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This study assesses the stress-related impacts of the construction of the Thwake Multipurpose Dam in Makueni, Kenya by examining salivary cortisol concentrations and patterns of diurnal variation. One set of evening, waking, and 30-min post-waking saliva samples was collected across 221 women who were displaced by the dam or who lived upstream or downstream of the dam development site. Salivary cortisol concentration was analyzed using a commercially available assay kit. Multivariable linear regression was used to assess the relationship between displacement status and waking cortisol concentration, evening cortisol concentration, cortisol awakening response, and diurnal difference. Log-transformed evening cortisol concentration (displaced: β = 0.365, p = 0.018; downstream: β = 0.675, p = 0.007) and diurnal difference (displaced: β = 0.034, p = 0.049) were significantly associated with displacement status. Both displaced and downstream communities demonstrate stress-related hormonal differences associated with dam-induced disruption. Future policy and research addressing the health impacts of hydroelectric dam development should include downstream communities in addition to those directly displaced by development.

Surface subsidence resulting from underground mining presents a significant challenge in mining engineering, potentially damaging surface structures, environmental concerns, the safety of personnel, and causing substantial economic losses for the mine. This study aims to investigate and predict ground subsidence resulting from underground mining activities, focusing on a copper ore mine in India, adopting the blast-hole stopping technique. In this study, various subsidence prediction modeling techniques (empirical, influence function, and theoretical/numerical/analytical) have been outlined for assessing mining-induced subsidence. A three-dimensional (3-D) numerical model was developed using the Finite Element Method (FEM) code Strand7, encompassing geologic complexities such as joints and faults, connections between different lithologies, and the stoping sequence selected from the mine plans to investigate the mechanism of surface subsidence induced by underground mining. The model simulates various mining phases, including the virgin state, ongoing operations, and predictions for the subsequent five and ten years, to assess the related strain and displacement in all directions. To improve model precision, rock mass characteristics and in-situ stress conditions were optimised via backward modeling. The novelty of the work lies in the coupling of backward modeling for geotechnical parameter improvement within a three-dimensional FEM framework, applied to a real-world mining sequence in a complex geological setting. This method enhances the accuracy of predictions and provides a practical approach to subsidence prediction in operational mines. This research helps to promote safer mine planning and enables the construction of dependable, site-specific methods.

Predicting foundation pit deformation is a significant challenge for foundation pit engineering. The inaccuracy of deformation prediction is increased by the intricacy of subterranean space and the variety of construction conditions. Thus, this study develops a deformation prediction model that combines the attention mechanism and bidirectional long short term memory network (BiLSTM) in order to increase the accuracy of deep foundation pit deformation prediction. Meanwhile, to enhance the generalization ability of the model, this study introduces combined regularization in the loss function and adds a Dropout mechanism in the network structure. This study takes a deep foundation pit excavation project in Guangzhou as an example. Experiments shows that the model proposed in the study can complete convergence in about 30 training rounds, and the training loss is maintained at the 0.03 level. Meanwhile, the maximum absolute error of the model in the prediction of verification data is 1.44 mm, and the minimum error is 0.001 mm. The mean absolute error of the model is 0.311 mm, the root mean square error is 0.433 mm, and the R2 is 0.906, which is better than the comparison model. The attention mechanism and BiLSTM model suggested in this study provides good generalization performance and high prediction accuracy in deep foundation pit deformation prediction, according to experimental results. Its potential for use in engineering safety management is promising.

Single-fiber bidirectional transmission (Bidi transmission) enables efficient reuse of existing global communication cables for sensing, yet environmental disturbances induce link noise that fundamentally limits long-distance sensing performance. Despite extensive research efforts dedicated to Bidi transmission link noise suppression, existing methods fail to eliminate link noise, forcing the abandonment of the Bidi transmission scheme in large-scale deployments. Here, we uncover that the origin of this residual noise is the "ghost noise," which can be decomposed into polarization cross-coupling noise (PCN) and calculation cross-coupling noise (CCN). To address this ghost noise, we develop a polarization-traversal interrogation (PTI) technique integrated with pseudo-random binary sequence (PRBS) phase modulation. This hybrid approach achieves zero noise increase over a 7 km Bidi transmission link and realizes 94.8% background noise reduction in a 10 km Bidi transmission link. This breakthrough extends the low-noise Bidi analog transmission distance by six times, holding significant implications for the construction of sensing networks through global communication infrastructure.

Remote sensing using uncrewed aerial vehicles (UAVs) and AI, particularly machine learning and deep learning, is increasingly applied to crop pest and disease detection. However, the real-world robustness of these models remains uncertain. We conducted a meta-analysis of 121 UAV-based studies published between 2018 and 2024, examining dataset construction and model validation practices. We found that 89% of studies lacked truly independent test datasets, resulting in inflated performance estimates and limited generalisability. Only 11% evaluated models on independent fields, and successful transferability was uncommon. Our analysis identifies key methodological limitations underlying this issue and provides recommendations to improve robustness, reproducibility, and practical relevance. Overall, current validation practices require substantial improvement to ensure reported model performance reflects field-level applicability.

As a critical area for the ecological security barrier in the middle and lower reaches of the Yangtze River, the Jianghuai Watershed Area holds significant importance in coordinating ecological conservation and economic development through the assessment of its water ecosystem service value (ESV). This study focuses on Chuzhou, a typical city within the Jianghuai Watershed Area, to explore the spatiotemporal evolution of water ESV and analyze the spatiotemporal change of the Consistency between water Ecosystem services and Economy (CEE) from 2000 to 2022 based on remote sensing. Firstly, the water was extracted through normalized snow water index (NSWI) and Otsu threshold method (OTSU). Subsequently, the water ESV dynamic evaluation model was constructed. Using this model, the water ESV in Chuzhou was quantified, and its spatiotemporal distribution characteristics were analyzed. Finally, by calculating the coefficient of variation (CV) between ESV and GDP, the deviation between water ESV and GDP in Chuzhou was obtained. At the same time, the coordination between water ESV and economic development of Chuzhou was obtained by calculating CEE, so as to evaluate the Spatiotemporal coordination and regional differences between them. This study has found that: (1) The water ESV in Chuzhou showed a phased change of "fluctuating decline and subsequent recovery" from 2000 to 2022. (2) In terms of spatial distribution, the water ESV exhibits a gradient pattern of "high ESV in the northwest and low ESV in the southeast of the Chuzhou". (3) In terms of ecological and economic coordination, the northwest of the Chuzhou maintain a healthy CEE. However, the southeast of Chuzhou has exacerbated the imbalance in CEE due to the water space occupied by construction land. This study could provide a decision-making basis for formulating ecological priority development strategies in the Jianghuai Watershed Area.

The growing demand for sustainable construction materials has accelerated the need for alternatives to natural aggregates in concrete. Although steel slag has been extensively studied, the use of steel sludge as a fine aggregate substitute in concrete paver blocks has not been sufficiently examined, especially regarding durability and microstructural characteristics. This study investigates the performance of steel sludge as a micro-filler and partial replacement of fine aggregate at 10%, 20%, and 30% by mass, maintaining a constant water-cement ratio of 0.38. Mechanical and durability properties were evaluated using compressive strength, water absorption, sorptivity, rapid chloride penetration, and weight loss tests, complemented by SEM, EDX, and XRD analyses. The results indicate a consistent improvement in performance with increasing steel sludge content. The 28-day compressive strength increased from 42.77 MPa for the control mix to 54.13 MPa at 30% replacement, representing an approximate 26.6% increase. Water absorption decreased from 6.12% to 4.20%, the initial absorption rate decreased from 0.0332 to 0.0250 mm/min⁰·⁵. and RCPT values declined from 640 to 552 Coulombs, reflecting reduced permeability. Microstructural analysis demonstrated pore refinement, a lower Ca/Si ratio, and enhanced formation of polymerized C-(A)-S-H gel. XRD analysis confirmed the absence of new crystalline phases. Within the investigated range, steel sludge enhances strength and durability through matrix densification and microstructural refinement. These findings demonstrate its potential as a sustainable industrial waste management to natural sand in paver block applications.

Accurate and highly sensitive detection of pathogenic bacteria is essential to public health. Conventional biosensors often rely on bulk signal amplification, which suffers from diffusion-limited kinetics and high background signals. Herein, we present a programmable, surface-confined biosensing strategy that constructs a target-responsive nucleic acid-enzyme microenvironment directly on the bacterial surface, enabling localized catalytic reactions for ultrasensitive detection. Upon target recognition, the aptamer-primer (AP) strand triggers surface-confined rolling circle amplification (RCA) to form high-density DNA network scaffolds. These scaffolds recruit alkaline phosphatase (ALP), establishing a confined enzymatic reaction microenvironment. The localized ALP catalyzes the hydrolysis of p-aminophenol phosphate (APP) to generate p-aminophenol (PAP), creating a reducing microenvironment that drives in situ nucleation of silicon quantum dots (SiQDs) from the silane precursor N-[3-(trimethoxysilyl)propyl] ethylenediamine (DAMO). Using Staphylococcus aureus as a model pathogen, this approach provides dual-mode readouts: an ultrasensitive fluorescence response with a detection limit of 23 CFU mL-1 and a rapid colorimetric response with a detection limit of 134 CFU mL-1. Importantly, the recognition module was highly programmable. By simply replacing the target-specific aptamer domain within the AP strand, this strategy can be universally adapted for various pathogens. Moreover, by generating complementary multidimensional signals, including fluorescence, UV-vis absorbance, and hydrodynamic diameter, the strategy enables the construction of a multidimensional optical sensor array. Integration with machine-learning algorithms allows the platform to interpret distinct chemo-optical fingerprints, thereby enabling the accurate classification of multiple pathogenic bacteria. Overall, this strategy provides a versatile framework for intelligent multiplexed pathogen diagnostics by integrating programmable biomolecular recognition, spatially confined enzymatic nanosynthesis, and data-driven analyses.

N7-methylguanosine (m7G) modification plays a critical role in RNA metabolism and is increasingly recognized for its implications in cancer biology. It can influence RNA stability, translation efficiency, and gene expression regulation. However, the specific role of m7G modification and its downstream genes in thyroid carcinoma (THCA) is not well understood. To comprehensively explore the impact of m7G methylation modification and the m7G-related gene ZNF831 on THCA, this study aims to identify key genes influencing m7G modification in THCA, with a particular focus on clarifying the role of ZNF831. This study is expected to further elucidate the pathological mechanisms of THCA and fill the current research gap in this field. Weighted gene co-expression network (WGCNA) analysis was used to evaluate the expression of m7G-related genes in the THCA expression data from the GEO (Gene Expression Omnibus) datasets. Machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), gradient boosting decision tree (XGBoost), and random forest (RF), were used to identify the feature genes, including GPSM3 and ZNF831, in the TCGA-THCA dataset. Immunohistochemistry was used to identify the expression difference of ZNF831 in 3 THCA tissues and 3 normal tissues. Finally, the changes of proliferation and migration of THCA cells after overexpression of ZNF831 were investigated. This study investigated m7G-related genes in THCA, focusing on ZNF831 as a key tumor suppressor. Differential expression analysis revealed significant dysregulation of m7G-related genes in THCA. Functional and bioinformatics analyses, including gene set enrichment analysis and protein-protein interaction network construction, identified ZNF831 as a candidate gene. Experimental validation demonstrated that ZNF831 overexpression significantly reduced the proliferation and migration of THCA cells. Additionally, tumor microenvironment analysis showed a positive correlation between ZNF831 expression and immune cell infiltration, indicating its potential role in enhancing anti-tumor immunity. These findings underscore the importance of m7G modifications and m7G-related gene ZNF831 in THCA pathogenesis, highlighting their potential as therapeutic targets. Further research is needed to elucidate the molecular mechanisms and explore clinical applications of these findings.

In this work, we develop a mathematical model that captures both the early and late phases of Long-Term Potentiation (LTP) and Long-Term Depression (LTD), incorporating NMDAR-dependent induction and changes in AMPAR conductance. The model combines multiple essential properties. First, it emphasizes a detailed representation of biochemical processes within the postsynaptic neuron, thereby illustrating the interaction between LTD and distinct forms of LTP. Second, the dynamic modulation of postsynaptic AMPA receptor conductance is represented through nonlinear differential equations and algebraic relations. Third, the model incorporates input specificity, associativity, and cooperativity, allowing synaptic changes at one site to influence the strength of neighboring synapses. These features provide a comprehensive description of synaptic dynamics, allowing the simulation of plasticity at both the cellular and the network levels. Overall, the model offers a valuable framework for studying NMDAR-dependent LTP and LTD by explicitly incorporating changes in AMPAR conductance. We believe that this model provides deeper insights into the molecular mechanisms of synaptic plasticity and paves the way for the construction of network-level models by linking multiple cells through AMPA receptor conductance. Significance statement: We present a comprehensive mathematical framework that integrates early (E-LTP), late (L-LTP), and LTD by incorporating NMDAR-dependent signaling and changes in AMPAR conductance. By combining and extending established biochemical models, our approach links molecular signaling, receptor trafficking, and postsynaptic membrane dynamics to changes in synaptic strength. The model reproduces key experimental phenomena, including input specificity, associativity, and cooperativity, and clarifies how pathways, such as CaMKII and PKA govern the stability of synaptic modifications. By capturing both cellular- and network-level properties, this framework provides a foundation for building scalable neural models grounded in the biophysics of learning and memory.

Chronic ocular graft-versus-host disease (coGVHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT) may lead to irreversible ocular surface damage and even vision loss. Current management of coGVHD faces challenges, with frequent missed or misdiagnosed cases. This study aimed to leverage a multimodal large language model (MLLM) to develop an early warning and diagnostic system for coGVHD. A total of 666 post-allo-HSCT patients (early warning model) and 805 post-allo-HSCT patients (1574 eyes, diagnostic model) were enrolled for construction, internal validation, and external validation of the corresponding models. We proposed the GVHD-MLLM, a multitask multimodal network that fused latent representations from four modal sequences to provide high-precision, real-time predictions for two tasks. The GVHD-MLLM achieved high performance in internal testing, with AUROCs of 93.44% (95% CI: 91.85-95.03%) for early warning, 98.98% (95% CI: 98.59-99.36%) for diagnosis, and 98.24% (95% CI: 98.05-98.43%) for disease severity grading. In external validation, the early warning AUROC was 83.45%, while diagnostic AUROCs across three external sites were all above 96.0%. The disease severity of patients seeking medical treatment after using the early warning model was significantly lower. Junior ophthalmologists also improved diagnostic accuracy using the model as an auxiliary tool. The GVHD-MLLM can process rich multi-modal information collected in clinical practice, and is expected to become an effective tool for managing coGVHD.

Oral cancer is a significant global health challenge, ranking as the sixth most prevalent cancer worldwide, with approximately 377,000 new cases diagnosed annually. The high morbidity and mortality rates are largely attributed to tobacco and alcohol use. While conventional treatments such as surgery, radiation, and chemotherapy have improved survival rates, they often lead to unfavourable aesthetic and functional outcomes. Tissue engineering offers a promising alternative, providing regenerative solutions aimed at restoring both oral function and appearance. By integrating biomaterials, biological systems, and engineering principles, tissue engineering enables the creation of functional tissue replacements. The current review examines different t pathways the potential applications of autologous tissue, oral cancer cell lines, CRISPR, gene-editing technologies, and epigenetic modifications for tissue regeneration. Advanced scaffold technologies that mimic the natural extracellular matrix, along with stem cell-based therapies and bioactive molecules, are employed to support tissue growth and differentiation. Mesenchymal stem cells (MSCs) and induced pluripotent stem cells (iPSCs) show significant potential in regenerating hard and soft oral tissues, while also targeting cancer stem cells (CSCs) to prevent recurrence. Furthermore, innovative technologies like 3D bio printing, combined with vascularization strategies, hold promise for developing patient-specific tissue constructs for reconstructive procedures. In conclusion, tissue engineering offers transformative potential for oral cancer treatment, presenting regenerative therapies that can significantly enhance patient outcomes and quality of life.

Accurate microbial identification based on lipid profiling offers promising applications in diagnostics and microbial ecology. In this study, five lipid pretreatment methods-Maldixin, chloroform-methanol, tert-butyl methyl ether, isobutyric acid-ammonium hydroxide, and sodium acetate (SA) buffer (pH 4.0)-were systematically evaluated using MALDI-TOF mass spectrometry operated in linear negative-ion mode. Among the tested methods, the SA buffer method provided a favorable balance between operational feasibility, extraction efficiency, and spectral quality. Compared with positive-ion mode, negative-ion mode yielded high-intensity lipid peaks and superior spectral clarity, particularly for Acinetobacter baumannii. Lipid profiles were generated from 203 bacterial strains representing six species, revealing distinct species-specific lipid fingerprints. A lipid fingerprint database was subsequently constructed and validated using an independent set of 144 strains, achieving identification accuracies of 81.9% at the species-level and 97.2% at the genus-level. While overall species-level accuracy remains lower than that of conventional protein-based systems, these results indicate that linear negative-ion mode MALDI-TOF MS provides complementary lipid information that may enhance microbial characterization. Further optimization of sample preparation and database algorithms is warranted to support broader clinical application.

PIWI-interacting RNAs (piRNAs) are an important class of non-coding RNA molecules in epigenetic regulation. It plays a crucial role in maintaining genomic stability and inhibiting transposable elements, and have been proven to participate in various diseases by regulating gene expression and influencing signaling pathways. Traditional biological experimental methods have limitations such as low throughput, long cycles, and high costs, making them difficult to meet the requirements of large-scale systematic screening. In this study, we develop a predictive framework named PiDA-DVLSA. We integrate autoencoder, dual graph transformer, and multi-head self-attention mechanisms, and construct an end-to-end multimodal deep learning system. We use autoencoder to perform nonlinear dimensionality reduction and denoising on piRNA sequence features and disease phenotype semantic features, and extract potential representations with strong discriminative ability. Then, we use graph transformers to model the high-order topological relationships between nodes in isomorphic similar graphs, and input heterogeneous graph transformers to learn complex cross-entity interaction patterns in heterogeneous networks. Finally, we achieve adaptive fusion of multi-source information through multi-head self-attention mechanisms. PiDA-DVLSA performs excellently on the benchmark dataset, with AUC and AUPR reach 0.9437 and 0.9195, respectively, significantly outperform eight mainstream algorithms. In independent case validations for breast cancer, clioblastoma, and Alzheimer disease, our model successfully predicts multiple biologically significant potential associations, further confirming its practicality and effectiveness in real scientific research scenarios and providing a solid computational basis for future precision diagnostic and therapeutic applications. PiDA-DVLSA is freely available at https://github.com/zhaoqi106/PiDA-DVLSA .

Impaired ventricular-arterial coupling (VAC) is associated with adverse health outcomes. However, the predictive values of VAC calculated by different non-invasive methods remain uncertain. We aimed to assess prognostic values of VAC calculated as the ratio between arterial elastance (Ea) and left ventricular end-systolic elastance (Ees) and between carotid-femoral pulse wave velocity (PWV) and global longitudinal strain (GLS). In 3634 Atherosclerosis Risk In Communities study participants (57.4% women; mean age, 75.1 years), Cox proportional hazard models were constructed to determine associations of VAC metrics with heart failure (HF) and all-cause mortality. Risk prediction models were employed to examine prediction improvement of VAC beyond established risk factors. Over approximately 6.3 years (median), 316 participants experienced HF, and 482 died. The hazard ratios of HF related to 1-SD increment in VAC metrics were 1.28 (95% CI, 1.18-1.38; P&#x2009;<&#x2009;0.001) for Ea/Ees and 1.40 (1.27-1.54; P&#x2009;<&#x2009;0.001) for PWV/GLS with adjustments applied for potential confounders. PWV/GLS was the only VAC parameter associated with mortality (adjusted HR, 1.18; 1.08-1.28; P&#x2009;<&#x2009;0.001). PWV/GLS was observed to have stronger associations with all outcomes in individuals aged &#x2264;74 years than those aged >74 years (P for interaction &#x2264;0.034). The addition of VAC maker to the conventional risk factors improved risk prediction for incident HF (P&#x2009;&#x2264;&#x2009;0.010) assessed by C statistics, net reclassification improvement, and integrated discrimination improvement for Ea/Ees (0.702, 22.4%, and 1.40%) and for PWV/GLS (0.701, 22.1%, and 1.46%). In the general population, impaired VAC was associated with a higher risk of incident HF and total mortality.

Our brains dynamically adapt to a multisensory world by orchestrating diverse inputs across sensory streams. This process engages multiple brain regions, but it remains unclear how audiovisual stimuli are represented and evolve over time, especially in naturalistic scenarios. Here, we employed a movie-viewing paradigm to explore this question. We recorded intracranial electrocorticography (iEEG) to measure brain activity in 19 participants watching a short multilingual movie. Using unsupervised clustering and supervised encoding models, we identified a robust modality-specific gradient in the frontal cortex, wherein the ventral division primarily processes auditory information and the dorsal division processes visual inputs. Further, we found that this cortical organization dynamically changed, adapting to different movie contexts. This result potentially reflects flexible audiovisual-resource assignment to construct a coherent percept of the movie. Leveraging behavioral ratings, we found that the frontal cortex is the primary site in this modality assignment process. Together, our findings shed new light on the functional architecture of the frontal cortex underlying flexible multisensory representation and integration in natural contexts.

Although there are ongoing studies on innovative green materials, building materials are mostly composed of cementitious compounds as binders. Cement is known to significantly increase CO2 emissions both during its production and utilization. Furthermore, while the utilization of industrial by-products in waste storage and disposal processes offers positive contributions to the circular economy, this approach does not constitute a holistic and adequate solution in terms of the environment and human health. This study presents the results of the effect of mud powder obtained from Rize province on the mechanical and microstructural properties of cement mortars to the academic environment. The mud was dried and ground, then calcined at 600, 700 and 800&#xa0;&#xb0;C. The calcined mud powder obtained at the optimum calcination temperature was substituted into cementitious mortars at 0%, 10%, 20%, 30% and 40% by weight. The parameters used in the study were material/ball weight&#x2009;=&#x2009;1/12 and binder/cement weight&#x2009;=&#x2009;0.45. Compressive and flexural tests were performed for mechanical strength and XRD, SEM-EDS, FTIR, Specific gravity, and UPV tests were performed to explain the microstructures. The numerical and test data imply that the maximum 28-day compressive strength of 46. 65&#xa0;MPa was obtained as a result of 10% substitution of calcined mud powder, which is a natural resource. These results indicated that calcined powder mud (CPS) has strong potential as a sustainable material for construction and structural applications.