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Abstract Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. This is particularly valuable for understanding the intricate dynamics of spinal surgery and its multifaceted impacts on patient prognosis. This review aims to delineate the role of artificial intelligence in spinal surgery. A search of the PubMed database from 1992 to 2023 was conducted using relevant English publications related to the application of artificial intelligence in spinal surgery. The search strategy involved a combination of the following keywords: "Artificial neural network," "deep learning," "artificial intelligence," "spinal," "musculoskeletal," "lumbar," "vertebra," "disc," "cervical," "cord," "stenosis," "procedure," "operation," "surgery," "preoperative," "postoperative," and "operative." A total of 1,182 articles were retrieved. After a careful evaluation of abstracts, 90 articles were found to meet the inclusion criteria for this review. Our review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events. By utilizing these technologies, surgical outcomes can be improved, ultimately enhancing the quality of life for patients.

Abstract Background Many personnel respond to natural disasters like earthquakes and tsunamis and frequent public health events like Ebola and COVID-19. However, research on emergency assistance personnel remains limited. This study aims to describe the perceived well-being among responders deployed in isolated or emergency international missions while providing practical references to intervene in developing similar missions. Methods For this qualitative phenomenological study, purposive sampling was used following the principle of maximum differentiation to select personnel deployed on an emergency mission for over a year. Data collection continued until data saturation. Phenomenologically semi-structured interviews helped explore the physical and psychological status of the participants with Colaizzi’s method. Results Eleven personnel were interviewed after the mission, with four major themes being identified: ‘perceived somatic change,’ ‘perceived emotional change,’ ‘behavioral change,’ and ‘coping with perceived change.’ Conclusions The mental health status of the emergency assistance personnel was affected by multiple factors from external and internal environments. The current study explored the physical and psychological feelings and emotions of emergency assistance personnel during an emergency mission. The study provided a practical reference for health management under similar missions. Registrations Not registered.

Abstract Understanding the impacts of COVID-19 on citizens from different cities is crucial for urban resilience-building and reducing inequal resilience distribution. However, little research focuses on urban life at the individual level, particularly in second- and third-tier cities. An online survey was therefore conducted to collect data on how COVID-19 affected the cities and urban residents in mainland China. The results indicate that COVID-19 limited citizens’ access to healthcare facilities and socioeconomic activities apart from the immediate health crisis. Most citizens suffered reduced income, unemployment, and social anxiety. However, COVID-19 also raised social awareness and actions for disaster adaptation. The Chinese pandemic management has strengthened governmental leadership and credibility among most citizens in the early stage. Importantly, the results suggested that citizens in first-tier cities appeared more resilient to pandemics than those in second-tier cities. A networked resilience framework was therefore discussed for resilience-building policy implications.

Solid oral controlled release formulations feature numerous clinical advantages for drug candidates with adequate solubility and dissolution rate. However, most new chemical entities exhibit poor water solubility, and hence are exempt from such benefits. Although combining drug amorphization with controlled release formulation is promising to elevate drug solubility, like other supersaturating systems, the problem of drug recrystallization has yet to be resolved, particularly within the dosage form. Here, we explored the potential of an emerging, non-leachable terpolymer nanoparticle (TPN) pore former as an internal recrystallization inhibitor within controlled release amorphous solid dispersion (CRASD) beads comprising a poorly soluble drug (celecoxib) reservoir and insoluble polymer (ethylcellulose) membrane. Compared to conventional pore former, polyvinylpyrrolidone (PVP), TPN-containing membranes exhibited superior structural integrity, less crystal formation at the CRASD bead surface, and greater extent of celecoxib release. All-atom molecular dynamics analyses revealed that in the presence of TPN, intra-molecular bonding, crystal formation tendency, diffusion coefficient, and molecular flexibility of celecoxib were reduced, while intermolecular H-bonding was increased as compared to PVP. This work suggests that selection of a pore former that promotes prolonged molecular separation within a nanoporous controlled release membrane structure may serve as an effective strategy to enhance amorphicity preservation inside CRASD.

Abstract Background Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

This study delves into the intricate usage of certainty stance adverbs in the academic writings of the Chinese. The object of this study is to conduct a contrastive analysis of certainty stance adverbs between Chinese linguistic MA novices and linguistic experts. The subject of this study is to explore the similarities and differences in the use of these adverbs within the academic discourse of the two groups mentioned. In addressing these disparities, the study seeks to analyze them from the perspective of interlanguage and interpersonal function, with the ultimate goal of enhancing the production of high-quality academic papers by the Chinese. The study employs two key methods: the contrastive analysis method and the corpus-based method. The novelty of the study lies in its contrastive examination of certainty stance adverbs between Chinese linguistic MA novices and experts. Through an in-depth analysis of theoretical frameworks and linguistic data extracted from the MA theses of Chinese linguistic novices and research articles of linguistic experts, the study underscores the importance of considering the similarities and differences in the use of certainty stance adverbs within the realm of second language acquisition and pragmatic studies. This consideration aims to refine learners’ syntactical and pragmatic command of certainty stance adverbs to align with expert academic discourse, ultimately fostering effective interpersonal communication in academic writing. The findings of this research offer valuable insights into the specific linguistic challenges encountered by the Chinese, thereby laying a solid groundwork for the development of targeted pedagogical strategies to bolster their academic writing skills.

The investigation of staggered finned manifold microchannel (MMC) for addressing thermal management challenges in high heat flux electronic devices presents promising insights. The research indicates that staggered finned MMCs exhibit superior heat transfer characteristics in comparison to traditional rectangular microchannels, particularly under single-phase flow conditions. When staggered finned microchannels reduce the maximum temperature of the heating surface by around 10 K, the pressure drop is reduced by 1.3 kPa. Notably, as the heat flux escalates, the advantages of staggered finned MMCs become increasingly pronounced, while higher flow rates lead to a gradual diminishment of these advantages. Furthermore, in scenarios involving flow boiling with low heat flux and high flow conditions, staggered finned MMCs demonstrate enhanced heat transfer performance over traditional rectangular microchannels. When staggered finned microchannels reduce the maximum temperature of the heating surface by around 3 K, the pressure drop is improved by 1.2 kPa. Moreover, the study underscores that at lower flow rates, staggered finned MMCs experience a reduced pressure drop relative to traditional rectangular microchannels, whereas at higher flow rates, this trend is reversed. These findings carry significant implications for the optimization of thermal management strategies for electronic devices operating under high heat flux conditions.

Background: Symptomatic Tarlov cysts are extremely rare, and there is no consensus on the optimal treatment for this condition. Here, we encountered a patient with a symptomatic giant sacral Tarlov cyst and reviewed the relevant literature.Case descriptionA 55-year-old male patient was diagnosed with a large sacral canal cyst based on an MRI examination four years ago. Due to mild symptoms, the patient received conservative treatment. In 2023, the patient's symptoms worsened, significantly affecting daily activities. It was suspected that the symptoms were related to the cyst. Subsequently, the patient underwent laminectomy and microsurgical excision of the cyst. Postoperatively, the patient experienced an improvement in symptoms. We conducted a review of the surgical approach used for this patient and explored alternative treatment methods proposed in the literature for similar conditions. Conclusions: We achieved favorable outcomes by performing microsurgical excision of symptomatic giant Tarlov cysts in a 55-year-old patient. Additionally, we reviewed relevant literature regarding Tarlov cysts. The treatment approaches for this condition vary, and conservative, interventional, or surgical treatments have all shown efficacy. Therefore, it is crucial to choose an appropriate treatment plan based on the patient's specific circumstances.

Abstract Purpose We aimed to explore the relationship between bone mineral density (BMD), bone metabolism markers, and blood lipid-related indicators, body mass index (BMI) in elderly individuals. Methods A retrospective analysis was conducted on 710 patients. Patients’ gender, age, height, weight, bone density values, T-scores, bone metabolism markers (including serum N-terminal propeptide of type I collagen (s-PINP), serum C-terminal telopeptide of type I collagen (s-CTX) and 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) and lipid-related indicators (including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and Castelli index 1 (TC/HDL-C index) and Castelli index 2 (LDL-C/HDL-C index) were recorded. Correlations between variables were analyzed, and patients were grouped according to gender and T-score for intergroup comparisons. Results HDL-C negatively correlates with BMD and s-CTX. TG, Castelli index, and BMI positively correlate with BMD. BMI negatively correlates with s-PINP. 1,25(OH)2D3 negatively correlates with TC, LDL-C, and Castelli index. LDL-C positively correlates with BMD in males, and TC negatively correlates with s-PINP. In females, HDL-C negatively correlates with BMD, and s-CTX positively correlates with Castelli index. 1,25(OH)2D3 negatively correlates with TC, LDL-C, and Castelli index. TG and Castelli index were higher in normal bone mass group, while HDL-C is higher in the osteoporosis group. TG and BMI positively predicted bone mass density, while HDL-C negatively predicted bone mass density. Conclusions HDL-C may have a predictive role in osteoporosis, particularly in women. The likelihood of osteoporosis is lower in individuals with high BMI or hyperlipidemia. Some lipid metabolism markers can be used to predict osteoporosis, and further research is needed.

For the protection, restoration, and sustainable management of wetland ecosystems, precision in extracting high-quality wetland land cover information is crucial. This study focused on the National Nature Reserve of Liaohe Estuary in Panjin City, Liaoning Province, China. To enhance the classification accuracy of wetland land covers exhibiting similar spectral characteristics and alleviate the occurrence of the ‘salt-and-pepper’ effect, where certain land parcels are erroneously classified into multiple categories by pixel-based methods, an approach integrating object-oriented techniques and temporal features was employed for precise wetland land cover classification. The analysis utilized multi-temporal Sentinel-2 multispectral images. Initially, the images underwent segmentation using the SNIC method to generate uniform polygons, effectively mitigating misclassification issues. Subsequently, texture, geometry, band reflectance, and spectral deviation features were extracted for each segmented object. A total of 57 features, including vegetation and moisture components, were integrated to construct temporal characteristics. By applying the Random Forest (RF) algorithm in combination with Extreme Randomized Trees (ERT), 18 significant features influencing wetland extraction were identified. These selected features were then utilized to train a Random Forest (RF) model for classifying wetland land cover in the study area. The findings revealed that the integrated object-oriented and temporal feature classification approach achieved an impressive overall accuracy of 95.52% and a Kappa coefficient of 0.95 for the Liaohe Estuary wetland region. The accuracy for various land cover types reached 0.87 for both user and producer accuracy. Compared to alternative machine learning algorithms such as SegUnet++, SVM, and RF, the proposed method demonstrated a performance increase of 16.35%, 14.06%, and 6.14%, respectively. The incorporation of temporal features notably reduced land cover misclassifications, resulting in a 6.14% increase in overall accuracy and a 0.07 improvement in the Kappa coefficient compared to a method lacking temporal features. Particularly for categories like canals, aquaculture, rivers, and reservoirs, producer accuracy improved by over 7.5% and user accuracy by more than 2.9%. The effectiveness of the object-oriented approach was evident in addressing the “salt-and-pepper” effect, showcasing a rise of 2.81% in overall accuracy and 0.03 in Kappa coefficient compared to an approach not utilizing object-oriented techniques. In summary, the proposed classification method, integrating object-oriented methods and temporal features, offers superior accuracy in fine wetland land cover classification and mapping.

The response capacities of urban flood forecasting and risk control can be improved by strengthening the computational abilities of urban flood numerical models. In this work, a GPU-based hydrodynamic model is developed to simulate urban rainstorm inundations. By simulating rainstorm floods in a certain area of Xixian New City, the established model can implement high-resolution urban rainstorm inundation simulations with significantly accelerated computing performances. The accelerated computation efficiencies of the different rainstorm event simulations under resolutions of 5 and 2 m are quantitatively analysed, showing that the absolute and relative speedup ratios for all scenarios of applying two GPUs range from 10.8 to 12.6 and 1.32 to 1.68 times as much as those of a CPU and a single GPU, respectively. The application of a large-scale rainstorm inundation simulation shows the excellent acceleration performance of the model compared to previous research. In addition, the greater the number of computational grids included in the simulation, the more significant the effect on the acceleration computing performance. The proposed model efficiently predicts the spatial variation in the inundation water depth. The simulation results provide guidance for urban rainstorm inundation management, and they improve the time and efficiency of urban flood emergency decision-making. HIGHLIGHTS The GPU-based hydrodynamic model is used to evaluate sponge city rainstorm inundation.; Significant speedup of the model executed on multi-GPU compared to a single GPU.; The model is efficient for performing high-resolution sponge city inundation simulation.; Validation and speedup computing performance tests on real urban city-scale areas.;

High-resolution (HR) optical remote sensing images are typically small in swath and, due to cloud cover, their revisit period, mosaic error, and other problems, it is often infeasible to obtain a large range of remote sensing images for a study area. Meanwhile, low-resolution (LR) satellite images suffer from insufficient spatial and texture information for ground objects. Therefore, classifying a study area with high spatial resolution, large area, and no cloud occlusion using optical remote sensing imagery is very difficult. In recent years, the rapid development of super-resolution reconstruction (SRR) technology has made high-quality spatial resolution reconstruction possible. The SRR of real images is usually accompanied by problems such as sensor spectral range differences, cloud occlusion in the research area, and the SRR algorithm sacrificing a lot of the original information. In this study, with an improved PGGAN, we use only a small number of samples, the wide-swath medium-resolution satellite was restored to the same resolution as the high-resolution satellite, a new method for SRR multi-spectral optical remote sensing image classification based on texture reconstruction information is proposed, and a wide range of high-precision feature classifications are achieved in the study area. In order to solve the problem of spectral distortion in the process of multi-spectral image SRR and the weak generalization of optical remote sensing image ground object classification due to the difference between temporal and spatial features, we combine the idea of ground object classification with texture features obtained after super-resolution reconstruction. We used the support vector machine (SVM) and random forest (RF) classification methods to evaluate the classification effect of each texture spectral feature combination, with the overall accuracy (OA) of the SVM and RF classifiers reaching 98.93% and 98.51%, respectively. The land-use and land-cover (LULC) classification accuracy of the SRR images combined with texture features is much higher than that when directly classifying the original GaoFen-1 and Sentinel-2 images. The obtained results imply that the method of superimposing texture features allows for better classification results in the Liaohe estuary area, providing a new technical idea for the study of LULC classification.

Abstract Background To date, few reports have evaluated the long-term outcome of percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) and the factors influencing the long-term outcome of this procedure are uncertain. Methods A total of 91 patients underwent PKP for thoracolumbar OVCFs from June 2012 to December 2012. Pain Visual Analogue Scores (VAS) and Oswestry Disability Index (ODI) were recorded preoperatively and after 10-year follow-up. Factors that may affect surgical outcome, such as gender, age, height, weight, hypertension, diabetes, cause of injury, fracture segment, length of hospitalization, history of previous spinal surgery, preoperative bone mineral density (BMD), preoperative VAS and ODI scores, length of surgery, bone cement dosage, postoperative standardized anti-osteoporosis treatment, and other new vertebral fractures, were analyzed by multiple linear regression with VAS and ODI scores at the last follow-up. The correlation factors affecting the efficacy were analyzed. Results The preoperative and final follow-up pain VAS was 7.9 ± 1.1 and 2.2 ± 1.1. ODI scores were 30.4 ± 4.2 and 10.7 ± 2.6. The difference was statistically significant (P 0.05). Conclusion The long-term outcome after PKP is satisfactory. Age, gender, cause of injury, and standardized postoperative anti-osteoporosis treatment may be factors affecting the long-term outcome.

Long-term exposure to ultraviolet (UV) rays can result in skin photoaging, which is primarily characterized by dryness, roughness, pigmentation, and a loss of elasticity. However, the clinical drugs commonly employed to treat photoaged skin often induce adverse effects on the skin. Anthocyanins (ACNs) are water-soluble pigments occurring abundantly in various flowers, fruits, vegetables, and grains and exhibiting a range of biological activities. Studies have demonstrated that ACNs contribute to the repair of photoaged skin due to their diverse biological characteristics and minimal side effects. Evidence suggests that the stability of ACNs can be enhanced through encapsulation or combination with other substances to improve their bioavailability and permeability, ultimately augmenting their efficacy in repairing photoaged skin. A growing body of research utilizing cell lines, animal models, and clinical studies has produced compelling data demonstrating that ACNs mitigate skin photoaging by reducing oxidative stress, alleviating the inflammatory response, improving collagen synthesis, alleviating DNA damage, and inhibiting pigmentation. This review introduces sources of ACNs while systematically summarizing their application forms as well as mechanisms for repairing photoaged skin. Additionally, it explores the potential role of ACNs in developing functional foods. These findings may provide valuable insight into using ACNs as promising candidates for developing functional products aimed at repairing photoaged skin.

In recent years, accurate field monitoring has been a research hotspot in the domains of aerial remote sensing and satellite remote sensing. In view of this, this study proposes an innovative cross-platform super-resolution reconstruction method for remote sensing images for the first time, aiming to make medium-resolution satellites capable of field-level detection through a super-resolution reconstruction technique. The progressive growing generative adversarial network (PGGAN) model, which has excellent high-resolution generation and style transfer capabilities, is combined with a deep residual network, forming the Res-PGGAN model for cross-platform super-resolution reconstruction. The Res-PGGAN architecture is similar to that of the PGGAN, but includes a deep residual module. The proposed Res-PGGAN model has two main benefits. First, the residual module facilitates the training of deep networks, as well as the extraction of deep features. Second, the PGGAN structure performs well in cross-platform sensor style transfer, allowing for cross-platform high-magnification super-resolution tasks to be performed well. A large pre-training dataset and real data are used to train the Res-PGGAN to improve the resolution of Sentinel-2’s 10 m resolution satellite images to 0.625 m. Three evaluation metrics, including the structural similarity index metric (SSIM), the peak signal-to-noise ratio (PSNR), and the universal quality index (UQI), are used to evaluate the high-magnification images obtained by the proposed method. The images generated by the proposed method are also compared with those obtained by the traditional bicubic method and two deep learning super-resolution reconstruction methods: the enhanced super-resolution generative adversarial network (ESRGAN) and the PGGAN. The results indicate that the proposed method outperforms all the comparison methods and demonstrates an acceptable performance regarding all three metrics (SSIM/PSNR/UQI: 0.9726/44.7971/0.0417), proving the feasibility of cross-platform super-resolution image recovery.

Groundwater resources are essential for sustaining ecosystems and human activities, especially under the pressures of climate change. This study employed Electrical Resistivity Tomography (ERT) to assess the impact of Gully Land Consolidation (GLC) engineering on the groundwater hydrological field of small watersheds in the China Loess Plateau (CLP). Results revealed ample subsurface water storage in backfilled areas, primarily migrating along the original river path owing to topographical limitations. Although the distribution patterns of soil moisture in each backfilling block varied slightly, the boundaries of soil moisture content and variation mainly appeared at depths of 8 m and 20 m underground. Significant moisture variation occurred across the 0–20 m underground layers, suggesting the 8–20 m layer could function as a groundwater collection zone in the study area. Human activities could disturb groundwater, altering migration pathways from the original river path. An optimized “Drainage–Conveyance–Barrier” system is proposed to enhance GLC sustainability, involving upstream groundwater level control, midstream soil moisture management, and downstream hydrological connectivity improvement. These findings carry substantial implications for guiding the planning and execution of GLC engineering initiatives. The novelty of this study lies in its application of ERT to provide a detailed spatial and temporal understanding of soil moisture dynamics in the GLC areas. Future research should focus on factors such as soil types and topographical changes for a comprehensive assessment of GLC’s impact on small watershed groundwater hydrology.

BackgroundThe long non-coding RNA cancer susceptibility candidate (CASC) has abnormal expression in lung cancer tissues and may correlate with lung cancer prognosis. This study aimed to comprehensively evaluate the association between CASC expression and the cancer prognosis.MethodsPubMed, Embase, Web of Science, Google Scholar, Cochrane Library, and China National Knowledge Infrastructure databases were searched until April 1, 2023, to obtain the relevant literature. Studies that met the predefined eligibility criteria were included, and their quality was independently assessed by 2 investigators according to the Newcastle-Ottawa Scale (NOS) score. Detailed information was obtained, such as first author, year of publication, and number of patients. Hazard ratio (HR) with a 95% confidence interval (CI) was extracted and grouped to assess the relationship between CASC expression and cancer prognosis. The dichotomous data was merged and shown as the odds ratio (OR) with a 95% CI was extracted to assess the relationship between CASC expression and clinicopathological parameters.ResultsA total of 12 studies with 746 patients with lung cancer were included in the meta-analysis. The expression levels of lncRNA CASC2 and CASC7 were decreased, while those of CASC9, 11, 15, and 19 were induced in lung cancer tissues compared with paracancerous tissues. In the population with low CASC expression (CASC2 and CASC7), high CASC expression indicated a good lung cancer prognosis (HR = 0.469; 95% CI, 0.271-0.668). Conversely, in the population with high CASC expression (CASC9, 11, 15, and 19), high CASC expression predicted a poor lung cancer outcome (HR = 1.910; 95% CI, 1.628-2.192). High CASC expression also predicted worse disease-free survival (DFS) (HR = 2.803; 95% CI, 1.804-6.319). Combined OR with 95% CI revealed an insignificant positive association between high CASC expression and advanced TNM stage (OR = 1.061; 95% CI, 0.775-1.454), LNM (OR = 0.962; 95% CI, 0.724-1.277), tumor size (OR = 0.942; 95% CI, 0.667-1.330), and histological grade (OR = 1.022; 95% CI, 0.689-1.517).ConclusionThe CASC expression levels negatively correlate with lung cancer prognosis. Therefore, CASC expression may serve as a prognostic marker and a potential therapeutic target for lung cancer.

This study investigated the bactericidal effects of ultraviolet (UV) radiation, a high-voltage electric field (HVEF), and their combination on Escherichia coli. The results indicated that UV and combined disinfection were more effective with longer exposure, leading to significant reductions in microbial activity. Specifically, the single UV disinfection alone reduced activity by 3.3 log after 5 min, while combined disinfection achieved a 4.2 log reduction. In contrast, short-term HVEF treatment did not exhibit significant bactericidal effects, only achieving a reduction of 0.17 log in 5 min. Furthermore, prolonged exposure to both UV disinfection and an HVEF was found to damage cell membranes, ultimately causing cell death, while shorter durations did not. Despite rapid cell count decreases, flow cytometry did not detect apoptotic or necrotic cells, likely due to rapid cell rupture. This study suggests that combining UV radiation and an HVEF could be a promising approach for inhibiting bacterial reproduction, with HVEF enhancing UV effects. These findings provide insights for using combined HVEF and UV disinfection in food safety and preservation.

Equatorial plasma bubbles (EPBs) occur frequently in low-latitude areas and have a non-negligible impact on navigation satellite signals. To systematically analyze the effects of a single EPB event on multi-frequency signals of GPS, Galileo, GLONASS, and BDS, all-sky airglow images over South China are jointly used to visually determine the EPB structure and depletion degree. The results reveal that scintillations, or GNSS signal fluctuations, are directly linked to EPBs and that the intensity of scintillation is positively correlated with the airglow depletion intensity. The center of the airglow depletion often corresponds to stronger GNSS scintillation, while the edge of the bubble, which is considered to have the largest density gradient, corresponds to relatively smaller scintillation instead. This work also systematically analyzes the responses of multi-constellation and multi-frequency signals to EPBs. The results show that the L2 and L5 frequencies are more susceptible than the L1 frequency is. For different constellations, Galileo’s signal has the best tracking stability during an EPB event compared with GPS, GLONASS, and BDS. The results provide a reference for dual-frequency signal selection in precise positioning or TEC calculation, that is, L1C and L2L for GPS, L1C and L5Q for Galileo, L1P and L2C for GLONASS, and L1P and L5P for BDS. Notably, BDS-2 is significantly weaker than BDS-3. And inclined geosynchronous orbit (IGSO) satellites have abnormal data error rates, which should be related to the special signal path trajectory of the IGSO satellite.

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

We report that diazepam binding inhibitor (DBI) is a glial messenger mediating crosstalk between satellite glial cells (SGCs) and sensory neurons in the dorsal root ganglion (DRG). DBI is highly expressed in SGCs of mice, rats, and humans, but not in sensory neurons or most other DRG-resident cells. Knockdown of DBI results in a robust mechanical hypersensitivity without major effects on other sensory modalities. In vivo overexpression of DBI in SGCs reduces sensitivity to mechanical stimulation and alleviates mechanical allodynia in neuropathic and inflammatory pain models. We further show that DBI acts as an unconventional agonist and positive allosteric modulator at the neuronal [GABA.sub.A] receptors, particularly strongly affecting those with a high-affinity benzodiazepine binding site. Such receptors are selectively expressed by a subpopulation of mechanosensitive DRG neurons, and these are also more enwrapped with DBI-expressing glia, as compared with other DRG neurons, suggesting a mechanism for a specific effect of DBI on mechanosensation. These findings identified a communication mechanism between peripheral neurons and SGCs. This communication modulates pain signaling and can be targeted therapeutically., Introduction Despite remarkable progress in our understanding of the fundamental biology of pain, current therapies for chronic pain are often inadequate and prone to serious side effects within the central [...]