Modulate the laser phase to improve the ns-LIBS spectrum signal based on orbital angular momentum.

Aiming to enhance the ns-LIBS signal, in this work, we introduced orbital angular momentum to modulate the laser phase of the Gaussian beam into the vortex beam. Under similar incident laser energy, the vortex beam promoted more uniform ablation and more ablation mass compared to the Gaussian beam, leading to elevated temperature and electron density in the laser-induced plasma. Consequently, the intensity of the ns-LIBS signal was improved. The enhancement effects based on the laser phase modulation were investigated on both metallic and non-metallic samples. The results showed that laser phase modulation resulted in a maximum 1.26-times increase in the peak intensities and a maximum 1.25-times increase in the signal-to-background ratio (SBR) of the Cu spectral lines of pure copper for a laser energy of 10 mJ. The peak intensities of Si atomic spectral lines were enhanced by 1.58-1.94 times using the vortex beam. Throughout the plasma evolution process, the plasma induced by the vortex beam exhibited prolonged duration and a longer continuous background, accompanied by a noticeable reduction in the relative standard deviation (RSD). The experimental results demonstrated that modulation the laser phase based on orbital angular momentum is a promising approach to enhancing the ns-LIBS signal.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue.

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C2 molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

Comparison of whole blood and serum samples of breast cancer based on laser-induced breakdown spectroscopy with machine learning.

To identify cancer from non-cancer is one of the most challenging issues nowadays in the early diagnosis of cancer. The primary issue of early detection is to choose a suitable type of sample collection to diagnose cancer. A comparison of whole blood and serum samples of breast cancer was studied using laser-induced breakdown spectroscopy (LIBS) with machine learning methods. For LIBS spectra measurement, blood samples were dropped on a substrate of boric acid. For the discrimination of breast cancer and non-cancer samples, eight machine learning models were applied to LIBS spectral data, including decision tree, discrimination analysis, logistic regression, naïve byes, support vector machine, k-nearest neighbor, ensemble and neural networks classifiers. Discrimination between whole blood samples showed that narrow neural networks and trilayer neural networks both provided 91.7% highest prediction accuracy and serum samples showed that all the decision tree models provided 89.7% highest prediction accuracy. However, using whole blood as sample achieved the strong emission lines of spectra, better discrimination results of PCA and maximum prediction accuracy of machine learning models as compared to using serum samples. These merits concluded that whole blood samples could be a good option for the rapid detection of breast cancer. This preliminary research may provide the complementary method for early detection of breast cancer.

Full-Stokes polarization multispectral images of various stereoscopic objects.

Polarization multispectral imaging (PMI) has been applied widely with the ability of characterizing physicochemical properties of objects. However, traditional PMI relies on scanning each domain, which is time-consuming and occupies vast storage resources. Therefore, it is imperative to develop advanced PMI methods to facilitate real-time and cost-effective applications. In addition, PMI development is inseparable from preliminary simulations based on full-Stokes polarization multispectral images (FSPMI). Whereas, FSPMI measurements are always necessary due to the lack of relevant databases, which is extremely complex and severely limits PMI development. In this paper, we therefore publicize abundant FSPMI with 512 × 512 spatial pixels measured by an established system for 67 stereoscopic objects. In the system, a quarter-wave plate and a linear polarizer are rotated to modulate polarization information, while bandpass filters are switched to modulate spectral information. The required FSPMI are finally calculated from designed 5 polarization modulation and 18 spectral modulation. The publicly available FSPMI database may have the potential to greatly promote PMI development and application.

Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy.

Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.

Polarimetry-inspired feature fusion spectroscopy (PIFFS) for ammonia sensing in water.

The sustainable use of water resources is inseparable from water pollution detection. The sensing of toxic ammonia nitrogen in water currently requires auxiliary reagents, which may cause secondary pollution. Benefiting from the ability of substances to change light characteristics, this work proposes polarimetry-inspired feature fusion spectroscopy (PIFFS) to detect ammonia. The PIFFS system mainly includes a light source, a quarter-wave plate (QWP), a linear polarizer (LP) and a fiber spectrometer. The target light containing substance information is polarization modulated by adjusting the QWP and LP angles. Then, the Stokes parameters of target light can be calculated by appropriate modulations. The feasibility of PIFFS method to detect ammonia nitrogen is verified by experiments on both standard water samples and environmental water samples. Experimental results show that inspired by the first Stokes parameter, the fused features provide superiority in classifying ammonia concentration. The results also demonstrate the effectiveness of support vector machine-based concentration classification and random forests-based spectral selection. The interaction between light and substances ensures that the proposed PIFFS method has the potential to detect other pollutants.

Self-adjusting inverse design method for nanophotonic devices.

Nanophotonic devices, which consist of multiple cell structures of the same size, are easy to manufacture. To avoid the optical proximity effect in the ultraviolet lithography process, the cell structures must be maintained at a distance from one another. In the inverse design process, the distance is maintained by limiting the optimized range of the location. However, this implementation can weaken the performance of the devices designed during transmission. To solve this problem, a self-adjusting inverse design method based on the adjoint variable method is developed. By introducing artificial potential field method, the location of one cell structure is modified only when the distances between this cell structure and other cell structures are smaller than a threshold. In this case, the range of the location can be expanded, and thus the performance of the designed devices can be improved. A wavelength demultiplexer with a channel spacing of 1.6 nm is designed to verify the performance of the proposed method. The experiment reveals that the transmission of the designed devices can be improved by 20%, and the self-adjusting inverse design process is 100 times faster than the inverse-design process based on the genetic algorithm.

Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy.

As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.

In-vitro study on the identification of gastrointestinal stromal tumor tissues using laser-induced breakdown spectroscopy with chemometric methods.

Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.

Evaluation of human melanoma and normal formalin paraffin-fixed samples using Raman and LIBS fused data.

In this research, we developed a novel method of quantitative analysis to increase the detection potential for screening and classification of skin cancer (melanoma). We fused two distinct optical approaches, an atomic spectroscopic detection technique laser-induced breakdown spectroscopy (LIBS) and a vibrational molecular spectroscopic technique known as Raman spectroscopy. Melanoma is a kind of skin cancer, also known as malignant melanoma, that developed in melanocytes cells, which produced melanin. Classification of melanoma cancerous tissues is a fundamental problem in biomedicine. For early melanoma cancer diagnosis and treatment, precise and accurate categorizing is critically essential. Laser-based spectroscopic approaches can be used as an operating instrument for simultaneous tissue ablation and ablated tissue elemental and molecular analysis. For this purpose, melanoma and normal paraffin-embedded tissues are used as a sample for LIBS and Raman measurement. We studied the data provided by laser-based spectroscopic methods using different machine learning classification techniques of extreme learning machine (ELM), partial least square discriminant analysis (PLS-DA), and K nearest neighbors (kNN). For visualization of melanoma and normal data, principal component analysis (PCA) is also used. Three different ways are used to process the data, LIBS measurement, Raman measurement, and combine data measurement (merged/fused data), and then compared the results. ELM classification model achieved the highest accuracy (100%) for combined data as well as for Raman and LIBS data, respectively. According to the experimental results, we can assume that Raman spectroscopy and LIBS combine can significantly improve the identification and classification accuracy of melanoma and normal specimens.

Acceleration of inverse design process on adaptive mesh.

In inverse design, the design and background areas can be represented by different spatial resolutions; thus, adaptive meshes are more efficient than structured meshes. In this study, a second-order interpolation scheme is introduced to realize an inverse design process on an adaptive mesh. Experiment results show that the proposed scheme yields a 1.79-fold acceleration over that achieved using a structured mesh, aiding design time reduction or design area expansion. As the design area can be divided into multiple areas with different spatial resolutions, in future work, adaptive meshes can be combined with machine learning algorithms to further improve the inverse-design-process efficiency.

Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis.

Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.

Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy.

The identification and preservation of parathyroid glands (PGs) is a major issue in thyroidectomy. The PG is particularly difficult to distinguish from the surrounding tissues. Accidental damage or removal of the PG may result in temporary or permanent postoperative hypoparathyroidism and hypocalcemia. In this study, a novel method for identification of the PG was proposed based on laser-induced breakdown spectroscopy (LIBS) for the first time. LIBS spectra were collected from the smear samples of PG and non-parathyroid gland (NPG) tissues (thyroid and neck lymph node) of rabbits. The emission lines (related to K, Na, Ca, N, O, CN, C2, etc.) observed in LIBS spectra were ranked and selected based on the important weight calculated by random forest (RF). Three machine learning algorithms were used as classifiers to distinguish PGs from NPGs. The artificial neural network classifier provided the best classification performance. The results demonstrated that LIBS can be adopted to discriminate between smear samples of PG and NPG, and it has a potential in intra-operative identification of PGs.

Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network.

Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.

Discrimination of hazardous bacteria with combination laser-induced breakdown spectroscopy and statistical methods.

Real-time biohazard detectors must be developed to facilitate the rapid implementation of appropriate protective measures against foodborne pathogens. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the real-time detection of hazardous bacteria (HB) in the field. However, distinguishing among various HBs that exhibit similar C, N, O, H, or trace metal atomic emissions complicates HB detection by LIBS. This paper proposes the use of LIBS and chemometric tools to discriminate Staphylococcus aureus, Bacillus cereus, and Escherichia coli on slide substrates. Principal component analysis (PCA) and the genetic algorithm (GA) were used to select features and reduce the size of spectral data. Several models based on the artificial neural network (ANN) and the support vector machine (SVM) were built using the feature lines as input data. The proposed PCA-GA-ANN and PCA-GA-SVM discrimination approaches exhibited correct classification rates of 97.5% and 100%, respectively.

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING.

Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.

Comparison of Performance of Equations for Estimated Glomerular Filtration Rate in Chinese Patients with Biopsy-Proven Diabetic Nephropathy.

BACKGROUND: The performance of various equations for estimated glomerular filtration rate (eGFR) in patients with diabetes remains controversial. We aimed to evaluate the performance of equations for eGFR in Chinese patients with diabetic nephropathy (DN). METHODS: This is a retrospective study included in 308 patients with type 2 diabetes and biopsy-proven DN who were followed up at least one year. eGFR was calculated using chronic kidney disease epidemiology (CKD-EPI) equations based on serum creatinine (eGFRCKD-EPI-Cr), cystatin C (eGFRCKD-EPI-CysC), and joint equations (eGFRCKD-EPI-Cr-CysC), respectively. End-stage kidney disease was defined by initiation of renal replacement therapy. The eGFR concordance between equations was assessed by Bland-Altman plots. Log-rank and multivariable logistic regression were employed to evaluate the performance of equations. RESULTS: Overall, the proportion of patients with eGFR < 60 mL/min/1.73m2 was 53%, 70%, and 61% by the equations of eGFRCKD-EPI-Cr, eGFRCKD-EPI-CysC, and eGFRCKD-EPI-Cr-CysC, respectively. Higher disconcordance was observed between equations when eGFR > 60 mL/min/1.73m2. Compared with eGFRCKD-EPI-Cr, 39% of patients were reclassified (reclassified group) from CKD 1-2 stages to CKD 3-5 stages by eGFRCKD-EPI-CysC and they presented significantly longer diabetic duration, heavier proteinuria, advanced pathological lesions, and poorer kidney outcomes. Multivariable logistic regression indicated cystatin C was independently associated with advanced glomerular classifications. CONCLUSION: eGFR equations incorporating cystatin C are superior to eGFR based on creatinine alone for detecting kidney injury in the early stage. The independent association between cystatin C and glomerular classifications might contribute to it.

Identification and classification of explosives using semi-supervised learning and laser-induced breakdown spectroscopy.

Public places are often under threat from explosion events, which pose health and safety risks to the public. Therefore, the detection of explosive materials has become an important concern in the fields of antiterrorism and security. Laser-induced breakdown spectroscopy (LIBS) has been demonstrated to be useful in identifying explosives but has limitations. This study focuses on using semi-supervised learning combined with LIBS for explosive identification. Labeled data were utilized for the construction of a semi-supervised model for distinguishing explosive clusters and improving the accuracy of the K-nearest neighbor algorithm. The method requires only minimal prior information, and the time for obtaining a large amount of labeled data can be saved. The results of our investigation demonstrated that semi-supervised learning with LIBS can be used to discriminate explosives from interfering substances (plastics) containing similar components. The algorithm exhibits good robustness and practicability.

Thyroid hormones and diabetic nephropathy: An essential relationship to recognize.

AIMS: Although abnormal thyroid hormone metabolism is common in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), the relationship between thyroid hormones and DN is unclear and has been ignored during clinical practice. This study aimed to investigate the relationship between thyroid hormones and clinicopathologic changes in biopsy-proven DN patients. METHODS: Clinical and pathological data for 146 biopsy-proven DN patients were collected. The patients were divided into four groups: euthyroid group, high-thyroid stimulating hormone (TSH) group (SCH), low-free triiodothyronine (FT3) group (with normal levels of TSH and FT4), and high-TSH + low-FT3 group (with normal levels of FT4). The clinicopathologic features among the four groups were investigated. We evaluated the risks of abnormal thyroid hormone levels on DN by logistic regression with multivariable adjustments for other risk factors. We also performed quarterback and eight-point analyses of TSH and FT3 levels to determine their influences on DN. RESULTS: The overt proteinuria (>5 g/24 h) (P = 0.008) and severity of glomerular lesions (P = 0.011) differed between euthyroid group and high-TSH group significantly. Moreover, the levels of estimated glomerular filtration rate (P =0.019), serum creatinine (P =0.014), and severity of glomerular lesions (P =0.003) differed between the euthyroid group and low-FT3 group significantly. There were also significant differences between high-TSH, low-FT3 and high-TSH + low-FT3 patients, respectively. Respective correlations between high-TSH, low-FT3 and renal clinicopathologic changes were found to be significant according to logistic regression analyses. Quarterback and eight-point analyses indicated that patients with TSH levels of 4.54-5.67 mU/L had the most severe renal clinicopathologic changes, and the severity of renal changes decreased with increased FT3 levels. CONCLUSIONS: Diabetic nephropathy patients with high-TSH and/or low-FT3 had more severe proteinuria, renal insufficiency, and glomerular lesions, suggesting that regulating thyroid hormones might have a renoprotective effect.

Importance evaluation of spectral lines in Laser-induced breakdown spectroscopy for classification of pathogenic bacteria.

The correct classification of pathogenic bacteria is significant for clinical diagnosis and treatment. Compared with the use of whole spectral data, using feature lines as the inputs of the classification model can improve the correct classification rate (CCR) and reduce the analyzing time. In order to select feature lines, we need to investigate the contribution to the CCR of each spectral line. In this paper, two algorithms, important weights based on principal component analysis (IW-PCA) and random forests (RF), were proposed to evaluate the importance of spectra lines. The laser-induced plasma spectra (LIBS) of six common clinical pathogenic bacteria species were measured and a support vector machine (SVM) classifier was used to classify the LIBS of bacteria species. In the proposed IW-PCA algorithm, the product of the loading of each line and the variance of the corresponding principal component were calculated. The maximum product of each line calculated from the first three PCs was used to represent the line's importance weight. In the RF algorithm, the Gini index reduction value of each line was considered as the line's importance weight. The experimental results demonstrated that the lines with high importance were more suitable for classification and can be chosen as feature lines. The optimal number of feature lines used in the SVM classifier can be determined by comparing the CCRs with a different number of feature lines. Importance weights evaluated by RF are more suitable for extracting feature lines using LIBS combined with an SVM classification mechanism than those evaluated by IW-PCA. Furthermore, the two methods mutually verified the importance of selected lines and the lines evaluated important by both IW-PCA and RF contributed more to the CCR.