Search Results (728)

Background: Phototherapy utilizes targeted irradiation to inactivate bacteria or treat various medical conditions. Depending on the therapeutic goal, wavelengths from violet to infrared (IR) are applied. Within the visible and near-IR spectrum, photodynamic therapy (PDT) combines light with photosensitizers that generate reactive oxygen species (ROS), leading to bacterial inactivation. Optimizing photodynamic efficacy can involve either enhancing ROS formation through specific topical agents that modulate ROS generation or employing dual-wavelength light irradiation (DWLR) to achieve synergistic excitation. Established DWLR protocols typically combine blue and red light or IR to activate distinct photosensitizers. Materials and Methods: This study investigates whether a similar synergistic effect can be achieved within the green spectral range by simultaneously exciting a single photosensitizer—coproporphyrin III (CP III)—at 496 nm and 547 nm. Results: Convolution analysis and in vitro bacterial reduction experiments with Cutibacterium acnes subsp. elongatum revealed that cyan irradiation (496 nm) achieved the strongest photoreduction (2.31 log steps at 1620 J/cm²), whereas PC-lime irradiation (547 nm) produced a smaller effect (0.74 log steps). DWLR protocols (simultaneous and sequential irradiation) resulted in intermediate reductions (1.64 and 1.73 log steps, respectively), exceeding PC-lime but not surpassing cyan irradiation alone. Conclusions: These findings demonstrate that excitation efficiency at the local absorption maximum of CP III is the primary determinant of ROS generation, while spectral broadening through DWLR does not enhance bacterial inactivation within this wavelength range and in vitro setup. Full article

Soil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment essential for sustainable agriculture. Visible and near-infrared (Vis–NIR) spectroscopy offers a non-destructive and cost-effective alternative to conventional laboratory analyses, allowing for the simultaneous estimation of multiple soil properties from a single spectrum. This study aimed to predict SOC content using machine learning techniques applied to Vis–NIR spectra of 860 soil samples collected in the Sierra Nevada de Santa Marta, Colombia. The spectra (400–2500 nm) were acquired using a NIR spectrophotometer, and the soil organic carbon (SOC) content was quantified using a wet oxidation method that employs dichromate in an acidic medium. A hybrid modeling framework combining Random Forest (RF) with support vector regression (SVR) and XGBoost was implemented. Spectral pretreatments (Savitzky–Golay first derivative, MSC, and SNV) were compared, and spectral bands were selected every 10 nm. The 30 most relevant wavelengths were identified using RF importance analysis. Data were divided into training (80%) and test (20%) subsets using stratified random sampling, and five-fold cross-validation was applied for parameter optimization and overfitting control. The RF–XGBoost (R² = 0.86) and RF–SVR (R² = 0.85) models outperformed the individual RF and SVR models (R² < 0.7). The proposed hybrid approach, optimized through features, and advanced spectral preprocessing demonstrate a robust and scalable framework for rapid prediction of SOC and sustainable soil monitoring. Full article

Background/Objectives: Autism spectrum disorder is associated with executive functioning (EF) challenges, yet the neural correlates of EF challenges in autistic youth remain unclear. This study aimed to examine EF performance and cortical activation in autistic versus non-autistic youth, using functional near-infrared spectroscopy (fNIRS) during a modified Flanker task. Methods: Thirty age-matched (11.6 ± 0.8 years) autistic (N = 15) and non-autistic youth (N = 15) completed congruent and incongruent conditions of a modified Flanker task while cortical activation in prefrontal, parietal, and temporal regions was recorded using fNIRS. The Behavior Rating Inventory of Executive Function (BRIEF) was used to assess general EF impairments. Behavioral data (i.e., Flanker task mean reaction time/accuracy, and reaction time variability) and cortical activation were analyzed using ANCOVAs. Pearson correlations were used to determine the relationship between cortical activation, EF performance, and clinical measures. The significance level was set at p < 0.05, with FDR corrections for multiple comparisons. Results: While mean reaction time and accuracy were comparable across groups, autistic youth exhibited greater reaction time variability (autistic youth = 34.8 ± 10.36; controls = 26.4 ± 1.94, p = 0.02, Hedges’ g = 0.85) and higher BRIEF index scores compared to controls (p_s < 0.001, Hedges’ gs > 1.3; e.g., Global Executive Composite Score for autistic youth = 71.3 ± 3.7; controls = 47.8 ± 2.4), indicative of delayed EF development. During the incongruent condition, compared to non-autistic controls, autistic youth showed lower left inferior parietal lobe (IPL) activation (Mean HbO₂ in autistic youth = −0.02 ± 0.006 mmol.mm; controls = 0.01 ± 0.006 mmol.mm, p_s < 0.001, Hedges’ g = 0.5) and a lack of left-lateralized activation (e.g., left vs. right STS activation, p < 0.001, Hedges’ g = 0.41 in the non-autistic youth). In the ASD group, lower activation in the left STS was associated with lower EF performance (r = −0.28, p = 0.007), whereas greater activation in various right-hemispheric ROIs was associated with better EF performance (r = −0.31 to −0.35, p_s < 0.005), suggesting potential compensatory activation. Conclusions: The findings revealed ASD-specific differences in the neural correlates of EF performance and possible alternative compensatory activation patterns. These potential neural correlates of EF performance highlight the utility of fNIRS-based neural measures to better understand the neural bases of EF differences in autism. Study Registration: This study was approved by the Institutional Review Board (IRB) at the University of Delaware (Protocol #: 1947455) on 4 October 2022. Full article

Through this work, an experimental setup and pre-processing method for obtaining fluorescence and quasi-noise-free Raman spectra of minerals for in situ mineral identification in an underground environment is proposed. It uses a combination of methodologies like dual excitation wavelengths, Shifted Excitation Raman Difference Spectroscopy (SERDS), and deep learning-based U-Net model for background and noise correction. The dual excitation wavelengths technique employs a near-infrared SERDS laser for the fingerprint and a red laser for the large Raman shift region. The SERDS laser operates at two excitation wavelengths and is tuneable in the vicinity of 785 nm. The red laser uses 671 nm excitation wavelength. The obtained fingerprint and large Raman shift Raman spectra are then fed to a pre-processing method containing the trained U-Net model for obtaining a background-corrected and quasi-noise-free Raman spectrum. The proposed method addresses issues of existing handheld Raman systems in terms of spectrometer sensitivity, spectrum acquisition speed, pre-processing time, fluorescence effects, and other interferences due to surrounding light or vibration. The obtained final processed Raman spectrum is then deconstructed into pseudo-Voigt peaks. The identification of the minerals can be based on the number and the positions of the pseudo-Voigt peaks. Samples of gypsum (CaSO₄·2H₂O) and anhydrite (CaSO₄) were used for evaluating the performance of the proposed method. The influence of measurement time on the reproducibility and precision of the peak identification and, thus, mineral identification is also analyzed. Full article

Near-infrared (NIR) spectroscopy combined with multivariate calibration methods is widely used for the quantitative analysis of complex samples. However, the high-dimensional redundancy of spectra may compromise model predictive accuracy, making it necessary to select variables before modeling. The beluga whale optimization (BWO) algorithm is known for its fast convergence speed, high accuracy and few parameters. The present study employed the discretized BWO (DBWO) algorithm in conjunction with partial least squares (PLS) for spectral quantitative analysis of complex samples. After the optimal number of iterations and transfer function were determined, the PLS models were established based on the randomization test (RT), uninformative variable elimination (UVE) and Monte Carlo uninformative variable elimination (MC-UVE). The predictive performance of DBWO-PLS was compared with full-spectrum PLS, RT-PLS, UVE-PLS and MC-UVE-PLS using wheat, tablet and cocoa bean samples. The results show that all four variable selection methods enhanced model prediction accuracy, with the DBWO-PLS model notably achieving superior performance. Full article

Objectives: Pretargeting strategies enhance the specificity and safety of radiopharmaceuticals by separating tumor targeting from radionuclide delivery. To address the rapid clearance and systemic exposure of directly labeled small-molecule agents, a DZ-1–based pretargeting system was developed, utilizing its broad-spectrum tumor-targeting characteristics. Methods: Three DZ-TCO precursors (DZ-1-TCO, DZ-Lys-TCO, and DZ-Lys-PEG₄-TCO) were synthesized and evaluated by near-infrared fluorescence imaging in HeLa and U87MG tumor-bearing mice. Two tetrazine probes (methyl-tetrazine and mono-substituted tetrazine) were labeled with ⁶⁸Ga to yield ⁶⁸Ga-DOTA-Me-Tz and ⁶⁸Ga-DOTA-H-Tz, whose stability was assessed in PBS and serum. Pretargeted PET imaging was performed using different precursor/probe combinations and pretargeting intervals (24, 48, and 72 h). Results: All precursors exhibited tumor accumulation peaking at 24 h and signal retention up to 96 h. Both ⁶⁸Ga-DOTA-Me-Tz and ⁶⁸Ga-DOTA-H-Tz maintained >85% radiochemical stability after 4 h. PET imaging identified DZ-Lys-TCO as the most effective precursor (1.98 ± 0.72 %ID/g, T/M 3.86 ± 0.91). Using ⁶⁸Ga-DOTA-H-Tz, the 48 h interval achieved optimal uptake (3.24 ± 0.95 %ID/g) with the highest tumor-to-muscle ratio (8.30 ± 3.39). Biodistribution confirmed rapid renal clearance, low off-target accumulation, and peak tumor uptake of 3.53 ± 1.76 %ID/g (T/M 10.9 ± 0.3 at 30 min). Conclusions: The DZ-TCO/⁶⁸Ga-DOTA-Tz pretargeting system enables high-contrast tumor imaging with low background. The combination of DZ-Lys-TCO and ⁶⁸Ga-DOTA-H-Tz at a 48 h interval provides optimal performance, representing a promising platform for precise and safe radiopharmaceutical imaging. Full article

Amphotericin: B (AmpB)-resistant Candida (C.) species, such as C. parapsilosis, are among the most common causes of invasive fungal infections, posing significant challenges in hospital settings. Although AmpB is considered the first-line treatment owing to its broad-spectrum fungicidal activity, its use is hampered by severe side effects and the emergence of acquired resistance, particularly in C. parapsilosis, which exhibits reduced susceptibility to polyene, azole, and echinocandin-based antifungal drugs. Here, we present findings on photodynamic therapy (PDT) that targets the opportunistic fungal pathogens C. parapsilosis and C. albicans via the use of photosensitizers from the iodocyanine and newly developed iodinated Methylene blue families. These compounds contain heavy iodine atoms that increase the production of reactive oxygen species (ROS), the agents responsible for oxidative cellular damage, via the heavy-atom effect, which promotes intersystem crossing (ISC) and triplet-state formation. A strong antifungal effect was observed against AmpB-resistant C. parapsilosis, indicating a correlation between the quantum yield of ROS generation and the photosensitizing efficacy under near-infrared (NIR) light irradiation. The combination of efficient cellular uptake and enhanced ROS generation positions iodinated photosensitizers as promising candidates for the treatment of drug-resistant Candida strains. Full article

Rapid and accurate moisture detection is critical for alfalfa quality control, yet conventional methods are slow, and non-destructive techniques are challenged by different product forms. This study leveraged Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) to acquire spatially representative spectra, aiming to develop and validate robust, form-specific moisture prediction models for compressed and powdered alfalfa. For compressed alfalfa, a full-spectrum Support Vector Regression (SVR) model demonstrated stable and good performance (mean Prediction Coefficient of Determination ${\mathrm{R}}_{\mathrm{P}}^2$ = 0.880, Ratio of Performance to Deviation RPD = 2.93). In contrast, powdered alfalfa achieved superior accuracy (mean ${\mathrm{R}}_{\mathrm{P}}^2$ = 0.953, RPD = 5.29) using an optimized pipeline of Savitzky–Golay’s first derivative, Successive Projections Algorithm (SPA) for feature selection, and an SVR model. A key finding is that the optimal model for powdered alfalfa frequently converged to an ultra-sparse, single-band solution near water absorption shoulders (~970/1450 nm), highlighting significant potential for developing low-cost, filter-based agricultural sensors. While this minimalist model showed excellent average accuracy, rigorous repeated evaluations also revealed non-negligible performance variability across different data splits—a crucial consideration for practical deployment. Our findings underscore that tailoring models to specific product forms and explicitly quantifying their robustness is essential for reliable NIR sensing in agriculture and provides concrete wavelength targets for sensor development. Full article

Background: The intravascular laser irradiation of blood (ILIB) is a low-power laser technique that has been studied since the 1970s, and it is associated with the substantial capability to modulate various physiological processes. Indeed, ILIB involves the irradiation of blood with low-intensity light, typically within the red or near-infrared spectrum, to trigger a cascade of photochemical and photobiological events. Objective: This study aimed to analyze previous findings regarding ILIB effects on physical performance. Methods: This study is a narrative review of the literature, addressing the effects of ILIB on multiple organ systems and its impact on physical performance. Results: The most found effects include antioxidant activation, inhibition of inflammatory processes, increased blood fluidity, and improved hemorheological properties. The ILIB affects blood rheological properties based on vasodilatation and decreasing aggregation of thrombocytes. Other effects include improved deformability of erythrocytes, which results in a better supply of oxygen and a decrease in the partial pressure of carbon dioxide. Since ILIB is a photobiomodulation procedure, other applications can be considered, such as ergogenic intervention. In this context, ILIB may favor performance in aerobic exercises and contribute to practices involving anaerobic metabolism by facilitating phosphocreatine resynthesis and ATP restoration. Conclusions: Multiple findings seek to support the potential benefits of ILIB on metabolic and cardiovascular responses associated with exercise training, providing potential improvements in athletic performance. Full article

The cereal leaf beetle (CLB; Oulema melanopus L., Coleoptera: Chrysomelidae) is a serious pest of wheat, capable of causing yield losses of up to 40% through photosynthetic impairment. Early detection and severity assessment are essential for effective and sustainable pest management. This study evaluates the potential of hyperspectral remote sensing (RS) combined with machine learning (ML) for non-invasive detection of CLB-induced stress in winter wheat. Spectral reflectance was measured using a full-range spectroradiometer (350–2500 nm) from flag leaves categorized into four damage levels (healthy, slightly, moderately, and severely damaged). Three input datasets were used for ML classification: full spectral reflectance, a set of 13 vegetation indices (VIs), and outputs of dimensionality reduction technique. CLB stress increased reflectance in the visible range (400–700 nm) and reduced it in the near-infrared (700–1400 nm), consistent with chlorophyll degradation and mesophyll damage. Several VIs, including RIGreen, NDVI750, GNDVI, and NDVI, correlated strongly with damage severity (τ = 0.78–0.81). Among the six ML models tested, Support Vector Machine (SVM) achieved the highest classification accuracy of 90.0% (precision = 0.90, recall = 0.90, F1 = 0.90) across the four severity classes, and achieved 91.9% accuracy at the early-detection threshold. As far as the currently available literature indicates, this study provides one of the earliest quantitative assessments of CLB damage severity based on full-spectrum leaf-level hyperspectral reflectance integrated with ML classification. These findings were obtained under controlled, leaf-level measurement conditions and therefore represent a proof-of-concept; future validation using UAV and satellite platforms is needed to assess performance under operational field variability. Overall, our findings highlight the potential of hyperspectral RS and ML for precision pest monitoring, supporting threshold-based decision-making and more sustainable insecticide use. Full article

Active metasurfaces, whose optical properties can be tuned by an external stimulus such as electric or laser pulses, have attracted great research interest recently. The phase change material (PCM), antimony sulfide (Sb₂S₃), has been reported to modulate resonance wavelengths from the visible to the infrared. Here, we present a purely numerical study of an active and nonvolatile narrow-band perfect absorber in the infrared region based on a nanostructured metal–insulator–metal (MIM) metasurface incorporating Sb₂S₃. The proposed absorber exhibits a high quality factor and achieves near-unity absorption at resonance wavelengths. In addition, the absorption spectrum can be dynamically modulated by the phase transition of Sb₂S₃, with a modulation range approaching 1 μm. Moreover, the designed absorber shows insensitivity to the angle of incidence. This study offers a feasible strategy for developing Sb₂S₃-integrated metasurface perfect absorbers with potential applications in selective thermal emitters and bolometers. Full article

Rapid, non-destructive assessment of biomass composition is essential for advancing Miscanthus spp. breeding and bioenergy production. This study aimed to develop and validate high-throughput near-infrared spectroscopy (NIRS) models for key chemical components in Miscanthus biomass. A robust calibration set was constructed from 107 diverse samples by combining two key species, Miscanthus sacchariflorus and M. lutarioriparius, to enhance chemical variability and create broadly applicable models. Partial Least Squares (PLS) regression models were developed using this dataset, comparing full-spectrum performance against models optimized with three feature selection algorithms: CARS, VCPA-GA, and VCPA-IRIV. All feature selection methods significantly enhanced predictive accuracy. Notably, the CARS-PLS models yielded excellent performance for cellulose (R²v = 0.98; RPD = 7.38), hemicellulose (R²v = 0.95, RPD = 4.35), lignin (R²v = 0.96, RPD = 5.40), and moisture (R²v = 0.98, RPD = 7.18), while the VCPA-IRIV-PLS model was superior for ash content (R²v = 0.96, RPD = 5.13). Overall, NIRS coupled with advanced feature selection provides a powerful, rapid protocol for Miscanthus biomass analysis, poised to accelerate germplasm evaluation and industrial quality control in the bioenergy sector. Full article

Ensuring food safety and quality is critical in the food-processing industry, where the detection of contaminants remains a persistent challenge. This study assesses the feasibility of hyperspectral imaging (HSI) for detecting foreign objects on pork belly meat. A Specim FX17 hyperspectral camera was used to capture data across various bands in the near-infrared spectrum (900–1700 nm), enabling identification of contaminants that are often missed by traditional visual inspection methods. The proposed solution combines a segmentation approach based on a lightweight Vision Transformer with specific pre- and post-processing strategies to distinguish contaminants from meat, fat, and conveyor belt, while emphasizing on a low false-positive rate. On a test set of 55 images with contaminants, the method retained most true positives; on 183 clean images, the full pipeline eliminated all false positives. Across 208 additional images acquired under production-line temperature variation (10–55 °C), only one image exhibited small false positives, and on a challenging 95-image set with fat-like spectra the pipeline produced zero false positives. These results demonstrate high detection accuracy and training efficiency while addressing issues such as noise, temperature drift, and spectral similarity. The findings support the feasibility of real-time HSI for automated quality control. Full article

To address the demand for precise sorghum tannin control in liquor brewing, and to overcome the inefficiency and high cost of traditional methods, this study developed a non-destructive approach by fusing features from dual hyperspectral sensors. Based on 240 representative sorghum samples covering different varieties and production regions, visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data were sequentially collected, and the tannin content was determined using standard chemical methods as reference values. Using the competitive adaptive reweighted sampling (CARS) method, characteristic wavelength bands were extracted and fused feature subsets were constructed. Combined with partial least squares (PLS), support vector machine (SVM), and convolutional neural network (CNN) algorithms, the performance of models built from both full-data concatenation and feature fusion of VNIR and SWIR data was systematically compared. The results demonstrated that the feature-based models exhibited superior performance to the full-spectrum models, while the model incorporating dual-sensor feature fusion achieved the best overall results. The fused-feature-CNN model achieved the optimal prediction performance, with values of 0.83 for coefficient of determination for the prediction set (R_P²), 0.29 for root mean squared error for the prediction set (RMSE_P), and 2.42 for residual predictive deviation for the prediction set (RPD_P). This study confirms that the integration of multi-sensor feature fusion with deep learning strategies can provide an effective technical pathway for the rapid, non-destructive detection of sorghum tannin content and the development of online sorting equipment. Full article

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems. Full article