Search Results (30,885)

Phosphorus (P) deficiency severely limits maize growth and yield, yet early detection remains challenging, as visible symptoms appear only after prolonged starvation. This study evaluated the capability of hyperspectral imaging (HSI) combined with machine learning to detect P deficiency in maize seedlings at both symptomatic and pre-symptomatic stages. Two greenhouse experiments were conducted: a long-term pot system under high and low P conditions and a short-term hydroponic experiment with three P concentrations of 500, 100, and 0 μmol/L phosphate (Pi). After long-term P deficiency, significant reductions in shoot biomass and Pi content were observed, while root biomass increased and nutrient profiles were altered. Hyperspectral signatures revealed distinct wavelength-specific differences across visible, red-edge, and near-infrared (NIR) regions, with P-deficient leaves showing lower reflectance in green and NIR regions but higher reflectance in the red band. A multilayer perceptron machine learning model achieved 99.65% accuracy in discriminating between P treatments. In the short-term experiment, P deficiency significantly reduced tissue Pi content within one week without affecting pigment composition or photosynthetic parameters. Despite the absence of visible symptoms, hyperspectral measurements detected subtle spectral changes, particularly in older leaves, enabling classification accuracies of 80.71–84.56% in the first week and 85.88–90.98% in the second week of P treatment. Conventional vegetation indices showed weak correlations with Pi content and failed to detect early P deficiency. These findings demonstrate that HSI combined with machine learning can effectively detect P deficiency before visible symptoms emerge, offering a non-destructive, rapid diagnostic tool for precision nutrient management in maize production systems. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

This article develops a boundary-adaptive nonparametric methodology for estimating the geometric conditional quantiles of a multivariate response when the conditioning covariate is supported on the simplex—an important case, as it is the natural domain of compositional data. The statistical difficulty addressed here is twofold. First, geometric conditional quantiles for multivariate responses must be defined and estimated through a genuinely directional and convex framework rather than through any scalar ordering. Second, when the covariate is compositional or otherwise simplex-constrained, conventional symmetric kernel procedures suffer from intrinsic support mismatch and severe boundary distortion, thereby compromising both estimation accuracy and inferential validity near faces and edges of the simplex. The method proposed in this paper is designed precisely to overcome this combined obstacle. Our main innovation consists in embedding the spatial quantile formalism of Chaudhuri within a Dirichlet–Kernel smoothing scheme whose shape parameters depend deterministically on the evaluation point. This produces a convex M-estimator that respects the simplex geometry exactly, automatically adapts its local shape to the position of the target point, and removes the need for artificial boundary corrections. To the best of our knowledge, this is the first contribution to provide a complete asymptotic treatment of geometric conditional quantile estimation under simplex-supported covariates with location-adaptive asymmetric kernels. We establish a Bahadur-type linear representation with an explicit negligible remainder, from which we derive refined asymptotic bias and variance expansions. The variance analysis reveals a distinctive geometric phenomenon: each coordinate direction approaching the simplex boundary induces an additional $b^{-1/2}$ inflation factor, so that the variance at a face of codimension $\left|\mathcal{J}\right|$ scales as $n^{-1}{b}^{-(s+|\mathcal{J}\left|\right)/2}$. We further obtain the asymptotic mean squared error, an explicit optimal bandwidth rate, asymptotic normality under the nonstandard normalization $n^{1/2}{b}^{-s/4}$, and consistent plug-in covariance estimators yielding valid confidence ellipsoids. Numerical experiments and a real-data illustration based on the GEMAS data confirm the practical merit of the approach, especially in boundary regions where classical methods are known to deteriorate. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Radiotherapy is one of the conventional methods for the treatment of cancers. Boron neutron capture therapy (BNCT) has emerged as a promising and well-recognized modality for treating certain types of cancers. BNCT is a binary radiotherapy that largely depends on neutron beams and ¹⁰B carriers. Although an “ideal” boron carrier should fulfill multiple criteria, high tumor/normal tissue ratio (T/N > 5) and high tumor uptake of boron (>20 μg/g) are critically important. First-generation (boric acid and derivatives) and second-generation (BPA and BSH) boron carriers suffer from poor T/N and extremely high dose in clinical use (500 mg/kg and usually >30 g for each patient). Glucose transporter 1 (GLUT1) is overexpressed on the membrane surface of multiple tumors and is a potential target for third-generation boron carrier to achieve high T/N and high tumor uptake of boron. However, the boron-bearing sugar derivatives designed in the last few decades have suffered from suboptimal T/N values and significant cytotoxicity. In the present study, a total of two categories comprising 6 series (28 in total) of boron-bearing sugar derivatives were designed and synthesized and their cellular boron uptake, T/N, and cytotoxicity were evaluated. The structure–activity relationship (SAR) of these target compounds was analyzed, and one of the target compounds, B3, a phenyl C-mannoside with an o-carborane moiety, exhibited the best boron-carrying profile, which featured 10.6-fold higher boron uptake by the SCC-9 cell line and a largely improved T/N (3.3 for B3 vs. 1.4 for BPA) compared with the current clinical gold standard BPA. Therefore, the chemical structure of B3 represents a privileged candidate structure for the future design of “ideal” boron carriers for BNCT. Full article

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the proposed model achieves a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 2.38 MPa, and a mean absolute percentage error (MAPE) of 2.02% on the test set, with prediction accuracy significantly outperforming benchmark models such as BPNN, XGBoost, and LSTM. Ablation experiments verify that both the SOM clustering and GAN augmentation modules effectively enhance model performance. Analysis of acid treatment parameters indicates that hydrofluoric acid (HF) concentration is the dominant factor influencing fracture pressure reduction, and the mud acid system exhibits a stronger synergistic effect compared to the single hydrochloric acid system. Reasonable optimization of acid concentration and dosage can significantly reduce fracture pressure (3.14–5.28 MPa). This method provides a theoretical basis and engineering guidance for accurate fracture pressure prediction and optimal design of acid pretreatment in tight conglomerate reservoirs. Full article

Avian influenza infections cause substantial economic losses in the poultry industry and raise public health concerns due to viral adaptation and cross-species transmission. The frequent antigenic drift of influenza viruses further complicates the prevention and treatment of avian respiratory infections. In this study, we generated high-affinity heavy-chain variable domain (VHH) nanobodies from naïve alpaca/camelid VHH libraries using phage display combined with H9N2 influenza A virus (IAV)-infected Madin-Darby Canine Kidney (MDCK) cells. Based on binding affinity and neutralization potential, we identified seven hemagglutinin (HA)-specific and two neuraminidase (NA)-specific VHHs. Molecular docking predicted the interaction sites of HA-specific VHHs (L1-2, L1-4, A5) and NA-specific VHHs (L1-3, L2-2), providing mechanistic insights. Notably, the three HA-specific VHHs (L1-2, L1-4, A5) showed cross-reactivity to representative HA subtypes (H1, H3, and influenza B), indicating recognition of conserved epitopes across divergent influenza strains. For the first time, these camelid nanobodies were fused to the chicken IgY Fc domain, and the expression cassette was integrated into the Saccharomyces cerevisiae genome, achieving a secretion yield of 15–20 mg/L of VHH-Fc antibodies. Experimental validation confirmed that the three HA-specific VHHs-Fc constructs effectively blocked viral infection, while the two NA-specific VHH-Fc constructs (L1-3, L2-2) inhibited NA activity, demonstrating the functional efficacy of the yeast-secreted VHH–IgY Fc platform. This novel IgY Fc fusion approach offers a scalable platform with enhanced stability, extended circulation potential, and applicability in poultry. Full article

Livestock represent a key asset in the livelihood of smallholder farmers and play a critical role in the social dynamics and nutritional security of resource-poor communities. However, within these resource-poor communities, livestock productivity remains low. This is often due to seasonal changes in the quantity and quality of available feed from the natural veld, which in turn also contributes to methane production. This study aimed to evaluate the effects of supplementing Eragrostis curvula hay with Searsia lancea leaf or silage meal on in vitro fermentation efficiency and gas and methane production. Therefore, an in vitro study using a semi-automated pressure transducer technique was conducted on grass hay alone (control) and grass hay supplemented with 15% or 30% of either S. lancea leaf or silage meal. The dietary treatments were arranged in a complete randomized design, with each treatment replicated four times. Total gas and methane production was recorded at 3, 6, 12, 24 and 48 h using a pressure transducer attached to a data logger. After incubation, samples were collected to determine volatile fatty acids. Supplementing grass hay with 15% S. lancea leaf meal increased gas production by 76%, 52%, 32% and 12% in the first 24 h of incubation. Similarly, increasing the supplementation level to 30% increased gas production by 75%, 63%, 45% and 14%. However, supplementing grass hay with silage meal at 15% significantly reduced gas production by 37% during the first 3 h of incubation, whereas supplementation at 30% had no effect. Supplementing grass hay with S. lancea meals effectively reduced methane production at 24 and 48 h. Grass hay supplemented with 15% or 30% silage meal reduced methane by 46% and 39% at 24 h, while at 48 h, methane was reduced by 39% and 49%, respectively. Supplementing grass hay with S. lancea meals, however, did not affect volatile fatty acids. In conclusion, S. lancea can be strategically used as a supplementary feed source to modulate the rumen ecosystem by attenuating enteric methane production. Further studies are required to determine the effect of S. lancea on rumen microbial composition and its metabolic function. Full article

Background: Uterus transplantation (UTx) is an emerging treatment for absolute uterine factor infertility. However, the use of deceased donors is limited, and donation after circulatory death (DCD) has not yet been utilized. Ischemic injury remains a major barrier, particularly compared with living donor procedures. Hypothermic oxygenated perfusion (HOPE), which has shown protective effects in heart, liver, and kidney transplantation, may offer similar benefits for uterine grafts. Methods: We report the first series applying HOPE to human uteri to improve preservation and enable metabolic injury assessment during perfusion. Six uteri (3 DBD, 3 DCD; median donor age 53 years) underwent 8 h of HOPE following procurement, while paired tissue controls were preserved using static cold storage (SCS). Perfusion was delivered using a pressure-controlled system (15 mmHg, 10 ± 1 °C, VitaSmart^®). Perfusate and tissue samples were analyzed for mitochondrial injury, inflammation, and transcriptional responses. Results: HOPE maintained stable flows (70–150 mL/min), delivered high oxygen levels (pO₂ ≈ 1000 hPa), and increased tissue ATP levels. Stratification based on perfusate flavin mononucleotide (FMN) release identified grafts with greater Complex I/II injury. HOPE was associated with lower levels of mitochondrial injury markers and inflammatory signals, preserved tissue architecture, and promoted gene expression patterns consistent with metabolic recovery compared with paired SCS tissue controls. Conclusions: These findings suggest that HOPE may serve as a preservation approach that enables metabolic and ischemic injury assessment and may facilitate broader use of deceased donor uteri for transplantation. Full article

The plant-specific TCP transcription factor family plays crucial roles in morphogenesis and stress adaptation. While characterized in many species, this family remains unstudied in Punica granatum. We performed the first genome-wide analysis of the TCP family in pomegranate, identifying 24 PgTCP genes classified into the PCF, CIN, and CYC/TB1 subclades, supported by conserved gene structures and motifs. Evolutionary analysis indicated segmental duplication and purifying selection shaped this family. Expression profiling revealed distinct spatiotemporal patterns: PgTCP2/9/14/21 were highly expressed in flowers, with PgTCP21 also notably abundant in fruit tissues (seed coats and pericarp), suggesting roles in reproductive development. PgTCP19, an ortholog of the branching suppressor BRC1, showed dominant expression in dormant buds, implicating it in shoot architecture regulation. Furthermore, PgTCP5 and the miR319-targeted PgTCP22 were leaf-predominant, indicating a function in leaf development. Under abiotic stress, PgTCPs displayed dynamic, treatment-specific responses. A subset of genes was rapidly induced by cold, while PgTCP14 and PgTCP23 showed sustained upregulation during drought. Several light-responsive PgTCPs were suppressed under shading. This study provides a foundational resource, functionally classifies the PgTCP family, and identifies key candidates regulating organ development and stress resilience for future functional validation and molecular breeding in pomegranate. This work provides the first comprehensive overview of the TCP gene family in pomegranate and offers candidate genes for future functional studies related to development and stress responses. Full article

Systemic lupus erythematosus (SLE) is an autoimmune disease that predominantly affects women of childbearing age, and active disease during pregnancy is associated with increased maternal and fetal morbidity. Belimumab is an effective biologic therapy for active SLE; however, its use during pregnancy has long been limited by the scarcity of safety data. Recent evidence and updated international recommendations suggest that belimumab may be considered in selected cases when required to maintain maternal disease control. We report the case of a woman with SLE who experienced three consecutive pregnancies with live births between 2019 and 2024 while receiving belimumab, allowing an intra-individual comparison of different exposure strategies. During the first pregnancy, belimumab was discontinued at conception and was followed by a disease flare in late pregnancy and postpartum. In the second and third pregnancies, belimumab was continued until gestational week 20 following shared decision-making with the patient; nevertheless, disease flares occurred during the third trimester of both pregnancies. All pregnancies resulted in live births at term, with no congenital anomalies, placental insufficiency, or fetal growth restriction. One neonate from the third pregnancy developed early-onset neonatal sepsis and meningitis, which resolved completely after antibiotic treatment. All children are currently growing and developing normally. This case supports a risk-adapted approach to belimumab use during pregnancy. In selected women with SLE at high risk of disease reactivation, continuation of belimumab until mid-gestation may contribute to improved maternal disease control without evident adverse fetal outcomes. Full article

The performance of biological wastewater treatment processes directly impacts water resource recycling and ecological safety. This year-long study compared full-scale wastewater treatment plants (WWTPs) using either the anaerobic/anoxic/aerobic (AAO) or modified Bardenpho process. By integrating water quality analysis with 16S rRNA sequencing, we examined how process type, influent quality, and seasonal factors affect microbial communities and treatment performance. Systems with high chemical oxygen demand (COD) and biochemical oxygen demand (BOD)/COD influent exhibited the best pollutant removal performance, with average nitrogen and phosphorus concentrations in the effluent as low as 7.0 mg/L and 0.1 mg/L, respectively. Optimizing a 1:9 influent distribution ratio between the pre-anoxic and first anoxic zones in the modified Bardenpho process increased total nitrogen (TN) removal efficiency by an average of 14 percentage points compared to the AAO process. Additionally, the modified Bardenpho process identified 1100 bacterial genera, indicating a more complex and stable community. Influent water quality had the most significant impact on microbial communities and treatment efficiency, followed by seasonal factors and process type. This study provides theoretical and data support for the optimization of wastewater treatment processes and seasonal regulations. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article