Gas geochemistry of the hot springs gas in Fujian province, SE China: insight into the deep faults and seismic activity

Fujian province is located at the forefront of the South China continental margin, situated on the edge of the Circum-Pacific seismic belt, and it is one of the regions with the most active neotectonic and geothermal activities in Chinese mainland. To explore the geochemical signals of hot spring gases to tectonic activity and earthquakes, a collection of geothermal gas samples was collected from 29 locations in Fujian from January 2021 to December 2022 (many of which were multiply collected at several sites quarterly). The gas samples were tested for their gas composition, helium, neon, carbon isotopes, radon contents, and gas flow rates. The results show that the dominant component of the hot spring outgassing is N2, and the increase in CO2 content is often associated with the increasing 13C. The variation range of the helium isotope ratio (3He/4He) in the hot spring gases is between 0.06 and 2.20Ra, and Rc/Ra varies between 0.06 and 1.58, with peak values occurring at the intersections of deep faults. Radon contents range from 18 to 2000 Bq/L. Calculations revealed that the maximum proportion of mantle-derived helium is 30.2%, and the mantle-derived heat contribution ranges from 37.6% to 63.4%. These data indicate a significant mantle degassing process in Fujian, with a high degree of mantle-crust connectivity, and mantle-derived heat as the main source of geothermal activity in the area. Comparative analysis with regional seismic activity indicates that areas with relatively strong upwelling of deep fluids are the main regions of regional seismic activity, and seismic intensity is positively correlated with mantle-derived heat flow. Thus, deep thermal fluid actives are closely genetically correlated to regional seismic activity. Additionally, the correlation analysis with the Taiwan ML6.0 earthquake suggests that high 3He/4He, δ13CCO2 values of hot spring gas and gas flow velocity in Nancheng Hot Spring (QZ6) indicate significant short-term and imminent anomaly indications preceding ML6.0 earthquakes in the Taiwan region

DHA dietary intervention caused different hippocampal lipid and protein profile in ApoE-/- and C57BL/6J mice

Background: Changes in protein and lipid levels may occur in the Alzheimer’s disease brain, and DHA can have beneficial effects on it. To investigate the impact of DHA dietary intervention on brain protein and lipid profile in ApoE-/- mice and C57 mice. Method: Three-month-old ApoE-/- mice and C57 mice were randomly divided into two groups respectively, and fed with control diet and DHA-fortified diet for five months. Cortical TC, HDL-C and LDL-C levels and cholesterol metabolism-related protein expression were measured by ELISA or immunohistochemistry methods. Hippocampus were collected for proteomic and lipidomics analysis by LC-MS/MS and differential proteins and lipid metabolites were screened and further analyzed by GO functional annotation and KEGG pathway enrichment analysis. Results: DHA intervention decreased cortical TC level in both C57 and ApoE-/- mice (P < 0.05), but caused different change of cortical HDL-C, LDL-C level and LDL-C/HDL-C ratio in C57 and ApoE-/- mice (P < 0.05). Discrepant cortical and hippocampal LDLR, ABCG1, Lox1 and SORT1 protein expression was found between C57 and ApoE-/- mice (P < 0.05), and DHA treatment caused different changes of these proteins in C57 and ApoE-/- mice (P < 0.05). Differential hippocampal proteins and lipids profile were found in C57 and ApoE-/- mice before and after DHA treatment, which were mainly involved in vesicular transport and phospholipid metabolic pathways. Conclusion: ApoE genetic defect caused abnormal cholesterol metabolism, and affected protein and lipid profile, as well as discrepant response of hippocampal protein and lipids profile in the brain of mice given DHA fortified diet intervention

In Situ Representation of Soil/Sediment Conductivity Using Electrochemical Impedance Spectroscopy

The electrical conductivity (EC) of soil is generally measured after soil extraction, so this method cannot represent the in situ EC of soil (e.g., EC of soils with different moisture contents) and therefore lacks comparability in some cases. Using a resistance measurement apparatus converted from a configuration of soil microbial fuel cell, the in situ soil EC was evaluated according to the Ohmic resistance (Rs) measured using electrochemical impedance spectroscopy. The EC of soils with moisture content from 9.1% to 37.5% was calculated according to Rs. A significant positive correlation (R2 = 0.896, p &lt; 0.01) between the soil EC and the moisture content was observed, which demonstrated the feasibility of the approach. This new method can not only represent the actual soil EC, but also does not need any pretreatment. Thus it may be used widely in the measurement of the EC for soils and sediments

Extracting Indoor Space Information in Complex Building Environments

Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings&rsquo; indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings

An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment

Real-time earthquake magnitude estimation via a deep learning network based on waveform and text mixed modal

Abstract Rapid and accurate earthquake magnitude estimations are essential for earthquake early warning (EEW) systems. The distance information between the seismometers and the earthquake hypocenter can be important to the magnitude estimation. We designed a deep-learning, multiple-seismometer-based magnitude estimation method using three heterogeneous multimodalities: three-component acceleration seismograms, differential P-arrivals, and differential seismometer locations, with a specific transformer architecture to introduce the implicit distance information. Using a data-augmentation strategy, we trained and selected the model using 5365 and 728 earthquakes. To evaluate the magnitude estimation performance, we use the root mean square error (RMSE), mean absolute error (MAE), and standard deviation error (ϭ) between the catalog and the predicted magnitude using the 2051 earthquakes. The model could achieve RMSE, MAE, and ϭ less than 0.38, 0.29, and 0.38 when the passing time of the earliest P-arrival is 3 s and stabilize to the final values of 0.20, 0.15, and 0.20 after 14 s. The comparison between the proposed model and model ii, which is retrained without the specific architecture, indicates that the architecture contributes to the magnitude estimation. The P-arrivals picking error testing indicates the model could provide robust magnitude estimation on EEW with an absolute error of less than 0.2 s. Graphical Abstrac

Association of physical activity and dietary diversity with cognitive function in the elderly with Type 2 diabetes mellitus: findings from a cross-sectional study

Aims: Mild cognitive impairment (MCI) is a common complication of type 2 diabetes mellitus (T2DM). Changes in lifestyle and dietary patterns play a crucial role in preventing both diabetes and cognitive impairment. Methods: A cross-sectional study was conducted on 899 aging participants. The Dietary Diversity Score (DDS) was used to evaluate dietary diversity. The physical activity (PA) levels were divided based on metabolic equivalents and weekly activity time. Individual PA levels were further re-scored and combined with DDS scores to obtain each participant's total score. Results: Regardless of T2DM status, individuals with MCI had lower DDS and plant-derived DDS compared to non-MCI individuals. Non-MCI subjects had higher total PA and DDS scores than MCI subjects. There were differences in the correlation between DDS or PA scores and blood glucose and MoCA scores among different groups. The subjects with high DDS levels showed a significantly decreased risk of MCI and T2DM+MCI. Those with a total PA and DDS score in Q4 showed a significantly decreased risk of MCI and T2DM+MCI compared to Q1. Conclusions: A diversified diet improved blood glucose levels and cognitive function. Elderly individuals with diverse diets and adequate PA had a reduced risk of developing T2DM and MCI

Establishment of a modified percutaneous CT-guided paraspinal intramuscular VX-2 squamous cell carcinoma dual tumor model in rabbits

Background The rabbit VX-2 tumor model is a commonly used transplanted tumor model and is widely used in surgical, radiological, and interventional studies. Most of the known tumor models for each site are single solid tumors. This study aimed to establish an accurate and stable intramuscular dual tumor model guided by computed tomography (CT). Methods In this study, we compared three different inoculation methods to select the most appropriate dual tumor model. Six New Zealand White rabbits were used as tumor-carrying rabbits for tumor harvesting. Thirty rabbits were divided into three groups as experimental rabbits. Group A applied the tumor cell suspension method, in which the suspension was injected into the designated location with a syringe under CT guidance. Groups B and C used tumor tissue strips obtained in vivo or under direct in vitro vision. The tumor tissue strips were implanted into the designated locations using a guide needle under CT guidance. The differences in tumorigenic rate, the size difference between bilateral tumors, and metastasis between the three methods were compared. Results It was found that group A obtained a 100% tumor survival rate, but the size of the tumor was more variable, and needle tract implantation metastasis occurred in 5 cases. In group B, tumor tissue strips were taken in vivo for implantation, in which one case failed to survive. Tumor tissue strips in group C were obtained in vitro under direct vision. The tumor tissue strips obtained in vitro by puncture using a biopsy needle in group C had a 100% tumorigenicity rate and stable tumor size. No significant needle tract implantation metastases were found in either group B or C. The variance of tumor size obtained in group A was significantly higher than in groups B and C. The variance of tumor size in group C was the smallest. Group C had high tumorigenicity and a more stable size and morphology of the formed tumors. Conclusion The results showed that the method of obtaining tumor tissue strips using in vitro direct vision puncture and implanting them into the muscle with CT guidance and guide needles can establish an accurate and stable dual tumor model. This dual tumor model can provide substantial support for relevant preclinical studies