Near-infrared Spectroscopy Data Research Articles

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data. We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination. Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach. When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results. This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

Correlation of intraoperative near-infrared-spectroscopy values to neurologic outcome in patients undergoing total aortic arch replacement using the frozen elephant trunk technique

ObjectiveTo correlate intraoperative near-infrared-spectroscopy (NIRS) values to neurologic outcome in patients undergoing total aortic arch replacement using the frozen elephant trunk (FET) technique. DesignRetrospective, single centre registry study using a two-way repeated measures ANOVA. SettingBetween 11/2013 and 12/2023, 427 patients were treated for acute and chronic aortic pathologies using FET procedure. ParticipantsOne-hundred sixty-six patients who had complete NIRS data at all predefined timepoints were included and retrospectively divided into two groups: patients with stroke diagnosed by experienced radiologists and clinicians and patients without stroke after FET. MeasurementsBilateral NIRS values were recorded continuously and at seven critical timepoints in each patient a correlation was made between left- and right-sided NIRS values and stroke. Main Results23 patients (13.9%) were diagnosed with a stroke. There was a significantly higher in-hospital mortality in patients with stroke (21.7%) compared to patients without stroke (7.7%) (p = 0.037). Left sided regional cerebral oxygenation (rSO2) did not show any significant difference in patients with stroke versus patients without stroke (p = 0.550). There were differences detected in right sided rSO2 trends (p < 0.001) and the inter-hemisphere NIRS at the specified timepoints (p = 0.009). The interaction Time x Stroke, however, did not show any significant results in all our analyses (p > 0.05). ConclusionNIRS is a reliable tool to monitor intraoperative frontal lobe cerebral oxygen saturation. However, its use to predict postoperative stroke remains limited. Further refinements are needed to develop the technique to a prediction tool.

Comparison of local muscle oxygenation and whole-body VO2 during incremental exercise

The use of near-infrared spectroscopy (NIRS) in sports science has become increasingly important in the past few years (Perrey et al., 2024). One application is to determine thresholds from different NIRS parameters like oxygenated (O2Hb) or deoxygenated (HHb) hemoglobin to distinguish between hard and severe-intensity exercise (Caen et al., 2018). Such applications are frequently validated by comparison to established threshold determination concepts. However, it is not yet known which of the two parameters is more closely related to classical concepts. Therefore, the aim of this project was do analyze which NIRS parameter (oxygenated [O2Hb] or deoxygenated [HHb] hemoglobin) better reflects systemic behavior determined from ventilation (VE). Thirteen physically active participants (4 females, 9 males; mean age: 26.3 ± 4.4 years, height: 176.0 ± 10.3 cm, weight: 71.9 ± 9.3 kg) were included in the study. VE and NIRS data from a major locomotor muscle (vastus lateralis) during a step-incremental single leg cycle ergometer test were recorded. We used a custom MATLAB script to fit piecewise linear continuous functions by regression to estimate NIRS and VE thresholds. We used two distinct segments for NIRS data and three distinct segments for VE data. NIRS thresholds were defined as the point of intersection of the two segments in O2Hb and HHb signals, respectively. The ventilatory threshold was defined as the second increase in VE. We used a resampling strategy to reduce the impact of outliers or peculiar data distributions on threshold estimates. Thresholds from O2Hb and HHb in the active leg and VE were observed at 78.24%, 76.37%, and 84.15% of the maximum power achieved. Both O2Hb (r = 0.85, p &lt; 0.001) and HHb (r = 0.75, p = 0.0051) thresholds were significantly correlated, but mean values were significantly different (p &lt; 0.05) from the respiratory compensation point (RCP) mean value. Correlations between O2Hb and HHb values from the passive leg and the RCP values were in the same range (r = 0.88, p &lt; 0.001 and r = 0.79, p = 0.0023, respectively). The results indicate that, based on the correlations, O2Hb is slightly closer related to the second ventilatory threshold compared to HHb. The present study shows that thresholds from both NIRS parameters are closely related to the second ventilatory threshold which indicates a physiological relationship. However, thresholds cannot be used interchangeably as NIRS parameters derived from the working muscle appear earlier than systemic thresholds that demonstrate a delay in kinetics between the working muscle and the system.

A Spatial-Temporal Analysis of Brain Activation to Explain the Take-Over Failure in Conditionally Automated Driving.

This study aims to investigate the causes of take-over failures in conditional automated driving with spatial-temporal analysis of brain zone activation. Take-over requires a human driver to resume the control of the vehicle when its automation system disengages. Existing studies have found that take-over failures occur frequently on some drivers, but the causes have not been thoroughly studied. In a driving simulator experiment, 40 drivers took over in critical freeway cut-in situations. Functional near-infrared spectroscopy (fNIRS) data were collected before and during the take-over process to evaluate brain zone activation. Successful and failed take-overs were compared with changes in fNIRS data based on spatial-temporal comparisons and cluster analysis. The results suggested a significant difference in temporal brain activation between take-over failure and success conditions. Take-over failure conditions are mostly related to earlier and longer brain activation in most brain zones and repeated activation of the cognition brain zones. Drivers' attention switches, steering, and braking patterns are also related to different brain zone activation orders. The results indicate the need to reduce the mental workload caused by the sudden system disengagement to prevent take-over failure. Future research and implementation should focus on earlier warnings of upcoming hazards and driver education in dealing with sudden system disengagement.

Research on model transfer strategies based on the fusion of NIR-MIR spectral data

This study investigated the impact of different data fusion strategies on the performance of soluble solids content (SSC) prediction models based on near-infrared and mid-infrared spectroscopic techniques. In the data-level fusion approach, we applied standard normal variate and multiplicative scatter correction for pre-processing the NIR and MIR data. For the feature-level fusion, we utilized successive projections algorithm and competitive adaptive reweighted sampling to select informative wavelengths, and then applied direct orthogonal projection (DOP) for model transfer. The study employed a dataset of 150 honey samples to evaluate the impact of different data fusion strategies on model performance. To effectively evaluate model performance, we utilized the coefficient of R2 and RMSEP as evaluation metrics. By comparing the results of data-level fusion, feature-level fusion and single-spectrum model transfer, the results showed that spectral data fusion improved the model transfer performance compared to the single-spectrum approach, with feature-level fusion exhibiting the most significant advantages. The effective variable selection techniques in feature-level fusion successfully removed a substantial amount of interfering data and significantly reduced noise influence, thereby improving the model accuracy. Specifically, the use of feature-level fusion improved the predictive model’s R2 from 0.319 to 0.878 and reduced the RMSEP from 1.974 to 0.613°Brix, demonstrating the significant advantages of this approach in enhancing model transfer performance. The research findings provide important reference and theoretical support for future studies in the field of food quality assessment and other near-infrared spectroscopic data applications. This not only validates the effectiveness of the feature-level fusion approach, but also lays the foundation for establishing efficient and reliable predictive models.

Synchronization processes in fNIRS visibility networks

We employ Kuramoto model to assess the presence of synchronization in individuals who fulfill a cooperation task. Our input data is a couple of signals obtained from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. We consider 1 min signal for individuals in three distinct states: (i) rest; (ii) before a disturb happens; (iii) after the disturbance. We estimate global and local order parameters synchronization with the purpose to compare the conditions of reaching a synchronous state in the networks corresponding to different states for distinct individuals and hemispheres of the prefrontal cortices of same individual. Experimental results confirmed once more that coherent state is reached not for same conditions in both individuals and hemispheres of the prefrontal cortices. Furthermore, condition changes even for different events. The computation of the effective frequencies for each degree class indicates clearly the network difference in rest, before and after disturb. Finally, we investigate the dynamic connectivity matrix and consider the similarity between distinct prefrontal cortices over time.

WD-1D-VGG19-FEA: An Efficient Wood Defect Elastic Modulus Predictive Model.

As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood with defects was established by using the inverse image, and the finite element analysis was carried out to predict the elastic modulus of wood. In the experiment, 270 points were selected from each of the four regions of the wood, totaling 1080 sets of near-infrared data, and the 1D-VGG19 model was used for classification. The results showed that the identification accuracy of the knot area was 95.1%, the fiber deviation area was 92.7%, the transition area was 90.2%, the net wood area was 100%, and the average accuracy was 94.5%. The error range of the elastic modulus prediction of the three-dimensional model established by the VGG19 classification model in the finite element analysis is between 2% and 10%, the root mean square error (RMSE) is about 598. 2, and the coefficient of determination (R2) is 0. 91. This study shows that the combination of the VGG19 algorithm and finite element analysis can accurately describe the nonlinear defect morphology of wood, thus establishing a more accurate prediction model of wood mechanical properties to maximize the use of wood mechanical properties.

Combination of Near-Infrared Spectroscopy and Multisource Data Fusion for the Geographical Origin Authentication of Panax notoginseng

Panax notoginseng (Burk.) F.H. Chen (P. notoginseng), a precious medicinal herb, provides both considerable values. There is confusion in the market regarding the geographical origins of P. notoginseng. It is of paramount importance to authenticate geographical origins of P. notoginseng, which will facilitate the rational utilization of resources and sustainable development. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the influence of the tissue and data fusion strategy for the authentication of P. notoginseng origins. The PLS-DA model employing the main root spectra demonstrated great potential for authenticating P. notoginseng origin. The PLS-DA training and test sets based on feature-level fusion (VIP) spectral data were 100% accurate, indicating excellent potential in authenticating P. notoginseng. The data fusion strategy improved the authentication. The article presents a reliable and efficacious novel methodology for authenticating P. notoginseng origins and a scientific foundation for its quality control.

Authentication of the shelf-life of NPH injectable insulins samples using NIR and MS spectral fingerprints

Insulin is essential for regulating glucose levels, and its hormonal deficiency leads to diabetes. Treatment involves the use of different types of insulin tailored to individual needs. Properly stored and transported Neutral Protamine Hagedorn (NPH) insulin remains effective and stable, highlighting the importance of quality control to ensure its efficacy and patient safety. This study aims to develop an analytical method to evaluate the shelf-life of NPH injectable insulin samples while preserving packaging integrity. The approach consisted of analyzing intact insulin samples, with and without expired shelf-life dates, using near-infrared spectroscopy (NIR) obtained from two instruments: a benchtop and a portable device. These measurements were coupled with chemometric methods, specifically single-class classification algorithms. The classifiers considered were Data Driven-Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS). For benchtop NIR, the DD-SIMCA method, with Savitzky-Golay smoothing preprocessing (41-point window) and baseline offset (BO), achieved excellent results, reaching 100% sensitivity, specificity, and efficiency in both training and test sets. As for portable NIR, the most effective methods were DD-SIMCA and OC-PLS, using datasets preprocessed with Savitzky-Golay smoothing with first derivative, second-order polynomial (SGD-1st-2nd), and 9-point windows combined with Standard Normal Variate (SNV), and Savitzky-Golay smoothing filters (SGS) using second-order polynomial and 7-point windows combined with baseline offset (BO) and Multiplicative Scattering Correction (MSC). The results were corroborated by mass spectrometry, correlating the structural changes of degraded insulin with NIR data. The proposed methodology is a promising tool for non-destructive, rapid, and cost-effective authentication of the preservation state of NPH insulin, aligning with green chemistry principles and allowing for in-situ evaluation, thereby preserving medication integrity.

Optimizing protein content prediction in rice bean (Vigna umbellata L.) by integrating near-infrared reflectance spectroscopy, MPLS, deep learning, and key wavelengths selection algorithms

Rice bean, a potential protein-rich underutilized crop, can serve as a valuable source for food and feed, contributing to sustainable food and nutrition security. However, traditional methods for measuring protein content are laborious, tedious, and expensive. This study evaluates the performance of Modified Partial Least Squares (MPLS) and 1D Convolutional Neural Network (1D CNN) models in predicting protein content in rice bean using Near-Infrared Reflectance Spectroscopy (NIRS) data. Both models were validated with whole spectra, key wavelengths, and spectral segments derived from Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Competitive Adaptive Reweighted Sampling (CARS). The 1D CNN model demonstrated superior predictive performance, with an 11 % increase in R² and an 18 % increase in RPD using the whole spectra compared to the MPLS model. Specifically, the 1D CNN model with CARS-selected wavelengths achieved an R² of 0.82 and an RPD of 2.15, while the MPLS model achieved an R² of 0.84 and an RPD of 2.25. The segmented spectra approach further enhanced the 1D CNN model's performance, achieving the highest R² of 0.84 and an RPD of 2.28 with CARS-selected segments. These results highlight that the 1D CNN model with CARS-selected wavelengths provides the best predictive performance for protein content in rice bean. The optimized method can lead to rapid early-phase breeding practices and improved quality assessments in leguminous crops, emphasizing the importance of integrating advanced modeling techniques with NIRS for agricultural applications.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

VR rehabilitation system evaluator: A fNIRS-based and LLM-enabled evaluation paradigm for Mild Cognitive Impairment

Virtual Reality (VR) has significant potential for Mild Cognitive Impairment (MCI) rehabilitation by offering dynamic and engaging tasks designed to stimulate various cognitive domains. However, how efficiently evaluating the VR tasks is always challenging. The current effectiveness evaluation methods for task evaluation and optimisation typically rely on post-training metrics like game performance or subjective questionnaires, which cannot directly reflect the comprehensiveness and intensity of cognitive stimulation. To bridge these gaps, we propose a novel approach integrating functional near-infrared spectroscopy (fNIRS) data and a Large Language Model (LLM) for VR rehabilitation task evaluation and optimisation. Firstly, we leverage fundamental cognitive theories to propose a systematic evaluation paradigm for assessing the scope of the cognitive domains stimulated by VR tasks. Secondly, we propose fNIRS-derived graph parameters to objectively evaluate the cognitive stimulation with high-time resolution. Finally, we develop a prompt-generating strategy to facilitate Large Language Model (LLM)-based analysis, which transforms fNIRS-derived metrics combined with the scope of stimulated cognitive domains into easy-to-understand evaluation reports and actionable recommendations for task optimisation. We demonstrate this approach with two VR rehabilitation tasks. Several experts evaluated the LLM-based task evaluation results and task optimisation recommendation and agreed that the LLM-generated results are meaningful and applicable. This approach offers a rapid, objective method to evaluate VR task effectiveness and improves their design, marking a significant step in developing more effective MCI interventions.

Investigating cortical hypoxia in multiple sclerosis via time-domain near-infrared spectroscopy.

Hypoperfusion and tissue hypoxia have been implicated as contributory mechanisms in the neuropathology of multiple sclerosis (MS). Our objective has been to study cortical oxygenation in vivo in patients with MS and age-matched controls. A custom, multiwavelength time-domain near-infrared spectroscopy system was developed for assessing tissue hypoxia from the prefrontal cortex. A cross-sectional case-control study was undertaken assessing patients with secondary progressive MS (SPMS) and age-matched controls. Co-registered magnetic resonance imaging was used to verify the location from which near-infrared spectroscopy data were obtained through Monte Carlo simulations of photon propagation. Additional clinical assessments of MS disease severity were carried out by trained neurologists. Linear mixed effect models were used to compare cortical oxygenation between cases and controls, and against measures of MS severity. Thirty-three patients with secondary progressive MS (median expanded disability status scale 6 [IQR: 5-6.5]; median age 53.0 [IQR: 49-58]) and 20 age-matched controls were recruited. Modeling of photon propagation confirmed spectroscopy data were obtained from the prefrontal cortex. Patients with SPMS had significantly lower cortical hemoglobin oxygenation compared with controls (-6.0% [95% CI: -10.0 to -1.9], P = 0.004). There were no significant associations between cortical oxygenation and MS severity. Using an advanced, multiwavelength time-domain near-infrared spectroscopy system, we demonstrate that patients with SPMS have lower cortical oxygenation compared with controls.

Multi-Scale Spatial Attention-Based Multi-Channel 2D Convolutional Network for Soil Property Prediction.

Visible near-infrared spectroscopy (VNIR) is extensively researched for obtaining soil property information due to its rapid, cost-effective, and environmentally friendly advantages. Despite its widespread application and significant achievements in soil property analysis, current soil prediction models continue to suffer from low accuracy. To address this issue, we propose a convolutional neural network model that can achieve high-precision soil property prediction by creating 2D multi-channel inputs and applying a multi-scale spatial attention mechanism. Initially, we explored two-dimensional multi-channel inputs for seven soil properties in the public LUCAS spectral dataset using the Gramian Angular Field (GAF) method and various preprocessing techniques. Subsequently, we developed a convolutional neural network model with a multi-scale spatial attention mechanism to improve the network's extraction of relevant spatial contextual information. Our proposed model showed superior performance in a statistical comparison with current state-of-the-art techniques. The RMSE (R²) values for various soil properties were as follows: organic carbon content (OC) of 19.083 (0.955), calcium carbonate content (CaCO3) of 24.901 (0.961), nitrogen content (N) of 0.969 (0.933), cation exchange capacity (CEC) of 6.52 (0.803), pH in H2O of 0.366 (0.927), clay content of 4.845 (0.86), and sand content of 12.069 (0.789). Our proposed model can effectively extract features from visible near-infrared spectroscopy data, contributing to the precise detection of soil properties.

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes.

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Deciphering the imprint of active galactic nucleus feedback in Seyfert galaxies. Nuclear-scale molecular gas deficits

We study the distribution of cold molecular gas in the circumunuclear disks (CND; $r pc) of a sample of 64 nearby L $=7-45 Mpc) disk galaxies ---including 45 active galactic nuclei (AGN) and 19 nonAGN--- for which high-spatial-resolution (median value $ pc) multiline CO interferometer observations have been obtained at millimeter wavelengths with the Atacama Large Millimeter Array (ALMA) and/or Plateau de Bure Interferometer (PdBI). We decipher whether or not the concentration and normalized radial distribution of cold molecular gas change as a function of X-ray luminosity in the 2--10 keV range X $) in order to analyze the imprint left by AGN feedback. We also look for similar trends in the concentration and normalized radial distribution of the hot molecular gas and in the hot-to-cold-molecular gas mass ratio in a subset of 35 galaxies using near-infrared (NIR) integral field spectroscopy data obtained for the H$_2$ 1-0 S(1) line. We find a significant turnover in the distribution of the cold molecular gas concentration as a function of X-ray luminosity with a breakpoint that divides the sample into two branches: (1) the `AGN build-up branch'' ($L_ X pm0.3 $erg s$^ $) and (2) the ``AGN feedback branch'' ($L_ X pm0.3 $erg s$^ $) . Lower-luminosity AGN and nonAGN of the AGN build-up branch show high cold molecular gas concentrations and centrally peaked radial profiles on nuclear ($r pc) scales. Higher-luminosity AGN of the AGN feedback branch show a sharp decrease in the concentration of molecular gas and flat or inverted radial profiles. The cold molecular gas concentration index ($CCI$) --- defined as the ratio of surface densities at $r pc ($ gas $) and $r pc $( gas $), namely $CCI gas gas $)--- spans a 0.63 dex range, equivalent to a factor simeq 4-5, between the galaxies lying at the high end of the AGN build-up branch and the galaxies showing the most extreme nuclear-scale molecular gas deficits in the AGN feedback branch. The concentration and radial distributions of the hot molecular gas in our sample follow qualitatively similar but less extreme trends as a function of X-ray luminosity. As a result, we find higher values of hot-to-cold molecular gas mass ratios on nuclear scales in the highest luminosity AGN sources of the AGN feedback branch. These observations confirm ---with a three times larger sample--- previous evidence found in the context of the Galaxy Activity Torus and Outflow Survey (GATOS) that the imprint of AGN feedback on the CND-scale distribution of molecular gas is more extreme in higher luminosity Seyfert galaxies of the local Universe.

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Adjusting effective multiplicity ( ) for family-wise error rate in functional near-infrared spectroscopy data with a small sample size.

The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity ( ) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of calculations. We aimed to reevaluate the effectiveness of the method for fNIRS data with a small sample size. In experiment 1, we used resampling simulations to explore the relationship between sample size and values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid could be predicted from a small sample size. Experiment 1 revealed that the values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. The findings from these two experiments indicate the potential for the effective application of correction in fNIRS studies with sample sizes smaller than the number of channels.