Search Results (1,163)

COVID-19, caused by SARS-CoV-2, remains a global health challenge because of viral evolution and immune escape. Although current therapies primarily target viral entry and replication, agents that directly interfere with the Spike receptor-binding domain (S-RBD) remain limited. Here, we combined virtual screening with biological validation to identify compounds capable of interfering with S-RBD function. A total of 3014 compounds from a customized drug library were screened by molecular docking. Candidate binding and functional activity were subsequently evaluated using cellular thermal shift assays, surface plasmon resonance, immunoprecipitation, immunofluorescence, and pseudovirus-entry assays against 2019-nCoV, Delta, and Omicron pseudoviruses. DOTAP chloride (K_D = 49.94 μM), cefotiam hexetil hydrochloride (K_D = 142.26 μM), melittin (K_D = 34.98 μM), and teicoplanin (K_D = 73.58 μM) showed detectable binding to the S-RBD and inhibited Spike-mediated pseudovirus entry. Molecular docking suggested that these interactions were mediated by potential binding modes involving hydrogen bonds and π-interactions. The compounds interfered with S-RBD/hACE2 colocalisation and inhibited pseudovirus entry with variant-dependent efficacy. DOTAP chloride (25 μM) inhibited entry of the 2019-nCoV and Omicron pseudoviruses, whereas the other three compounds showed activity across the tested variants. These findings identify four compounds with RBD-binding and entry-inhibition properties for further development as entry inhibitors. Full article

Coastal salt-marsh wetlands sustain diverse native halophytes whose root-associated microbiomes mediate plant adaptation to saline-alkaline environments. Host filtering and niche differentiation between rhizosphere soil and root endosphere jointly shape bacterial community assembly, yet comparative information for co-existing native halophytes in the northern Yellow River Delta remains limited. Here, we collected 80 rhizosphere soil and root tissue samples from replicated plots in the Binzhou coastal salt-marsh wetland (northern Yellow River Delta), and applied Illumina MiSeq 16S rRNA sequencing to compare bacterial communities of four native pioneer halophytes (Phragmites australis, Suaeda salsa, Tamarix chinensis, Cynanchum chinense). Rhizosphere soils had markedly higher alpha diversity than root endospheres, with C. chinense rhizosphere reaching the highest diversity; Proteobacteria dominated roots, whereas Actinobacteriota, Bacteroidota and Chloroflexi accumulated in rhizosphere habitats. Root bacterial communities displayed greater interspecific divergence, and rhizospheres of S. salsa and P. australis possessed enhanced nitrogen metabolism and hydrocarbon degradation. LEfSe identified host-specific biomarkers, and Chloroflexi Subgroup_10 functioned as a core hub whereas no shared keystone genus existed across root endosphere networks, underscoring stronger host filtering in endophytic habitats. This study advances our understanding of host-driven bacterial assembly of native halophytes and provides baseline references for microbiome-assisted wetland restoration in the northern Yellow River Delta. Full article

Ovine coenurosis, caused by the larval stage of Taenia multiceps (Coenurus cerebralis), is a parasitic disease that affects the central nervous system of livestock. While diagnosis traditionally relies on clinical signs, imaging, and postmortem examination, electroencephalography (EEG) remains underutilized despite its potential to detect brain abnormalities. This case report describes EEG and quantitative EEG (qEEG) findings in a 2-year-old sheep presenting with clinical signs suggestive of a chronic focal encephalopathy (ataxia, head tilt and circling) associated with lethargy and progressive weight loss. Computed tomography (CT) confirmed a single, discretely bilobate intra-axial cystic lesion within the left cerebral hemisphere, with a severe mass effect, marked deformation and displacement of the ventricular system, compression of the thalamic structures, and contralateral deviation of the median line. A solitary parasitic cyst with no lesion in the right hemisphere was subsequently confirmed at necropsy and on histopathology. Under ketamine–diazepam sedation, both EEG recordings showed a continuous, delta-dominant background consistent with the anesthetic protocol. Epoch-wise asymmetry indices with bootstrap confidence intervals distinguished an unstable component (which reversed in hemispheric sign between recordings) from a reproducible one confined to the occipital derivations, where the asymmetry lay over the right hemisphere in both recordings in every band except delta. This reproducible preponderance was contralateral to the cyst, over a hemisphere in which no structural lesion was demonstrable either on imaging or at necropsy, and absolute powers indicated that it arose principally from attenuation of the signal recorded over the diseased hemisphere. These findings provide the first electrophysiological characterization of ovine coenurosis and show that in the presence of a severe mass effect, the side of the surface qEEG abnormality may not indicate the side of the lesion (an electrophysiological false localizing sign described in human patients but not previously in a ruminant). EEG therefore reflects the functional brain disturbance caused by Coenurus cerebralis but should be regarded as a complement to, rather than a substitute for, cross-sectional imaging in neurolocalization. Full article

Under climate change, the demand for high-quality urban ecological security is rising. This study focuses on how rapid urbanization and climate change affect ecological risk, resilience, and land use functions (LUFs) spatial co-variation in the Pearl River Delta. Land use was simulated under Shared Socioeconomic Pathways (SSPs) using system dynamics (SD) and the interaction network–Patch-generating Land Use Simulation (intPLUS) model. Ecological risk was quantified via the landscape ecological risk index (LERI) and habitat degradation index (HDI), while ecological resilience was obtained using an adaptability–resistance–recovery framework, producing a comprehensive ecological risk–resilience index (CERRI). Ecosystem services were assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, geographically weighted regression (GWR), constraint lines, and extreme gradient boosting with SHapley Additive exPlanations (XGBoost-SHAP) revealed LUFs patterns, nonlinear relationships and threshold effects, and driving factors. The results indicate that construction land expands mainly at the expense of cropland (~4049–4157 km²) from 2023 to 2035, while woodland and water remain largely stable. CERRI shows a concentric pattern (2023 domain mean = 0.638), with safer peripheral belts and a more risk-dominated central–southern core; under coupling-weight uncertainty with 2023-fixed common-reference normalization, SSP245 was preferred in all Monte Carlo iterations (best-scenario probability = 1.000). EF–LF, EF–PF and LF–PF retain stable nonlinear forms. Elevation, economic vitality and transport accessibility are the leading drivers, with model-derived breakpoints near low-elevation, high-vitality and moderately accessible transport nodes. This study provides the CERRI framework to support ecological security monitoring and adaptive land-use management, contributing to more sustainable regional development under climate change. Full article

This paper proposes a supervisory soft disagreement framework for adaptive uncertainty regulation in Interval Type-2 (IT2) fuzzy servo control and validates its performance through embedded implementation on an industrial servo platform. The proposed framework introduces a supervisory learning layer that combines supervised classification and unsupervised fuzzy clustering to characterize servo operating conditions using experimentally extracted performance indicators, including rise time, settling time, overshoot, steady-state error, Integral Absolute Error (IAE), control-effort energy, tracking-error standard deviation, and maximum control effort. Operating condition confidence is quantified by measuring the soft disagreement between the posterior class probabilities of a Support Vector Machine (SVM) classifier and the normalized membership degrees of a Fuzzy C-Means (FCM) clustering algorithm using the Bhattacharyya coefficient. The resulting disagreement index adaptively regulates the Footprint of Uncertainty (FOU) of the antecedent membership functions in IT2 fuzzy controller. A closed-form Uncertainty Avoider Defuzzification (UAD) strategy enables computationally efficient uncertainty-aware type reduction for real-time embedded implementation without iterative procedures. The framework was trained using experimental data collected from a Delta ASDA-B2 400 W industrial servo drive under diverse operating conditions. The complete controller was implemented on a Raspberry Pi and experimentally compared with conventional Proportional–Integral–Derivative (PID), Type-1, and fixed-FOU IT2 fuzzy controllers. Experimental results show that the proposed controller achieved an average IAE of 1.08, representing improvements of 55.6% and 27.5% over the PID and fixed-FOU IT2 controllers, respectively. Overshoot was reduced to 2.2% and settling time to 0.24 s, while the supervisory computation required only 4.55 ms, confirming real-time feasibility. The scientific significance of this work lies in introducing a new disagreement-driven supervisory paradigm that links probabilistic machine learning confidence with adaptive fuzzy uncertainty regulation. By establishing a principled connection among supervised learning, unsupervised learning, and Interval Type-2 fuzzy control, the proposed framework provides a general foundation for confidence-aware adaptive uncertainty management in intelligent control systems operating under uncertain and time-varying conditions. Full article

Critically ill patients frequently move between the intensive care unit (ICU) and the operating room while remaining dependent on ventilatory, hemodynamic, antimicrobial, sedative, analgesic, and renal support. Although extensive perioperative data are generated, clinically relevant information may remain fragmented across devices and electronic systems. This narrative review synthesizes the literature published primarily between 2016 and 2026 on perioperative digital continuity, structured ICU–operating room handoff, interoperability, artificial intelligence (AI)-supported decision support, continuous renal replacement therapy (CRRT)-related pharmacokinetic personalization, and digital twin concepts. Based on this synthesis, we develop a conceptual framework in which the Intelligent Operating Room and Smart ICU function as connected clinical information nodes. The framework is organized around a minimum perioperative continuity dataset, a perioperative delta view, and the author-proposed concept of Time-to-Truth at ICU readmission. Current evidence supports structured handoff and selected AI-assisted physiological prediction, but evidence for improvement in major patient-centered outcomes remains limited and heterogeneous. CRRT illustrates the importance of preserving information on delivered therapy, interruptions, residual kidney function, and treatment-related changes when interpreting drug exposure. The proposed framework is not clinically validated and is intended as a basis for future implementation and prospective evaluation. Overall, the review suggests that improving digital continuity should precede more complex forms of automation in perioperative critical care. Full article

The Northwest Xizang White Cashmere goat has a high global reputation for good goat cashmere germplasm characteristics, and its cashmere industry is the economic pillar of the herdsmen in the northwest plateau of Xizang. Cashmere is produced by the secondary hair follicles of the skin, and the key period of hair follicle development is from birth to 3 months old. This study aimed to explore how early compound nutritional intervention affects lamb growth performance and secondary hair follicle development. We assessed skin follicle density and performed skin transcriptomic and multi-omics analyses to clarify this regulatory effect. The trial was performed at the Northwest Xizang White Cashmere Goat Breeding Farm, Ritu County, Ngari region, Xizang. Thirty newborn single lambs of comparable birth status were randomly divided into control and early compound nutritional intervention groups (n = 15 per group). The results showed that early compound nutritional intervention significantly increased the secondary hair follicle density, total hair follicle density, and secondary/primary hair follicle (S/P) value of lambs (p < 0.05). However, there was no significant difference in the average cashmere fineness between the two groups (p > 0.05). The transcriptome, proteome, and metabolomics analyses of the skin revealed candidate genes related to hair follicle development and the regulation of cashmere fineness: keratin structural genes (KRT10, KRT18, KRT26), cell proliferation and adhesion-related regulatory genes (ITGB2, CSF1R, AKT1), and extracellular matrix coding genes (COL6A1, COL6A2, COL6A6). Moreover, the results revealed that key proteins related to hair follicle development include RBP4, KAP13-3, KRTAP3-1, KRTAP27-1, DKK3, ITGB2, and DUSP23. The differentially expressed metabolites, 15-Deoxy-delta12,14-Prostaglandin J2 and Thromboxane B2 (TXB2), modulate prostaglandin-associated pathways; altered pathway activity subsequently regulates the expression of hair follicle functional genes and key proteins, which may in turn indirectly regulate hair follicle growth and development, thereby affecting hair follicle density and, by extension, cashmere production performance. Therefore, early compound nutritional intervention of Northwest Xizang White Cashmere goats promotes the growth and development of secondary hair follicles. The identified candidate genes and key proteins involved in secondary hair follicle development provide a reference for exploring the mechanism of hair follicle development. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders with partially overlapping clinical manifestations, making early and differential diagnosis challenging. This study investigated whether electroencephalography (EEG)-derived spectral features and Granger-causality (GC)-based directed functional connectivity features can characterize and classify AD, FTD, and healthy control (HC) subjects. Resting-state eyes-closed EEG recordings from 88 participants were analyzed, including 36 AD, 23 FTD, and 29 HC subjects. Spectral features included absolute and relative band power and spectral ratios, while directed connectivity features were extracted from broadband and frequency-specific GC matrices. Statistical analyses identified theta/alpha ratio (TAR) as the dominant spectral marker, with the strongest three-group differences observed in frontal and global TAR features. GC analysis revealed group-related alterations mainly in alpha-band regional directed connectivity, although three-group GC features did not survive false discovery rate (FDR) correction at q < 0.05. In the main nested cross-validation analysis, the spectral-only model achieved the best three-class performance, with balanced accuracy of 0.572 and macro-F1 of 0.557. For dementia group (DEM) vs. HC classification, the combined GC + spectral feature (GC + SPEC) set achieved balanced accuracy of 0.710 and macro-F1 of 0.665. For AD vs. FTD classification, the combined GC + SPEC feature set achieved the highest numerical performance in the main nested cross-validation (CV) comparison, with balanced accuracy of 0.584 and macro-F1 of 0.559. In the separate long permutation-testing analysis, which used a reduced hyperparameter grid for computational feasibility, above-chance performance was confirmed for the three-class spectral model and the DEM vs. HC GC + SPEC model (p < 0.001), but not for AD vs. FTD (p = 0.270). An exploratory photobiomodulation (PBM) single-case analysis showed longitudinal EEG reorganization, including increased alpha power, reduced delta/alpha ratio (DAR) and beta/alpha ratio (BAR), mixed TAR changes, and HC-like GC/GC + SPEC centroid projections. Overall, the results support the value of spectral and directed connectivity EEG markers for dementia-related EEG characterization, while highlighting the persistent difficulty of AD vs. FTD differentiation. Full article

Clarifying ecosystem service interactions under urbanization facilitates integrated ecological governance. This study took the rapidly urbanizing Su-Xi-Chang agglomeration in the Yangtze River Delta as the research area and applied the InVEST model to evaluate spatiotemporal variations in carbon storage, soil retention, water yield, and habitat quality from 2000 to 2023. The coupling coordination degree model and self-organizing map model were adopted to analyze urbanization–ecosystem interactions and the evolution of ecosystem service bundles, while the GeoDetector model was used to identify key socio-ecological drivers. The results reveal significant spatiotemporal divergences among the four ecosystem services. Carbon storage and habitat quality declined, while water yield and soil retention increased. Carbon storage and soil retention hotspots were persistently concentrated in the Yixing–Liyang Mountainous Area. Pairwise correlations further show that the synergy between water yield and soil retention strengthened over time, whereas their trade-offs with habitat quality intensified. The coupling coordination between urbanization and ecosystem services remained at a primary level yet exhibited a clear trajectory from imbalance toward optimization after 2010. Four ecosystem service bundles were identified, among which the urban-function-dominated bundle expanded by 190.39% from 2000 to 2023, indicating progressive spatial lock-in of urban land use. Differentiated zoning and targeted restoration strategies are proposed to reconcile urban development and ecological conservation. This study offers scientific references for ecological spatial optimization and refined ecological management in the Yangtze River Delta. Full article

Soil salinisation and periodic flooding jointly shape coastal wetland ecosystems, but how coexisting halophytes differ in their rhizosphere bacterial communities under these conditions remains insufficiently understood. Here, we investigated rhizosphere bacterial communities associated with Suaeda glauca Bunge and Tamarix chinensis Lour. in saline–alkaline habitats of the Yellow River Delta, China. Twenty quadrat-level rhizosphere samples were collected across four plant–habitat groups, and near-full-length 16S rRNA gene amplicons were sequenced using Pacific Biosciences single-molecule real-time sequencing. Our analysis revealed that hydrological habitat and plant identity together contributed to differences in rhizosphere bacterial community composition. Across the dataset, 2325 bacterial operational taxonomic units were identified. T. chinensis showed higher Shannon and Gini–Simpson diversity, whereas richness patterns depended on habitat and the metric examined. Meanwhile, exploratory genus-level association networks revealed host- and habitat-dependent differences in node number, network density and average degree. PICRUSt2-based functional prediction suggested contrasting predicted functional response patterns: the S. glauca rhizosphere showed 19 significantly altered predicted pathways between flooded and non-flooded habitats, whereas the T. chinensis rhizosphere showed no significant pathway shifts after multiple-testing correction. These findings suggest that coexisting halophytes are associated with divergent rhizosphere bacterial community patterns under saline–alkaline and flooding-associated habitat conditions. Full article

Bottom-up ship emission inventories derived from Automatic Identification System (AIS) data are normally reported on kilometre-scale grids, which merge port waters that perform very different functions. Working with an AIS-based STEAM inventory for the Yangtze River Delta (YRD), we ask a question that gridded inventories rarely separate: are the cells where ships accumulate time the same cells where they emit? To answer it, we define the Static–Dynamic Ratio (SDR), the ratio of hotelling (auxiliary-engine) to propulsion (main-engine) emissions within a cell, and use it to classify YRD waters without recourse to external port charts. Hotelling-dominated cells occupy 17.7% of the sea area and accumulate 59.3% of all ship-hours, a ship-hour density seven times that of transit-dominated fairways, yet they carry only 22.4% of NO_x. A vessel at anchor emits about one-eighth as much NO_x per hour as one under way, and the two effects nearly cancel. The cancellation is species dependent: low-load correction factors are steeper for sulfur and particulate species than for NO_x, so hotelling zones reach relative SO₂ and PM_2.5 densities of 1.21 and 1.09 against 0.89 for NO_x. Coarsening the same activity field from 100 m to 1 km drops the share held by the busiest 1% of cells from 70% to 50%, showing how kilometre grids manufacture apparent continuity along shipping lanes. Activity hotspots are therefore not emission hotspots, and anchorage-targeted measures such as shore power are best justified by particulate and sulfur exposure near populated coasts rather than by their share of the regional NO_x burden. Full article

Macroplastic pollution in rivers is an increasing environmental concern because rivers function simultaneously as active transport pathways and temporary storage compartments for land-based plastic waste. This study investigated the spatial distribution, composition, and size characteristics of riverbank and floating macroplastic debris in the Can Tho River, a tidal tributary of the Hau River in the Mekong Delta, Vietnam, to improve understanding of macroplastic transport, selective retention, and environmental partitioning between active transport and temporary storage compartments. Riverbank debris was surveyed at twelve sites spanning urban, peri-urban, and rural sections, while floating debris was quantified using a net-based sampling system. Riverbank accumulations exhibited pronounced local spatial heterogeneity, although litter density and mass density did not differ significantly among river sections. Plastics dominated both environmental compartments, accounting for 53–60% of accumulated debris and more than 95% of floating debris by abundance. Riverbank accumulations were dominated by plastic bags, food packaging, and beverage containers, whereas floating debris was dominated by expanded polystyrene foam products. Significant differences were also observed in material composition, plastic-product composition, and size distribution. Riverbank accumulations contained proportionally larger macroplastics (100–500 mm), whereas floating debris was dominated by smaller macroplastics (50–200 mm), supporting the role of size-dependent transport and selective retention in environmental partitioning. These findings show that floating debris and riverbank accumulations represent complementary components of the riverine plastic continuum, linking active transport and temporary storage through selective environmental partitioning. Integrating floating and riverbank monitoring provides a more comprehensive framework for understanding macroplastic transport and environmental fate while informing management strategies to reduce downstream plastic transport to the Hau River and ultimately estuarine and coastal ecosystems. Full article

This study evaluates the unique ecological and floristic identity of the coastal dune vegetation in the Enez delta and adjacent dune coast of Saros Bay (southern Thrace) by comparing it with diverse dune systems across the Anatolian peninsula. A comprehensive data matrix of 97 phytosociological relevés across nine representative coastal dunes spanning the East Mediterranean, Aegean, Marmara, and Black Sea coasts was analyzed. Methodologically, univariate non-parametric approaches (Friedman variance analysis and Durbin–Conover tests) were integrated with multivariate techniques, including Hierarchical Cluster Analysis and Principal Coordinates Analysis (PCoA) based on a Bray–Curtis dissimilarity matrix. Ephedra distachya ssp. monostachya was found to be the most characteristic and differentiating taxa from the clustering. Friedman test results demonstrated highly heterogeneous species abundance across localities (χ² = 27.2, p < 0.001). The multivariate synthesis revealed a profound ecological decoupling for Saros Bay dunes: while macroclimatic filtering forces a powerful functional convergence with arid Mediterranean models dominated by therophyte, strict composition-based metrics isolate Saros Bay coastal dunes into an entirely independent taxonomic clade. PCoA ordination confirmed this distinctiveness (p < 0.05 against six of the eight national localities), with the first two axes explaining 33.58% of the total variation (Axis 1: 19.66%, Axis 2: 13.92%). This isolation is driven by a high density of Irano-Turanian elements and specialized local lineages like Silene kotschyi. Conversely, a sharp latitudinal bio-climatic macro-gradient was mapped, showing a transition toward humid Black Sea systems strictly dictated by macroclimatic filtering rather than biotic competition (p > 0.05). To preserve these specialized niches, designating coastal dunes of Saros Bay as a Priority Conservation Unit and establishing international, transboundary catchment to coast monitoring frameworks are essential. Full article

Manual handling and packing of thin metal plates remains a labour-intensive operation in the automotive manufacturing sector, frequently requiring multiple operators and resulting in limited productivity, reduced process repeatability, and ergonomic constraints. This paper presents a preliminary virtual and experimental proof-of-concept for the automation of such a packing task using a Delta robot, motivated by a representative industrial scenario involving automotive heat exchanger plates. The proposed study adopts an intentionally simplified problem formulation to support early-stage feasibility assessment and system design. In the considered scenario, nominal pickup and placement coordinates are predefined in the robot control program. The plates are manually aligned with marked pickup areas, while their actual physical poses are not measured or automatically corrected, allowing the study to focus on the robotic packing stage rather than on perception-based localization. A virtual robotic packing cell is developed using MATLAB-based simulation tools, with emphasis on functional sequence verification, workspace reachability, and end-effector integration. A set of preliminary feasibility indicators is considered, including reachability of the predefined positions, execution of the manipulation sequence, cycle time, end-effector behaviour, and correspondence between the planned and experimentally achieved layouts. The virtual model is used to support planning and preliminary analysis, while a simplified experimental demonstrator is implemented to verify the physical execution of the inclined-tray packing task using representative plate geometries. The experimental results provide a controlled functional feasibility baseline and identify part-presentation accuracy, sensing, calibration, and cycle-time reduction as the main requirements for further development. Full article

This paper develops a unified asymptotic theory for inverse-probability-weighted conditional U-statistics of arbitrary fixed order in the presence of missing-at-random responses and infinite-dimensional functional covariates. The target is a conditional higher-order functional generated by a measurable response kernel and evaluated locally on a separable Banach space. Localization is formulated through delta sequences, providing a common framework for kernel, partition, regressogram, orthogonal series, and related smoothing procedures without recourse to finite-dimensional density arguments. For bounded kernels, we establish uniform almost-complete convergence over pseudo-compact functional domains and obtain a sharp decomposition into deterministic localization bias and stochastic fluctuation. The latter is governed by the localized-kernel variance, the envelope of the delta sequence, the metric complexity of the indexing domain, and the small-ball concentration of the functional covariate. Unbounded kernels are treated under explicit weighted moment, truncation, and summability conditions. The feasible theory quantifies the additional perturbation induced by estimating the propensity score and identifies conditions under which this first-stage uncertainty is asymptotically negligible. Pointwise distributional theory is derived through a denominator linearization combined with the Hoeffding decomposition of the centered localized kernel. The Gaussian limit is driven by the first projection, while the higher-order canonical components are shown to be negligible under explicit local-mass, moment, and noncancellation assumptions. This yields oracle-equivalent feasible inference, a consistent first-projection variance estimator, and asymptotically valid studentized confidence intervals. A finite-grid adaptive comparison principle is also developed for data-driven resolution selection. The scope of the theory is illustrated through conditional rank functionals, discrimination with incomplete labels, metric-learning criteria, and functional prediction. Synthetic and semi-synthetic studies based on functional classification, phoneme log-periodograms, and growth trajectories document the finite-sample interaction between covariate-dependent label observation, local information loss, propensity estimation, and inverse-weighting variance. Full article