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Background This study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM. Methods This study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels. Results The MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes. Conclusion The study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

IntroductionSubjective cognitive decline (SCD) and mild cognitive impairment (MCI) are stages 2 and 3, respectively, of the Alzheimer’s continuum. The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-cog’s) ten-words recall test is a validated method for the early detection of cognitive impairment in Alzheimer’s disease. However, limited studies have investigated its ability to differentiate between SCD and MCI.Methods203 participants with SCD and 62 participants with MCI underwent multiple neuropsychological assessments. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment-Basic (MOCA-B) served as brief global cognition tests. A binary logistic regression model was used to analyze the potential factors affecting MCI. The accuracy of the ten-words recall test was assessed using the area under the receiver operating characteristic (ROC) and the area under the curve (AUC).ResultsThe neuropsychological assessment revealed significant differences in the ten-words recall test scores between the SCD (median age 61 years; 70.44% female) and MCI (median age 64 years; 61.29% female) groups (p < 0.001), with the MCI group scoring the highest. Using a cut-off value of 3.15 for the ten-words recall test, sensitivity for distinguishing MCI from SCD reached 87%, while specificity stood at 61% (AUC 0.777, p < 0.001). DeLong’s test indicated no statistically significant difference in the ten-words recall test’s ability to distinguish between SCD and MCI compared to the total score of ADAS-Cog (AUC 0.833, p) and MMSE (AUC 0.784, p > 0.05). However, a significant difference was observed when compared to MoCA-B (AUC 0.973, p < 0.001). In the population with an education level of ≤ 9 years, the optimal cut-off value for the ten-words recall test was 3.15, yielding a sensitivity of 91% and a specificity of 45% (AUC = 0.674, p = 0.030). In the population with an education level > 9 years, the optimal cut-off value was 3.63, resulting in a sensitivity of 79% and a specificity of 71% (AUC = 0.785, p < 0.001).DiscussionThe ten-words recall test from the ADAS-cog may detect MCI early owing to its simplicity and quick administration. It is an effective and convenient tool for rapidly identifying mild cognitive impairment.

Background and aimMetabolic-associated fatty liver disease (MAFLD) has gradually become one of the main health concerns regarding liver diseases. Postmenopausal women represent a high-risk group for MAFLD; therefore, it is of great importance to identify and intervene with patients at risk at an early stage. This study established a predictive nomogram model of MAFLD in postmenopausal women and to enhance the clinical utility of the new model, the researchers limited variables to simple clinical and laboratory indicators that are readily obtainable.MethodsData of 942 postmenopausal women from January 2023 to October 2023 were retrospectively collected and divided into two groups according to the collection time: the training group (676 cases) and the validation group (226 cases). Significant indicators independently related to MAFLD were identified through univariate logistic regression and stepwise regression, and the MAFLD prediction nomogram was established. The C-index and calibration curve were used to quantify the nomogram performance, and the model was evaluated by measuring the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsOf 37 variables, 11 predictors were identified, including occupation (worker), body mass index, waist-to-hip ratio, number of abortions, anxiety, hypertension, hyperlipidemia, diabetes, hyperuricemia, and diet (meat and processed meat). The C-index of the training group predicting the related risk factors was 0.827 (95% confidence interval [CI] 0.794–0.860). The C-index of the validation group was 0.787 (95% CI 0.728–0.846). Calibration curves 1 and 2 (BS1000 times) were close to the diagonal, showing a good agreement between the predicted probability and the actual incidence in the two groups. The AUC of the training group was 0.827, the sensitivity was 0.784, and the specificity was 0.735. The AUC of the validation group was 0.787, the sensitivity was 0.674, and the specificity was 0.772. The DCA curve showed that the nomogram had a good net benefit in predicting MAFLD in postmenopausal women.ConclusionsA predictive nomogram for MAFLD in postmenopausal women was established and verified, which can assist clinicians in evaluating the risk of MAFLD at an early stage.

Study region: Tabu Basin is located on a typical-deserted steppe in Inner Mongolia, China and plays a significant role in maintaining ecological stability of northern China.Study focus: Among rivers worldwide, 51–60 % cease to flow for at least one day annually, especially in arid and semiarid areas. Understanding interactions between intermittent rivers (IRs) and groundwater (GW) is crucial for protecting water resources and sustaining ecological balance. A comprehensive framework was developed to analyze these interactions.New hydrological insights for the region: Unlike previous studies, this study considered seasonal differences in hydrochemical and isotopic compositions of IR and GW, as well as the intensive influence of evaporation. Multitemporal datasets were employed to analyze the IR–GW interaction across spatial, temporal, and profiling scales. Hydrochemical evidence and isotopic analysis indicated the input of meltwater with depleted isotopic signatures to GW and GW recharge to IR. The isotopic compositions followed the order of rainfall < GW

Background and purposeArteriosclerotic cerebral small vessel disease (aCSVD) is a cause of cognitive impairment, dementia, and stroke. Developing a better understanding of the risk factor of aCSVD is key to reducing the incidence of these conditions. This study investigated the association between intracranial arterial calcification (IAC) and total cerebral small vessel disease (CSVD) burden score.Materials and methodsThis is a retrospective study, the subjects were transient ischemic attack (TIA) or acute ischemic stroke (AIS) patients. The data of 303 inpatients admitted to our study hospital between December 2018 and July 2020 were analyzed. Four imaging markers of CSVD (lacunes, white matter hyperintensities, cerebral microbleeds, and enlarged perivascular spaces) were evaluated by magnetic resonance imaging, and a total CSVD burden score was calculated. The experimental group was divided into four subgroups according to total CSVD burden score (1–4 points). Patients without CSVD (0 points) served as the control group. Head computerized tomography (CT) scans were used to assess ICA, using Babiarz’s method. The correlations between IAC and single imaging markers of CSVD were determined using Spearman’s rank correlation. Binary logic regression analysis and multivariate ordered logic regression analysis were used to determine the associations between IAC and aCSVD.ResultsIAC was positively correlated with total CSVD burden score (r = 0.681), deep white matter hyperintensities (r = 0.539), periventricular white matter hyperintensities (r = 0.570), cerebral microbleeds (r = 0.479), lacunes (r = 0.541), and enlarged perivascular spaces (r = 0.554) (all p

Abstract Lung squamous cell carcinoma (LUSC) is the second most common lung cancer worldwide, leading to millions of deaths annually. Although immunotherapy has expanded the therapeutic choices for LUSC and achieved considerable efficacy in a subset of patients, many patients could not benefit, and resistance was pervasive. Therefore, it is significant to investigate the mechanisms leading to patients’ poor response to immunotherapies and explore novel therapeutic targets. Using multiple public LUSC datasets, we found that Kallikrein-8 (KLK8) expression was higher in tumor samples and was correlated with inferior survival. Using a LUSC cohort (n = 190) from our center, we validated the bioinformatic findings about KLK8 and identified high KLK8 expression as an independent risk factor for LUSC. Function enrichment showed that several immune signaling pathways were upregulated in the KLK8 low-expression group and downregulated in the KLK8 high-expression group. For patients with low KLK8 expression, they were with a more active TME, which was both observed in the TCGA database and immune marker immunohistochemistry, and they had extensive positive relations with immune cells with tumor-eliminating functions. This study identified KLK8 as a risk factor in LUSC and illustrated the associations between KLK8 and cancer immunity, suggesting the potentiality of KLK8 as a novel immune target in LUSC.

The zonal detached-eddy simulation (ZDES) method divides the flow field into different computational modes, offering flexibility in the simulation of complex flows. The transonic shock buffet on the upper surface of the OAT15A supercritical airfoil is numerically simulated by ZDES based on the k-ω SST model. The results show that the error in the low-frequency characteristics of the transonic shock buffet predicted by ZDES compared to experiments is within 3%. Its predictions of pressure fluctuations and averaged velocity agree better with the experimental data. Overall, ZDES is effective in predicting the periodic oscillations of shock waves along the streamwise direction, flow separation induced by shock wave motion, and the small-scale vortex structures in the wake region.

Abstract As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

BackgroundPrevious observational studies have demonstrated a link between diabetes mellitus(DM) and primary biliary cholangitis (PBC). Nevertheless, since these relationships might be confused, whether there is any causal connection or in which direction it exists is unclear. Our investigation aimed to identify the causal associations between DM and PBC.MethodsWe acquired genome-wide association study (GWAS) datasets for PBC, Type 1 diabetes(T1DM), and Type 2 diabetes(T2DM) from published GWASs. Inverse variance-weighted (IVW), MR-Egger, weighted median (WM), Simple mode, and weighted mode methods were used to determine the causal relationships between DM(T1DM or T2DM) and PBC. Sensitivity analyses were also carried out to ensure the results were robust. To determine the causal relationship between PBC and DM(T1DM or T2DM), we also used reverse MR analysis.ResultsT1DM was associated with a higher risk of PBC (OR 1.1525; 95% CI 1.0612-1.2517; p = 0.0007) in the IVW method, but no evidence of a causal effect T2DM on PBC was found (OR 0.9905; 95% CI 0.8446-1.1616; p = 0.9071) in IVW. Results of the reverse MR analysis suggested genetic susceptibility that PBC was associated with an increased risk of T1DM (IVW: OR 1.1991; 95% CI 1.12-1.2838; p = 1.81E-07), but no evidence of a causal effect PBC on T2DM was found (IVW: OR 1.0101; 95% CI 0.9892-1.0315; p = 0.3420).ConclusionThe current study indicated that T1DM increased the risk of developing PBC and vice versa. There was no proof of a causal connection between PBC probability and T2DM. Our results require confirmation through additional replication in larger populations.

The effect of carbonation curing on depth-wise carbonation gradient and compressive strength development of RCM was evaluated. The RCM mortars were carbonated for seven curing periods, complemented by a thorough investigation of carbonation degree, phase assemblage, and microstructural at varying depths. Results showed a significant increase in compressive strength, from 3.3 MPa on the first day to 51.7 MPa after seven days and 63.5 MPa after 28 days of carbonation curing. A notable gradient in CaCO3 concentration was observed, decreasing from 41.4% at the surface to 16.8% at 25 mm depths. Additionally, the formation of CaCO3 polymorphs and Ca-modified silica gel in the surface layers was found to enhance microstructural density and impede CO2 penetration, while a higher prevalence of micropores was noted in the inner regions. These findings highlight the potential of optimizing the carbonation regime or employing internal carbonation methods to achieve a more homogeneous microstructure.

BackgroundMultiple studies have confirmed the significant role of cathepsins in the development and progression of digestive system tumors. However, further investigation is needed to determine the causal relationships.MethodsWe conducted a two-sample bidirectional Mendelian randomization (MR) study using pooled data from a genome-wide association study (GWAS) to assess the causal associations between nine cathepsins (cathepsin B, E, F, G, H, L2, O, S, and Z) and six types of digestive system tumors, including hepatocellular carcinoma (HCC), pancreatic cancer (PCa), biliary tract cancer (BTC), colorectal cancer (CRC), gastric carcinoma (GC), and esophageal cancer (EC). We employed the following methods including inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran’s Q, MR-PRESSO, MR-Egger intercept test and leave-one-out sensitivity analysis. The STROBE-MR checklist for the reporting of MR studies was used in this study.ResultsThe risk of HCC increased with high levels of cathepsin G (IVW: p = 0.029, odds ratio (OR) = 1.369, 95% confidence interval (CI) = 1.033-1.814). Similarly, BTC was associated with elevated cathepsin B levels (IVW: p = 0.025, OR = 1.693, 95% CI = 1.070-2.681). Conversely, a reduction in PCa risk was associated with increased cathepsin H levels (IVW: p = 0.027, OR = 0.896, 95% CI = 0.812-0.988). Lastly, high levels of cathepsin L2 were found to lower the risk of CRC (IVW: p = 0.034, OR = 0.814, 95% CI = 0.674-0.985).ConclusionOur findings confirm the causal relationship between cathepsins and digestive system tumors, which can offer valuable insights for the diagnosis and treatment of digestive system tumors.

Background and objective: Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). Methods: A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). Results: The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. Conclusion: The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.

Diverse cognitive processes place different demands on locally segregated and globally integrated brain activities. Motor imagery (MI) is a complex mental operation characterized by sensorimotor rhythms. However, how the brain network acts on MI rhythms is not well-known. The present work aimed to explore the effects of brain integration and segregation on brain rhythmic oscillations. The power spectrum dynamics, topography distribution, and brain network metrics in the alpha and beta bands were calculated. And the correlations were investigated with the network metrics and sensorimotor rhythm. The results showed that the degree of event-related desynchronization/synchronization (ERD/ERS) was higher in alpha band than in beta band during [−1, 1 s] (p

The laminar flow meter (LFM) boasts several advantages such as no moving parts, a wide range ratio, high measurement accuracy, quick dynamic response, etc., and is a promising technology for micro gas flow measurement. In order to explore the influence of different curvature radii on curved surface gap LFM, three curved structures with different curvature radii were designed. The computational fluid dynamics method is applied to simulate the flow feature of three structures. The simulated velocity cloud and pressure distribution show that the larger the curvature radius, the more stable the flow of gas medium. The relationship between differential pressure and volume flow was obtained through the test within a flow range of 0~540 sccm. Regression analysis revealed that the volume flow measured by the curved surface LFM had a high linear relationship with the differential pressure. Experimental findings indicate that differential pressure of the structure with a curvature radius of 2 mm was greater than that of other two structures (curvature radius of 6 mm and 3 mm) at the same point. This indicates that adding the number of surfaces can effectively increase the pressure loss, so as to obtain a larger range ratio, but will increase the measurement error.

Objective: A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography. Methods and procedures: The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit $\Sigma $ - $\Delta $ analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups’ data were analyzed. Results: Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were $0.89 \mu $ Vpp, 1.25 GN), 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p $>$ 0.05). Conclusion: The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data. Clinical impact: The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.

The diabetic kidney disease (DKD) is chronic kidney disease caused by diabetes and one of the most common comorbidities. It is often more difficult to treat end-stage renal disease once it develops because of its complex metabolic disorders, so early prevention and treatment are important. However, currently available DKD therapies are not ideal, and novel therapeutic strategies are urgently needed. The potential of stem cell therapies partly depends on their ability to secrete exosomes. More and more studies have shown that stem cell-derived exosomes take part in the DKD pathophysiological process, which may offer an effective therapy for DKD treatment. Herein, we mainly review potential therapies of stem cell-derived exosomes mainly stem cell-derived exosomal microRNAs in DKD, including their protective effects on mesangial cells, podocytes and renal tubular epithelial cells. Using this secretome as possible therapeutic drugs without potential carcinogenicity should be the focus of further research.

ObjectiveUnilateral spatial neglect (USN) is an impaired contralesional stimulus detection, response, or action, causing functional disability. After a stroke, the right hemisphere experiences USN more noticeably, severely, and persistently than the left. However, few studies using fNIRS have been reported in cases of USN. This study aimed to confirm weaker RSFC in USN and investigate the potential inherent features in hemodynamic fluctuations that may be associated with USN. Furthermore, these features were combined into a mathematical model for more accurate classification.MethodsA total of 33 stroke patients with right-sided brain damage were chosen, of whom 12 had non-USN after stroke, and 21 had USN after stroke (the USN group). Graph theory was used to evaluate the hemodynamic signals of the brain's right cerebral cortex during rest. Furthermore, a support vector machine model was built to categorize the subjects into two groups based on the chosen network properties.ResultsFirst, mean functional connectivity was lower in the USN group (0.745 ± 0.239) than in the non-USN group (0.843 ± 0.254) (t = −4.300, p < 0.001). Second, compared with the non-USN group, USN patients had a larger clustering coefficient (C) (t = 3.145, p < 0.001), local efficiency (LE) (t = 3.189, p < 0.001), and smaller global efficiency (GE) (t = 3.047, p < 0.001). Notably, there were differences in characteristic path length (L) and small worldness (σ) values between the two groups at certain thresholds, mainly as higher L (t = 3.074, p < 0.001) and lower small worldness (σ) values (t = 2.998, p < 0.001) in USN patients compared with non-USN patients. Finally, the classification accuracy of the SVM model based on AUC aC (t = −2.259, p = 0.031) and AUC aLE (t = −2.063, p = 0.048) was 85%, the sensitivity was 75%, and the specificity was 89%.ConclusionThe functional network architecture of the right cerebral cortex exhibits significant topological alterations in individuals with USN following stroke, and the sensitivity index based on the small-world property AUC may be utilized to identify these patients accurately.

Due to the shortage and high price of river sand, the use of sea sand as a fine aggregate for concrete is gradually being considered. Seashells are fragile and have an undesirable effect on the compressive strength of concrete. However, the exact effect of seashells is still unclear and quality control of concrete is not possible since there are no effective methods for seashell characterization. In this study, we investigated the feasibility of segmenting photos of sea sand and analyzing seashells by using three typical machine learning methods, i.e., PointRend, DeepLab v3 + , and Weka. A new imaging method was proposed to avoid overlapping sea sand particles and preserve the smallest particles with sufficient resolution. A total of 960 photos were captured, and 2199 seashells were labeled, of which 80% and 20% were used for model training and validation, respectively. As a result, PointRend could efficiently recognize seashells with different shapes, sizes, and surface textures. It also had the highest Intersection over Union (IOU) and pixel accuracy (PA) scores due to the well-defined boundaries of the seashells, followed by DeepLab v3 + and Weka. From the segmentation results, the size of the seashells showed a left-skewed distribution with a mean diameter of 0.747 mm, which was smaller than the size of the sea sand. There was also considerable variation in the irregularity and roundness of the seashells. As the size of the seashells increased, their shapes became more irregular. The automated analysis of the seashells can provide further insights into the effect of shells on the properties of concrete.

In order to solve the problems of low cathode conductivity, shuttle effect, and poor electrochemical performance of Li-S batteries, we designed a porous carbon/nano-sized ferric oxide (Fe _3 O _4 ) composite modified membrane. This improved both conductivity and adsorption capacity of lithium polysulfides (LiPSs) prepared with mesopores and high specific surface area C-Fe _3 O _4 @PE. The membrane can bind polysulfide via chemical and physical adsorption. Owing to these advantages, a Li-S battery based on C-Fe _3 O _4 @PE and with a separator exhibited excellent rate performance (965.9 mA h g ^–1 at 0.2 C and 437.5 mA h g ^–1 at 5 C), as well as a long cycle life (416.6 mA h g ^–1 after 300 cycles at 0.5 C, with a capacity retention rate of 50.5%).

Abstract Introduction A high perceptual load can effectively prevent attention from being drawn to irrelevant stimuli; however, the neural pattern underlying this process remains unclear. Methods This study adopted a perceptual load paradigm to examine the temporal processes of attentional modulation by incorporating conditions of perceptual load, distractor‐target compatibility, and eccentricity. Results The behavioral results showed that a high perceptual load significantly reduced attentional distraction caused by peripheral distractors. The event‐related potential results further revealed that shorter P2 latencies were observed for peripheral distractors than for central distractors under a high perceptual load and that a suppressed compatibility effect with increasing load was reflected by the P3 component. Conclusion These findings suggested that (1) P2 and P3 components effectively captured different sides of attentional processing modulated by load (i.e., the filter processing of the object and the overall attentional resource allocation) and (2) response patterns of selective attention modulated by perceptual load were influenced by eccentricity. Our electrophysiological evidence confirmed the behavioral findings, indicating the neural mechanisms of attentional modulation.

The Xiaolangdi Dam is a key project for the control and development of the Yellow River. It bears the functions of flood control, controlling water and sediment in the lower reaches, ice prevention, industrial and agricultural water supply, power generation, and so on. Its safety is related to people’s life and property safety and local economic and social development. It is of great significance to carry out comprehensive and regular deformation monitoring for dams since the deformation is an important evaluation index for dam safety. Interferometric Synthetic Aperture Radar (InSAR) technology has been a rapidly evolving technology in the field of space geodesy in recent years. It offers advantages such as high monitoring precision, extensive coverage, and high monitoring point density, making it a powerful tool for monitoring deformations in hydraulic engineering projects. Based on Sentinel-1 data covering the Xiaolangdi Dam from September 2020 to November 2022, the PS-InSAR technique was used to obtain the surface deformation of the Xiaolangdi Dam, and reservoir water level data on image acquisition dates were obtained for joint analysis. The results show that there is a large deformation in the center of the dam crest of the Xiaolangdi Dam, while both sides of the slope and downstream dam foot are relatively stable. The time series deformation of the dam body is closely related to the reservoir water level change. When the water level increases, the dam body tends to deform downstream; when the water level decreases, the dam body tends to deform upstream. The deformation and water level of the Xiaolangdi Dam exhibit a clear negative correlation. There is no significant cumulative deformation on the dam slopes or at the base of the dam. However, cumulative deformation occurs over time in the central area of the dam’s crest. The deformation process at the central area of the dam’s crest follows a continuous and non-disruptive pattern, which is consistent with the typical deformation behavior of the Xiaolangdi earth–rock dam structure. Therefore, it is judged that the current deformation of the Xiaolangdi Dam does not impact the safe operation of the dam. InSAR technology enables the rapid acquisition of high-precision, high-density deformation information on the surfaces of reservoir dams. With an increasing number of radar satellites in various frequency bands, such as Sentinel-1 and TerraSAR-X, there is now an ample supply of available data sources for InSAR applications. Consequently, InSAR technology can be extended to routine monitoring applications for reservoir dam deformations, especially for small and medium-sized reservoirs that may not be equipped with ground measurement tools like GNSS. This holds significant importance and potential for enhancing the safety monitoring of such reservoirs.

In recent decades, multiple primary lung cancer (MPLC) has been increasingly prevalent in clinical practice. However, many details about MPLC have not been completely settled, such as understanding the driving force, clinical management, pathological mechanisms, and genomic architectures of this disease. From the perspective of diagnosis and treatment, distinguishing MPLC from lung cancer intrapulmonary metastasis (IPM) has been a clinical hotpot for years. Besides, compared to patients with single lung lesion, the treatment for MPLC patients is more individualized, and non-operative therapies, such as ablation and stereotactic ablative radiotherapy (SABR), are prevailing. The emergence of next-generation sequencing has fueled a wave of research about the molecular features of MPLC and advanced the NCCN guidelines. In this review, we generalized the latest updates on MPLC from definition, etiology and epidemiology, clinical management, and genomic updates. We summarized the different perspectives and aimed to offer novel insights into the management of MPLC.

A clear understanding of the spatiotemporal evolution and driving factors of ecosystem service value (ESV) and the landscape ecological risk index (ERI) can effectively link human well-being and ecosystem security, which is essential for sustainable ecosystem management. Based on multitemporal land use data (1990, 2000, 2010, and 2018), the spatiotemporal evolution of ESV and the ERI in the Ulan Buh Desert was evaluated. The driving forces of ESV and the ERI were quantitatively evaluated by the Geodetector model. The results show that 1) from 1990 to 2018, total ESV in the Ulan Buh Desert increased from 7.00×108 yuan to 11.09×108 yuan. Low-ESV areas accounted for approximately 72.28% of the study area. High-ESV and moderate-high-ESV areas were mainly distributed along the Yellow River and the northeastern region. 2) During the study period, the ecological risk of the Ulan Buh Desert generally decreased; only the ecological risk of the northeastern region improved significantly, and high-risk areas were mainly distributed across sand dunes located in the middle of the desert. 3) There was a negative correlation between ESV and the ERI in the study area, and the main relationship was low value-high risk. 4) Driving force analysis results show that natural and human impact factors jointly affected the spatiotemporal differentiation of ESV and the ERI in the Ulan Buh Desert. Among the influencing factors, the interaction between the distance to a highway and annual precipitation had the strongest impact. The implementation of relevant policies in the study area should be guided by ESV and the ERI, and the protection and restoration of various ecosystems in the study area must be strengthened.

Abstract Background Aging and senescence can alter immune cell fitness and influence the efficacy of lung cancer treatments, especially immunotherapy. However, the correlations between cellular senescence and tumor microenvironment are still not clearly clarified and the value of cellular senescence-related genes in evaluating the immune infiltration and clinical outcomes of lung adenocarcinoma (LUAD) need further investigated. Methods We identified three cellular senescence clusters by NMF algorithm and correlated the cellular senescence clusters with the immune landscape in LUAD patients. A prognostic scoring system was established using random survival forest algorithm and validated in 4 external cohorts. Multivariate Cox regression analysis was performed to evaluate the prognostic value of the scoring system. Expression of LYPD3 was evaluated by immunohistochemistry in LUAD samples. Results Based on the mRNA expression profiles of 278 cellular senescence-related genes, three cellular senescence clusters with distinct prognosis were identified. We characterized three cellular senescence clusters by differences in biological processes, EMT score, expression of immunomodulatory genes, extent of intratumor heterogeneity and response to immunotherapy. Meanwhile, a cellular senescence-related scoring system (CSS) was established and validated as an independent prognostic factor and immunotherapy predictor of LUAD. Patients with low CSS was characterized by prolonged survival time. In response to anti-cancer drugs, patients with low CSS exhibited higher sensitivities to molecular drugs, such as Roscovitine (CDKs inhibitor), Lenaidornide (TNF-α inhibitor), MK2206 (Akt 1/2/3 inhibitor), and especially increased response to anti-PD-1/L1 immunotherapy. Conclusions This study demonstrated the correlations between cellular senescence patterns and tumor immune landscape in LUAD, which enhanced our understanding of the tumor immune microenvironment and provided new insights for improving the outcome of immunotherapy for LUAD patients.

Ultra-high performance cementitious composite (UHPCC) is an advanced composite material with superior compressive strength. UHPCC with low steel fiber content typically fails in a single-crack mode under tension, resulting in a high risk of catastrophic structural collapse and limiting its wide application. However, high steel fiber content adversely affects the electrochemical corrosion resistance and long-term performance of UHPCC. Therefore, enhancing the tensile performance of UHPCC with low fiber content has attracted great attention. Polyvinyl alcohol (PVA) fiber is characterized by outstanding crack control ability in cementitious materials. In this study, the macroscale flowability, tensile and flexural properties, ductility and failure mode of UHPCC containing steel and PVA fibers at low fiber content level were explored and analyzed. Results indicate that the post-crack energy absorption of UHPCC improved by more than 40% following fibers hybridization. The failure mode of UHPCC with low fiber content shifted from single crack mode to ductile multiple cracks mode. Microscale observations show that PVA fibers can bridge crack and transfer load at the interface after first cracking, and impede slippage between steel fiber and matrix, thus endows UHPCC with desirable ductile multiple cracks failure mode. The findings of this study provide a foundation for designing UHPCC with ductile tensile performance and give an in-depth understanding of underlying mechanisms of hybrid fibers, promoting the application of ductile UHPCC in innovative structures.

Microglial cell activation and neuroinflammation after intracerebral hemorrhage (ICH) lead to secondary brain damage. Ubiquitin-specific protease 11 (USP11) has been correlated with ICH-induced neuron apoptosis. This study aims to explore the molecular mechanism of USP11 regulating neuroinflammation in ICH. First, an ICH rat model was developed by intracranial administration of collagenase. Silencing USP11 was found to alleviate nerve injury in rats with ICH-like symptoms. Then, through loss- and gain-of-function assays, USP11 knockdown was revealed to alleviate ICH-induced symptoms, corresponding to reduced modified neurological severity scores (mNSS) value, brain water content, blood-brain barrier permeability, neuron apoptosis, microglial cell activation, neutrophil infiltration, and inflammatory factor secretion. It was subsequently shown in microglial cells that USP11 stabilized p53 by deubiquitination and p53 targeted the Kruppel-like factor 2 (KLF2) promoter to repress KLF2 transcription, thereby activating the nuclear factor κB (NF-κB) pathway. Further, rescue experiments were conducted in vivo to validate the function of the USP11/p53/KLF2/NF-κB axis in ICH-induced inflammation, which confirmed that USP11 silencing blocked the release of pro-inflammatory cytokines following ICH by downregulating p53, thus protecting against neurological impairment. Hence silencing USP11 may be a novel anti-inflammatory method for ICH treatment.

Research showed that action real-time strategy gaming (ARSG) experience is related to cognitive and neural plasticity, including visual selective attention and working memory, executive control, and information processing. This study explored the relationship between ARSG experience and information transmission in the auditory channel. Using an auditory, two-choice, go/no-go task and lateralized readiness potential (LRP) as the index to partial information transmission, this study examined information transmission patterns in ARSG experts and amateurs. Results showed that experts had a higher accuracy rate than amateurs. More importantly, experts had a smaller stimulus-locked LRP component (250 – 450 ms) than amateurs on no-go trials, while the response-locked LRP component (0 – 300 ms) on go trials did not differ between groups. Thus, whereas amateurs used an asynchronous information transmission pattern, experts used a reduced asynchronous information transmission pattern or a synchronous pattern where most of processing occurred prior to response execution – an information transmission pattern that supports rapid, error-free performance. Thus, experts and amateurs may use different information transmission patterns in auditory processing. In addition, the information transmission pattern used by experts is typically observed only after long-term auditory training according to past research. This study supports the relationship between ARSG experience and the development of information processing patterns.

There is no cost-effective, accurate, and non-invasive method for the detection of esophageal squamous cell carcinoma (ESCC) in clinical practice. We aimed to investigate the diagnostic potential of tumor-educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into a training cohort (23 patients and 27 healthy individuals) and a validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants, and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2, and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor-educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found that keratin and collagen protein families and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics.

ObjectiveTo develop and validate a radiomic nomogram for individualized prediction of hepatocellular carcinoma (HCC) in HBV cirrhosis patients based on baseline magnetic resonance imaging examinations and clinical data.Methods364 patients with HBV cirrhosis from five hospitals were assigned to the training, internal validation, external validation-1 or external validation-2 cohort. All patients underwent baseline magnetic resonance image (MRI) scans and clinical follow-up within three-year time. Clinical risk factors and MRI-based features were extracted and analyzed. The radiomic signatures were built using the radiomics-score (Rad-score) that calculated for each patient as a linear weighted combination of selected MRI-based features. Prognostic performances of the clinical and radiomic nomograms were evaluated with Cox modeling in the training and validation cohorts.ResultsEighteen features were selected for inclusion in the Rad-score prognostic model. The radiomic signature from multi-sequence MRI yielded a concordance index (C-index) of 0.710, 0.681, 0.632 and 0.658 in the training, internal validation, external validation-1, external validation-2 cohorts, respectively. Sex and Child-Turcotte-Pugh (CTP) class were the most prognostic clinical risk factors in univariate Cox proportional hazards analyses. The radiomic combined nomogram that integrated the radiomic signature with the clinical factors yielded a C-index of 0.746, 0.710, and 0.641 in the training, internal validation, and external validation-1 cohorts, respectively, which was an improvement over either the clinical nomogram or radiomic signature alone.ConclusionWe developed an MRI-based radiomic combined nomogram with good discrimination ability for the individualized prediction of HCC in HBV cirrhosis patients within three-year time.

Soil microbial communities are essential to phosphorus (P) cycling, especially in the process of insoluble phosphorus solubilization for plant P uptake. Phosphate-solubilizing microorganisms (PSM) are the dominant driving forces. The PSM mediated soil P cycling is easily affected by water condition changes due to extreme hydrological events. Previous studies basically focused on the effects of droughts, floods, or drying-rewetting on P cycling, while few focused on drought-flood abrupt alternation (DFAA), especially through microbial activities. This study explored the DFAA effects on P cycling mediated by PSM and P metabolism-related genes in summer maize field soil. Field control experiments were conducted to simulate two levels of DFAA (light drought-moderate flood, moderate drought-moderate flood) during two summer maize growing periods (seeding-jointing stage, tasseling-grain filling stage). Results showed that the relative abundance of phosphate-solubilizing bacteria (PSB) and phosphate-solubilizing fungi (PSF) increased after DFAA compared to the control system (CS), and PSF has lower resistance but higher resilience to DFAA than PSB. Significant differences can be found on the genera Pseudomonas, Arthrobacter, and Penicillium, and the P metabolism-related gene K21195 under DFAA. The DFAA also led to unstable and dispersed structure of the farmland ecosystem network related to P cycling, with persistent influences until the mature stage of summer maize. This study provides references for understanding the micro process on P cycling under DFAA in topsoil, which could further guide the DFAA regulations.

The current evolution of “open neuroscience” has led to an increased amount of research on large-scale electroencephalography (EEG) applications, resulting in large quantities of accumulated EEG data. The batch sharing and processing of these massive EEG data play an important role in EEG studies within or across laboratories and result in an increasing requirement for a standard data file structure for existing EEG data. In this work, a new and more flexible data structure, named the Standard EEG Data Structure (SEDS), was proposed to meet the needs of both small-scale EEG data batch processing in single-site studies and large-scale EEG data sharing and analysis in single-/multisite studies (especially on cloud platforms). Furthermore, two versions (MATLAB and Docker versions) of the EEG Datafile Restructuring Toolbox (DRT) were developed to restructure EEG data files according to the SEDS. The DRT GUI (MATLAB version) dramatically reduces the time required for novice researchers, while the DRT (Docker version) is more efficient for experienced researchers. All materials including SEDS documents, tools, example datasets, etc., are available on the WeBrain website (https://webrain.uestc.edu.cn/) and Wiki (https://github.com/WeCloudHub/DRT). We hope that these two user-friendly toolboxes can make the relatively novel SEDS easier to collaboratively study, especially for applications in large-scale EEG studies.

The dynamics of plants’ carbon and water use efficiency and their responses to drought are crucial to the sustainable development of arid and semi-arid environments. This study used trend analysis and partial correlation analysis to examine the carbon use efficiency (CUE) and water use efficiency (WUE) of Inner Mongolia’s vegetation from 2001 to 2020. MODIS data for gross primary productivity (GPP), net primary productivity (NPP), potential evapotranspiration (PET), evapotranspiration (ET), drought severity index (DSI), and plant type were used. Altered trends were observed for drought during 2001–2020 in the study area. The results revealed that 98.17% of the research area’s drought trend was from dry to wet and 1.83% was from wet to dry, and the regions with decreased drought regions were broadly dispersed. In 2001–2020, CUE in Inner Mongolia declined by 0.1%·year−1, whereas WUE reduced by 0.008 g C·mm−1·m−2·year−1, but the total change was not significant. CUE decreased from west to east, whereas WUE increased from southwest to northeast. DSI and CUE had the highest negative connection, accounting for 97.96% of the watershed area, and 71.6% passed the significance test. The correlation coefficients of DSI and WUE were spatially opposite to those of CUE and DSI. In total, 54.21% of the vegetation cover exhibited a negative connection with DSI. The CUE and WUE of different vegetation types in Inner Mongolia were negatively correlated with the DSI index except for grasslands (GRA). Drought in Inner Mongolia mostly influenced the CUE of different plant types, which had a higher negative correlation than WUE. The study’s findings can inform climate change research on Inner Mongolia’s carbon and water cycles.

Accumulated evidence reminds one that abrupt transitions between dry and wet spells, though attracting less attention, have harmful influences upon global continents as extensively investigated droughts and floods. This study attempts to incorporate dryness–wetness transitions into the current hazard assessment framework through bivariate frequency analysis and causal attribution from a teleconnection perspective. In the study, regional dry and wet conditions are monitored using the multivariate standardized drought index (MSDI) which facilitates the integrated evaluation of water deficits/surplus from a combined viewpoint of precipitation (largely denoting the received atmospheric water) and runoff (representing an important source of surface water). On such a basis, a copula-based method is subsequently utilized to calculate joint return periods of dryness–wetness combinations in three (i.e., moderate, severe and extreme) severity scenarios. The changing frequency of diverse dryness–wetness combinations is also estimated under a changing climate using a 25-year time window. Furthermore, the cross-wavelet transform is applied to attribute variations in dry and wet conditions to large-scale climate indices, which benefits the early warning of dryness–wetness combinations by providing predictive information. A case study conducted during 1952–2010 in the Huai River basin (HRB)—a typical climatic transition zone in China—shows that the HRB is subject to prolonged dryness with the highest frequency, followed by the abrupt transition from dryness to wetness. Spatially, abrupt dryness–wetness transitions are more likely to occur in the southern and central parts of the HRB than in the rest of the proportion. The past half-century has witnessed the dominantly higher frequency of occurrence of dryness–wetness combinations under three severity scenarios. In particular, the occurrence of the continued dry/wetness escalates more rapidly than transition events under climate change. Moreover, a preliminary attribution analysis discloses the link of the dry and wet conditions in the HRB with climate indices, such as the El Niño southern oscillation, the Pacific decadal oscillation and the Arctic oscillation, as well as sunspot activities. The results of the study enrich the current atlas of water-related hazards, which may benefit more effective hazard mitigation and adaptation.

The current evolution of ‘cloud neuroscience’ leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Isothermal compression behaviors of as-extruded AA 2055 alloy (T6 state) were studied at temperature of 320, 380, 440 and 500 °C with strain rate of 0.001, 0.01, 0.1 and 1 s−1 by a Gleeble-3800 testing machine. A modified Johnson–Cook model fitted by polynomial and power-exponential functions were established to describe the flow stress of the alloy. The constitutive models fitted by higher-order polynomials were more accurate than the ones fitted by second-order polynomial and power-exponential functions. The constitutive model fitted by a fourth-order polynomial was chosen for the optimal constitutive model in order to balance the prediction accuracy and model complexity. The modified Johnson–Cook constitutive model could predict the flow stress well, especially in high-temperature zone (around 500 °C) and low-temperature zone (around 320 °C). The dynamic precipitation and dissolution of the T1 phase during hot compression were discussed. The unusual dynamic precipitation of the T2 phase was investigated during hot compression by XRD and TEM. The massive dense fine precipitates effectively pinned dislocations or subgrain boundaries to accelerate DRV but suppressed DRX, leading to a low frequency of HAGBs in compressed samples.

Understanding the adaptative behaviors of dominant desert-steppe species under varying levels of grazing-related environmental degradation is an important step in elucidating the survival strategies and environmental response mechanisms of desert plants. In this study, the Xilamuren desert steppe in Northwest China was used as a case study location to systematically investigate the adaptative traits of three dominant plant species (Stipa breviflora, Cleistogenes squarrosa, and Convolvulus ammannii) from the perspectives of plant morphology, leaf structure, and photosynthesis. As degradation intensified, the adaptive changes in the three dominant plant species manifested in the following ways: 1) adjustment of substance allocation strategy, such that biomass was preferentially concentrated in the leaves and roots for photosynthesis and resource acquisition; 2) miniaturization, including reduced plant height, reduced leaf size, and leaf narrowing; 3) thickening of the leaves, epidermis, and cuticle to increase water storage and resist illumination and temperature elevation and thickening of the midrib veins to increase water and nutrient transportation; and 4) increased water-use efficiency in Stipa breviflora, which showed a stronger capacity to adjust to water stress than Cleistogenes squarrosa and Convolvulus ammannii. The results suggested that the morphological, structural, and functional adjustments of desert plants were related to the development of drought-resistance mechanisms through water-use strategies, including increased water absorption and water-use efficiency and decreased water consumption. These results enrich the existing knowledge of the environmental adaptation mechanisms of dominant plants in desert steppes, providing a scientific basis for the restoration of desert steppe ecologies and the formulation of suitable grazing policies.

The canonical correlation analysis (CCA)-based frequency recognition method is one of the widely used methods in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), and several extended version were proposed in the past decade. However, these methods only adopt the maximal correlation coefficient provided by the CCA and discard other coefficients. This operation may lead to the loss of discriminative information that exists in the discarded coefficients. In the current study, we proposed to fuse all the correlation coefficients of the CCA with a nonlinear weighting function when performing frequency recognition with CCA method, termed as FoCCA. Evaluated on a benchmark dataset of thirty-five subjects, the experimental results demonstrated that the classification accuracy and information transfer rate (ITR) of FoCCA are significantly higher than those of the standard CCA at various time windows. Fusing correlation coefficients could be a new strategy to improve the performance of other extended CCA methods, which holds the potential to implement high-performance SSVEP-based BCI systems in the future.

Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the μ and β rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.

Action real-time strategy gaming (ARSG)—a major genre of action video gaming (AVG)—has both action and strategy elements. ARSG requires attention, visual working memory (VWM), sensorimotor skills, team cooperation, and strategy-making abilities, thus offering promising insights into the learning-induced plasticity. However, it is yet unknown whether the ARSG experience is related to the development of VWM capacity. Using both behavioral and event-related potential (ERP) measurements, this study tested whether ARSG experts had larger VWM capacity than non-experts in a change detection task. The behavioral results showed that ARSG experts had higher accuracy and larger VWM capacity than non-experts. In addition, the ERP results revealed that the difference wave of the contralateral delay activity (CDA) component (size 4–size 2) elicited by experts was significantly larger than that of non-experts, suggesting that the VWM capacity was higher in experts than in non-experts. Thus, the findings suggested that prolonged ARSG experience is correlative with the enhancement of VWM.

Action real-time strategy gaming (ARSG) is a cognitively demanding task which requires attention, sensorimotor skills, team cooperation, and strategy-making abilities. A recent study found that ARSG experts had superior visual selective attention (VSA) for detecting the location of a moving object that could appear in one of 24 different peripheral locations (Qiu et al., 2018), suggesting that ARSG experience is related to improvements in the spatial component of VSA. However, the influence of ARSG experience on the temporal component of VSA—the detection of an item among a sequence of items presented consecutively and quickly at a single location—still remains understudied. Using behavioral and electrophysiological measures, this study examined whether ARSG experts had superior temporal VSA performance compared to non-experts in an attentional blink (AB) task, which is typically used to examine temporal VSA. The results showed that the experts outperformed the non-experts in their detection rates of targets. Furthermore, compared to the non-experts, the experts had faster information processing as indicated by earlier P3 peak latencies in an AB period, more attentional resources distributed to targets as indicated by stronger P3 amplitudes, and a more flexible deployment of attentional resources. These findings suggest that experts were less prone to the AB effect. Thus, long-term ARSG experience is related to improvements in temporal VSA. The current findings support the benefit of video gaming experience on the development of VSA.

AimThis study investigated the neural mechanisms of brain-wave music on sleep quality.BackgroundSleep disorders are a common health problem in our society and may result in fatigue, depression, and problems in daytime functioning. Previous studies have shown that brain-wave music generated from electroencephalography (EEG) signals could emotionally affect our nervous system and have positive effects on sleep. However, the neural mechanisms of brain-wave music on the quality of sleep need to be clarified.MethodsA total of 33 young participants were recruited and randomly divided into three groups. The participants listened to rapid eye movement (REM) brain-wave music (Group 1: 13 subjects), slow-wave sleep (SWS) brain-wave music (Group 2: 11 subjects), or white noise (WN) (Control Group: 9 subjects) for 20 min before bedtime for 6 days. EEG and other physiological signals were recorded by polysomnography.ResultsWe found that the sleep efficiency increased in the SWS group but decreased in REM and WN groups. The sleep efficiency in the SWS group was ameliorated [t(10) = −1.943, p = 0.076]. In the EEG power spectral density analysis, the delta power spectral density in the REM group and in the control group increased, while that in the SWS group decreased [F(2,31) = 7.909, p = 0.005]. In the network analysis, the functional connectivity (FC), assessed with Pearson correlation coefficients, showed that the connectivity strength decreased [t(10) = 1.969, p = 0.073] between the left frontal lobe (F3) and left parietal lobe (C3) in the SWS group. In addition, there was a negative correlation between the FC of the left frontal lobe and the left parietal lobe and sleep latency in the SWS group (r = −0.527, p = 0.064).ConclusionSlow-wave sleep brain-wave music may have a positive effect on sleep quality, while REM brain-wave music or WN may not have a positive effect. Furthermore, better sleep quality might be caused by a decrease in the power spectral density of the delta band of EEG and an increase in the FC between the left frontal lobe and the left parietal lobe. SWS brain-wave music could be a safe and inexpensive method for clinical use if confirmed by more data.

The purpose of this paper is to obtain the pore distribution of asphalt mixture accurately by nondestructive technology. Specimens prepared with four gradations of asphalt mixtures were scanned using X-ray computed tomography (CT) which was used to measure air void sizes at different depths within specimens. The air void distributions of obtained CT images were analyzed using ring blocking segmentation combining Otsu’s method, which provided an accurate estimate of air voids in asphalt mixtures. The image processing results showed that air void distribution was not uniform in the specimens; higher air void concentrations were found at the top and bottom of the specimen, and lower, in the rest of the sample depth. The air void sizes of SUP13 and AC13 are mainly distributed between 0.15 to 0.2 mm, while PA13 and SMA13 are 0.4 to 0.65 mm and 0.4 to 0.7 mm, respectively. It is believed that the CT pictures processed by the ring blocking segmentation combining Otsu’s method is feasible and rational to capture the air voids size and content of asphalt mixtures.+

Droughts have significantly damaged the environment of the Inner Mongolia Autonomous Region, China. In this study, the region was divided into two subregions. Soil moisture was used as the basic parameter to analyze the characteristics of agricultural droughts. Based on a geographical detector, the spatial stratified heterogeneity in different seasons was discussed. Moreover, the copula joint functions of characteristics and dominant factors of agricultural droughts were constructed. Based on the Soil Moisture Anomaly Percentage Index (SMAPI), the results demonstrate that the climate tendency rate of droughts in the summer and in spring in Subregion I shows an increasing trend, while it decreases in the autumn and winter. In Subregion II, the climate tendency rate of droughts in different seasons has no significant change. Through geographical detection, the single factor detection illustrates that temperature and Precipitation Conversion Efficiency (PCE) show the highest explanatory power in different subregions. The interactive detection also demonstrates the explanatory powers of the combination of the PCE and temperature, respectively. The t-copula function describes the correlation coefficients of the SMAPI with the PCE and temperature, with the optimal tail dependence. In short, agricultural droughts are most significantly affected by temperature and the PCE, and their balance has a significant impact on agricultural droughts.

Abstract Background The aim of this retrospective study is to identify epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer patients and to compare the long-term postoperative outcomes in different EGFR-TKI-targeted therapy effects between the different EGFR mutation groups. Methods A total of 2094 postoperative non-small cell lung cancer (NSCLC) patients with EGFR gene detection were collected in the Department of Pathology in the Cancer Hospital Chinese Academy of Medical Sciences from January 2003 to January 2014. Three hundred sixty-three patients were treated with EGFR tyrosine kinase inhibitor (TKI) after surgery: 184 harbored the exon 19 deletion mutation and 179 cases carried the exon 21 L858R point mutation. The end points included progression-free survival (PFS), overall survival (OS), and the response rate. Results OS was increased in the EGFR exon 19 deletion group compared with the exon 21 L858R point mutation group (92 vs. 65 months; P

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.

The changes in sap flow of Salix psammophila growing on a gentle slope (lower slope, P1), a middle slope (P2), and an upper slope (P3), and the response of sap flow to meteorological factors at the different slope positions were studied using the continuous and synchronized observations, the instrument were wrapped stem flowmeter EMS 62 sap-flow heat-balance-based system and the LSI-LASTEM automatic weather station. The results revealed that the soil moisture content was the highest and the growth conditions of Salix psammophila were the best at P1, followed by P2. At P3, however, although good apical dominance was observed, the proportion of dead branches was the highest. Furthermore, the daily variation patterns of sap flow on the three slopes presented as multi-peak bell-shaped curves. The daily accumulation changes in sap flow showed a trend of P1 > P3 > P2, and within the same diameter range, the sap flow at P1 was significantly different from that at P2 and P3, whereas the sap flow at P2 and P3 did not vary significantly. All the three slopes showed a significant and positive correlation with photosynthetically active radiation, atmospheric temperature, and vapor pressure difference, and a significant and negative correlation with relative humidity; however, the degrees of correlation varied slightly. The stepwise regression analysis showed that, at different slopes, different variables were selected for different branch diameters, but photosynthetically active radiation and atmospheric temperature played dominant roles on all slopes. This study reveals the sap flow pattern of Salix psammophila on different slopes and its response mechanism to meteorological factors, which was essential for understanding the restoration ability, physiological adaptability, and ecosystem stability of Salix psammophila communities.

Electronic-sports (e-sports) is a form of organized, online, multiplayer video game competition, which requires both action skills and the ability and process of forming and adapting a strategy (referred to as strategization hereafter) to achieve goals. Over the past few decades, research has shown that video gaming experience has an important impact on the plasticity of the sensorimotor, attentional, and executive brain areas. However, little research has examined the relationship between e-sports experience and the plasticity of brain networks that are related to strategization. Using resting-state fMRI data and the local functional connectivity density (lFCD) analysis, this study investigated the relationship between e-sports experience (League of Legends [LOL] in this study) and brain plasticity by comparing between top-ranking LOL players and lower-ranking (yet experienced) LOL players. Results showed that the top-ranking LOL players had superior local functional integration in the executive areas compared to lower-ranking players. Furthermore, the top-ranking players had higher lFCD in the default mode areas, which have been found related to various subprocesses (e.g., memory and planning) essential for strategization. Finally, the top-ranking players’ lFCD was related to their LOL expertise rank level, as indicated by a comprehensive score assigned by the gaming software based on players’ gaming experience and expertise. Thus, the result showed that the local functional connectivity in central executive and default mode brain areas was enhanced in the top-ranking e-sports players, suggesting that e-sports experience is related to the plasticity of the central executive and default mode areas.

Abstract Background Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear. Methods We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows. Results The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows. Conclusions Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.