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Abstract Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Abstract Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Abstract Background To evaluate the impact of implementation of 2019 European respiratory distress syndrome (RDS) guidelines on the incidence of bronchopulmonary dysplasia (BPD). Method We retrospectively collected the clinical data of very preterm infants (VPIs) born before 32 gestational weeks from January 1st 2018 to December 31st 2021. VPIs were divided into group A and group B according to their birth date which was before or at/after January 1st 2020, when the 2019 European RDS guidelines were introduced. BPD is considered as primary outcome. We statistically analyzed all the data, and we compared the general characteristics, ventilation support, medication, nutrition and the outcomes between the two groups. Results A total of 593 VPIs were enrolled, including 380 cases in group A and 213 cases in group B. There were no statistic differences regarding to gender ratio, gestational age, birth weight and delivery mode between the two groups. Compared with group A, group B showed higher rate of antenatal corticosteroid therapy (75.1% vs. 65.5%). The improvement of ventilation management in these latter patients included lower rate of invasive ventilation (40.4% vs. 50.0%), higher rate of volume guarantee (69.8% vs. 15.3%), higher positive end expiratory pressure (PEEP) [6 (5, 6) vs. 5 (5, 5) cmH2O] and higher rate of synchronized nasal intermittent positive pressure ventilation (sNIPPV) (36.2% vs. 5.6%). Compared with group A, group B received higher initial dose of pulmonary surfactant [200 (160, 200) vs. 170 (130, 200) mg/Kg], shorter antibiotic exposure time [13 (7, 23) vs. 17 (9, 33) days], more breast milk (86.4% vs. 70.3%) and earlier medication for hemodynamically significant patent ductus arteriosus (hsPDA) treatment [3 (3, 4) vs. 8 (4, 11) days] (p 0.05). Conclusions After implementation of 2019 European RDS guidelines, the overall incidence of BPD was significantly decreased in VPIs. Continuous quality improvement is still needed in order to decrease the incidence of BPD in smaller infants who are less than 28 gestational weeks or less than 1,000 g.

Abstract Conventional dielectric solid materials, both natural and artificial, lack electromagnetic self-duality and thus require additional coatings to achieve impedance matching with free space. Here, we present a class of dielectric metamaterials that are effectively self-dual and vacuum-like, thereby exhibiting full-polarization omnidirectional impedance matching as an unusual Brewster effect extended across all incident angles and polarizations. With both birefringence and reflection eliminated regardless of wavefront and polarization, such anisotropic metamaterials could establish the electromagnetic equivalence with “stretched free space” in transformation optics, as substantiated through full-wave simulations and microwave experiments. Our findings open a practical pathway for realizing unprecedented polarization-independence and omnidirectional impedance-matching characteristics in pure dielectric solids.

In this study, nano-Nb-modified AlSi10Mg composites were fabricated using laser powder bed fusion (LPBF). The effects of Nb addition on the microstructure and mechanical properties of the AlSi10Mg alloy were systematically investigated. Compared with the as-built AlSi10Mg alloy, the microstructure along the building direction of the AlSi10Mg–Nb composites changed from coarse columnar grains to ultrafine equiaxed grains. Moreover, the preferential grain growth orientation was significantly weakened. Grain refinement occurs due to the presence of abundant in-situ Al3Nb precipitates dispersed within the grain, serving as heterogeneous nucleation inoculants. Simultaneously, the precipitation of NbSi2 particles along the eutectic network also contributed to refining the structure of the eutectic network. As a result, the LPBF-processed AlSi10Mg–Nb samples demonstrated a very high ultimate tensile strength (∼560 MPa) and good ductility (∼7%), comparable to those of high-strength aluminum alloys. The underlying mechanisms for this strength enhancement are discussed based on the improved mechanical properties. This study provides a novel perspective for the fabrication of high-performance AlSi10Mg alloys using LPBF.

In the context of sustainable urban development, effective garbage management plays a crucial role. However, traditional methods encounter limitations in terms of data quality and quantity. The research on automatic garbage image recognition and classification methods based on deep learning has been gaining attention. This study proposes an integrated garbage image recognition and classification method that combines ResNet-50, YOLOv5, and weakly supervised CNN algorithms. The aim is to enhance both the accuracy and efficiency of image recognition, optimize intelligent garbage management, and promote urban sustainable development planning. The ResNet-50 model is employed to extract meaningful features from images and train weakly supervised CNN models for subsequent training and prediction. This enables the analysis of urban environmental development trends and the formulation of planning measures. Through evaluation on four representative public datasets, the proposed method outperforms several traditional algorithms in terms of accuracy, efficiency, and stability in garbage image recognition systems. Notably, on the HGI-30 dataset, the algorithm achieves significant improvements by reducing inference time by over 48.6%, FLOPs by over 46.5%, and MAPE by over 41%. These enhancements greatly enhance the accuracy and robustness of garbage image classification, highlighting the substantial significance of this method in the realms of garbage management and environmental protection.

Abstract Biophysical processes of forests affect climate through the regulation of surface water and heat fluxes, which leads to further effects through the adjustment of clouds and water cycles. These indirect biophysical effects of forests on clouds and their radiative forcing are poorly understood but highly relevant in the context of large-scale deforestation or afforestation, respectively. Here, we provide evidence for local decreases in global low-level clouds and tropical high-level clouds from deforestation through both idealized deforestation simulations with climate models and from observations-driven reanalysis using space-for-time substitution. The decreased cloud cover can be explained by alterations in surface turbulent heat flux, which diminishes uplift and moisture to varying extents. Deforestation-induced reduction in cloud cover warms the climate, partially counteracting the cooling effects of increased surface albedo. The findings from idealized deforestation experiments and space-for-time substitution exhibit disparities, with global average offsets of, respectively, approximately 44% and 26%, suggesting the necessity for further constraints.

Abstract Palm leaf manuscripts, which are crucial carriers of historical, religious, scientific, and artistic information in East and Southeast Asia, specifically encapsulate significant aspects of Buddhist culture and thus require comprehensive research and preservation efforts. The base material of palm leaf manuscripts is processed palm leaves, which are hygroscopic and profoundly affected by environmental humidity. Currently, there is a research gap regarding the impact of traditional processing crafts and natural aging on the hygroscopicity of palm leaf manuscripts. Utilizing dynamic water vapor sorption (DVS), the hygroscopic properties of palm leaves from various years were assessed before and after traditional processing in Yunnan Province, China. The results show that traditional processing slightly increases the equilibrium moisture content (EMC) in environments with 0 to 60% relative humidity (RH), but significantly lowers EMC in high humidity environments, with reductions up to 19.01%. Additionally, hysteresis doubled post-processing, indicating enhanced stability under fluctuating humidity conditions. Sorption models suggest that traditional processing increases the number of adsorption sites while reducing physical adsorption or capillary condensation. FT-IR (Fourier-transform infrared spectroscopy) analysis indicates that the relative contents of cellulose and hemicellulose were reduced by 39.90% and 3.97%, respectively. Degradation occurring in both the crystalline and amorphous regions of cellulose. After natural aging, the hygroscopicity of processed palm leaves improved across the entire humidity range of 0 to 95%, and there was a slight increase in hysteresis. This is due to the increase in both adsorption sites and physical adsorption capabilities. FT-IR results also indicate that the relative contents of cellulose and hemicellulose were decreased by 57.52% and 19.83% after nature aging, respectively. These findings confirm that traditional processing improves the writability and humidity resilience of the leaves, while natural aging enhances their overall hygroscopic properties. This research contributes to our understanding of how humidity damages palm leaf manuscripts. aids in determining optimal RH ranges for storage, and assesses the effectiveness of consolidation treatments in their long–term preservation.

Abstract Antarctic sea ice extent (SIE) reached a new record low in February 2023. Here we examine the evolution of the coupled ocean-atmosphere-sea ice system during the 12 months preceding the record. The impact of preceding conditions is assessed with observations, reanalyses, and output from the regional ocean-sea ice coupled model NEMO3.6-LIM3. We find that the 2022-2023 sea ice annual cycle was characterized by consistently low SIE throughout the year, anomalously rapid sea ice retreat in December 2022, and nearly circumpolar negative SIE anomalies in February 2023. While advection-induced positive air temperature anomalies inhibited the sea ice growth in most regions, strong southerly winds in the Amundsen-Ross Sea caused by an anomalously deep Amundsen Sea Low in spring transported notable volumes of sea ice northward, triggering an unusually active ice-albedo feedback onshore and favoring accelerated melt towards the minimum. This study highlights the impacts of multifactorial processes during the preceding seasons to explain the recent summer sea ice minima.

Abstract Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

ObjectiveIn order to improve the combined navigation accuracy of unmanned surface vehicles (USVs) by strapdown inertial navigation system (SINS) and doppler range finder (DVL), the DVL error needs to be accurately calibrated. MethodsThis paper carries out error modeling of the installation error angle, lever arm, and scale factor of the DVL, and focuses on the analysis of the impact of the lever arm error term on the combined navigation positioning in the vessels, pointing out the necessity of its calibration; proposes a DVL error calibration method based on the parameter estimation between two point sets, which treats the SINS/GNSS navigation parameters and DVL output parameters as two point sets, converting the calibration problem into an estimation problem of the difference between the two point sets, and using the Kalman filter to estimate the error parameters between the two point sets; using the SVD（singular value decomposition）-based observability analysis to quantify the observability of the filter in different motion condtions, giving the motion recommendations of the carrier during the calibration process.ResultsThe results of mathematical simulation and real ship experiments show that the global relative accuracy of the calibrated SINS/DVL algorithm can reach a voyage of 0.122%. During cornering maneuvers, the lever arm causes abrupt changes in SINS/DVL positioning. After compensating for the lever arm, the positioning errors become smoother and the overall positioning error is reduced.ConclusionThe proposed method provides a feasible way to calibrate each DVL error including the lever arm.

Abstract To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

Abstract The damage of road base course has the characteristics of strong concealment and difficulty in detecting. For this reason, the impact imaging method has been used for detection of road base course. This paper discussed systematically collection points setting, excitation mode and data processing method. Through the application in testing for highway pavement base before and after grouting maintenance, the results show that the method is simple and accurate. The detection results can be displayed in a two-dimensional image form and it is easy to be used in road maintenance. This method can be used to identify and locate the damages of the pavement base, to judge the uniformity of the pavement base structure. It can also be used to evaluate the effectiveness of internal damage after grouting repairing.

Summary: Extrachromosomal circular DNAs (eccDNAs) are defined as distinct genomic entities of circular and mobile DNA molecules, but their molecular functions in and impact on breast cancer (BC) are rarely known. This study used Circle-seq to analyze eccDNAs from 19 BC tissues and 17 adjacent normal tissues. We found that eccDNAs are present on all chromosomes and enriched in seven eccDNA hotspot genes (HSGs) associated with the BC pathway. Several eccDNAs harboring entire genes (eccGenes) and eccDNAs harboring miRNAs (eccMIRs) were identified and linked to cancer-relevant pathways. Synthetic eccMIR6748, eccMIR6508, and eccMIR3142 elevated miRNA expression in MCF-7 cells, with eccMIR6748 promoting BC cell migration and invasion by upregulating miR-6748, which suppresses tumor suppressor candidate factor 5 (TUSC5) at the post-transcriptional level. eccMIR6748 also influences BC progression via the p38 mitogen-activated protein kinase (MAPK) signaling pathway. These findings suggest that eccDNAs, which contain functional genomic segments, play a role in BC initiation and progression, offering a dynamic source of genomic plasticity and potential as biomarkers and therapeutic targets.

The nuclear factor erythroid 2-related factor 2 (Nrf2) is overexpressed in multiple tumor cells. Nevertheless, the role of Nrf2 in malignant transformation induced by hydroquinone (HQ) is unknown. Here, we hypothesized that Nrf2 might participate in HQ-induced malignant transformation of TK6 cells, a line of normal human lymphoblastoid cells, by accelerating cell proliferation and regulating cell cycle progression. The data indicated that TK6 cells chronically exposed to HQ continuously activated Nrf2-Keap1 signaling pathway. Furthermore, we found that defects in Nrf2 inhibited cell proliferation and prevented cells from entering S phase from G1 phase. Mechanistically, Nrf2 is involved in cell cycle abnormalities induced by prolonged exposure to HQ by binding to p16, thereby activating the p16/Rb signaling pathway. Taken together, Nrf2 might be a potential driver of carcinogenesis that promotes malignant cell proliferation and affects cell cycle distribution.

Abstract Two-dimensional (2D) AMX2 compounds are a family of mixed ionic and electronic conductors (where A is a monovalent metal ion, M is a trivalent metal, and X is a chalcogen) that offer a fascinating platform to explore intrinsic coupled ionic-electronic properties. However, the synthesis of 2D AMX2 compounds remains challenging due to their multielement characteristics and various by-products. Here, we report a separated-precursor-supply chemical vapor deposition strategy to manipulate the chemical reactions and evaporation of precursors, facilitating the successful fabrication of 20 types of 2D AMX2 flakes. Notably, a 10.4 nm-thick AgCrS2 flake shows superionic behavior at room temperature, with an ionic conductivity of 192.8 mS/cm. Room temperature ferroelectricity and reconfigurable positive/negative photovoltaic currents have been observed in CuScS2 flakes. This study not only provides an effective approach for the synthesis of multielement 2D materials with unique properties, but also lays the foundation for the exploration of 2D AMX2 compounds in electronic, optoelectronic, and neuromorphic devices.

Abstract Background Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well-studied. In this population-based cohort study, we examined the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression. Methods Using the territory-wide electronic medical records in Hong Kong, we identified all patients aged ≥ 10 years with new diagnoses of depression from 2014 to 2022. We performed an interrupted time-series (ITS) analysis to examine changes in incidence of medically attended depression before and during the pandemic. We then divided all patients into nine cohorts based on year of depression incidence and studied their initial and ongoing service use patterns until the end of 2022. We applied generalized linear modeling to compare the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among prevalent patients with depression. Results We found an immediate increase in depression incidence (RR = 1.21, 95% CI: 1.10–1.33, p

Abstract It is known that the heterogeneity caused by thermally induced micro-cracks and thermal stress can affect the mechanical behavior of granite. The laboratory-scale tests have the intrinsic limitation of non-repeatability and lack of effective methods to characterize the interaction effect between thermal micro-cracks and thermal stresses. In this study, we demonstrate how advancements in particle bonded model and moment tensor can help better understand the roles of high temperature in weakening granite and thermally induced cracking process in Brazilian test. Our results show that the types of micro-cracks (intergranular, intragranular, and transcrystalline ones) are related to their thermal expansion coefficients of mineralogical compositions. The intergranular tensile micro-cracks are predominant during the heating and heating–cooling processes. An obvious weakening of granite and non-central initiation is associated with the heterogeneity caused by the thermal damage and thermal stress. We also quantitatively evaluate the thermal damage based on orientation distribution, b-value, and nature of the sources, which gives a new microcracking perspective on tensile characteristics subjected to high temperature.

Abstract Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.

Abstract Despite epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) have shown remarkable efficacy in patients with EGFR-mutant non-small cell lung cancer (NSCLC), acquired resistance inevitably develops, limiting clinical efficacy. We found that TET2 was poly-ubiquitinated by E3 ligase CUL7FBXW11 and degraded in EGFR-TKI resistant NSCLC cells. Genetic perturbation of TET2 rendered parental cells more tolerant to TKI treatment. TET2 was stabilized by MEK1 phosphorylation at Ser 1107, while MEK1 inactivation promoted its proteasome degradation by enhancing the recruitment of CUL7FBXW11. Loss of TET2 resulted in the upregulation of TNF/NF-κB signaling that confers the EGFR-TKI resistance. Genetic or pharmacological inhibition of NF-κB attenuate the TKI resistance both in vitro and in vivo. Our findings exemplified how a cell growth controlling kinase MEK1 leveraged the epigenetic homeostasis by regulating TET2, and demonstrated an alternative path of non-mutational acquired EGFR-TKI resistance modulated by TET2 deficiency. Therefore, combined strategy exploiting EGFR-TKI and inhibitors of TET2/NF-κB axis holds therapeutic potential for treating NSCLC patients who suffered from this resistance.

The evolution of lattice dislocations in metals can be induced by electric current. However, the effect of electric current on the interaction between lattice dislocations and grain boundaries (GBs) remains unclear. Herein, we investigate the evolution of lattice dislocations and grain boundary dislocations (GBDs) induced by electric current in M50 steel via in-situ TEM characterization, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations. Our findings reveal that electric current induces the directional slip of lattice dislocations towards GBs, while the reduction of GB contrast is attributed to the decomposition and reorganization of GBDs. DFT calculations demonstrate that electric current enhances the force on atoms in defect regions. These in-situ TEM observations are well reproduced by MD simulations incorporating electric current, which include a virtual temperature rise at defect regions to reflect the electric current force on defect atoms. Based on these observations, we propose a novel GB accommodation model under electric current. These results provide valuable insights into the mechanisms by which electric current tailors lattice dislocation behavior and enhances GBDs ordering in metallic materials.

Background Gestational diabetes can lead to increased blood pressure in offspring, accompanied by impaired renal sodium excretion function and vasoconstriction and diastole dysfunction. However, there are few studies on whether it is accompanied by increased sympathetic nerve activity.Methods Pregnant C57BL/6 mice were intraperitoneally injected with streptozotocin (35 mg/kg) or citrate buffer at day 0 of gestation. The mice of control mother offspring (CMO) and diabetic mother offspring (DMO) at 16 weeks of age were infused with vehicle (artificial cerebrospinal fluid, aCSF, 0.4 μL/h) or tempol (1 mmol/L, 0.4 μL/h) into the bilateral paraventricular nucleus (PVN) of mice for 4 weeks, respectively.Results Compared with CMO group, SBP and peripheral sympathetic nerve activity (increased heart rate, LF/HF and plasma norepinephrine and decreased SDNN and RMSSD) were increased in DMO group, which was accompanied by increased angiotensin II type-1 receptor (AT1R) expression and function in PVN. The increase in AT1R expression levels was attributed to a decrease in the methylation level of the AT1R promoter region, resulting in an increase in AT1R mRNA levels in PVN of DMO. Moreover, compared with CMO group, the levels of oxidative stress were increased and DNMT1 expression was decreased in PVN of DMO. Bilateral PVN infusion of tempol attenuated oxidative stress increased the level of DNMT1 expression and the binding of DNMT1 to the AT1R promoter region, which reduced mRNA and protein expression level of AT1R, heart rate and SBP in DMO, but not in CMO.Conclusions The present study provides evidence for overactive sympathetic nervous systems in the pathogenesis of gestational diabetes-induced hypertension in offspring. Central antioxidant intervention in the PVN may be an important treatment strategy for fetal-programmed hypertension.

Background Fibroblast growth factor 21 (FGF21) has a protective effect against cardiovascular disease. However, the role of FGF21 in hypertension remains elusive.Methods Ten-week-old male C57BL/6 mice were randomly divided into normal-salt (NS) group, NS+FGF21 group, deoxycorticosterone acetate-salt (DOCA) group and DOCA+FGF21 group. The mice in NS group underwent uninephrectomy without receiving DOCA and 1% NaCl and the mice in DOCA group were subjected to uninephrectomy and DOCA-salt (DOCA and 1% NaCl) treatment for 6 weeks. At the same time, the mice were infused with vehicle (artificial cerebrospinal fluid, aCSF) or FGF21 (1 mg/kg) into the bilateral paraventricular nucleus (PVN) of mice.Results Here, we showed that FGF21 treatment lowered DOCA salt-induced inflammation and oxidative stress in the PVN, which reduced sympathetic nerve activity and hypertension. Mechanistically, FGF21 treatment decreased the expression of HNF4α and inhibited the binding activity of HNF4α to the promoter region of ACE2 in the PVN of DOCA salt-treated mice, which further up-regulated ACE2/Ang (1–7) signals in the PVN. In addition, ACE2 deficiency abolished the protective effect of FGF21 in DOCA salt-treated mice, suggesting that FGF21-mediated antihypertensive effect was dependent on ACE2.Conclusions The results demonstrate that FGF21 protects against salt-sensitive hypertension via regulating HNF4α/ACE2/Ang (1–7) axis in the PVN of DOCA salt-treated mice via multi-organ crosstalk between liver, brain and blood vessels.

ABSTRACTBackground Endothelial dysfunction of the pulmonary artery contributes to hypoxia-induced pulmonary arterial hypertension (PAH). Omentin-1, as a novel adipocytokine, plays an important protective role against cardiovascular diseases. However, the effect and underlying mechanisms of omentin-1 against PAH remain unclear.Methods PAH was induced in SD (Sprague & Dawley) rats via a low-oxygen chamber for 4 weeks. Hemodynamic evaluation was undertaken using a PowerLab data acquisition system, and histopathological analysis was stained with hematoxylin and eosin (H&E). Endothelial function of pulmonary artery was assessed using wire myography.Results We found that omentin-1 significantly improved pulmonary endothelial function in rats exposed to hypoxia and attenuated PAH. Mechanistically, we found that omentin-1 increased phosphorylated 5’‑adenosine monophosphate‑activated protein kinase (p‑AMPK) level and reduced endoplasmic reticulum (ER) stress and increased NO production in pulmonary artery from rats exposed to hypoxia. However, the effect of omentin-1 was abolished by treatment with AMPK inhibitor (Compound C).Conclusions Our results reveal a protective effect of omentin-1 in PAH via inhibiting ER stress through AMPKα signaling and provide an agent with translational potential for the treatment of PAH.

We propose an acoustic-mechanical metasurface with simultaneous sound absorption and vibration reduction functionalities. This acoustic-mechanical feature results from a nonlocal coupling effect in the designed composite meta-structure. A nonlocal meta-mesh is designed at the acoustic-mechanical interface and embedded in the metasurface array to connect adjacent unit cells, which can make a nonlocal coupling effect. This effect can improve the acoustic property, which strengthens sound absorption by 58.6 %, and the mechanical interface strengthens vibration acceleration reduction by 88.45 %. The resulting nonlocal acoustic-mechanical metasurface achieves an average absorption coefficient of higher than 0.9 within 267–638 Hz, and a vibration level difference of 18.75 dB within 490–920 Hz. The sample has an ultra-thin thickness of 8.5 cm (λ/14 for the lowest working frequency) and is lightweight (305.1 g), whose weight reduction reaches 42.8 % compared with conventional designs with similar functionalities. Our proposed design strategy will be enlightened for multi-functional acoustic-mechanical coupling meta-device.

Gastric cancer (GC) is the fifth most prevalent type of cancer in the whole world. Cuproptosis is discovered as a programmed cell death pathway and connected to cells' growth and death, as well as tumorigenesis. The relationship between cuproptosis and GC is still elusive. Two aspects of this study will elaborate the relationship between cuproptosis and immunotherapy as well as biomarkers in GC. Notably, the herein review is intended to highlight what has been accomplished regarding the cuproptosis for the diagnosis, immunotherapy, and prognosis in GC. The aim of this study is to offer a potential directions and the strategies for future research regarding cuproptosis inside the GC.

Ferroptosis is a lipid peroxidation-driven and iron-dependent form of programmed cell death, which is involved in a variety of physical processes and multiple diseases. Numerous reports have demonstrated that ferroptosis is closely related to the pathophysiological processes of Mycobacterium tuberculosis (M. tuberculosis) infection and is characterized by the accumulation of excess lipid peroxides on the cell membrane. In this study, the various functions of ferroptosis, and the therapeutic strategies and diagnostic biomarkers of tuberculosis, were summarized. Notably, this review provides insights into the molecular mechanisms and functions of M. tuberculosis-induced ferroptosis, suggesting potential future therapeutic and diagnostic markers for tuberculosis.

Radiation-induced brain injury (RIBI) is a significant challenge in radiotherapy for head and neck tumors, impacting patients' quality of life. In exploring potential treatments, this study focuses on memantine hydrochloride and hydrogen-rich water, hypothesized to mitigate RIBI through inhibiting the NLRP3/NLRC4/Caspase-1 pathway. In a controlled study involving 40 Sprague-Dawley rats, divided into five groups including a control and various treatment groups, we assessed the effects of these treatments on RIBI. Post-irradiation, all irradiated groups displayed symptoms like weight loss and salivation, with notable variations among different treatment approaches. Particularly, hydrogen-rich water showed a promising reduction in these symptoms. Histopathological analysis indicated substantial hippocampal damage in the radiation-only group, while the groups receiving memantine and/or hydrogen-rich water exhibited significant mitigation of such damage. Molecular studies, revealed a decrease in oxidative stress markers and an attenuated inflammatory response in the treatment groups. Immunohistochemistry further confirmed these molecular changes, suggesting the effectiveness of these agents. Echoing recent scientific inquiries into the protective roles of specific compounds against radiation-induced damages, our study adds to the growing body of evidence on the potential of memantine and hydrogen-rich water as novel therapeutic strategies for RIBI.

Background: Gallstone disease is one of the most common gastrointestinal disorders. Despite extensive research exploring the risk factors associated with gallstones, the association between depressive symptoms and gallstones remains inadequately understood. This study aimed to assess the association between depressive symptoms and the prevalence of gallstones among adults in the United States. Methods: In this study, a cross-sectional design utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2017 to 2020. The assessment of depressive symptoms was conducted through the utilization of the Patient Health Questionnaire-9 (PHQ-9), which assigns total scores ranging from 0 to 27. Participants with PHQ-9 scores equal to or exceeding 10 were categorized as having clinically relevant depressive symptoms. Multivariable adjusted logistic regression and subgroup analysis were used to assess the association between depressive symptoms and gallstone prevalence. Results: A total of 7,797 participants aged 20 years or older were enrolled in this study, of whom 835 had a self-reported history of gallstones. After multiple adjustments, each one-point increase in PHQ-9 scores was associated with a 5 % increase in the risk of gallstones (odds ratio [OR], 1.05; 95 % confidence interval [CI], 1.03, 1.07, P

To realize nanoscale manufacturing based on laser direct writing technology, objective lenses with high numerical apertures immersed in water or oil are necessary. The use of liquid medium restricts its application in semiconductors. Achieving nanoscale features on silicon by laser direct writing in a low refractive index medium has been a challenge. In this work, a microsphere assisted femtosecond laser far-field induced dewetting approach is proposed. A reduction in the full-width at half-maximum of the focused light spot is realized by modulating tightly focused light through microspheres and achieving a minimum feature size of 9 nm on silicon in ambient air with energy smaller than the ablation threshold. Theoretical analysis and numerical simulation of laser processing are performed based on a two-temperature model. Furthermore, we explored the potential of femtosecond laser-induced dewetting in nanolithography and demonstrated its ability to achieve an arbitrary structure on silicon. Our work enables laser-based far-field sub-10-nm feature etching on a large-scale, providing a novel avenue for nanoscale silicon manufacturing.

Abstract Structural materials such as ceramics, metals, and carbon fiber‐reinforced plastics (CFRP) are frequently threatened by large compressive and impact forces. Energy absorption layers, i.e., polyurethane and silicone foams with excellent damping properties, are applied on the surfaces of different substrates to absorb energy. However, the amount of energy dissipation and penetration resistance are limited in commercial polyurethane foams. Herein, a distinctive nacre‐like architecture design strategy is proposed by integrating hard porous ceramic frameworks and flexible polyurethane buffers to improve energy absorption and impact resistance. Experimental investigations reveal the bioinspired designs exhibit optimized hardness, strength, and modulus compared to that of polyurethane. Due to the multiscale energy dissipation mechanisms, the resulting normalized absorbed energy (≈8.557 MJ m−3) is ≈20 times higher than polyurethane foams under 50% quasi‐static compression. The bioinspired composites provide superior protection for structural materials (CFRP, glass, and steel), surpassing polyurethane films under impact loadings. It is shown CFRP coated with the designed materials can withstand more than ten impact loadings (in energy of 10 J) without obvious damage, which otherwise delaminates after a single impact. This biomimetic design strategy holds the potential to offer valuable insights for the development of lightweight, energy‐absorbent, and impact‐resistant materials.

An item bank is key to applying computerized adaptive testing (CAT). The traditional approach to developing an item bank requires content experts to design each item individually, which is a time-consuming and costly process. The cognitive design system (CDS) approach offers a solution by automating item generation. However, the CDS approach has a specific way of calibrating or predicting item difficulty that affects the measurement efficiency of CAT. A simulation study was conducted to compare the efficiency of CAT using both calibration and prediction models. The results show that, although the predictive model (linear logistic trait model; LLTM) shows a higher root mean square error (RMSE) than the baseline model (Rasch), it requires only a few additional items to achieve comparable RMSE. Importantly, the number of additional items needed decreases as the explanatory rate of the model increases. These results indicate that the slight reduction in measurement efficiency due to prediction item difficulty is acceptable. Moreover, the use of prediction item difficulty can significantly reduce or even eliminate the need for item pretesting, thereby reducing the costs associated with item calibration.

Virtual urban green environment images and audio stimuli had been proven to have restorative effects on subjects’ physical and mental health. In this area, researchers predominantly focused on visual, auditory and olfactory aspects, while tactile and gustatory senses have been minimally explored. However, the optimal combination of sensory stimuli for promoting physical and mental recovery remains unclear. Therefore, a simulated sensory stimulation approach involving 240 participants was employed, with 30 individuals included in each of the eight experimental groups: the visual–auditory (VA), visual–auditory-olfactory (VAO), visual–auditory-tactile (VAT), visual–auditory-gustatory(VAG), visual–auditory-olfactory-tactile (VAOT), visual–auditory-olfactory-gustatory (VAOG), visual–auditory-tactile-gustatory (VATG), and visual–auditory-olfactory-tactile-gustatory (VAOTG) groups. This study aimed to explore the differences in participants’ physiological and psychological health recovery after exposure to different combinations of simulated sensory stimuli in virtual UGSs. The results indicated that the following: (1) In terms of physiological recovery, the blood pressure of the 8 experimental groups decreased significantly after the experiment, indicating that the virtual urban green space environment has a certain recovery effect on physiological state. The combination of VAOTG stimuli in the multisensory group resulted in the best blood pressure recovery (p

Summary: Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15–35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69–0.81; partial-model: C = 0.73, 95% C.I. = 0.65–0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Plasma membrane intrinsic proteins (PIPs) are conserved plant aquaporins that transport small molecules across the plasma membrane to trigger instant stress responses and maintain cellular homeostasis under biotic and abiotic stress. To elucidate their roles in plant immunity to pathogen attack, we characterized the expression patterns, subcellular localizations, and H2O2-transport ability of 11 OsPIPs in rice (Oryza sativa), and identified OsPIP2;6 as necessary for rice disease resistance. OsPIP2;6 resides on the plasma membrane and facilitates cytoplasmic import of the immune signaling molecule H2O2. Knockout of OsPIP2;6 increases rice susceptibility to Magnaporthe oryzae, indicating a positive function in plant immunity. OsPIP2;6 interacts with OsPIP2;2, which has been reported to increase rice resistance to pathogens via H2O2 transport. Our findings suggest that OsPIP2;6 cooperates with OsPIP2;2 as a defense signal transporter complex during plant–pathogen interaction.

To reveal the dynamic mechanical characteristics of deep rocks, a series of impact tests under triaxial static stress states corresponding to depths of 300–2400 m were conducted. The results showed that both the strain rates and the stress environments in depth significantly affect the mechanical characteristics of rocks. The sensitivity of strain rate to the dynamic strength and deformation modulus shows a negative correlation with depth, indicating that producing penetrative cracks in deep environments is more difficult when damage occurs. The dynamic strength shows a tendency to decrease and then increase slightly, but decreases sharply finally. Transmissivity demonstrates a similar trend as that of strength, whereas reflectivity indicates the opposite trend. Furthermore, two critical depths with high dynamically induced hazard possibilities based on the China Jinping Underground Laboratory (CJPL) were proposed for deep engineering. The first critical depth is 600–900 m, beyond which the sensitivity of rock dynamic characteristics to the strain rate and restraint of circumferential stress decrease, causing instability of surrounding rocks under axial stress condition. The second one lies at 1500–1800 m, where the wave impedance and dynamic strength of deep surrounding rocks drop sharply, and the dissipation energy presents a negative value. It suggests that the dynamic instability of deep surrounding rocks can be divided into dynamic load dominant and dynamic load induced types, depending on the second critical depth.

Waveguide-integrated photodetectors (PDs) play a crucial role in mid-infrared (MIR) silicon photonics, serving vital functions in sensing and communication applications. III-V semiconductors are widely used in MIR PDs, and many state-of-the-art III-V PDs on Si still require complicated integration methods. Heteroepitaxial growth technology is a competitive approach for large-scale integration; however, buffers capable of simultaneously achieving heteroepitaxial growth and optical coupling are limited in the MIR region. In this paper, we report a waveguide-integrated InAs PD on Si, incorporating a GaAs/Ge buffer design based on interfacial misfit (IMF) technology. We optimize the geometric structure and calculate the optoelectronic properties at a wavelength of 3 μm. For our simulated parameters, the optimal PD achieves a responsivity of 2.77 A/W and a detectivity of 4.68×109 cm·Hz1/2·W-1 at -1V. This work suggests a promising avenue to further develop high-detectivity and high-speed PDs for MIR silicon photonics.

Abstract Background Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital cardiac anomaly, mortality rates in infancy reach approximately 90%, with only a small number of patients surviving into adulthood, therefore, most of the literature reports mainly focus on infantile type. Case presentation A 55-year-old female was admitted due to persistent repeated chest pain experienced and had worsened for unknown reasons. Color doppler echocardiography, coronary computed tomographic angiography, and coronary angiography confirmed the diagnosis of ALCAPA and concurrent right coronary artery-right atrial fistula. The symptoms of chest pain exhibited notable improvement subsequent to corrective surgery for the anomalous origin of the coronary artery. Conclusions This report shows an unique case of ALCAPA in an adult patient, characterizing the condition’s combination with a right coronary-right atrial fistula, and it is prone to misdiagnosis and misdiagnosis. We aim to provide valuable insights for clinical diagnosis and treatment of ALCAPA.

As a renewable energy solution for remote marine environments, marine raft microgrid clusters differ from terrestrial multi-microgrid systems and traditional single-island microgrids. In the absence of large-scale grid support, these marine raft microgrids must maintain the stability and economic efficiency of power supply within a collaborative multi-microgrid context. To address this, a multi-objective optimization approach for energy scheduling is proposed. This study initially constructs a microgrid cluster system model and introduces two economic objective functions. These functions consider both inter-microgrid power scheduling and the economic benefits of power procurement. By applying the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Constraint-based Multi-objective Evolutionary Algorithm based on Decomposition (CMOEA/D) to solve the objective functions, the results indicate that the CMOEA/D algorithm demonstrates high efficiency and accuracy in pursuing economically optimal solutions. Compared to the NSGA-II algorithm, CMOEA/D outperforms in terms of the quality of optimal solutions and iteration time, thereby enhancing the economic benefits of the microgrid cluster and validating the effectiveness of the proposed model. This research provides significant theoretical and practical guidance for energy management in remote marine environments, showcasing its profound theoretical significance and application value.

The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.

Abstract About 15–40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

In order to address the challenges of high loss density, difficult heat dissipation, and irreversible demagnetization in high temperature of high-speed permanent magnet machine, an innovative cooling structure with axial ventilation and self-cooling rotor is proposed to enhance temperature distribution. The cooling efficiency of the cooling structure is defined, and the influence of the rotor vent on the electromagnetic and air friction loss is analyzed. The governing equation for the fluid flow in a rotating pipe is derived from a fluid model analysis of a ventilation hole rotating at any angle to the axis of rotation. The calculated results are validated against the results obtained from computational fluid dynamics. The sensitivity of rotor cooling performance to various control factors using the variance index method. Subsequently, the cooling structure was enhanced based on the findings of the analysis. The calculation results indicate that the heat dissipation effect of the axial ventilation self-cooled rotor has decreased by 4.17 °C compared to before the improvement. The temperature rise experiment verified the accuracy of temperature calculation and the effectiveness of the cooling structure, providing strong support for the design of axial self-ventilation cooling system for high-speed machines.

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash–Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Coalburst is one of the most serious disasters that threaten the safe production of coal mines, and this disaster is particularly serious in China. This paper presents an overview of coalbursts in China since 1980s. From the ''stress and energy'' and ''regional and local'' perspectives, the achievements in the theory, practice and management of coalbursts in China are systematically summarized. A theoretical system of coalbursts has been formed to reveal the deformational behavior of coalbursts and explain the mechanism of coalbursts. The occurrence conditions of coalbursts are put forward and the critical stress is obtained. The stress index method for risk evaluation of coalbursts before mining is proposed, and the deformation localization prediction method of coalbursts is put forward. The relationship between energy release and absorption in the process of coalbursts is found, and the prevention and control methods of coalbursts, including the regional method, the local method and support, are presented. The safety evaluation index of coalburst prevention and control is put forward. The integrated prevention and control method for coal and gas outbursts is proposed. The prevention and control technology and equipment of coalbursts have also been developed. Amongst them, the distribution law of the critical stress in China coalburst mines is discovered. The technology and equipment for monitoring, prevention and control of coalbursts, as well as for integrated prevention and control of combined coalbursts and other disasters, have been developed. The energy-absorbing and coalburst-preventing support technology for roadways is invented, and key engineering parameters of coalburst prevention and control are pointed out. In China, coalburst prevention and control laws and standards have been developed. Technical standards for coalbursts are formulated, statute and regulations for coal mines are established, and regulatory documents are promoted.

Scandium is very expensive and limits the application potential of Sc containing alloys. A novel high-strength Al–Mg–Mn-Sc-Zr alloy was designed for reducing Sc. Multiple strengthening mechanisms were utilized for designing this alloy, especially for the solid solution strengthening which benefits from ultrahigh cooling. With the addition of Mn and Si, the strength of our alloy reaches comparable level of similar alloys while the Sc content is 40–60 % less than them. The effect of Mn addition on microstructure and mechanical properties were carefully studied. Near full-dense samples with relative density >99.88 % were obtained with the help of overlapping prediction model. The continuous Mg2Si network was observed with Si introduction and broken up with increasing Mn content. According to the tensile experiment results and corresponding calculations, it was considered that the thermal stability and the effect of precipitate strengthening was slightly deteriorated. However, obvious improvement of the tensile strength was observed with increasing Mn addition, and the ductility maintains in a certain range. After aging at 280 °C for 4 h, the sample of 1.4 wt% Mn exhibits the highest ultimate strength of 497.08 ± 7.22 MPa, and possesses a considerable ductility of 8.01 ± 0.34 %. The Portevin-Le Chatelier (PLC) effect was observed in stress-strain curves, which reflects a dynamic strain aging of mobile dislocations and solute atoms. After aging, the stress amplitude and waiting time of serration flow decrease obviously, because of the introduction of bypassing precipitates and reduction of diffusion time. Compared with other similar alloys, our alloy exhibited comparable yield strength and elongation, but considerable advantages in material costs resulting from lower Sc content. It will offer not only an idea for alloy design utilizing the ultra-high solid solubility of elements, but also a feasible way to further expand application field of high-strength Al-based alloys through reducing the material costs.

Abstract Objective As a single‐transmembrane protein of the FXYD family, FXYD6 plays different roles under physiological and pathological status, especially in the nervous system. This study aims to identify FXYD6 as a biomarker for glioma, by analyzing its expression and methylation patterns. Methods Using TCGA and GTEx datasets, we analyzed FXYD6 expression in various tissues, confirming its levels in normal brain and different glioma grades via immunoblotting and immunostaining. FXYD6 biological functions were explored through enrichment analysis, and tumor immune infiltration was assessed using ESTIMATE and TIMER algorithms. Pearson correlation analysis probed FXYD6 associations with biological function‐related genes. A glioma detection model was developed using FXYD6 methylation data from TCGA and GEO. Consistently, a FXYD6 methylation‐based prognostic model was constructed for glioma via LASSO Cox regression. Results FXYD6 was observed to be downregulated in GBM and implicated in a range of cellular functions, including synapse formation, cell junctions, immune checkpoint, ferroptosis, EMT, and pyroptosis. Hypermethylation of specific FXYD6 CpG sites in gliomas was identified, which could be used to build a diagnostic model. Additionally, FXYD6 methylation‐based prognostic model could serve as an independent factor as well. Conclusions FXYD6 is a promising biomarker for the diagnosis and prognosis of glioma, with its methylation‐based prognostic model serving as an independent factor. This highlights its potential in clinical application for glioma management.

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

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.