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Abstract To analyze the clinical characteristics of patients with metastatic calcification undergoing maintenance dialysis. Patients diagnosed with metastatic calcification via 99mTc-MDP bone scintigraphy between June 2019 and March 2023 at our hospital were included in this study. All patients were followed from the time of diagnosis until death, loss to follow-up, or the study endpoint (July 2023). The primary endpoint was all-cause mortality. The cohort comprised 7 males and 3 females, with a mean age of (48.7 ± 11.1) years. The most common sites of metastatic calcification included the lungs, joints, and skin, while rarer sites involved the stomach, myocardium, and mandibular angle. Nine patients received treatment with sodium thiosulfate, with a mean duration of STS administration of 23.5 days (range: 13.25 to 56.5 days). The mean follow-up duration was 14 months (range: 10 to 20 months). One patient died, one was lost to follow-up, while 8 out of 10 patients survived, and 6 out of 10 patients were followed for more than one year. 99mTc-MDP bone scintigraphy demonstrates high sensitivity and specificity for detecting metastatic calcification. The most frequently affected sites are the lungs, joints, and skin, with less common sites being the stomach, myocardium, and mandibular angle. Eight patients (8/10) survived, and 6 patients (6/10) remained alive with over one year of follow-up.

Abstract Accurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.

During the operation of a deep geological repository in crystalline rocks for disposal of high-level radioactive waste, understanding the seepage behaviors of fractured crystalline rocks under coupled thermo-hydro-mechanical conditions is essential for the performance assessment of deep geological repositories. In this study, radial flow tests on cylindrical Beishan granite specimens with a single artificial fracture were conducted using the MTS 815 rock mechanics testing system to investigate the influence of normal stress and temperature on radial flow behaviors of rough rock fractures. Steady state method was used to measure fracture permeability, and an axial extensometer was used to measure fracture deformation during compression. A three-dimensional blue light scanner was used to characterize fracture surface morphology. Experimental results indicate that fracture permeability decreases nonlinearly with the increase of normal stress or temperature, and normal stress has a more significant influence on fracture permeability than temperature. The evolution of three-dimensional non-uniform distribution of voids under compression was numerically obtained, and the variogram was employed to quantify the non-uniform distribution characteristics of mechanical apertures. In addition, a radial flow model considering non-uniform distribution of apertures is proposed to predict the normal stress- and temperature-dependent seepage behaviors of rock fractures, and the predictions were found to be in good agreement with experimental data.

Abstract Background This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005–2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China. Methods Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025. Results Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3–7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing’s urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015–2019) and the dynamic zero-COVID period (2020–2022), respectively, compared to the pre-upsurge period (2005–2014) (P

Land surface temperature (LST) is a crucial factor for reflecting climate change. High spatial resolution LST is particularly significant for environmental monitoring in plateau and mountainous areas, which are characterized by rugged landscapes, diverse ecosystems, and high spatial variability in LST. Typical plateau mountainous areas in Diqing Tibetan Autonomous Prefecture and Dali Bai Autonomous Prefecture were selected as study areas. Three machine learning models, including Back Propagation (BP) Neural Network, random forest (RF), and extreme gradient boosting (XGBoost), and two classic single-factor linear regression models (DisTrad and TsHARP) were compared. Particle Swarm Optimization (PSO) was introduced to optimize hyperparameters of three machine learning methods. Regression factors suitable for plateau mountainous areas, including normalized vegetation index (NDVI), normalized multi-band drought index (NMDI), bare soil index (BSI), normalized difference snow index (NDSI), elevation, surface roughness (SR), and Hillshade were selected. The performance of five models was analyzed from the perspective of different spatial resolutions and land cover types. The results revealed that the performance of machine learning models is better than traditional linear models in both study areas. Based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), XGBoost demonstrated the best performance. For study area A, the results were R2 = 0.891, RMSE = 2.67 K, and MAE = 1.83 K, while for study area B, the values were R2 = 0.832, RMSE = 1.98 K, and MAE = 1.54 K. In addition, among different land cover types, the XGBoost model has the best performance in both study areas. Moreover, the larger the ratio of initial resolution to target resolution, the lower the accuracy of downscaled LST (DLST). In summary, the XGBoost model is more suitable for downscaling LST in plateau mountainous areas.

Biological characteristics of pregnant women during early pregnancy make them susceptible to both poor sleep quality and metal/metalloid exposure. However, the effects of metal(loid) exposure on sleep quality in pregnant women remain unknown and unexplored. We aimed to examine the relationship between exposure to a mixture of metal(loid)s and pregnant women’s sleep quality during early pregnancy. We recruited 493 pregnant women in the first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected their spot urine samples. All urine specimens were assessed for eight metal(loid)s: arsenic (As), cadmium (Cd), iron (Fe), zinc (Zn), molybdenum (Mo), lead (Pb), selenium (Se), and mercury (Hg). We used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. Linear regression, logistic regression, generalized additive models (GAMs), quantile g-computation, and Bayesian kernel machine regression (BKMR) were applied to investigate the relationships between metal(loid) exposure and sleep quality. The results from single metal(loid) models, quantile g-computation models, and BKMR models consistently suggested that Fe was positively related to women’s sleep quality. Moreover, in the quantile g-computation models, As was the most critical contributor to the negative effects of the metal(loid) mixture on sleep quality. In addition, we found significant As by Fe interaction for scores of PSQI and habitual sleep efficiency, Pb by Fe interaction for PSQI and sleep latency, and Hg by Fe interaction for PSQI, suggesting the interactive effects of As and Fe, Pb and Fe, Hg and Fe on sleep quality and specific sleep components. Our study provided the first-hand evidence of the effects of metal(loid) exposure on pregnant women’s sleep quality. The underlying mechanisms need to be explored in the future.

Introduction: As one of the common psychiatric diseases, depression poses serious threats to human health. Although many genes have been nominated for depression, few of them were investigated in details at the molecular level. Objectives: To demonstrate Frizzled class receptor 6 (FZD6) functions in depression through disrupting Wnt/β-catenin signal pathway. Methods: The FZD6 edited cell line and mouse model were generated by using CRISPR/Cas9 technique. The expression of key genes and proteins in Wnt/β-catenin pathway was determined by qRT-PCR and Western blotting, respectively. Animal behavioral tests, including open field test (OFT), elevated plus maze test (EPM), forced swimming test (FST), tail suspension test (TST), and sucrose preference test (SPT), were employed to determine anxiety- and depressive-like behaviors. Immunofluorescent staining was used to assess cell proliferation in the hippocampus of mouse brain. Results: Among patients with depression, FZD6, one of the receptors of Wnt ligand, was significantly decreased. In CRISPR/Cas9-based FZD6 knockdown cells, we showed that FZD6 plays a significant role in regulating expression of genes involved in Wnt/β-catenin pathway. Subsequently behavioral studies on Fzd6 knockdown mice (with a 5-nucleotide deletion; Fzd6-Δ5) revealed significant changes in depressive symptoms, including increased immobility duration in FST, less preference of sucrose in SPT, reduction of distance traveled in OFT, and decreased time spent in open arms in EPM. Immunofluorescent staining showed decreased cell proliferation in the hippocampus of Fzd6-Δ5 mice with reduced number of Ki67+ and PCNA+ cells.Moreover, decreased Gsk3β mRNA expression, phosphorylated GSK3β, and cytoplasmic β-catenin in the hippocampus of Fzd6-Δ5 mice provided further evidence supporting the role of Fzd6 in depression. Conclusion: Together, above findings proved the significant role of FZD6 in depression through its effect on hippocampal cell proliferation and its ability to regulate canonical Wnt/β-catenin pathway.

Abstract Background HIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors. Methods We conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW). Results The successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings. Conclusions HIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment.

G-quadruplex (G4), an important secondary structure of nucleic acids, is polymorphic in structure. G4 monomers can associate with each other to form multimers, which show better application performance than monomers in some aspects. G4 dimers, the simplest and most widespread multimeric structures, are often used as a representative for studying multimers. RHAU, a G4 ligand, has been reported to recognize G4 dimers. However, there are few reports focusing on interactions between RHAU and different G4 dimers. In this work, interactions between RHAU peptide and six G4 dimers were investigated by size-exclusion chromatography (SEC). It was revealed that compared to the hybrid G4 monomer, the hybrid tandem unstacked G4 dimer could form special binding sites, leading to a weak interaction with RHAU. It was also found that the steric hindrance at terminal G-tetrads of a special Z-G4 structure greatly weakened their interactions with RHAU. Additionally, RHAU exhibited stronger interactions with intermolecular stacked/interlocked parallel dimers than with intramolecular tandem stacked parallel dimers. This work enriches the understanding of interactions between RHAU and G4 dimers, which is conducive to the elucidation of G4 polymorphism, and provides a strong reference for studying G4 multimer–peptide interactions.

Three-dimensional geological modeling is essential for simulating natural gas hydrate (NGH) productivity and formulating development strategies. Current approaches primarily concentrate on the single-phase modeling of either hydrate or free gas layers. However, an increasing number of instances suggest that the three-phase coexistence zone, which includes hydrate, gas, and water, is common and has become a focal point of international research, as this type of reservoir may present the most viable opportunities for exploitation. At present, there exists a significant gap in the research regarding modeling techniques for such reservoirs. This study undertakes a comprehensive modeling investigation of the three-phase zone reservoir situated in the sand layer of the Qiongdongnan Basin. By employing deterministic complex geological modeling techniques and integrating existing seismic and logging data, we have developed a three-phase coexistence zone model that precisely characterizes the interactions between geological structures and utilizes them as auxiliary constraints. This approach effectively mitigates the potential impact of complex geological conditions on model accuracy. Through a comprehensive analysis of 105 seismic profiles, we enhanced the model’s accuracy, resulting in the creation of a three-phase coexistence zone model comprising 350,000 grids. A comparison between the modeling results and well data indicates a relatively small error margin, offering valuable insights for future development efforts. Furthermore, this method serves as a reference for modeling hydrates in marine environments characterized by three-phase coexistence on a global scale.

The increasing Ozone (O3) concentration in various regions of China has garnered significant attention, highlighting the need to understand the mechanisms of O3 formation. This study focuses on the source apportionment of O3 in Jilin City during and after the COVID-19 lockdown countermeasure, and also the influence of anthropogenic emissions on O3 concentration. The contributions of different O3 emission sources were quantified using the Weather Research and Forecasting Community Multi-Scale Air Quality (WRF-CMAQ) model in conjunction with the Integrated Source Apportionment Method (ISAM). The results indicate a significant increase in O3 concentrations during the lockdown in Jilin City, which were particularly characterized by long-distance transportation. Transportation is identified as the primary direct source of O3 in Jilin City, with Yongji County contributing the most among the six designated regions. This study highlights variations in the causes and sources of O3 pollution among the different regions of Jilin City. Simply controlling anthropogenic emissions is inadequate for effectively managing O3 pollution and may even worsen the situation. It is more effective to focus on controlling O3’s precursors. These findings improve the understanding of O3 pollution in Jilin City and provide valuable insights for developing O3 control policies. Similarly, this research is applicable to other countries and regions.

Summary: This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.

High-resolution radar systems encounter a challenge where dispersed backscatter energy from targets results in a non-uniform power distribution across range cells. Although autoregressive (AR) modeling of heterogeneous clutter can enhance the probability of range-spread target detection, the performance of detectors significantly decreases in the case of a small number of pulses due to the discard of samples equivalent to the AR order. Therefore, to mitigate performance degradation, this paper presents improved AR model-based detectors for range-spread targets in heterogeneous clutter environments, derived under the assumption of known clutter covariance matrix based on the Rao test, Wald test and generalized likelihood ratio test. Furthermore, the covariance matrix is reconstructed using estimated AR parameters through the relationship between the AR process and triangular matrix decomposition. Additionally, the asymptotic expressions for the probability of detection and false alarm show the new detectors are asymptotically constant false alarm rate with respect to the clutter covariance matrix. Experiments are conducted on both simulated and real clutter data to validate the performance of the newly derived AR model-based detectors. Both sets of results demonstrate that the enhanced detectors maintain robust performance even with a limited number of pulses, outperforming the conventional AR model-based detectors in such scenarios.

To investigate energy-saving approaches in wastewater treatment plants and decrease aeration energy consumption, this study successfully established a floc–granule coexistence system in a sequencing batch airlift reactor (SBAR) employing micro-bubble aeration. The analysis focused on granule formation and pollutant removal under various aeration intensities, and compared its performance with a traditional floc-based coarse-bubble aeration system. The results showed that granulation efficiency was positively associated with aeration intensity, which enhanced the secretion of extracellular polymeric substances (EPSs) and facilitated granule formation. The SBAR with the micro-aeration intensity of 30 mL·min−1 showed the best granulation performance (granulation efficiency 52.6%). In contrast to the floc-based system, the floc–granule coexistence system showed better treatment performance, and the best removal efficiencies of NH4+-N, TN, and TP were 100.0, 77.0, and 89.5%, respectively. The floc–granule coexistence system also enriched higher abundance of nutrients removal microbial species, such as Nitrosomonas (0.05–0.14%), Nitrospira (0.14–2.32%), Azoarcus (2.95–12.17%), Thauera (0.43–1.95%), and Paracoccus (0.76–2.89%). The energy-saving potential was evaluated, which indicated it is feasible for the micro-aeration floc–granule coexistence system to decrease the aeration consumption by 14.4% as well as improve the effluent. HIGHLIGHTS A floc–granule sludge coexistence system was constructed in an SBAR system by micro-bubble aeration.; Excessive aeration favors granular sludge formation, but not phosphorus removal.; Floc–granule sludge coexistence systems have better functional bacterial genera and energy-saving potential compared to flocculated sludge systems.;

Abstract Chongqing was seriously affected by hand, foot, and mouth disease (HFMD), but the relationships between daily mean temperature and the incidence of HFMD remain unclear. This study used distributed lag nonlinear model to evaluate the effect of daily mean temperature on the incidence of HFMD in children aged

The significant increase in ambient ozone (O3) levels across China highlights the urgent need to investigate the sources and mechanisms driving regional O3 events, particularly in densely populated urban areas. This study focuses on Xi’an, located in northwestern China on the Guanzhong Plain near the Qinling Mountains, where the unique topography contributes to pollutant accumulation. Urbanization and industrial activities have significantly increased pollutant emissions. Utilizing the Weather Research and Forecasting–Community Multiscale Air Quality Model (WRF-CMAQ), we analyzed the contributions of specific regional and industrial sources to rising O3 levels, particularly during an atypical winter event characterized by unusually high concentrations. Our findings indicated that boundary conditions were the primary contributor to elevated O3 levels during this event. Notably, Xianyang and Baoji accounted for 30% and 22% of the increased O3 levels in Xi’an, respectively. Additionally, residential sources and transportation accounted for 31% and 28% of the O3 increase. Within the Xi’an metropolitan area, Baqiao District (18–27%) and Weiyang District (23–30%) emerged as leading contributors. The primary industries contributing to this rise included residential sources (28–37%) and transportation (35–43%). These insights underscore the need for targeted regulatory measures to mitigate O3 pollution in urban settings.

Classical frequency-stabilized lasers have achieved high-frequency stability and reproducibility; however, their extensive wavelength spacing limits their utility in various scenarios. This study introduces a novel frequency-stabilized laser scheme that integrates a Fabry-Perot etalon (FPE) with digital control technology and wavelength modulation techniques. The FPE, characterized by multiple transmission peaks at minimal frequency intervals, provides stable frequency references for different lasers, thereby enhancing the system’s flexibility and adaptability. An error signal is derived from the first-order differentiation of the FPE’s transmission curve. A 180° phase difference was observed in the feedback output signal when the laser’s central frequency diverged from the reference, determining that the direction of the frequency control was accordingly determined.Employing feedback control, the laser’s output frequency is stabilized at the transmission peak frequency of the FPE. Experimental results demonstrate that this stabilization scheme effectively locks the laser’s output wavelength to different transmission peak frequencies of the FPE, achieving 25 GHz wavelength spacing. The frequency stability is improved by two orders of magnitude on a second-level timescale, maintained within hundreds of kHz, equating to a frequency stability level of 10−10.

This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin.

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios.

Abstract Type 2 diabetes (T2D) and its complications can have debilitating, sometimes fatal consequences for afflicted individuals. The disease can be difficult to control, and therapeutic strategies to prevent T2D-induced tissue and organ damage are needed. Here we describe the results of administering a potent and selective inhibitor of Protein Kinase C (PKC) family members PKCα and PKCβ, Cmpd 1, in the ZSF1 obese rat model of hyperphagia-induced, obesity-driven T2D. Although our initial intent was to evaluate the effect of PKCα/β inhibition on renal damage in this model setting, Cmpd 1 unexpectedly caused a marked reduction in the hyperphagic response of ZSF1 obese animals. This halted renal function decline but did so indirectly and indistinguishably from a pair feeding comparator group. However, above and beyond this food intake effect, Cmpd 1 lowered overall animal body weights, reduced liver vacuolation, and reduced inguinal adipose tissue (iWAT) mass, inflammation, and adipocyte size. Taken together, Cmpd 1 had strong effects on multiple disease parameters in this obesity-driven rodent model of T2D. Further evaluation for potential translation of PKCα/β inhibition to T2D and obesity in humans is warranted.

Given the complex nonlinear problem between the control and prediction of the surrounding surface settlement deformation caused (GWO), the GWO-ELM deep foundation pit excavation deformation prediction model was proposed. Extreme learning machine and Grey Wolf optimization algorithm combining training and predicting land subsidence. Based on MIDAS GTS NX software, we established a finite element simplified model for deep foundation pit construction, conducted structural calculations, and utilized the Grey Wolf optimization algorithm to optimize the deep foundation pit excavation and its influencing factors, input weights, and hidden layer thresholds in the ELM neural network. Taking the deep foundation pit project of Baoding Automobile Science and Technology Industrial Park as an example, the actual monitoring value is compared with the simulated value, verifying the model's accuracy. The number of soil nails in the finite element model, the excavation depth, the settlement of surrounding buildings and other factors are taken as the input factors of the prediction model. The DB-2 surface settlement of the monitoring point in the finite element model is taken as the output factor of the prediction model. The predicted value of the GWO-ELM model was compared with that of the ELM model. We draw three main conclusions from the results. First, the surface settlement of a bottomless foundation pit can be predicted in advance by using finite element software and the distribution law of surface settlement and horizontal displacement is consistent with the measured values. Second, the Grey Wolf optimization algorithm optimizes the input weights and thresholds in the extreme learning machine neural network. The GWO-ELM prediction model has good generalization ability, can effectively reduce human errors and can improve the accuracy of the prediction model. Third, through practical engineering verification, the average absolute error of the GWO-ELM model is 0.26145, the mean square error is 0.31258 and the R2 is 0.98725, all of which are superior to the ELM model and are an effective method for predicting deformation and settlement of deep foundation pit excavation.

Abstract Background Hip fractures are still unsolved problems nowadays. We evaluated the functional outcomes and complications in the treatment of hip fractures (AO/OTA31A1-A3) to find potential difference and risk between intramedullary nail (IMN) and dynamic hip screw (DHS). Method We searched PubMed, Embase, Cochrane library up to 19 June 2023 and retrieved any studies comparing IMN and DHS in treatment of Hip fractures. The main outcomes and complications were extracted from the included studies. The fixed-effect model was selected to pool the data for homogeneous studies (I2 50%). The analysis of sensitivity and subgroup was performed to explore the homogeneous studies among studies. The p-value of less than 0.05 was considered statistically significant. Results 30 RCT studies were included in this meta-analysis. There were significant difference of in the items of blood loss, screening time, femoral neck shortening, non-union, and femoral fractures (p

Background Dysfunction of vascular endothelial cells (ECs) plays a central role in the pathogenesis of cardiovascular complications in diabetes. SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A member 5 (SMARCA5) is a key regulator of chromatin structure and DNA repair, but its role in ECs remains surprisingly unexplored. The current study was designed to elucidate the regulated expression and function of SMARCA5 in diabetic ECs. Methods SMARCA5 expression was evaluated in ECs from diabetic mouse and human circulating CD34+ cells using quantitative reverse transcription polymerase chain reaction and Western blot. Effects of SMARCA5 manipulation on ECs function were evaluated using cell migration, in vitro tube formation and in vivo wound healing assays. Interaction among oxidative stress, SMARCA5 and transcriptional reprogramming was elucidated using luciferase reporter assay, electrophoretic mobility shift assay and chromatin immunoprecipitation. Results Endothelial SMARCA5 expression was significantly decreased in diabetic rodents and humans. Hyperglycemia-suppressed SMARCA5 impaired EC migration and tube formation in vitro, and blunted vasculogenesis in vivo. Contrarily, overexpression of SMARCA5 in situ by a SMARCA5 adenovirus-incorporated hydrogel effectively promoted the rate of wound healing in a dorsal skin punch injury model of diabetic mice. Mechanistically, hyperglycemia-elicited oxidative stress suppressed SMARCA5 transactivation in a signal transducer and activator of transcription 3 (STAT3)-dependent manner. Moreover, SMARCA5 maintained transcriptional homeostasis of several pro-angiogenic factors through both direct and indirect chromatin-remodeling mechanisms. In contrast, depletion of SMARCA5 disrupted transcriptional homeostasis to render ECs unresponsive to established angiogenic factors, which ultimately resulted in endothelial dysfunction in diabetes. Conclusion Suppression of endothelial SMARCA5 contributes to, at least in part, multiple aspects of endothelial dysfunction, which may thereby exacerbate cardiovascular complications in diabetes.

In recent years, O3 pollution events have occurred frequently in Chinese cities. Utilizing the WRF-CMAQ model, this study analyzed the causes of an O3 pollution event in Suqian on 18–21 June 2020, considering meteorological conditions, process analysis, and source analysis. It also designed 25 emission reduction scenarios to explore more effective O3 emission reduction strategies. The results show that meteorological conditions such as temperature and wind field play an important role in the formation and accumulation of O3. During the heavy pollution period, the contribution of vertical transport (VTRA) and horizontal transport (HTRA) to O3 concentration is significantly enhanced. The photochemical reactions of precursors, such as NOx and VOCs transported from long distances and O3 directly transported to Suqian from other regions, contribute greatly to O3 pollution in Suqian; local sources contribute very little, between 12.22% and 18.33%. Based on the simulation of 25 emission reduction scenarios, it was found that excessive emission reduction of NOx is not conducive to the reduction of O3 concentration, and it is best to control the emission reduction ratio at about 10%. Without affecting normal production and life, it is recommended to reduce VOCs as much as possible, particularly those generated by traffic sources.

A microwave photonic frequency multiplier with low phase noise based on an optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. In this scheme, a dual-parallel Mach–Zehnder modulator (DPMZM) is employed to generate the third-harmonic frequency of the input radio frequency (RF) signal, while the oscillation frequency of the OEO is also three times the RF signal frequency. By adjusting the bias voltages of different arms in the DPMZM, a triple-frequency signal with a high side-mode suppression ratio of 64.8 dB can be obtained. The experimental results indicate that the output of the frequency-multiplier has a better single-sideband phase noise value, for instance, −126 dBc/Hz@10 kHz at 20.019 GHz. It has improvements of 34 dB and 43.5 dB compared with the input RF signal and the simulated electrical frequency tripler module, respectively.

The classical 1.5 μm band frequency-stabilized laser using acetylene gas saturated absorption can achieve high frequency stability and reproducibility, but its system design is complex and bulky. For some practical applications, a simple, compact system containing anti-interference abilities is preferred. In this study, a low-cost and simple-structured 1.5 μm frequency-stabilized laser is constructed using digital control methods, wavelength modulation technology, and acetylene gas absorption. The fiber input and output optical devices of the system significantly simplify the optical path and reduce the volume of the system. The error signal is obtained by the first-order differential method, and a combination of the high-speed comparator circuit and the microcontroller unit (MCU) is used to detect the error signal. Through the feedback control method of coarse temperature adjustment and fine current adjustment, the second-level frequency stability of the laser is stabilized within 100 kHz, that is, the frequency stability reaches 10​−10. The designed system achieved continuous and stable operation for more than 6 h, and the long-term frequency stability reached 10​−9.

In this paper, an optoelectronic oscillator (OEO) with ultra-low phase noise and high stability based on the injection-locked and phase-locked loop is proposed. In theory, the injection-locked frequency range of the injection signal is studied based on the phase dynamics equation, and the phase noise performance of the injection-locked OEO is analyzed. The role of the phase-locked loop on the frequency stability of the OEO is analyzed based on the phase-locked loop transfer function. In addition, this paper builds an injection-locked OEO based on a phase-locked loop. The injection-locked signal is the high-frequency output of the multiplication crystal oscillator (MCO). At the same time, this MCO synchronously outputs a low-frequency signal, which is used as the reference signal of the phase-locked loop. The experimental results show that the proposed OEO output frequency is 10 GHz, and the phase noise is −89.25 dBc/Hz@100 Hz, −121.71 dBc/Hz@1 kHz, and −145.39 dBc/Hz@10 kHz; the side-mode suppression ratio is 80 dB; the frequency stability is 2.06 × 10−11@1 s, 9.03 × 10−11@10 s, 1.03 × 10−10@100 s, and 3.03 × 10−10@1000 s. Consistent with the theoretical analysis results, the solution takes into account the frequency stability, side-mode suppression ratio, and phase noise performance. The simple structure is more advantageous in practical applications.

The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM2.5 reduction strategies.

Rheumatoid arthritis (RA) displays very similar characteristics to those of tumor cells, platycodin D (PD) is a triterpenoid saponin abundant in Platycodon grandiflorum (PG), plays an important role in the inhibition of tumor growth. Our previous experiments confirmed that PD inhibited MH7A cell proliferation and migration, but it’s possible mechanism remain unclear. This study aimed to reveal the mechanism of PD on RA, based on network pharmacology analysis. Rat of CIA was treated with the different doses PD. The arthritis score and paw volume were evaluated, ankle imaging changes were observed via myosseous ultrasound, all rats were anaesthetized by intraperitoneal injection of 25% urethane (1 mL/100 g), and ankle histopathology was observed using hematoxylin and eosin (HE) staining. Cell (MH7A) Counting Kit 8 (CCK8) was used to measure cell activity, and JC-1 assay kit and flow cytometry were employed to examine the cell mitochondrial membrane potential and apoptosis. The expression levels of Sonic hedgehog (Shh) signaling pathway-related proteins were observed by Western blotting. Cell inflammation levels of tumor necrosis factor alpha (TNF-α) and interleukin (IL)-6 being determined via enzyme-linked immunoassay ELISA and q-PCR. In total, the saponin PD significantly improves joint synovium inflammation and apoptosis in CIA rats. The activity of administered MH7A was significantly inhibited, the mitochondrial membrane potential decreased, the expression level of the Shh signaling pathway-related protein SuFu increased, the expression levels of SHh and Gli decreased, and cell serum levels of TNF-a and IL-6 decreased significantly. Therefore, PD exhibits therapeutic potential for synovial hyperplasia in RA.

In order to effectively control and predict the settlement deformation of the surrounding ground surface caused by deep foundation excavation, the deep foundation pit project of Baoding City Automobile Technology Industrial Park is explored as an example. The initial population approach of the whale algorithm (WOA) is optimized using Cubic mapping, while the weights of the shrinkage envelope mechanism are adjusted to avoid the algorithm falling into local minima, the improved whale algorithm (IWOA) is proposed. Meanwhile, 10 benchmark test functions are selected to simulate the performance of IWOA, and the advantages of IWOA in learning efficiency and convergence speed are verified. The IWOA-LSTM deep foundation excavation deformation prediction model is established by optimizing the input weights and hidden layer thresholds in the deep long short-term memory (LSTM) neural network using the improved whale algorithm. The IWOA-LSTM prediction model is compared with LSTM, WOA-optimized LSTM (WOA-LSTM) and traditional machine learning, the results show that the final prediction score of the IWOA-LSTM prediction model is higher than the score of other models, and the prediction accuracy is better than that of traditional machine learning.

The mitochondrial permeability transition pore is a nonspecific transmembrane channel. Inhibition of mitochondrial permeability transition pore opening has been shown to alleviate mitochondrial swelling, calcium overload, and axonal degeneration. Cyclophilin D is an important component of the mitochondrial permeability transition pore. Whether cyclophilin D participates in mitochondrial impairment and axonal injury after intracerebral hemorrhage is not clear. In this study, we established mouse models of intracerebral hemorrhage in vivo by injection of autologous blood and oxyhemoglobin into the striatum in Thy1-YFP mice, in which pyramidal neurons and axons express yellow fluorescent protein. We also simulated intracerebral hemorrhage in vitro in PC12 cells using oxyhemoglobin. We found that axonal degeneration in the early stage of intracerebral hemorrhage depended on mitochondrial swelling induced by cyclophilin D activation and mitochondrial permeability transition pore opening. We further investigated the mechanism underlying the role of cyclophilin D in mouse models and PC12 cell models of intracerebral hemorrhage. We found that both cyclosporin A inhibition and short hairpin RNA interference of cyclophilin D reduced mitochondrial permeability transition pore opening and mitochondrial injury. In addition, inhibition of cyclophilin D and mitochondrial permeability transition pore opening protected corticospinal tract integrity and alleviated motor dysfunction caused by intracerebral hemorrhage. Our findings suggest that cyclophilin D is used as a key mediator of axonal degeneration after intracerebral hemorrhage; inhibition of cyclophilin D expression can protect mitochondrial structure and function and further alleviate corticospinal tract injury and motor dysfunction after intracerebral hemorrhage. Our findings provide a therapeutic target for preventing axonal degeneration of white matter injury and subsequent functional impairment in central nervous diseases.

The adaptive Frequency-Hopping(FH) technique based on interference sensing can further enhance the anti-interference performance of hybrid Direct-Sequence/FH Spread Spectrum(DS/FH SS) system and FH Orthogonal Frequency Division Multiplexing(FH-OFDM) system. Existing interference sensing algorithms cannot provide an accurate detection performance for both stationary and non-stationary LFM interference, and have the problem of misjudging useful signal as interference due to the dynamic time-frequency characteristics of useful signal. To improve the sensing detection capability with low false alarm rate, this paper proposes an algorithm, by analyzing the duration units of frequency points and combining the least-squares fitting method to sense stationary and non-stationary LFM interference. Simulations have been carried out to demonstrate the effectiveness of our interference sensing algorithm.

ABSTRACT Objective: To explore the feasibility of 68Ga-PSMA PET/CT in diagnosing primary prostate cancer. Materials and Methods: Embase, PubMed and Cochrane Library databases were searched for studies published before July 2020. The studies that used 68Ga-PSMA PET/CT for detecting primary prostate cancer, and pathological biopsy as the reference standard were included. The selecting process used preferred reporting items for systematic reviews and meta-analyses (PRISMA). The quality of enrolled studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results: According to our search strategy, 9 studies were included for analysis. A total of 547 patients with primary prostate cancer and 443 lesion segments that underwent 68Ga-PSMA PET/CT scans were included and their pathological biopsies were compared. The results of these studies showed some differences. For instance, the lowest sensitivity of 68Ga-PSMA PET/CT in diagnosing primary prostate cancer was 67%, while the highest sensitivity recorded was 97%. Conclusions: Compared with conventional imaging examinations, 68Ga-PSMA PET/CT had higher sensitivity and specificity in detecting primary prostate cancer. At present, most of the studies that used 68Ga-PSMA PET/CT for detecting prostate cancer are retrospective studies. Based on its advantage of high detection rate, the use of 68Ga-PSMA PET/CT in the detection of primary prostate cancer is worthy of promotion.

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

Cotton production in Xinjiang is increasing year by year, and the improved crop yields have had an impact on the environment. This study investigated the changes in six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) from 2017 to 2022 in Xinjiang. We compiled a biomass burning emission inventory to make the MEIC emission inventory more complete. The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was employed to simulate air quality in different reduction scenarios in 2025, and it explored ways to alleviate air pollution in the main cotton areas of Xinjiang. The result shows that the main pollutant in Xinjiang is particulate matter (PM particles with aerodynamic diameters less than 2.5 µm and 10 µm), and the concentration of particulate matter decreased from the northern mountains toward the south. The concentrations of O3 (ozone) were highest in summer, while the concentrations of other pollutants were high in autumn and winter. If the pollution is not strictly controlled in terms of emission reduction, it is impossible to achieve the target of a 35 μg/m3 PM2.5 concentration in the planting area. In the scenario of enhanced emission reduction measures and the scenario of higher intensity emission reduction measures, there was a failure to reach the target, despite the reduction in the PM2.5 concentration. In the best emission reduction scenario, PM2.5 in Xinjiang is expected to drop to 22.5 μg/m3 in November and 34 μg/m3 in March, respectively. Therefore, in the optimal emission reduction scenario, the target of 35 μg/m3 will be reached. This study emphasized the importance of future air pollution mitigation and identified a feasible pathway to achieve the target of 35 μg/m3 PM2.5 concentration by 2025. The research findings provide useful insights for the local government which can be used to develop strategies aimed at mitigating substantial pollution emissions.

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method.Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method.Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

With the continuous promotion of urbanization in China, air pollution problems have become increasingly prominent in recent years. Various factors, such as emissions, meteorology, and physical and chemical reactions, jointly affect the severity of PM2.5 pollution to a large extent. This study selected five meteorological variables (planetary boundary layer height (PBLH), wind speed (WS), temperature(T), water vapor mixing ratio(Q), and precipitation (PCP)) for perturbation, and 21 different scenarios were set up. In this study, the effects of changes in a single meteorological variable on the pollutants produced in the area were represented by subtracting the baseline scenario (i.e., without perturbation of meteorological variables) simulated in January 2017 separately from each post-disturbance scenario. The results showed that Handan (HD) has the highest annual mean PM2.5 concentration of 85.75 μg/m3 in 2017, while all cities in study area exceeded the secondary concentration limit of urban atmospheric particulate matter. The correlation coefficient (R) between the simulation values of models and the actual monitoring values ranges from 0.41 to 0.74, indicating good model performance and acceptable simulation errors. PBLH (±10%-±20%), WS(±10%-±20%), and PCP(±10%-±20%) all showed a single adverse effect among the five meteorological variables, meaning that a reduction in these three factors led to an increase in PM2.5 concentrations. However, T (±1 K-±1.5 K) and Q (±10%-±20%) could indicate a positive impact under certain conditions. From the sensitivity calculations of single meteorological variables, it is clear that WS, PBLH, and PCP show a highly linear trend in all cities at the 0.01 level of significance. The hypothesis that T changes linearly in 10 cities in the study area is valid, while for Q, the hypothesis that Q changes linearly only occurs in Shijiazhuang and Baoding. When different meteorological variables are disturbed, there are significant spatial differences in the main affected areas of PM2.5 concentrations. By discussing the impact of meteorological variable disturbance on air quality in critically polluted cities in China, this study identified the meteorological variables that can substantially affect PM2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures.

Objective To investigate the effect of cardiac valve calcification (CVC) on the prognosis of patients with chronic kidney disease (CKD). Methods A total of 343 CKD patients were retrospectively analyzed, and divided into two groups according to the presence or absence of cardiac valve calcification. All patients were followed until death, loss to follow-up, or the end point of the study (December 2021). Results The incidence of CVC among the 343 CKD patients was 29.7%, including 21 cases of mitral valve calcification, 63 cases of aortic valve calcification, and 18 cases of mitral valve combined with aortic valve calcification. The incidence of CVC in CKD stages 1–2 was 0.3%, 5.2% in CKD stages 3–4, and 24.2% in CKD stage 5 (P

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar.

Quantifying the genetic diversity present in plant populations is crucial for the success of selection of elite plants and breeding programs. The population structure and partition of genetic variance within and between plant populations allows inferences about the distribution and extent of variation available in plant breeding populations. The Chinese pine (Pinus tabuliformis Carriere) is a native species that is ecologically and economically important to Northern China. Studying the genetic variation and partition of P. tabuliformis will be invaluable for its efficient utilization. In this study, we selected 21 pairs of SSR primers to estimate the genetic diversity of 35 clones from four populations and 216 progenies from 22 full-sib families of P. tabuliformis. Our results showed that P. tabuliformis was a typical outcrossing plant (FIS = −0.053/−0.165) containing moderately high genetic diversity (He = 0.542/0.605) both for the clone populations and the progenies produced by the clone crosses via controlled pollination. The genetic differentiation between the clones (FST = 0.076) was lower than that between the full-sib progenies (FST = 0.155). Such diverse FST may be promoted by different gene flows (Nm = 3.881/1.546) due to distinct pollination patterns. The clustering methods showed a lack of significant division of the clone populations, with a similar genetic admixture found for each of the clones. However, two groups were found between the full-sib families with genetic admixture between them. The analysis of molecular variance (AMOVA) showed that the most plentiful genetic variability exists between the cloned individuals (98%) as well as within the full-sib progenies (89%). Such distribution of genetic variation suggests rich internal variance and thus it is feasible to select the elite plants within each population or family. These results provide genetic materials and theoretical guidance for the selection and breeding of the Chinese pine.

Severe COVID-19 patients exhibit impaired IFN-I response due to decreased IFN-β production, allowing persistent viral load and exacerbated inflammation. While the SARS-CoV-2 nucleocapsid (N) protein has been implicated in inhibiting innate immunity by interfering with IFN-β signaling, the specific underlying mechanism still needs further investigation for a comprehensive understanding. This study reveals that the SARS-CoV-2 N protein enhances interaction between the human SUMO-conjugating enzyme UBC9 and MAVS. Increased MAVS-UBC9 interaction leads to enhanced SUMOylation of MAVS, inhibiting its ubiquitination, resulting in the inhibition of phosphorylation events involving IKKα, TBK1, and IRF3, thus disrupting IFN-β signaling. This study highlights the role of the N protein of SARS-CoV-2 in modulating the innate immune response by affecting the MAVS SUMOylation and ubiquitination processes, leading to inhibition of the IFN-β signaling pathway. These findings shed light on the complex mechanisms utilized by SARS-CoV-2 to manipulate the host’s antiviral defenses and provide potential insights for developing targeted therapeutic strategies against severe COVID-19.

Abstract Background Risk genes linked to the development of gout have been identified, and lifestyle factors are related to gout risk. It remains unclear whether healthy lifestyle factors can mitigate the genetic risk of gout. Therefore, we aimed to explore whether and to what extent a healthy lifestyle can mitigate the risk of gout related to genetic factors. Methods Within the UK Biobank, 416,481 gout-free participants (aged 37–74) were identified at baseline. Polygenic risk for gout was assessed and categorized as low (lowest tertile), middle (tertile 2), and high (highest tertile). Healthy lifestyle factors included no/moderate alcohol consumption, no smoking, physical activity, and a healthy diet. Participants were categorized into three groups according to their number of healthy lifestyle factors: unfavorable (0 or 1), intermediate (any 2), and favorable (3 or 4). Data were analyzed using Cox proportional hazard models. Results Over the follow-up (median: 12.1 years), 6206 participants developed gout. Compared to low genetic risk, the hazard ratios (HRs) and 95% confidence intervals (CIs) of gout was 1.44 (1.35–1.54) for middle and 1.77 (1.66–1.89) for high genetic risk. The HRs (95% CIs) of gout were 0.63 (0.59–0.67) for a favorable lifestyle and 0.79 (0.75–0.85) for an intermediate lifestyle, compared to an unfavorable lifestyle. In joint effect analysis, compared to participants with low genetic predisposition and a favorable lifestyle, the HRs (95% CIs) of gout were 2.39 (2.12–2.70)/3.12 (2.79–3.52) in those with middle and high genetic predisposition plus unfavorable lifestyle profiles, and 1.53 (1.35–1.74)/1.98 (1.75–2.24) for those with middle and high genetic predisposition plus favorable lifestyle profiles, respectively. Moreover, compared to an unfavorable lifestyle, the HRs of gout related to a favorable lifestyle was 0.64 (95% CI, 0.56–0.73) for low genetic risk, 0.65 (95% CI, 0.58–0.72) for middle genetic risk, and 0.62 (95% CI, 0.57–0.69) for high genetic risk. There was a significant additive interaction between unfavorable lifestyle and high genetic risk on gout. Conclusions Healthy lifestyle was associated with a lower risk of gout and may attenuate the risk of gout related to genetic factors by almost a third.

Mismatched filters (MMF) have been intensively explored for linear-frequency modulation (LFM) waveform sidelobe reduction. Unlike other design strategies, the use of convex optimization (CO) techniques is able to find the optimal design. However, existing studies for CO-based MMF design have focused on minimizing peak sidelobe level (PSL), which might not take the mainlobe widening sacrifice into account. To address this issue, this paper proposes a weighted CO model targeting a flexible tradeoff between sidelobe suppression and mainlobe widening. Moreover, to better control the optimization problem via the presented CO model, the cyclic algorithm (CA) is employed to determine upper limits on mainlobe width. Consequently, the proposed tradeoff MMF design by combining CA with CO would be feasible to meet the requirements of different target detection scenarios. Comprehensive simulations have been carried out to demonstrate the effectiveness of our presented MMF design.

Groundwater flow in mountainous areas is controlled by climate conditions, topography, geological structure, and other factors.Due to the restrictions such as data acquisition, the groundwater flow pattern and control mechanism in mountainous areas are still not clearly understood.Taking Beishan area as an example, which locates the pre-selected area of the Geological Repository for High-Level radioactive Waste in China, groundwater flow pattern is of great significance for the safety of the repository on the ten-thousand-year time scale.In this study, characteristics of the hydrogeological structure and permeability in Beishan area, Hexi Corridor, and Qilian Mountains were obtained based on regional remote sensing interpretation, geological structure evolution analysis, geophysical exploration, and hydrogeological drilling.A conceptual model was proposed through comprehensive hydrogeological survey, hydrogeochemical and isotope analysis.In addition, regional groundwater flow numerical models and multi-scenario simulations were used to understand the groundwater flow patterns in the Beishan-Hexi Corridor-Qilian Mountains in Gansu.Results show that topography plays a dominant role in the groundwater flow pattern in the area.Groundwater from Qilian Mountains is difficult to pass through the Hexi Corridor to discharge in the Beishan Mountains.With the lowest altitude in the region, the Hexi Corridor can be regarded as the boundary of the groundwater system between the Qilian Mountains and the Beishan Mountain.Under the control of topography and lithology, the groundwater in Beishan mountain area only develops local flow systems and the flow velocity is small.At the same time, due to the blocking effect of the faults in this area, the groundwater from Xinchang, the key pre-selected high level radioactive waste disposal site in Beishan, cannot flow directly to the south into the Huahai Basin.In addition, its length of the flow path is greatly increased.Only the groundwater on the south side of the F95 fault can discharge to the Huahai Basin, but due to the limited catchment basin, slow flow velocity and poor circulation condition, the discharge is weak.This study investigates the mountain-basin groundwater circulation pattern, and provides a scientific basis for the site evaluation of the geological repository for high-level radioactive waste in Beishan, Gansu.

The extent to which neurodevelopment is affected by prenatal lead exposure has not been conclusive. In addition, studies on the effects of sex on these relationships are inconsistent. The aim of this study was to investigate the impact of cord blood lead on neurodevelopment in children within sex subgroups. A total of 275 mother-child pairs from the Shanghai mother-child cohort were included. Umbilical cord blood lead was measured using graphite furnace atomic absorption spectrophotometry. The Bayley Scales for Infant Development-III (BSID-III) was used to measure the neurodevelopment of infants at the age of 18 ± 1.5 months. The median and interquartile range of cord blood lead levels in the total participants, male, and female children were 44.0 (24.5) μg/L, 44.0 (24.3) μg/L, and 46.0 (24.0) μg/L, respectively. According to multiple linear regression, cord blood lead concentrations showed a negative association with fine motor scores in all models associated with female children (β = −1.5; 95%confidence interval: −2.6, −0.4). However, prenatal lead levels were not associated with any of the BSID-III scores in male children. In addition, cord serum DHA was found positively related to fine motor scores in male children. Our findings suggest that prenatal lead exposure could lead to decreased motor function, although this phenomenon was only observed in female children. And DHA may be a protective factor against lead exposure in boys. Thus, further studies are needed to investigate the associations between prenatal lead exposure and neurobehavioral development, as well as the mechanism of sex differences.

ObjectiveTo determine the effectiveness of transcranial magnetic stimulation in improving cerebellar ataxia.Data sourcesPubMed, EMBASE, the Cochrane Library, Springer, Science Direct, the China National Knowledge Infrastructure (CNKI) and the China Science and Technology Journal Database (VIP) were searched until 2022.Review methodsTrials with transcranial magnetic stimulation on the effects on cerebellar ataxia were included, and the effect size was evaluated using the standardized mean difference (SMD) or mean difference (MD) and a 95% confidence interval (CI).ResultsEight studies comprising 272 participants, published between 2014 and 2022, were included. The results revealed that the effect of TMS on patients with cerebellar ataxia as assessed by the International Cooperative Ataxia Rating Scale (ICRAS), the Scale for the Assessment and Rating of Ataxia (SARA), the Berg Balance Scale (BBS), and the Timed Up and Go (TUG) test was statistically significant (P < 0.01) with low heterogeneity among the studies (I2 = 4, 27, 0, and 0% respectively).ConclusionThe effects of transcranial magnetic stimulation in improving cerebellar ataxia in the affected patients are significant. TMS targeting the cerebellar structures can induce changes in the excitability of the cerebellar-thalamus-cortical pathways; thus, it is necessary to carry out large-scale research with good design and high quality in the future.