Scientific Reports最新文献

Exposure to plastic nanoparticles (PNPs) has become a significant public health and environmental concern due to their pervasive presence and potential toxic effects. However, the long-term effects of different PNPs types, their interactions with other nanoparticles, and effects across sexes, remain poorly understood. This study aimed to evaluate sex-specific physiological, biochemical, and genotoxic effects of chronic exposure to polystyrene nanoparticles (PS-NPs), silver nanoparticles (AgNPs), high-density polyethylene nanoparticles (HDPE-NPs) isolated from food packaging, and a mixture of PS-NPs and AgNPs in male and female rats. Nanoparticles were administered daily for 28 days via oral gavage, after which selected systemic, metabolic, and genotoxic endpoints were assessed. Despite no overt systemic toxicity or major liver damage, we found changes in cholesterol levels, especially in females, and signs of DNA damage, suggesting potential genotoxicity. The combination of PS-NPs/AgNPs triggered liver stress responses, implying additive or synergistic effects. Importantly, females showed greater sensitivity in terms of lipid metabolism, whereas HDPE-NPs-treated male group reduced testicular weight. These findings underscore the necessity of including both sexes in nanoparticle toxicity studies and highlight the need for a better understanding of the health risks of nanoplastics and their interactions with other co-occurring contaminants under realistic exposure conditions.

Accurate gland segmentation in colorectal cancer histopathology is crucial, but the scarcity of pixel-level annotations limits robust model development. This study aims to develop a highly accurate gland segmentation method that leverages weakly labeled data, specifically image-level labels, to overcome the need for extensive pixel-level annotations. We propose a novel three-stage framework that uniquely combines self-supervised fine-tuning of the DINOv2 vision transformer, attention-based pseudo-label generation, and a boundary-aware loss function. Initially, an off-the-shelf DINOv2 encoder is fine-tuned on a large unlabeled dataset of histopathology images. This fine-tuned encoder is then integrated into a classification network equipped with an attention mechanism, which is trained using image-level labels to generate initial pseudo-labels via attention maps. These maps are refined through blending, thresholding, and Conditional Random Field (CRF) post-processing. Finally, a segmentation network, employing the same fine-tuned encoder and a lightweight decoder, is trained using these refined pseudo-labels and a boundary-aware loss. Ablation studies demonstrated the significant benefit of the fine-tuned encoder and the comprehensive post-processing steps for pseudo-label generation. Further studies confirmed the effectiveness of the boundary-aware loss in improving segmentation accuracy. Our method achieved superior performance on the GlaS dataset compared to several state-of-the-art methods, including both fully supervised and weakly supervised approaches, demonstrating higher F1-score, Object Dice, and lower Object Hausdorff distance. This approach effectively addresses the challenge of limited pixel-level annotations by utilizing more readily available image-level data, offering a promising solution for improved colorectal cancer diagnosis. The proposed framework shows potential for generalization to other histopathology image analysis tasks.

The application of biostimulants represents a sustainable strategy to enhance crop productivity and resilience. This study investigated the efficacy of Delonix regia pollen aqueous extract as a biostimulant on coriander (Coriandrum sativum). Seeds were primed with different concentrations of the extract (0%, 0.5%, 1%, and 1.5%) for 24-48 h. The 1% extract applied for 48 h was the most effective treatment, significantly increasing shoot fresh and dry weight, shoot length, and the content of total protein, phenolics, flavonoids, and terpenes. This treatment also led to a significant upregulation of the key biosynthetic genes Chalcone synthase (CHS) and geranylgeranyl pyrophosphate synthase (GGPS) by 1.4-fold and 2.1-fold, respectively. Principal component analysis confirmed a positive correlation among shoot fresh weight, total protein, terpene content, and GGPS expression. These findings demonstrate that D. regia pollen extract is a potent biostimulant that enhances coriander growth and the production of valuable bioactive compounds through the modulation of key metabolic pathways.

With the growing demand for non-contact monitoring of vital signs such as respiration and heartbeat, frequency-modulated continuous wave (FMCW) radars have emerged as a promising solution for precise analysis of these signals. However, in complex environments such as indoors or inside vehicles, masking effects significantly degrade the accuracy of the target's distance. Additionally, multiple harmonics of the respiration frequency can easily leak into the heartbeat frequency range, resulting in biased heart rate estimation. To address these challenges, we propose the Matrix Coefficient Selection Method (MCSM), a robust distance detection approach that suppresses interference between targets and mitigates the impact of other obstacles in the environment, thereby improving the robustness of distance detection. Inspired by the harmonic mitigation techniques employed in power systems, we propose the Recursive Least Squares Respiratory Harmonic Suppression (RLSRHS) method, which is derived from an improved adaptive filter structure, to suppress respiratory harmonics. Simulation experiments demonstrate that the MCSM method reduces the MAE by approximately 40% at distance detection compared to traditional methods, while the accuracy of heart rate estimation after RLSRHS respiratory harmonic suppression reaches 83%. Extensive actual experiments, compared with contact instruments such as electrocardiogram monitors, Xiaomi wristbands, and respiratory sensors, show that the error is about 4%.

This paper focuses on an effective control technique for enhancing the Maximum Power Point Tracking (MPPT) performance of a grid-connected DFIG-based wind power plant under continuously varying wind conditions. However, rapid fluctuations in wind speeds, uncertainties in parameters, and grid disturbances are key challenges to enhancing the MPPT performance capability. Considering these struggles, this study aims to model a modified dynamic DFIG-based wind turbine system and develop a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Proportional-Integral (PI) controller on the back-to-back converter at the rotor and grid sides. The actual limited ranges of wind speed and output power generation data of the Adama II wind power plant in Ethiopia are utilized as input and output variables for the ANFIS controller. The simulation results from the latest version of R2024a-MATLAB-Simulink software show that the proposed ANFIS-PI reached an MPPT of 2.22 MW compared to the FLC-PI controller attained 2.2 MW using the benchmark as the reference value of 1.561 MW in the PI controller, by improving the maximum power coefficient of 0.5504 compared to 0.5473 using the baseline as the reference value of 0.4109, respectively, at a rated wind speed of 12.5 m/s and an optimal pitch angle of 0°.

The interplay of sleep quality, social hierarchy, and social isolation remains elusive. We evaluated such interplay using two mouse lines: C57BL/6J (B6) mice with relatively weak social hierarchy, and ICR×B6 F1 hybrid mice with relatively robust social hierarchy. Considering the potential effects of group housing on sleep - both through direct physical contact and other social interactions, which complicates interpretation-we designed a neighbor-housing condition that eliminates effects of direct physical contact while preserving social context. Under this condition, sleep architecture did not differ significantly between dominant and subordinate mice of either line. Under the single-housing condition, sleep differences emerged, some of which depended on both social rank and mouse line. In both mouse lines, single housing had opposite effects on oscillatory activities during sleep between dominant and subordinate mice. Notably, single housing significantly increased rapid eye movement sleep (REMS) amount only in subordinate B6 mice, but not in subordinate F1 hybrids or dominant mice of either lines, suggesting a genetically modulated sensitivity to social conditions. Our findings suggest complicated interactions between social environment, social hierarchy, and genetic factors in REMS regulation.

An efficient siderophore-producing bacterium, Burkholderia sp. Bmkn7, was isolated from the rhizosphere of an inland rice variety cultivated in the underexplored coastal saline-affected rice fields of Kerala, India. The complete genome of Bmkn7 possessed a single circular chromosome of 8,397,732 bp with an average GC content of 66.5%. Phylogenetic and comparative genome studies identified Bmkn7 as a member of the genus Burkholderia, closely related to the plant-associated Burkholderia cepacia complex genomovar I. The antiSMASH analysis identified a rich repertoire of 20 biosynthetic gene clusters (BGCs) involved in the production of diverse specialized secondary metabolites, including non-ribosomal peptide synthetases (NRPSs) encoding siderophores (pyochelin, ornibactin), a pyrrolnitrin-encoding cluster, and terpenes. Furthermore, the presence of several orphan BGCs suggests the potential genetic ability of Bmkn7 to produce novel bioactive compounds. In addition, Bmkn7 exhibited potential antimicrobial activity against various bacterial and fungal phytopathogens involving metabolites dependent and independent of siderophores, including unidentified bioactive molecules. Additionally, Bmkn7 harbors several plant-associated and plant growth-promoting genes, including those involved in phosphate solubilization, 1-aminocyclopropane-1-carboxylate (ACC) deaminase production, mitigation of plant-derived oxidative stress, and the utilization of various plant-derived substrates. Notably, the Bmkn7 genome lacks key genes associated with animal-host interactions and virulence, suggesting a plant-associated lifestyle. Combining genomic analyses and phenotypic assays, we provide evidence suggesting Bmkn7 as an ideal candidate for phytopathogen suppression and plant growth promotion, further expanding knowledge on plant-associated Burkholderia strains.

In response to the problem of poor quantification ability and unclear feature correlation of pipeline defect magnetic flux leakage signals, a neural network is proposed to establish the relationship between the characteristic quantities of pipeline defect magnetic flux leakage signals and defect sizes. The characteristic quantities of pipeline defect magnetic flux leakage signals that measure the length, width and depth of pipeline defects are determined by combining the characteristic quantities of the radial and axial components of defect magnetic flux leakage signals in actual pipelines. A database of magnetic leakage signal characteristic quantities for pipeline defects is established by extracting and organizing the characteristic quantities of magnetic leakage signals. Moreover, a particle swarm optimization (PSO)-radial basis function (RBF) neural network model that combines the PSO algorithm and the RBF neural network to quantify pipeline defects is designed. Results show that the average quantification error of the PSO-RBF network model reached 21.08%, representing an improvement of 12.94% compared with that of the traditional RBF network model. The Pearson correlation analysis shows that these feature quantities are significantly positively correlated with the defect size, which provides a reliable feature basis for quantitative modeling.It meets the requirements of practical engineering applications for pipeline defect quantification and has a good application prospect in pipeline magnetic leakage internal detection technology.

Numerous 3D shape descriptors have been proposed in recent years, among which spectral descriptors have gained significant prominence. However, widely used spectral signatures, such as the Heat Kernel Signature (HKS), Scale-Invariant HKS (SIHKS), and Wave Kernel Signature (WKS), suffer from parameter dependence, where heuristic and sub-optimal scale selection limits their robustness and generalizability. To address this limitation, this paper introduces a novel class of descriptors termed Geometric Moments of Spectral Shape Descriptors (GMSDs). By integrating temporal and spatial domains, GMSDs leverage invariant moment theory to calculate six moment terms, creating a theoretical framework that significantly enhances performance in non-rigid 3D shape analysis. GMSDs not only inherit the desirable properties of standard spectral signatures, such as isometric invariance and robustness to noise and topological changes, but also effectively mitigate parameter sensitivity. Extensive experiments on the TOSCA, SCAPE, SHREC 2011, and SHREC 2015 benchmarks demonstrate that GMSDs achieve superior performance in both shape correspondence and retrieval tasks compared to state-of-the-art methods.

To investigate the factors associated with self-injury among patients in Psychiatry Outpatient Clinic. A total of 67 patients presenting with self-injury behavior were recruited from the Psychiatry Outpatient Clinic of Qilu Hospital, Shandong University, between October 2021 and July 2023. All participants underwent a diagnostic interview, which collected data on basic demographic variables and was assessed using the Columbia-Suicide Severity Rating Scale (C-SSRS). Based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), participants were categorized into a diagnosed NSSI group (n = 26) and a non-diagnosed NSSI group (n = 41). Logistic regression analysis identified that being a non-only child and the presence of suicidal ideation were significant risk factors for an NSSI diagnosis. Further analysis showed that among only child, self-injury was more frequently associated with "classmate relationship" compared to non-only child. In contrast, self-injury related to "parental relationship", "parent-child relationship", "teacher-student relationship" and "academic pressure" was less common in only child than in non-only child. Additionally, participants with suicidal ideation were more likely to report self-injury influenced by "parent-child relationship" and "teacher-student relationship", but less likely to attribute self-injury to "parental relationship", "peer relationship", or "academic pressure" compared to those without suicidal ideation. Non-only child and individuals with suicidal ideation are the risk influencing factors for the diagnosis of NSSI (non-suicidal self-injury). Regarding influencing factors of self-injury, particular attention should be paid to the "parental relationship", "parent-child relationship" and "teacher-student relationship" in the non-only child group, and to the "parent-child relationship" and "teacher-student relationship" in the group with suicidal ideation.