the development of the renewable energy industry

Structural alteration of montmorillonite by acid activation and its effect on the decolorization of rapeseed oil

Low-temperature atmospheric calcination is a new technology for solving the current environmental issues associated with activated clay production. In this study, the structural alteration of montmorillonite during the production of activated clay was investigated by this new technology. The results revealed that the increase in temperature aggravates the destruction of montmorillonite layers, which mainly constituted octahedral sheets due to the continuous dissolution of cations in the sheets, with relatively stable tetrahedral sheets. Activated montmorillonite layers became curled and stacked in disorder, which was different from that in the original. The maximum acidity of 230 mmol/kg was achieved at an optimum temperature of 200°C. Under this condition, the specific surface area and total pore volume increased from 78.4 m2/g to 226 m2/g and from 0.107 cm3/g to 0.318 cm3/g, respectively. With the improvement in the decolorization ability of the clay, the absorbance of the rapeseed oil decreased to 0.867 from 4.070