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Precipitation strengthening is a crucial method for enhancing the mechanical properties of steel. The bridgman directional solidification furnace, optical microscope (OM), scanning electron microscope (SEM), electron probe analyzer (EPMA) and transmission electron microscope (TEM) were used to investigate the effect of solidification cooling rate on the solidification microstructure, microsegregation, and nanoprecipitates in medium carbon CrMo cast steel. The results indicate that as the cooling rate increases, the solidification microstructure is significantly refined. The relationship between the cooling rate (CA) and primary dendrite arm spacing (PDAS, λ1) and secondary dendrite arm spacing (SDAS, λ2) are given by λ1 = 123.74 × C-0.4088A, λ2 = 66.63 × C-0.6532 A. Solute elements like Cr, Mo, and V exhibit noticeable positive segregation in the inter-dendritic. When the cooling rate does not exceed 0.47 °C/s, the solute concentrations in the inter-dendritic increase with higher cooling rate, leading to a decrease in the effective solute partition coefficient. This phenomenon is primarily attributed to the back diffusion of solute elements during solidification. Following heat treatment, precipitation of M6C, V(C,N), and M23C6 occurs within the inter-dendritic regions. If the solidification cooling rate does not exceed 0.47 °C/s, the density and volume fraction of nanoprecipitates increase with higher cooling rate. This trend primarily results from the reduction in critical nucleation radius and critical nucleation energy, facilitating easier nucleation of nanoprecipitates.

Abstract More than 70% of the potash fertilizer globally is produced by the froth flotation process, in which 4-dodecylmorpholine (DMP) serves as a reverse flotation agent. As the potash fertilizer production rapidly rises, the increased DMP levels in discharged brine pose a threat to the production of high-value chemicals. In this paper, composite particles of basic magnesium sulfate@TiO2 (BMS@TiO2) were prepared using a simple and mild loading method. These particles were utilized for the adsorption and photocatalytic degradation of DMP in brine. Compared with normal powdered materials, the granular BMS@TiO2 in this study can be easily separated from liquid, and the degradation intermediates will not enter the brine without causing secondary pollution. BMS@TiO2 consists of 5·1·7 phase (5Mg(OH)2·MgSO4·7H2O) whisker clusters embedding 2.3% TiO2. The adsorption equilibrium of DMP on BMS@TiO2 particles was achieved through hydrogen bonding and pore interception with the adsorption capacity of approximately 5 mg g−1 after 6 h. The photodegradation efficiency of DMP adsorbed on BMS@TiO2 reached about 92% within 16 h, which is compared with that of pure TiO2 nanoparticles. Additionally, excellent stability and recyclability of BMS@TiO2 were also observed in five cycle tests of adsorption and photocatalytic degradation of DMP, and the possible photocatalytic degradation pathways and mechanism of DMP are proposed following molecular electrostatic potential analysis. This work provides a sustainable and environmentally friendly approach for eliminating organic micropollutants from water environments.

Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

In northwest China, changes in cultivation patterns and the scarcity of preferred hosts have forced Helicoverpa armigera to feed on the marginal host walnut (Juglans regia). However, the mechanisms allowing this adaptation remain poorly understood. Here, we investigated the behavioral, physiological, and molecular mechanisms underlying the local adaptation of this pest to walnut fruits. The green husk and shell generally contained higher levels of phytochemicals than the kernel. Bioassays revealed that the phytochemical-rich green husk and shell were less preferred, reduced larval fitness and growth, and elevated the activity of detoxification enzymes compared to the nutrient-rich kernel, which were further supported by a larger number of upregulated detoxification genes in insects fed green husks or shells based on transcriptome sequencing. Together, these data suggest that P450 genes (LOC110371778) may be crucial to H. armigera adaptation to the phytochemicals of walnuts. Our findings provide significant insight into the adaptation of H. armigera to walnut, an alternative host of lower quality. Meanwhile, our study provides a theoretical basis for managing resistance to H. armigera larvae in walnut trees and is instrumental in developing comprehensive integrated pest management strategies for this pest in walnut orchards and other agricultural systems.

Rubidium and cesium are critical strategic elements, and their development and utilization are of great significance. In this study, a magnesium ammonium phosphate (MAP) adsorbent was prepared and characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, and Fourier transform infrared spectroscopy (FTIR). The adsorption performance of the adsorbent for Rb+ and Cs+ in solution was investigated. The results showed that the adsorbent exhibited high adsorption capacities of 2.83 mol/g for Rb+ and 4.37 mol/g for Cs+. In simulated brine, the adsorbent demonstrated excellent selectivity for Cs+. Kinetic and thermodynamic studies indicated that the adsorption process followed a pseudo-second order kinetic model and Langmuir isotherm model. The primary adsorption mechanism was an ion exchange. The development of this adsorbent holds significant promise for the extraction of rubidium and cesium from liquid resources.

When shield tunneling is carried out in poor geological areas with high water head and low strength, there is a great construction risk, and the external water pressure is one of the key factors affecting the stability and safety of the tunnel during shield tunneling. Taking the Yinguruxin tunnel of Xin’an County as the engineering background, the geological conditions and water-bearing characteristics of the water-rich area in the tunnel excavation path are analyzed by means of drilling and high-density electrical method, and the pumping test is used to evaluate the groundwater linkage in the water-rich area. The 2022 version of Midas GTS NX is used to study the distribution characteristics of external water pressure during tunnel excavation in fault zones, and the influence of different drainage rates on the external water pressure of the tunnel is analyzed. The results show that the rock mass in the unfavorable geological section of the tunnel excavation is broken and rich in water, but the complexity of the stratum leads to uneven water richness in the axis direction of the tunnel. The drainage rate is the key to affecting the external water pressure of the lining. The drainage rate is the key to affecting the external water pressure of the tunnel. The correct drainage rate is an effective measure to reduce the external water pressure of the tunnel. The internal and external water pressure of the tunnel decreases with the increase of the drainage rate. When the drainage rate reaches 66.67% of the water inflow, the external water pressure of the tunnel can be reduced to 0.3 MPa to ensure the safety of the earth pressure balance shield machine in the tunneling process. The conclusion provides a useful reference for the high water pressure control of the tunnel shield tunneling process.

In order to solve the problems of insufficient communication rate between networked fighters, signals being easily eavesdropped and interfered, and unsatisfactory networking performance in the existing air-to-air inter-aircraft data links, a millimeter wave communication air-to-air data link system was proposed.The millimeter wave communication technology was introduced into the data link system, and reconfigurable intelligent surface, orthogonal time frequency space modulation, integrated sensing and communication, and wireless ad hoc network technology were integrated.The analysis and simulation results show that the proposed system is superior to existing systems in terms of transmission bandwidth, anti-interception and anti-interference performance and hardware complexity and cost.At the same time, the proposed system faces technical challenges such as real-time beam tracking, which need further research to solve.

The soft and fractured strata can cause significant deformation of surrounding rock during tunnel excavation. This study analyzes field monitoring test results and compares numerical simulations from the third bid project of the Dali I section construction within the water diversion project in central Yunnan to address the issue of significant deformation following tunnel excavation in soft and fractured strata. It proposes an optimized support scheme consisting of a densified steel arch and enhanced initial support strength and stiffness. In addition, the research investigates support effectiveness considering varying support strengths and steel arch ring spacing. The study findings indicated the following: (1) The tunnel traverses soft and fractured strata, causing unevenly distributed vertical convergence deformation around the cavern. The maximum settlement occurs at the crown, showing pronounced nonlinearity. (2) The maximum stress in the steel arch is concentrated at the arch crown, measuring −19.02 MPa. The arch remains compressed, with stress decreasing from the crown to the waist. (3) The axial force in the anchor bolt reduces from the crown to the arch’s waist on both sides. As the depth of the rock mass increases, the axial force in each anchor bolt decreases and the tension state is maintained. The maximum axial force reaches 46.57 kN. (4) The maximum displacement decreases from 4.21 to 0.15 cm after the optimized support structure is implemented, demonstrating the optimization scheme’s effectiveness. Future constructions can refer to this scheme and make necessary adjustments based on various terrain conditions to ensure safety.

Background: Stroke is one of the leading causes of mortality and permanent disability worldwide. However, the current stroke treatment has a limited effect. Therefore, a new treatment is urgently needed. Stem cell therapy is a cutting-edge treatment for stroke patients. This study aimed to gain better understanding of global stem cell trends in stroke via a bibliometric analysis.Methods: We used the Web of Science Core Collection to search pertinent articles about stem cells in stroke published between 2004 and 2022. Analysis was conducted using CiteSpace, VOSviewer, and the R package “bibliometrix” to identify publication outputs, countries/regions, institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords.Results: A total of 6,703 publications were included in the bibliometric analysis. The total number of citations significantly and rapidly increased between 2004 and 2022, with the most pronounced growth pattern observed in the period of 2008–2009. In terms of authoritarian countries, the USA had the most publications among the countries. As for institutions and authors, the most prolific institution was the University of South Florida, followed by Oakland University and then Shanghai Jiao Tong University, and Chopp, M. and Borlongan, Cesario V, had the most output among the authors. Regarding the journals, Cell Transplantation had the highest publication, followed by Brain Research. As for references, “Mesenchymal stem cells as trophic mediators” was the most frequently cited (2,082), and the article entitled Neuronal replacement from endogenous precursors in the adult brain after stroke had the strongest burstiness (strength = 81.35). Emerging hot words in the past decade included “adhesion molecule,” “mesenchymal stromal cell,” “extracellular vesicle,” “pluripotent stem cells,” “signaling pathway,” “plasticity,” and “exosomes.”Conclusion: Between 2004 and 2022, the terms “neurogenesis,” “angiogenesis,” “mesenchymal stem cells,” “extracellular vesicle,” “exosomes,” “inflammation,” and “oxidative stress” have emerged as the hot research areas for research on stem cells in stroke. Although stem cells exert a number of positive effects, the main mechanisms for mitigating the damage caused by stroke are still unknown. Clinical challenges may include complicating factors that can affect the efficacy of stem cell therapy, which are worth a deep exploration.

The research is aimed at exploring the influences of microwave irradiation on the physicomechanical properties and Cerchar abrasivity index (CAI) of rocks. For this purpose, basalt collected in Chifeng (the Inner Mongolia Autonomous Region, China) was taken as research objects to carry out microwave irradiation tests for different durations in a multimode cavity. By using the MTS815 mechanical testing machine and the abrasion servo tester of rocks, mechanical tests and Cerchar abrasion tests were conducted before and after microwave irradiation. Changes in the mass, volume, surface fractures, surface temperature, ultrasonic wave velocity, uniaxial compressive strength (UCS), and CAI of the basalt samples before and after microwave irradiation were analyzed. Results show that the surface temperature of basalt samples linearly increases with the duration of microwave irradiation. The volume and fracture coalescence of the rock samples both increase with the prolonging duration of microwave irradiation. The mass, ultrasonic wave velocity, UCS, and CAI of the basalt samples all decrease with the increase in the duration of microwave irradiation. The reduction of the physicomechanical properties and CAI of rocks indicates that microwave irradiation can reduce the wear of rock-breaking tool and thus improve the efficiency of rock breaking.

Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.

Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue, so the trained model cannot well generalize to the unseen data without calibrating the logs. In this paper, we formulated the geophysical logs calibration problem and give its statistical explanation, and then exhibited an interpretable machine learning method, i.e., Unilateral Alignment, which could align the logs from one well to another without losing the physical meanings. The involved UA method is an unsupervised feature domain adaptation method, so it does not rely on any labels from cores. The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.

Background: Ischemic stroke, the most common stroke type, has threatened human life and health. Currently, intravenous thrombolysis and endovascular thrombectomy are the mainstream treatment methods, but they may cause cerebral ischemia-reperfusion injury (CIRI), which aggravates brain injury. Consequently, it is worthwhile to start with a study of CIRI mechanism to identify better prevention and treatment methods. Applying single-cell RNA sequencing (scRNA-seq) technology to further understand the biological functions of various cell types in CIRI will facilitate the intervention of CIRI. Methods: This study aimed to establish a rat middle cerebral artery occlusion (MCAO) model to simulate cerebral ischemia-reperfusion, perform enzymatic hydrolysis, and suspend cerebral cortex tissue edema. Single-cell transcriptome sequencing was used, combined with cluster analysis, t-distributed stochastic neighbor embedding (t-SNE) visualization, and other bioinformatics methods to distinguish cell subgroups while using gene ontology (GO) function enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment to reveal the biological function of each cell subgroup. Results: We identified 21 brain clusters with cell type-specific gene expression patterns and cell subpopulations, as well as 42 marker genes representing different cell subpopulations. The number of cells in clusters 0–3 increased significantly in MCAO group compared to that in the sham group, and nine-cell subpopulations exhibited remarkable differences in the number of genes. Subsequently, GO and KEGG analyses were performed on the top 40 differentially expressed genes (DEGs) in the six cell subpopulations with significant differences. These results indicate that biological processes and signaling pathways are involved in different cell subpopulations. Conclusions: ScRNA-seq revealed the diversity of cell differentiation and the unique information of cell subpopulations in the cortex of rats with acute ischemic stroke, providing novel insight into the pathological process and drug discovery in stroke.

The types, evolution processes, formation mechanisms, and depositional models of deep-water gravity flow deposits in a lacustrine rift basin are studied through core observation and systematic analysis. Massive transport of slide and slump, fluid transport of debris flow and turbidity currents are driven by gravity in deep-water lacustrine environment. The transformation between debris flow and turbidity current, and the transformation of turbidity current between supercritical and subcritical conditions are the main dynamic mechanisms of gravity flow deposits in a lake basin. The erosion of supercritical turbidity current controls the formation of gravity-flow channel. Debris flow deposition gives rise to tongue shape lobe rather than channel. Deep-water gravity flow deposits are of two origins, intrabasinal and extrabasinal. Intrabasinal gravity flow deposits occur as single tongue-shape lobe or fan of stacking multiple lobes. Extrabasinal gravity-flow deposits occur as sublacustrine fan with channel or single channel sand body. However, the nearshore subaqueous fan is characterized by fan of stacking multiple tongue shape lobes without channel. The differential diagenesis caused by differentiation in the nearshore subaqueous fan facies belt results in the formation of diagenetic trap. The extrabasinal gravity flow deposits are one of the important reasons for the abundant deep-water sand bodies in a lake basin. Slide mass-transport deposits form a very important type of lithologic trap near the delta front often ignored. The fine-grained sediment caused by flow transformation is the potential “sweet spot” of shale oil and gas.

To explore the influences of microwave irradiation on the rock-breaking efficiency of tunnel boring machine (TBM) cutters, a multimode cavity with a microwave power of 2100 W was used to conduct microwave irradiation tests on Chifeng basalt for different durations. A combined abrasion test device for the reduced-scale SP3-I TBM cutter was adopted to conduct cutter wear and rock-breaking tests on basalt samples before and after microwave irradiation. Influences of microwave irradiation on the wear loss of the reduced-scale TBM cutter, rock removal, cutting force, and specific energy were investigated. The results show that the wear loss of the reduced-scale TBM cutter linearly reduces while the rock removal linearly increases with increasing exposure to microwave irradiation. When the penetration depth and the cutter spacing are fixed, the cutting force and the work done by the cutting force exerted by the reduced-scale TBM cutter decrease linearly with increasing exposure to microwave irradiation. The rock-breaking specific energy decreases with increasing exposure to microwave irradiation in an exponential manner. These results indicate that microwave irradiation can prolong the service life of the TBM cutter in the rock-breaking process, reduce the required thrust and torque of the cutter, and improve the rock-breaking efficiency of the TBM.

Cerebral ischemia and the subsequent cerebral ischemia-reperfusion (I/R) injury usually result in neuronal impairment with serious disabilities. Although neural stem cell (NSC) transplantation can help with functional recovery after stroke, due to the improper cellular milieu after brain injury, direct NSC transplantation will result low cell survival rates and differentiation efficiency into neurons. Here a conductive hybrid hydrogel based on gelatin methacryloyl (GelMA) and poly(3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT:PSS) was created as a NSC niche for the treatment of cerebral I/R injury. GelMA/PEDOT:PSS hybrid hydrogel promoted the development of NSCs into neurons. GelMA/PEDOT:PSS hydrogel along with NSCs could enhance neuronal activity and minimize apoptosis when co-cultured with oxygen-glucose deprivation/reperfusion (OGD/R) neurons. Furthermore, after 7 days of implantation, GelMA/PEDOT:PSS/NSCs on the infarcted brain of rats subjected to reperfusion injury after middle cerebral artery occlusion (MCAO) was verified to attenuate inflammatory responses. These findings show that the conductive GelMA/PEDOT:PSS hybrid hydrogel could regulate NSC development and act as promising cell niches for the treatment of cerebral I/R injury.

Abstract Transcription factors represent the crucial role of controlling gene transcription in cancer development and progression. However, their functions in gastric cancer have not been thoroughly characterized. For this study, we comprehensively evaluated the correlation between infiltration patterns of tumor microenvironment (TME) cells and TFs expression in the cohort of stomach adenocarcinoma (STAD) from TCGA database. We integrally explored differential expression panel and prognostic value of candidate TFs in TCGA‐STAD cohort. Notably, we found a key transcription factor named HEYL, which its expression level was correlated with stromal component transformation of TME. HEYL was regularly high expressed in gastric cancer and correlated with patients’ poor prognosis. Knockdown of HEYL prominently abrogated the tendency of cell proliferation, migration, and progression in gastric cancer. Consistently, overexpression of HEYL strikingly accelerated the gastric carcinoma development through activating oncogenic signaling pathways and transcriptional activation of cadherin 11 (CDH11). Our findings not only identified the close relationship between TFs and TME phenotype, but also emphasized the crucial importance of TFs, especially HEYL, which could be identified as a candidate biomarker to evaluate prognostic risk and therapeutic effect in gastric cancer.

The common occurrence of various number and distribution of hole flaws complicates the mechanical behavior and fracture mode of the rock masses that contain them. This study develops seven numerical models of limestone samples with different numbers and distributions of circular hole flaws using 2-D particle flow discrete element code (PFC2D) to investigate their impact on the mechanical properties of limestone while maintaining the same flaws area. In addition, it analyzes the effect of these factors on the mesomechanical features of each model, including the characteristic stress values (peak stress, crack initiation, and damage stress), crack evolution, stress, and displacement field. The results showed that the peak stress, crack initiation, and damage stress of the single-hole model are between those of multihole models. As the arrangement of dip angle increases, the peak stress, crack initiation, and damage stress of models with the same number of multihole flaws exhibit a V-shaped change. The characteristic stress values are the largest when the holes are vertically aligned. Model differences in crack development path, shape, and number, as well as stress concentration area and failure mode, are primarily due to the number and distribution of holes. The circular holes are arranged at approximately 45°, and the greater the number of defects, the more likely the model is to fail. The study’s findings can provide support and reference for the research system of deformation and failure of rock mass with hole flaws.

Abstract Ginger is traditionally used as a sexual enhancer in folk medicine. Despite extensive studies on the effect of ginger on reproduction, the molecular mechanism of ginger prevention effect on ethanol-induced reproductive disorder is not fully understood. Twenty-four adult male ratswereallocated into control, ethanol (4 g/kg of body weight (BW)/day), ginger (250 mg/kg of BW/day) and ginger-ethanol group. Ginger and ethanol were administrated by gavage for 28 days. Testicular concentration of testosterone, TNF-α, and antioxidant enzymes activity and serum concentration of gonadotropins hormone and testosterone were measured. The gene expression of Nrf2 and NF-κB which regulate oxidative damage and inflammation, respectively, and StAR, P450scc and 17βHSD which are involved in testosterone synthesis were detected. Ethanol significantly decreased gonadotropin hormones, oxidative markers, expression of genes involved in testosterone synthesis and Nrf2, and in reverse significantly increased TNF-α, MDA and gene expression of NF-κB compared to control (p

In recent years, grassland degradation has become a global ecological problem. The identification of degraded grassland species is of great significance for monitoring grassland ecological environments and accelerating grassland ecological restoration. In this study, a ground spectral measurement experiment of typical grass species in the typical temperate grassland of Inner Mongolia was performed. An SVC XHR-1024i spectrometer was used to obtain field measurements of the spectra of grass species in the typical grassland areas of the study region from 6–29 July 2021. The parametric characteristics of the grass species’ spectral data were extracted and analyzed. Then, the spectral characteristic parameters + vegetation index, first-order derivative (FD) and continuum removal (CR) datasets were constructed by using principal component analysis (PCA). Finally, the RF, SVM, BP, CNN and the improved CNN model were established to identify Stipa grandis (SG), Cleistogenes squarrosa (CS), Caragana microphylla Lam. (CL), Leymus chinensis (LC), Artemisia frigida (AF), Allium ramosum L. (AL) and Artemisia capillaris Thunb. (AT). This study aims to determine a high-precision identification method based on the measured spectrum and to lay a foundation for related research. The obtained research results show that in the identification results based on ground-measured spectral data, the overall accuracy of the RF model and SVM model identification for different input datasets is low, but the identification accuracies of the SVM model for AF and AL are more than 85%. The recognition result of the CNN model is generally worse than that of the BP neural network model, but its recognition accuracy for AL is higher, while the recognition effect of the BP neural network model for CL is better. The overall accuracy and average accuracy of the improved CNN model are all the highest, and the recognition accuracy of AF and CL is stable above 98%, but the recognition accuracy of CS needs to be improved. The improved CNN model in this study shows a relatively significant grass species recognition performance and has certain recognition advantages. The identification of degraded grassland species can provide important scientific references for the realization of normal functions of grassland ecosystems, the maintenance of grassland biodiversity richness, and the management and planning of grassland production and life.

For geological analysis tasks such as reservoir characterization and petroleum exploration, lithology identification is a crucial and foundational task. The logging lithology identification tasks at this stage generally build a lithology identification model, assuming that the logging data share an independent and identical distribution. This assumption, however, does not hold among various wells due to the variations in depositional conditions, logging apparatus, etc. In addition, the current lithology identification model does not fully integrate the geological knowledge, meaning that the model is not geologically reliable and easy to interpret. Therefore, we propose a cross-domain lithology identification method that incorporates geological information and domain adaptation. This method consists of designing a named UAFN structure to better extract the semantic (depth) features of logging curves, introducing geological information via wavelet transform to improve the model’s interpretability, and using dynamic adversarial domain adaptation to solve the data-drift issue cross-wells. The experimental results show that, by combining the geological information in wavelet coefficients with semantic information, more lithological features can be extracted in the logging curve. Moreover, the model performance is further improved by dynamic domain adaptation and wavelet transform. The addition of wavelet transform improved the model performance by an average of 6.25%, indicating the value of the stratigraphic information contained in the wavelet coefficients for lithology prediction.

Abstract Background N7‐methylguanosine (m7G) modification is one of the most common transfer RNA (tRNA) modifications in humans. The precise function and molecular mechanism of m7G tRNA modification in hepatocellular carcinoma (HCC) remain poorly understood. Methods The prognostic value and expression level of m7G tRNA methyltransferase complex components methyltransferase‐like protein‐1 (METTL1) and WD repeat domain 4 (WDR4) in HCC were evaluated using clinical samples and TCGA data. The biological functions and mechanisms of m7G tRNA modification in HCC progression were studied in vitro and in vivo using cell culture, xenograft model, knockin and knockout mouse models. The m7G reduction and cleavage sequencing (TRAC‐seq), polysome profiling and polyribosome‐associated mRNA sequencing methods were used to study the levels of m7G tRNA modification, tRNA expression and mRNA translation efficiency. Results The levels of METTL1 and WDR4 are elevated in HCC and associated with advanced tumour stages and poor patient survival. Functionally, silencing METTL1 or WDR4 inhibits HCC cell proliferation, migration and invasion, while forced expression of wild‐type METTL1 but not its catalytic dead mutant promotes HCC progression. Knockdown of METTL1 reduces m7G tRNA modification and decreases m7G‐modified tRNA expression in HCC cells. Mechanistically, METTL1‐mediated tRNA m7G modification promotes the translation of target mRNAs with higher frequencies of m7G‐related codons. Furthermore, in vivo studies with Mettl1 knockin and conditional knockout mice reveal the essential physiological function of Mettl1 in hepatocarcinogenesis using hydrodynamics transfection HCC model. Conclusions Our work reveals new insights into the role of the misregulated tRNA modifications in liver cancer and provides molecular basis for HCC diagnosis and treatment.

Studies investigating age-related positivity effects during facial emotion processing have yielded contradictory results. The present study aimed to elucidate the mechanisms of cognitive control during attentional processing of emotional faces among older adults. We used go/no-go detection tasks combined with event-related potentials and source localization to examine the effects of response inhibition on age-related positivity effects. Data were obtained from 23 older and 23 younger healthy participants. Behavioral results showed that the discriminability index (d') of older adults on fear trials was significantly greater than that of younger adults [t(44)=2.37, p=0.024, Cohen’s d=0.70], whereas an opposite pattern was found in happy trials [t(44)=2.56, p=0.014, Cohen’s d=0.75]. The electroencephalography results on the amplitude of the N170 at the left electrode positions showed that the fear-neutral face pairs were larger than the happy-neutral ones for the younger adults [t(22)=2.32, p=0.030, Cohen’s d=0.48]; the older group’s right hemisphere presented similar tendency, although the results were not statistically significant [t(22)=1.97, p=0.061, Cohen’s d=0.41]. Further, the brain activity of the two hemispheres in older adults showed asymmetrical decrement. Our study demonstrated that the age-related “positivity effect” was not observed owing to the depletion of available cognitive resources at the early attentional stage. Moreover, bilateral activation of the two hemispheres may be important signals of normal aging.

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Electronic properties of superatomic crystals have not been sufficiently explored due to the versatility of their building units; moreover, their interunit couplings are even poorly understood. Here, we present a joint experiment-theory investigation of a rationally designed layered superatomic crystal of Au_{6}Te_{12}Se_{8} (ATS) cubes stacked by noncovalent intercube quasibonds. We find a sequential-emerged anisotropic triple-cube charge density wave (TCCDW) and polarized metallic states below 120 K, as revealed via scanning tunneling microscopy and spectroscopy, angle-resolved photoemission spectroscopy, transport measurement, Raman spectra, and density-functional theory. The polarized states are locked in an antiparallel configuration, which is required for maintaining the inversion symmetry of the center cube in the TCCDW. The antipolar metallic states are thus interweaved by the CDW and the polarized metallic states, and primarily ascribed to electronic effects via theoretical calculations. This work not only demonstrates a microscopic picture of the interweaved CDW and polarized charge orders in the superatomic crystal of ATS, but also sheds light on expanding the existing category of quantum materials to noncovalent solids.

The degradation of a machine is nonlinear, which brings challenges to its performance assessment during condition monitoring, especially when there is a run-in period. Technically, the quantification of mechanical degradation is to define a distance metric from a health baseline. This paper develops an integrated condition monitoring scheme, where the degradation evaluation and fault diagnosis are combined by using one technical framework. Specifically, an optimum healthy state (OHS) is determined based on the clustering center of the self-organizing map (SOM) neural network instead of the commonly used initial working state. Then, the distance metric deviating from the OHS is defined as a health index, where the perceptual vibration hashing is improved to make it more sensitive to degradation. Visualized fault diagnosis is carried out by the SOM when the health index exceeds the preset threshold. Two cases with experiments are conducted to demonstrate the accuracy and robustness of the proposed method.

High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.

Background: Although numerous studies have suggested that the gradually increasing selective preference for positive information over negative information in older adults depends on cognitive control processes, few have reported the characteristics of different attention stages in the emotional processing of older individuals. The present study used a real-time eye-tracking technique to disentangle the attentional engagement and disengagement processes involved in age-related positivity effect (PE).Methods: Eye movement data from a spatial-cueing task were obtained for 32 older and 32 younger healthy participants. The spatial-cueing task with varied cognitive loads appeared to be an effective way to explore the role of cognitive control during the attention engagement and disengagement stages of emotion processing.Results: Compared with younger adults, older participants showed more positive gaze preferences when cognitive resources were sufficient for face processing at the attention engagement stage. However, the age-related PE was not observed at the attention disengagement stage because older adults had more difficulty disengaging from fearful faces than did the younger adults due to the consumption of attention by the explicit target judgment.Conclusion: The present study highlights how cognitive control moderates positive gaze preferences at different attention processing stages. These findings may have far-reaching implications for understanding, preventing, and intervening in unsuccessful aging and, thus, in promoting active and healthy aging.

Background: Transcriptional factors (TFs) are responsible for orchestrating gene transcription during cancer progression. However, their roles in gastric cancer (GC) remain unclear.Methods: We analyzed the differential expressions of TFs and, using GC cells and tissues, investigated plexin C1 (PLXNC1) RNA levels, as well as PLXNC1's clinical relevance and functional mechanisms. The molecular function of PLXNC1 was evaluated in vitro and in vivo. Kaplan-Meier curves and the log-rank test were used to analyze overall survival (OS) and disease-free survival (DFS).Results: PLXNC1 was frequently up-regulated in GC and associated with poor prognosis. The expression level of PLXNC1 could serve as an independent biomarker to predict a patient's overall survival. Notably, knockdown of PLXNC1 significantly abolished GC cell proliferation, and migration, and overexpression of PLXNC1 accelerated carcinogenesis in GC. The gene set enrichment analysis (GSEA) indicated that high-expression of PLXNC1 was positively correlated with the activation of epithelial-mesenchymal transition (EMT), TNF-α, and IL-6/STAT3 signaling pathways. PLXNC1 promoted proliferation and migration of GC cells through transcriptional activation of the interleukin 6 signal transducer (IL6ST), which could rescue the malignant behavior of PLXNC1-deficient GC cells.Conclusions: Our study demonstrated that the PLXNC1 plays an oncogenic role in GC patients. The PLXNC1-IL6ST axis represents a novel potential therapeutic target for GC.

8-Aminoquinolines (8-AQs) are an important class of anti-infective therapeutics. The monoamine oxidases (MAOs) play a key role in metabolism of 8-AQs. A major role for MAO-A in metabolism of primaquine (PQ), the prototypical 8-AQ antimalarial, has been demonstrated. These investigations were further extended to characterize the enantioselective interactions of PQ and NPC1161 (8-[(4-amino-1-methylbutyl) amino]-5-[3, 4-dichlorophenoxy]-6-methoxy-4-methylquinoline) with human MAO-A and -B. NPC1161B, the (R)-(−) enantiomer with outstanding potential for malaria radical cure, treatment of visceral leishmaniasis and pneumocystis pneumonia infections is poised for clinical development. PQ showed moderate inhibition of human MAO-A and -B. Racemic PQ and (R)-(−)-PQ both showed marginally greater (1.2- and 1.6-fold, respectively) inhibition of MAO-A as compared to MAO-B. However, (S)-(+)-PQ showed a reverse selectivity with greater inhibition of MAO-B than MAO-A. Racemic NPC1161 was a strong inhibitor of MAOs with 3.7-fold selectivity against MAO-B compared to MAO-A. The (S)-(+) enantiomer (NPC1161A) was a better inhibitor of MAO-A and -B compared to the (R)-(−) enantiomer (NPC1161B), with more than 10-fold selectivity for inhibition of MAO-B over MAO-A. The enantioselective interaction of NPC1161 and strong binding of NPC1161A with MAO-B was further confirmed by enzyme-inhibitor binding and computational docking analyses. Differential interactions of PQ and NPC1161 enantiomers with human MAOs may contribute to the enantioselective pharmacodynamics and toxicity of anti-infective 8-AQs therapeutics.

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

SMAD ubiquitination regulatory factor 1 (Smurf1) is a Nedd4 family E3 ubiquitin ligase that regulates cell motility, polarity and TGFβ signaling. Smurf1 contains an N-terminal protein kinase C conserved 2 (C2) domain that targets cell membranes and is required for interactions with membrane-localized substrates such as RhoA. Here, we investigated the lipid-binding mechanism of Smurf1 C2, revealing a general affinity for anionic membranes in addition to a selective affinity for phosphoinositides (PIPs). We found that Smurf1 C2 localizes not only to the plasma membrane but also to negatively charged intracellular sites, acting as an anionic charge sensor and selective PIP-binding domain. Site-directed mutagenesis combined with docking/molecular dynamics simulations revealed that the Smurf1 C2 domain loop region primarily interacts with PIPs and cell membranes, as opposed to the β-surface cationic patch employed by other C2 domains. By depleting PIPs from the inner leaflet of the plasma membrane, we found that PIP binding is necessary for plasma membrane localization. Finally, we used a Smurf1 cellular ubiquitination assay to show that the amount of ubiquitin at the plasma membrane interface depends on the lipid-binding properties of Smurf1. This study shows the mechanism by which Smurf1 C2 targets membrane-based substrates and reveals a novel interaction for non-calcium-dependent C2 domains and membrane lipids.

The full N,N-methylation of 4,4′-methylenedianiline (MDA) with dimethyl carbonate (DMC) was investigated. The yield of the major product 4,4′-methylene bis(N,N-dimethylaniline) (MBDMA) reached as high as 97% over NaY catalyst at 190°C for 6 h. The catalyst could be used for two more times with acceptable MBDMA yields higher than 90%. The main by-products were identified as three N-methylated derivatives. Surprisingly, the formation of the N-methoxycarbonylation product was extremely restrained, which could be produced in high yields of 98% on zinc acetate catalyst. Furthermore, the reaction pathway to the major product MBDMA was proposed. Finally, a feasible synthetic route of 4,4′-methylene bis(N,N-dimethylaniline) (MBDMA) was established, featuring a high yield, mild reaction conditions, and simple operations.

BACKGROUND:Cognitive bias modification (CBM), a set of techniques for modifying bias in information processing-is considered a novel intervention for social anxiety disorder (SAD), which has drawn considerable interest from researchers. However, the effects of CBM on SAD are not consistent. Some studies have demonstrated significant positive effects compared to control groups, while others have found no such effects. AIMS:We conducted a meta-analysis aimed at quantitatively assessing the effects of CBM on SAD at post-test. METHOD:Through a systematic literature search by two independent raters, 34 articles (36 randomized studies) including 2,550 participants were identified. A multilevel modeling approach was employed to assess the effects of CBM on SAD, and to explore the potentially crucial procedures and sample characteristics that enhance the effectiveness of benign training. RESULTS:In general, there were small but significant effects of CBM on the primary symptoms of SAD (g = 0.17), cognitive bias (CB) toward threat (g = 0.32), and reactivity in stressful situations (g = 0.25), but non-significant effects on secondary symptoms. However, the interpretation modification program was more effective than was attentional bias modification in reducing SAD primary symptoms and negative CB. Laboratory training procedures produced larger primary symptom reductions compared to Internet-based training, whereas the percentage of contingency and feedback about training performance boosted cognitive effects only. Finally, the following groups were more likely to benefit from CBM: younger participants (primary symptoms and cognitive effects), women (primary symptom effects), and samples with stronger CB (stressor effects). The quality of the randomized controlled trials was less than desirable, as there was some indication of publication bias in our study. CONCLUSIONS:Current findings broadly supported cognitive theories of SAD that consider a bidirectional or mutually reinforcing relationship between symptoms and CBs. However, the small therapeutic effect observed here indicates that it is necessary to develop more reliable and efficient CBM interventions that are specific to SAD.

Natural products are an abundant source of potential drugs, and their diversity makes them a rich and viable prospective source of bioactive cannabinoid ligands. Cannabinoid receptor 1 (CB1) antagonists are clinically established and well documented as potential therapeutics for treating obesity, obesity-related cardiometabolic disorders, pain, and drug/substance abuse, but their associated CNS-mediated adverse effects hinder the development of potential new drugs and no such drug is currently on the market. This limitation amplifies the need for new agents with reduced or no CNS-mediated side effects. We are interested in the discovery of new natural product chemotypes as CB1 antagonists, which may serve as good starting points for further optimization towards the development of CB1 therapeutics. In search of new chemotypes as CB1 antagonists, we screened the in silico purchasable natural products subset of the ZINC12 database against our reported CB1 receptor model using the structure-based virtual screening (SBVS) approach. A total of 18 out of 192 top-scoring virtual hits, selected based on structural diversity and key protein–ligand interactions, were purchased and subjected to in vitro screening in competitive radioligand binding assays. The in vitro screening yielded seven compounds exhibiting >50% displacement at 10 μM concentration, and further binding affinity (Ki and IC50) and functional data revealed compound 16 as a potent and selective CB1 inverse agonist (Ki = 121 nM and EC50 = 128 nM) while three other compounds—2, 12, and 18—were potent but nonselective CB1 ligands with low micromolar binding affinity (Ki). In order to explore the structure–activity relationship for compound 16, we further purchased compounds with >80% similarity to compound 16, screened them for CB1 and CB2 activities, and found two potent compounds with sub-micromolar activities. Most importantly, these bioactive compounds represent structurally new natural product chemotypes in the area of cannabinoid research and could be considered for further structural optimization as CB1 ligands.

Existing studies on the adsorption of alkaline earth metal ions have mainly focused on the adsorption of one alkaline earth metal ion. However, under natural conditions, alkaline earth metal ions usually co-exist. While separating them using an adsorbent, they compete with each other for adsorption onto the adsorbent. In this work, comparative and competitive adsorption of Mg, Ca and Sr ions onto four resins (D001, DJH003, LSC-100 and LSC-500) of sodium form were investigated in batch experiments at 25 °C. Our results showed that in all the experiments, the equilibrium point was attained in approximately 4 hours. In general, the pseudo-first-order model is more suitable to describe the adsorption kinetic data than the pseudo-second-order model. For both D001 and DJH003 resins, there are bigger differences in the adsorption amounts of Mg, Ca and Sr ions for the competitive adsorption than for the non-competitive adsorption. Under competitive adsorption conditions, the separation factor follows the order of β Sr/Mg > β Ca/Mg , and the order of preference for adsorption is Sr 2+ > Ca 2+ > Mg 2+ . Therefore, it can be seen that there is a clear inhibitory effect for the adsorption of Mg ions owing to the competition of other ions. However, for both LSC-100 and LSC-500 resins, the competitive adsorption of Mg, Ca and Sr ions does not show obvious differences. The competitive adsorption results for all the resins were explained by considering hydration of the metal ions, electrostatic attractions and complexation between the metal ions and the resins. The results obtained would be helpful for understanding the competitive adsorption processes and the recovery or removal of one or more alkaline earth metal ions from aqueous solutions.

Composite adsorbents, Ca(ALG) 2 –Cu 2 O, Ca(ALG) 2 –AgCl and Ca(ALG) 2 –Ag, were prepared using Cu 2 O, AgCl and Ag as the adsorption-active components and calcium alginate as the matrix material. The adsorption of iodide from aqueous solutions onto these adsorbents was investigated as a function of initial solution pH, temperature and co-existing NaCl. The loading of the adsorption-active components in the composite adsorbents is much higher than that in the reported composite adsorbents. In neutral solutions, the equilibrium adsorption amount follows the order of Ca(ALG) 2 –AgCl <> Ca(ALG) 2 –Ag >> Ca(ALG) 2 –Cu 2 O. The adsorption by Ca(ALG) 2 –AgCl is insensitive to pH, while the adsorption by Ca(ALG) 2 –Cu 2 O and Ca(ALG) 2 –Ag is pH dependent. The effect of temperature on the adsorption is more pronounced for Ca(ALG) 2 –Cu 2 O than for Ca(ALG) 2 –AgCl and Ca(ALG) 2 –Ag. The co-existing NaCl does not affect the adsorption by Ca(ALG) 2 –AgCl very much; however, it suppresses the adsorption by Ca(ALG) 2 –Ag, and especially, Ca(ALG) 2 –Cu 2 O. Chemical adsorption is the main mechanism for all the three adsorbents. Ca(ALG) 2 –AgCl was found to be the best adsorbent owing to its highest adsorption capacity, suitable adsorption rate and insensitivity to solution pH, temperature and co-existing NaCl.