Search Results (665)

Background: Sport-related concussion is common in rugby union, yet female players remain underrepresented in research. This study examined seasonal changes in cerebral oxygenation, cardiac function, and concussion symptomology in adult female rugby players, and explored acute physiological responses following a single documented concussion. Methods: A total of 29 adult females (19 amateur rugby, 10 control) completed pre-, mid-, and end-season assessments. Measures included functional near-infrared spectroscopy (fNIRS) of the pre-frontal cortex, seismocardiography (SCG)-derived cardiac timing indices, and Sport Concussion Assessment Tool 6 (SCAT6). Group and time effects were analysed using general linear models and statistical parametric mapping. Typical error (TE) and its 90% confidence intervals (90% CI) were used to determine meaningful changes post-concussion. Results: Rugby players reported more SCAT6 symptoms (number: p = 0.006, η²_p = 0.23; severity: p = 0.020, η²_p = 0.17). They also had shorter systolic time (p = 0.002, η²_p = 0.19) and higher twist force values (p = 0.014, η²_p= 0.21) than controls. fNIRS revealed higher right-hemisphere oxyhaemoglobin (ΔO₂Hb) responses for both tasks (ps < 0.001, η²_p = 0.77 and η²_p = 0.80) and lower activation in specific prefrontal channels. No seasonal changes occurred in global oxygenation or frequency band activity. In the exploratory single-concussion case, symptomology, SCG twist force, ΔO₂Hb, and cardiac band power exceeded TE and its 90% CI at 5 days post-injury. Conclusions: The multimodal approach detected stable group-level physiology alongside localised cortical and cardiac differences, and acute changes following concussion. While these results highlight the potential of combined fNIRS and SCG measures to capture physiological disturbances, the small sample size and single-concussion case necessitate cautious interpretation. Further validation in larger, longitudinal cohorts is required before any biomarker utility can be inferred. Full article

The calorific value of coal is a key parameter for pricing, trade, and combustion management. Conventional bomb calorimetry provides accurate results but is time-consuming, labor-intensive, and destructive. Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, yet its application is limited by strong band correlations, nonlinear spectral responses, and the lack of interpretability in many predictive models. In this study, the Kolmogorov–Arnold Network (KAN) is applied to the prediction of coal calorific value, demonstrating its capability to describe nonlinear spectral relationships within an interpretable mathematical structure. Based on this framework, a Radial Basis Function KAN (RBF-KAN) is further developed by replacing the B-spline bases in the KAN with radial basis functions, allowing improved representation of localized and irregular spectral variations while maintaining model transparency. Using 671 coal-powder samples measured by a portable MicroNIR spectrometer, the RBF-KAN achieved an RMSE of 1.35 MJ/kg and an MAE of 0.92 MJ/kg under five-fold cross-validation, outperforming conventional regression models, deep neural networks, and other KAN variants. Analysis of RBF activations and spectral attribution maps indicates that the model consistently responds to characteristic O-H and C-H overtone regions, which correspond to known absorption features in coal. These results suggest that the RBF-KAN provides a practical and interpretable framework for on-site estimation of coal calorific value, complementing traditional calorimetric analysis. Full article

Ensuring the quality and safety of agricultural and food products is crucial for protecting consumer health, meeting market expectations, and complying with regulatory requirements. Quality and safety parameters are commonly assessed using chemical and microbiological analyses, which are time-consuming, impractical, and involve the use of toxic solvents, often disrupting the material’s original structure. An alternative technique, infrared spectroscopy, including near-infrared (NIR), mid-infrared (MIR), and short-wave infrared (SWIR), has emerged as a rapid, powerful, and minimally destructive technique for evaluating the quality and safety of food and agricultural products. This review focuses on discussing MIR spectroscopy, particularly Fourier transform infrared (FTIR) techniques, with emphasis on the attenuated total reflectance (ATR) measurement mode (globar infrared light source is commonly used) and on the use of synchrotron radiation (SR) as an alternative high-brightness light source. Both approaches enable the extraction of detailed spectral data related to molecular and functional attributes concerning quality and safety, thereby facilitating the assessment of crop disorders, food chemical composition, microbial contamination (e.g., mycotoxins, bacteria), and the detection of food adulterants, among several other applications. In combination with advanced chemometric techniques, FTIR spectroscopy, whether employing ATR as a measurement mode or SR as a high-brightness light source, is a powerful analytical tool for classification based on attributes, variety, nutritional and geographical origins, with or without minimal sample preparation, no chemical use, and short analysis time. However, limitations exist regarding calibrations, validations, and accessibility. The objective of this review is to address recent technological advancements and existing constraints of FTIR conducted in ATR mode and using SR as a light source (not necessarily in combination). It defines potential pathways for the comprehensive integration of FTIR and chemometrics for real-time quality and safety monitoring systems into the global food supply chain. Full article

Although widely used across disciplines, the terms emotion and feeling remain conceptually ambiguous, particularly within developmental science. Emotion is defined as an evolutionarily conserved, biologically embedded system of action readiness and intersubjective communication, shaped by attentional, neural, and physiological reactivity to environmental salience. In contrast, feeling is conceptualized as the consciously experienced, representational outcome of emotional activation, emerging through cognitive appraisal and symbolic processing. Building upon this distinction, the review explores how emotion develops within parent–child dyads through coregulated neurocognitive and psychophysiological mechanisms. Drawing on empirical evidence from eye-tracking studies of visual attention to emotional faces, functional near-infrared spectroscopy (fNIRS) research on social-emotional activation in prefrontal brain regions, and cortisol-based assessments of hormonal synchrony, the paper highlights how emotional attunement and transmission are embedded in early caregiving interactions. The review also emphasizes the moderating role of environmental sensitivity—both in children and parents—in shaping these developmental pathways. By positioning emotion as a dynamic, intersubjective process and feeling as its emergent experiential correlate, this review offers a novel developmental framework for understanding affect and proposes directions for future research on resilience, dysregulation, and intervention. Full article

Background/Objectives: Lightweight and portable functional near-infrared spectroscopy (fNIRS) systems enable neuromonitoring in clinical environments such as operating rooms. Patient posture is known to influence physiology, behavior, and brain activity, and may affect fNIRS measurements. However, the effects of some postures commonly used in clinical care—such as Fowler’s and semi-Fowler’s—remain largely unexamined in fNIRS research. Methods: We conducted a singular study in a mock operating room exploring the effects of five postures—standing, upright sitting, Fowler’s, semi-Fowler’s, and supine—on fNIRS data during resting-state conditions and under various auditory stimuli. We collected hemodynamic data and extracted the characteristic hemodynamic response function (HRF) at each posture in response to the presented auditory stimulus and the amplitude of the resting-state signal. Results: For the auditory task condition, we found that posture had no statistically significant impact on the amplitude of the global HRF for Fowler’s and semi-Fowler’s postures. We also found no significant relationships across different postures when analyzing the amplitude of the global resting-state signal; however, binning of frequency-dependent postural effects revealed statistically significant differences between Fowler’s and semi-Fowler’s postures at low frequencies (f < 0.09 Hz). Conclusions: Our results suggest posture effects need not require complex data processing pipelines or data segmentation efforts on an auditory task-induced condition or on the general analysis of the global resting signal; however, not all reclined postures are equivalent, and we recommend that researchers report the angle of reclination measurements for seated data collection sessions for improved reliability and data context. Full article

Background: Exercise capacity is markedly impaired in patients with end-stage renal disease (ESRD) due to neuromuscular dysfunction and reduced oxygen delivery. This study aimed to investigate the effects of NMES during HD on exercise capacity in patients with ESRD. It specifically examined neuromuscular and hemodynamic adaptations. Methods: Twenty-two patients with ESRD were randomized to a neuromuscular electrical stimulation training group (NSTG, n = 11) or a control group (CG, n = 11). The NSTG underwent intradialytic quadriceps NMES three times per week for 12 weeks (40 min/session). Exercise capacity was evaluated via sustained isometric contraction at 50% of maximal voluntary contraction (MVC) until exhaustion. Neuromuscular function was assessed through voluntary activation (ΔVA) and potentiated twitch force (ΔQ_tw,pot), while muscle oxygenation (ΔO₂Hb, ΔHHb, ΔTHb) of the vastus lateralis was continuously monitored using near-infrared spectroscopy. Results: After the intervention, the NSTG showed a significant +20% increase in Tlim (103.9 ± 14.4 s to 123.3 ± 16.6 s; p = 0.01) and +30% improvement in MVC (421.3 ± 24.9 N to 550.4 ± 20.3 N; p < 0.01), while no improvements were observed in CG. Resting VA increased by ≈7% in NSTG (90.2 ± 3.7% to 96.8 ± 2.5%; p = 0.012). Improved muscle oxygenation and reduced twitch force suggest enhanced oxidative capacity and greater peripheral fatigue tolerance. Conclusions: Intradialytic NMES elicited robust improvements in exercise capacity, muscle strength, and oxygen utilization in ESRD patients by enhancing both central activation and peripheral oxidative adaptations. These findings support NMES as a feasible and effective rehabilitative strategy to counteract fatigue and functional decline in the ESRD population. Full article

Background/Objectives: It has become a major direction of research in affective computing to explore the brain-information-processing mechanisms based on physiological signals such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, existing research has mostly focused on feature- and decision-level fusion, with little investigation into the causal relationship between these two modalities. Methods: In this paper, we propose a novel emotion recognition framework for the simultaneous acquisition of EEG and fNIRS signals. This framework integrates the Granger causality (GC) method and a modality–frequency attention mechanism within a convolutional neural network backbone (MFA-CNN). First, we employed GC to quantify the causal relationships between the EEG and fNIRS signals. This revealed emotional-processing mechanisms from the perspectives of neuro-electrical activity and hemodynamic interactions. Then, we designed a 1D2D-CNN framework that fuses temporal and spatial representations and introduced the MFA module to dynamically allocate weights across modalities and frequency bands. Results: Experimental results demonstrated that the proposed method outperforms strong baselines under both single-modal and multi-modal conditions, showing the effectiveness of causal features in emotion recognition. Conclusions: These findings indicate that combining GC-based cross-modal causal features with modality–frequency attention improves EEG–fNIRS-based emotion recognition and provides a more physiologically interpretable view of emotion-related brain activity. Full article

This study uses a pointwise statistical approach to analyze Near-Infrared Spectroscopy (NIRS) signals in preterm infants with and without Patent Ductus Arteriosus (PDA). The analysis focuses on three signals: blood oxygenation (SpO₂), cerebral oxygenation (rSO₂-1), and renal oxygenation (rSO₂-2), across three newborn groups: without PDA (no-PDA), with hemodynamically insignificant PDA (PDA), and with hemodynamically significant PDA (hsPDA). While NIRS is widely used in medicine, its research, featuring statistical analysis, has been limited. Smoothed signals were tested using pointwise ANOVA and Tukey HSD to detect significant group differences. Results showed distinct patterns in rSO₂-1 and rSO₂-2, with the hsPDA group standing out in rSO₂-1 and the no-PDA group in rSO₂-2, demonstrating the value of this method in biomedical signal analysis. Pointwise ANOVA shows more time periods with significant differences compared to the SpO₂ signal. The time period with the most significant differences is between 2 and 6 h, with additional peaks of p-values below 0.05 occurring before 2 h. These findings demonstrate the value of FDA in improving statistical analysis of biomedical NIRS signals and support its use in future research. Full article

In this study, we employed functional near-infrared spectroscopy (fNIRS) to explore dynamic functional connectivity (dFC) responses to olfactory stimulation in thirteen healthy control participants and seven patients with vascular dementia (VD). Participants underwent five rest and odor exposure cycles, and dFC was estimated using a sliding window correlation approach. The healthy control group exhibited limited changes, while the VD group exhibited more extensive fluctuations in both oxy- and deoxyhemoglobin dFC across multiple regions during several stimulation periods. Between-group analyses revealed differences, particularly during olfactory stimulation, with moderate to large effect sizes. These preliminary findings suggest that olfactory-evoked dFC may reflect altered brain network dynamics in VD and could potentially serve as a non-invasive, accessible tool to help understand vascular dementia. Full article

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination ($\mathit{ROC}\text{-}\mathit{AUC}$ = 0.909) under subject-aware CV5; at the default threshold, $\mathit{Sensitivity}$ was moderate and $\mathit{Specificity}$ was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited $\mathit{Sensitivity}$ despite high $\mathit{Specificity}$. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms. Full article

The possibility of modifying the surface chemistry of materials and synthetizing inorganic particles in natural aqueous extracts of plants (avoiding calcination), opens the doors to undoubtedly interesting scenarios for innovative functionalization strategies that are increasingly eco-sustainable and rich in interesting chemical–physical and biochemical properties. Among the aerial plants, Satureja montana exhibits interesting antibacterial, antifungal, antimicrobial, and antioxidant activities due to the rich volatile and non-volatile compounds (characterized by gas chromatography–mass spectrometry), contained in the aqueous extracts. For the first time, the latter was applied for the green synthesis of TiO₂ and CaCO₃ particles, characterized by X-ray diffraction, Raman, infrared spectroscopies, and scanning electron microscopy, coupled with microanalysis. Screening through antimicrobial assays under indoor passive sedimentation conditions showed opposite trends for two kinds of particles. TiO₂ anatase spherical particles (400 < φ < 600 nm) increase microbial growth, proportionally to increasing particle concentration. Instead, S. montana-functionalized CaCO₃ prismatic microparticles (1 µm × 1 µm × 1 µm) exhibit strong and dose-dependent antimicrobial activities, achieving near-complete inhibition at 50 mg/mL. Full article

Background/Objectives: This study aimed to investigate the effects of low and high glycemic index (LGI and HGI) breakfasts on post-exercise cognitive functions and prefrontal hemodynamic responses. Methods: Ten male athletes aged 18–22 years participated in this study. The athletes conducted two laboratory visits in a randomized manner. Athletes were given different glycemic index (GI) levels (low and high) for pre-exercise meals on different days, with the same calorie values, carbohydrate, and fat content. A total of 90 min after breakfast, a 30 min submaximal exercise was performed using a cycling ergometer. During the laboratory visits, blood glucose measurements were performed at the 0th (fasting), 90th (pre-exercise), and 120th (post-exercise) min. Additionally, the “3-Back test” was performed pre- and post-exercise to assess working memory and their prefrontal hemodynamic responses were monitored via functional Near-Infrared Spectroscopy. The collected data were evaluated in the SPSS 22 statistical program. Results: The HGI breakfast led to higher blood glucose levels at the 90th (pre-exercise) and 120th min (post-exercise) than LGI breakfast (p < 0.05). No difference was observed between HGI and LGI breakfasts in the results of the “3-Back Test” performed pre- and post-exercise. In terms of prefrontal hemodynamic responses, no difference was observed in post-exercise oxy-hemoglobin responses between the conditions. Conclusions: The findings of the study indicate that an increase in the glycemic index of breakfast has the potential to affect prefrontal oxygenation responses during cognitive tasks. However, no effect of glycemic index level was observed on cognitive and hemodynamic values at the end of the exercise. Full article

Smart polyester materials with embedded near-infrared (NIR) functionalities offer a promising pathway for low-cost, covert tagging, and object identification. In this study we present the development and characterization of polyester packaging surfaces printed with spectrally matched twin dyes that are invisible under visible light but selectively absorbed in the NIR region. The dye patterns were applied using a Direct-to-Film transfer (DTF) method onto polyester substrates. To validate their optical behavior, we applied a dual measurement approach. Laboratory grade NIR absorbance spectroscopy was used to characterize the spectral profiles of the twin dyes in the 400–900 nm range. A custom photodiode-based detection system was constructed to evaluate the feasibility of low-cost, embedded NIR absorbance sensing. Results from both methods show correlation in absorbance contrast between the dye pairs, confirming their suitability for spectral tagging. The developed materials were evaluated in a real-world detection scenario using commercially available NIR cameras. Under dark field conditions with edge illuminated planar lighting, the twin dye patterns were successfully recognized through custom software, enabling non-contact identification and spatial localization of the NIR codes. This work presents a low-cost, scalable approach for smart packaging applications based on optical detection of actively illuminated twin dyes using accessible NIR imaging systems. Full article

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees. Full article

Insights into motor cortex remodeling may enable the development of more effective rehabilitation strategies during the acute phase. We aim to assess the affected and unaffected motor/premotor/somatosensory cortex resting state functional connectivity (RSFC) and reactivity with continuous wave functional near-infrared spectroscopy (cw-fNIRS) in patients with ICH compared to age, sex, and comorbidity-matched subjects. We enrolled patients with acute–subacute hemispheric ICH (n = 37; two were excluded due to artifacts) and grouped them according to the side (right and left) of the stroke. Matched participants or patients with recent transient ischemic attack were enrolled as control subjects for the study (n = 44; five were excluded due to artifacts). RSFC was assessed in both affected and unaffected hemispheres by group-level seed-based (primary motor cortex, priMC) correlation analysis. FT-associated relative oxyhemoglobin (ΔHbO) changes were analyzed in affected and unaffected hemispheres with generalized linear model regression. In left hemispheric ICH, the resting state coherence between the affected priMC and the affected premotor cortex (preMC) increased (β = 0.83, 95% CI = 0.19, 1.47, p = 0.01). In contrast, in right hemispheric ICH, the coherence between the unaffected priMC and the affected preMC decreased (β = −0.6, 95% CI = −1.12, −0.09, p = 0.02). In the left hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the affected preMC (β = 0.01, 95% CI = 0.003, 0.02, p = 0.01). In contrast, in right hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the unaffected preMC (β = 0.02, 95% CI = 0.006, 0.04, p = 0.01). Left hemispheric preMC may be involved in motor cortex reorganization in acute ICH in either hemisphere. Further studies may be required to assess longitudinal changes in motor cortex reorganization to inform acute motor rehabilitation. Full article