Search Results (2,700)

Diabetic retinopathy (DR), a critical ocular disease that can lead to blindness, demands early and accurate diagnosis to prevent vision loss. Current automated DR diagnosis methods face two core challenges: first, subtle early lesions such as microaneurysms are often missed due to insufficient feature extraction; second, there is a persistent trade-off between model accuracy and efficiency—lightweight architectures often sacrifice precision for real-time performance, while high-accuracy models are computationally expensive and difficult to deploy on resource-constrained edge devices. To address these issues, this study presents a novel deep learning framework integrating depthwise separable convolution and a multi-view attention mechanism (MVAM) for efficient DR diagnosis using retinal images. The framework employs multi-scale feature fusion via parallel 3 × 3 and 5 × 5 convolutions to capture lesions of varying sizes and incorporates Gabor filters to enhance vascular texture and directional lesion modeling, improving sensitivity to early structural abnormalities while reducing computational costs. Experimental results on both the diabetic retinopathy (DR) dataset and ocular disease (OD) dataset demonstrate the superiority of the proposed method: it achieves a high accuracy of 0.9697 on the DR dataset and 0.9669 on the OD dataset, outperforming traditional methods such as CNN_eye, VGG, and UNet by more than 1 percentage point. Moreover, its training time is only half that of U-Net (on DR dataset) and VGG (on OD dataset), highlighting its potential for clinical DR screening. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

Urban wastewater is composed of nutrients such as nitrogen and phosphorus, organic matter, heavy metals, pathogens, and micropollutants. If untreated, these contribute to eutrophication and environmental degradation. Microalgae-based bioremediation offers a sustainable solution, showing promise for pollutant removal and high-value bioproduct generation. This study evaluates the efficacy of Galdieria sulphuraria ACUF 427 in treating urban wastewater, with a focus on nutrient removal and phycocyanin production at different optical densities (OD 2, OD 4, and OD 6). Nutrient removal rates (RRs) were analysed for ammonium nitrogen (N-NH₄⁺), ammonia nitrogen (N-NH₃), phosphate phosphorus (P-PO₄³⁻), and chemical oxygen demand (COD). The RR for N-NH₄⁺ increased with optical density, reaching 7.49 mg/L/d at an optical density of 6. Similar trends were observed for N-NH₃ and P-PO₄³⁻, with peak removal at OD 6. COD removal remained high across all ODs, though differences between OD 4 and OD 6 were not statistically significant. Significant variations (p < 0.05) in nutrient removal were noted across the ODs, except for COD between OD 4 and OD 6. Biomass growth and phycocyanin production were significantly higher in the wastewater compared to the control (Allen Medium), with the most effective performance observed at an optical density (OD) of 6. Maximum growth rates were 0.241 g/L/d at OD 6, 0.178 g/L/d at OD 4, and 0.120 g/L/d at OD 2. These results highlight the potential of G. sulphuraria as an agent for wastewater bioremediation and the production of high-value compounds, particularly at elevated cell densities, where we achieved superior nutrient removal and biomass production. Full article

The Zigui Basin, located in the Three Gorges Reservoir Area, has developed numerous landslides due to its interlayering of sandstone and mudstone, geological structure, and reservoir operations. This study identifies a fourth type of landslide failure mode: an oblique-dip slope wedge (OdSW) landslide, based on the Wanshuitian landslide. Following four heavy rainfall events from 3 to 13 July 2024, this landslide exhibited significant deformation on the 17th and was completely destroyed within 40 min. The dimensions of the landslide were 350 m in length, 160 m in width, and 20 m in thickness, with a volume estimated at 8.0 × 10⁵ m³. The characteristics of landslide deformation and the changes in moisture content within the shallow slide body were ascertained using unmanned aerial vehicles, moisture meters, and mobile phone photography. The landslide was identified to have occurred within the weathered residual layer of mudstone, situated between two sandstone layers, with the eastern boundary defined by an inclined rock layer. Upon transitioning into the accelerated deformation stage, the landslide initially exhibited uniform overall sliding deformation, culminating in accelerated deformation destruction. The dip structure created terrain disparities, resulting in a step-like terrain on the left bank and gentler slopes on the right bank, with interbedded soil and rock in a shallow layer, because the interlayered soft and hard geological conditions caused varied weathering and erosion patterns on the riverbank slopes. The interbedded weak–hard stratum layer fostered the development of the oblique-dip slope wedge landslide. Based on the improved Green–Ampt model, we developed a stability prediction methodology for an oblique-dip slope wedge landslide and determined the rainfall infiltration depth threshold of the Wanshuitian landslide (9.8 m). This study aimed not merely to sharpen the evolution mechanism and stability prediction of the Wanshuitian landslide but also to formulate more effective landslide-monitoring strategies and emergency management measures. Full article

Background/Objectives: The COVID-19 pandemic highlighted the limitations of pulse oximetry in detecting occult hypoxemia. The superiority of the alveolar gas monitor (AGM) compared to pulse oximetry (SpO₂) in predicting respiratory deterioration among COVID-19-positive individuals has previously been demonstrated. Here, we combine COVID-19 and non-COVID-19 individuals as a combined cohort of participants to determine if the AGM has similar utility across a larger, more generalizable cohort. Methods: Adult patients (n = 75) at risk of respiratory deterioration in the emergency department (ED) underwent prospective assessments of their oxygen deficit (OD) and SpO₂, simultaneously measured during quiet breathing on the AGM. The OD and SpO₂ were then compared for their ability to predict the dichotomous outcome of the need for supplemental oxygen. The administration of supplemental oxygen was ordered by the clinical care team with no knowledge of the patients’ enrollment in this study. Results: In the logistic regression analysis, both SpO₂ and OD significantly predicted the need for supplemental oxygen among COVID-19-negative individuals. However, in the multivariable regression, only OD (p < 0.001) significantly predicted the need for supplemental oxygen, while SpO₂ (p = 0.05) did not in the combined cohort of COVID-19-negative and -positive individuals. Receiver operating characteristic (ROC) curve analysis demonstrated the superior discriminative ability of OD (area under ROC curve = 0.937) relative to SpO₂ (area under ROC curve = 0.888) to predict the need for supplemental oxygen. Conclusions: The noninvasive AGM, which combines the measurement of exhaled partial pressures of gas with SpO₂, outperforms SpO₂ alone in predicting the need for supplemental oxygen among individuals in the ED at risk of respiratory deterioration regardless of the etiology for their symptoms (COVID-19-positive or -negative). Full article

The state of the art in the thermodynamics of calix[4]resorcinarene derivatives and its metal ion complexes is briefly discussed in the introduction. This is followed by the synthesis and characterization of a recyclable calix[4]resorcinarene amide derivative (L). The 1H NMR analyses in CD3CN and CD3OD showed solvent-dependent conformational changes with a notable downfield chemical shift in the aromatic proton (H-2) in moving from deuterated methanol to acetonitrile, indicating an interaction of the solvent within the ligand cavity as suggested by molecular dynamic simulations. 1H NMR complexation in acetonitrile revealed that L forms relatively strong 1:1 complexes with cations, with selectivity for Ca(II) and, to lesser extent, with Pb(II) over other metal cations. The composition of the complexes is corroborated by conductance measurements. The thermodynamics of these systems indicate that the complexation process is predominantly enthalpy controlled in acetonitrile, while it is entropy controlled in methanol. A remarkable outcome of fundamental studies is found in its application as new material for the removal of Ca(II) from water. The capacity of L to remove Ca(II) from water is 24 mmol/g which exceeds by far the capacity of cation exchange resins. Full article

Neural ordinary differential equations (neural ODEs) are a well-established tool for optimizing the parameters of dynamical systems, with applications in image classification, optimal control, and physics learning. Although dynamical systems of interest often evolve on Lie groups and more general differentiable manifolds, theoretical results for neural ODEs are frequently phrased on ${\mathbb{R}}^n$. We collect recent results for neural ODEs on manifolds and present a unifying derivation of various results that serves as a tutorial to extend existing methods to differentiable manifolds. We also extend the results to the recent class of neural ODEs on Lie groups, highlighting a non-trivial extension of manifold neural ODEs that exploits the Lie group structure. Full article

Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed significant outcomes in the identification of OD using deep learning (DL) models. Thus, this work aims to develop a multi-classification DL-based model for the classification of seven ODs, including normal (NOR), age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma (GLU), maculopathy (MAC), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR), using color fundus images (CFIs). This work proposes a custom model named the ocular disease detection model (ODDM) based on a CNN. The proposed ODDM is trained and tested on a publicly available ocular disease dataset (ODD). Additionally, the SMOTE Tomek (SM-TOM) approach is also used to handle the imbalanced distribution of the OD images in the ODD. The performance of the ODDM is compared with seven baseline models, including DenseNet-201 (R₁), EfficientNet-B0 (R₂), Inception-V3 (R₃), MobileNet (R₄), Vgg-16 (R₅), Vgg-19 (R₆), and ResNet-50 (R₇). The proposed ODDM obtained a 98.94% AUC, along with 97.19% accuracy, a recall of 88.74%, a precision of 95.23%, and an F1-score of 88.31% in classifying the seven different types of OD. Furthermore, ANOVA and Tukey HSD (Honestly Significant Difference) post hoc tests are also applied to represent the statistical significance of the proposed ODDM. Thus, this study concludes that the results of the proposed ODDM are superior to those of baseline models and state-of-the-art models. Full article

The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utilized to boost the water-based fluid’s thermal effect. This study primarily focuses on heat transfer over a horizontal sheet, exploring different scenarios by varying key factors such as the magnetic field and thermal radiation properties. The mathematical model is formulated using partial differential equations (PDEs), which are then transformed into a corresponding set of ordinary differential equations (ODEs) through appropriate similarity transformations. The bvp4c solver is then used to simulate the numerical behavior. The effects of relevant parameters on the temperature, velocity, skin friction, and local Nusselt number profiles are examined. It is discovered that the parameters of the Prandtl fluid have a considerable impact. The local skin friction and the local Nusselt number are improved by increasing these parameters. The dataset is split into 70% training, 15% validation, and 15% testing. The ANN model successfully predicts skin friction and Nusselt number profiles, showing good agreement with numerical simulations. This hybrid framework offers a robust predictive approach for heat management systems in industrial applications. This study provides important insights for researchers and engineers aiming to comprehend flow characteristics and their behavior and to develop accurate predictive models. Full article

This study examined the membrane permeability of E. coli, which were exposed to a moderate pulsed electric field (PEF) (3.3 kV/cm). The membrane permeability of E. coli was examined as a function of time after exposure to PEF. When comparing the percentage of propidium iodide (PI) permeability at a given time from PEF exposure, it appeared that as the bacterial density increased, there was a decrease in PI permeability. The permeability to PI in the bacterial suspensions of 0.05, 0.1, and 0.5 OD, 90 min after exposure, was 56.4 ± 4.08%, 43.91 ± 0.75%, and 29.47 ± 3.31%, respectively. Membrane permeability was also examined in different phosphate-buffered saline (PBS) concentrations. At 0.05 OD there was a linear correlation between PBS concentrations (0.56, 0.75, and 1 mM) and PI permeability (28.36 ± 2.22%, 61.08 ± 3.17%, and 98.2 ± 0.9%, respectively). At the higher bacterial densities of 0.1 and 0.5 OD, this phenomenon was not evident. Examination of bacterial membrane permeability using 4, 70, and 250 kDa fluorescein isothiocyanate (FITC)-dextran revealed that PEF led to 4kDa FITC-dextran permeabilization of 27.94 ± 3.76%. The PEF parameters used did not influence the bacterial cell size and viability. This study shed light on bacterial membrane permeability as a function of conductivity and bacterial density under PEF exposure. Full article

Hospital-acquired infections (HAIs) remain a major clinical and economic burden, with pathogens such as Escherichia coli contributing to high rates of morbidity and mortality. Traditional manual disinfection methods are often insufficient, particularly in high-risk hospital environments. In this study, we investigated innovative strategies to enhance surface decontamination and reduce infection risk. First, we assessed the efficacy of the SMEG BPW1260 bedpan washer-disinfector, a thermal disinfection system for human waste containers. Our results demonstrated a reduction in Clostridium difficile and Escherichia coli contamination by >99.9% (>3 log reduction), as measured by colony-forming units (CFU) before and after treatment. Molecular techniques, including spectrophotometry, cell counting, and quantitative PCR (qPCR) for DNA quantification, confirmed reduction in bacterial contamination. Specifically, Clostridium difficile showed a reduction of approximately 89% in both optical density (OD) and cell count (cells/mL). In the case of Escherichia coli, a reduction of around 82% in OD was observed, with an even more pronounced decrease in cell count, reaching approximately 99.3%. For both bacteria, DNA quantification by qPCR was below detectable limits. Furthermore, we optimized the energy efficiency of the disinfection cycle, achieving a 45% reduction in power consumption compared to standard protocols without compromising antimicrobial efficacy. Secondly, we developed a sustainable cleaning solution based on methyl ester sulfonate surfactants derived from waste cooking oil. The detergent’s antibacterial activity was tested on contaminated surfaces and further enhanced through the incorporation of nanoassemblies composed of silver, electrostatically bound either to biomimetic magnetic nanoparticles or to conventional magnetic nanoparticles. Washing with the detergent alone effectively eliminated detectable contamination, while the addition of nanoparticles inhibited bacterial regrowth. Antimicrobial testing against E. coli revealed that the nanoparticle-enriched formulations reduced the average MIC values by approximately 50%, with MIC₅₀ values around 0.03–0.06 mg/mL and MIC₉₀ values between 0.06 and 0.12 mg/mL, indicating improved inhibitory efficacy. Finally, recognizing the infection risks associated with intra-hospital transport, we tested the SAFE-HUG Wheelchair Cover, a disposable non-woven barrier designed to reduce patient exposure to contaminated wheelchair surfaces. Use of the cover resulted in a 3.3 log reduction in surface contamination, based on viable cell counts. Optical density and bacterial DNA were undetectable in all covered samples at both 1 and 24 h, confirming the strong barrier effect. Together, these approaches—thermal no-touch disinfection, eco-friendly detergent boosted with nanoparticles, and protective transport barriers—respond to the urgent need for effective, sustainable infection control methods in healthcare settings. Our findings demonstrate the potential of these systems to counteract microbial contamination while minimizing environmental impact, offering promising solutions for the future of infection prevention in healthcare settings. Full article

Background and Clinical Significance: This case report highlights the rare but potentially sight-threatening presentation of corneal ulcers associated with methamphetamine abuse. Identifying the signs of illicit drug use is critical, as ocular complications may be overlooked without proper social history or lab confirmation. Case Presentation: A 48-year-old Hispanic male presented with progressive bilateral vision loss over four weeks, describing his condition as “blind vision.” Two weeks earlier, he had visited the emergency room after a fall caused by impaired vision and was prescribed insulin, metronidazole, and fluoroquinolone drops. At ophthalmology follow-up, visual acuity was 20/400 OD and 20/800 OS. Examination revealed bilateral stromal corneal ulcers with infiltrates. Notable systemic signs—pockmarks, poor dentition, thin body habitus, and jittery behavior—raised suspicion for methamphetamine use. He was treated with bandage contact lenses, dehydrated amniotic membranes, and a steroid-antibiotic combination drop. Conclusions: This case underscores the importance of recognizing physical signs of methamphetamine abuse, even in the absence of disclosed history. Emergency room laboratory testing confirmed methamphetamine use. Awareness of drug-induced ocular effects allows for appropriate patient education, timely intervention, and referral to addiction services. Patients should be warned that continued drug use may result in irreversible vision loss. Full article

This paper establishes the global existence of solutions to a chemotaxis system with indirect signal production in the whole two-dimensional space. This system exhibits a mass threshold phenomenon governed by a critical mass $m_c=8\pi \delta$, where $\delta$ represents the decay rate of the static individuals. When the total initial mass $m={\int }_{R^2}{u}_{0}dx<m_c$, all solutions exist globally and remain bounded. In the critical case of $m=m_c$, the global existence or finite-time blow-up may occur depending on the initial conditions. The critical mass obtained in the whole space coincides with that previously derived in radially symmetric bounded domains. A key novelty lies in extending the analysis to the full plane, where the absence of compactness is overcome by constructing a suitable Lyapunov functional and employing refined Trudinger–Moser-type inequalities. Full article

Metabolite fingerprint profiling is a robust tool for verifying suppliers of authentic botanical ingredients. While comparative studies exist, few apply identical conditions across multiple species; this study utilized a cross-species comparison to identify versatile solvents despite biochemical variability. Multiple solvents were used for sample extraction prior to analysis by proton NMR and liquid chromatography–mass spectrometry (LC-MS) for multiple botanicals including Camellia sinensis, Cannabis sativa, Myrciaria dubia, Sambucus nigra, Zingiber officinale, Curcuma longa, Silybum marianum, Vaccinium macrocarpon, and Prunus cerasus. Comparisons were normalized by total intensity; deuterated methanol aids NMR lock but is not required for LC-MS. Hierarchical clustering analysis (HCA) evaluated solvent efficacy. Methanol–deuterium oxide (1:1) was the most effective extraction method, yielding 155 NMR spectral metabolite variables for Camellia sinensis, while methanol (90% CH₃OH + 10% CD₃OD) produced 198 for Cannabis sativa and 167 for Myrciaria dubia, with 11, 9, and 28 assigned metabolites, respectively. LC-MS detected 121 metabolites in Myrciaria dubia in methanol as the most effective extraction method. Methanol (10% deuterated) is the most effective extraction method for comprehensive metabolite fingerprinting using NMR and LC-MS protocols because it provides the broadest metabolite coverage. This study advances fit-for-purpose methods to qualify suppliers of botanical ingredients in food and NHP quality control programs. Full article

Researchers traditionally calculate growth rates using the natural logarithm of optical density (OD), with existing script packages facilitating this process. Automatic plate readers, capable of simultaneously measuring OD across 384 cultures, significantly enhance data collection efficiency. Furthermore, these readers also measure luminescence and fluorescence, providing valuable insights into gene expression. However, current analysis software often struggle with data generated by robotic systems measuring multiple plates, limiting the integration of growth and reporter analyses. This method paper addresses three key challenges: (a) the incompatibility of robotic multi-plate systems with existing analysis software, (b) the integration of growth and reporter analyses, and (c) the development of user-friendly interfaces for non-programmers. To address these challenges, we offer optimized script packages and a relevant case study on matrix expression in response to antibiotics. Our platform facilitates the efficient and integrated analysis of multi-plate growth and reporter data. Full article