Search Results (85,775)

Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article

Background: White light cystoscopy (WLC) is the gold standard for bladder cancer (BC) detection and surveillance, but has limited sensitivity, particularly for small and flat lesions. We evaluated the diagnostic performance and safety of a real-time artificial intelligence (AI)-assisted support tool (CystoAID©) for bladder cancer detection. Methods: This single-center, randomized, controlled non-inferiority trial included 64 patients undergoing transurethral resection of bladder tumor or laser fulguration for suspected primary or recurrent BC. Patients were randomized 1:1 to WLC alone or WLC followed by AI-assisted cystoscopy. The primary outcome was per-lesion sensitivity of CystoAID vs. WLC within the intervention group. The reference standard was histopathology or clinical evaluation. The predefined non-inferiority margin was −5%. Secondary outcomes included sensitivity for lesions ≤ 5 mm, false positives, procedural duration, and safety (30-day adverse events). Results: A total of 142 lesions were identified (84 intervention, 58 control). Sensitivity was 96.2% (95% CI 87.0–99.5) for CystoAID and 88.7% (95% CI 77.0–95.7) for WLC (difference 7.5%; 95% CI −2.7 to 17.8), demonstrating non-inferiority. In a sub-analysis for lesions ≤ 5 mm (n = 25), sensitivity was 100% (95% CI 86.3–100) vs. 80% (95% CI 59.3–93.2). AI assistance added a median of 2.1 min without further affecting workflow. Adverse event rates were low and similar between groups. Conclusions: CystoAID demonstrated non-inferior sensitivity to WLC and could be safely integrated into the routine clinical workflow. Larger studies with improved representation of flat lesions are warranted, as well as to assess whether CystoAID can achieve superior sensitivity compared with standard WLC. Full article

This manuscript aims at the introduction of interpolative $\mathcal{FG}$-contractions and their application to derive fixed-point results. As an application, one of the obtained results is used to analyze the fractional-order Aizawa model. Moreover, we introduce a Hutchinson–Barnsley operator defined in terms of interpolative $\mathcal{FG}$-contractions and a related iterated function system to prove the existence of a unique fractal. The theoretical findings are supported with illustrative examples and graphical demonstrations. This manuscript also sheds light on a theoretical framework for analyzing material decomposition and recycling processes. Full article

Scutellaria baicalensis Georgi is a crop with significant economic and medicinal value, but no studies have examined the GATA gene family in S. baicalensis or its expression patterns following foliar spray with nanomaterials. In this study, the GATA gene family in S. baicalensis was identified and analyzed using bioinformatics. The results showed that 25 SbGATAs genes were identified, distributed across seven chromosomes, and could be classified into four subfamilies based on phylogenetic analysis. The physicochemical properties of SbGATAs proteins showed differences in the number of amino acids, molecular weight, and isoelectric point, but all were hydrophilic nuclear proteins. Analysis of cis-acting elements in the promoter regions revealed that the SbGATAs gene promoters were enriched for light, hormone response, and stress response elements. In this study, RT-qPCR was used to investigate the expression patterns of the SbGATAs family members in S. baicalensis leaves sprayed with different concentrations of carbon dot solutions. This study employed the RT-qPCR method to elucidate the expression of members of the SbGATAs family in the leaves of S. baicalensis after different treatments. The results showed that at low concentrations, except for SbGATA6, whose expression was upregulated by 2.1 times, the expressions of the other four genes were all downregulated; at high concentrations—except for SbGATA6, whose expression was upregulated by 3.75 times, and SbGATA14, the expression of which was basically the same as that of the control—the expressions of the other three genes were all downregulated. SbGATA6 was the only gene that was significantly upregulated at both concentrations and whose upregulation ratio increased with the increase in concentration, indicating that it may have a wide response to carbon dot treatment and may be involved in physiological regulation at different concentrations. Full article

The Ros/MucR family is constituted by proteins controlling the expression of genes crucial for the interaction with eukaryotic hosts. Ros/MucR family members were classified as H-NS-like proteins in α-proteobacteria, as they share fundamental features with H-NS proteins playing a pivotal role in controlling gene expression by structuring the bacterial genome. Here, we identified two new Ros/MucR family members in Sinorhizobium meliloti. They differ from classical MucR homologs since MucR2 lacks the circular oligomeric structure typical of other family members and MucR3 shows a concentration-dependent oligomerization ability with a low propensity to form circular particles, as shown by cryogenic electron microscopy. Moreover, MucR2 and MucR3 present a new zinc coordination sphere. The newly identified MucRs bind DNA, but lack the DNA bridging activity, which is crucial for structuring the bacterial genome. Using mass spectrometry, light scattering, NMR, EMSA and bridging assay, our study reports the first identification and characterization of two new MucRs and indicates that Ros/MucR family members control gene expression through distinct mechanisms. These results provide an important framework for future studies aimed at dissecting the interplay among MucR proteins and understanding how they can jointly orchestrate condition-dependent gene expression in bacterial species expressing multiple mucR homologous genes. Full article

Skeleton-based human action recognition has achieved significant progress, but local occlusions and missing joints in complex environments (e.g., occlusion and low-light conditions) still degrade recognition accuracy and stability. Existing GCN-based methods aggregate features uniformly across joints and lack mechanisms to suppress unreliable observations or recover structural semantics under large-area occlusion. To address this, we propose a Robust Occlusion-Compensated Graph Convolutional Network (ROC-GCN) with two complementary components: an adaptive dropout module that suppresses spatiotemporal noise via attention-guided Bernoulli sampling with dynamic spatial–temporal fusion, and an Occlusion Compensation Graph Convolution Module that compensates occluded features through Local–Global Body-Prior-Guided Attention together with feature-guided and multi-hop aggregation. To enable systematic evaluation, we further construct two complementary occlusion benchmarks on NTU RGB+D 60/120 covering spatial-random and spatiotemporal-continuous occlusion, and additionally validate the model on a real-world missing-joint subset. On standard NTU60/120 X-Sub, ROC-GCN improves Top-1 accuracy by +0.41% and +0.48% over the baseline, with the Top-1 standard deviation reduced from 0.61 → 0.17 and 0.47 → 0.10. On the occlusion benchmarks, Top-1 accuracy further improves by +0.98% and +0.73%, and consistent gains are also observed on the real-world missing-joint validation, confirming improved robustness and training stability. Full article

Sleep disturbance is a central feature of depression and a proposed pathway through which Bright-Light Therapy (BLT) exerts antidepressant effects. However, little is known about how sleep reorganises day by day during BLT or whether these dynamics relate to symptom improvement. We analysed daily sleep diaries from 66 patients with depression undergoing three weeks of BLT in routine outpatient care. Generalised Additive Mixed Models characterised daily trajectories in sleep timing, continuity, duration, and Subjective Sleep Quality, and weekly changes in sleep regularity were assessed using Root Mean Square of the Successive Differences. Structural Equation Modelling examined whether within-person deviations in sleep parameters mediated changes in depressive symptoms. Sleep timing showed gradual adjustment across treatment, with a progressive 48 min advance in weekday sleep onset. Sleep regularity improved from Week 1 to Week 2 before partially reversing, and the probability of nocturnal awakenings followed a non-linear trajectory. Other sleep parameters showed weaker directional trends. Improvements in Subjective Sleep Quality accounted for a modest portion of the association between treatment progression and reductions in depressive symptoms, whereas changes in sleep timing and regularity were not associated with symptom change. These findings indicate that sleep reorganises gradually during outpatient BLT, with different sleep dimensions evolving on distinct timescales and Subjective Sleep Quality emerging as one observable component linked to symptom improvement. More broadly, the results highlight the value of day-to-day modelling for understanding sleep–mood dynamics during real-world chronotherapy. Full article

Sugarcane is a globally important crop, widely cultivated for sugar production and bioenergy. However, leaf spot disease leads to a reduction in its quality and yield. In this study, pathogen identification, biological characteristic analysis, and screening of antagonistic bacteria against the causal pathogens were done as a basis for epidemic prediction and green control of sugarcane leaf spot disease. The causal pathogens of sugarcane leaf spot disease were identified as Epicoccum latusicollum El532 and Fusarium sacchari Fs64, respectively, based on morphological characteristics, multi-gene phylogenetic analysis (ITS, TUB2, and RPB2 for El532; ITS, TEF1α, and RPB2 for Fs64), and pathogenicity tests. Biological characterization revealed that both pathogens exhibited optimal mycelial growth at 25 °C and under continuous darkness. However, light-dark cycles inhibited their growth. The optimal pH ranges for both isolates were 6–9 and 5–10, respectively. Maltose was the optimal carbon source for El532, whereas maltose, lactose, and starch were optimal for Fs64. Yeast extract served as the optimal nitrogen source for both. Isolation and screening of bacterial strains from healthy sugarcane roots, leaves, and rhizosphere soil yielded 13 antagonistic bacterial strains. Among them, six strains exhibited inhibition rates exceeding 57% against both pathogens. Bacillus subtilis A5 exhibited the highest antagonistic activity (68.85% against El532, 71.69% against Fs64), underscoring its potential as a promising biocontrol candidate. These findings provide a scientific basis for the diagnosis and management of sugarcane leaf spot disease. Full article

Accurate and stable indoor occupancy information is essential for occupant-based intelligent ventilation control. Under a single-camera setting, existing indoor occupancy detection methods commonly suffer from missed detections caused by occlusion and blind zones, false detections caused by people outside the room, and cumulative entry–exit errors that are difficult to correct. These problems lead to false fluctuations in detected occupancy, affect control performance, and may further reduce indoor comfort or cause unnecessary energy use. To address the practical situation in which indoor spaces are commonly equipped with a single security camera, this study proposes an indoor occupancy detection method by fusing field-of-view information and entry–exit events with a single camera. The study covers method development, multi-scenario validation, parameter analysis, and a ventilation control application. The proposed method uses YOLOv8x and DeepSORT as front-end models and performs post-processing on their outputs to extract field-of-view occupancy information, entry–exit events, and blind-zone events. An occupancy confirmation and correction module is then constructed. The blind-zone event mechanism reduces the influence of missed entry–exit events and camera blind zones on occupancy judgment. The correction module integrates frame-by-frame ID counts, historical outputs, and multiple event signals to verify and suppress false occupancy changes caused by false detections, missed detections, and blind zones, thereby producing more stable indoor occupancy results. Experimental results show that the proposed method outperforms the baseline methods based on front-end object detection and tracking in terms of score, RMSE, and F1 score in three typical scenarios: an office, a home, and a classroom. In the office scenario, the proposed method achieved a score of 99.36%, an RMSE of 0.081, and an F1 score of 0.781. The detection stability was also improved in the home and classroom scenarios. In the high-density and strongly occluded classroom scenario, the absolute detection performance of the fusion-based detection method was limited by the front-end models, indicating that the method still has certain applicability boundaries in complex high-density scenes. Parameter sensitivity analysis shows that key parameters, including the entry–exit area depth, confidence threshold, and time threshold, affect the detection results of the fusion-based detection method. Under the test conditions of this study, the method performs well when the entry–exit area depth is approximately 1.5d, the YOLOv8x confidence threshold is 40%, and the time threshold is 5 × FPS. These results can provide a reference for initial parameter setting and on-site calibration in similar scenarios. Using the office scenario as a case study, the method was further applied to occupant-based ventilation control. The average CO₂ concentration during occupied periods under the proposed method was 622.43 ppm, which was closest to the result under ground-truth occupancy control, with a deviation of only 0.9 ppm. This indicates that the method can help improve indoor air quality. Compared with conventional schedule-based control, occupant-based ventilation control driven by the proposed fusion method reduced cumulative fan energy consumption by approximately 65.2%, showing good energy-saving potential at the ventilation-control level. In summary, the proposed method can effectively improve the accuracy and stability of indoor occupancy detection under a single-camera setting and provide more reliable input for occupant-based ventilation control. The framework is modular, and the front-end object detection and tracking models can be replaced according to actual deployment needs. However, the validation in this study is still mainly based on scenarios where existing security cameras can cover the main activity areas and all entry–exit passages. The applicability of the method under more complex camera arrangements, lighting variations, and automatic region configuration requires further investigation. Full article

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. Full article

Gaucher disease is a prototypical lysosomal sphingolipid storage disorder caused by pathogenic variants in GBA1, resulting in glucocerebrosidase deficiency and accumulation of bioactive lipids, including glucosylceramide and glucosylsphingosine (lyso-Gb1). While non-neuronopathic Gaucher disease is effectively managed with enzyme replacement and substrate reduction therapies, neuronopathic forms remain largely refractory to treatment due to progressive central nervous system (CNS) involvement and limited penetration of current therapies across the blood–brain barrier. Disease pathobiology extends beyond lysosomal substrate accumulation to encompass dysregulated sphingolipid signaling, particularly sphingosine-1-phosphate (S1P)-mediated “inside-out” signaling, alongside neuroinflammation, oxidative stress, and glial activation, which collectively drive neurodegeneration. In this review, we synthesize current knowledge on sphingolipid metabolism and signaling in neuronopathic Gaucher disease and integrate these mechanisms into a three-tier, CNS-focused biomarker framework. The first tier comprises substrate-proximal markers of lysosomal burden (lyso-Gb1), which reflect GCase deficiency and correlate with systemic disease severity but incompletely capture CNS pathology. The second tier comprises markers of glial activation and neuroinflammation (glial fibrillary acidic protein [GFAP], glycoprotein non-metastatic melanoma protein B [GPNMB]), which reflect the downstream neuroimmune response to sphingolipid accumulation. The third tier comprises markers of neuroaxonal injury (neurofilament light chain [NfL]), which index irreversible neuronal damage as the terminal consequence of uncontrolled CNS disease. Together, these tiers map distinct but mechanistically interconnected stages of disease progression, from lysosomal dysfunction through glial activation to neuroaxonal loss, enabling stage-specific interpretation of biomarker signals that single-analyte approaches cannot provide. We further examine how S1P-mediated inside-out signaling links intracellular lipid dysregulation to extracellular neuroimmune and neurovascular responses and how the blood–brain barrier shapes compartment-dependent biomarker behavior across cerebrospinal fluid and blood. By grounding biomarker selection in this mechanistic cascade, the framework provides explicit criteria for pairing analytes across tiers, interpreting discordance between peripheral and CNS compartments, and designing multi-modal endpoints for clinical trials of CNS-penetrant therapies. Despite these advances, significant challenges remain, including limited longitudinal datasets, variability in assay methodologies, and incomplete validation of biomarkers as surrogates of CNS disease progression. Addressing these gaps will require harmonized, multi-modal approaches integrating biochemical, functional, and imaging measures. By positioning neuronopathic Gaucher disease as a model of sphingolipid-driven neurodegeneration, this review highlights opportunities for biomarker-guided therapeutic development relevant to Gaucher disease and the broader spectrum of sphingolipid-associated neurological disorders. Full article

This work of historical theology profiles the unique role of Sergius Bulgakov (1877–1944) in the unfolding of Russian Orthodox theology over the last century. It narrates the role in his thought of the contested figure of Sophia (the personification of divine wisdom), with special focus on to how his creative appropriation of this ancient concept led him to propose modifications of the church’s doctrines of creation and deification. Our focus on deification acknowledges that patristic sources regarding this topic were only made ready for Russian theological research in the first decades of the twentieth century. Concurrently, another novelty came into vogue among the Russian intelligentsia during these same years: the evocation of both esoteric and biblical versions of Sophia, along with the promulgation of an original theological framework known as sophiology, a development pioneered by Vladimir Solovyov. These unique cultural and religious vectors, deification, creation theology, and sophiology, and their relation to Orthodox trinitarianism, converged around the year 1910 in the life and scholarship of Sergius Bulgakov, while also being firmly resisted by conservatives. A brilliant, wide-ranging, and independent thinker, Bulgakov began as a Marxist economist whose midlife conversion led him to the priesthood and a professorship at a prominent Russian seminary. Bulgakov went on to create one of the last century’s most ambitious and creative Orthodox systematic theologies, an endeavor made possible through his sheer mastery of numerous scholarly sources that, over three decades, he explicated in light of the emergence of sophiology, the worldview of which he became the leading proponent. Against opposition by colleagues at the St. Sergius Theological Institute, where he served as dean, Bulgakov expanded sophiology into a panentheistic theological system that highlighted and integrated concepts of religious living, deification, and cosmology within a vast panoply of other Christian theological dogmas that he examined. This introductory historical account summarizes a selection of Bulgakov’s pioneering innovations, with special emphasis on the reception of his ideas during his lifetime and the decades since. Full article

The use of plant extracts in the production of fermented meat products can help protect fats from oxidation, improve color stability, and extend their shelf life. The study evaluated the effect of natural extracts (acerola—A, rosemary—R, and their combination—M) on the quality of Tokaj salami stored in MAP at 4 °C for 35 days, compared to negative (N) and positive (K, sodium erythorbate) controls. While the initial chemical composition showed differences due to raw material variability (p < 0.001), fat and protein content remained stable during storage (p > 0.05). In contrast, acidity and water activity (aw) were significantly affected (p < 0.001). Regarding oxidative stability, plant extracts significantly inhibited lipid oxidation during storage (p < 0.05). By day 35, the negative control reached the highest malondialdehyde (MDA) level of 0.67 mg/kg, whereas samples with acerola (A) maintained the lowest values at 0.38 mg/kg, performing comparably to the synthetic antioxidant (0.43 mg/kg; p > 0.05). Acerola extract (A) demonstrated the highest efficacy in stabilizing oxidative changes, with results comparable to the synthetic antioxidant (p > 0.05). Colorimetric analysis revealed that lightness (L*) ranged from 45.98 to 49.75, with L*, a*, and b* parameters significantly influenced by both the antioxidant type and storage phase (p < 0.001). Sensory evaluation remained unaffected by the antioxidants, being affected only by storage time (p < 0.05). These results confirm that acerola and rosemary extracts are viable natural alternatives to sodium erythorbate for maintaining the oxidative and color stability of fermented salami. Full article

Calcium copper titanate (CaCu₃Ti₄O₁₂, abbreviated as CCTO) has emerged as a versatile, high-performance material distinguished by its remarkable dielectric, photocatalytic, and environmental properties, positioning it at the forefront of ongoing research and technological innovation. This review provides a comprehensive analysis of CCTO, emphasizing its growing relevance in catalytic and environmental applications. Beginning with an overview of its unique structural and dielectric properties, we discuss how these attributes underpin CCTO’s multifunctionality. Various synthesis methods are examined for their effects on CCTO’s microstructure and performance. Furthermore, we investigate the photocatalytic potential of CCTO under visible light, particularly for applications such as water splitting, CO₂ reduction, and degradation of organic pollutants. Environmental applications, including gas sensing and wastewater treatment, are also evaluated, highlighting CCTO’s chemical robustness and suitability under diverse operating conditions. Lastly, key challenges in scalability, cost, and environmental adaptability are discussed, along with future directions, including hybrid composite development and machine-learning-assisted material design. Together, these insights position CCTO as a promising material for advancing sustainable technologies in energy and the environment. Full article

Background/Objectives: Clostridioides difficile infection (CDI) is the leading cause of colitis and hospital-acquired diarrhea. Patients with Chronic Obstructive Pulmonary Disease (COPD) frequently have infectious exacerbations requiring treatment with antibiotics, which may be predisposing them to CDI. This study examines the prevalence and in-hospital outcomes of CDI in patients with COPD. Methods: Data for hospitalized patients with CDI was extracted from the National Inpatient Sample database for the years 2016 through 2020. Baseline risk factors were identified using the International Classification of Diseases codes. Patients were stratified into two groups: with COPD and without COPD. The primary outcome was in-hospital mortality. The secondary outcomes were septic shock, hypovolemic shock, AKI, cardiac arrest, need for intensive care unit (ICU) level of care and length of stay. Statistical analyses were conducted using SPSS. Results: 290,172 patients were included in this study. Patients with COPD had more comorbidities overall and higher in-hospital mortality rates compared to patients without COPD (7.7% vs. 5.9%, p < 0.001). On multivariate logistic regression analysis, patients with CDI and COPD had higher risk of in-hospital mortality (OR = 1.346, p < 0.001), septic shock (OR = 1.289, p < 0.001), hypovolemic shock (OR = 1.184, p < 0.001), cardiac arrest (OR = 1.362, p < 0.001) and required more ICU level of care. Conclusions: Patients with COPD experience frequent exacerbations, often requiring hospitalizations and broad-spectrum antibiotics, steroids, proton pump inhibitors and antacids. These factors contribute to the higher prevalence of CDI in this patient population. Patients with CDI and COPD are also more likely to require ICU level of care, shedding the light on the significant burden of CDI, long hospital stays and substantial hospital charges. Recognizing mortality outcomes is essential to guide patient-specific therapies and highlights the need for closer monitoring and targeted management of CDI in patients with COPD. Full article