Scientific Reports最新文献

The intensification of abiotic stresses, particularly drought and elevated temperatures driven by climate change, requires the widespread adoption of protected cultivation. A pivotal innovation within this system is the use of spectral-selective filters, which modify the solar spectrum to provide environmental protection and direct physiological control over plant growth, especially for high-value ornamental species. The application of colored shade nets in this study, positively influenced nutrient uptake and growth parameters except for stem diameter, spike length, number of flowers and plant and flower dry mass in Polianthes cut flowers. The highest concentration of potassium (2.78%), nitrogen (2.02%), and phosphorus (0.14%) was found in the green and blue shade treatments, but there was no significant difference between the treatments. Furthermore, the most substantial improvement in flower diameter was observed under the green shade net, resulting in a 20.75% increase, while the white shade net showed the smallest enhancement at 9.66% in comparison with full sunlight. Additionally, peroxidase activity and photosynthetic pigment levels were elevated in plants under green and white shading compared to those under blue nets. Proline content was significantly reduced by all colored shade treatments, with the highest accumulation observed in plants exposed to full sunlight. In conclusion, although the use of all shade nets improved some flower quality indices and somewhat increased the potassium, chlorophyll, and carotenoids contents and reduced proline in the plant, overall, the use of green shades is recommended.

The long-term effects of the COVID-19 pandemic on asthma exacerbations across sociodemographic groups is unknown. We conducted a retrospective cohort study of 162,113 asthma patients within the Montefiore Health System from March 2018 to February 2024 to evaluate pandemic-related changes in exacerbation risk across demographic, socioeconomic, and social determinant subgroups. Difference-in-differences logistic regression compared pre-pandemic (2018-2020) and late-pandemic (2022-2024) periods by age, sex, race and ethnicity, income, insurance, and unmet social needs. Interrupted time series regression assessed level and trend changes in March 2020, with sensitivity analyses confirming robustness. Before the pandemic, exacerbation odds were higher among Black, Hispanic, and Other race patients, females, Medicaid-insured individuals, and children. After pandemic onset, risk increased disproportionately for patients with unmet social needs, children, males, Medicaid, and low-income groups, while racial and ethnic disparities persisted. Interrupted time series showed immediate decreases among children and adults, with subsequent rebound to baseline in children but sustained suppression among adults. The pandemic was associated with widened socioeconomic disparities, a transient reduction in pediatric exacerbations, and a lasting decline among adults, underscoring the need for interventions addressing socioeconomic and social drivers of asthma outcomes.

Medication-related osteonecrosis of the jaw (MRONJ) is a severe complication associated with antiresorptive or antiangiogenic agents, often leading to pain, infection, and reduced quality of life. Current imaging-based diagnostics have limitations in detecting lesions smaller than 10 mm. In this study, we propose a label-free saliva screening approach for MRONJ diagnosis using a three-dimensional plasmonic structure based on M13 bacteriophage. Raman spectroscopy was employed to detect metabolite alterations in saliva, which are known to be associated with MRONJ. The M13 bacteriophage facilitates controlled interparticle gap of gold nanoparticles, thereby increasing hotspot density and enhancing Raman signal intensity. Data preprocessing was conducted on saliva Raman spectra collected from MRONJ patients and controls. To filter outliers, we computed Pearson correlation coefficients between each spectra and the group mean and excluded those with coefficients lower than 0.9. A total of 90 spectra were classified using an optimized multi-layer perceptron model, yielding a specificity of 84.6%, sensitivity of 100.0%, and an AUC of 0.92. This study demonstrates the potential of a saliva-based, non-invasive MRONJ screening strategy. Subsequent research should expand clinical datasets and investigate broader diagnostic applications.

Textile dyes such as Reactive Blue 21 and Reactive Red 195 are complex organic compounds widely used in the fabric industry, which often cause significant environmental pollution due to their resistance to conventional wastewater treatment methods. The aim of this study is photo-degradation of these organic dyes by copper (II) oxide nanoparticles (CuO-NPs) that biosynthesized via a method based on pistachio hulls extract. The synthesized nanoparticles were characterized by various techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), and ultraviolet-visible (UV-Vis) spectroscopy. The characterization results confirm the formation of homogeneous, spherical, crystalline CuO-NPs with an average size of approximately 90 nm. The high quality of CuO-NPs synthesized using pistachio hull extract is attributed to the presence of natural reducing and capping agents in the extract. These agents play a dual role: they reduce the copper precursor to CuO-NPs and simultaneously stabilize the nanoparticles by preventing agglomeration. The biosynthesized CuO-NPs showed high photocatalytic activity in degrading organic dyes under UV light irradiation. Specifically, the degradation efficiencies reached about 83% for Reactive Blue 21 and 75% for Reactive Red 195 after 180 min of irradiation.

Automated detection of surface defects on three-dimensional (3D) parts is vital for ensuring product quality and safety in manufacturing. However, three key challenges hinder reliable detection: geometric context ambiguity across complex part shapes, domain mismatch between generic pretrained features and industrial scans (with their unique noise and reflectivity), and the scarcity of diverse defect examples for training. To overcome these issues, we propose a novel single-forward-pass framework for point cloud anomaly detection, comprising three new modules: (1) Spatial Context Aggregation, which grounds each local patch in a set of learned global prototypes via an optimal-transport alignment to resolve context ambiguity; (2) Feature Adaptor, a lightweight two-layer multilayer perceptron (MLP) that fine-tunes self-supervised Point-MAE embeddings to the specific characteristics of industrial scans; and (3) Selective Anomalous Feature Generator, which synthesizes realistic hard negatives by corrupting random subsets of feature tokens, thus mitigating the need for extensive defect labels. An attention-based discriminator trained with patch-wise supervision learns to distinguish these hard negatives from genuine defect-free patterns. At inference, our pipeline delivers dense per-point anomaly scores in a single pass at up to 13.5 frames per second (FPS). On the Real3D-AD benchmark, we observe point-level improvements of 2.8% in area under the receiver operating characteristic curve (AUROC) and 5.7% in area under the precision-recall curve (AUPR), with object-level gains of 3.0% (AUROC) and 3.5% (AUPR). Evaluated on our newly released Industrial3D-AD dataset, which captures realistic sensor noise and reflective materials, we see similar enhancements (2.9%/5.3% point-level, 2.8%/3.3% object-level).