Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

Spatial sensitivity to absorption changes for various near-infrared spectroscopy methods:A compendium review

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media,particularly for measurements relevant to near-infrared spectroscopy.The three temporal domains,continuous wave,frequency domain,and time domain,each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient.Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change.Therefore,spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains.The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media.These works are experimental and theoretical,presenting one-,two-,and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods,domains,and data types.Following this history,we present a compendium of sensitivity maps organized by temporal domain and then data type.This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document.Methods for one to generate these maps are provided in Appendix A,including the code.This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize,investigate,compare,and generate sensitivity to localized absorption change maps.

Cortical activity in patients with high-functioning ischemic stroke during the Purdue Pegboard Test:insights into bimanual coordinated fine motor skills with functional near-infrared spectroscopy

After stroke,even high-functioning individuals may experience compromised bimanual coordination and fine motor dexterity,leading to reduced functional independence.Bilateral arm training has been proposed as a promising intervention to address these deficits.However,the neural basis of the impairment of functional fine motor skills and their relationship to bimanual coordination performance in stroke patients remains unclear,limiting the development of more targeted interventions.To address this gap,our study employed functional near-infrared spectroscopy to investigate cortical responses in patients after stroke as they perform functional tasks that engage fine motor control and coordination.Twenty-four high-functioning patients with ischemic stroke(7 women,17 men;mean age 64.75±10.84 years)participated in this cross-sectional observational study and completed four subtasks from the Purdue Pegboard Test,which measures unimanual and bimanual finger and hand dexterity.We found significant bilateral activation of the sensorimotor cortices during all Purdue Pegboard Test subtasks,with bimanual tasks inducing higher cortical activation than the assembly subtask.Importantly,patients with better bimanual coordination exhibited lower cortical activation during the other three Purdue Pegboard Test subtasks.Notably,the observed neural response patterns varied depending on the specific subtask.In the unaffected hand task,the differences were primarily observed in the ipsilesional hemisphere.In contrast,the bilateral sensorimotor cortices and the contralesional hemisphere played a more prominent role in the bimanual task and assembly task,respectively.While significant correlations were found between cortical activation and unimanual tasks,no significant correlations were observed with bimanual tasks.This study provides insights into the neural basis of bimanual coordination and fine motor skills in high-functioning patients after stroke,highlighting task-dependent neural responses.The findings also suggest that patients who exhibit better bimanual performance demonstrate more efficient cortical activation.Therefore,incorporating bilateral arm training in post-stroke rehabilitation is important for better outcomes.The combination of functional near-infrared spectroscopy with functional motor paradigms is valuable for assessing skills and developing targeted interventions in stroke rehabilitation.

Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R2 0.85) is better than the whole seed (0.33 R2 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R2_whole seed = 0.78, R2_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system(CNS)and autonomic nervous system(ANS)play a crucial role in modulating perception,cognition,and emotion production.Previous studies on CNS–ANS interactions,or heart–brain coupling,have often used heart rate variability(HRV)metrics derived from electrocardiography(ECG)recordings as empirical measurements of sympathetic and parasympathetic activities.Functional near-infrared spectroscopy(fNIRS)is a functional brain imaging modality that is increasingly used in brain and cognition studies.The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms.Therefore,fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously,allowing analysis of CNS–ANS interactions.In this proof-of-concept study,fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena(MOBA)game.From the fNIRS recordings,neural and heartbeat frequency bands were extracted to assess prefrontal activities and shortterm pulse rate variability(PRV),an approximation for short-term HRV,respectively.Under the experimental conditions used,fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards,in terms of absolute measurements and video game playing(VGP)-related changes.It was also observed that,similar to previous studies on physical activity and exercise,the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP.It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP,facilitating the understanding of VGP-related heart–brain coupling.

Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality seedlings.Nearinfrared spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor N.Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this task.Plants were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 days.Then we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using NIRS.With a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant function.It was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant occurred.Thereby,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A.emarginata.

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy

Modeling Light propagation within human head to deduce spatial sensitivity distribution(SSD)is important for Near-infrared spectroscopy(NIRS)/imaging(NIRI)and diffuse correlation tomography.Lots of head models have been used on this issue,including layered head model,artificial simplified head model,MRI slices described head model,and visible human head model.Hereinto,visible Chinese human(VCH)head model is considered to be a most faithful presentation of anatomical structure,and has been highlighted to be employed in modeling light propagation.However,it is not practical for all researchers to use VCH head models and actually increasing number of people are using magnet resonance imaging(MRI)head models.Here,all the above head models were simulated and compared,and we focused on the effect of using di®erent head models on predictions of SSD.Our results were in line with the previous reports on the effect of cerebral cortex folding geometry.Moreover,the in fluence on SSD increases with thefidelity of head models.And surprisingly,the SSD percentages in scalp and gray matter(region of interest)in MRI head model were found to be 80%and 125%higher than in VCH head model.MRI head models induced nonignorable discrepancy in SSD estimation when compared with VCH head model.This study,as we believe,is the first to focus on comparison among full serials of head model on estimating SSD,and provided quantitative evidence for MRI head model users to calibrate their SSD estimation.

Strength prediction model for water-bearing sandstone based on nearinfrared spectroscopy

The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a near-infrared spectrum acquisition experiment in the field and laboratory uniaxial compression strength tests on sandstone that had different water saturation levels.The correlations between the peak height and peak area of the nearinfrared absorption bands of the water-bearing sandstone and uniaxial compressive strength were analyzed.On this basis,a strength prediction model for water-bearing sandstone was established using the long short-term memory full convolutional network(LSTM-FCN)method.Subsequently,a field engineering test was carried out.The results showed that:(1)The sandstone samples had four distinct characteristic absorption peaks at 1400,1900,2200,and 2325 nm.The peak height and peak area of the absorption bands near 1400 nm and 1900 nm had a negative correlation with uniaxial compressive strength.The peak height and peak area of the absorption bands near 2200 nm and 2325 nm had nonlinear positive correlations with uniaxial compressive strength.(2)The LSTM-FCN method was used to establish a strength prediction model for water-bearing sandstone based on near-infrared spectroscopy,and the model achieved an accuracy of up to 97.52%.(3)The prediction model was used to realize non-destructive,quantitative,and real-time determination of uniaxial compressive strength;this represents a new method for the non-destructive testing of grotto rock mass at sites of cultural relics protection.

Near-infrared spectroscopy as a promising tool in stroke:Current applications and future perspectives

Stroke is caused by an acute focal disruption of the vasculature in the central nervous system.Neurological-related functional deficits are the most devastating consequences for stroke survi-vors.Neural signals from stroke patients can reflect the functional statuses of patients and provide insights into the neuronal recovery mechanism for functioning,which could be used as the basis for designing optimal treatment strategies.Near-infrared spectroscopy(NIRS)is a low-cost,noninvasive,easily operated neuroimage method and it is compatible with various rehabilitative programs.These advantages make NIRS an excellent candidate in research for stroke recovery.Here,we focused on the brain functions and recovery for stroke patients at stable status,conducted a systematic literature review about NIRS applications in stroke since 2000 and identified a total of 72 references through ScienceDirect and PubMed database retrieval.The NIRS studies in stroke include resting-state function and its recovery,motor function and itsrecovery,motor and cognition interference,cognitive function and its recovery,language function and its recovery,emotional function and its recovery and other applications.Based on the results of the quality assessment,we identified some study gaps from the previous research and provided suggestions for some methodological improvement in the future.The trend of NIRS gives a boost to its application in stroke,and the potential research directions for NIRS in stroke are pros-pected,including multi-center clinical research,treatment efficacy prediction research and brain-muscle coupling research.Finally,limitations of NIRS are discussed.

Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

Coptidis Rhizoma(Chinese:Huanglian)and Phellodendri Chinensis Cortex(Chinese:Huangbo)are widely used Traditional Chinese Medicine,and often used in combination because of their similar pharmacological effects in clinical practice.However,the quality control methods of the two drugs are different and complicated,which is time consuming and laborious in practical application.In this paper,rapid and simultaneous determination of moisture and berberine in Coptidis Rhizoma(CR)and Phellodendri Chinensis Cortex(PC)was realized by near-infrared spectroscopy(NIRs)combined with global models.Competitive adaptive reweighted sampling(CARS)and successive projection algorithm(SPA)method were applied for variable selection.Principal component analysis(PCA)and partial least squares regression method(PLSR)were applied for qualitative and quantitative analysis,respectively.The characteristic variables of berberine showed similarity and consistency in distribution,providing basis for the global models.For moisture content,the global model had relative standard error of prediction set(RSEP)value of 3.04%and 2.53%for CR and PC,respectively.For berberine content,the global model had RSEP value of 5.41%and 3.97%for CR and PC,respectively.These results indicated the global models based on CARS-PLS method achieved satisfactory prediction for moisture and berberine content,improving the determination e±ciency.Furthermore,the greater range and larger number of samples enhanced the reliance of the global model.The NIRs combined with global models could be a powerful tool for quality control of CR and PC.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Identification of Salmonella and Listeria monocytogenes by Near-infrared Spectroscopy and PCA

Near-infrared spectra of pathogenic bacteria (salmonella and Listeria monocytogenes ) were determined, and the spectral data were analyzed by the projection discriminant analysis based on principal component analysis (PCA). The expected results were obtained. The results showed that salmonella and L. monocytogenes could be distinguished from each other by the near-infrared spectroscopy of the whole cells, cell walls or cytoplasm.

Assessment of cerebral oxygenation response to hemodialysis using near-infrared spectroscopy (NIRS):Challenges and solutions

To date,the clinical use of functional near-infrared spectroscopy(NIRS)to detect cerebral ischemia has been largely limited to surgical settings,where motion artifacts are minimal.In this study,we present novel techniques to address the challenges of using NIRS to monitor ambu-latory patients with kidney disease during approximately eight hours of hemodialysis(HD)treatment.People with end-stage kidney disease who require HD are at higher risk for cognitive impairment and dementia than age-matched controls.Recent studies have suggested that HD-related declines in cerebral blood flow might explain some of the adverse outcomes of HD treatment.However,there are currently no established paradigms for monitoring cerebral per-fusion in real-time during HD treatment.In this study,we used NIRS to assess cerebral hemo-dynamic responses among 95 prevalent HD patients during two consecutive HD treatments.We observed substantial signal attenuation in our predominantly Black patient cohort that required probe modifications.We also observed consistent motion artifacts that we addressed by devel-oping a novel NIRS methodology,called the HD cerebral oxygen demand algorithm(HD-CODA),to identify episodes when cerebral oxygen demand might be outpacing supply during HD treatment.We then examined the association between a summary measure of time spent in cerebral deoxygenation,derived using the HD-CODA,and hemodynamic and treatment-related variables.We found that this summary measure was associated with intradialytic mean arterial pressure,heart rate,and volume removal.Future studies should use the HD-CODA to implement studies of real-time NIRS monitoring for incident dialysis patients,over longer time frames,and in other dialysis modalities.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Feasibility study on the use of near-infrared spectroscopy for rapid and nondestructive determination of gossypol content in intact cottonseeds

Background:Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products.It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program,so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method.Results:Gossypol content in cottonseeds was investigated by near-infrared spectroscopy(NIRS)and high-performance liquid chromatography(HPLC).Partial least squares regression,combined with spectral pretreatment methods including Savitzky-Golay smoothing,standard normal variate,multiplicative scatter correction,and first derivate were tested for optimizing the calibration models.NIRS technique was efficient in predicting gossypol content in intact cottonseeds,as revealed by the root-mean-square error of cross-validation(RMSECV),root-mean-square error of prediction(RMSEP),coefficient for determination of prediction(R_(p)^(2)),and residual predictive deviation(RPD)values for all models,being 0.05∼0.07,0.04∼0.06,0.82∼0.92,and 2.3∼3.4,respectively.The optimized model pretreated by Savitzky-Golay smoothing+standard normal variate+first derivate resulted in a good determination of gossypol content in intact cottonseeds.Conclusions:Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares(PLS)regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds,rapidly and non destructively.It could be used as an alternative method to substitute for traditional one to determi ne the gossypol content in intact cottonseeds.

Determination of the immunoglobulin G precipitation end-point by an intelligent near-infrared spectroscopy system

Precipitation is a key manufacturing unit during the immunoglobulin G(IgG)production,which guarantees the quality of the final product.Ethanol is usually used to purify IgG during the precipitation process,so it is important to monitor the ethanol concentration online.Near-infrared(NIR)spectroscopy is a powerful process analytical technology(PAT)which has been proved to be feasible to determine the ethanol concentration during the precipitation process.However,the NIR model is usually established based on the specific process,so a universal model is needed.And the clarity degree of solution will affect the quality of the spectra.Therefore,in this study an integrated NIR system was introduced to establish a universal NIR model which could predict the ethanol concentration online and determine the end-point of the whole process.First,a spectra acquisition device was designed and established in order to get high-quality NIR spectra.Then,a simple prepared ethanol NIR model was constructed to predict the actual manufacturing process.Finally,the end-point was determined to stop the peristaltic pump when the ethanol concentration reached 20%.The results showed that the spectra quality was good,model prediction was accurate,and process monitoring was accurate.In conclusion,all results indicated that the integrated NIR system could be used to monitor the biopharmaceutical process to help us understand the pharmaceutical process.

Working Memory Function in Chinese Dyslexic Children:A Near-Infrared Spectroscopy Study

The deficiency theories of dyslexia are quite contradictory and the cross-cultural studies in recent years mainly focused on whether the dyslexics among cultures shared the same cognitive profile or just based on the language.This study used Near-Infrared Spectroscopy (NIRS) imaging to measure the regional cerebral blood volume (BV) and the changes of cerebral activation in the left prefrontal cortex of 12 Chinese dyslexic children and their 12 age-matched normal controls during the Paced Vis-ual Serial Addition Test (PVSAT).Results showed that the scores of PVSAT of dyslexic children were significantly lower than those of the normal children (t=3.33,P<0.01).The activations of the left pre-frontal cortex in the normal group were significantly greater than those of dyslexic children (all P<0.01).Our results indicated that Chinese dyslexia had a general deficiency in working memory and this may be caused by the abnormal metabolic activity of brain blood volume in the left prefrontal cortex and the deficits in brain function might be the basis of neuropathology of Chinese dyslexia.Present study sup-ports the difference on brain activation of dyslexics from different languages may be caused by the same cognitive system related to reading.