Cancer stem cells (CSCs) have been demonstrated to have a close association with cancer initiation, metastasis, drug resistance, and recurrence. Consequently, the exploration and identification of new compounds that target CSCs have become a prominent area in recent drug development. Natural products, characterized by their structural and bioactive variety, offer substantial inspiration for drug design and development. From an innovative vantage point of structure - activity relationship (SAR) investigations, this paper comprehensively reviews various categories of anti-CSCs natural products, all of which share Michael acceptors as a common pharmacophore. This review not only furnishes fresh perspectives for the research on anti-CSCs natural products but also presents alternative strategies for the molecular design and structural modification of potential drug molecules aimed at CSCs.

Lithium-sulfur (Li-S) batteries are promising for high-energy storage, yet practical devices are constrained by the coupled challenges of polysulfide shuttle and slow sulfur conversion, which become particularly severe at high sulfur loading and lean electrolyte. Here we report a vermiculite-derived nanofluidic separator interface that couples ion-selective transport with conversion regulation to accelerate sulfur electrochemistry and enhance sulfur utilization. Li-intercalated vermiculite nanosheets (VMT-Li) are assembled onto a commercial polypropylene (PP) separator to form a laminated coating with negatively charged, interlayer-confined galleries. This nanofluidic interface preferentially conducts Li+ while rejecting anionic polysulfides, and simultaneously creates a confined reaction microenvironment that enriches intermediates and promotes Li2S formation, thereby reconciling shuttle suppression with accelerated sulfur redox kinetics. Consequently, Li-S cells using VMT-Li/PP exhibit markedly reduced interfacial impedance (81.9 Ω vs 227.1 Ω for PP) and stable long-term cycling (retaining 581.4 mAh g-1 after 500 cycles at 2 C). Importantly, the benefits persist under practical conditions (sulfur loading ∼9 mg cm-2, E/S = 6 μL mg-1) and extend to a 1 Ah-class pouch cell delivering ∼200 Wh kg-1 based on total pouch-cell mass. This work establishes nanofluidic interface engineering as a scalable strategy to enable fast sulfur conversion and realize long-life, high-areal-capacity Li-S batteries.

Sulfamethoxazole (SMX) is an emerging contaminant and its degradation mechanisms in paddy soils are important for risk control. This study combined DNA stable-isotope-probing (DNA-SIP) and Raman-Activated Cell Sorting (RACS) to explore the active SMX degraders and degradation pathways in paddy soils driven by seawater rice straw biochar amendment. Biochar significantly accelerated SMX degradation rate with the highest degradation efficiency of 96.38% achieved in the presence of 2% biochar. Across treatments, DNA-SIP and RACS individually identified the active SMX degraders belonging to 24 and 43 bacterial families, respectively, and 20 of them were jointly recognized by both methods. Particularly Anaeromyxobacteraceae, Intrasporangiaceae, Micromonosporaceae, Myxococcaceae, and Nocardioidaceae were dominant, and the degradation pathway was reconstructed based on DNA-SIP-derived metagenomic data and metabolites. Biochar changed the composition of the active SMX degraders by enriching Burkholderiaceae, Caulobacteraceae, Geodermatophilaceae, Mycobacteriaceae, Nocardiaceae, Oxalobacteraceae, Longimicrobiaceae, Planococcaceae, as well as some key degradation genes (sadA, sadC, chqB, E1.3.1.32, pcaJ, pcaI, pcaF and fadA), shifting the pathway from “benzene hydroxylation → sulfonamide cleavage” to “direct sulfonamide cleavage → desulfonation → ring opening and mineralization”. RACS captured a broader diversity and higher abundance of active degraders at the single-cell level, whereas DNA-SIP preferentially enriched core functional degraders harboring degradation genes, highlighting their complementary roles in resolving microbial identity–function relationships. Overall, this study reveals that biochar enhances SMX biodegradation by shaping microbial communities, functional genes, and metabolic pathways, providing molecular mechanisms of biochar-mediated remediation in paddy soils.

富水隧道围岩中孔洞缺陷会加剧软岩时效劣化，其应力松弛损伤机制及长期力学行为预测仍有待深入研究。以郑万高铁巴东段某隧洞泥岩为对象，开展水–岩作用下含不同孔深泥岩应力松弛及加载破坏试验，结合溶液pH值、离子浓度、扫描电镜和数字图像相关技术，研究孔洞深度对泥岩松弛特性、细观结构及破裂演化的影响；融合一维卷积神经网络的局部时间特征提取能力与长短期记忆神经网络的长期依赖表征能力构建CNN-LSTM应力松弛预测模型。结果表明：随孔洞深度增加，泥岩应力松弛量和稳定时间均增大，剩余应力比及峰值强度降低、破坏程度加剧；水–岩作用导致矿物颗粒胶结弱化、黏土矿物水化及孔隙裂隙扩展，且深孔试样的细观结构劣化更显著；松弛阶段孔周形成环状应变集中区，加载破坏阶段张拉裂纹多由孔洞附近应变集中区域起裂并逐渐贯通。CNN-LSTM融合模型能够在本文室内试验数据范围内较好表征应力松弛曲线的非线性时序变化特征，均方根误差（RMSE）降至0.001 7，决定系数（R²）增至0.995 7。研究结果可为富水软岩应力松弛行为分析及数据驱动预测方法建立提供试验参考。本文所建模型适用范围受室内试验参数限制尚不能直接外推至实际隧洞围岩长期稳定性评价，仍需结合现场监测数据、原位应力条件、裂隙网络特征和地下水化学环境进一步校准与验证。

Diffuse intrinsic pontine glioma (DIPG) is a rare and fatal pediatric brainstem malignancy for which effective treatment options are lacking. Cerebrospinal fluid (CSF) analysis can reveal intrinsic alterations and characteristic metabolic profiles of the tumor microenvironment. In this study, the proteome and metabolome of CSF from DIPG patients were comprehensively analyzed to identify potential biomarkers and the pathways involved. Functional annotation and pathway enrichment analyses were performed using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases. Bioinformatics methods were used to comprehensively analyze the proteomic and metabolomic results to identify key differentially expressed proteins, metabolites, and potential signaling pathways involved in DIPG. In total, 885 DEPs (differentially expressed proteins) were identified in cerebrospinal fluid from DIPG patients, of which 54 were upregulated and 831 were downregulated, primarily originating from the cytoplasm and cell membrane. Among the top 20 upregulated proteins, URB1 (nucleolar pre-ribosomal-associated protein 1) had the greatest statistical significance, while the remaining proteins were mostly immunoglobulin fragments. GO enrichment analysis revealed that the downregulated proteins were enriched primarily in cellular processes, metabolic processes, and binding functions. KEGG analysis revealed that upregulated proteins were significantly enriched in complement and coagulation cascades, whereas downregulated proteins were primarily associated with endocytosis and certain microbial infections. A total of 1372 metabolites were identified, of which 40 were differentially expressed: 24 were upregulated, and 16 were downregulated. Pathway analyses of the differentially expressed metabolites revealed that they were primarily related to purine metabolism and tyrosine metabolism. The multiomics analysis revealed that purine metabolism is particularly important in DIPG.

Serum biomarkers for early cancer detection often suffer from limited sensitivity and specificity due to the biochemical complexity of blood. Here, we report a surface-enhanced Raman scattering (SERS) artificial nose that integrates a set of chemically distinct molecular receptors to generate multidimensional spectral responses to human serum. These receptors exhibit large Raman cross sections and well-defined vibrational signatures, enabling high signal-to-noise readouts and subtle yet reproducible spectral perturbations upon exposure to serum components. By applying machine-learning analysis to the resulting multivariate Raman patterns, we extract a diagnostic molecular fingerprint capable of distinguishing early-stage non-small-cell lung cancer (NSCLC) from controls. Using an optimized multireceptor array, the model achieves 100% sensitivity at 98% specificity, markedly outperforming conventional serological biomarkers and imaging-based screening approaches. This work establishes a chemically tunable SERS artificial nose as a powerful strategy for serum-based cancer detection and highlights the potential of multiplexed, receptor-driven sensing for disease-associated metabolic phenotyping.

Currently, there is an increasingly urgent need of high-efficient chemosensors for detecting highly toxic chromium ions (Cr3+ and Cr2O72-). Herein we propose an innovative lanthanide-based fluorescence sensing material, i.e., terbium coordination hybrid nanoparticles (6-MP)0.01CF0.004/Tb1 that are obtained based on the coordination self-assembly strategy. Its terbium luminescence is strongly associated with the ligand-assisted terbium sensibilization energy transfer approach that is sensitive to specific inorganic stimuli. It is further revealed that (6-MP)0.01CF0.004/Tb1 can be served as a real-time, selective, competitive and quantitative fluorescence chemosensor to detect Cr3+, Cr2O72-, and NO2- in aqueous solutions based on the quenched fluorescence of hybrid. Using statistical analysis methods, the hybrid (6-MP)0.01CF0.004/Tb1 even can discriminate several groups of cation or anion analogues, especially Cr3+ and Cr2O72- that are distinguished from each other by the pH regulation. Moreover, this hybrid enables operators to monitor different contents of Cr3+, Cr2O72-, and NO2- in real water samples. This study not only develops a novel luminescent lanthanide-based hybrid but also provides a promising fluorescence sensing platform for multianalyte detection of toxic Cr3+, Cr2O72-, and NO2- in potential fields.

Lung cancer remains the leading cause of cancer-related mortality worldwide, highlighting the urgent need for accurate and minimally invasive diagnostic strategies. Metabolic reprogramming provides a promising basis for disease detection, yet sensitive analysis of low-molecular-weight metabolites in complex clinical samples remains challenging. Here, we report the rational design and systematic evaluation of noble metal nanomaterials as laser desorption/ionization mass spectrometry (LDI-MS) matrixes for high-performance metabolic fingerprinting. Compared with the conventional organic matrix α-cyano-4-hydroxycinnamic acid (CHCA), these nanomaterials exhibited reduced background interference and improved ionization efficiency. Among them, gold platinum alloy nanoparticles (Au&Pt NP) delivered superior spectral quality and sensitivity and were selected as the optimal matrix. Using Au&Pt NP-assisted LDI-MS, serum metabolic fingerprints were obtained from 45 lung cancer patients and 22 healthy controls. A support vector machine (SVM) model achieved robust discrimination with an area under the ROC curve (AUC) of 0.957 and 92.1% accuracy in a blinded test set. Furthermore, subtype discrimination between lung adenocarcinoma (LUAD) and minimally invasive adenocarcinoma (MIA) yielded an AUC of 0.951. These results establish structure and performance relationships for noble metal-assisted LDI and demonstrate the potential of nanomaterial-enhanced metabolic fingerprinting for accurate lung cancer diagnosis and subtype stratification, providing a basis for future clinical screening after large-scale validation.

Our investigation focuses on developing and testing a radiomics nomogram based on contrast-enhanced mammography (CEM) and clinical factors to predict ductal carcinoma in situ (DCIS) in breast cancer. A retrospective analysis was performed on 731 breast cancer cases who underwent CEM examination and subsequent surgical treatment with complete pathological results, enrolled from five centers. Radiomics features were derived from both low-energy and recombined CEM images for each patient. The Minimum Redundancy Maximum Relevance (mRMR）and least absolute shrinkage and selection operator (LASSO) methods were used to select radiomics features. The radiomics signature (Rad-score) was calculated as a weighted linear combination of the most discriminative features. The univariate and multivariate logistic regression were used to select the clinical factors. A radiomics nomogram was established by integrating the Rad-score and independent clinical risk factors. The receiver operator characteristic curves (ROCs) and calibration curves were used to assess the performance of the radiomics nomogram. The Rad-score was calculated through the integration of 11 radiomics features. The radiomics nomogram was developed from Rad-score, age, menstrual status and background parenchymal enhancement (BPE) by logistic regression, which showed better predictive performance in both internal and pooled external test sets, with AUCs of 0.889 (95

The realization of data-driven prediction regarding river water quality is of great significance for the maintenance of river ecosystems' health and also provides a scientific basis for the rational development, utilization, and protection of water resources. In order to verify the feasibility and superiority of the water quality prediction model based on whale optimization algorithm (WOA)-radial basis function neural network (RBFNN) for river water quality prediction, the water quality of the Xiangyang section of the Hanjiang River Basin was selected as the object of investigation. Drawing upon monitoring data collected from the Yujiahu cross-section between April 2023 and June 2023, a water quality prediction model based on WOA-RBFNN was devised. This entailed the utilization of the WOA to optimize the parameter values of the RBFNN, subsequently predicting dissolved oxygen concentration by inputting data pertaining to a total of nine influencing factors, encompassing air temperature, precipitation, relative humidity, water temperature, pH, ammonia nitrogen, total nitrogen, total phosphorus, and permanganate index, into the WOA-RBFNN-based water quality prediction model. The results revealed that the WOA-RBFNN-based water quality prediction model exhibited minimal deviation between the predicted and measured dissolved oxygen concentration. Comparative analysis with the dissolved oxygen prediction results of the RBFNN model and the back- propagation neural network (BPNN) model underscored the WOA-RBFNN-based water quality prediction model's minimized prediction errors and heightened accuracy. Hence, it is affirmed that the water quality prediction model based on WOA-RBFNN can effectively predict the water quality in the Xiangyang section of the Hanjiang River basin.

Determining comprehensive anisotropic parameters for thin-walled tubes is extremely challenging due to the curved surface geometry, causing constitutive modeling and applications to lag significantly behind those for sheets. Although a segment ring expansion test (SRET) has been established to enable precise measurement of the circumferential normal anisotropy coefficient r90, limitations remain in modeling the biaxial tensile hardening of anisotropic aluminum alloy tubes (Cui et al., 2025). In this paper, a novel pyramid-segment ring expansion test (PRET) method was first developed to determine the σ90-ε90 curve of tubes through mechanical analysis. Quantitative relationships between test parameters and stress state were established to produce a uniform, near-uniaxial stress state, and its high accuracy was verified, with a measurement error of only 1.6%. Then, the predictive characteristics of the Barlat89 model are analyzed to clarify the effects of the model coefficients and exponent. Based on theoretical analysis and testing conditions, a hybrid calibration strategy was proposed to model biaxial tensile hardening. Then, experimental tests on 6061 and 5B02 aluminum alloy tubes confirmed distinct anisotropies in hardening and flow under uniaxial and biaxial states. After precisely quantifying their anisotropies, a chain of relationships was established for the Barlat89 model, linking anisotropic parameters to model coefficients and exponent and to prediction accuracy, thereby comprehensively revealing how different calibration strategies and material characteristics affect prediction performance. Results show that the hybrid calibration strategy reduces the average error from 2.9% to 0.7% for 6061 tubes and from 7.0% to 2.8% for 5B02 tubes under M = 8 compared with (Cui et al., 2025), and benefiting from lower M-sensitivity, yields stable predictions over a wide range of M. Thus, an efficient modeling approach was established for anisotropic aluminum alloy tubes by synergistically integrating a novel test method, a simpler yield criterion, and a flexible modeling concept. It not only predicts the load-displacement response of the PRET but also precisely describes the biaxial hardening behavior in finite element (FE) simulation, thereby significantly enhancing the simulation accuracy for strain distribution and tube geometry in the biaxial tension test. These can contribute to high-precision simulations of load matching and deformation behavior during the axisymmetric forming processes of aluminum alloy tubular components, which primarily involve biaxial tension.

Enzymes mediate diverse cancer processes by exerting their functional activities. Precise profiling of enzyme activity in the tumor microenvironment (TME) is therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we report biomodified nanoprobes for the highly sensitive detection of specific protease activities in medulloblastoma (MB) across scales. The surface-enhanced Raman scattering (SERS)-based nanoprobes use peptide-functionalized three-dimensional (3D) surfaces rich in electromagnetic hotspots to facilitate both enzymatic hydrolysis and plasmonic enhancement of Raman signals. We apply the nanoprobes to analyze multiple protease activities in 2D in vitro culture and 3D tumor cell spheroids, uncovering heterogeneous activity maps among different cell types, therefore allowing for recognition of distinct MB cellular subtypes. Through spatially resolved in situ localization of protease activity, we observe the suppressed protease function in the core region of tumor spheroids. Furthermore, in a pilot clinical assay (n = 27), the nanoprobe-based SERS assay reveals elevated levels of protease activity in sera of MB patients, and achieves great accuracy in discriminating MB from noncancer controls with the area under the receiver operating characteristic curve of 1.0. Together, this study offers a framework for functional, multiscale measurement of protease dysregulation in cancer.

Acute Myeloid Leukemia (AML) is a devastating hematologic malignancy. Chemotherapy remains the primary treatment, offering rapid disease control and potential complete remission. However, more than half of the patients develop resistance and relapse, significantly reducing patient survival. Research has shown that drug-resistance and recurrence of AML are closely linked to leukemic stemness. Consequently, discovering new anti-Leukemia stem cell (LSC) compounds is a promising strategy for the treatment of AML in clinic. Additionally, the recent focus on inducing non-apoptotic programmed cell death in AML cells presents an alternative direction for therapeutic drug development, targeting current anti-apoptotic pathways. In this study, novel Sitolactone analogues, potential anti-LSCs compounds, were designed and synthesized based on the "biomimetic design" strategy. Compound 42 was found to significantly inhibit proliferation of AML cells. Subsequent biological evaluation revealed that this compound not only reduced the population of LSCs but also effectively induced PANoptosis in AML cells. Given the active compound's poor water solubility, a prodrug modification strategy was employed to enhance in vivo delivery with superior oral bioavailability and PK properties. This approach significantly suppressed AML cell growth in a mouse orthotropic model with favorable in vivo tolerance.

The mining roadways of fully mechanized mining faces are susceptible to complex disturbances, leading to deformation of the surrounding rock, breaking the mechanical balance of the advanced support section of the mining roadway, and potential roof safety incidents and equipment damage. Accurate prediction of the spatial attitude of the advanced hydraulic support group can provide a foundation for dynamically adjusting the spatial attitude to accommodate the deformation of the roadway's surrounding rock. The selection of training parameters in the conventional Long Short-Term Memory (LSTM) network is often random and involves a significant amount of effort, which further limits prediction accuracy and real-time performance. Based on this, the WOA algorithm was utilized to search for the optimal number of neurons in the hidden layer and the learning rate. A spatial attitude prediction method for the advanced hydraulic support group based on WOA-LSTM was proposed. Relying on the experimental monitoring platform, the attitude parameters of key nodes during the movement of the support group were obtained, and comparative experiments were carried out. The results indicate that, with 3,200 training samples for the top beam pitch angle and 600 iterations, the Mean Absolute Error (MAE) of the WOA-LSTM prediction model is 0.18°, the Root Mean Squared Error (RMSE) is 0.23°, and the Mean Absolute Percentage Error (MAPE) is 1.3%. Compared to the traditional LSTM model, these three error metrics are reduced by 0.2°, 0.2°, and 1.6%, respectively. This improvement enhances the accuracy and parameter optimization efficiency of the advanced support attitude prediction model, thereby providing robust theoretical and technical support for the intelligent, safe, and efficient mining operations of the advanced coupling support system.

Tumor heterogeneity poses a major challenge to the precision treatment of medulloblastoma (MB). Rapid and accurate subtyping tools are urgently needed for informed clinical decision-making. Herein, we demonstrate the utility of Raman spectroscopy (RS) as a label-free, noninvasive approach for molecular characterization and subtype discrimination of MB at the single-cell level. Five representative MB cell lines from group 3 and sonic hedgehog (SHH) subtypes, along with microglial controls, were profiled by RS and validated using mass spectrometry-based metabolomics. RS-derived single-cell metabolic fingerprints revealed subtype-specific variations in nucleic acids, amino acids, lipids, and proteins. Group 3 cells, exemplified by metastatic D283, exhibited elevated levels of unsaturated lipids compared with most SHH cells. Notably, Daoy cells from the SHH subtype displayed unsaturation levels comparable to D283, reflecting intragroup heterogeneity and membrane remodeling in MB. Machine learning classifiers achieved high diagnostic performance, with an average area under the curve of 0.994 and a Matthews correlation coefficient of 0.935. Furthermore, cisplatin-treated D283 cells showed increased unsaturated lipid content relative to Daoy cells, revealing subtype-dependent metabolic shifts in drug response. These findings underscore the metabolic diversity of MB subtypes and the role of lipid metabolism in tumor progression and therapy. Collectively, our results exemplify the power of RS combined with metabolomics for molecule-resolved, label-free MB subtyping and therapeutic stratification.

O3-NaNi0.4Fe0.2Mn0.4O2 is a prominent cathode for sodium-ion batteries, recognized for its high reversible capacity (>180 mAh g(-1)). However, it suffers from poor structural stability and interfacial stability due to complex phase transitions, large volume change, and transition metal (TM) ions dissolution/migration during charge/discharge processes, which results in severe performance degradation. Here, a high-entropy oxyfluoride cathode with a high configurational entropy of 1.865 R, NaNi0.2Fe0.2Mn0.2Cu0.1Ti0.2Li0.1O1.95F0.05 (NFMCTLF) is designed. The synergy of high-entropy design and fluorine doping strengthens TM-O bonds and prevents interlayer sliding, thereby enhancing reversible O3-P3-OP2 phase transitions and reducing volume change. Moreover, such a high-entropy oxyfluoride cathode also provides a thin, uniform cathode electrolyte interface layer and elevates the Ni/Fe/Mn ions migration energy barrier, which significantly suppresses interfacial side reactions and inhibits TM ions dissolution/migration. Thus, the NFMCTLF cathode exhibits a specific capacity of 182.4 mAh g(-1) with minimal volume change (<1%) in a broad voltage range of 2.0-4.2 V, achieving a capacity retention of 91.45% after 200 cycles at 0.5C and 80.43% after 1000 cycles at 5C. The full battery also exhibits excellent performance with 80.14% capacity retention after 1350 cycles at 5C. This work highlights the great potential in developing high-performance cathodes with high-entropy oxyfluoride.

Purpose:This study aims to investigate the feasibility of cellular microstructural mapping by the diffusion MRI (IMPULSED, imaging microstructural parameters using limited spectrally edited diffusion) of breast tumors, and further to evaluate whether the MRI-derived microstructural features is associated with the prognostic factors in breast cancer. Materials and methods:This prospective study collected 232 patients with suspected breast tumors from March to August 2023. The IMPULSED MRI scan included acquisitions of diffusion MRI using both pulsed (PGSE) and oscillating (OGSE) gradient spin echo with the oscillating frequencies up to 33 Hz. The OGSE and PGSE data were fitted by the IMPUSLED method using a two-compartment model to estimate mean cell diameter (d mean), intracellular fraction (fin ), extracellular diffusivity (D ex), and cellularity index (f in/d) within breast tumor lesions. The apparent diffusion coefficients (ADCs) were calculated from the conventional diffusion weighted imaging, PGSE, and OGSE (17 Hz and 33 Hz) sequences (ADCDWI, ADCPGSE, ADC17Hz, and ADC33Hz). The independent samples test was used to compare the d mean, fin , Dex , cellularity index, and ADC values between benign and malignant breast tumors, and between breast cancer subgroups with different risk factors. The receiver operating characteristic (ROC) curve was used to access the diagnostic performance. Results:213 patients were finally included and divided into malignant (n=130) and benign (n=83) groups according to the histopathological results. The d mean (15.74 ± 2.68 vs. 14.28 ± 4.65 μm, p<0.001), f in (0.346 ± 0.125 vs. 0.279 ± 0.212, p<0.001) and cellularity index (21.19 ± 39.54 vs. 19.38 ± 14.87 ×10-3 um-1, p<0.005) values of malignant lesions were significantly higher than those of benign lesions, and the D ex (2.119 ± 0.395 vs. 2.378 ± 0.332 um2/ms, p<0.001) and ADCDWI (0.877 ± 0.148 vs. 1.453 ± 0.356 um2/ms, p<0.001) of malignant lesions were significantly lower than those of benign lesions. For differentiation between benign and malignant breast lesions, ADCDWI showed the highest AUC of 0.951 with the sensitivity of 80.49% and specificity of 98.28%. The combination of d mean, f in, D ex, and cellularity for differentiation between benign and malignant breast lesions showed AUC of 0.787 (sensitivity = 70.73%, and specificity = 77.86%), and the combination of IMPULSED-derived parameters with ADCs by PGSE and OGSE further improve the AUC to 0.897 (sensitivity = 81.93%, and specificity = 81.54%). The f in values of HER-2(+) tumors were significantly lower than those of HER-2(-) tumors (0.313 ± 0.100 vs. 0.371 ± 0.137, p=0.015), and the ADCDWI, ADC17Hz and ADC33Hz values of HER-2(+) tumors were significantly higher than those of HER-2(-) tumors (ADCDWI: 0.929 ± 0.115 vs. 0.855 ± 0.197 um2/ms, p=0.023; ADC17Hz: 1.373 ± 0.306 vs. 1.242 ± 0.301 um2/s, p =0.025; ADC33Hz: 2.042 ± 0.545 vs. 1.811 ± 0.392 um2/s, p = 0.008). The f in (0.377 ± 0.136 vs. 0.300 ± 0.917, p=0.001) and cellularity index (27.22 ± 12.02 vs. 21.66 ± 7.76 ×10-3 um-1, p=0.007) values of PR(+) tumors were significantly higher than those of PR(-) tumor. The ADC17Hz values of PR(+) tumors were significantly lower than those of PR(-) tumors(1.227 ± 0.299 vs. 1.404 ± 0.294 um2/s, p =0.002).The ADC17Hz and D ex values of ER(+) tumors were significantly lower than those of ER(-) tumors (ADC17Hz: 1.258 ± 0.313 vs. 1.400 ± 0.273 um2/s, p = 0.029; D ex: 2.070 ± 0.405 vs. 2.281 ± 0.331 um2/ms, p=0.011). For differentiation between ER(+) and ER(-), the ADC17Hz and D ex showed AUCs of 0.643 (sensitivity = 76.67%, and specificity = 47.06%) and 0.646 (sensitivity = 80.0%, and specificity = 45.98%), and the combination of D ex and ADC17Hz showed AUCs of 0.663 (sensitivity =93.33%, specificity = 36.78%). For differentiation of PR(+) and PR(-), the ADC17Hz, f in, and cellularity index showed AUCs of 0.666 (sensitivity = 68.18%, and specificity = 61.97%), 0.697 (sensitivity = 77.27%, and specificity = 60.27%) and 0.661 (sensitivity = 68.18%, and specificity = 61.64%), respectively, and their combination showed AUCs of 0.729 (sensitivity =72.73%, specificity = 65.75%). For differentiation of HER-2(+) and HER-2(-), the ADCDWI, ADC17Hz, and ADC33Hz, and f in showed AUCs of 0.625 (sensitivity = 59.42%, specificity = 63.04%), 0.632 (sensitivity = 43.66%, and specificity = 84.78%), 0.664 (sensitivity = 47.95%, and specificity = 82.67%) and 0.650 (sensitivity = 77.46%, and specificity = 56.52%), respectively, and their combination showed AUCs of 0.693 (sensitivity = 69.57%, specificity = 64.79%) of HER-2(+) and HER-2(-). Conclusion:The IMPULSED method demonstrates promise for characterizing cellular microstructural features in breast tumors, which may be helpful for prognostic risk evaluation in breast cancer.

This study addresses the common issues of slow dynamic response and significant hysteresis in nonlinear electro-hydraulic systems, with a particular focus on optimizing the control performance of shotcrete robots operating under complex working conditions. The electro-hydraulic proportional control system was first designed and mathematically modeled. Based on input-output data collected under actual operating conditions of the shotcrete manipulator, signal preprocessing was performed using a wavelet soft-threshold denoising algorithm. Subsequently, system parameters were identified using a particle swarm optimization (PSO) algorithm enhanced by least squares estimation, resulting in a high-accuracy system transfer function. To overcome the limited robustness of traditional PID controllers under strong nonlinearities, as well as the real-time computational burden of standalone model predictive control (MPC), a dual-loop control strategy was proposed-employing PID feedback as the inner loop and MPC as the outer loop-to optimize and simulate the electro-hydraulic system. Experimental validation was conducted on a six-degree-of-freedom shotcrete robot platform through extension and rotational motion control tests of the robotic boom. Results show that, compared to conventional PID control, the proposed PID-MPC approach significantly improved system responsiveness and tracking accuracy. Specifically, in the extension test, the maximum tracking error decreased from 0.13 m/s to 0.04 m/s, and the maximum settling time was reduced from 3.0 s to 0.45 s; in the rotational test, the maximum tracking error dropped from 8°/s to 4°/s, and the maximum settling time shortened from 2.6 s to 0.8 s. This study offers a practical and effective solution for accurate modeling and high-performance control of complex electro-hydraulic systems, providing a solid theoretical foundation for the development and engineering application of intelligent and efficient shotcrete equipment.

Wafer warpage is one of the most critical challenges for 3D NAND flash fabrication. In this study, room temperature patterned ion implant with various width is applied to release the stress in plasma enhanced chemical vapor deposition (PECVD) fabricated Si3N4 film for curl-shape wafer warpage modulation. The implant pattern method can be used to precisely control the local stress in Si3N4 films, and thus the 2D (top-view) and 3D wafer profile can be flexibly adjusted from curl-shape to flat. The properties, microstructure, film stress and wafer warpage of Si3N4 film before and after implant are analysed by patterned wafer geometry (PWG), Transmission Electron Microscope (TEM), Fourier-transform infrared spectroscopy (FTIR), Electron Paramagnetic Resonance (EPR), X-ray Photoelectron Spectroscopy (XPS), atomic force microscopy (AFM) and nanoindentation. From these analyses, we conclude that the improvement of wafer warpage is related to stress compensation between the implant and non-implant regions. The stress compensation mechanism in PECVD fabricated Si3N4 film is attributed to breaking of Si-N, Si-H and N-H bonds in Si3N4 film which induces a stress difference between the implant and non-implant regions, ultimately, the film stress on the front and back (Si3N4) of 3D NAND can be balanced, causing the wafer warpage change of 3D NAND flash from curl-shape to flat. Since ion implantation is performed at room temperature and can improve the wafer surface roughness, ion implantation is helpful for both the reliability of device and the subsequent process of 3D NAND flash. It is first time reported that a novel room-temperature patterned ion implant method can be used to adjust the local stress and regulate the wafer warpage in 3D NAND flash fabrication.