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Smilax glabra Roxb. (SGR) is known for its high nutritional and therapeutic value. However, the frequent appearance of counterfeit products causes confusion and inconsistent quality among SGR varieties. Herein, this study collected the proportion of SGR adulteration and used high-performance liquid chromatography (HPLC) to measure the astilbin content of SGR. Then Fourier-transform near-infrared (FT-NIR) technology, combined with multivariate intelligent algorithms, was used to establish partial least squares regression quantitative models for detecting SGR adulteration and measuring astilbin content, respectively. The method conducted a quantitative analysis of dual indicators through single-spectrum data acquisition (QADS) to comprehensively evaluate the authenticity and superiority of SGR. The coefficients of determination (R 2 ) for both the calibration and prediction sets exceeded 0.96, which successfully leverages FT-NIR combined with multivariate intelligent algorithms to considerably enhance the accuracy and reliability of quantitative models. Overall, this research holds substantial value in the comprehensive quality evaluation in functional health foods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study aimed to investigate integration of alternating speech, a stimulus which classically produces a V-shaped speech intelligibility function with minimum at 2-6 Hz in typical-hearing (TH) listeners. We further studied how degraded speech impacts intelligibility across alternating rates (2, 4, 8, and 32 Hz) using vocoded speech, either in the right ear or bilaterally, to simulate single-sided deafness with a cochlear implant (SSD-CI) and bilateral CIs (BiCI), respectively. To assess potential cortical signatures of across-ear integration, we recorded activity in the bilateral auditory cortices (AC) and dorsolateral prefrontal cortices (DLPFC) during the task using functional near-infrared spectroscopy (fNIRS). For speech intelligibility, the V-shaped function was reproduced only in the BiCI condition; TH (with ceiling scores) and SSD-CI conditions had significantly higher scores across all alternating rates compared to the BiCI condition. For fNIRS, the AC and DLPFC exhibited significantly different activity across alternating rates in the TH condition, with altered activity patterns in both regions in the SSD-CI and BiCI conditions. Our results suggest that degraded speech inputs in one or both ears impact across-ear integration and that different listening strategies were employed for speech integration manifested as differences in cortical activity across conditions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sobczak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

SO 2 derivatives, sulfite/bisulfite, are widely employed in both the food processing and drug synthesis industries. Despite their widespread application, excessive levels of sulfite/bisulfite can negatively impact human health. Most probes for detecting sulfite/bisulfite are restricted by their fluorescence within the visible spectrum range and poor solubility in aqueous solution, which limit their use in food testing and biological imaging. Herein, a near-infrared probe comprising of the cyanopyridine cyanine skeleton, 4-((Z)-2-((E)-2-chloro-3-(2-cyano-2-(1-methylpyridine-4(1H)-ylidene)ethylidene)cyclohex-1-en-1-yl)-1-cyanovinyl)-1-methylpyridin-1-ium (abbreviated as CCP), was developed. This probe enables precise quantification of bisulfite (HSO 3 - ) in almost pure buffered solutions, showing a near-infrared fluorescence emission at 784 nm with an impressively low detection limit of 0.32 μM. The probe stands out for its exceptional selectivity, minimal susceptibility to interference, and strong adaptability. The probe CCP utilizes the CC bond to trigger a near-infrared fluorescence quenching reaction with HSO 3 - via nucleophilic addition, which effectively disrupts the large delocalization within the molecule for accurate HSO 3 - identification. Moreover, the probe has been successfully applied in detecting HSO 3 - in various food products and living cells, simplifying the measurement of HSO 3 - content in water samples. This advancement not only enhances the analytical capabilities but also contributes to ensuring food safety and environmental protection. ENVIRONMENTAL IMPLICATION: SO 2 derivatives including sulfite/bisulfite, serving dual roles as preservatives and antioxidants, have widespread application across various sectors including food preservation, water sanitation, and the pharmaceutical industry. Despite their widespread application, excessive levels of sulfite/bisulfite can affect human health. Developing methods for precisely and sensitively detecting sulfite/bisulfite in food products and biological samples is important for ensuring food safety and environmental protection. Here, a sensitive near-infrared and multifunctional fluorescent probe in a 99.9 % buffered solution, along with water gel encapsulation, has been successfully applied for the detection of bisulfite in food, authentic water samples, and biological cells., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions., Methods: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists., Results: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively., Conclusion: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems., Competing Interests: Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Children with developmental coordination disorder (DCD) have impaired online motor control. Researchers posit that this impairment could be due to a deficit in utilizing the internal model control process. However, there is little neurological evidence to support this view because few neuroimaging studies have focused specifically on tasks involving online motor control. Therefore, the aim of this study was to investigate the differences in cortical hemodynamic activity during an online movement adjustment task between children with and without DCD., Methods: Twenty children with DCD (mean age: 9.88 ± 1.67 years; gender: 14M/6F) and twenty age-and-gender matched children with typical development (TD) (mean age: 9.87 ± 1.59 years; gender: 14M/6F) were recruited via convenience sampling. Participants performed a double-step reaching task under two conditions (with and without online adjustment of reaching). Cortical hemodynamic activity during task in ten regions of interest, including bilateral primary somatosensory cortex, primary motor cortex, premotor cortex, superior parietal cortex, and inferior parietal cortex was recorded using functional near-infrared spectroscopy. In the analyses, change in oxyhemoglobin (ΔHbO) concentration was used to characterize hemodynamic response. Two-way analyses of variance were conducted for each region of interest to compare hemodynamic responses between groups and conditions. Additionally, Pearson's r correlations between hemodynamic response and task performance were performed., Results: Outcome showed that children with DCD required significantly more time to correct their reaching movements compared to the control group (t = 3.948, P < 0.001). Furthermore, children with DCD have a significantly lower ΔHbO change in the left superior parietal cortex during movement correction, compared to children with TD (F = 4.482, P = 0.041). Additionally, a significant negative correlation (r = - 0.598, P < 0.001) was observed between the difference in movement time of reaching and the difference in ΔHbO between conditions in the left superior parietal cortex., Conclusions: The findings of this study suggest that deficiencies in processing real-time sensory feedback, considering the function of the superior parietal cortex, might be related to the impaired online motor control observed in children with DCD. Interventions could target this issue to enhance their performance in online motor control., (© 2024. The Author(s).)

Objective. We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively. Approach. Using functional near-infrared spectroscopy, we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient. Main results. Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65, p=0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r=-0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance. Significance. A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive. Near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV-Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration. Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV-Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v. With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R 2 CV's up to 0.8799 and RMSECV's lower than 3 ml/100 ml for NIR spectra and R 2 CV's up to 0.81 and RMSECV's lower than 4 ml/100 ml for UV-Vis spectra. NIR spectra produced better models as compared to UV-Vis spectra., (© 2024. The Author(s).)

While it is widely acknowledged that exercise has positive effects on cognitive function, the specific impacts of different types of exercises, particularly open and closed skill exercises, on cognitive impairment continue to be a debated topic. In this study, we used fNIRS and cognitive psychology tasks to investigate the effects of different types of exercises on cognitive function and brain activity in young adults. We conducted an observational study to assess the cognitive function of participants who had engaged in these exercises for a long period. Additionally, we examined the effects of open skill exercise (badminton) and closed skill exercise (calisthenics) on localized blood flow in the prefrontal lobe of the brain using an experimental research method. Specifically, during the Stroop task, the badminton group exhibited significantly higher △HbO 2 in channel 18, corresponding to the dorsolateral prefrontal cortex, compared to the calisthenics group (F = 4.485, P < 0.05, η 2  = 0.074). In the 2-back task, the calisthenics group showed significantly higher △HbO 2 in channel 17, corresponding to the frontopolar area, dorsolateral prefrontal cortex and inferior prefrontal gyrus, than the badminton group (F = 8.842, P < 0.01, η 2  = 0.136). Our findings reveal that open skill exercises are more effective in enhancing cognitive inhibition, thereby increasing attention capacity, self-regulation, and flexibility in response to environmental changes. Conversely, closed skill exercises demonstrate greater efficacy in improving working memory within cognitive functions, showcasing an enhanced capacity for information processing and storage. These data indicate that while both open and closed skill exercises are beneficial for cognitive function, they exhibit significant distinctions in some aspects., (© 2024. The Author(s).)

Near-infrared (NIR) probes are highly sought after as fluorescent tags for multicolor cellular and in vivo imaging. Here we develop small NIR fluorescent nanobodies, termed NIR-Fb LAG16 and NIR-Fb LAG30 , enabling background-free visualization of various GFP-derived probes and biosensors. We also design a red-shifted variant, NIR-Fb (718) , to simultaneously target several antigens within the NIR spectral range. Leveraging the antigen-stabilizing property of the developed NIR-Fbs, we then create two modular systems for precise control of gene expression in GFP-labeled cells. Applying the NIR-Fbs in vivo, we target cells expressing GFP and the calcium biosensor GCaMP6 in the somatosensory cortex of transgenic mice. Simultaneously tracking calcium activity and the reference signal from NIR-Fb LAG s bound to GCaMP6 enables ratiometric deep-brain in vivo imaging. Altogether, NIR-Fb LAG s present a promising approach for imaging and manipulating various processes in live cells and behaving animals expressing GFP-based probes., (© 2024. The Author(s).)

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R 2 ), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R 2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R 2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R 2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R 2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients., (© 2024. The Author(s).)

Objective. Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved. Approach. To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches. Main results. We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing the n -Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline. Significance. Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

A fast, simple and reagent-free detection method for aflatoxin B 1 (AFB 1 ) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB 1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RAR V + , RAR V - , and RAR V ) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RAR V + , RAR V - , and RAR V were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B 1 in feedstuff materials., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Sport nutrition supplements (SNS) are vulnerable to adulteration with melamine, artificially augmenting their protein content as determined by conventional assay methodologies. Vibrational spectroscopy techniques are suitable for the detection of adulteration because they allow rapid analysis, require minimal sample preparation, and can perform numerous analyses in a short time. The aim of this study was to develop rapid quantification models for the determination of melamine adulteration in a variety of SNS matrices using NIRS (near-infrared spectroscopy) in combination with multivariate data processing. Moreover, a comparison of benchtop and portable NIR instruments was carried out. Employing a stepwise approach involving OPLS-DA and PLS analysis, matrix discrimination and prediction ability were investigated. The benchtop instrument effectively discriminated among matrices (R2Y = 0.964, Q2 = 0.933), while the portable device, although showing a slightly altered pattern, maintained favorable discrimination capability (R2Y = 0.966, Q2 = 0.931). The quantitative PLS models for each SNS matrix exhibited comparable statistical indicators for both instruments with reasonable errors for melamine content estimation and prediction (RMSEE: 0.3-2.4 %, RMSEP: 0.98-2.99 %). Higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, probably due to the tendency of protein agglomeration and adhesion to different surfaces, which affects the homogeneity of the powder. Despite data loss due to the narrower spectral range and lower resolution of the portable instrument, all models were found to be suitable for predicting melamine content in sport nutrition supplements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The advancement of biological imaging techniques critically depends on the development of novel near-infrared (NIR) fluorescent probes. In this study, we introduce a designed NIR fluorescent probe, NRO-βgal, which exhibits a unique off-on response mechanism to β-galactosidase (β-gal). Emitting a fluorescence peak at a wavelength of 670 nm, NRO-βgal showcases a significant Stokes shift of 85 nm, which is indicative of its efficient energy transfer and minimized background interference. The probe achieves a remarkably low in vitro detection limit of 0.2 U/L and demonstrates a rapid response within 10 min, thereby underscoring its exceptional sensitivity, selectivity, and operational swiftness. Such superior analytical performance broadens the horizon for its application in intricate biological imaging studies. To validate the practical utility of NRO-βgal in bio-imaging, we employed ovarian cancer cell and mouse models, where the probe's efficacy in accurately delineating tumor cells was examined. The results affirm NRO-βgal's capability to provide sharp, high-contrast images of tumor regions, thereby significantly enhancing the precision of surgical tumor resection. Furthermore, the probe's potential for real-time monitoring of enzymatic activity in living tissues underscores its utility as a powerful tool for diagnostics in oncology and beyond., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

SO 2 derivatives play an important role in many metabolic processes, excessive ingestion of them can lead to serious complications of various diseases. In this work, a novel dual ratiometric NIR fluorescent probe XT-CHO based on ICT effect was synthesized for detecting SO 2 derivative. In the design of the probe, the α, β-unsaturated bond formed between benzopyran and coumarin was used as the reaction site for SO 2 , meanwhile, the extended π-conjugate system promoted maximum emission wavelength of the probe up to 708 nm. Notably, the probe exhibited high selectivity and sensitivity for detecting SO 2 , the limit of detection reached 2.13 nM and 58.5 nM in fluorescence spectra and UV-Vis absorption spectra, respectively. The reaction mechanism of SO 2 and XT-CHO had been verified by 1 H NMR, ESI-MS spectra and DFT calculation. Moreover, the probe was successfully applied in detecting endogenous and exogenous SO 2 in living cells and proved possessed the mitochondrial targeted ability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 - 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 - 7 %) whereas at low contents (5 %) significantly higher values (9 - 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings., Competing Interests: Competing interests statement:Dr. Androu Abdalmalak is a Scientific Consultant for NIRx Medical Technologies, LLC. Dr. Abdalmalak completed the work on this manuscript prior to employment at NIRx. NIRx did not review the results nor financially supported this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships.

Introduction: To date, radical surgery remains the best curative option in patients with early-stage lung cancer. In patients with small lung lesions, video-assisted thoracic surgery (VATS) should be increasingly chosen as a fundamental alternative to thoracotomy as it is associated with less postoperative pain and better quality of life. This scenario necessarily increases the need for thoracic surgeons to implement new localization techniques. The conventional near-infrared (NIR) indocyanine green (ICG) method demonstrated a significant limitation in deep cancer recognition, principally due to its intrinsic low-depth tissue penetration. Similarly, the lymph-node sentinel approach conducted by the ICG method was demonstrated to be inefficient, mainly due to the non-specificity of the tracker and the irregular pathway of pulmonary lymph node drainage. Our study aims to evaluate the effectiveness of Cetuximab- IRDye800CW in marking lung nodules and mediastinal lymph nodes., Methods and Analysis: This study is defined as an open-label, single-arm, single-stage phase II trial evaluating the effectiveness of Cetuximab-IRDye800CW in detecting tumors and lymph-node metastases in patients with lung cancer who are undergoing video-assisted thoracic surgery (VATS). Cetuximab is a monoclonal antibody that binds, inhibits, and degrade the EGFR. The IRDye ® 800CW, an indocyanine-type NIR fluorophore, demonstrated enhanced tissue penetration compared to other NIR dyes. The combination with the clinical approved monoclonal antibody anti-epidermal growth factor EGFR Cetuximab (Cetuximab-IRDye800) has shown promising results as a specific tracker in different cancer types (i.e., brain, pancreas, head, and neck). The study's primary outcome is focused on the proportion of patients with lung nodules detected during surgery using an NIR camera. The secondary outcomes include a broad spectrum of items, including the proportion of patients with detection of unexpected cancer localization during surgery by NIR camera and the proportion of patients with negative surgical margins, the evaluation of the time spawns between the insertion of the NIR camera and the visualization of the nodule and the possible morbidity of the drug assessed during and after the drug infusion., Ethics and Dissemination: This trial has been approved by the Ethical Committee of Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino (Torino, Italy) and by the Italian Medicines Agency (AIFA). Findings will be written as methodology papers for conference presentations and published in peer-reviewed journals. The Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, the University of Torino, and the AIRC Public Engagement Divisions will help identify how best to publicize the findings.Trial registration EudraCT 202,100,645,430., Clinicaltrials: gov NCT06101394 (October 23, 2023)., (© 2024. The Author(s).)

Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth., Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression., Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast., Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths., Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression., (© 2024 The Authors.)

In technology education, there has been a paradigmatic shift towards student-centered approaches such as learning by doing, constructionism, and experiential learning. Educational robotics allows students to experiment with building and interacting with their creations while also fostering collaborative work. However, understanding the student's response to these approaches is crucial to adapting them during the teaching-learning process. In this sense, neuroscientific tools such as Functional Near-Infrared Spectroscopy and Eye-tracker could be useful, allowing the investigation of relevant states experienced by students. Although they have already been used in educational research, their practical relevance in the teaching-learning process has not been extensively investigated. In this perspective article expressing our position, we bring four examples of learning experiences in a robotics class with children, in which we illustrate the usefulness of these tools., Competing Interests: Declaration of competing interest We declare no conflict of interest in this publication., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

The efficacy of fluorescence-guided surgery in facilitating the real-time delineation of tumours depends on the optical contrast of tumour tissue over healthy tissue. Here we show that CJ215-a commercially available, renally cleared carbocyanine dye sensitive to apoptosis, and with an absorption and emission spectra suitable for near-infrared fluorescence imaging (wavelengths of 650-900 nm) and shortwave infrared (SWIR) fluorescence imaging (900-1,700 nm)-can facilitate fluorescence-guided tumour screening, tumour resection and the assessment of wound healing. In tumour models of either murine or human-derived breast, prostate and colon cancers and of fibrosarcoma, and in a model of intraperitoneal carcinomatosis, imaging of CJ215 with ambient light allowed for the delineation of nearly all tumours within 24 h after intravenous injection of the dye, which was minimally taken up by healthy organs. At later timepoints, CJ215 provided tumour-to-muscle contrast ratios up to 100 and tumour-to-liver contrast ratios up to 18. SWIR fluorescence imaging with the dye also allowed for quantifiable non-contact wound monitoring through commercial bandages. CJ215 may be compatible with existing and emerging clinical solutions., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

The control of posture is guided by the integration of sensory information. Because blind individuals cannot apply visual information to control posture as sighted individuals do they must compensate by the remaining senses. We therefore hypothesize that blind individuals alter their brain activation in the sensorimotor cortex during postural control to compensate for balance control without vision by the increased integration of somatosensory information. Ten blind and ten sighted (matched) individuals controlled posture during conditions with (I) eyes closed / open, and (II) stable / unstable surface conditions. Postural sway was recorded by applying a pressure distribution measuring plate. Brain activation was collected by functional Near InfraRed Spectroscopy (fNIRS) above motor-sensory cortices of the right and left hemispheres. Blind individuals showed significantly increased postural sway when balancing with open eyes on an unstable surface and when compared to sighted individuals. Whereas blind individuals showed significantly increased brain activation when balancing with open eyes on stable and unstable surface conditions, sighted individuals increased their brain oxygenation only during closed eyes and unstable surface conditions. Overall conditions, blind individuals presented significantly increased brain activation in two channels of the left and right hemispheric motor-sensory cortex when compared to sighted individuals. We therefore conclude that sighted individuals increase their brain oxygenation in the sensorimotor cortex during postural control tasks that demand sensory integration processes. Blind individuals are characterized by increased brain activation overall conditions indicating additional sensory integration during postural control. Thus, the sensorimotor cortex of blind individuals adapts to control posture without vision., (© 2024. The Author(s).)

Human beings represent spatial information according to egocentric (body-to-object) and allocentric (object-to-object) frames of reference. In everyday life, we constantly switch from one frame of reference to another in order to react effectively to the specific needs of the environment and task demands. However, to the best of our knowledge, no study to date has investigated the cortical activity of switching and non-switching processes between egocentric and allocentric spatial encodings. To this aim, a custom-designed visuo-spatial memory task was administered and the cortical activities underlying switching vs non-switching spatial processes were investigated. Changes in concentrations of oxygenated and deoxygenated haemoglobin were measured using functional near-infrared spectroscopy (fNIRS). Participants were asked to memorize triads of geometric objects and then make two consecutive judgments about the same triad. In the non-switching condition, both spatial judgments considered the same frame of reference: only egocentric or only allocentric. In the switching condition, if the first judgment was egocentric, the second one was allocentric (or vice versa). The results showed a generalized activation of the frontal regions during the switching compared to the non-switching condition. Additionally, increased cortical activity was found in the temporo-parietal junction during the switching condition compared to the non-switching condition. Overall, these results illustrate the cortical activity underlying the processing of switching between body position and environmental stimuli, showing an important role of the temporo-parietal junction and frontal regions in the preparation and switching between egocentric and allocentric reference frames., (© 2024. The Author(s).)

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The identification of the intersegmental plane (ISP) is a crucial step in segmentectomy for children with congenital pulmonary airway malformation (CPAM) due to complex anatomical variations. However, there is very limited literature available on this aspect specifically for infant. In this study, we compared the intravenous indocyanine green (ICG)-guided near-infrared fluorescence (NIRF) imaging method with the modified inflation-deflation method in terms of their perioperative characteristics and summarized our experience., Methods: From June 2021 to November 2022, the data of 83 patients with CPAM who underwent segmentectomy by video-assisted thoracoscopic surgery were retrospectively reviewed. Twenty-eight patients underwent ICG-guided NIRF method, and 56 patients underwent the modified inflation-deflation method, characteristics and clinical outcomes were compared., Results: The median age of the patients was 4.99 months (4.99 ± 1.51) with a mean body weight of 7.54 kg (7.54 ± 1.99). Both methods could accurately identify the ISP. The time taken to clearly display the ISP was shorter in ICG group than in the modified inflation-deflation group (0.18 ± 0.08 vs. 6.49 ± 1.67 min; P < 0.001), and the surgical duration (61.32 ± 14.28 vs. 88.18 ± 8.03 min; P < 0.001) were significantly shorter in the ICG group too. The two groups exhibited differences in the length of chest tube drainage (1.75 ± 1.24 vs. 2.36 ± 1.54 days; P = 0.072) and the length of hospital stay (4.61 ± 1.75 vs. 5.20 ± 3.07 days; P = 0.078), however, the differences were not statistically significant. There were no significant differences between the two groups in the blood lost and postoperative complications. At a follow-up of more than 1 year after operation, all patients had recovered well without recurrence., Conclusions: According to our experience, the ICG-guided NIRF method was safe and feasible for infants during thoracoscopic segmentectomy, it can quickly display the ISP and shorten the surgical duration compared with the modified inflation-deflation method., Competing Interests: Conflicts of interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Functional near-infrared spectroscopy (fNIRS) technology has been widely used to analyze biomechanics and diagnose brain activity. Despite being a promising tool for assessing the brain cortex status, this system is susceptible to disturbances and noise from electrical instrumentation and basal metabolism. In this study, an alternative filtering method, maximum likelihood generalized extended stochastic gradient (ML-GESG) estimation, is proposed to overcome the limitations of these disturbance factors. The proposed algorithm was designed to reduce multiple disturbances originating from heartbeats, breathing, shivering, and instrumental noises as multivariate parameters. To evaluate the effectiveness of the algorithm in filtering involuntary signals, a comparative analysis was conducted with a conventional filtering method, using hemodynamic responses to auditory stimuli and psycho-acoustic factors as quality indices. Using auditory sound stimuli consisting of 12 voice sources (six males and six females), the fNIRS test was configured with 18 channels and conducted on 10 volunteers. The psycho-acoustic factors of loudness and sharpness were used to evaluate physiological responses to the stimuli. Applying the proposed filtering method, the oxygenated hemoglobin concentration correlated better with the psychoacoustic analysis of each auditory stimulus than that of the conventional filtering method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Significance: Near-infrared spectroscopy (NIRS) measurement is a widely used technique to measure muscle oxygenation. A knowledge of the reproducibility of NIRS measurements is essential for the correct interpretation of data., Aim: Our aim was to test the reproducibility and sex differences of NIRS measurements during brachial artery occlusion in healthy participants., Approach: An NIRS device was used to measure muscle oxygenation and microvascular function during a 5 min brachial occlusion. Muscle oxygen consumption (mVO 2 ) and tissue saturation index (TSI%) were used. The occlusion test was performed three times on separate days for males (n = 13, 28 ± 8 years) and females (n = 13, 29 ± 7 years)., Results: During the occlusion phase, the reproducibility of mVO 2 was excellent (intraclass correlation; ICC = 0.90). During the reperfusion phase, the maximal change in TSI% revealed the best reproducibility (ICC = 0.77). There were no sex differences in reproducibility. Male participants had higher muscle oxygenation during occlusion (mVO 2 , 0.054 ± 0.010 vs. 0.038 ± 0.012 mLO 2 /min/100 g, p = 0.001, male and female, respectively). There were no sex differences during the reperfusion phase., Conclusion: The reproducibility of NIRS to measure muscle oxygenation and microvascular function during circulation occlusion and reperfusion is good to excellent. Muscle oxygen capacity measured during occlusion is higher in males compared to females, and there are no sex differences in microvascular function during the reperfusion phase., (© 2024 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine.)

Deception is an essential part of children's moral development. Previous developmental studies have shown that children start to deceive at the age of 3 years, and as age increased to 5 years, almost all children were able to deceive for their own benefit. Although behavioral studies have indicated that the emergence and development of deception are related to cognitive abilities, their neural correlates remain poorly understood. Therefore, the present study examined the neural correlates underlying deception in preschool-aged children (N = 89, 44 % boys, age 3.13 to 5.96 years, Han Chinese) using functional near-infrared spectroscopy. A modified hide-and-seek paradigm was applied to elicit deceptive and truth-telling behaviors. The results showed that activation of bilateral dorsolateral prefrontal cortex was positively associated with the tendency to deceive an opponent in a competitive game in the 3-year-olds. In addition, 3-year-olds who showed a high tendency to deceive showed the same brain activation in the frontopolar area as 5-year-olds did when engaged in deception, whereas no such effect was found in 3-year-olds who never engaged in deception. These findings underscore the link between preschoolers' deception and prefrontal cortex function., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cerebral monitoring during immediate fetal-to-neonatal transition is of increasing interest. The cerebral fractional tissue oxygen extraction (cFTOE) is a useful parameter to gain insight in the balance between tissue oxygen delivery and consumption during this complex process. The aim of this study was to review the literature on cFTOE during the first 15 min immediately after birth. A systematic qualitative literature research was last performed on 23 November 2023 of PubMed and EMBASE with the following search terms: neonate, infant, newborn, transition, after birth, delivery room, NIRS, near-infrared spectroscopy, spectroscopy, cFTOE, cerebral fractional tissue oxygenation extraction, cerebral oxygenation, and fractional oxygen extraction. Additional published reports were identified through a manual search of references in retrieved articles and in review articles. The methodological quality of the included studies was assessed by predefined quality criteria. Only human studies with data of cFTOE in the first 15 min after birth were included. Accordingly, exclusion criteria were defined as no measurement of cFTOE or no measurement within the first 15 min after birth. Across all studies, a total of 3566 infants (2423 term, 1143 preterm infants) were analysed. Twenty-five studies were identified describing cFTOE within the first 15 min after birth. Four studies established reference ranges for cFTOE and another four studies focused on the effect of pre-/perinatal circumstances on cFTOE in the first 15 min after birth. Six studies investigated the course of cFTOE after transition in infants without complications. Eleven studies analysed different potentially influencing parameters on cFTOE during transition., Conclusion: This systematic review provides a comprehensive insight on cFTOE during uncomplicated transition as well as the influence of perinatal circumstances, respiratory, haemodynamic, neurological, and laboratory parameters in preterm and term infants., What Is Known: • The NIRS-measured cerebral fractional tissue oxygen extraction (cFTOE) is a useful parameter to estimate the balance between oxygen delivery and consumption. • During normal transition, the cFTOE decreases in the first minutes after birth and then remains at a stable plateau., What Is New: • The cFTOE is a promising parameter that gives additional information on cerebral oxygenation and perfusion in preterm and term infants. • Several hemodynamic, metabolic, respiratory, and perinatal factors are identified, influencing the oxygen extraction of the newborn's brain after birth., (© 2024. The Author(s).)

Monitoring the metabolic cost or oxygen consumption associated with rest and exercise is crucial to understanding the impact of disease or physical training on the health of individuals. Traditionally, measuring the skeletal muscle oxygen cost associated with exercise/muscle contractions can be rather expensive or invasive (i.e., muscle biopsies). More recently, specific protocols designed around the use of near-infrared spectroscopy (NIRS) have been shown to provide a quick, noninvasive easy-to-use tool to measure skeletal muscle oxygen consumption ([Formula: see text]). However, the data and results from NIRS devices are often misunderstood. Thus the primary purpose of this sourcebook update is to provide several experimental protocols students can utilize to improve their understanding of NIRS technology, learn how to analyze results from NIRS devices, and better understand how muscle contraction intensity and type (isometric, concentric, or eccentric) influence the oxygen cost of muscle contractions. NEW & NOTEWORTHY Compared to traditional methods, near-infrared spectroscopy (NIRS) provides a relatively cheap and easy-to-use noninvasive technique to measure skeletal muscle oxygen uptake following exercise. This laboratory not only enables students to learn about the basics of NIRS and muscle energetics but also addresses more complex questions regarding skeletal muscle physiology.

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynamic variables of right proximal oxyhemoglobin (HbO 2 ) in maintenance (MNT), emergence (EM) and the consciousness (CON) stage were collected and then the differences between the three stages were compared by phase-amplitude coupling (PAC). Then combined with time-domain including linear (mean, standard deviation, max, min and range), nonlinear (sample entropy) and power in frequency-domain signal features, feature selection was performed and finally classification was performed by support vector machine (SVM) classifier. The results show that the PAC of the NIRS signal was gradually enhanced with the deepening of anesthesia level. A good three-classification accuracy of 69.27% was obtained, which exceeded the result of classification of any single category feature. These results indicate the feasibility of NIRS signals in performing three or even more anesthesia stage classifications, providing insight into the development of new anesthesia monitoring modalities.

Background: Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism., Method: Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD., Result: A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs., Conclusion: Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Neuromonitoring has been widely accepted as an important part in neonatal care. Amplitude-integrated EEG (aEEG) and near-infrared spectroscopy (NIRS) are often mentioned in this context, though being only a part of the fully array of methods and examinations that could be considered neuromonitoring. Within the broad array of medical conditions that could be encountered in a neonatal patient, it is important to be aware of the indications for neuromonitoring and especially which neuromonitoring technique to use best for the individual condition. aEEG is now a widely accepted neuromonitor in neonatology with its value in hypoxic events and seizures only rarely questioned. Other methods like NIRS still have to prove themselves in the future. The SafeBoosC-III trial showed that it still remains difficult for some of these methods to prove their value for the improvement of outcome. Bute future developments such as multimodal neuromonitoring with data integration and artificial intelligence analysis could improve the value of these methods., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Industrial starch hydrolysis allows the production of syrups with varying functionality depending on their Brix value and dextrose equivalent (DE). As the current methods for evaluating these products are labor-intensive and time-consuming, the objective of this study was to investigate the potential of near-infrared (NIR) spectroscopy for classifying the different tapioca starch hydrolysis products., Results: NIR spectra of samples of seven products (n = 410) were recorded in transflectance mode in the 12 000-4000 cm -1 range. Next, orthogonal partial least squares (OPLS) regression models were built to predict the Brix and DE values of the different samples. To classify the different starch hydrolysis products, support vector machines (SVM) were trained using either the raw spectra or latent variables (LVs) obtained from the OPLS models. The best classification accuracy was obtained by the SVM classifier based on the LVs from the OPLS model for DE prediction, resulting in 95% correct classification over all classes., Conclusion: These results show the potential of NIR spectroscopy for classifying tapioca starch hydrolysis products with respect to their functional properties related to the Brix and DE values. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes., (© 2024. The Author(s).)

Vis-NIR spectroscopy coupled with chemometric models is frequently used for pear soluble solid content (SSC) prediction. However, the model robustness is challenged by the variations in pear cultivars. This study explored the feasibility of developing universal models for predicting SSC of multiple pear varieties to improve the model's generalizability. The mature fruits of 6 pear cultivars with green skin (Pyrus pyrifolia Nakai cv. 'Cuiyu', 'Sucui No.1' and 'Cuiguan') and brown skin (Pyrus pyrifolia Nakai cv. 'Hosui','Syusui' and 'Wakahikari') were used to establish single-cultivar models and multi-cultivar universal models using convolutional neural network (CNN), partial least square (PLS), and support vector regression (SVR) approaches. Multi-cultivar universal models were built using full spectra and important variables extracted by gradient-weighted class activation mapping (Grad-CAM), respectively. The universal models based on important variables obtained satisfactory performances with RMSEPs of 0.76, 0.59, 0.80, 1.64, 0.98, and 1.03°Brix on 6 cultivars, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Due to its central role in cognitive control, the dorso-lateral prefrontal cortex (dlPFC) has been the target of multiple brain modulation studies. In the context of the present pilot study, the dlPFC was the target of eight repeated neurofeedback (NF) sessions with functional near infrared spectroscopy (fNIRS) to assess the brain responses during NF and with functional and resting state magnetic resonance imaging (task-based fMRI and rsMRI) scanning. Fifteen healthy participants were recruited. Cognitive task fMRI and rsMRI were performed during the 1st and the 8th NF sessions. During NF, our data revealed an increased activity in the dlPFC as well as in brain regions involved in cognitive control and self-regulation learning (pFWE < 0.05). Changes in functional connectivity between the 1st and the 8th session revealed increased connectivity between the posterior cingulate cortex and the dlPFC, and between the posterior cingulate cortex and the dorsal striatum (pFWE < 0.05). Decreased left dlPFC-left insula connectivity was also observed. Behavioural results revealed a significant effect of hunger and motivation on the participant control feeling and a lower control feeling when participants did not identify an effective mental strategy, providing new insights on the effects of behavioural factors that may affect the NF learning., (© 2024. The Author(s).)

Spectral fingerprinting has emerged as a powerful tool that is adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Utilizing Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intraendosomal environments of these cell types give rise to significant differences in many optical features, such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of >95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications, such as cell phenotyping or mapping intraendosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT 6 give rise to more improved model accuracy (>87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

The trail making test (TMT) is a commonly used tool for evaluating executive functions, and the activation of cerebral oxygenation in the prefrontal cortex (PFC) during the test can reflect the participation of executive function. This study aimed to compare the differences in cerebral oxygenation in the PFC between the computer- and paper-based versions of the TMT and provide a theoretical basis for the optimization and clinical application of the computer-based version. A total of 32 healthy adult participants completed the computer- and paper-based TMT Types A and B. Cerebral oxygenation changes in the PFC were monitored during the experiment using near-infrared spectroscopy. Moreover, average changes in oxyhemoglobin (Δoxy-Hb) levels at the baseline and during activation periods in different types of testing were compared and analyzed. The number of correct connections in the computer-based version Type B was less than that in the paper-based version Type B (p < .001). The task time of the computer-based version was longer than that of the paper-based version (p < .001). The B/A ratio of the number of correct connections in the computer-based version was lower than that in the paper-based version (p < .001). The Δoxy-Hb in the PFC of the paper-based version was higher than that of the computer-based version (p < .001). Significant differences in oxygenation in the PFC were observed between the paper- and computer-based versions of TMT. After further improvement and correction in the subsequent development of the computer-based TMT, and taking into account the psychological feelings and preferences of the participants when performing different versions of the TMTs, the computer-based TMT is expected to play a good auxiliary role in clinical evaluation., (© 2024. The Author(s).)

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, range, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-s) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a two-step validation process using multiple linear regression models, model fitness (adjusted R 2 ) and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability., (© 2024. The Author(s).)

In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R 2 CV and R 2 pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R 2 CV and R 2 pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky-Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R 2 values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies., (© 2024 The Authors.)

Neuroimaging studies using functional magnetic resonance imaging (fMRI) have provided unparalleled insights into the fundamental neural mechanisms underlying human cognitive processing, such as high-level linguistic processes during reading. Here, we build upon this prior work to capture sentence reading comprehension outside the MRI scanner using functional near infra-red spectroscopy (fNIRS) in a large sample of participants (n = 82). We observed increased task-related hemodynamic responses in prefrontal and temporal cortical regions during sentence-level reading relative to the control condition (a list of non-words), replicating prior fMRI work on cortical recruitment associated with high-level linguistic processing during reading comprehension. These results lay the groundwork towards developing adaptive systems to support novice readers and language learners by targeting the underlying cognitive processes. This work also contributes to bridging the gap between laboratory findings and more real-world applications in the realm of cognitive neuroscience., (© 2024. The Author(s).)

Background: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology., Methods: This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content., Results: The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh&#95;1)), with R 2 , RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh&#95;1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam., Competing Interests: Lihua Luo is an employee of the Yunnan Institute of Water and Hydropower Engineering Investigation and Design, Co., LTD., (©2024 Chen et al.)

Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

This work describes our recent PCB-based plasmonic nanostructured platform patent (US 11,828,747B2) for the detection of biomarkers in breast cancer serum (BCS). A 50 nm thin gold film (TGF) was immersion-coated on PCB (i.e., PCB-TGF) and immobilized covalently with gold nanourchin (GNU) via a 1,6-Hexanedithiol (HDT) linkage to produce a plasmonic activated nanostructured thin film (PANTF) platform. A label-free SERS immunosensor was fabricated by conjugating the platform with monoclonal HER-II antibodies (mAb) in a directional orientation via adipic acid dihydrazide (ADH) to provide higher accessibility to overexpressed HER-II biomarkers (i.e., 2+ (early), 3+ (locally advanced), and positive (meta) in BCS. An enhancement factor (EF) of 0.3 × 10 5 was achieved for PANTF using Rhodamine (R6G), and the morphology was studied by scanning electron microscopy (SEM) and atomic force microscope (AFM). UV-vis spectroscopy showed the peaks at 222, 231, and 213 nm corresponding to ADH, mAb, and HER-II biomarkers, respectively. The functionalization and conjugation were investigated by Fourier Transform Near Infrared (FT-NIR) where the most dominant overlapped spectra of 2+, 3+, and Pos correspond to OH-combination of carbohydrate, RNH 2 1st overtone, and aromatic CH 1st overtone of mAb, respectively. SERS data were filtered using the filtfilt filter from scipy.signals , baseline corrected using the Improved Asymmetric Least Squares ( isals ) function from the pybaselines.Whittaker library. The results showed the common peaks at 867, 1312, 2894, 3026, and 3258 cm -1 corresponding to glycine, alanine ν (C-N-C) assigned to the symmetric C-N-C stretch mode; tryptophan and α helix; C-H antisymmetric and symmetric stretching; NH 3 + in amino acids; and N-H stretch primary amide, respectively, with the intensity of Pos > 3 + > 2 + . This trend is justifiable considering the stage of each sample. Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA) were employed for the statistical analysis of data.