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Time-resolved resonant inelastic X-ray scattering (RIXS) is one of the developing techniques enabled by the advent of X-ray free electron laser (FEL). It is important to evaluate how the FEL jitter, which is inherent in the self-amplified spontaneous emission process, influences the RIXS measurement. Here, we use a microchannel plate (MCP) based Timepix soft X-ray detector to conduct a time-resolved RIXS measurement at the Ti L3-edge on a charge-density-wave material TiSe2. The fast parallel Timepix readout and single photon sensitivity enable pulse-by-pulse data acquisition and analysis. Due to the FEL jitter, low detection efficiency of spectrometer, and low quantum yield of RIXS process, we find that less than 2% of the X-ray FEL pulses produce signals, preventing acquiring sufficient data statistics while maintaining temporal and energy resolution in this measurement. These limitations can be mitigated by using future X-ray FELs with high repetition rates, approaching MHz such as the European XFEL in Germany and LCLS-II in the USA, as well as by utilizing advanced detectors, such as the prototype used in this study.

Microorganisms play key roles in ecosystem processes and biogeochemical cycling, however, the relationship between soil microbial taxa diversity and their function in natural ecosystems is largely unknown. To determine how soil bacteria community and function are linked from the local to regional scale, we studied soil bacteria community composition, potential function and environmental conditions in natural and mature broadleaf forests along a latitudinal gradient in China, using the Illumina 16S rRNA sequencing and GeoChip technologies. The results showed strong biogeographic endemism pattern in soil bacteria were existed, and the spatial distance and climatic variables were the key controlling factors for this pattern. Therefore, dispersal limitation and environmental selection may represent two key processes in generating and maintaining the soil bacterial biogeographic pattern. By contrast, the soil bacterial potential function is highly convergent along the latitudinal gradient and there were highly differing bacterial community compositions, and the soil chemistry may include the main factors active in shaping the soil bacterial potential function. Therefore, the soil bacterial potential function may be affected by local gradients in resource availability, and predicting soil bacterial potential function requires knowledge of abiotic and biotic environmental factors.

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research., (© 2024. The Author(s).)

In case of mitigate the reliance of aviation users on the Global Navigation Satellite System (GNSS) in an increasingly interference-prone environment, utilizing opportunistic signals from Low-Earth Orbit (LEO) for navigation and positioning is an alternative approach. However, LEO satellite SOPs are not intended for navigation. Therefore, it is necessary to design methods to extract navigation observables from these signals. In this paper, we proposed a lightweight deep learning model with a two-branch structure called CLOCFC, designed to extract navigation observables. Furthermore, we have established a low Earth orbit satellite signal dataset by using ORBCOMM constellation signals as the input to the model and Doppler frequency as the label for the model. The results show that CLOCFC, as a lightweight model, demonstrates a significantly faster convergence rate and higher accuracy in navigation observables extraction compared to other models (ResNet, Swin Transformer, and Clo Transformer). In CLOCFC, we introduce the CFC module, a kind of Liquid Neural Network, to enhance the information acquisition capability through the spatiotemporal information in the data sequence. Finally, we have also conducted extensive experiments with the Doppler shift extraction of LEO satellites as an example, under various noise and resolution conditions, demonstrating the superiority of the CLOCFC., (© 2024. The Author(s).)

With the rapid advancement of drone technology and the growing applications in the field of drone engineering, the demand for precise and efficient path planning in complex and dynamic environments has become increasingly important. Traditional algorithms struggle with complex terrain, obstacles, and weather changes, often falling into local optima. This study introduces an Improved Crown Porcupine Optimizer (ICPO) for drone path planning, which enables drones to better avoid obstacles, optimize flight paths, and reduce energy consumption. Inspired by porcupines' defense mechanisms, a visuo-auditory synergy perspective is adopted, improving early convergence by balancing visual and auditory defenses. The study also employs a good point set population initialization strategy to enhance diversity and eliminates the traditional population reduction mechanism. To avoid local optima in later stages, a novel periodic retreat strategy inspired by porcupines' precise defenses is introduced for better position updates. Analysis on the IEEE CEC2022 test set shows that ICPO almost reaches the optimal value, demonstrating robustness and stability. In complex mountainous terrain, ICPO achieved optimal values of 778.1775 and 954.0118; in urban terrain, 366.2789 and 910.1682 and ranked first among the compared algorithms, proving its effectiveness and reliability in drone delivery path planning. Looking ahead, the ICPO will provide greater efficiency and safety for drone path planning in navigating complex environments., (© 2024. The Author(s).)

This study aims to analyze the risk factors associated with delayed postoperative bleeding (DPPB) following colorectal polyp surgery, develop a dynamic nomogram and evaluate the model efficacy, provide a reference for clinicians to identify the patients at high risk of DPPB. Retrospective study was done on patients who underwent endoscopic colorectal polypectomy at the First Hospital of Lanzhou University from January 2020 to March 2023. Differences between the group with and without DPPB were compared, and independent risk factors for DPPB occurrence were identified through univariate analysis and combination LASSO and logistic regression. A dynamic nomogram was constructed based on multiple logistic regression to predict DPPB following colorectal polyp surgery. Model evaluation included receiver operating characteristic (ROC), Calibration curve, Decision curve analysis (DCA). DPPB occurred in 38 of the 1544 patients included. multivariate analysis showed that direct oral anticoagulants (DOACs), polyp location in the right hemi colon, polyp diameter, drink, and prophylactic hemoclips were the independent risk factors for DPPB and dynamic nomogram were established. Model validation indicated area under the ROC curve values of 0.936, 0.796, and 0.865 for the training set, validation set, and full set, respectively. The calibration curve demonstrated a strong alignment between the predictions of the column-line diagram model and actual observations. The decision curve analysis (DCA) displayed a significant net clinical benefit across the threshold probability range of 0-100%. The dynamic nomogram aids clinicians in identifying high-risk patients, enabling personalized diagnosis and treatment., (© 2024. The Author(s).)

The cross beam of a mining linear vibrating screen is prone to cracking under long-term cyclic load. In order to accurately predict the fatigue life of the cracked cross beam, a coupled analysis method of vibration and crack propagation is proposed. A 2D dynamic model of the vibrating screen is established based on the finite element method, which is verified by the vibration test platform. The cracked Euler beam element is used to model the cracked cross beam. The effects of crack depth, amplitude of excitation force, frequency of excitation force, and crack location on the crack-tip stress intensity factor of the cracked cross beam are investigated in detail. In addition, an iterative method is proposed on the basis of the Paris model. The residual service life of the beam under different frequencies of excitation force, amplitudes of excitation force, spring stiffness, crack positions, and degrees of stiffness imbalance are discussed. The results demonstrate that the fatigue life of the beam increases as the frequency of excitation force and spring stiffness increase. The increase of the amplitude of excitation force and spring permanent deformation reduces the fatigue life. The conclusion obtained provide some theoretical guidance for the design and routine maintenance of mining linear vibrating screens., (© 2024. The Author(s).)

We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed in the training cohort and subjected to transfer learning, and then the test cohort was validated for generalization. The features extracted from the visualization model were used for model interpretation. After migration learning, the area under the receiver operating characteristic curve for the deep learning-based pathomics model in the test cohort was 0.860 (95% CI 0.752-0.969), with good agreement between the migration training cohort and the test cohort in predicting recurrence, and the predicted values matched well with the observed values, with p values of 0.667766 and 0.140233 for the Hosmer-Lemeshow test, respectively. The good clinical application was observed using a decision curve analysis method. We developed a deep learning-based pathomics model showed promising performance in predicting recurrence within one year in NMIBC patients. Including 10 state prediction NMIBC recurrence group pathology features be visualized, which may be used to facilitate personalized management of NMIBC patients to avoid ineffective or unnecessary treatment for the benefit of patients., (© 2024. The Author(s).)

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset. A novel evaluation approach is introduced in this research based on k-fold cross-validation and statistical t-tests to assess the fairness of ML algorithms. This approach was exercised across five benchmark datasets using six classical ML algorithms. Considering four fair ML definitions guided by the current literature, our analysis showed that the same dataset generates a fair outcome for one ML algorithm but an unfair result for another. Such an observation reveals complex, context-dependent fairness issues in ML, complicated further by the varied operational mechanisms of the underlying ML models. Our proposed approach enables researchers to check whether deploying any ML algorithms against a protected attribute within datasets is fair. We also discuss the broader implications of the proposed approach, highlighting a notable variability in its fairness outcomes. Our discussion underscores the need for adaptable fairness definitions and the exploration of methods to enhance the fairness of ensemble approaches, aiming to advance fair ML practices and ensure equitable AI deployment across societal sectors., (© 2024. The Author(s).)

This study aimed to evaluate the expression and clinical significance of budding uninhibited by benzimidazole 1 (BUB1) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) in endometrial carcinoma (EC). BUB1 and BUBIB expressions were evaluated by bioinformatics. Protein expression, clinical features, prognosis and immune cell infiltration were explored in 20 EC tumors. siRNA was used to evaluate BUB1 and BUBIB function in EC cells. BUB1 and BUBIB were highly expressed in 26 cancers. BUB1 was associated with overall survival (OS) in eight cancers and disease-free survival in ten; BUB1B was associated with OS in nine cancers and DFS in eleven. BUB1 and BUBIB exhibited high frequencies of gene changes (mainly mutations, > 5%) in cancer. BUB1 was negatively correlated and BUB1B was positively correlated with cancer-associated fibroblasts and endothelial cell infiltration. BUB1 and BUBIB knockdown decreased migration and invasion in EC cells. High BUB1 expression correlated with tumor malignant phenotypes (P < 0.05). High BUB1 mRNA expression reduced OS (P = 0.00036) and recurrence-free survival (P = 0.0011). High BUB1B mRNA expression reduced OS (P = 0.0024). BUB1/BUB1B correlated with activated CD8 + T and CD4 + T cell infiltration. BUB1 and BUBIB are highly expressed and correlated with clinicopathological characteristics in EC. BUB1 and BUBIB are potential prognosis markers and immunotherapy targets., (© 2024. The Author(s).)

Planting potatoes through plastic film with incomplete or excessive soil coverage over seed holes significantly impairs yield. Existing covering methods rely solely on mechanical transmissions, leading to bulky and inconsistent soil coverage of the seed holes. This paper reports an innovative method using a precise soil covering device based on the YOLOv4-tiny real-time object detection system to accurately identify potato plastic film holes and cover them with soil. The system adopts a lightweight and high-precision detection scheme, balancing increased network depth with reduced computation. It can identify holes in the plastic film in real-time and with high accuracy. To verify the effectiveness of YOLOv4-tiny real-time object detection system, a precise soil covering device based on this detection system has been designed and applied to a double crank multi-rod hill-drop planter. Field tests revealed that the system's average accuracy rate for detecting holes is approximately 98%, with an average processing time of 15.15 ms per frame. This fast and accurate performance, combined with the device's robust real-time operation and anti-interference capabilities during soil covering, effectively reduce the problems of soil cover omission and repeated covering caused by existing mechanical transmission methods. The findings reported in this paper are valuable for the development of autonomous potato plastic film precise soil covering devices for commercial use., (© 2024. The Author(s).)

To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions., (© 2024. The Author(s).)

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance., (© 2024. The Author(s).)

In prior investigations, a correlation was established between patient outcomes in locally advanced non-small cell lung cancer (LA-NSCLC) following thoracic irradiation and parameters, such as pre/post-treatment neutrophil-to-lymphocyte ratio (NLR) and NLR change (ΔNLR). However, these parameters could potentially be influenced by radiation-related variables, such as gross tumor volume (GTV). The primary aim of this study was to elucidate the factors impacting post-treatment NLR and ΔNLR and to further assess their prognostic relevance. In this retrospective study, a cohort of 188 LA-NSCLC patients who underwent thoracic radiation between 2012 and 2017 was assessed. The calculation of pre/post-treatment NLR involved the use of absolute neutrophil and lymphocyte counts. ΔNLR was defined as the difference between post- and pre-treatment NLR values. To assess the relationships between various variables and overall survival (OS), local progression-free survival (LPFS), and distant metastasis-free survival (DMFS), the Kaplan-Meier technique and Cox proportional hazards regression were employed. Additionally, Spearman's rank correlation analysis was carried out to investigate correlations between the variables. The analysis revealed that both post-treatment NLR (r = 0.315, P < 0.001) and ΔNLR (r = 0.156, P = 0.032) were associated with GTV. However, OS, LPFS, and DMFS were not independently correlated with pre/post-treatment NLR. ΔNLR, on the other hand, exhibited independent associations with OS and DMFS (HR = 1.054, P = 0.020, and P = 0.046, respectively). Elevated ΔNLR values were linked to poorer OS (P = 0.023) and DMFS (P = 0.018) in the Kaplan-Meier analysis. Furthermore, when stratifying by GTV, a higher ΔNLR remained to be associated with worse OS and DMFS (P = 0.047 and P = 0.035, respectively) in the GTV ≤ 67.41 cm 3 group, and in the GTV > 67.41 cm 3 group (P = 0.028 and P = 0.042, respectively), highlighting ΔNLR as the sole independent predictive factor for survival and metastasis, irrespective of GTV., (© 2024. The Author(s).)

Evidence for seed transmission of phytoplasmas has grown in several pathosystems including coconut (Cocos nucifera). Bogia coconut syndrome (BCS) is a disease associated with the lethal yellowing syndrome associated with the presence of 'Candidatus Phytoplasma noviguineense' that affects coconut, betel nut (Areca catechu) and bananas (Musa spp.) in Papua New Guinea. Coconut and betel nut drupes were sampled from BCS-infected areas in Papua New Guinea, dissected, the extracted nucleic acid was used in polymerase chain reaction (PCR), and loop mediated isothermal amplification (LAMP) used to check for presence of phytoplasma DNA. In a second study, drupes of both plant species were collected from multiple field sites and grown in insect-proof cages. Leaf samples taken at 6 months were also tested with PCR and LAMP. The studies of dissected coconut drupes detected phytoplasma DNA in several tissues including the embryo. Drupes from betel nut tested negative. Among the seedlings, evidence of possible seed transmission was found in both plant species. The results demonstrate the presence of 'Ca. P. noviguineense' in coconut drupes and seedlings, and in seedlings of betel nut; factors that need to be considered in ongoing management and containment efforts., (© 2024. The Author(s).)

The syncytiotrophoblast is a multinucleated structure that arises from fusion of mononucleated cytotrophoblasts, to sheath the placental villi and regulate transport across the maternal-fetal interface. Here, we ask whether the dynamic mechanical forces that must arise during villous development might influence fusion, and explore this question using in vitro choriocarcinoma trophoblast models. We demonstrate that mechanical stress patterns arise around sites of localized fusion in cell monolayers, in patterns that match computational predictions of villous morphogenesis. We then externally apply these mechanical stress patterns to cell monolayers and demonstrate that equibiaxial compressive stresses (but not uniaxial or equibiaxial tensile stresses) enhance expression of the syndecan-1 and loss of E-cadherin as markers of fusion. These findings suggest that the mechanical stresses that contribute towards sculpting the placental villi may also impact fusion in the developing tissue. We then extend this concept towards 3D cultures and demonstrate that fusion can be enhanced by applying low isometric compressive stresses to spheroid models, even in the absence of an inducing agent. These results indicate that mechanical stimulation is a potent activator of cellular fusion, suggesting novel avenues to improve experimental reproductive modelling, placental tissue engineering, and understanding disorders of pregnancy development., (© 2024. The Author(s).)

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil., (© 2024. The Author(s).)

Acute thrombosis secondary to atherosclerotic plaque rupture is the main cause of acute cardiac and cerebral ischemia. An animal model of unstable atherosclerotic plaques is highly important for investigating the mechanism of plaque rupture and thrombosis. However, current animal models involve complex operations, are costly, and have plaque morphologies that are different from those of humans. We aimed to establish a simple animal model of vulnerable plaques similar to those of humans. Rabbits were randomly divided into three groups. Group A was given a normal formula diet for 13 weeks. Group C underwent surgery on the intima of the right carotid artery with - 80 °C cryofluid-induced injury after 1 week of a high-fat diet and further feeding a 12-week high-fat diet. Group B underwent the same procedure as Group C but without the - 80 °C cryofluid. Serum lipid levels were detected via ELISA. The plaque morphology, stability and degree of stenosis were evaluated through hematoxylin-eosin (HE) staining, Masson trichrome staining, Elastica van Gieson staining (EVG), and oil red O staining. Macrophages and inflammatory factors in the plaques were assessed via immunohistochemical analysis. The serum low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and total cholesterol (TC) levels in groups B and C were significantly greater than those in group A. No plaque formation was observed in group A. The plaques in group B were very small. In group C, obvious plaques were observed in the blood vessels, and the plaques exhibited a thin fibrous cap, a large lipid core, and partially visible neovascularization, which is consistent with the characteristics of vulnerable plaques. In the plaques of group C, a large number of macrophages were present, and matrix metalloproteinase 9 (MMP-9) and lectin-like oxidized LDL receptor 1 (LOX-1) were abundantly expressed. We successfully established a rabbit model of vulnerable carotid plaque similar to that of humans through the combination of cryofluid-induced endothelial injury and a high-fat diet, which is feasible and cost effective., (© 2024. The Author(s).)

The Qinghai-Tibet Plateau belongs to the area of extremely fragile environment and sensitive to human activities. In recent years, more and more human interference has been detected in this area. In this study, 128 surface soil samples were collected from the Sabao Chaqu watershed of the Tuotuo river at the source of the Yangtze River on the Qinghai-Tibet Plateau. The soil pollution status and spatial distribution characteristics of Cd, Hg, As, Cu, Pb, Cr, Zn and Ni were evaluated by soil accumulation index, enrichment factor, pollution index and geographical detector. The results showed that the average contents of As, Cd, Pb and Zn in the study area were 1.2-3.64 times higher than soil background values of Tibet, while the contents of Hg, Cr, Cu and Ni were lower than the background values, while the average content of As was higher than the soil pollution risk screening value (GB15618-2018), and the pollution index showed that As was in a low pollution state, while the other 7 heavy metals were in a safe state. There were significant differences in the spatial distribution of 8 heavy metals and there was a significant correlation with soil properties and distance factors. Factor detection showed that natural factors had the strongest explanatory power to the contents of As, Cd, Cr, Cu and Ni, distance from the lake and soil Sc content had the strongest explanatory power to Hg content, and anthropogenic factors had the strongest explanatory power to Pb content. Interaction detection revealed that the q values of the strongest interaction explanatory power for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 2.81, 4.30, 1.26, 2.47, 2.33, 1.59, 6.37, and 5.08 times higher than their strongest factor detection explanatory power, respectively. The interaction between anthropogenic factors and other factors has an important influence on the spatial differentiation of heavy metals in the study area. Risk detection showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were the highest in the subregions of MgO, TS, Sc, X 6 , X 13 , MgO, TN and X 4 , respectively. Comprehensive study shows that the spatial differentiation of As, Cd, Cr, Cu, Ni and Zn is mainly affected by natural factors, but there are also some anthropogenic factors, the spatial differentiation of Hg is affected by both natural factors and atmospheric deposition, and the spatial distribution characteristics of Pb are mainly affected by anthropogenic factors., (© 2024. The Author(s).)

This mediation analysis aimed to investigate the associations among areal bone mineral density, mobility-related brain atrophy, and specific gait patterns. A total of 595 participants from the Taizhou Imaging Study, who underwent both gait and bone mineral density measurements, were included in this cross-sectional analysis. We used a wearable gait tracking device to collect quantitative gait parameters and then summarized them into independent gait domains with factor analysis. Bone mineral density was measured in the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of brain regions related to mobility were computed using FreeSurfer. Lower bone mineral density was found to be associated with higher gait variability, especially at the site of the lumbar spine (β = 0.174, FDR = 0.001). Besides, higher gait variability was correlated with mobility-related brain atrophy, like the primary motor cortex (β = 0.147, FDR = 0.006), sensorimotor cortex (β = 0.153, FDR = 0.006), and entorhinal cortex (β = 0.106, FDR = 0.043). Bidirectional mediation analysis revealed that regional brain atrophy contributed to higher gait variability through the low lumbar spine bone mineral density (for the primary motor cortex, P = 0.018; for the sensorimotor cortex, P = 0.010) and the low lumbar spine bone mineral density contributed to higher gait variability through the primary motor and sensorimotor cortices (P = 0.026 and 0.010, respectively)., (© 2024. The Author(s).)

The human skin microbiome comprises diverse populations that differ temporally between body sites and individuals. The virome is a less studied component of the skin microbiome and the study of bacteriophages is required to increase knowledge of the modulation and stability of bacterial communities. Staphylococcus species are among the most abundant colonisers of skin and are associated with both health and disease yet the bacteriophages infecting the most abundant species on skin are less well studied. Here, we report the isolation and genome sequencing of 40 bacteriophages from human skin swabs that infect coagulase-negative Staphylococcus (CoNS) species, which extends our knowledge of phage diversity. Six genetic clusters of phages were identified with two clusters representing novel phages, one of which we characterise and name Alsa phage. We identified that Alsa phages have a greater ability to infect the species S. hominis that was otherwise infected less than other CoNS species by the isolated phages, indicating an undescribed barrier to phage infection that could be in part due to numerous restriction-modification systems. The extended diversity of Staphylococcus phages here enables further research to define their contribution to skin microbiome research and the mechanisms that limit phage infection., (© 2024. The Author(s).)

Both regular flow and infrequent outburst floods shape the mountain landscape, but their relative contributions have been widely debated, in part due to the paucity of quantitative data on historical outburst floods. In June 2000, an outburst flood was triggered by a landslide-dam failure in a rapidly exhumed region of the Eastern Himalaya. To investigate the role of this kind outburst flood on landscape evolution, we employ topographic differencing, satellite imagery, and 2D hydraulic simulations to quantify the equivalent erosion and deposition within ~ 80 km flood route downstream of the breach. The flood lasted for ~ 10 h, with a peak discharge of 10 5 m 3 /s, leading to average erosion of 10 m, and contributed ~ 1-2 × 10 3 times more sediment than over long-term mean fluvial processes. The flood produced extensive lateral erosion, which triggered a threefold widening of the valley floor and abundant subsequent landslides. The ubiquitous boulder bars deposited in the channel inhibited incision, and facilitated lateral erosion after the flood. The resulting channel configuration and extensive bank erosion continue to affect fluvial dynamics until the next catastrophic flood that remobilizes the boulders. Our quantitative findings highlight the profound importance of recurrent outburst floods for gorge development and landscape evolution in Eastern Himalaya., (© 2024. The Author(s).)

Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan-Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan-Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC., (© 2024. The Author(s).)

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application., (© 2024. The Author(s).)

Currently, the relationship between serum uric acid (SUA) and bone mineral density (BMD) in men remains controversial. This study aims to investigate the relationship between SUA and lumbar spine BMD in American men using data from the National Health and Nutrition Examination Survey (NHANES). A total of 6254 male subjects aged 12-80 years (mean age 35.52 ± 14.84 years) in the NHANES from 2011 to 2020 were analyzed. SUA was measured by DxC using the timed endpoint method, and lumbar spine BMD was measured by dual-energy X-ray absorptiometry (DXA). Multivariate linear regression models were used to explore the relationship between SUA and BMD by adjusting for age, race/Hispanic origin, drinking behavior, smoking behavior, physical activity, body mass index (BMI), poverty-to-income ratio (PIR), total protein, serum calcium, cholesterol, serum phosphorus, and blood urea nitrogen. After correcting for the above confounders, it was found that SUA was positively associated with lumbar spine BMD in the range of SUA < 5 mg/dL (β = 0.006 95% CI 0.003-0.009, P < 0.001), and BMD of individuals in the highest quartile of SUA was 0.020 g/cm 2 higher than those in the lowest quartile of SUA (β = 0.020 95% CI 0.008-0.032, P = 0.003). This study showed that SUA was positively correlated with lumbar spine BMD in American men within a certain range. This gives clinicians some insight into how to monitor SUA levels to predict BMD levels during adolescence when bone is urgently needed for growth and development and during old age when bone loss is rapid., (© 2024. The Author(s).)

Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler-Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively., (© 2024. The Author(s).)

Disulfidptosis is a condition where dysregulated NAPDH levels and abnormal accumulation of cystine and other disulfides occur in cells with high SLC7A11 expression under glucose deficiency. This disrupts normal formation of disulfide bonds among cytoskeletal proteins, leading to histone skeleton collapse and triggering cellular apoptosis. However, the correlation between disulfidptosis and immune responses in relation to glioblastoma survival rates and immunotherapy sensitivity remains understudied. Therefore, we utilized The Cancer Genome Atlas and The Chinese Glioma Genome Atlas to identify disulfidptosis-related immune checkpoint genes and established an overall survival (OS) prediction model comprising six genes: CD276, TNFRSF 14, TNFSF14, TNFSF4, CD40, and TNFRSF18, which could also be used for predicting immunotherapy sensitivity. We identified a cohort of glioblastoma patients classified as high-risk, which exhibited an upregulation of angiogenesis, extracellular matrix remodeling, and epithelial-mesenchymal transition as well as an immunosuppressive tumor microenvironment (TME) enriched with tumor associated macrophages, tumor associated neutrophils, CD8  +  T-cell exhaustion. Immunohistochemical staining of CD276 in 144 cases further validated its negative correlation with OS in glioma. Disulfidptosis has the potential to induce chronic inflammation and an immunosuppressive TME in glioblastoma., (© 2024. The Author(s).)

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky-Golay (S-G) filter is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the window length of the S-G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method., (© 2024. The Author(s).)

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations., (© 2024. The Author(s).)

What dataset features affect machine learning (ML) performance has primarily been unknown in the current literature. This study examines the impact of tabular datasets' different meta-level and statistical features on the performance of various ML algorithms. The three meta-level features this study considered are the dataset size, the number of attributes and the ratio between the positive (class 1) and negative (class 0) class instances. It considered four statistical features for each dataset: mean, standard deviation, skewness and kurtosis. After applying the required scaling, this study averaged (uniform and weighted) each dataset's different attributes to quantify its four statistical features. We analysed 200 open-access tabular datasets from the Kaggle (147) and UCI Machine Learning Repository (53) and developed ML classification models (through classification implementation and hyperparameter tuning) for each dataset. Then, this study developed multiple regression models to explore the impact of dataset features on ML performance. We found that kurtosis has a statistically significant negative effect on the accuracy of the three non-tree-based ML algorithms of the Support vector machine (SVM), Logistic regression (LR) and K-nearest neighbour (KNN) for their classical implementation with both uniform and weighted aggregations. This study observed similar findings in most cases for ML implementations through hyperparameter tuning, except for SVM with weighted aggregation. Meta-level and statistical features barely show any statistically significant impact on the accuracy of the two tree-based ML algorithms (Decision tree and Random forest), except for implementation through hyperparameter tuning for the weighted aggregation. When we excluded some datasets based on the imbalanced statistics and a significantly higher contribution of one attribute compared to others to the classification performance, we found a significant effect of the meta-level ratio feature and statistical mean and standard deviation features on SVM, LR and KNN accuracy in many cases. Our findings open a new research direction in understanding how dataset characteristics affect ML performance and will help researchers select appropriate ML algorithms for a possible optimal accuracy outcome., (© 2024. The Author(s).)

The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population's characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics., (© 2024. The Author(s).)

To address the problem of elastic contact discrepancies between a variable-diameter internal drive device and a non-continuous surface during the transition, caused by the vibrations resulting from elastic collision impact when the motion speed of the elastic body increases, the following steps were taken. First, we established models for elastic collision, impact, and vibration during the inter-stage transition to analyze how motion speed and preload affect the elastic contact characteristics between the two components. Subsequently, we employed the finite element method to further analyze the elastic contact state, using identical loads but varying motion speeds and radial preloads as boundary conditions. Finally, an experimental prototype was developed to validate the elastic contact state of the elastic body during the inter-stage transition. The results indicated that the amplitude of elastic body vibration increased with higher motion speed, while it decreased with higher radial preload. Therefore, it is necessary to adjust radial preload at different times to ensure effective elastic contact between the elastic body and the non-continuous surface during the inter-stage transition. This approach not only enhances deployment speed but also guarantees the stability of the inter-stage transition., (© 2023. The Author(s).)

This study aimed to construct a Ginsenoside Rb1-PLGA nano drug delivery system, optimize its preparation process, characterize and evaluate the resulting Ginsenoside Rb1-PLGA Nanoparticles (GRb1@PLGA@NPs). GRb1@PLGA@NPs were prepared using the emulsion solvent evaporation method. The optimal preparation process was determined using Plackett-Burman design combined with Box-Behnken experiments. Physical characterization and in vitro release studies were conducted. LC-MS/MS technique was employed to investigate the pharmacokinetic characteristics of GRb1 and GRb1@PLGA@NPs in rat plasma. The optimal preparation process yielded GRb1@PLGA@NPs with a particle size of 120.63 nm, polydispersity index (PDI) of 0.172, zeta potential of - 22.67 mV, encapsulation efficiency of 75%, and drug loading of 11%. In vitro release demonstrated sustained drug release. Compared to GRb1, GRb1@PLGA@NPs exhibited a shortened time to peak concentration by approximately 0.72-fold. The area under the plasma concentration-time curve significantly increased to 4.58-fold of GRb1. GRb1@PLGA@NPs formulated using the optimal process exhibited uniform distribution and stable quality, its relative oral bioavailability was significantly improved compared to free GRb1., (© 2023. The Author(s).)

Lung adenocarcinoma (LUAD) remains an incurable disease with a poor prognosis. This study aimed to explore neutrophil‑related genes (NRGs) and develop a prognostic signature for predicting the prognosis of LUAD. NRGs were obtained by intersecting modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data and the marker genes of neutrophils identified from single-cell RNA-sequencing(scRNA-seq) data. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were run to construct a prognostic signature, follow by delineation of risk groups, and external validation. Analyses of ESTIMAT, immune function, Tumor Immune Dysfunction and Exclusion (TIDE) scores, Immune cell Proportion Score (IPS), and immune checkpoint genes between high- and low-risk groups were performed, and then analyses of drug sensitivity to screen for sensitive anticancer drugs in high-risk groups. A total of 45 candidate NRGs were identified, of which PLTP, EREG, CD68, CD69, PLAUR, and CYP27A1 were considered to be significantly associated with prognosis in LUAD and were used to construct a prognostic signature. Correlation analysis showed significant differences in the immune landscape between high- and low-risk groups. In addition, our prognostic signature was important for predicting drug sensitivity in the high-risk group. Our study screened for NRGs in LUAD and constructed a novel and effective signature, revealing the immune landscape and providing more appropriate guidance protocols in LUAD treatment., (© 2023. Springer Nature Limited.)

Mining entity and relation from unstructured text is important for knowledge graph construction and expansion. Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and redundancy of relation prediction. In this paper, we propose a novel hybrid attention and dilated convolution network (HADNet), an end-to-end solution for entity and relation extraction and mining. HADNet designs a novel encoder architecture integrated with an attention mechanism, dilated convolutions, and gated unit to further improve computation efficiency, which achieves an effective global receptive field while considering local context. For the decoder, we decompose the task into three phases, relation prediction, entity recognition and relation determination. We evaluate our proposed model using two public real-world datasets that the experimental results demonstrate the effectiveness of the proposed model., (© 2023. Springer Nature Limited.)

The usefulness of optical frequency domain imaging (OFDI) guidance on two-stenting at left main bifurcation has not been evaluated. Here, we used a novel bench model to investigate whether pre-defined optimal rewiring with OFDI-guidance decreases acute incomplete stent apposition (ISA) at the left main bifurcation segment. A novel bench simulation system was developed to simulate the foreshortening and overlapping of daughter vessels as well as left main bifurcation motion under fluoroscopy. Double-kissing (DK) culotte stenting was performed using the novel bench model under fluoroscopy with or without OFDI-guidance. In the OFDI-guidance group, if the guidewire did not pass through the pre-defined optimal cell according to the 3-dimensional OFDI, additional attempts of rewiring into the jailed side branch were performed. The success rate of optimal jailed side branch rewiring after implantation of the first and second stent under OFDI-guidance was significantly higher than that under only angio-guidance. After completion of the DK-culotte stenting, the incidence and volume of ISA at the bifurcation segment in the OFDI-guidance group was significantly lower than that in the angio-guidance group. Online 3-dimensional OFDI-guided DK-culotte stenting according to a pre-defined optimal rewiring point might be superior to only angio-guided rewiring for reducing ISA at the bifurcation., (© 2023. Springer Nature Limited.)

Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the automated diagnosis of DR, and in particular, multi-lesion segmentation tasks. In this paper, we propose a novel Transformer-based model for DR segmentation that incorporates hyperbolic embeddings and a spatial prior module. The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. Hyperbolic embeddings are used to classify feature matrices from the model at the pixel level. We evaluated the proposed model's performance on the publicly available datasets and compared it with other widely used DR segmentation models. The results show that our model outperforms these widely used DR segmentation models. The incorporation of hyperbolic embeddings and a spatial prior module into the Vision Transformer-based model significantly improves the accuracy of DR segmentation. The hyperbolic embeddings enable us to better capture the underlying geometric structure of the feature matrices, which is important for accurate segmentation. The spatial prior module improves the continuity of the features and helps to better distinguish between lesions and normal tissues. Overall, our proposed model has potential for clinical use in automated DR diagnosis, improving accuracy and speed of diagnosis. Our study shows that the integration of hyperbolic embeddings and a spatial prior module with a Vision Transformer-based model improves the performance of DR segmentation models. Future research can explore the application of our model to other medical imaging tasks, as well as further optimization and validation in real-world clinical settings., (© 2023. The Author(s).)

Gastric cancer has been one of the wide public health burdens with its high morbidity and mortality over several decades. As the unconventional modules among RNA families, circular RNAs present their blazing biological effects during gastric carcinogenesis. Though diverse hypothetical mechanisms were reported, further tests were necessitated for authentication. Herein, this study pinpointed a representative circDYRK1A which screened from vast amounts of public data sets using surprisingly novel bioinformatics approaches together with validations from the in vitro findings and then concluded that circDYRK1A tethered the biological behavior and swayed the clinicopathological features with gastric cancer patients thus providing an in-depth awareness for gastric carcinoma., (© 2023. The Author(s).)

Because of similar astronomical background, Marine Isotope Stage (MIS) 11 is viewed as an analogue of the Holocene, but the evolution of seasonal climatic instability during MIS 11 has not been well investigated. Here we present a time series of land-snail eggs-a recently-developed proxy of seasonal cooling events-from the Chinese Loess Plateau (CLP) to investigate seasonal climatic instability during MIS 11 and adjacent glacials. Due to the impact of low temperatures on egg hatching, egg-abundance peaks document seasonal cooling events. A total of five egg-abundance peaks were recorded in the CLP during MIS 12, MIS 11 and MIS 10. Three peaks are strong and occur close to glacial inception or interglacial-to-glacial transition; two weaker peaks occur during MIS11. These peaks imply seasonal climatic instability intensifies mainly during glacial initiation or transition. All these events correspond to ice-sheet growth and the loss of ice-rafted debris at high northern latitudes. Moreover, they occurred at the minima of local spring insolation during the MIS 12 and MIS 10 glacials, but at the maxima during the MIS 11 interglacial. This may contribute to the difference in the intensity of seasonal cooling events between low-eccentricity glacials and interglacials. Our results provide new evidence for understanding low-eccentricity interglacial-glacial evolution., (© 2023. The Author(s).)

Reducing the heat loss in wellbore is the key for efficient development of geothermal resource. It is a reliable solution to establish a long-term stable wellbore with good thermal insulation through cementing. In this paper, the cement-based composite thermal insulation material was prepared by using cement as the cementing material, hollow glass beads, foaming agent and stabilizer as main raw materials, and other conventional admixtures. Foams and hollow glass beads can introduce gas with low thermal conductivity into cement, so as to improve the thermal insulation of composite material. Foams are produced by chemical forming process, using foaming agent, which is prepared according electrochemistry and thermodynamics, and the foam stabilizer helps foam distribute in cement slurry stably and uniformly. 10-13% hollow glass beads can significantly reduce the thermal conductivity of hardened cement, without significant adverse effects on the rheology and strength of the material. The thermal conductivity of the composite thermal insulation material can be as low as 0.2998 W·(m·K) -1 , which is 62% lower than that of conventional cement, while the compressive strength is 6.10 MPa, meeting the engineering requirement. A thermal-conductivity prediction method is proposed correspondingly based on Maxwell model, and the prediction error of the newly established model is within 2%. This research can provide technical support for efficient development of geothermal resources., (© 2023. The Author(s).)

All the diagnostic criteria of autoimmune hepatitis (AIH) include histopathology. However, some patients may delay getting this examination due to concerns about the risks of liver biopsy. Therefore, we aimed to develop a predictive model of AIH diagnostic that does not require a liver biopsy. We collected demographic, blood, and liver histological data of unknown liver injury patients. First, we conducted a retrospective cohort study in two independent adult cohorts. In the training cohort (n = 127), we used logistic regression to develop a nomogram according to the Akaike information criterion. Second, we validated the model in a separate cohort (n = 125) using the receiver operating characteristic curve, decision curve analysis, and calibration plot to externally evaluate the performance of this model. We calculated the optimal cutoff value of diagnosis using Youden's index and presented the sensitivity, specificity, and accuracy to evaluate the model in the validation cohort compared with the 2008 International Autoimmune Hepatitis Group simplified scoring system. In the training cohort, we developed a model to predict the risk of AIH using four risk factors-The percentage of gamma globulin, fibrinogen, age, and AIH-related autoantibodies. In the validation cohort, the areas under the curve for the validation cohort were 0.796. The calibration plot suggested that the model had an acceptable accuracy (p > 0.05). The decision curve analysis suggested that the model had great clinical utility if the value of probability was 0.45. Based on the cutoff value, the model had a sensitivity of 68.75%, a specificity of 76.62%, and an accuracy of 73.60% in the validation cohort. While we diagnosed the validated population by using the 2008 diagnostic criteria, the sensitivity of prediction results was 77.77%, the specificity was 89.61% and the accuracy was 83.20%. Our new model can predict AIH without a liver biopsy. It is an objective, simple and reliable method that can effectively be applied in the clinic., (© 2023. The Author(s).)

Millions of traumatic brain injuries (TBIs) occur annually. TBIs commonly result from falls, traffic accidents, and sports-related injuries, all of which involve rotational acceleration/deceleration of the brain. During these injuries, the brain endures a multitude of primary insults including compression of brain tissue, damaged vasculature, and diffuse axonal injury. All of these deleterious effects can contribute to secondary brain ischemia, cellular death, and neuroinflammation that progress for weeks, months, and lifetime after injury. While the linear effects of head trauma have been extensively modeled, less is known about how rotational injuries mediate neuronal damage following injury. Here, we developed a new model of repetitive rotational head trauma in rodents and demonstrated acute and prolonged pathological, behavioral, and electrophysiological effects of rotational TBI (rTBI). We identify aberrant Cyclin-dependent kinase 5 (Cdk5) activity as a principal mediator of rTBI. We utilized Cdk5-enriched phosphoproteomics to uncover potential downstream mediators of rTBI and show pharmacological inhibition of Cdk5 reduces the cognitive and pathological consequences of injury. These studies contribute meaningfully to our understanding of the mechanisms of rTBI and how they may be effectively treated., (© 2023. The Author(s).)

Psychological flow is a state of full task immersion. The present study was conducted to test the hypothesis that psychological flow is positively related to activity of the phasic locus coeruleus-norepinephrine (LC-NE) system, which supports decisions on whether to engage in or disengage from the current activity. Subjective flow was assessed among 36 participants who engaged in a gamified version of the n-back task with various difficulty levels (0, 1, 2, and 3 back). During the tasks, continuous pupil diameter and EEG were recorded. We found that psychological flow and two presumed indicators of the phasic LC-NE activity (pupil dilation and EEG P300 amplitude) fit inverted U-shapes with increasing subjective task difficulty. Moreover, a positive linear relationship between psychological flow and pupil dilation (not with P300) was found. In conclusion, this study indicates the involvement of the LC-NE system in the peak experience of flow., (© 2023. The Author(s).)

Images are a significant source of inspiration for designers to carry out the color design. However, the absence of animated images in the product color design can create confusion for designers. To translate the colours of the animated images into product colours, this work used eye-tracking technology to aid colour extraction and the multilayer perceptron neural network (MLP) algorithm to train a product colour decision model to filter the best product colour schemes. Firstly, eye tracking technology is used to collect the distribution of hotspots of the subject while viewing the animated images. Based on the distribution of eye-tracking hotspots, the most interesting animated colours were extracted. Then, the MLP is applied to train a colour decision model for children's shopping cart products, and the colour decision model is used to filter the optimal solution for the product colour, and finally the colour design is completed from the animated colour to the three-colour children's shopping cart product. Experimental results show that the color extraction based on the eye-tracking technology and the color scheme screening based on the intelligent algorithm can realize the effective conversion from animated image colors to product colors. This work proposes a color scheme design method from animations to products, which further expands the image color sources in product color design and can accurately find the color scheme that matches the animated image and the product., (© 2023. The Author(s).)