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Abstract The application of biochar to soil is widely recognized as a promising strategy for enhancing the accumulation and stability of soil organic carbon (SOC), which is crucial in mitigating climate change. However, the influence of interactions between plants and biochar on soil microbial communities and their involvement in SOC mineralization and stability remains unclear. This understanding is essential for optimizing carbon (C) sequestration in systems involving plants, soil, and biochar. In this study, employing a 13C natural abundance approach, we investigated the effect of biochar on the maize rhizosphere priming effect (RPE) in paddy soil. We also examined alterations in microbial communities and functional genes related to C degradation and fixation. Over the 99 days of maize growth, biochar application increased RPE and total SOC while decreasing dissolved organic C. It also elevated soil pH, resulting in shifts in fungal and bacterial community structure, favoring oligotrophic species. Fungal and bacterial assemblies were dominated by deterministic and stochastic processes, respectively. While the abundance of fungal guilds varied irregularly, bacterial guilds were uniformly enriched under biochar-plant interactions. Functional traits such as ecoenzymatic activities, bacterial guilds, and functional genes predominantly affected RPE under biochar application. Bacterial functional genes associated with C degradation and fixation were concurrently enhanced with biochar application. Our results indicate that interactions between plants and biochar can enhance native SOC mineralization and accumulation in a short timeframe by modulating functional traits of soil microorganisms, particularly the bacterial community involved in C degradation and fixation. Graphical Abstract

Three groups of composite cementitious materials (CCMs) were prepared by separate grinding, mixed grinding, and cascade grinding using iron and steel solid waste (ISSW) materials (granulated blast furnace slag (GBFS), iron ore tailings (IOTs), steel slag (SS) and desulfurization gypsum (DG)), and the influence on the strength of CCMs was investigated under different water-binder ratios (w/b). Then, the hydration hardening mechanism of the CCMs was revealed via hydration heat analysis, phase composition and structural analysis of hydration products. The results show that the properties of the CCMs prepared by cascade grinding (CGC) is better than that prepared by separate grinding and mixed grinding. When the mass percentage of IOTs and SS in the CGC was 2:3, the mix proportion of CGC is SS+IOTs: GBFS: DG=36:51.5:12.5, mixed with 1.0 % Na2SO4 and 1.2 % Na2SiO3ˑ9 H2O of the CGC, the compressive strength (CS) of mortar sample containing CGC in 28-days and 180-days reach 52.3 MPa and 70.7 MPa respectively under the conditions of standard curing and low water-binder ratios. The hydration degree of pure cement is higher than that of CGC, but types of the hydration products were similar with cement, mainly were Ca(OH)2, iolite, ettringite (AFt) and C-S-H gel. The CGC can greatly reduce the hydration heat of CCMs system in early age, and is suitable for massive concrete structures in which elevated temperature can be kept for a long time.

Abstract Bone marrow stromal/stem cells (BMSCs) are generally considered as common progenitors for both osteoblasts and adipocytes in the bone marrow, but show preferential differentiation into adipocytes rather than osteoblasts under aging, thus leading to senile osteoporosis. Accumulated evidences indicate that rejuvenation of BMSCs by autophagic enhancement delays bone aging. Here we synthetized and demonstrated a novel autophagy activator, CXM102 that could induce autophagy in aged BMSCs, resulting in rejuvenation and preferential differentiation into osteoblasts of BMSCs. Furthermore, CXM102 significantly stimulated bone anabolism, reduced marrow adipocytes, and delayed bone loss in middle-age male mice. Mechanistically, CXM102 promoted transcription factor EB (TFEB) nuclear translocation and favored osteoblasts formation both in vitro and in vivo. Moreover, CXM102 decreased serum levels of inflammation and reduced organ fibrosis, leading to a prolonger lifespan in male mice. Our results indicated that CXM102 could be used as an autophagy inducer to rejuvenate BMSCs and shed new lights on strategies for senile osteoporosis and healthyspan improvement.

Abstract The optical appearance of the numerically black hole solutions within the higher derivative gravity illuminated by an accretion disk context is discussed. We obtain solutions for non-Schwarzschild black holes with $$r_0=1$$ r 0 = 1 , $$r_0=2$$ r 0 = 2 , and $$r_0=3$$ r 0 = 3 . Further analysis of spacetime trajectories reveals properties similar to Schwarzschild black holes, while the $$r_0=2$$ r 0 = 2 black hole exhibits significant differences. The results reveal the presence of a repulsive potential barrier for the black hole, allowing only particles with energies exceeding a certain threshold to approach it, providing a unique gravitational scenario for non-Schwarzschild black holes. Additionally, the optical images are derived through numerical simulations by discussing the trajectories of photons in the black hole spacetime. The distribution of radiation flux and the effects of gravitational redshift and Doppler shift on the observed radiation flux are considered. Interestingly, previous analyses of the optical appearance of black holes were conducted within the framework of analytic solutions, whereas the analysis of numerical black hole solutions first appears in our analysis.

In this study, a composite modifier (CMCDL) containing coal gasification residue, dicyandiamide waste slag, and lake sediments was used to modify steel slag at high-temperature. Five kind of raw materials was analyzed in physicochemical properties, mineral composition and microstructure, cementitious properties and soundness, and particle size analysis, the same to steel slag after high-temperature modification (SHTM). The new mineral phases were found that tricalcium aluminate (C3A), gehlenite (C2AS), magnetite (Fe3O4), spinels (MgFe2O4), quartz (SiO2), celite (C6AF2) and diopside (CMS2) formed in the SHTM when the mass fraction increase from 10% to 30% in CMCDL. The HTM with CMCDL increases the glass phase and hydrated mineral (C3A, C3S, C2S) and in raw steel slag, promoting the disassembled of RO phase, turning the phase from FeO into MgFe2O4 and Fe3O4. MgFe2O4 and Fe3O4 have similar crystal structures, and they form mixed spinels. When the content of CMCCL (coal gasification residue: dicyandiamide waste slag: lake sediments =7:9:4) is 20%, the temperature is 1250 °C, result showed that the mass fraction of f-CaO in SHTM using CMCCL decreases from 4.81% to 1.86%, with a decrease of 61.3%, the 28-day activity index of SHTM increases up to 17.6% compared with raw steel slag, reaching 86.2%. Therefore, the CMCDL-blended SHTM can be applied harmlessly in cement and concrete, enabling low-energy fine grinding of steel slag.

Abstract Background The effect of neoadjuvant immunotherapy on minimally invasive gastrectomy (MIG) in older patients with gastric cancer remains controversial. This study aimed to evaluate the safety, and efficacy of MIG for older patients who underwent neoadjuvant chemotherapy and immunotherapy (NICT). Methods The clinical data of 726 older patients aged over 65 years who underwent upfront MIG or MIG after NICT in the Department of General Surgery, Chinese PLA General Hospital First Medical Center between Jan 2020 and Nov 2023 were retrospectively analyzed. Propensity score-matched (PSM) analysis at a ratio of 1:2 was performed to reduce bias from confounding patient-related variables, short- and long-term outcomes were compared between the two groups. Results The baseline characteristics were comparable between 61 patients in the NICT-MIG group and 114 patients in the MIG group after PSM (P > 0.05). The major pathological response (MPR) rate and pathological complete response (pCR) rate were 44.2% and 21.3%, respectively, in the NICT-MIG group. Patients in the NICT-MIG group had longer operation times (P = 0.005) and postoperative days (P = 0.030) than those in the MIG group. No significant differences were found in intraoperative bleeding, number of retrieved lymph nodes, first flatus day, R0 resection rate, overall postoperative complication (POC) morbidity, severe POC morbidity, 2-year overall, and recurrence-free survival between the MIG and NICT-MIG groups (P > 0.05). Multivariate logistic analysis revealed that an estimated blood loss > 200 mL (P = 0.010) and a lymphocyte-to-monocyte ratio (LMR) ≤ 3.25 (P = 0.006) were independent risk factors for POCs after MIG in older patients. Conclusion The safety, and efficacy of NICT-MIG were comparable to those of upfront MIG in older patients with GC. Patients with an estimated blood loss > 200 mL or an LMR ≤ 3.25 should be carefully evaluated for an increased risk of POCs in older patients who undergo MIG. Trial registration Chinese Clinical Trial Registry (Registration Number: ChiCTR2400086827).

Abstract Background Although the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes. Methods Participants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan–Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes. Results Sixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54–3.90) for severe liver disease (SLD), and 2.81 (2.60–3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes. Conclusions High PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management.

Abstract Background Previous studies have shown the importance of energy deficiency and malfunctioning mitochondria in the pathophysiology of hypertrophic cardiomyopathy (HCM). There has been a little research into the relationship between plasma free fatty acids (FFA), one of the heart’s main energy sources, and HCM. We evaluated its clinical importance in HCM to see if there was a link between plasma FFA metabolism and HCM. Methods In a single-center retrospective observational study, we investigated 420 HCM patients diagnosed at Beijing Anzhen Hospital between January 1, 2018, and December 31, 2022. Meanwhile, 1372 individuals without HCM (non-HCM) were recruited. 391 non-HCM patients were chosen as controls via a propensity score matching (PSM) study with a 1:1 ratio. Results FFA in HCM patients showed statistically significant correlations with creatinine (r = 0.115, p = 0.023), estimated GFR (r=-0.130, p = 0.010), BNP (r = 0.152, p = 0.007), LVEF (r=-0.227, p

Rice, a crucial global food crop, necessitates accurate mapping for food security assessment. China, a major rice producer and consumer, includes Jiangsu Province as a significant rice production region. The Hongzehu (HZH) area in Jiangsu contributes substantially to rice supply, supporting food security locally and province-wide. Sentinel-1 SAR data, particularly Single Look Complex (SLC) products, holds promise for precise crop mapping with enhanced phase and polarization information, enhancing sensitivity to rice growth changes by analyzing rice surface features information. However, challenges persist, especially climate impacts and timing inconsistencies between fields for planting rice. To overcome this, our study proposes a progressive feature screening and fusion method using multi-temporal SAR images. We introduce fuzzy coarse screening based on statistical distribution characteristics and refine it with Gaussian fitting. A model incorporating time-series sample separation and polarization decomposition feature fusion based on rice growth height enhances rice growth expression. For more precise results, we advocate a multi-temporal feature fusion approach using optimized sample features in the BiLSTM network to characterize rice growth and ground features. Experimental results demonstrate the method’s efficacy in two cities with a limited number of sampling points. The progressive feature fusion (DF) method outperforms classical classification methods using single feature (SF) or combined features (CF). Our proposed strategy proves effective for rice mapping applications, providing a promising approach for leveraging Sentinel-1 SLC SAR data. In conclusion, our study enhances accuracy in identifying rice fields and characterizing rice growth, contributing to improved food security assessments despite challenges associated with rainy seasons and planting times.

IntroductionMagnetic particle imaging (MPI), a radiation-free, dynamic, and targeted imaging technique, has gained significant traction in both research and clinical settings worldwide. Signal-to-noise ratio (SNR) is a crucial factor influencing MPI image quality and detection sensitivity, and it is affected by ambient noise, system thermal noise, and the magnetization response of superparamagnetic nanoparticles. Therefore to address the high amplitude system and inherent thermal noise present in conventional MPI systems is essential to improve detection sensitivity and imaging resolution.MethodThis study introduces a novel open-loop, narrow-band MPI signal acquisition system based on mixed-frequency harmonic magnetization response. Allowing superparamagnetic nanoparticles to be excited by low frequency, high amplitude magnetic fields and high frequency, low amplitude magnetic fields, the excitation coil generates a mixed excitation magnetic field at a mixed frequency of 8.664 kHz (fH + 2fL), and the tracer of superparamagnetic nanoparticles can generate a locatable superparamagnetic magnetization signal with rich harmonic components in the mixed excitation magnetic field and positioning magnetic field. The third harmonic signal is detected by a Gradiometer coil with high signal-to-noise ratio, and the voltage cloud image is formed.ResultThe experimental results show that the external noise caused by the excitation coil can be effectively reduced from 12 to about 1.5 μV in the imaging area of 30 mm × 30 mm, which improves the stability of the detection signal of the Gradiometer coil, realizes the detection of high SNR, and makes the detection sensitivity reach 10 μg Fe. By mixing excitation, the total intensity of the excitation field is reduced, resulting in a slight improvement of the resolution under the same gradient field, and the spatial resolution of the image reconstruction is increased from 2 mm under the single frequency excitation (20.7 kHz) in the previous experiment to 1.5 mm under the mixed excitation (8.664 kHz).ConclusionsThese experimental results highlight the effectiveness of the proposed open-loop narrowband MPI technique in improving signal detection sensitivity, achieving high signal-to-noise ratio detection and improving the quality of reconstructed images by changing the excitation magnetic field frequency of the excitation coil, providing novel design ideas and technical pathways for future MPI systems.

Background The clinical characteristics and survival outcomes of patients who underwent concomitant coronary artery bypass grafting during septal myectomy have not been well studied. Methods and Results We reviewed patients who underwent both septal myectomy and coronary artery bypass grafting from 2009 to 2020. Causes of concomitant grafting and their impact on survival were analyzed. The median follow‐up period was 5.1 years. A total of 320 patients underwent both grafting and myectomy. Of these, 69.7% and 28.1% underwent grafting attributed to atherosclerotic coronary artery disease and myocardial bridging, respectively. Patients who underwent grafting for coronary artery disease tended to be older, had a longer bypass time, and required more grafts compared with patients undergoing procedures because of myocardial bridging (all P95% and improved functional class of >90%.

Star sensors play a crucial role as high-precision optical attitude navigation devices in satellite attitude control systems. The CMOS image sensor is an important component of the imaging system of the star sensor and experiences performance degradation due to the total ionizing dose effect and displacement damage effect in the radiation environment of its long-term operational workspace. As a result, the detection capability and attitude positioning accuracy of the star sensor gradually deteriorate. The performance of CMOS image sensors can be partially restored after annealing but the impact on the performance of star sensors is unclear. This study conducted 100 MeV proton irradiation experiments with different fluences on CMOS image sensors and performed room temperature and high temperature annealing treatments. By testing and analyzing the performance of star sensors equipped with irradiated and different annealing treatments on CMOS image sensors, the impact mechanism of CMOS image sensor radiation damage and annealing recovery on the performance variation of star sensors was obtained. This study provides theoretical support for reliability of long-term in-orbit star sensors.

Abstract Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism‐related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre‐emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in‐silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism‐related outcomes, effectively classifying individuals into low‐ and high‐risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high‐risk group shows increased susceptibility to metabolism‐related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism‐related outcomes. The multimodal machine learning‐based ISHUA marker enables personalized risk stratification for gout and metabolism‐related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high‐risk group, providing guidance for preventive interventions.

Abstract Purpose This study aimed to identify ultrasound and clinicopathological characteristics related to recurrence in HER2‐positive (HER2+) breast cancer, and to develop nomograms for predicting recurrence. Methods In this dual‐center study, we retrospectively enrolled 570 patients with HER2+ breast cancer. The ultrasound and clinicopathological characteristics of hormone receptor (HR)−/HER2+ patients and HR+/HER2+ patients were analyzed separately according to HR status. Eighty percent of the original samples from HR−/HER2+ and HR+/HER2+ patients were extracted by bootstrap sampling as the training cohorts, while the remaining 20% were used as the external validation cohorts. Informative characteristics were screened through univariate and multivariable Cox regression in the training cohorts and used to develop nomograms for predicting recurrence. The predictive accuracy was calculated using Harrell's C‐index and calibration curves. Results Three informative characteristics (axillary nodal status, calcification, and Adler degree) were identified in HR−/HER2+ patients, and another three (histological grade, axillary nodal status, and echogenic halo) in HR+/HER2+ patients. Based on these, two separate nomograms were constructed to assess recurrence risk. In the training cohorts, the C‐index was 0.740 (95% CI: 0.667–0.811) for HR−/HER2+ nomogram, and 0.749 (95% CI: 0.679–0.820) for HR+/HER2+ nomogram. In the validation cohorts, the C‐index was 0.708 (95% CI: 0.540–0.877) for HR−/HER2+ group, and 0.705 (95% CI: 0.557–0.853) for HR+/HER2+ group. The calibration curves also indicated the excellent accuracy of the nomograms. Conclusions Ultrasound performance of HER2+ breast cancers with different HR status was significantly different. Nomograms integrating ultrasound and clinicopathological characteristics exhibited favorable performance and have the potential to serve as a reliable method for predicting recurrence in heterogeneous breast cancer.

Wastewater treatment plants (WWTPs) are important and energy-intensive municipal infrastructures. High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality. However, water–energy nexus analysis and models for WWTPs have rarely been reported to date. In this study, a cloud-model-based energy consumption analysis (CMECA) of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters. The principal component analysis (PCA) and K-means clustering were applied to classify the influent condition using water quality and volume data. The energy consumption of the WWTP is divided into five standard evaluation levels, and its cloud digital characteristics (CDCs) were extracted according to bilateral constraints and golden ratio methods. Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity. The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption, represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption. The local WWTP has high energy consumption with 0.3613 kW·h·m−3 despite low influent concentration and volumes, across four consumption levels from low (I) to relatively high (IV), showing an unsatisfactory operation and management level. The average oxygenation capacity, internal reflux ratio, and external reflux ratio during high energy efficiency days recognized by further clustering were obtained (0.2924–0.3703 kg O2·m−3, 1.9576–2.4787, and 0.6603–0.8361, respectively), which could be used as a guide for the days with low energy efficiency. Consequently, this study offers a water–energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Abstract The significance of carbon trading policy (CTP) for China’s carbon reduction goals cannot be overstated. Examining the practical impacts and inherent features of this policy is crucial for fostering its healthy development and effectiveness. This study utilizes the directional SBM super-efficiency model to calculate the combined emission efficiency (CEE) of greenhouse gases and atmospheric pollutants across 30 provinces and cities in China from 2005 to 2020. Through spatiotemporal analysis of the CEE evolution using hotspot analysis, it is evident that hotspots gradually shift towards the southeast coastal areas over time, while cold spots shift towards the northwest. Additionally, employing the differences-in-differences (DID) model and conducting robustness tests, the study finds that the CTP significantly enhances the CEE development. Spatial econometric analysis reveals that the CEE primarily follows a distribution pattern characterized by low-low (LL) and high-high (HH) regions, with positive spatial spillover effects. However, due to the incomplete state of early green development in China, the CTP temporarily exhibits negative spatial spillover effects. Finally, considering the current state of China's carbon trading policy, corresponding policy recommendations are proposed in this paper.

Bacteria are the causative agents of various infectious diseases; however, the anti‐tumor effect of some bacterial species has attracted the attention of many scientists. The human oral cavity is inhabited by abundant and diverse bacterial communities and some of these bacterial communities could play a role in tumor suppression. Therefore, it is crucial to find oral bacterial species that show anti‐tumor activity on oral cancers. In the present study, we found that a high abundance of Porphyromonas gingivalis, an anaerobic periodontal pathogen, in the tumor microenvironment (TME) was positively associated with the longer survival of patients with oral squamous cell carcinoma (OSCC). An in vitro assay confirmed that P. gingivalis accelerated the death of OSCC cells by inducing cell cycle arrest at the G2/M phase, thus exerting its anti‐tumor effect. We also found that P. gingivalis significantly decreased tumor growth in a 4‐nitroquinoline‐1‐oxide‐induced in situ OSCC mouse model. The transcriptomics data demonstrated that P. gingivalis suppressed the biosynthesis of mucin O‐glycan and other O‐glycans, as well as the expression of chemokines. Validation experiments further confirmed the downregulation of mucin‐1 (MUC1) and C‐X‐C motif chemokine 17 (CXCL17) expression by P. gingivalis treatment. Flow cytometry analysis showed that P. gingivalis successfully reversed the immunosuppressive TME, thereby suppressing OSCC growth. In summary, the findings of the present study indicated that the rational use of P. gingivalis could serve as a promising therapeutic strategy for OSCC.

Abstract Background Laser-assisted hatching (LAH) stands as the predominant technique for removing the zona pellucida (ZP) in embryos, primarily consisting of two methods: drilling laser-assisted hatching (D-LAH) and thinning laser-assisted hatching (T-LAH). Presently, both methods have limitations, and their comparative efficacy for embryo implantation and clinical pregnancy remains uncertain. Aim Evaluate the impact of D-LAH and T-LAH on clinical pregnancy rates within assisted reproductive technology (ART). Methods We systematically searched electronic databases including PubMed, Web of Science, and Cochrane Library until July 20, 2022. This study encompassed observational studies and randomized controlled trials (RCTs). A 95% confidence interval (CI) was utilized for assessing the risk ratio (RR) of pregnancy outcomes. The level of heterogeneity was measured using I2 statistics, considering a value exceeding 50% as indicative of substantial heterogeneity. Results The meta-analysis scrutinized 9 studies involving 2405 clinical pregnancies from D-LAH and 2239 from T-LAH. Findings suggested no considerable variation in the clinical pregnancy rates between the two techniques (RR = 0.93, 95% CI: 0.79–1.10, I2 = 71%, P = 0.41). Subgroup analyses also revealed no substantial differences. However, D-LAH exhibited a notably higher occurrence of singleton pregnancies compared to T-LAH (RR = 2.28, 95% CI: 1.08–4.82, I2 = 89%, P = 0.03). There were no noteworthy distinctions observed in other secondary outcomes encompassing implantation rate, multiple pregnancies, ongoing pregnancy, miscarriage, premature birth, and live birth. Conclusion Both the primary findings and subgroup analyses showed no marked variance in clinical pregnancy rates between D-LAH and T-LAH. Therefore, patients with varying conditions should select their preferred LAH technique after assessing their individual situation. However, due to the restricted number of studies involved, accurately gauging the influence of these laser techniques on clinical outcomes is challenging, necessitating further RCTs and high-quality studies to enhance the success rate of ART. Trial registration PROSPERO: CRD42022347066.

Abstract Aging is a global challenge, marked in the lungs by function decline and structural disorders, which affects the health of the elderly population. To explore anti-aging strategies, we develop a dynamic atlas covering 45 cell types in human lungs, spanning from embryonic development to aging. We aim to apply the discoveries of lung’s development to address aging-related issues. We observe that both epithelial and immune cells undergo a process of acquisition and loss of essential function as they transition from development to aging. During aging, we identify cellular phenotypic alternations that result in reduced pulmonary compliance and compromised immune homeostasis. Furthermore, we find a distinctive expression pattern of the ferritin light chain (FTL) gene, which increases during development but decreases in various types of lung cells during the aging process.

Abstract Photothermal therapy is favored by cancer researchers due to its advantages such as controllable initiation, direct killing and immune promotion. However, the low enrichment efficiency of photosensitizer in tumor site and the limited effect of single use limits the further development of photothermal therapy. Herein, a photo-responsive multifunctional nanosystem was designed for cancer therapy, in which myeloid-derived suppressor cell (MDSC) membrane vesicle encapsulated decitabine-loaded black phosphorous (BP) nanosheets (BP@ Decitabine @MDSCs, named BDM). The BDM demonstrated excellent biosafety and biochemical characteristics, providing a suitable microenvironment for cancer cell killing. First, the BDM achieves the ability to be highly enriched at tumor sites by inheriting the ability of MDSCs to actively target tumor microenvironment. And then, BP nanosheets achieves hyperthermia and induces mitochondrial damage by its photothermal and photodynamic properties, which enhancing anti-tumor immunity mediated by immunogenic cell death (ICD). Meanwhile, intra-tumoral release of decitabine induced G2/M cell cycle arrest, further promoting tumor cell apoptosis. In vivo, the BMD showed significant inhibition of tumor growth with down-regulation of PCNA expression and increased expression of high mobility group B1 (HMGB1), calreticulin (CRT) and caspase 3. Flow cytometry revealed significantly decreased infiltration of MDSCs and M2-macrophages along with an increased proportion of CD4+, CD8+ T cells as well as CD103+ DCs, suggesting a potentiated anti-tumor immune response. In summary, BDM realizes photothermal therapy/photodynamic therapy synergized chemotherapy for cancer.

Abstract Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

Residual stress is an important surface integrity index to evaluate the crack initiation and failure of die surface. The efficient prediction of cutting residual stress can guide the high-quality machining of die and improve its service life. The existing cutting residual stress prediction models are complex, time-consuming and inefficient. In this paper, a hybrid prediction method of cutting residual stress based on finite element-analytical-neural network is proposed. Firstly, the stress, strain and temperature of the cutting surface are obtained based on the orthogonal cutting finite element model. Then, the stress relaxation analytical algorithm considering the elastic-plastic state of the material is used to replace the stress release process of the finite element, and the residual stress distribution data were obtained based on the joint model of finite element and analytical algorithm. Secondly, the surrogate model of BP neural network (SSA-BP) is improved based on SSA algorithm to realize the rapid prediction of characteristic value of residual stress. The effectiveness of the finite element-analytical-neural network hybrid model was verified by the cutting residual stress test of H13 steel. Finally, the effects of tool structure parameters and cutting parameters on the residual stress distribution and the maximum compressive stress and maximum tensile stress of H13 steel were studied. This method can provide a flexible and efficient basic model for obtaining the optimal cutting conditions for controlling the residual stress of H13 steel and other metals.

The clinical application of cancer immunotherapy is unsatisfied due to low response rates and systemic immune-related adverse events. Microwave hyperthermia can be used as a synergistic immunotherapy to amplify the antitumor effect. Herein, we designed a Gd-based metal-organic framework (Gd-MOF) nanosystem for MRI-guided thermotherapy and synergistic immunotherapy, which featured high performance in drug loading and tumor tissue penetration. The PD-1 inhibitor (aPD-1) was initially loaded in the porous Gd-MOF (Gd/M) nanosystem. Then, the phase change material (PCM) and the cancer cell membrane were further sequentially modified on the surface of Gd/MP to obtain Gd-MOF@aPD-1@CM (Gd/MPC). When entering the tumor microenvironment (TME), Gd/MPC induces immunogenic death of tumor cells through microwave thermal responsiveness, improves tumor suppressive immune microenvironment and further enhances anti-tumor ability of T cells by releasing aPD-1. Meanwhile, Gd/MPC can be used for contrast-enhanced MRI. Transcriptomics data revealed that the downregulation of MSK2 in cancer cells leads to the downregulation of c-fos and c-jun, and ultimately leads to the apoptosis of cancer cells after treatment. In general, Gd/MPC nanosystem not only solves the problem of system side effect, but also achieves the controlled drug release via PCM, providing a promising theranostic nanoplatform for development of cancer combination immunotherapy.

Accumulating evidence has proved the close association between alterations in gut microbiota and resistance to chemotherapeutic drugs. However, the potential roles of gut microbiota in regulating oxaliplatin sensitivity in gastric cancer (GC) have not been investigated before. We first found that antibiotic treatment diminished the therapeutic efficacy of oxaliplatin in a GC mouse model. Importantly, this effect could be transmitted to germ-free mice via fecal microbiota transplantation, indicating a potential role of gut microbiota modulation in oxaliplatin efficacy. Further, metagenomics data showed that Akkermansia muciniphila (A. muciniphila) ranked first among the bacterial species with decreased relative abundances after antibiotic treatment. Metabolically active A. muciniphila promotes oxaliplatin efficacy. As shown by metabolomics analysis, the metabolic pattern of gut microbiota was disrupted with significantly downregulated levels of pentadecanoic acid (PEA), and the use of PEA significantly promoted oxaliplatin efficacy. Mechanistically, FUBP1 positively regulated aerobic glycolysis of GC cells to hinder the therapeutic efficacy of oxaliplatin. A. muciniphila-derived PEA functioned as an inhibitory factor of glycolysis by directly antagonizing the activity of FUBP1, which potentiated GC responses to oxaliplatin. Our research suggested a key role for intestinal A. muciniphila and its metabolite PEA in promoting oxaliplatin efficacy, thus providing a new perspective for probiotic and prebiotic intervention in GC patients during chemotherapy.

IntroductionThe combination of neoadjuvant immunotherapy and chemotherapy (NICT) has become a common treatment regimen for locally advanced gastric cancer (LAGC). However, the safety and efficacy of radical gastrectomy following NICT (NICT-G) remain controversial. This study aimed to analyze the risk factors influencing postoperative complications (POCs) after NICT-G. Additionally, it aimed to construct a nomogram to provide a clinical reference for predicting POCs.MethodsThis study included 177 patients who received NICT-G at the Chinese PLA General Hospital First Medical Center from January 2020 to January 2024. Univariable and multivariable logistic regression models were used to evaluate the risk factors influencing POCs, and a nomogram model was constructed. To evaluate the discrimination and accuracy of the nomogram model, the area under the receiver operating characteristic curve (AUC) and the calibration curve were measured.ResultsIn 177 patients who received NICT-G, the pathological complete response and major pathological response rates were 15.8% and 45.2%, respectively, whereas the rates of the overall and severe treatment-related adverse events were 71.8% and 15.8%, respectively. In addition, 43 (24.3%) patients developed overall POCs (Clavien–Dindo classification ≥ II). Univariable and multivariable logistic analyses showed that age ≥70 years, greater estimated blood loss, platelet/lymphocyte ratio (PLR) ≤196, neutrophil/lymphocyte ratio (NLR) >1.33, non-R0 resection, and body mass index (BMI) < 18.5 kg/m2 were independent risk factors for overall POCs (p < 0.05). The nomogram model developed using the abovementioned variables showed that the AUC (95% confidence interval [CI]) was 0.808 (95% CI): 0.731–0.885 in predicting the POC risk. The calibration curves showed that the prediction curve of the nomogram was a good fit for the actual POCs (Hosmer–Lemeshow test: χ2 = 5.76, P = 0.451).ConclusionThe independent risk factors for overall POCs in the NICT-G were age ≥ 70 years, greater estimated blood loss, PLR ≤ 196, NLR > 1.33, non-R0 resection, and BMI < 18.5 kg/m2. The nomogram model developed based on the abovementioned indicators showed better accuracy in predicting the POC risk.

Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a considerable health risk. Nevertheless, its risk factors are not thoroughly comprehended, and the association between the reticulocyte count and MASLD remains uncertain. This study aimed to explore the relationship between reticulocyte count and MASLD.Methods: A total of 310,091 individuals from the UK Biobank were included in this cross-sectional study, and 7,316 individuals were included in this prospective study. The cross-sectional analysis categorized reticulocyte count into quartiles, considering the sample distribution. Logistic regression models examined the connection between reticulocyte count and MASLD. In the prospective analysis, Cox analysis was utilized to investigate the association.Results: Our study findings indicate a significant association between higher reticulocyte count and an elevated risk of MASLD in both the cross-sectional and prospective analyses. In the cross-sectional analysis, the adjusted odds ratios (ORs) of MASLD increased stepwise over reticulocyte count quartiles (quartile 2: OR 1.22, 95% CI 1.17–1.28, p < 0.001; quartile 3: OR 1.44; 95% CI 1.38–1.51, p < 0.001; quartile 4: OR 1.66, 95% CI 1.59–1.74, p < 0.001). The results of prospective analyses were similar.Conclusion: Increased reticulocyte count was independently associated with a higher risk of MASLD. This discovery offers new insights into the potential of reticulocytes as biomarkers for MASLD.

ObjectiveThis study investigates the interactive collision avoidance （CA） problem of autonomous and manned ships in mixed navigation scenarios in order to achieve the efficient collision avoidance and safe navigation of ships. To this end, a multi-agent interactive ship dynamic game collision avoidance decision-making method is proposed. MethodsAccording to sailing practices, the ship collision avoidance problem in mixed navigation environments is analyzed and understood, and the ship encounter situation and collision risk are quantified on the basis of the International Regulations for Preventing Collisions at Sea (COLREGs). Dynamic game theory is introduced to model individual ships with collision risk as participants with independent thinking in the game, and the course changes of ships are taken as the strategy for determining the optimal action sequence under the constraints of ship safety and social and economic benefits. Different ship maneuvering modes are introduced to the simulation experiment, and the effectiveness of the collision avoidance decision-making method is verified using Python. ResultsThe results show that this method can realize the safe encounter of multiple ships in a mixed navigation environment of autonomous and manned ships, and each ship can adjust its behavior strategy to achieve safe avoidance when facing target ships with different sailing styles. ConclusionThis study can provide valuable references for the collision avoidance decision-making of autonomous and manned ships.

Abstract This paper reports lithium concentrations and isotopic compositions of olivines in the oceanized subcontinental lithospheric mantle (SCLM) peridotites of the Tibetan Yunzhug ophiolite. The results show systematic Li isotope changes with distance from the rim of olivine grains. δ7Li values of olivine in dunites decrease from + 10.46 to + 1.33‰ with increasing distance to olivine rim from 26.15 to 124.71 μm. A negative correlation of δ7Li and Li content in olivine from dunite and harzburgite indicates recent diffusive ingress of Li into the peridotites. The extremely heavy Li isotopic composition requires the seawater or seawater alteration endmember in the mixing model, and reveals Li diffusion from seawater into olivine. As in dunites, olivines in a harzburgite sample show similar variations in δ7Li as a function of distance from the grain rim (e.g., 6.01 to 1.73 in sample 14YZ13). We suggest that the behavior of Li in the oceanized SCLM peridotites may be controlled by Li diffusion from seawater, as Li activity in the liquid state is higher than the solid state in transporting Li through the olivines in the peridotites. This study supports that seawater Li diffusion is one of the important factors for the heterogeneity of mantle Li isotopes in ophiolites.

Abstract Background Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically. Methods This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required. Results The manual reviewers demonstrated acceptable interrater reliability (Cohen’s kappa 0.65–0.88). The performance results for the CTMS were as follows: accuracy, 92.9–98.0%; sensitivity, 51.9–83.5%; specificity, 99.0–99.1%; PPV, 75.7–85.1%; and NPV, 97.4–98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively. Conclusions We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials.

Animal transcranial magnetic stimulation (TMS) research is meaningful for exploring the mechanism of TMS and promoting the clinical promotion of new therapeutic methods. Tracing back the physiological phenomena caused by TMS is limited by the spatial resolution of the induced electric field. Through human and animal experiments, life science has proven that electric field direction affects the effect of magnetic stimulation, but this factor has not been introduced into the design of animal TMS coils. Therefore, this paper aims to design the TMS coil (EC-X) with controllable induced electric field direction, by finding the most favorable electric field direction for target activation, which can reduce interference to non-target areas and improve stimulation accuracy and energy utilization. Firstly, the structure of EC-X is proposed, establishing the Electromagnetic field Analytical Calculation Model (EM-ACM) for the arbitrarily shaped coil in the air domain, it is theoretically proven that EC-X can achieve direction control of the electric field. Then, the coil structure (such as coil length and angle) is optimized based on maximum ampere-turn $AN_{max}$ and stimulation focusing degree $F_{c}$ . Finally, the measurement of xy plane magnetic field distribution proves the accuracy of EM-ACM calculation results, which indirectly proves the feasibility of EC-X. Compared with the Figure-8 coil, EC-X can change the planar electric field direction without moving the coil, which has potential application value.

With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition accuracy. Therefore, an improved expectation maximization algorithm based on the constraint matrix Z and Tsallis entropy is proposed. The core goal of this algorithm is to accelerate the convergence of classification and improve accuracy. Furthermore, to enhance the classification accuracy, the spatial expectation maximization algorithm is introduced, which innovatively converts the sample mean and covariance matrix into $L_{1}$ -median and modified rank covariance matrix. According to the experimental data, the recall rate of the original expectation maximization algorithm is only 74%. However, the recall rate of the spatial expectation maximization algorithm in the Attack service has significantly increased to 85%. In other tests, such as Www and Peer-to-peer services, the recall rate has also significantly improved, increasing from 96% and 95.3% to 97.7% and 96.1%, respectively. These experimental results highlight the superior robustness of the spatial expectation maximization algorithm in handling outlier data. It further proves the outstanding performance in improving the accuracy of network traffic recognition. This research has brought significant innovation and potential practical value to the network traffic identification.

Cost prediction for construction projects provides important information for project feasibility studies and design scheme selection. To improve the accuracy of early-stage cost estimation for construction projects, an improved neural network prediction model was proposed based on BP (back propagation) neural network and Snake Optimizer algorithm (SO). SO algorithm is adopted to optimize the initial weights and thresholds of the BP neural network. Cost data for 50 construction projects undertaken by Shandong Tianqi Real Estate Group in China was collected, and the data samples were clustered into three categories using cluster analysis. 18 engineering feature indicators were determined through a literature review and 10 feature indicators were selected using Boruta algorithm for the input set. Compared to BP neural network and PSO–BP neural network, the results show that the improved SO–BP model has higher prediction accuracy, stability, better generalization ability and applicability. Therefore, based on reasonable feature indicators, the method proposed in this paper has certain guiding significance for predicting engineering costs.

To meet the practical demand of wearable/portable electronics, developing high-efficiency and durable multifunctional catalyst and in-situ assembling catalysts into electrodes with flexible features are urgently needed but challenging. Herein, we report a simple route to fabricate bendable multifunctional electrodes by in-situ carbonization of metal ion absorbed polyaniline precursor. Alloy nanoparticles encapsulated in graphite layer are uniformly distributed in the N-doping carbon nanorod skeleton. Profiting from the favorable free-standing structure and the cooperative effect of metallic nanoparticles, graphitic layer and N doped-carbon architecture, the trifunctional electrodes exhibit prominent activities and stability toward HER, OER and ORR. Notably, due to the protection of carbon layer, the electrocatalysts show the reversible catalytic HER/OER properties. The overall water splitting device can continuously work for 12 h under frequent exchanges of cathode and anode. Importantly, the bendable metal air batteries fabricated by self-supported electrode not only displays the outstanding battery performance, achieving a decent peak power density (125 mW cm−2) and exhibiting favorable charge-discharge durability of 22 h, but also holds superb flexible stability. Specially, a lightweight self-driven water splitting unit is demonstrated with stable hydrogen production.

In this paper, a data processing approach was developed to accurately extract the ring-down time and amplitude of the saturated cavity ring-down (CRD) signal; both were utilized to determine simultaneously the high reflectance and residual transmittance of highly reflective (HR) mirrors with a dual-channel CRD configuration. The influence of saturation was eliminated by deleting the beginning saturated data points of the saturated CRD signal and fitting the remaining non-saturated CRD signal to a single-exponential function. By comparing the reflectance/transmittance measurement results of HR samples obtained via data processing of saturated CRD signals and via single-exponentially fitting non-saturated CRD signals with utilization of neutral density filter(s) to eliminate saturation, it was found that the reflectances obtained with both methods were in excellent agreement, while the residual transmittance obtained with the saturated CRD signal was more accurate than that obtained with the neutral-density-filter-attenuated non-saturated CRD signal. The proposed data processing method eliminated the need to use the neutral density filters, therefore avoiding the adding of the optical density error to the uncertainty of residual transmittance measurement and improving the measurement accuracy. The proposed data processing method also extended the dynamic range of the dual-channel CRD scheme for simultaneous measurement of reflectance, transmittance and optical loss.

Using first-principles theory, this work purposes Ru-doped Janus WSSe (Ru-WSSe) monolayer as a potential gas sensor for detection of three typical gas species (CO, C2H2, and C2H4), in order to evaluate the operation status of the oil-immersed transformers. The Ru-doping behavior on the WSSe surface is analyzed, giving rise to the preferred doping site by the replacement of a Se atom with the formation energy of 0.01 eV. The gas adsorption of three gas species onto the Ru-WSSe monolayer is conducted, and chemisorption is identified for all three gas systems with the adsorption energy following the order: CO (−2.22 eV) > C2H2 (−2.01 eV) > C2H4 (−1.70 eV). Also, the modulated electronic properties and the frontier molecular orbital are investigated to uncover the sensing mechanism of Ru-WSSe monolayer upon three typical gases. Results reveal that the sensing responses of the Ru-WSSe monolayer, based on the variation of energy gap, to CO, C2H2, and C2H4 molecules are calculated to be 1.67 × 106, 2.10 × 105, and 9.61 × 103, respectively. Finally, the impact of the existence of O2 molecule for gas adsorption and sensing is also analyzed to uncover the potential of Ru-WSSe monolayer for practical application in the air atmosphere. The obtained high electrical responses manifest strong potential as a resistive sensor for detection of three gases. The findings hold practical implications for the development of novel gas sensing materials based on Janus WSSe monolayer. We anticipate that our results will inspire further research in this domain, particularly for applications in electrical engineering where the reliable detection of fault gases is paramount for maintaining the integrity and safety of power systems.

For the detections of typical faults gases, namely NO2 and O3, in the air insulated switchgears (AIS), we purpose Au- and B-embedded graphene (Au- and B-graphene) as promising sensing material using the first-principles theory. We firstly investigate the Au- and B-embedding behavior on the graphene, and find that both Au and B atoms are chemical stably adsorbed on the vacancy-defected graphene; in the gas adsorbed systems, it is found that NO2 and O3 adsorptions on Au-graphene are identified as chemisorption, while those on B-graphene are physisorption. The electronic properties of Au- and B-graphene in the gas adsorptions indicate the resistance-type sensing potential of Au-graphene upon O3 as well as that of B-graphene upon NO2. Besides, Au-graphene is a promising WF-based gas sensor upon NO2 and O3. The findings in this work uncover the strong potentials of Au- and B-graphene for detections of the NO2 and O3 in AIS, which gives the guidance to explore novel graphene-based gas sensors for applications in the power system.

Abstract Bile acids and salts have been shown to play a role in liver carcinogenesis through DNA damage, inflammation, and tumor proliferation. However, the correlation between bile acid metabolism and hepatocellular carcinoma (HCC) prognosis remains unclear. This study aimed to identify a predictive signature of bile acid and bile salt metabolism-related long non-coding RNAs (lncRNAs) for HCC prognosis and treatment response. The study used HCC RNA-sequencing data and corresponding clinical and prognostic data from The Cancer Genome Atlas. A prognostic model consisting of five bile acid and bile salt metabolism-related lncRNAs was developed and evaluated in a training set, a validation set and an external set. The model demonstrated good performance in predicting HCC prognosis and was shown to be an independent biomarker for prognosis. Additionally, our study revealed a significant association between the signature and immune cell infiltration, as well as its predictive value for therapeutic responses to both immunotherapy and chemotherapy. Furthermore, three LncRNAs (LUCAT1, AL031985.3 and AC015908.3) expression levels in our signature were validated through qRT-PCR in a cohort of 50 pairs of HCC patient tumor samples and corresponding adjacent non-tumor samples, along with 10 samples of normal liver tissue adjacent to benign lesions. These findings suggest that this novel bile acid and bile salt metabolism-related lncRNA signature can independently predict the prognosis of patients with HCC and may be utilized as a potential predictor of response to treatment in this setting.

Abstract The optical characteristics of three types of black holes (BHs) surrounded by a thin accretion disk are discussed, namely the Schwarzschild BH, Bardeen BH, and Hayward BH. We calculate the deflection angle of light as it traverses the vicinity of each BH using numerical integration and semi-analytical methods, revealing that both approaches can effectively elucidate the deflection of light around the BH. We investigate the optical appearance of the accretion disk and its corresponding observational images at various viewing angles, discovering that the luminosity in the region near the BH on the inner side of the accretion disk is higher than that on the outer side owing to higher material density in closer proximity to the BH. We observe a significant accumulation of brightness on the left side of the accretion disk, attributed to the motion of matter and geometric effects. Our findings emphasize the significant influence of the observation inclination angle on the observed outcomes. An increase in the observation inclination angle results in the separation of higher-order images. With the improvement in EHT observation accuracy, we believe that the feature of a minimal distance between the innermost region of the direct image of the Hayward BH and the outermost region of the secondary image can be used as an indicator for identifying Hayward BHs.

Abstract Accurate mastery of the creep characteristics of unsaturated saline soil is extremely important for the long-term stability and safe operation of all types of buildings. In this paper, the research object focused on the saline soil of the Zhangye area, Hexi corridor. The indoor triaxial CU creep test was carried out by means of graded loading to study the creep characteristics of saline soil under different salt content and loading stress. The Merchant and Burgers models were used to predict the creep behavior of the saline soils, and the predicted results were compared with the experimental values. The results showed that the triaxial creep curve of saline soil developed in stage III. Namely, transient creep stage, deceleration creep stage and steady-state creep stage. The creep deformation increases with the increase of salt content and loading stress. The stress–strain isochronous curve has non-linear growth, and the cluster of curves develops from dense to sparse after increasing to long-term strength (100∼150 kPa). The parameters of the Merchant and Burgers model vary with salt content and loading stress, and the creep curve predicted by the Burgers model is closer to the test value.

The deep learning neural network method is used to complete the non-intrusive load decomposition task, which often has the problems of poor model performance and low decomposition accuracy. In order to solve the problem that the decomposition accuracy is reduced because the model cannot fully learn all the power characteristics of a specific load in deep learning, a non-intrusive method based on the combination of TCN-LSTM two-layer neural network and strongly correlated power sequence is proposed. Firstly, the load decomposition algorithm learns the time-attribute power information mapping relationship through TCN, obtains the load state pre-identification result and coupling it with the prior data, obtains the independent strongly correlated power sequence of each electrical device to replace the original aggregate power sequence to improve the proportion of the target signal in the mixed signal, and inputs it into the LSTM to capture advanced time-domain information. The measured power sequence is used as a label to implement the neural network model. After training, the decomposed power sequences of five experimental loads were obtained and compared with the actual power sequences. In the part of case analysis, the UK-DALE public data set is used to verify the algorithm in the paper, and by comparing the algorithm in the paper with two typical algorithms, the effectiveness of the model in the paper is verified, and the high-precision load decomposition task is completed.