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Abstract This study is to analyse the failure patterns and long-term survival after stereotactic body radiotherapy (SBRT) in patients with T1-3N0M0 inoperable non-small cell lung cancer (NSCLC). Early-stage NSCLC patitents who received SBRT at Zhejiang Cancer Hospital from January 2012 to September 2018 were retrospectively analyzed. The primary endpoint were the patterns of disease progression, which were divided into local recurrence, regional failure, and distant metastasis. Kaplan-Meier method survival analysis was used to calculate overall survival (OS), progression-free survival (PFS). Cox model was used for univariate analysis and multivariate analysis. A total of 215 patients with 224 lesions were enrolled. After the median follow-up time of 50.8 months (1.0-117.9 months), 76 (35.3%) patients progressed, with regional progression occurring in 4 cases (1.8%), local and local-regional progression in 17 cases (7.9%), various distant metastases developing in 55 cases (25.6%). The OS rates at 1, 3, and 5 years were 97.1%, 80.9%, and 63.8%, respectively, with a median OS of 92.2 months (95%CI, 61.5-122.9 months). The PFS rates at 1, 3, and 5 years were 87.5%, 65.9%, and 50.8%, respectively, with a median PFS of 62.2 months (95% CI, 45.0-59.4 months). There was no significant difference in OS (P = 0.832) and PFS (P = 0.672) between the two groups with or without pathology. Multivariate analysis showed that BED and patient age were independent prognostic factors affecting early-stage lung cancer survival (all P

Abstract Biomass pyrolysis greatly impacts climates, ecosystem dynamics, air quality, human health, global carbon and nitrogen cycle. The emissions of nitrogen-containing compounds from biomass pyrolysis highly depend on the protein nitrogen existing in biomass. However, the quantitative kinetic information, including the rate constant and apparent activation energy of individual amino acid induced by pyrolysis are still yet to be well-constrained. Towards this, we performed a series of controlled pyrolysis experiments where 18 equimolar free amino acids standard mixtures were pyrolyzed under ambient oxygen at temperatures between 160 and 240 °C. Additionally, straw samples were pyrolyzed to understand the mechanism of combined amino acids in protein liberation to free amino acids during pyrolytic processes. Our observations indicated that an increase in heating duration and temperature promote the degradation of free amino acids. Further, the heating stability of the 18 examined amino acids varied, which could be related to the length and functional groups present in their side chains. Our result shows that the degradation processes of all examined 18 amino acids followed irreversible first-order reaction kinetics in air within the given temperature range, with their activation energy ranging from 88.5 to 137.44 kJ mol−1. The distinct distribution patterns of both combined and free amino acids in aerosol samples from straw pyrolytic processes were obtained. The kinetic information of amino acids garnered herein helps to elucidate the transformation mechanisms of nitrogenous compounds during biomass burning.

Abstract To address the conservative excavator working device configuration obtained by traditional design methods, a lightweight structure design method was proposed in this study based on automatically generated surrogate model. A finite element model of the working device was first constructed, and its accuracy was verified against experimental results. By incorporating the Gini coefficient to compute the information entropy pertaining to the relationship between variables and responses, the entropy-weighted TOPSIS method has been significantly enhanced. Subsequently, the comprehensive contributions of the boom design parameters to the mass, deformation, and stress responses were calculated, and the parameters exerting significant effects were selected as the design variables through the application of the improved TOPSIS. Next, the working device optimisation model was subsequently established using the minimum mass as the target and the stress and deformation as constraints. And then, a surrogate model of the working device was constructed using 500 sample points. Finally, a genetic algorithm was employed to derive a lightweight design of the excavator boom based on the surrogate model. The results indicate that the proposed method reduced the weight of the considered hydraulic excavator boom by 9.30%; further, a finite element simulation verified that the stress and deformation met the design requirements of the boom.

Abstract Slurry pipeline transportation is widely used in dredging and serves as an essential method for conveying solid materials. However, accurately describing the interaction between slurry and particles through numerical simulations, while optimizing the pipeline structure to improve the performance of slurry pipelines, poses a significant engineering challenge. In this study, a Z-shaped continuous pipeline, designed using B-spline curves, is implemented in the slurry circulation system. The validity of the CFD–DEM method is confirmed through slurry circulation experiments, and the effects of various structural parameters on transportation efficiency and flow characteristics are investigated. By integrating the Kriging surrogate model with the NSGA-II algorithm, a multi-objective optimization method is proposed to reduce computational complexity and improve the sediment-carrying capacity of the Z-shaped pipeline. Using a slurry shield tunneling machine with a diameter of 6.24 m as an example, this study optimizes a Z-shaped pipeline with an inner diameter of 0.3 m and an axial height of 1.5 m. The optimized structural parameters increase the average particle velocity by 8.9% and reduce particle accumulation by 21.3%. Additionally, the interaction between the pipeline’s inclination angle $$\theta$$ , the B-spline control parameter $$l_{4}$$ , and the sediment-carrying capacity is analyzed.

Abstract Soil microbial communities play a crucial role in maintaining diverse ecosystem functions within the saline-alkali soil ecosystems. Therefore, in this study, we collected various saline-alkaline soils from across the Inner Mongolia Hetao irrigation area. The soil chemical properties were analyzed, and the microbial diversity of bacteria and fungi was measured using 16 S rRNA and ITS rRNA amplicon sequencing. The dynamic relationship between the soil microbial community and soil factors was analyzed using the ABT (Aggregate Enhanced tree) model, the co-occurrence network, and the structural equation model. The results indicated that electrical conductivity (EC) was the biggest driving force of various saline-alkaline soils, affecting the community structure of bacteria (22.80%) and fungi (21.30%). The soil samples were categorized into three treatment levels based on their EC values: the low-salinity group (L, EC: 0–1 ms/cm, n = 10), the medium-salinity group (M, EC: 1–2 ms/cm, n = 8), and the high-salinity group (H, EC > 2 ms/cm, n = 6). The results demonstrated a negative correlation between microbial abundance and salinity-alkalinity, while revealing an enhanced interrelationship among species. The alterations in bacterial (12.36%) and fungal (22.92%) communities in various saline-alkali soils were primarily driven by saline-alkali ions, which served as the principal direct factors. The negative correlation between EC and SOM exhibited the highest magnitude, whereas the positive correlation between soil organic carbon and EC demonstrated the greatest strength. Therefore, it was further substantiated that EC played a pivotal role in shaping the distinct microbial communities in saline-alkali soils.

Abstract Liver cirrhosis, a common liver disease, currently lacks specific targeted therapies. This study investigates the potential therapeutic effects of serum circulating proteins on cirrhosis from a genetic perspective, and identified six associated plasma proteins (SERPINA1, PSG5, NCAN, APOE, ADH1B, GM2A). To search for therapeutic drugs associated with circulating proteins, databases such as DrugBank and DGIdb are utilized. Phenome-wide Mendelian Randomization analysis of the six significantly associated proteins revealed that GM2A exhibited no notable side effects as a therapeutic target for cirrhosis, SERPINA1 may offer additional therapeutic benefits for cholelithiasis and emphysema. ADH1B serves as a potential drug target that could simultaneously reduce the risk of alcohol-related disorders and hypertension. Furthermore, PSG5 and APOE might increase the risk of cardiovascular and neurological diseases, and NCAN has the potential to additionally reduce the risk of developing non-alcoholic fatty liver disease NAFLD. In conclusions, this study substantiates, from a genetic perspective, the potential therapeutic target role of six plasma proteins in cirrhosis, while comprehensively evaluating their side effects.

Abstract The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.

Developmental scientists have long described mid-adolescents emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14-18 years-old youths proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents neural development, and suggest the importance of attending to adolescents proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimers disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Maternal use of e-cigarette (e-cig) aerosols poses significant risks to fetal brain development, potentially increasing susceptibility to neurodevelopmental disorders in later life. However, the underlying mechanisms remain incompletely understood. This study aimed to understand the effects of fetal e-cig exposure on DNA methylome and transcriptomic changes in the neonatal brain. Pregnant rats were exposed to e-cig aerosols, and neonatal brains (5 males and 5 females/group) from both control and e-cig-exposed groups were used for experimental analysis. Results indicated that prenatal e-cig exposure altered site-specific DNA methylation patterns at both CpG and CH (non-CpG) sites, predominantly in intergenic and intronic regions, with sex dimorphism in methylation and gene expression changes. Gene ontology analysis revealed that e-cig exposure not only affected neuron projection development and axonogenesis but also altered pathways related to neurodegeneration and long-term depression. These findings provide novel insights into the dynamic changes of CpG and CH methylation induced by e-cig exposure, underscoring the susceptibility of the developing brain to maternal e-cig exposure and its potential implications for developmental disorders and neurodegenerative diseases later in life.