Search Results (80)

Optical oxygen sensors have attracted considerable attention owing to their high sensitivity, rapid response, and broad applicability. However, their test results may be affected by fluctuations in the pump light source and instability of the detection equipment. In this study, the intrinsic luminescence of pine wood was utilized as the reference signal, and the luminescence of platinum(II) octaethylporphyrin (PtOEP) was employed as the oxygen indication signal, to fabricate a dual-signal ratiometric oxygen sensor PtOEP/PDMS@Pine. The ratio of the luminescence of pine wood to that of PtOEP was defined as the optical parameter (OP). OP increased linearly with oxygen concentration ([O₂]) in the range of 10–100 kPa, and a calibration curve was obtained. The sensor exhibits excellent anti-interference capabilities, effectively resisting fluctuations from laser sources and detection equipment. It also displays stable hydrophobicity with a contact angle of 118.3° and maintains excellent photostability under continuous illumination. The sensor exhibited long-term stability within 90 days and robust recovery performance during cyclic tests, wherein the response time and recovery time were determined to be 1.4 s and 1.7 s, respectively. Finally, the effects of temperature fluctuations and photobleaching on the sensor’s performance have been effectively corrected, enabling accurate oxygen concentration measurements in complex environments. Full article

Spirodela polyrhiza is a suitable model organism for investigating plant developmental influences due to its intracolonial variations in response to various environmental fluctuations, like nutrient deficiency. In this study, transmission electron microscopy was used to examine age-dependent variation in chloroplast ultrastructure, while pigment levels (chlorophyll and anthocyanins), starch accumulation, and metabolic activity (photosynthetic and respiratory rates) were measured to determine metabolic responses to sulfur deficiency. For a comprehensive insight into electron transport efficiency and the redox states of the photosynthetic apparatus, rapid light curves, chlorophyll fluorescence (JIP test parameters), and modulated reflection at 820 nm were analyzed. Under S deficit, mother fronds relied on stored reserves to maintain functional PSII but accumulated reduced PQ pools, slowing electron flow beyond PSII. The first-generation daughter fronds, despite having higher baseline photosynthetic capacity, exhibited the largest decline in photosynthetic indicators (e.g., rETR fell about 50%), limitations in the water-splitting complex, and reduced PSI end-acceptor capacity that resulted in donor- and acceptor-side bottlenecks of electron transport. The youngest granddaughter fronds avoided these bottlenecks by absorbing less light per PSII, channeling electrons through the alternative pathway to balance PQ pools and redox-stable PSI while diverting more carbon into starch and anthocyanin production up to 5-fold for both. These coordinated and age-specific adjustments that provide response flexibility may help maintain photosynthetic function of the colony and facilitate rapid recovery when sulfur becomes available again. Full article

The toxic effects of copper (Cu) excess in brown macroalgae have been well characterized. However, the interactive effects of increased temperatures, associated with climate change, and Cu stress on these macrophytes remain almost unexplored. In this study, we exposed the model brown seaweed Ectocarpus to different Cu concentrations (0, 0.8, 1.6, and 3.2 μM) at two different temperatures (15 and 25 °C). Relative growth rates decreased at 25 °C for the two highest Cu concentrations after 8 days of exposure, but a contrasting pattern was observed in the photosynthetic maximum quantum yield (F_v/F_m) and photosynthetic efficiency (α), where reductions were observed at 15 °C for the same Cu concentrations. Although no differences among treatments were observed for chlorophyll a (Chla) and chlorophyll c (Chlc), a reduction in concentration of the accessory pigment fucoxanthin (Fx) was only observed at 15 °C in all Cu treatments. Interestingly, at 25 °C, 20.1% less total Cu (intracellular + extracellularly bound) accumulated compared to 15 °C upon exposure to 3.2 μM Cu. Likewise, 33.1 and 23.8% less Cu accumulated intracellularly at 25 °C after exposure to 1.6 μM and 3.2 μM Cu, respectively. Additionally, at 25 °C about half of the Cu ions accumulated intracellularly and half extracellularly compared to 15 °C, where Cu accumulated mostly intracellularly at the two highest Cu concentrations. The results presented here provide valuable information to better understand the interactive effects of increased temperature and excess Cu in the stress response of Ectocarpus, suggesting that increased temperature helps to offset the negative impacts of exposure to high Cu concentrations. Full article

Background/Objectives: Emerging infectious diseases pose significant global threats due to their rapid transmission, limited therapeutic options, and profound socioeconomic impact. Conventional diagnostic techniques that rely on sequencing and polymerase chain reactions (PCR) frequently lack the speed necessary to efficiently respond to rapidly evolving pathogens. This study describes the development of BioInnovate AI to overcome these limitations using machine learning to expedite PCR assay development. Methods: The ability of BioInnovate AI to predict optimal PCR reagents across multiple pathogens was assessed. Additionally, random forest classifier, light gradient boosting machine (LGBM), and gradient boosting classifier models were evaluated for their ability to predict effective PCR primer–probe combinations. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score, were assessed to identify the optimal model for platform integration. Results: All machine learning models performed well, with the LGBM model achieving the highest metrics (AUC: 0.97, sensitivity: 0.93, specificity: 0.91). BioInnovate AI significantly reduced PCR assay development time by approximately 90%, enabling rapid design and reagent optimization for multiple pathogens. Conclusions: BioInnovate AI provides a rapid, accurate, and efficient method for PCR reagent design, significantly enhancing global diagnostic preparedness by optimizing primers and probes for the timely detection of infectious diseases. Full article

Coal gangue is a fine-grained mineral with nutrient content, which can be used as a potential soil amendment. Nevertheless, current research on using coal gangue to improve soil water and support plant growth is still insufficient. In this study, coal gangue powder (CGP) was added to aeolian sandy soil. We compared the soil hydraulic properties and plant growth of original aeolian sandy soil (CK) and different CGP application rates (10% and 20%). The results indicated that the application of CGP transformed the soil texture from sandy to loamy, significantly reduced soil bulk density and saturated hydraulic conductivity (Ks) values, altered the soil water characteristic curve, enhanced soil water-holding capacity, and increased plant-available water. Compared with the CK group, the emergence rate of alfalfa seeds increased from approximately 50% to over 70% after CGP application. During the growth process, CGP application significantly elevated the net photosynthetic rate, transpiration rate, and stomatal conductance of alfalfa leaves. Rapid fluorescence kinetics monitoring of leaves demonstrated that alfalfa treated with CGP had a higher efficiency in light energy utilization. However, the photosynthetic capacity of leaves did not improve as the CGP application rate increased from 10% to 20%, suggesting that excessive CGP addition did not continuously benefit plant gas exchange. In conclusion, CGP application can improve the soil hydraulic properties of aeolian sandy soil and support plant growth and development, which is conducive to reducing the accumulated amount of coal gangue, alleviating plant water stress, and promoting ecological restoration in arid mining areas. We recommend a 10% addition of coal gangue powder as the optimal amount for similar soils. Full article

Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors and heterogeneous data fusion. This manuscript proposes an enhanced credit rating model based on an improved TabNet framework. First, the Kaggle “Give Me Some Credit” dataset undergoes preprocessing, including data balancing and partitioning into training, testing, and validation sets. Subsequently, the model architecture is refined through the integration of a multi-head attention mechanism to extract both global and local feature representations. Bayesian optimization is then employed to accelerate hyperparameter selection and automate a parameter search for TabNet. To further enhance classification and predictive performance, a stacked ensemble learning approach is implemented: the improved TabNet serves as the feature extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Categorical Boosting), KNN (K-Nearest Neighbors), and SVM (Support Vector Machine) are selected as base learners in the first layer, with XGBoost acting as the meta-learner in the second layer. The experimental results demonstrate that the proposed TabNet-based credit rating model outperforms benchmark models across multiple metrics, including accuracy, precision, recall, F1-score, AUC (Area Under the Curve), and KS (Kolmogorov–Smirnov statistic). Full article

A new TLC method combined with densitometry was developed for the determination of donepezil hydrochloride in Cogiton Biofarm and Donecept Actavis tablets. The analyses were performed on TLC silica gel 60F₂₅₄ plates with mobile phase of n-butanol + n-propanol + acetone + water + glacial acetic acid at ratio of 2:2:1:1:1, v/v. The proposed mobile phase is miscible and after development the chromatographic plate has a homogeneous background in visible light. Densitometric analysis at λ = 319 nm was used for quantitative studies. The method was linear from 1.0 to 5.0 µg/spot and from 0.2 to 1.0 µg/spot and it was validated for both concentration ranges. The presented method is rapid, selective, linear, accurate, precise, robust, and economical. The results of the donepezil content in drugs calculated from both calibration curves were that no statistically significant differences were observed. The obtained content of donepezil in Cogiton (99.2%) and Donecept (99.0%) tablets is within the deviations permitted by the European Pharmacopoeia in relation to the amount declared by the manufacturer. The novelty of the study consists of the development of chromatographic conditions allowing the separation of as many as six donepezil degradation products with the simultaneous use of TLC chromatographic plates. As a result, the proposed method is economical, since it is several times cheaper than using HPTLC plates. While Ali et al. separated a maximum of three degradation products from donepezil, Pandey et al. successfully separated only two donepezil-related substances from donepezil. The proposed new TLC method combined with densitometry can be used for the routine control of donepezil in pharmaceutical preparations (tablets). Since TLC is less sensitive and precise compared to HPLC, it can be used as a complementary technique. Full article

With an increasing number of elderly individuals, the demand for advanced technologies to treat cardiac diseases has become more critical than ever. Additionally, there is a pressing need to reduce the learning curve for cardiac interventionalists to keep pace with the rapid development of new types of procedures and devices and to expand the adoption of established procedures in more hospitals. This comprehensive review aims to shed light on recent advancements in novel robotic systems for cardiac interventions. To do so, this review provides a brief overview of the history of previously developed robotic systems and describes the necessity for advanced technologies for cardiac interventions to address the technological limitations of current systems. Moreover, this review explores the potential of cutting-edge technologies and methods in developing the next generation of intra-procedure autonomous navigation. Each highlighted topic undergoes a critical analysis to evaluate its technical limitations and the challenges that must be addressed for successful clinical implementation. Full article

Sargassum is an important primary producer of rocky bottom communities in coastal ecosystems. Like other parts of the planet, benthic populations of S. natans from Ilha Grande Bay (IGB), southeastern Brazil, have been suffering from different forms of natural and anthropogenic disturbances, in particular increasing seawater temperatures. The aim of this study was to understand the effects of temperature on the photosynthetic performance of S. natans using the pulse amplitude modulated (PAM) fluorometry. In the field experiments, the occurrence of photoprotection resulted in a difference between the effective and maximum quantum yields [(ΔF (F’_m − F_s)/F’_m and F_v/F_m, respectively) that was maximized at noon. The stress induced by incubation at 32–35 °C caused a decrease in F_v/F_m by 33% on the first day and approximately 20% on subsequent days. In the laboratory, using two co-occurred species of S. natans and Padina gymnospora, we verified that the photosynthetic apparatus of S. natans collapses at 34 °C. The fate of the energy absorbed by photosystem II (PSII) antenna showed that, in S. natans, photochemical activity and non-photochemical quenching of chlorophyll fluorescence (NPQ) drastically decrease, and only the passive dissipation in the form of heat and fluorescence remains. Our results indicate the disappearance of the NPQ photoprotection at 34 °C before the decline of F_v/F_m as the reason for the collapse of photochemistry of Sargassum. Full article

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters—including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)—was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. Performance was assessed using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior diagnostic performance, achieving a precision of 99%, recall of 100%, and an AUC of 1.00. It outperformed traditional diagnostic methods and other standalone machine learning algorithms, offering high accuracy and generalizability. Conclusions: The lightweight machine learning metamodel provides a cost-effective, non-invasive, and rapid diagnostic alternative for typhoid fever, particularly suited for resource-limited settings. Its reliance on accessible clinical parameters ensures practical applicability and scalability, potentially improving patient outcomes and aiding in disease control. Future work will focus on broader validation and integration into clinical workflows to further enhance its utility. Full article

A PhenoCam is a near-surface remote sensing system traditionally used for monitoring phenological changes in diverse landscapes. Although initially developed for forest landscapes, these near-surface remote sensing systems are increasingly being adopted in agricultural settings, with deployment expanding from 106 sites in 2020 to 839 sites by February 2025. However, agricultural applications present unique challenges because of rapid crop development and the need for precise phenological monitoring. Despite the increasing number of PhenoCam sites, clear guidelines are missing on (i) the phenological analysis of images, (ii) the selection of a suitable color vegetation index (CVI), and (iii) the extraction of growth stages. This knowledge gap limits the full potential of PhenoCams in agricultural applications. Therefore, a study was conducted in two soybean (Glycine max L.) fields to formulate image analysis guidelines for PhenoCam images. Weekly visual assessments of soybean phenological stages were compared with PhenoCam images. A total of 15 CVIs were tested for their ability to reproduce the seasonal variation from RGB, HSB, and Lab color spaces. The effects of image acquisition time groups (10:00 $\mathrm{h}$–14:00 $\mathrm{h}$) and object position (ROI locations: far, middle, and near) on selected CVIs were statistically analyzed. Excess green minus excess red (EXGR), color index of vegetation (CIVE), green leaf index (GLI), and normalized green red difference index (NGRDI) were selected based on the least deviation from their loess-smoothed phenological curve at each image acquisition time. For the selected four CVIs, the time groups did not have a significant effect on CVI values, while the object position had significant effects at the reproductive phase. Among the selected CVIs, GLI and EXGR exhibited the least deviation within the image acquisition time and object position groups. Overall, we recommend employing a consistent image acquisition time to ensure sufficient light, capture the largest possible image ROI in the middle region of the field, and apply any of the selected CVIs in order of GLI, EXGR, NGRDI, and CIVE. These results provide a standardized methodology and serve as guidelines for PhenoCam image analysis in agricultural cropping environments. These guidelines can be incorporated into the standard protocol of the PhenoCam network. Full article

Infrared deflectometry is an efficient and accurate measuring method for curved surfaces fabricated via grinding or finish milling. The emitting properties and geometrical configurations of the infrared light source is a core component governing the measurement performance. In this paper, an infrared slit light source is designed based on the cavity structure of a polyimide heating film. This design ensures good stability and uniformity of the light source whilst effectively reducing background noise. Additionally, the light source can be applied as a calibration board for calibrating infrared cameras. The light source is aligned using a theodolite and cubic prism to control the positional deviations during scanning. Experimental results demonstrate that the proposed slit light source and calibration method can achieve a measurement accuracy of 1 µm RMS, which can meet the needs of rapid measurement in grinding. This approach provides a reliable, cost-effective, and efficient tool for surface quality assessments in optical workshops and has a broad application potential. Full article

Porcine epidemic diarrhea virus (PEDV) is a major pathogen that causes serious economic losses to the swine industry. To aid PEDV clinical diagnosis and vaccine development, sensitive and precise serological methods are demanded for rapid detection of (neutralizing) antibodies. Aiming for the development of a novel virus-free hemagglutination inhibition (HI) assay, the N-terminal region of the PEDV S1 subunit, encompassing the sialic acid-binding motif, was first expressed as an Fc-fusion protein with a C-terminal Spy Tag (S1^0A-Spy). The S1^0A-Spy protein was then presented on SpyCatcher-mi3 nanoparticles, forming virus-like particles designated S1^0A-NPs. Electron microscopy and dynamic light scattering analysis confirmed its topology, and the hemagglutination assay showed that S1^0A-NPs can efficiently agglutinate red blood cells. The HI assay based on S1^0A-NPs was then validated with PEDV-positive and -negative samples. The results showed that the HI assay had high specificity for the detection of PEDV antibodies. Next, a total of 253 clinical serum samples were subjected to the HI testing along with virus neutralization (VN) assay. The area under the receiver operating characteristic curve with VN was 0.959, and the kappa value was 0.759. Statistical analysis of the results indicated that the HI titers of the samples tested exhibited high consistency with the VN titers. Taken together, a novel virus-free HI assay based on the multivalent display of a chimeric PEDV spike protein upon self-assembling nanoparticles was established, providing a new approach for PEDV serological diagnosis. Full article

With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors. Full article

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings. Full article