Search Results (6,283)

The present work proposes a method for the synthesis of a nanoparticle with a superparamagnetic Fe₃O₄ core coated with SiO₂-NH₂ by ultrasound-assisted coprecipitation. Additionally, the nanoparticle is functionalized with a microinflammation biomarker peptide, and its effects on the viability of monkey kidney endothelial cells and the Vero cell line were evaluated. The main physicochemical properties of the nanoparticles were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), X-ray Photoemission Spectroscopy (XPS), a vibrating sample magnetometer (VSM), a field emission scanning electron, Scanning Electron Microscopy (SEM), and High-Resolution Transmission Electron Microscopy (HR-TEM). The results showed that the nanoparticles are spherical, with sizes smaller than 10 nm, with high thermal stability and superparamagnetic properties. They also demonstrated cell viability rates exceeding 85% through Magnetic Resonance Imaging (MRI). The results indicate the potential of these nanoparticles to be used as a contrast agent in magnetic resonance to detect mild brain lesions. Full article

Background: Zinc oxide nanobiocomposites were successfully synthesized using a green synthesis approach. The process involves the utilization of the isoflavone puerarin, resulting in the formation of PUE-ZnO NPs. Methods: Physico-chemical and biological characterization techniques including X-ray dif-fraction (XRD), UV-vis spectroscopy, Fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), atomic force microscopy (AFM), and in ovo methods were employed to study the main characteristics of this novel hybrid material. Results: The PUE-ZnO NPs were confirmed to have been successfully synthesized with a UV absorption peak at 340 nm, the XRD analysis demonstrating their high purity and crystallinity. The energy band-gap value of 3.30 eV suggests possible photocatalytic properties. Both SEM and AFM images revealed the nanoparticle`s quasi-spherical shape, roughness, and size. Good tolerability and anti-irritative effects were recorded in ovo on the chorioallantoic membrane (CAM). Conclusions: According to these results, the synthesis of green PUE-ZnO NPs may be a promising future approach for biomedical and personal care applications. Full article

Near-infrared autofluorescence (NIA) is a non-invasive retinal imaging technique used to examine the retinal pigment epithelium (RPE) based on the autofluorescence of melanin. Melanin has several functions within RPE cells. It serves as a protective antioxidative factor and is involved in the phagocytosis of photoreceptor outer segments. Disorders affecting the photoreceptor–RPE complex result in alterations of RPE cells which are detectable by alterations of NIA. NIA allows us to detect early alterations in various chorioretinal disorders, frequently before they are ophthalmoscopically visible and often prior to alterations in lipofuscin-associated fundus autofluorescence (FAF) or optical coherence tomography (OCT). Although NIA and FAF relate to disorders affecting the RPE, the findings for both imaging methods differ and the area involved has been demonstrated to be larger in NIA compared to FAF in several disorders, especially inherited retinal dystrophies (IRDs), indicating that NIA detects earlier alterations compared to FAF. Foveal alterations can be much more easily detected using NIA compared to FAF. A reduced subfoveal NIA intensity is the earliest sign of autosomal dominant Best disease, when FAF and OCT are still normal. In other IRDs, a preserved subfoveal NIA intensity is associated with good visual acuity. So far, the current knowledge on NIA in IRD has been presented in multiple separate publications but has not been summarized in an overview. This review presents the current knowledge on NIA in IRD and demonstrates NIA biomarkers. Full article

Adipocytes derived from 3T3-L1 cells are a gold standard for analyses of adipogenesis processes and the metabolism of fat cells. A widely used histological and immunohistochemical staining and mass spectrometry lipidomics are mainly aimed for examining lipid droplets (LDs). Visualizing other cellular compartments contributing to the cellular machinery requires additional cell culturing for multiple labeling. Here, we present the localization of the intracellular structure of the 3T3-L1-derived adipocytes utilizing vibrational spectromicroscopy, which simultaneously illustrates the cellular compartments and provides chemical composition without extensive sample preparation and in the naïve state. Both vibrational spectra (FTIR—Fourier transform infrared and RS—Raman scattering spectroscopy) extended the gathered chemical information. We proved that both IR and RS spectra provide distinct chemical information about lipid content and their structure. Despite the expected presence of triacylglycerols and cholesteryl esters in lipid droplets, we also estimated the length and unsaturation degree of the fatty acid acyl chains that were congruent with known MS lipidomics of these cells. In addition, the clustering of spectral images revealed that the direct surroundings around LDs attributed to lipid-associated proteins and a high abundance of mitochondria. Finally, by using quantified markers of biomolecules, we showed that the fixative agents, paraformaldehyde and glutaraldehyde, affected the cellular compartment differently. We concluded that PFA preserves LDs better, while GA fixation is better for cytochromes and unsaturated lipid analysis. The proposed analysis of the spectral images constitutes a complementary tool for investigations into the structural and molecular features of fat cells. Full article

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R² of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R² of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R² of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection. Full article

Ship image classification identifies the type of ships in an input image, which plays a significant role in the marine field. To enhance the ship classification performance, various research focuses on studying multi-modal ship classification, which aims at combining the advantages of visible images and infrared images to capture complementary information. However, the current methods simply concatenate features of different modalities to learn complementary information, which neglects the multi-level correlation between different modalities. Moreover, the existing methods require a large amount of labeled ship images to train the model. How to capture the multi-level cross-modal correlation between unlabeled and labeled data is still a challenge. In this paper, a novel semi-supervised multi-modal ship classification approach is proposed to solve these issues, which consists of two components, i.e., multi-level cross-modal interactive network and semi-supervised contrastive learning strategy. To learn comprehensive complementary information for classification, the multi-level cross-modal interactive network is designed to build local-level and global-level cross-modal feature correlation. Then, the semi-supervised contrastive learning strategy is employed to drive the optimization of the network with the intra-class consistency constraint based on supervision signals of unlabeled samples and prior label information. Extensive experiments on the public datasets demonstrate that our approach achieves state-of-the-art semi-supervised classification effectiveness. Full article

Background: Choroideremia is a monogenic inherited retinal dystrophy that manifests in males with night blindness, progressive loss of peripheral vision, and ultimately profound sight loss, commonly by middle age. It is caused by genetic defects of the CHM gene, which result in a deficiency in Rab-escort protein-1, a key element for intracellular trafficking of vesicles, including those carrying melanin. As choroideremia primarily affects the retinal pigment epithelium, fundus autofluorescence, which focuses on the fluorescent properties of pigments within the retina, is an established imaging modality used for the assessment and monitoring of affected patients. Methods and Results: In this manuscript, we demonstrate the use of both short-wavelength blue and near-infrared autofluorescence and how these imaging modalities reveal distinct disease patterns in choroideremia. In addition, we show how these structural measurements relate to retinal functional measures, namely microperimetry, and discuss the potential role of these retinal imaging modalities in clinical practice and research studies. Moreover, we discuss the mechanisms underlying retinal autofluorescence patterns by imaging with a particular focus on melanin pigment. Conclusions: This could be of particular significance given the current progress in therapeutic options, including gene replacement therapy. Full article

The quality evaluation of processed potatoes is vital in the food industry. In this study, the effect of three different drying methods on the post-processing quality of potatoes utilizing 4, 8, 12, and 16 h of freeze drying (FD), infrared drying (ID), and oven drying (OD) was investigated. The impact of the drying methods on the potato’s microstructure was analyzed and quantified using 3D X-ray micro-computed tomography images. A new Hybrid Quality Score Evaluator (HQSE) was introduced and used to assess the Quality Index (QI) and Specific Energy Consumption Index (SECI) across various drying methods and durations. Mathematical models were developed to predict the optimal drying method. FD showed significantly higher (p < 0.05) colour retention, rehydration ratio, and total porosity, with minimal shrinkage, although it had higher energy consumption. ID had the shortest drying time, followed by OD and FD. The optimization showed that for FD, the optimal time of 5.78 h increased QI by 9.7% and SECI by 30.6%. The mathematical models could accurately predict the QI and SECI under different drying methods, balancing quality preservation with energy efficiency. The findings suggest that a hybrid drying system could optimize potato quality and energy consumption. Full article

In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article

Background: Green synthesized nanoparticles (NPs) have gained increasing popularity in recent times due to their broad spectrum of antimicrobial properties. This study aimed to develop a phytofabrication approach for producing cuprous (Cu₂O) and cupric oxide (CuO) NPs using a simple, non-hazardous process and to examine their antimicrobial properties. Methods: The synthesis employed Bidens pilosa plant extract as a natural reducing and stabilizing agent, alongside copper chloride dihydrate as the precursor. The biosynthesized NPs were characterized through various techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared (FT-IR) spectroscopy, ultraviolet–visible (UV-Vis) spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). Results: XRD analysis confirmed that the synthesized CuO and Cu₂O NPs exhibited a high degree of crystallinity, with crystal structures corresponding to monoclinic and face-centered cubic systems. SEM images revealed that the NPs displayed distinct spherical and sponge-like morphologies. EDS analysis further validated the purity of the synthesized CuO NPs. The antimicrobial activity of the CuO and Cu₂O NPs was tested against various pathogenic bacterial strains, including Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Bacillus cereus, with the minimum inhibitory concentration (MIC) used to gauge their effectiveness. Conclusions: The results showed that the phytosynthesized NPs had promising antibacterial properties, particularly the Cu₂O NPs, which, with a larger crystal size of 68.19 nm, demonstrated significant inhibitory effects across all tested bacterial species. These findings suggest the potential of CuO and Cu₂O NPs as effective antimicrobial agents produced via green synthesis. Full article

The microstructure of aluminum thin films, including the grain morphology and surface roughness, are key parameters for improving the thermal or electrical properties and optical reflectance of films. The first step in optimizing these parameters is a thorough understanding of the grain growth mechanisms and film structure. To investigate these issues, thin aluminum films with thicknesses ranging from 25 to 280 nm were coated on SiO_x/Si substrates at ambient temperature under high-vacuum conditions and a low argon pressure of 3 × 10⁻³ mbar (0.3 Pa) using the radio frequency magnetron sputtering method. Quantitative analyses of the surface roughness and nanograin characteristics were conducted using atomic force microscopy (AFM), transmission electron microscopy (TEM), and X-ray diffraction. Changes in specular reflectance were measured using ultraviolet–visible and near-infrared spectroscopy. The low roughness values obtained from the AFM images resulted in high film reflectivity, even for thicker films. TEM and AFM results indicate monomodal, randomly oriented grain growth without a distinct columnar or spherical morphology. Using TEM cross-sectional images and the dependence of the grain size on the film thickness, we propose a grain growth mechanism based on the diffusion mobility of aluminum atoms through grain boundaries. Full article

The phosphors with persistent luminescence in the NIR (near-infrared) region and the NIR-to-NIR Stokes luminescence properties have received considerable attention owing to their inclusive application prospects in the in vivo imaging field. In this paper, Nd³⁺ doped SrSnO₃ phosphors with remarkable NIR emission performance were prepared using a high temperature solid state reaction method; the phase structure, morphology, and luminescence properties were discussed systematically. The SrSnO₃ host exhibits broadband NIR emission (800–1300 nm) with absorptions in the near ultraviolet region. Nd³⁺ ions emerge excellent NIR-to-NIR Stokes luminescence under 808 nm laser excitation, with maximum emission at around ~1068 nm. The concentration-dependent luminescence properties, temperature dependent emission, and the luminescence decay curves of Nd³⁺ in the SrSnO₃ host were also studied. The Nd³⁺ doped SrSnO₃ phosphors exhibit exceptional thermal stability; the integrated emission intensity can retain approximately 66% at 423 K compared to room temperature. Most importantly, NIR persistent luminescence also can be observed for the SrSnO₃:Nd³⁺ samples, which is in the first and second biological windows. A possible mechanism was proposed for the persistent NIR luminescence of Nd³⁺ based on the thermo-luminescence spectra. Consequently, the exciting results indicate that multifunctional NIR luminescence has been successfully realized in the SrSnO₃:Nd³⁺ phosphors. Full article

A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network’s capacity for multi-scale complicated targets. Replacing YOLOv8’s C2f module with C2f-DCNv3 increases the network’s ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone’s SPPF module to improve the model’s detection performance by directing the network’s detection resources to the major road target detection zone. Experimental results show that on the FLIR_ADAS_v2 dataset retaining eight main categories, using YOLO-APDM compared to YOLOv8n, mAP_@0.5 and mAP_@0.5:0.95 increased by 6.6% and 5.0%, respectively. On the M3FD dataset, mAP_@0.5 and mAP_@0.5 increased by 8.1% and 5.9%, respectively. The number of model parameters and model size were reduced by 8.6% and 4.8%, respectively. The design requirements of the high-precision detection of infrared road targets were achieved while considering the requirements of model complexity control. Full article

PHPs (pulsating heat pipes) are widely used as an efficient heat transfer element in equipment thermal management and waste heat recovery due to their flexibility. The purpose of this study was to design a heat transfer device that utilizes an asymmetric pulsating heat pipe structure by adjusting the lengths of selected pipes within the entire circulation pipeline. In the experiment, a constant temperature water bath was used as the heat source, with heat dissipated in the condensing section via natural convection. An infrared thermal imager was used to record the temperature of the condensing section, and the local wall temperature distribution was measured in different channels of the condensing section. Based on an in-depth analysis of the wavelet frequency, the following research conclusions are drawn: Firstly, as the heat source temperature increases, the start-up time of the pulsating heat pipe is shortened, the operating state changes from start–stop–start to stable and continuous oscillation, and the oscillation mode changes from high amplitude and low frequency to low amplitude and high frequency. These changes are especially pronounced when the heat source temperature is 80 °C, which is when the thermal resistance reaches its lowest value of 0.0074 K/W, and the equivalent thermal conductivity reaches its highest value of 666.29 W/(m·K). Secondly, the flow and oscillation of the working fluid can be effectively promoted by appropriately shortening the length of the condensing section of the pulsating heat pipes or the heat transfer distance between the evaporation and condensing sections. Third, under a low-temperature heat source, the oscillation frequency of each channel of a pulsating heat pipe is found to be low based on wavelet analysis. However, as the heat source temperature increases, the energy content of the temperature signal of the working fluid in each channel changes from a low- to a high-frequency value, gradually converging to the same characteristic frequency. At this point, the working fluid in the pipes no longer flows randomly in multiple directions but rather in a single direction. Finally, we determined that the maximum oscillation frequency of working fluid in a PHP is around 0.7 HZ when using the water bath heating method. Full article

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows. Full article