CaSR Induces Osteoclast Differentiation and Promotes Bone Metastasis in Lung Adenocarcinoma

Objective: Explore the mechanism of CaSR's involvement in bone metastasis in lung adenocarcinoma. Methods: Immunohistochemistry (IHC) was used to detect the expression of calcium-sensing receptor (CaSR) in 120 cases of lung adenocarcinoma with bone metastasis. Stably transfected cell lines with CaSR overexpression and knockdown based on A549 cells were constructed. The expression of CaSR was verified by western blot and qPCR. The proliferation and migration abilities of A549 cells were tested using cholecystokinin-8 (CCK-8) and Transwell assays, respectively. Western blotting was used to detect the expression of matrix metalloproteinases MMP2, MMP9, CaSR, and NF-κB. The supernatant from each cell culture group was collected as a conditional co-culture solution to study the induction of osteoclast precursor cells and osteoblasts. Western blot and qPCR were used to validate the expression of bone matrix degradation-related enzymes cathepsin K and hormone calcitonin receptor (CTR) and osteoblast-induced osteoclast maturation and differentiation enzyme receptor activator of nuclear factor-κB ligand (RANKL), macrophage colony-stimulating factor (M-CSF), osteoprotegerin (OPG), and PTHrP. Immunofluorescent staining was used to detect F-actin ring formation and osteocalcin expression. Western blot results for NF-κB expression identified a regulatory relationship between NF-κB and CaSR. Results: CaSR expression in lung cancer tissues was significantly higher than that in adjacent and normal lung tissues. The expression of CaSR in lung cancer tissues with bone metastasis was higher than that in non-metastatic lung cancer tissues. The proliferation and migration ability of A549 cells increased significantly with overexpressed CaSR. The co-culture solution directly induced osteoclast precursor cells and the expression of bone matrix degradation-related enzymes significantly increased. Osteoblasts were significantly inhibited and osteoblast-induced osteoclast maturation and differentiation enzymes were significantly downregulated. It was found that the expression of NF-κB and PTHrP increased when CaSR was overexpressed. Osteoclast differentiation factor expression was also significantly increased, which directly induces osteoclast differentiation and maturation. These results were reversed when CaSR was knocked down. Conclusions: CaSR can positively regulate NF-κB and PTHrP expression in A549 cells with a high metastatic potential, thereby promoting osteoclast differentiation and maturation, and facilitating the occurrence and development of bone metastasis in lung adenocarcinoma

Indo-Pacific finless porpoises presence in response to pile driving on the Jinwan Offshore Wind Farm, China

The Jinwan Offshore Wind Farm project in the Pearl River Estuary (PRE) is a new stressor for the resident marine mammals there, especially for the Indo-Pacific finless porpoise. A broadband recording system was deployed in the Jinwan Offshore Wind Farm, before and during the construction period, in order to determine how the finless porpoise responded to pile driving activity. The results showed that the wind farm area was an important habitat for the finless porpoise during the monitoring period. The finless porpoise also showed avoidance behavior of pile driving activity. There was a significant negative correlation between porpoise detection and pile driving detection, and the time between porpoise’s acoustic detections increased during pile driving compared to periods without pile driving. Our results indicated that acoustic protection measures are strongly recommended in future offshore wind farm developments in order to protect finless porpoises

H-infinity Variable-Pitch Control for Wind Turbines Based on Takagi-Sugeno Fuzzy Theory

When the wind speed is above the rated value, the output power of the wind turbine should be maintained at the rated value in order to prevent the power generation system from overheating. In addition, the natural wind speed will fluctuate randomly in a large range of values, making the traditional control effect not ideal. This paper presents a novel H-infinity (H∞) pitch control strategy for Wind Turbine Generators (WTGs), which can make the rotor speed and output power constant when the wind speed changes in a large range. In order to shorten response time and reduce overshoot, in the specific solution, the control method combines the H∞ theory and the Takagi-Sugeno (T-S) fuzzy theory. Firstly, the linearized models of several operating points were obtained with the T-S fuzzy theory. Then, a robust controller was designed for each linear sub-system based on the H∞ control theory. Furthermore, the controllers of the sub-systems were superimposed into a global controller for the entire system through the membership function. Finally, modeling and simulation were carried out in MATLAB/SIMULINK. The simulation results show that when the wind speed changes above the rated speed, the rotor speed can be maintained at the rated value, and the output power also can be maintained at the rated value. Compared with the optimal control, the response speed of this method is faster and the overshoot is smaller. It provides a new idea for the pitch angle control of wind turbine

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of ‘Urban population ratio’, ‘Retail sales of consumer goods’, ‘Number of tourists’, ‘Tourism Income’, ‘Ratio of the elderly population (> 60 year old) in this city’, ‘population density’, ‘Mobility scale (move in/inbound) during the spring festival’, ‘Ratio of Population and Health facilities’, ‘Jobless rate (%)’, ‘The straight-line distance from original epicenter Wuhan to this city’, ‘urban per capita GDP’, and ‘the prevalence of the COVID-19’. The study’s results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic

Insights into technical challenges in the field of microplastic pollution through the lens of early career researchers (ECRs) and a proposed pathway forward

Early career researchers (ECR) face a series of challenges related to the inherent difficulties of starting their careers. Microplastic (MP) research is a topical field attracting high numbers of ECRs with diverse backgrounds and expertise from a wealth of disciplines including environmental science, biology, chemistry and ecotoxicology. In this perspective the challenges that could hinder scientific, professional, or personal development are explored, as identified by an international network of ECRs, all employed in MP research, that was formed following a bilateral workshop for scientists based in the UK and China. Discussions amongst the network were grouped into four overarching themes of technical challenges: in the field, in the laboratory, in the post data collection phase, and miscellaneous. The three key areas of representativeness, access to appropriate resources, training, and clean labs, and the use of databases and comparability, as well as the overarching constraint of available time were identified as the source of the majority of challenges. A set of recommendations for pathways forward are proposed based on the principles of research openness, access to information and training, and widening collaborations. ECRs have great capacity to promote research excellence in the field of MPs and elsewhere, when provided with appropriate opportunities and suitable support