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Abstract Background Previous studies have shown that migraines are associated with brain structural changes. However, the causal relationships between these changes and migraine, as well as its subtypes, migraine with aura (MA) and migraine without aura (MO), remain largely unclear. Methods We utilized genome-wide association study (GWAS) summary statistics from European cohorts for 2,347 cortical structural magnetic resonance imaging (MRI) phenotypes, derived from both T1-weighted and diffusion tensor imaging scans (n = 36,663), with migraine and its subtypes (n = 147,970–375,752). Cortical phenotypes included both macrostructural (e.g., cortical thickness, surface area) and microstructural (e.g., fractional anisotropy, mean diffusivity) features. Genetic correlations were first assessed to identify significant associations, followed by bidirectional Mendelian randomization (MR) analyses to determine causal relationships between these brain phenotypes and migraine, as well as its subtypes (MA and MO). Sensitivity analyses were applied to ensure the robustness of the results. Results Genetic correlation analysis identified 510 significant associations between cortical structural phenotypes and migraine across 401 distinct traits. Forward MR analysis revealed nine significant causal effects of cortical structural changes on migraine risk. Specifically, increased cortical thickness and local gyrification index in specific cortical regions were associated with a decreased risk of overall migraine, MA, and MO, while intracellular volume fraction and orientation diffusion index in specific regions increased the risk of MA and MO. Reverse MR analysis demonstrated that MA causally increased mean diffusivity in the insular and frontal opercular cortex. Sensitivity analyses confirmed the robustness of these findings, with no evidence of horizontal pleiotropy or heterogeneity. Conclusion This study identifies causal relationships between cortical neuroimaging phenotypes and migraine, highlighting potential biomarkers for migraine diagnosis, treatment, and prevention.

This article presents a Robust Servo Linear Quadratic Regulator (RSLQR) method based on the Linear Extended State Observer (LESO), which solves the problem of optimal tracking control of electro-optical system under disturbance. For the problem that the accuracy of the electro-optical tracking system is reduced due to disturbance and modeling error, the LESO is adopted in this article to obtain and compensate the influence of internal and external disturbances in the controlled object model, so as to improve the disturbance suppression ability of the electro-optical tracking system. Considering the influence of weighting matrix in the RSLQR method on system's dynamic response performance, this article presents a selection criterion of weighting matrix in the RSLQR to meet the performance requirements of closed-loop system. Through simulation analysis and experimental verification, the design method proposed in this article has great reference value for the optimization of the dynamic response performance of the electro-optical tracking system and the improvement of the disturbance suppression ability.

Abstract Tropospheric ozone is an air pollutant and a greenhouse gas whose anthropogenic production is limited principally by the supply of nitrogen oxides (NOx) from combustion. Tropospheric ozone in the northern hemisphere has been rising despite the flattening of NOx emissions in recent decades. Here we propose that this sustained increase could result from the photolysis of nitrate particles (pNO3−) to regenerate NOx. Including pNO3− photolysis in the GEOS‐Chem atmospheric chemistry model improves the consistency with ozone observations. Our simulations show that pNO3− concentrations have increased since the 1960s because of rising ammonia and falling SO2 emissions, augmenting the increase in ozone in the northern extratropics by about 50% to better match the observed ozone trend. pNO3− will likely continue to increase through 2050, which would drive a continued increase in ozone even as NOx emissions decrease. More work is needed to better understand the mechanism and rates of pNO3− photolysis.

Abstract This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012–2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 μg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

Selenium-enriched Lentinus edodes (SL) is a kind of edible fungi rich in organic selenium and nutrients. Monascus purpureus with high monacolin K (MK) production and Saccharomyces cerevisiae were selected as the fermentation strains. A single-factor experiment and response surface methodology were conducted to optimize the production conditions for MK with higher contents from selenium-enriched Lentinus edodes fermentation (SLF). Furthermore, we investigated the nutritional components, antioxidant capacities, and volatile organic compounds (VOCs) of SLF. The MK content in the fermentation was 2.42 mg/g under optimal fermentation conditions. The organic selenium content of SLF was 7.22 mg/kg, accounting for 98% of the total selenium content. Moreover, the contents of total sugars, proteins, amino acids, reducing sugars, crude fiber, fat, and ash in SLF were increased by 9%, 23%, 23%, 94%, 38%, 44%, and 25%, respectively. The antioxidant test results demonstrated that 1.0 mg/mL of SLF exhibited scavenging capacities of 40%, 70%, and 79% for DPPH, ABTS, and hydroxyl radicals, respectively. Using gas chromatography–ion mobility spectrometry technology, 34 unique VOCs were identified in SLF, with esters, alcohols, and ketones being the main components of its aroma. This study showed that fungal fermentation provides a theoretical reference for enhancing the nutritional value of SL.

Drosophila spermatogenesis involves the renewal of germline stem cells, meiosis of spermatocytes, and morphological transformation of spermatids into mature sperm. We previously demonstrated that Ocnus (ocn) plays an essential role in spermatogenesis. The ValRS-m (Valyl-tRNA synthetase, mitochondrial) gene was down-regulated in ocn RNAi testes. Here, we found that ValRS-m-knockdown induced complete sterility in male flies. The depletion of ValRS-m blocked mitochondrial behavior and ATP synthesis, thus inhibiting the transition from spermatogonia to spermatocytes, and eventually, inducing the accumulation of spermatogonia during spermatogenesis. To understand the intrinsic reason for this, we further conducted transcriptome-sequencing analysis for control and ValRS-m-knockdown testes. The differentially expressed genes (DEGs) between these two groups were selected with a fold change of ≥2 or ≤1/2. Compared with the control group, 4725 genes were down-regulated (dDEGs) and 2985 genes were up-regulated (uDEGs) in the ValRS-m RNAi group. The dDEGs were mainly concentrated in the glycolytic pathway and pyruvate metabolic pathway, and the uDEGs were primarily related to ribosomal biogenesis. A total of 28 DEGs associated with mitochondria and 6 meiosis-related genes were verified to be suppressed when ValRS-m was deficient. Overall, these results suggest that ValRS-m plays a wide and vital role in mitochondrial behavior and spermatogonia differentiation in Drosophila.

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

As an omnivorous species, crayfish (Procambarus clarkii) have varying feeding habits in different environments. However, little is known about the diet of crayfish under different fertilization patterns in rice-crayfish cocultures. In this study, we investigated the effect of fertilization on the growth performance, dietary composition and isotopic niche of crayfish in a rice-crayfish coculture system. Four experimental treatments were established: no fertilizer (CK), slow-release fertilizer (HS), crayfish rice fertilizer (XD) and compound fertilizer (FH). The results showed that HS and FH could significantly promote crayfish growth, and the growth of crayfish treated with HS was the best. Fertilization patterns had significant effects on the dietary composition of crayfish. The food source of unfertilized crayfish was relatively balanced. However, in the HS, XD and FH treatments, particulate organic matter increased with contribution rates of 34.04%, 56.89% and 42.86%, respectively. In addition, the artificial feed intake of crayfish in the FH group was the lowest. The isotopic niche revealed that crayfish in the HS group significantly increased in the direction of the δ15N axis. Based on routine economic indicators, slow-release fertilizer was suitable for the rice-crayfish coculture system. These findings could provide scientific guidance for the selection of fertilization modes in rice-crayfish coculture systems and the exploration of healthy crayfish culture models.

Osteoporotic vertebral compression fracture (OVCF) is a complication of osteosis, whose main manifestations are percussion pain and limited activity. OVCF at high levels of vertebral body is rare and usually treated conservatively. We reported a 65-year-old male patient suffering OVCF at T4 vertebral body, treated with PKP to relieve his serious percussion pain instantly. CT and MRI delineated acute compression fractures in T4 and T9 vertebrae as well as an old fracture in L1. As cranial CT scanning shows that the subdural hematoma was very little and no shifting of midline, we chose to perform PKP after a week of observation. This case suggested that as long as patients’ symptoms seriously affect their life quality, PKP is worth to perform with especial caution even if OVCF takes place at upper segments of thoracic vertebrae.

Radiant cooling floors combined with ventilation systems have been widely applied in large space buildings. However, there has been a lack of research on system control strategies for their adaptation to weather changes. This study aimed to find control strategies for radiant cooling floors combined with displacement ventilation systems used in large space buildings in order to achieve energy conservation and environmental improvement. Supply air temperature and cooling surface temperature were determined to be the control variables. It was found that cooling capacity of the combined system and the comfort index, PMV (predicted mean vote), were linear in relation to the supply air temperature and cooling surface temperature. The linear equations regarding cooling capacity and PMV were established separately using environment data, and then the optimal region was determined. A case study on Terminal 3 of Xi’an Xianyang International Airport was conducted. The thermal environment was investigated through on-site measurements, questionnaires, and numerical simulations with CFD (computational fluid dynamics). It was found that supply air temperature and cooling surface temperature had a significant impact on PMV, and less impact on the cooling capacity. Therefore, it was determined that the supply air temperature should be altered first when the indoor temperature exceeds the upper limit, and then the cooling surface temperature should be changed if the indoor environment continues to overheat with the supply air temperature set to 18 °C. Thus, the supply air temperature was kept at 18 °C, and the floor surface temperature was set to be 22 °C on a high-temperature day. The average PMV was 0.87, and the cooling capacity of the combined system was 200 W/(m2·K), according to the CFD simulation. In addition, the surface heat transfer coefficient of the cooling floor was found to be 10.26 W/(m2·K). This research provides important references for the design and operational management of radiant cooling floors in large space buildings.

A modified Extended State Kalman Filter (ESKF)-based Model Predictive Control (MPC) algorithm is introduced to tailor the enhanced disturbance suppression in electro-optical tracking systems. Traditional control techniques, although robust, often struggle in scenarios with concurrent internal, external disturbances, and sensor noise. The proposed algorithm effectively overcomes these limitations by precisely estimating system states and actively mitigating disturbances, thus significantly boosting noise and perturbation control resilience. The primary contributions of this study include the integration of ESKF for accurate system state and disturbance estimation in noisy environments, the embedding of an ESKF estimation-compensation loop to simulate an improved disturbance-free system, and a simplified modeling approach for the controlled device. This designed structure minimizes the reliance on extensive system identification, easing the predictive control model-based constraints. Moreover, the approach incorporates total disturbance estimation into the optimization problem, safeguarding against actuator damage and ensuring high tracking accuracy. Through rigorous simulations and experiments, the ESKF-based MPC has demonstrated enhanced model error tolerance and superior disturbance suppression capabilities. Comparative analyses under varying model parameters and external disturbances highlight its exceptional trajectory tracking performance, even in the presence of model uncertainties and external noise.

A multi-channel phase-compensated active disturbance rejection control (MPADRC) incorporating an improved backstepping strategy is proposed in this paper to handle the phase lag in the extended state observer (ESO) and the residual uncertainty in the system. Firstly, a multi-channel phase-compensated ESO (MPESO) is constructed by adding phase-advanced networks to all output channels of the ESO, which allows disturbances and system states to be compensated and feedback in a more timely manner, respectively. Then, to estimate and offset the residual uncertainty in the system, an improved backstepping control method is employed and a Lyapunov function is designed to verify the convergence of the error between the estimated and actual values of the residual uncertainty. After that, the improved backstepping control is combined with MPADRC, and comparisons with the conventional linear active disturbance rejection control (LADRC) are conducted for a range of cases. Finally, on an inertial stabilization platform in the electro-optical tracking system (ETS), simulation and experimental results verified the effectiveness of the proposed method.

Background and aimsHeart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death.Methods and resultsData (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C.ConclusionDecreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

In 2022, persistent extreme precipitation events occurred during the Dragon Boat Water (DBW) period (May to June) over South China, with rainfall-breaking historical extremes at many stations. In this study, the relative importance of the thermodynamic and dynamic effects on this anomalous event was explored through budget equations of moisture and moist static energy (MSE). The moisture budget analysis suggests that the thermodynamic effect contributes to the precipitation anomaly by less than 10% through the advection of abnormal moisture by mean horizontal and vertical motion. However, the dynamic effect, associated with the mean moisture advection by anomalous vertical motion, makes the largest contribution. This seems to imply that the increase in water vapor due to global warming does not play a critical role in this extreme event. Nevertheless, further analysis of the MSE budget equation reveals that the anomalous vertical motion is governed by both the dynamical effect of atmospheric circulation changes and the thermodynamic effect of atmospheric thermal state changes. The thermodynamic effect, which accounts for 47.21% of the total effect (sum of the thermodynamic and dynamic effects), is caused by anomalous moist enthalpy (mainly dry enthalpy) advection due to the mean horizontal motion. In contrast, the dynamical effect is related to the warm and humid air advection caused by the anomalous southwest wind caused by the anomalous anticyclone over the northwest Pacific. Simultaneously, the anomalous northeast wind brings dry cold air, resulting in the convergence of cold and warm air over the Pearl River basin and persistent heavy rainfall events. The results of this study provide valuable insights into the direct and indirect physical mechanisms leading to anomalous DBW period over South China in 2022.

To address the problems of a low tracking accuracy and slow error convergence in high-order single-input, single-output electro-optical tracking systems, a backstepping control method based on a Softsign linear–nonlinear tracking differentiator is proposed. First, a linear–nonlinear tracking differentiator is designed in conjunction with the Softsign excitation function, using its output as an approximate replacement for the conventional differentiation process. Then, this is combined with backstepping control to eliminate the “explosion of complexity” problem in conventional backstepping procedures due to repeated derivation of virtual control quantities. This reduces the workload of parameter tuning, takes into account the rapidity and stability of signal convergence, and improves the trajectory tracking performance. This method can ensure the boundedness of the system signal. The effectiveness and superiority of this control method are verified through simulations and experiments.

In recent years, vegetable production has been in the transitional stage from traditional cultivation mode to modern cultivation mode. Therefore, evaluating the actual productivity and economic benefits of different cultivation modes is instructive for leafy vegetable production. This research chose a vegetable production company (Shanghai, China) as a demonstration case to cultivate various leaf vegetables under different cultivation modes (hydroponic, substrate, and soil cultivation). By investigating the yield and quality of vegetables, as well as all production costs and selling prices, we drive a comparative evaluation of comprehensive features and economic benefits of different cultivation modes. The results showed that leafy vegetables cultivated by hydroponic mode gain the highest fresh weight at the early growth stage, but the greatest unit yield (0.98~1.21 kg·m−2) emerged under the soil cultivation mode. Vegetables grown under substrate mode had the best comprehensive quality with high soluble solids content and low nitrate content. The unit costs of the soilless cultivation modes (8.26~10.3 CNY·m−2) were 3–4-fold higher than that of soil cultivation, mainly due to the high fixed costs and input costs, especially for substrate mode. According to actual prices, the soil cultivation mode had the highest economic benefits as a whole (4.83~8.83 CNY·m−2), and only hydroponic cabbage which had comparable yield gained considerable benefits (8.94 CNY·m−2). Quality-based pricing scheme can make substrate mode achieve profitability. The pricing scheme that guarantees a 40% revenue is within acceptable limits for consumers. Based on this actual production case, it can be concluded that a low unit yield of hydroponic mode and a high unit cost of substrate mode are the predominant factors affecting their profitability and economic viability.

ObjectiveThis study aimed to investigate the acute effects of aerobic exercise (AE), resistance exercise (RE), and integrated concurrent exercise (ICE; i.e., AE plus RE) on executive function among hospitalized type 2 diabetes mellitus (T2DM) inpatients, and the mechanism of cerebral hemodynamics.MethodsA within-subject design was applied in 30 hospitalized patients with T2DM aged between 45 and 70 years in the Jiangsu Geriatric Hospital, China. The participants were asked to take AE, RE, and ICE for 3 days at 48-h intervals. Three executive function (EF) tests, namely, Stroop, More-odd shifting, and 2-back tests, were applied at baseline and after each exercise. The functional near-infrared spectroscopy brain function imaging system was used to collect cerebral hemodynamic data. The one-way repeated measurement ANOVA was used to explore training effects on each test indicator.ResultsCompared with the baseline data, the EF indicators have been improved after both ICE and RE (p < 0.05). Compared with the AE group, the ICE and RE groups have demonstrated significant improvements in inhibition (ICE: MD = − 162.92 ms; RE: MD = −106.86 ms) and conversion functions (ICE: MD = −111.79 ms; RE: MD = −86.95 ms). Based on the cerebral hemodynamic data, the beta values of brain activation in executive function related brain regions increased after three kinds of exercise, the EF improvements after the ICE showed synchronous activation of blood flow in the dorsolateral prefrontal cortex (DLPFC), the frontal polar (FPA) and orbitofrontal cortex (OFC), the improvement of inhibitory function after RE displayed synchronous activation of DLPFC and FPA, and AE mainly activates DLPFC. The HbO2 concentration in the pars triangularis Broca's area increased significantly after AE, but the EF did not improve significantly.ConclusionThe ICE is preferred for the improvements of executive function in T2DM patients, while AE is more conducive to the improvements of refresh function. Moreover, a synergistic mechanism exists between cognitive function and blood flow activation in specific brain regions.

Abstract Background Precision medicine aims to address the demand for precise therapy at the gene and pathway levels. We aimed to design software to allow precise treatment of osteoporosis (OP) with Chinese medicines (CMs) at the gene and pathway levels. Methods PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP database), and the Wanfang database were searched to identify studies treating osteoporosis with CMs. The TCMSP was used to identify bioactive ingredients and related genes for each CM. Gene expression omnibus (GEO) database and the limma package were used to identify differentially expressed genes in osteoporosis. Perl software was used to identify the shared genes between the bioactive components in CM and osteoporosis. R packages and bioconductor packages were used to define the target relationship between shared genes and their related pathways. Third-party Python libraries were used to write program codes. Pyinstaller library was used to create an executable program file. Results Data mining: a total of 164 CMs were included, but Drynariae Rhizoma (gusuibu) was used to present this process. We obtained 44 precise relationships among the bioactive ingredients of Drynariae Rhizoma, shared genes, and pathways. Python programming: we developed the software to show the precise relationship among bioactive ingredients, shared genes, and pathways for each CM, including Drynariae Rhizoma. Conclusions This study could increase the precision of CM, and could provide a valuable and convenient software for searching precise relationships among bioactive ingredients, shared genes, and pathways.

Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.

The support vector machine (SVM) model was applied to predict the color change of heat-modified wood after artificial weathering. In order to improve the prediction performance, the improved particle swarm optimization (IPSO) algorithm was used to optimize the parameters of the SVM model, and an improved particle swarm optimized support vector machine (IPSO-SVM) model was established on the basis of the nonlinear descending weight strategy to improve the particle swarm optimization. To verify the performance of the established model, the MAE, RMSE, and R2 of the test set and training set were compared with the PSO-SVM model and the SVM model. Analysis of the results showed that compared to the PSO-SVM model and the SVM model, the IPSO-SVM model reduced the RMSE of the training set data by 49% and 72%, the MAE by 52% and 78%, the STD by 14% and 68%, the test set data by 6% and 24%, the MAE by 2% and 25%, and the STD by 22% and 29%, respectively. The results show that modeling studies using the IPSO-SVM model provide results showing that color changes in heat-modified wood after artificial weathering can be successfully predicted without expensive and time-consuming experimental studies.

Brown adipose tissue (BAT) is a kind of adipose tissue engaging in thermoregulatory thermogenesis, metaboloregulatory thermogenesis, and secretory. Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases. Additionally, the activity of BAT presents certain differences between different ages and genders. Clinically, BAT segmentation based on PET/CT data is a reliable method for brown fat research. However, most of the current BAT segmentation methods rely on the experience of doctors. In this paper, an improved U-net network, ICA-Unet, is proposed to achieve automatic and precise segmentation of BAT. First, the traditional 2D convolution layer in the encoder is replaced with a depth-wise over-parameterized convolutional (Do-Conv) layer. Second, the channel attention block is introduced between the double-layer convolution. Finally, the image information entropy (IIE) block is added in the skip connections to strengthen the edge features. Furthermore, the performance of this method is evaluated on the dataset of PET/CT images from 368 patients. The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts. The average DICE coefficient (DSC) is 0.9057, and the average Hausdorff distance is 7.2810. Experimental results suggest that the method proposed in this paper can achieve efficient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.

Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC).Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients’ clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted.Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86–0.96) was higher than that of RM (0.82, 95% CI: 0.75–0.89) and DLM (0.90, 95% CI: 0.85–0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77–0.93) was also higher than that of RM (0.68, 95% CI: 0.52–0.84) and DLM (0.79, 95% CI: 0.67–0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models.Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

PurposeTo investigate whether the combination of radiomics derived from brain high-resolution T1-weighted imaging and automatic machine learning could diagnose subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) accurately.MethodsA total of 116 right-handed participants involving 40 SIVCIND patients and 76 gender-, age-, and educational experience-matched normal controls (NM) were recruited. A total of 7,106 quantitative features from the bilateral thalamus, hippocampus, globus pallidus, amygdala, nucleus accumbens, putamen, caudate nucleus, and 148 areas of the cerebral cortex were automatically calculated from each subject. Six methods including least absolute shrinkage and selection operator (LASSO) were utilized to lessen the redundancy of features. Three supervised machine learning approaches of logistic regression (LR), random forest (RF), and support vector machine (SVM) employing 5-fold cross-validation were used to train and establish diagnosis models, and 10 times 10-fold cross-validation was used to evaluate the generalization performance of each model. Correlation analysis was performed between the optimal features and the neuropsychological scores of the SIVCIND patients.ResultsThirteen features from the right amygdala, right hippocampus, left caudate nucleus, left putamen, left thalamus, and bilateral nucleus accumbens were included in the optimal subset. Among all the three models, the RF produced the highest diagnostic performance with an area under the receiver operator characteristic curve (AUC) of 0.990 and an accuracy of 0.948. According to the correlation analysis, the radiomics features of the right amygdala, left caudate nucleus, left putamen, and left thalamus were found to be significantly correlated with the neuropsychological scores of the SIVCIND patients.ConclusionsThe combination of radiomics derived from brain high-resolution T1-weighted imaging and machine learning could diagnose SIVCIND accurately and automatically. The optimal radiomics features are mostly located in the right amygdala, left caudate nucleus, left putamen, and left thalamus, which might be new biomarkers of SIVCIND.

Identifying intravenous immunoglobulin-resistant patients is essential for the prompt and optimal treatment of Kawasaki disease, suggesting the need for effective risk assessment tools. Data-driven approaches have the potential to identify the high-risk individuals by capturing the complex patterns of real-world data. To enable clinically applicable prediction of intravenous immunoglobulin resistance addressing the incompleteness of clinical data and the lack of interpretability of machine learning models, a multi-stage method is developed by integrating data missing pattern mining and intelligible models. First, co-clustering is adopted to characterize the block-wise data missing patterns by simultaneously grouping the clinical features and patients to enable (a) group-based feature selection and missing data imputation and (b) patient subgroup-specific predictive models considering the availability of data. Second, feature selection is performed using the group Lasso to uncover group-specific risk factors. Third, the Explainable Boosting Machine, which is an interpretable learning method based on generalized additive models, is applied for the prediction of each patient subgroup. The experiments using real-world Electronic Health Records demonstrate the superior performance of the proposed framework for predictive modeling compared with a set of benchmark methods. This study highlights the integration of co-clustering and supervised learning methods for incomplete clinical data mining, and promotes data-driven approaches to investigate predictors and effective algorithms for decision making in healthcare.

Sound stimulation is generally used for tinnitus and hyperacusis treatment. Recent studies found that long-term noise exposure can change synaptic and firing properties in the central auditory system, which will be detected by the acoustic startle reflex. However, the perceptual consequences of long-term low-intensity sound exposure are indistinct. This study will detect the effects of moderate-level noise exposure (83 dB SPL) on auditory loudness, and temporal processing was evaluated using CBA/CaJ mice. C-Fos staining was used to detect neural activity changes in the central auditory pathway. With two weeks of 83 dB SPL noise exposure (8 hours per day), no persistent threshold shift of the auditory brainstem response (ABR) was identified. On the other hand, noise exposure enhanced the acoustic startle response (ASR) and gap-induced prepulse inhibition significantly (gap-PPI). Low-level noise exposure, according to the findings, can alter temporal acuity. Noise exposure increased the number of c-Fos labeled neurons in the dorsal cochlear nucleus (DCN) and caudal pontine reticular nucleus (PnC) but not at a higher level in the central auditory nuclei. Our results suggested that noise stimulation can change acoustical temporal processing presumably by increasing the excitability of auditory brainstem neurons.

Toll stations are bottlenecks in the traffic flow of expressways, and the evaluation of their capacity is essential for the operation of the expressway. Traditional capacity studies are mostly based on theoretical modelling of traffic engineering or simulation, with a focus on parameter tuning and idealized hypotheses, resulting in poor reliability. In view of the coexistence of electronic toll collection lanes and compound toll collection lanes at toll stations of expressways in China, the present study analyses the capacity of entrance and exit lanes of toll stations under mixed traffic conditions using a real toll data-driven approach. Firstly, the service time of a single vehicle during the saturation period was taken as the starting point for the capacity estimates. Secondly, the variation in service time for multiple categories of vehicles is modelled using lognormal distribution. Finally, the capacity of the two types of toll lanes at the designated toll station is determined. The important outcome of this study is the calculation of authentic capacity at the toll stations and the discussion of individual special toll lanes. Accordingly, it contributes to the development of appropriate policies to manage the operation of the toll plaza effectively.

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

Based on the newly released China Merged Surface Temperature (China-MST) dataset, we diagnose the leading mode of covariation between surface temperature and the summer precipitation in Eastern China (EC). A possible connection between the surface temperature in East Asia and EC summer precipitation since the last century (1901–2016) is analyzed. Results indicate that there exists a remarkable meridional dipole pattern of surface temperature, defined as the meridional temperature difference index (MTDI). Meanwhile, there is a tripolar pattern of summer precipitation, which coincides with the first principal component of summer precipitation in EC. Further analyses show that when the MTDI is high in summer, an anomalous anticyclonic/cyclonic circulation is in East Asia’s mid/low latitude. The water vapor is down in the Yangtze and Huaihe River basins, and the ascending motion is weak, resulting in reduced precipitation over this region. At the same time, more water vapor and intensified ascending motion in North and South China tend to produce more summer precipitation. The opposite situations are found when the MTDI is low. Moreover, the MTDI closely relates with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation at interdecadal and multi-decadal scales. These results have been substantiated in the application of information flow analysis, a recently developed method in physics for the inference of causality between dynamical events.

Objectives We aimed to evaluate recompensation factors among patients with decompensated cirrhosis.Design A multicentre retrospective case–control study was conducted. Data were collected from and compared between groups of patients with recompensated and acute decompensated cirrhosis. Univariable and multivariable logistic regressions were used to select indicators associated with recompensation among patients with decompensated cirrhosis with different complications. A decision tree with 10-fold cross-validation was used to develop the model to identify patients with recompensation. We followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guideline for development and reporting of the new model.Setting The study was conducted in six tertiary public hospitals in Chongqing, China.Participants This study included 3953 patients with decompensated cirrhosis.Results In the total sample of included patients, there were 553 patients with recompensation and 3400 patients with acute decompensation, including 1158 patients with gastrointestinal bleeding, 1715 patients with a bacterial infection, 104 patients with hepatic encephalopathy and 423 patients with ascites. The most relevant indicator of recompensation selected by the decision tree model was albumin, with a threshold of 40 g/L. Total protein, haemoglobin, basophil percentage, alanine aminotransferase, neutrophil-to-lymphocyte ratio and diabetes were also selected to subsequently distinguish patients. The terminal nodes with a probability of recompensation was 0.89. The overall accuracy rate of the model was 0.92 (0.91–0.93), and it exhibited high specificity (86.9%) and sensitivity (92.6%).Conclusions The occurrence of recompensated cirrhosis could be identified by albumin, total protein, haemoglobin, basophil percentage, alanine aminotransferase, neutrophil-to-lymphocyte ratio and diabetes. These simple variables may help clinicians develop a treatment plan to encourage patients with decompensated cirrhosis to recompensate.

In order to improve the recovery of rare earth elements, finding a collector with a strong selectivity ability had become the focus of research. In this paper, phenylpropyl hydroxamic acid (PHA) was used as a new hydrophobic surfactant collector for the separation of bastnaesite from calcite, and salicylic hydroxamic acid (SHA) was used as a reference collector. The results of a single mineral flotation test with SHA show that the reagent has good collection performance and selectivity. In addition, Zeta potential measurements and FTIR analysis show that PHA is adsorbed on the surface of bastnaesite by chemical adsorption, and the surface state of bastnaesite changes after PHA treatment. By XPS analysis, PHA interacts with Ce, and forms a Ce–O bond with Ce. It is speculated that the hydroxamic acid forms a five-element-chelated hydroxamic group with Ce on bastnaesite surface, so as to improve the hydrophobicity of bastnaesite, and make bastnaesite float more easily out of the pulp. According to DFT calculation, PHA has better adsorption capacity and stronger hydrophobicity than SHA, and shows superior electronic group capacity and chemical reactions that promote its flotation performance.

Background and Aims: Patients with acute decompensated (AD) cirrhosis are frequently readmitted to the hospital. An accurate predictive model for identifying high-risk patients may facilitate the development of effective interventions to reduce readmission rates.Methods: This cohort study of patients with AD cirrhosis was conducted at six tertiary hospitals in China between September 2012 and December 2016 (with 705 patients in the derivation cohort) and between January 2017 and April 2020 (with 251 patients in the temporal validation cohort). Least absolute shrinkage and selection operator Cox regression was used to identify the prognostic factors and construct a nomogram. The discriminative ability, calibration, and clinical net benefit were evaluated based on the C-index, area under the curve, calibration curve, and decision curve analysis. Kaplan–Meier curves were constructed for stratified risk groups, and log-rank tests were used to determine significant differences between the curves.Results: Among 956 patients, readmission rates were 24.58, 42.99, and 51.78%, at 30, 60, and 90 days, respectively. Bacterial infection was the main reason for index hospitalization and readmission. Independent factors in the nomogram included gastrointestinal bleeding [hazard rate (HR): 2.787; 95% confidence interval (CI): 2.221–3.499], serum sodium (HR: 0.955; 95% CI: 0.933–0.978), total bilirubin (HR: 1.004; 95% CI: 1.003–1.005), and international normalized ratio (HR: 1.398; 95% CI: 1.126–1.734). For the convenience of clinicians, we provided a web-based calculator tool (https://cqykdx1111.shinyapps.io/dynnomapp/). The nomogram exhibited good discrimination ability, both in the derivation and validation cohorts. The predicted and observed readmission probabilities were calibrated with reliable agreement. The nomogram demonstrated superior net benefits over other score models. The high-risk group (nomogram score >56.8) was significantly likely to have higher rates of readmission than the low-risk group (nomogram score ≤ 56.8; p < 0.0001).Conclusions: The nomogram is useful for assessing the probability of short-term readmission in patients with AD cirrhosis and to guide clinicians to develop individualized treatments based on risk stratification.

The development of intelligent transportation systems (ITSs) faces the challenge of integrating data from multiple unrelated sources. As one of the core technologies of knowledge integration in ITS, an ontology typically provides a normative definition of transportation domain that can be used as a reference for information integration. However, due to the subjectivity of domain experts, a concept may be expressed in multiple ways, yielding the ontology heterogeneity problem. Ontology matching (OM) is an effective method of addressing it, which is of help to further realize the mutual communication between the ontology-based ITSs. In this work, we first propose to use Word2Vec to model the entities in vector space and calculate their similarity values. Then, a stable marriage-based alignment extraction algorithm is presented to determine high-quality alignment. In the experiment, the performance of the proposal is tested by using the benchmark track of OAEI and real transportation ontologies. The experimental results show that our approach is able to obtain higher quality alignment results than OAEI’s participants and other state-of-the-art ontology matching techniques.

Objective: Kawasaki disease (KD) is one of the most prevailing vasculitis among infants and young children, and has become the leading cause of acquired heart disease in childhood. Delayed diagnosis of KD can lead to serious cardiovascular complications. We sought to create a diagnostic model to help distinguish children with KD from children with other febrile illnesses [febrile controls (FCs)] to allow prompt treatment.Methods: Significant independent predictors were identified by applying multivariate logistic regression analyses. A new diagnostic model was constructed and compared with that from diagnostic tests created by other scholars.Results: Data from 10,367 patients were collected. Twelve independent predictors were determined: a lower percentage of monocytes (%MON), phosphorus, uric acid (UA), percentage of lymphocyte (%LYM), prealbumin, serum chloride, lactic dehydrogenase (LDH), aspartate aminotransferase: alanine transaminase (AST: ALT) ratio, higher level of globulin, gamma-glutamyl transpeptidase (GGT), platelet count (PLT), and younger age. The AUC, sensitivity, and specificity of the new model for cross-validation of the KD diagnosis was 0.906 ± 0.006, 86.0 ± 0.9%, and 80.5 ± 1.5%, respectively. An equation was presented to assess the risk of KD, which was further validated using KD (n = 5,642) and incomplete KD (n = 809) cohorts.Conclusions: Children with KD could be distinguished effectively from children with other febrile illnesses by documenting the age and measuring the level of %MON, phosphorus, UA, globulin, %LYM, prealbumin, GGT, AST:ALT ratio, serum chloride, LDH, and PLT. This new diagnostic model could be employed for the accurate diagnosis of KD.

Using citric acid (CA) and ethylenediamine (EDA) as precursors, stable nitrogen-doped carbon dots (CD) nanosols were prepared by microwave procedure and characterized in detail. It was found that CDNs catalyze ethanol (Et)-HAuCl4 to generate gold nanoparticles (AuNPs), which have strong surface plasmon resonance, Rayleigh scattering, (RRS) and a surface plasmon resonance (SPR) absorption (Abs) effect at 370 nm and 575 nm, respectively. Compled the new catalytic amplification indicator reaction with the specific As3+ aptamer reaction, a new RRS/Abs dual-mode aptamer sensor for the assay of trace As3+ was developed, based on the RRS/Abs signals increasing linearly with As3+ increasing in the ranges of 5–250 nmol/L and 50−250 nmol/L, whose detection limits were 0.8 nmol/L and 3.4 nmol/L As3+, respectively. This analytical method has the advantages of high selectivity, simplicity, and rapidity, and it has been successfully applied to the detection of practical samples.

Medical information retrieval plays an increasingly important role to help physicians and domain experts to better access medical-related knowledge and information, and support decision making. Integrating the medical knowledge bases has the potential to improve the information retrieval performance through incorporating medical domain knowledge for relevance assessment. However, this is not a trivial task due to the challenges to effectively utilize the domain knowledge in the medical knowledge bases. In this paper, we proposed a novel medical information retrieval system with a two-stage query expansion strategy, which is able to effectively model and incorporate the latent semantic associations to improve the performance. This system consists of two parts. First, we applied a heuristic approach to enhance the widely used pseudo relevance feedback method for more effective query expansion, through iteratively expanding the queries to boost the similarity score between queries and documents. Second, to improve the retrieval performance with structured knowledge bases, we presented a latent semantic relevance model based on tensor factorization to identify semantic association patterns under sparse settings. These identified patterns are then used as inference paths to trigger knowledge-based query expansion in medical information retrieval. Experiments with the TREC CDS 2014 data set: 1) showed that the performance of the proposed system is significantly better than the baseline system and the systems reported in TREC CDS 2014 conference, and is comparable with the state-of-the-art systems and 2) demonstrated the capability of tensor-based semantic enrichment methods for medical information retrieval tasks.

Person re-identification (Re-ID) is a fundamental subject in the field of the computer vision technologies. The traditional methods of person Re-ID have difficulty in solving the problems of person illumination, occlusion and attitude change under complex background. Meanwhile, the introduction of deep learning opens a new way of person Re-ID research and becomes a hot spot in this field. This study reviews the traditional methods of person Re-ID, then the authors focus on the related papers about different person Re-ID frameworks on the basis of deep learning, and discusses their advantages and disadvantages. Finally, they propose the direction of further research, especially the prospect of person Re-ID methods based on deep learning.

In view of the low efficiency of traditional electrostatic precipitators in removing fine particles, acoustic and pulsed corona discharge coupling fields were proposed to increase particle size. In this paper, monodisperse particles with three different sizes (0.5 μm, 2 μm, and 4 μm) were generated to investigate the agglomeration effect under different parameters in external fields. A larger reduction ratio of particle number concentration resulted in a higher agglomeration efficiency. Results indicated that, in the range from 800 to 2400 Hz, the acoustic agglomeration effect on 4-μm particles was better than that on 0.5-μm and 2-μm particles. In the pulsed corona discharge field, agglomeration efficiencies of the three particle sizes were lower than those in the acoustic field. However, application of the coupling field highly improved agglomeration efficiency compared with the single field. When a pulse input voltage of 50 kV with acoustic sound pressure level (SPL) of 143 dB and frequency of 1600 Hz was selected, the corresponding number reduction ratio of 0.5-μm, 2-μm, and 4-μm particles increased to 0.464, 0.526, and 0.918 from 0.254, 0.438, and 0.814 in the acoustic wave field and 0.226, 0.385, and 0.794 in the pulsed corona discharge field.