Differential diagnosis of Crohn’s disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

利用ATR-FTIR研究脂质纳米粒的鼻黏液渗透性

鼻黏液是影响疫苗和药物经鼻吸收的首要屏障。由于干扰因素复杂多变,在体评价黏液渗透性较难实施,多采用体外评价。现有的药物鼻黏液渗透性检测方法如细胞模型法、多粒子示踪技术等,存在细胞培养周期长、操作繁琐、成本高、可获得信息少且需要进行荧光标记等不足,对鼻黏膜制剂的体外评价具有很大的局限性,因此迫切需要建立一种快速、简便、灵敏的鼻黏膜制剂黏液渗透性评价方法。基于衰减全反射-傅里叶红外光谱(ATR-FTIR)对药物结构以及粘蛋白二级结构变化的敏感性,利用其对不同性质(粒径与电荷)脂质体的鼻黏液渗透性进行研究,通过FTIR图谱分析不同脂质纳米粒与黏液中粘蛋白的相互作用,建立了鼻黏膜制剂黏液渗透性的体外评价方法。方法学研究表明,对于聚乙二醇10000(PEG10000)、壳聚糖、海藻酸钠脂质体,该方法线性关系分别为Y=2.3866X+2.154、Y=1.8703X+0.2789、Y=1.13014X+0.0609,线性相关系数分别为0.9958、0.9945、0.9909,精密度RSD值分别为0.62%、0.73%、0.95%;重复性试验中RSD值分别为0.83%、0.97%、0.88%,说明该方法线性关系良好、精密度高、重复性好,可用于体外评价药物制剂在黏液中的渗透性。研究结果表明,利用ATR-FTIR在不同时间内扫描样品可得到不同脂质体制剂样品的强度增加的吸收带。对于不同粒径PEG脂质体而言,粒径越小其黏液渗透性越强;对于不同电荷脂质体而言,壳聚糖脂质体黏液渗透性最弱,海藻酸钠次之,PEG脂质体黏液渗透性最强。进一步研究表明,不同电荷脂质体黏液渗透性的差异源于其与粘蛋白相互作用,该结论可通过分析粘蛋白酰胺Ⅰ带所包含的各二级结构信息得到。综上,基于ATR-FTIR所建立的体外评价方法灵敏简便,可作为多种不同制剂的鼻黏液渗透性的快速测定,经改进后还可用于药物制剂在其他黏液中的渗透性评价,应用前景广阔。

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.

In situ infrared, Raman and X-ray spectroscopy for the mechanistic understanding of hydrogen evolution reaction

Hydrogen production by water reduction reactions has received considerable attention because hydrogen is considered a clean-energy carrier,key for a sustainable energy future.Computational methods have been widely used to study the reaction mechanism of the hydrogen evolution reaction(HER),but the calculation results need to be supported by experimental results and direct evidence to confirm the mechanistic insights.In this review,we discuss the fundamental principles of the in situ spectroscopic strategy and a theoretical model for a mechanistic understanding of the HER.In addition,we investigate recent studies by in situ Fourier transform infrared(FTIR),Raman spectroscopy,and X-ray absorption spectroscopy(XAS) and cover new findings that occur at the catalyst-electrolyte interface during HER.These spectroscopic strategies provide practical ways to elucidate catalyst phase,reaction intermediate,catalyst-electrolyte interface,intermediate binding energy,metal valency state,and coordination environment during HER.

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.

Laser spectroscopy imaging technique coupled withnanomaterials for cancer diagnosis: A review

Laser spectroscopic imaging techniques have received tremendous attention in the-eld of cancer diagnosis due to their high sensitivity,high temporal resolution,and short acquisition time.However,the limited tissue penetration of the laser is still a challenge for the in vivo diagnosis of deep-seated lesions.Nanomaterials have been universally integrated with spectroscopic imaging techniques for deeper cancer diagnosis in vivo.The components,morphology,and sizes of nanomaterials are delicately designed,which could realize cancer diagnosis in vivo or in situ.Considering the enhanced signal emitting from the nanomaterials,we emphasized their combination with spectroscopic imaging techniques for cancer diagnosis,like the surface-enhanced Raman scattering(SERS),photoacoustic,fluorescence,and laser-induced breakdown spectroscopy(LIBS).Applications ofthe above spectroscopic techniques offer new prospectsfor cancer diagnosis.

A dual-route optical emission spectroscopy diagnostic with wide spectral range and high wavelength resolution on HL-2A tokamak

A dual-route optical emission spectroscopy(D-OES)diagnostic is newly developed to monitor the optical emission from the X-point plasma region on the HL-2 A tokamak.This diagnostic is composed of an imaging system,a beam-splitting system for dual-route measurements,fiber bundles,a spectrometer system,and a control and acquisition system.One route is used to obtain wide-spectral-range spectra,and the other route is used to acquire high-wavelengthresolution line shapes.The spectral resolution of the wide-range spectrometers is 0.8 nm with a coverage of 800 nm(@200-1000 nm).The spectral resolution of the high-resolution spectrometer is 0.01 nm with a coverage of 6 nm(@200-660 nm).The spatial resolution of each route of D-OES is about 4 cm with 11 channels.The temporal resolution is 16 ms at maximum in the single-channel mode.Wide-range spectra(containing Balmer series and a Fulcher band)and highly resolved Ha line shapes are obtained by D-OES in the hydrogen glow discharge in the lab.D-OES measurements are carried out in the high-density deuterium experiments of HL-2A.The electron density n_(e)and deuterium temperature T_(D) in the X-point multifaceted asymmetric radiation from the edge(MARFE)region are derived simultaneously by fitting the measured D_(a) shape.The density n_(e)is observed to increase from~8.7×10^(18)m^(-3)to~7.8×10^(19)m^(-3),and the temperature T_(D)drops from~14.4 eV to~2.3 eV after the onset of MARFE in the discharge#38260.

Core-level spectroscopy of the photodissociation process of BrCN molecule

Fewest-switches surfacing hopping(FSSH) simulations have been performed with the high-level multi-reference electronic structure method to explore the coupled electronic and nuclear dynamics upon photoexcitation of cyanogen bromide(BrCN). The potential energy surfaces(PES) of BrCN are charted as functions of the Jacobi coordinates(R, θ). An indepth examination of the FSSH trajectories reveals the temporal dynamics of the molecule and the population changes of the lowest twelve states during BrCN's photodissociation process, which presents a rich tapestry of dynamical information.Furthermore, the carbon K-edge x-ray absorption spectroscopy(XAS) is calculated with multi-reference inner-shell spectral simulations. The rotation of the CN fragment and the elongation of the C–Br bond are found to be the reason for the peak shifting in the XAS. Our findings offer a nuanced interpretation for inner-shell probe investigations of BrCN, setting the stage for a deeper understanding of the photodissociation process of cyanogen halides molecules.

Negligible normal fluid in superconducting state of heavily overdoped Bi_(2)Sr_(2)CaCu_(2)O_(8+δ) detected by ultra-low temperature angle-resolved photoemission spectroscopy

In high temperature cuprate superconductors,it was found that the superfluid density decreases with the increase of hole doping.One natural question is whether there exists normal fluid in the superconducting state in the overdoped region.In this paper,we have carried out high-resolution ultra-low temperature laser-based angle-resolved photoemission measurements on a heavily overdoped Bi2212 sample with a T_(c) of 48 K.We find that this heavily overdoped Bi2212 remains in the strong coupling regime with 2Δ_(0)/(k_(B)T_(c))=5.8.The single-particle scattering rate is very small along the nodal direction(~5 meV) and increases as the momentum moves from the nodal to the antinodal regions.A hard superconducting gap opening is observed near the antinodal region with the spectral weight at the Fermi level fully suppressed to zero.The normal fluid is found to be negligibly small in the superconducting state of this heavily overdoped Bi2212.These results provide key information to understand the high T_(c) mechanism in the cuprate superconductors.

Estimating the yield stress of soft materials via laser-induced breakdown spectroscopy

Taking three typical soft samples prepared respectively by loose packings of 77-,95-,and 109-μm copper grains as examples,we perform an experiment to investigate the energy-dependent laser-induced breakdown spectroscopy(LIBS)of soft materials.We discovered a reversal phenomenon in the trend of energy dependence of plasma emission intensity:increasing initially and then decreasing separated by a well-defined critical energy.The trend reversal is attributed to the laser-induced recoil pressure at the critical energy just matching the sample's yield strength.As a result,a one-to-one correspondence can be well established between the samples'yield stress and the critical energy that is easily obtainable from LIBS measurements.This allows us to propose an innovative method for estimating the yield stress of soft materials via LIBS with attractive advantages including in-situ remote detection,real-time data collection,and minimal destructive to sample.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications.

Wavelength calibration and spectral analysis of vacuum ultraviolet spectroscopy in EAST

A vacuum ultraviolet(VUV)spectroscopy with a focal length of 1 m has been engineered specifically for observing edge impurity emissions in Experimental Advanced Superconducting Tokamak(EAST).In this study,wavelength calibration for the VUV spectroscopy is achieved utilizing a zinc lamp.The grating angle and charge-coupled device(CCD)position are carefully calibrated for different wavelength positions.The wavelength calibration of the VUV spectroscopy is crucial for improving the accuracy of impurity spectral data,and is required to identify more impurity spectral lines for impurity transport research.Impurity spectra of EAST plasmas have also been obtained in the wavelength range of 50–300 nm with relatively high spectral resolution.It is found that the impurity emissions in the edge region are still dominated by low-Z impurities,such as carbon,oxygen,and nitrogen,albeit with the application of fulltungsten divertors on the EAST tokamak.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy(LIBS)is investigated experimentally.The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness,ranging from 0 to 4.4 mm,by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm.It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases,implying that the method investigated here is feasible.It is also found that,for achieving the inspection of surface flatness within such a wide range,when univariate analysis is applied,a piecewise calibration model must be constructed.This is due to the complex dependence of plasma formation conditions on the surface flatness,which inevitably complicates the inspection procedure.To solve the problem,a multivariate analysis with the help of Back-Propagation Neural Network(BPNN)algorithms is applied to further construct the calibration model.By detailed analysis of the model performance,we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Initial measurement of ultrafast charge exchange recombination spectroscopy on the EAST tokamak

Ultrafast charge exchange recombination spectroscopy(UF-CXRS)has been developed on the EAST tokamak(Yingying Li et al 2019 Fusion Eng.Des.146522)to measure fast evolutions of ion temperature and toroidal velocity.Here,we report the preliminary diagnostic measurements after relative sensitivity calibration.The measurement results show a much higher temporal resolution compared with conventional CXRS,benefiting from the usage of a prismcoupled,high-dispersion volume-phase holographic transmission grating and a high quantum efficiency,high-gain detector array.Utilizing the UF-CXRS diagnostic,the fast evolutions of the ion temperature and rotation velocity during a set of high-frequency small-amplitude edgelocalized modes(ELMs)are obtained on the EAST tokamak,which are then compared with the case of large-amplitude ELMs.

Ultrafast carrier dynamics in GeSn thin film based on time-resolved terahertz spectroscopy

We measure the time-resolved terahertz spectroscopy of GeSn thin film and studied the ultrafast dynamics of its photo-generated carriers.The experimental results show that there are photo-generated carriers in GeSn under femtosecond laser excitation at 2500 nm,and its pump-induced photoconductivity can be explained by the Drude–Smith model.The carrier recombination process is mainly dominated by defect-assisted Auger processes and defect capture.The firstand second-order recombination rates are obtained by the rate equation fitting,which are(2.6±1.1)×10^(-2)ps^(-1)and(6.6±1.8)×10^(-19)cm^(3)·ps^(-1),respectively.Meanwhile,we also obtain the diffusion length of photo-generated carriers in GeSn,which is about 0.4μm,and it changes with the pump delay time.These results are important for the GeSn-based infrared optoelectronic devices,and demonstrate that Ge Sn materials can be applied to high-speed optoelectronic detectors and other applications.

A seven-crystal spectrometer for high-energy resolution X-ray spectroscopy at Shanghai Synchrotron Radiation Facility

A Johann-type X-ray spectrometer was successfully developed at the hard X-ray branch(in-vacuum undulator with a 24-mm periodic length)of the energy material beamline(E-line)at the Shanghai Synchrotron Radiation Facility(SSRF).This spectrometer was utilized to implement X-ray emission spectroscopy(XES),high-energy resolution fluorescence-detected X-ray absorption spectroscopy(HERFD-XAS),and resonant inelastic X-ray scattering.Seven spherically bent crystals were positioned on the respective vertical 500-mm-diameter Rowland circles,adopting an area detector to increase the solid angle to 1.75%of 4πsr,facilitating the study of low-concentrate systems under complex reaction conditions.Operated under the atmosphere pressure,the spectrometer covers the energy region from 3.5 to 18 keV,with the Bragg angle ranging from 73°to 86°during vertical scanning.It offers a promised energy resolution of sub-eV(XES)and super-eV(HERFD-XAS).Generally,these comprehensive core-level spectroscopy methods based on hard X-rays at the E-line with an extremely high photon flux can meet the crucial requirements of a green energy strategy.Moreover,they provide substantial support for scientific advances in fundamental research.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a priorguided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut(Arachis hypogaea L.)

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding.In this study,Near Infrared Spectroscopy(NIRS)was applied to rapidly assess germplasm variability from whole seed of 699 samples,field-collected and assembled in four genetic and environmentbased sets:one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population,evaluated in three environments in a large spatial scale of two countries,Mbalmayo and Bafia in Cameroon and Nioro in Senegal,under rainfed conditions.NIR elemental spectra were gathered on six subsets of seeds of each sample,after three rotation scans,with a spectral resolution of 16 cm-1over the spectral range of867 nm to 2530 nm.Spectra were then processed by principal component analysis(PCA)coupled with Partial least squares-discriminant analysis(PLS-DA).As results,a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments.The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm,usually related to oil content and fatty acid composition.PCA yielded the most chemical attributes in three significant PCs(i.e.,eigenvalues>10),which together captured 93%of the total variation,revealing genetic and environment structure of varieties and genotypes into four clusters,corresponding to the four samples sets.The pattern of genetic variability of the interspecific population covers,remarkably half of spectrum of the core-collection,turning out to be the largest.Interestingly,a PLS-DA model was developed and a strong accuracy of 99.6%was achieved for the four sets,aiming to classify each seed sample according to environment origin.The confusion matrix achieved for the two sets of Bafia and Nioro showed 100%of instances classified correctly with 100%at both sensitivity and specificity,confirming that their seed quality was different from each other and all other samples.Overall,NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection,as a source of nutritional diversity,to support the breeding efforts.

Emission and capture characteristics of deep hole trap in n-GaN by optical deep level transient spectroscopy

Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.