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In the process of oilfield exploitation and production, harmful pollutants, such as Crude oil that falls to the ground (generally refers to crude oil that leaks to the ground during oil production or transportation), production wastewater and oil-bearing mud are produced. In this contribution, the soil and crude oil from Daqing area are adopted as experimental materials to make a soil column-experimental device. The results show that the maximum migration depth of petroleum pollutants is 25 cm, most of the pollutants exist above 10 cm. The components of pollutants in disturbed soil column are complex, and the peak area of each component is large, mainly distributed in C 12 –C 28 , while in undisturbed soil column, the content of pollutants is small, and the peak area of each component is also small, mainly distributed in C 12 –C 22 . With the increase of depth, the relative content of aromatic hydrocarbons increases. The migration ability of low carbon component is weaker than the other components in crude oil. The components with high carbon number are significantly higher in shallow part. The relative contents of each component from high to low are saturates, aromatic hydrocarbons, resin and asphaltene in the soil. Compared with disturbed soil columns, the structure of undisturbed soil is complex, and the migration rate of pollutants in undisturbed soil is slower than that in disturbed soil. With the increase of depth, the light components of disturbed soil columns gradually decrease, and the relative content of heavy components changes little. The light components of the undisturbed soil column also gradually decreased, and the heavy components greater than C22 did not migrate to the depth of the soil column.

Background Beyond hemostasis, platelets’ immune and inflammatory role is recognized and thrombocytosis is generally regarded as a marker of the inflammatory response. However, the immune functions of platelets remain beneath the surface, and previous studies have reported conflicting findings regarding the significance of thrombocytosis in infection-related diseases. Can reactive thrombocytosis be viewed as a “higher-is-worse or higher-is-better” predictor of clinical outcome? Methods We analyzed 2754 patients discharged from the respiratory and infectious-disease wards between 1 January 2021 and 11 June 2025, and patients from the MIMIC-IV 3.1 database were used as validation cohorts, grouped by peak platelet counts (< 90, ≥ 400, 90–399 × 10⁹/L). Cox regression and multivariate logistic regression were performed to analyze the relationship between platelet count and in-hospital mortality. Multivariate linear regression was used to analyze the relationship between platelet count and length of hospital stay (LOS). Results Patients with reactive thrombocytosis had longer hospital stays (13 vs. 10 days; p  < 0.001) and higher inflammatory markers than those with normal platelet counts CRP (90.2 vs. 28.6 mg/L; p  < 0.001), PCT (0.195 vs. 0.092 ng/mL; p  < 0.001), WBC (12.6 vs. 8.4 × 10⁹/L; p  < 0.001). Despite these differences, mortality (3.4% vs. 4.4%) did not differ, nor did the rates of bloodstream, fungal, or polymicrobial infections. Clustering analyses confirmed comparable overall microbial spectra between the two groups. The impact of thrombocytosis on mortality (HR, 0.52; 95% CI, 0.27–0.99; p =  0.047) is context-dependent: it trended toward higher mortality in non-septic patients (OR, 1.42; 95% CI, 0.64–2.86, p  = 0.357) but toward lower mortality in those with sepsis (OR, 0.33; 95% CI, 0.05–1.51, p  = 0.18). The same opposing pattern and the interaction of sepsis and thrombocytosis re-emerged (OR, 1.70; 95% CI, 0.95–2.92, p  = 0.063 vs. OR, 0.58; 95% CI, 0.34–0.96, p  = 0.04) in MIMIC-IV data. Thrombocytopenia is an independent risk factor for mortality (HR, 2.08; 95% CI, 1.48–2.93; p <  0.001) and has good combined predictive ability. Conclusions Thrombocytosis trended toward higher mortality in non-septic patients yet toward lower mortality in those with sepsis.

Port green spaces are essential protective barriers, enhancing safety and environmental resilience in high-activity port regions. Given the intensity of human activities in these areas, understanding the factors influencing the carbon sequestration capacity and ecological benefits of port green spaces is crucial for developing sustainable green ports. This study integrated field investigations and remote sensing data to estimate carbon density and carbon sequestration capacity in the Dongjiakou Port area, examining their relationship with port green space planning. The results indicated that carbon density in green spaces showed a significant negative correlation with the number of lanes in adjacent roads, where an increase in lane numbers corresponded to lower carbon density. Additionally, carbon density decreased significantly with increasing distance from the shipping center. In contrast, a significant positive correlation was observed between carbon density and distance from large water bodies, indicating that green spaces closer to large water bodies exhibited smaller carbon density. Infrastructure development in Dongjiakou substantially negatively impacted vegetation carbon sequestration capacity, with effects not reversible in the short term. However, green space enhancement efforts provided additional ecological benefits, leading to a 50.9 ha increase in green space area. When assessing carbon density in urbanizing areas, geographical influences should be prioritized. Furthermore, the long-term environmental impacts of urban expansion must be considered at the early planning stages, ensuring the implementation of proactive protective measures to mitigate potential ecological disruptions.

Deep neural networks are vulnerable to adversarial attacks, bringing high risk to numerous security-critical applications. Existing adversarial defense algorithms primarily concentrate on optimizing adversarial training strategies to improve the robustness of neural networks, but ignore that the misguided decisions are essentially made by the activation values. Besides, such conventional strategies normally result in a great decline in clean accuracy. To address the above issues, we propose a novel RSA algorithm to counteract adversarial perturbations while maintaining clean accuracy. Specifically, RSA comprises three distinct modules: feature refinement, activation s uppression, and alignment modules. First, the feature refinement module refines malicious activation values in the feature space. Subsequently, the feature activation suppression module mitigates redundant activation values induced by adversarial perturbations across both channel and spatial dimensions. Finally, to avoid an excessive performance drop on clean samples, RSA incorporates a consistency constraint and a knowledge distillation constraint for feature alignment. Extensive experiments on five public datasets and three backbone networks demonstrate that our proposed algorithm achieves consistently superior performance in both adversarial robustness and clean accuracy over the state-of-the-art.

This study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly. A total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models. After matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (<58 mmHg) (OR 0.588, 95% confidence interval (CI): 0.362-0.955) was associated with a decreased risk for CI, and ≥50% stenosis of the left PCA (OR 2.854, 95% CI: 1.387-5.872) was associated with an increased risk for CI after adjusting for body mass index, myocardial infarction, and stroke history. Based on the means of various blood pressure (BP) parameters, the performance of the LR, DT, RF and SVM models accurately predicted CI (AUC 0.740, 0.786, 0.762, and 0.753, respectively) after adding the stenosis score of posterior circulatory artery. Elderly with low pulse differential pressure may have lower risk for cognitive impairment. The hybrid model combined with the stenosis score of posterior circulatory artery, clinical indicators, and the means of various BP parameters can effectively predict the risk of CI in elderly individuals.

Endoplasmic reticulum stress (ER stress) is a condition that is defined by abnormal accumulation of unfolded proteins. It plays an important role in maintaining cellular protein, lipid, and ion homeostasis. By triggering the unfolded protein response (UPR) under ER stress, cells restore homeostasis or undergo apoptosis. Chronic ER stress is implicated in many human diseases. Despite extensive studies on related signaling mechanisms, reliable image biomarkers for ER stress remain lacking. To address this deficiency, we have validated a morphological image biomarker for ER stress and have developed a deep learning-based assay to enable automated detection and analysis of this marker for screening studies. Specifically, ER under stress exhibits abnormal morphological patterns that feature ring-shaped structures called whorls (WHs). Using a highly specific chemical probe for unfolded and aggregated proteins, we find that formation of ER whorls is specifically associated with the accumulation of the unfolded and aggregated proteins. This confirms that ER whorls can be used as an image biomarker for ER stress. To this end, we have developed ER-WHs-Analyzer, a deep learning-based image analysis assay that automatically recognizes and localizes ER whorls similarly as human experts. It does not require laborious manual annotation of ER whorls for training of deep learning models. Importantly, it reliably classifies different patterns of ER whorls induced by different ER stress drugs. Overall, our study provides mechanistic insights into morphological patterns of ER under stress as well as an image biomarker assay for screening studies to dissect related disease mechanisms and to accelerate related drug discoveries. It demonstrates the effectiveness of deep learning in recognizing and understanding complex morphological phenotypes of ER.

Phosphorus is one of the key limiting factors for maize growth and productivity, and low-phosphorus stress severely restricts crop yield and stability. Enhancing the ability of maize to grow under low-phosphorus stress and improving phosphorus use efficiency (PUE) are crucial for achieving high and stable yields. Phosphate transporter (PHT) family proteins play a crucial role in the absorption, transport, and utilization of phosphorus in plants. In this study, we systematically identified the PHT gene family in maize, followed by the phylogenetic, gene structure, and expression profiles. The results show that these genes are widely distributed across the 10 chromosomes of maize, forming multiple subfamilies, with the PHT1 subfamily having the largest number. Cis-regulatory element analysis revealed that these genes might play key roles in plant stress responses and hormone regulation. Transcriptome analysis under phosphorus-deficient and normal conditions demonstrated developmental stage- and tissue-specific expression patterns, identifying candidate genes, such as ZmPHT1-3, ZmPHT1-4, ZmPHT1-10, and ZmPHO1-H3, involved in phosphorus stress response. This study presents a comprehensive and systematic analysis of the PHT gene family in maize, providing key molecular resources for improving phosphorus use efficiency and breeding phosphorus-efficient maize varieties.

Objects are often organized in a semantic hierarchy of categories, where fine-level categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.

Objective To establish morphological and radiomic models for early prediction of cognitive impairment associated with cerebrovascular disease (CI-CVD) in an elderly cohort based on cerebral magnetic resonance angiography (MRA). Methods One-hundred four patients with CI-CVD and 107 control subjects were retrospectively recruited from the 14-year elderly MRA cohort, and 63 subjects were enrolled for external validation. Automated quantitative analysis was applied to analyse the morphological features, including the stenosis score, length, relative length, twisted angle, and maximum deviation of cerebral arteries. Clinical and morphological risk factors were screened using univariate logistic regression. Radiomic features were extracted via least absolute shrinkage and selection operator (LASSO) regression. The predictive models of CI-CVD were established in the training set and verified in the external testing set. Results A history of stroke was demonstrated to be a clinical risk factor (OR 2.796, 1.359–5.751). Stenosis ≥ 50% in the right middle cerebral artery (RMCA) and left posterior cerebral artery (LPCA), maximum deviation of the left internal carotid artery (LICA), and twisted angles of the right internal carotid artery (RICA) and LICA were identified as morphological risk factors, with ORs of 4.522 (1.237–16.523), 2.851 (1.438–5.652), 1.373 (1.136–1.661), 0.981 (0.966–0.997) and 0.976 (0.958–0.994), respectively. Overall, 33 radiomic features were screened as risk factors. The clinical-morphological-radiomic model demonstrated optimal performance, with an AUC of 0.883 (0.838–0.928) in the training set and 0.843 (0.743–0.943) in the external testing set. Conclusion Radiomics features combined with morphological indicators of cerebral arteries were effective indicators for early signs of CI-CVD in elderly individuals. Key Points Question The relationship between morphological features of cerebral arteries and cognitive impairment associated with cerebrovascular disease (CI-CVD) deserves to be explored. Findings The multipredictor model combining with stroke history, vascular morphological indicators and radiomic features of cerebral arteries demonstrated optimal performance for the early warning of CI-CVD. Clinical relevance Stenosis percentage and tortuosity score of the cerebral arteries are important risk factors for cognitive impairment. The radiomic features combined with morphological quantification analysis based on cerebral MRA provide higher predictive performance of CI-CVD. Graphical Abstract

This study aimed to investigate the association between the plant community structure, leaf surface microstructure, nutrient element content, and the dust-retention capacity of garden plants in urban road green spaces. The plant community located along Ying Tian Street in Nanjing City was selected as the focal point of the investigation. Random sampling was performed on the urban road green spaces, determining the amount of dust trapped in plant leaves. Subsequently, the microstructure of the leaf surface was observed, and the content of nutrient elements in the plant leaves was determined. The study also entailed an analysis of the interrelationships between the leaf surface microstructure, plant nutrient element content, and the dust-retention ability of the plants. The findings of this study revealed notable variations in the dust-retention capacity of garden plants and the community structure observed along Ying Tian Street. Among the tree species, Cedrus deodara and Ginkgo biloba exhibited a remarkable dust-retention ability per unit leaf area. Among the shrub species, Abelia × grandiflora and Loropetalum chinense displayed a strong dust-retention capacity per unit leaf area. Similarly, Ophiopogon japonicus and Cynodon dactylon exhibited a robust dust-retention ability per unit leaf area among the herbaceous plants. Furthermore, the dust-retention ability of the plants exhibited a strong positive correlation with the dimensions of leaf stomata, specifically the length and width, while displaying a moderate positive correlation with the width of grooves on the upper and lower surfaces of the leaves. Conversely, the thickness of the leaves did not exhibit a significant correlation. Additionally, the nitrogen content of the leaves exerted a significant influence on the dust-retention ability of the plants (p < 0.05), although the phosphorus and potassium content factors did not exhibit a significant influence (p > 0.05). Based on the findings, it is recommended to prioritize the utilization of plants with robust dust-retention abilities, such as C. deodara, A. grandiflora, O. japonicus, and C. dactylon, and implement a mixed planting approach encompassing a combination of trees, shrubs, and herbaceous plants within urban road green spaces.