Enzymatic Metabolism of Ergosterol by Cytochrome P450scc to Biologically Active 17α,24-Dihydroxyergosterol

SummaryWe demonstrate the metabolism of ergosterol by cytochrome P450scc in either a reconstituted system or isolated adrenal mitochondria. The major reaction product was identified as 17α,24-dihydroxyergosterol. Purified P450scc also generated hydroxyergosterol as a minor product, which is probably an intermediate in the synthesis of 17α,24-dihydroxyergosterol. In contrast to cholesterol and 7-dehydrocholesterol, cleavage of the ergosterol side chain was not observed. NMR analysis clearly located one hydroxyl group to C24, with evidence that the second hydroxyl group is at C17. 17α,24-Dihydroxyergosterol inhibited cell proliferation of HaCaT keratinocytes and melanoma cells. Thus, in comparison with cholesterol and 7-dehydrocholesterol, the 24-methyl group and the C22-C23 double bond of ergosterol prevent side chain cleavage by P450scc and change the enzyme’s hydroxylase activity from C22 and C20, to C24 and C17, generating bioactive product

Studies of Metabolism and Disposition of Natural Products using Mass Spectrometry.

Studies of Metabolism and Disposition of Natural Products using Mass Spectrometry

LC-MS visual recording of drug secondary degradation

"High performance liquid chromatography-mass spectrometry (HPLC-MS) is an analytical technique used for many applications in many different stages of drug development including in vivo drug screening, metabolic stability screening, metabolite identification, impurity identification, degradant identification, quantitative bioanalysis, and quality control etc. This image, acquired in mechanistic studies of drug degradation, shows mass spectrometric monitoring of secondary degradation of a drug. In pharmaceutical manufacturing finishing line, vials are inspected by leak detection system. High voltage electric current is applied to glass vials, producing trace amount of ozone. In this particular case, drug, in aqueous solution, undergoes ozonolysis to form degradant #1, and further, in neutral or slight basic pH condition, amide bond breaks to give secondary degradant #2. HPLC-MS technique is sensitive and specific enough to detect small amount of degradation products and give structural information of degradant, facilitating mechanistic investigation during drug development.

Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China

Street vitality is a significant indicator of a city’s capacity for sustainable development. Significant progress has been made on the basis of measurements of a single indicator of street vitality, but few studies have used multisource data to measure street vitality in a comprehensive way. In this study, in order to explore the multidimensional vitality characteristics of streets, streets were taken as the analysis unit, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) evaluation model with combined weights was used to identify the spatial pattern of streets vitality from social, economic, and cultural dimensions using multisource spatial data such as Baidu heat map, Meituan store rating, and cultural facilities points of interest in the main urban area of Qingdao City, China. Using a Multiscale Geographically Weighted Regression (MGWR) model, the spatial correlations and differences between street built environment components and multidimensional street vitality were examined, to reveal the influence mechanism of street vitality creation in each street. The study found that the comprehensive vitality of the streets in the main urban area of Qingdao City exhibits the spatial differentiation features of “weak east–west, strong central, multicenter, cluster type”. Furthermore, although commercial and public services are essential for enhancing street vitality and attracting crowds, a very high degree of functional mix has not resulted in a high degree of street vitality. Lastly, high spatial heterogeneity between built environment factors and street vitality necessitates considering the functional positioning and development basis of the street, tailoring to local conditions and policies, considering the street’s vitality development status and development needs, complementing strengths, promoting coordinated development, and releasing and enhancing the street’s vitality. Therefore, it is essential to explore street vitality and its influencing mechanisms to improve people’s quality of life and promote sustainable urban development

The impact of carbon emissions on asset values and operating cash flows: evidence from Australian listed companies

In November 2011, the Australian government approved the legislation (Clean Energy Act 2011) to introduce a reduction plan of carbon emissions in Australia. This plan will be implemented from July 2012. This is one of the first accounting studies to investigate the potential impacts of this plan on long-lived asset values and operating cash flows for Australian listed companies. A sample of Australian Securities Exchange (ASX) 200 indexed companies from 2006 to 2010 is used. Hypotheses are tested based on Heckman’s (1979) two-stage approach. Three regression models are developed to examine the association between carbon emissions and asset values/operating cash flows. This study finds that asset values and operating cash flows will be adversely affected, if the reduction plan is implemented. Specifically, this study finds that the book value of long-lived assets will decrease, if listed companies are considered to be emissions-liable. The book value of long-lived assets is further found to be negatively associated with listed companies’ carbon emission levels. This study also demonstrates that operating cash flows of emissions-liable companies will be adversely affected. However, this study does not find a relationship between operating cash flows and companies’ emission levels. The empirical findings from Australian listed companies provide the evidence that the reduction plan of carbon emissions will adversely affect corporate entities’ asset values and operating cash flows. The results further indicate that the magnitude of the impact will be proportional to the companies’ emission levels. The implications of these empirical findings for listed companies, for the accounting profession, and for carbon emission regulators are also discussed

The Dynamics of Floating Macroalgae in the East China Sea and Its Vicinity Waters: A Comparison between 2017 and 2023

Ulva prolifera and Sargassum are two common floating macroalgae in China's coastal algal bloom events. Ulva prolifera frequently emerges concomitantly with Sargassum outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between Ulva prolifera and Sargassum, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between Ulva prolifera and Sargassum are observed. This study proposes a random forest-based algorithm for discriminating between Ulva prolifera and Sargassum in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing Ulva prolifera and Sargassum within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm's robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between Ulva prolifera and Sargassum. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation

Residual Stress and Distortion Prediction for Laser Directed Energy Deposition Based on Cyclic Heat Transfer Model

Predicting the residual stress and distortion caused by inhomogeneous temperature fields in the laser directed energy deposition (LDED) process is a challenging task. This study proposes a novel thermodynamic coupling simulation method based on the cyclic heat transfer model to accurately predict temperature, stress, and distortion evolution during the deposition process. The model effectively calculates the layer-by-layer superposition of thermal effects and cyclic accumulation of thermal stress during the deposition process, leading to improved prediction accuracy for temperature, residual stress, and distortion. Initially, the heat source model, the cyclic heat transfer model, and the thermoelastic matrix are established. The thermoelastic constitutive equation and the equilibrium differential equation are formulated to capture the actual process characteristics of the LDED accurately in order to achieve the thermodynamic coupling solution. Then, numerical simulations are performed on a typical model specimen, with simulation parameters consistent with the actual deposition parameters. Finally, the predicted results are validated through actual deposition experiments, and the temperature, stress, and distortion history are analyzed. The results demonstrate that the cyclic thermodynamic coupling model proposed in this study can effectively predict the deposited components’ temperature, residual stress, and distortion evolution. This study establishes a crucial foundation for achieving precision and performance control in the deposition process and reducing residual stress and distortion in the components

Position-Singularity Analysis of a Class of the 3/6-Gough-Stewart Manipulators Based on Singularity-Equivalent-Mechanism

This paper addresses the problem of identifying the property of the singularity loci of a class of 3/6-Gough-Stewart manipulators for general orientations in which the moving platform is an equilateral triangle and the base is a semiregular hexagon. After constructing the Jacobian matrix of this class of 3/6-Gough-Stewart manipulators according to the screw theory, a cubic polynomial expression in the moving platform position parameters that represents the position-singularity locus of the manipulator in a three-dimensional space is derived. Graphical representations of the position-singularity locus for different orientations are given so as to demonstrate the results. Based on the singularity kinematics principle, a novel method referred to as ‘singularity-equivalent-mechanism' is proposed, by which the complicated singularity analysis of the parallel manipulator is transformed into a simpler direct position analysis of the planar singularity-equivalent-mechanism. The property of the position-singularity locus of this class of parallel manipulators for general orientations in the principal-section, where the moving platform lies, is identified. It shows that the position-singularity loci of this class of 3/6-Gough-Stewart manipulators for general orientations in parallel principal-sections are all quadratic expressions, including a parabola, four pairs of intersecting lines and infinite hyperbolas. Finally, the properties of the position-singularity loci of this class of 3/6-Gough-Stewart parallel manipulators in a three-dimensional space for all orientations are presented

A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available

Analysis of Regional Economic Disparities in Guizhou Province Based on ESDA-GIS

Take the county as the research scale and the per capita GDP as the measure index as well to reveal the difference of Guizhou Province’s regional economy which based on ESDA and GeoDA-GIS. It shows that the level of economic develop of Guizhou’s central area is high and surrounded area is low. The difference between North and south is greater than the difference between East and West. There is a clear spatial correlation among them. Moran scatter diagram shows that the majority of counties are located in the first and third quadrants, which accounted for 73.86% of the total number of the county. The number of “L-L” type is more than the number of “H-H” type 37 counties. Most parts of the provinces are relatively poor. Finding the “H-H” area and “L-L” area and “L-H” area and “H-L” area of economic development level of county based on the spatial correlation model. That can provide scientific basis for the future economic construction and social development of Guizhou province