Origin identification of Polygonatum cyrtonema based on hyperspectral data / 中国中药杂志

In this study, visual-near infrared(VNIR), short-wave infrared(SWIR), and VNIR + SWIR fusion hyperspectral data of Polygonatum cyrtonema from different geographical origins were collected and preprocessed by first derivative(FD), second derivative(SD), Savitzky-Golay smoothing(S-G), standard normalized variate(SNV), multiplicative scatter correction(MSC), FD+S-G, and SD+S-G. Three algorithms, namely random forest(RF), linear support vector classification(LinearSVC), and partial least squares discriminant analysis(PLS-DA), were used to establish the identification models of P. cyrtonema origin from three spatial scales, i.e., province, county, and township, respectively. Successive projection algorithm(SPA) and competitive adaptive reweighted sampling(CARS) were used to screen the characteristic bands, and the P. cyrtonema origin identification models were established according to the selected characteristic bands. The results showed that(1)after FD preprocessing of VNIR+SWIR fusion hyperspectral data, the accuracy of recognition models established using LinearSVC was the highest, reaching 99.97% and 99.82% in the province origin identification model, 100.00% and 99.46% in the county origin identification model, and 99.62% and 98.39% in the township origin identification model. The accuracy of province, county, and township origin identification models reached more than 98.00%.(2)Among the 26 characteristic bands selected by CARS, after FD pretreatment, the accuracy of origin identification models of different spatial scales was the highest using LinearSVC, reaching 98.59% and 97.05% in the province origin identification model, 97.79% and 94.75% in the county origin identification model, and 90.13% and 87.95% in the township origin identification model. The accuracy of identification models of different spatial scales established by 26 characteristic bands reached more than 87.00%. The results show that hyperspectral imaging technology can realize accurate identification of P. cyrtonema origin from different spatial scales.

Origin identification of Poria cocos based on hyperspectral imaging technology / 中国中药杂志

To realize the non-destructive and rapid origin discrimination of Poria cocos in batches, this study established the P. cocos origin recognition model based on hyperspectral imaging combined with machine learning. P. cocos samples from Anhui, Fujian, Guangxi, Hubei, Hunan, Henan and Yunnan were used as the research objects. Hyperspectral data were collected in the visible and near infrared band(V-band, 410-990 nm) and shortwave infrared band(S-band, 950-2 500 nm). The original spectral data were divided into S-band, V-band and full-band. With the original data(RD) of different bands, multiplicative scatter correction(MSC), standard normal variation(SNV), S-G smoothing(SGS), first derivative(FD), second derivative(SD) and other pretreatments were carried out. Then the data were classified according to three different types of producing areas: province, county and batch. The origin identification model was established by partial least squares discriminant analysis(PLS-DA) and linear support vector machine(LinearSVC). Finally, confusion matrix was employed to evaluate the optimal model, with F1 score as the evaluation standard. The results revealed that the origin identification model established by FD combined with LinearSVC had the highest prediction accuracy in full-band range classified by province, V-band range by county and full-band range by batch, which were 99.28%, 98.55% and 97.45%, respectively, and the overall F1 scores of these three models were 99.16%, 98.59% and 97.58%, respectively, indicating excellent performance of these models. Therefore, hyperspectral imaging combined with LinearSVC can realize the non-destructive, accurate and rapid identification of P. cocos from different producing areas in batches, which is conducive to the directional research and production of P. cocos.

Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology / 中国中药杂志

This Fructus,study including and aimed to construct a rapid and nondestructive detection flavonoid,model betaine,for and of the content vitamin of(Vit four four quality C).index components Lycium barbarum polysaccharide,of inL ycii rawma total and C Hyperspectral data quantitative of terials modelswere powder developed Lycii using Fructus partial were squares effects collected,regression raw based LSR),on the support content vector the above components,the forest least(P regression compared,(SVR),the and effects random three regression(RFR)were algorithms.also The Four spectral predictive commonly data of the materialsand powder were were applied and of spectral quantitative for models reduction.compared.used were pre-processing screened methods feature to successive pre-process projection the raw algorithm data(SPA),noise competitive Thepre-processed for bands using adaptive reweigh ted sampling howed(CARS),the and maximal effects relevance based and raw minimal materials redundancy and(MRMR)were algorithms Following to optimize multiplicative the models.scatter The correction Based resultss(MS that prediction SPA on feature the powder prediction similar.PLSR C)denoising sproposed and integrated for model,screening the the coefficient bands,determination the effect(R_C~2)of(MSC-SPA-PLSR)coefficient was optimal.of on(R_P~2)thi of of calibration flavonoid,and and of all determination greater prediction0.83,L.barbarum inconte nt prediction of polysaccharide,total mean betaine,of Vit C were than smallest In the compared study,root with mean other prediction content squareserror models of the calibration(RMSEC)residual and deviation root squares was error2.46,prediction2.58,(RMSEP)and were the,and prediction(RPD)2.50,developed3.58,achieve respectively.rapid this the the quality mod el(MSC-SPA-PLSR)fourcomponents based Fructus,on hyperspectral which technology was approach to rapid and effective detection detection of the of Lycii in Lycii provided a new to the and nondestructive of of Fructus.

Application of partial least squares algorithm to explore bioactive components of crude and stir-baked hawthorn for invigorating spleen and promoting digestion / 中国中药杂志

This study was aimed at identifying the bioactive components of the crude and stir-baked hawthorn for invigorating spleen and promoting digestion, respectively, to clarify the processing mechanism of hawthorn by applying the partial least squares(PLS) algorithm to build the spectrum-effect relationship model. Firstly, different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions were prepared, respectively. Then, the contents of 24 chemical components were determined by ultra-high performance liquid chromatography-mass spectrometry. The effects of different polar fractions of crude hawthorn and stir-baked hawthorn aqueous extracts and combinations of different fractions were evaluated by measuring the gastric emptying rate and small intestinal propulsion rate. Finally, the PLS algorithm was used to establish the spectrum-effect relationship model. The results showed that there were significant differences in the contents of 24 chemical components for different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions, and the gastric emptying rate and small intestinal propulsion rate of model rats were improved by administration of different polar fractions of crude and stir-baked hawthorn aqueous extracts and combinations of different fractions. The bioactive components of crude hawthorn identified by PLS models were vitexin-4″-O-glucoside, vitexin-2″-O-rhamnoside, neochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, malic acid, quinic acid and fumaric acid, while neochlorogenic acid, cryptochlorogenic acid, rutin, gallic acid, vanillic acid, citric acid, quinic acid and fumaric acid were the bioactive components of stir-baked hawthorn. This study provided data support and scientific basis for identifying the bioactive components of crude and stir-baked hawthorn, and clarifying the processing mechanism of hawthorn.

Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations / 南方医科大学学报

OBJECTIVE@#To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.@*METHODS@#Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.@*RESULTS@#At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.@*CONCLUSION@#The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.

Integrated smart hyperspectral imaging and CARS-based characteristic band selection for rapid determination of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix / 中国中药杂志

In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.

Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium / 中国中药杂志

This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm / 法医学杂志

OBJECTIVES@#To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.@*METHODS@#Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established.@*RESULTS@#The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively.@*CONCLUSIONS@#In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

Pain and physical activity for one individual: a comparison of models / Dor e atividade física para um indivíduo: uma comparação de modelos

Aims: there is increasing awareness that for effective patient care we need more than only randomized controlled trials with groups of participants and that carefully collected single case (N = 1) data have several important advantages over traditional group-level studies. With the advance of technology, collecting relevant data from a single case is becoming easier by the day, and this offers tremendous opportunities for understanding how behaviors displayed by an individual can be influenced by one or several key variables. For example, how pain experienced influences the amount of time spent on physical exercise. Method: using publicly available observational single case data, five models are compared: a classical ordinary least squares (OLS) linear regression model; a dynamic regression model (DRM); a two-level random-intercepts model (2LRI); a continuous covariate first-order autoregressive correlation model (CAR1); and an ordinary least squares model with time trend (OLST). These models are compared in terms of overall model fit statistics, estimates of the relation between physical activity (response variable of interest) and pain (covariate of interest), and residual statistics. Results: 2LRI outperforms all other models on both overall model fit and residual statistics, and provides covariate estimates that are in between the relative extremes provided by other models. CAR1 and OLST demonstrate an almost identical performance and one that is substantially better than OLS ­ which performs worst ­ and DRM. Conclusion: for observational single case data, DRM, CAR1, OLST, and 2LRI account for the serial correlation that is typically present in single case data in somewhat different ways under somewhat different assumptions, and all perform better than OLS. Implications of these findings for observational, quasi-experimental, and experimental single case studies are discussed.

Objetivos: há uma crescente conscientização de que, para um atendimento eficaz ao paciente, precisamos de mais do que apenas ensaios clínicos randomizados com grupos de participantes e que os dados de caso único cuidadosamente coletados (N = 1) têm várias vantagens importantes sobre os estudos tradicionais em nível de grupo. Com o avanço da tecnologia, coletar dados relevantes de um único caso está se tornando mais fácil a cada dia, e isso oferece enormes oportunidades para entender como os comportamentos exibidos por um indivíduo podem ser influenciados por uma ou várias variáveis-chave. Por exemplo, como a dor experimentada influencia a quantidade de tempo gasto no exercício físico. Método: usando dados de caso único observacionais disponíveis publicamente, cinco modelos são comparados: um modelo clássico de regressão linear de mínimos quadrados ordinários (OLS); um modelo de regressão dinâmica (DRM); um modelo de interceptações aleatórias de dois níveis (2LRI); um modelo de correlação autorregressiva de primeira ordem covariável contínua (CAR1); e um modelo ordinário de mínimos quadrados com tendência temporal (OLST). Esses modelos são comparados em termos de estatísticas gerais de ajuste do modelo, estimativas da relação entre atividade física (variável de resposta de interesse) e dor (covariável de interesse) e estatísticas residuais. Resultados: o 2LRI supera todos os outros modelos tanto no ajuste geral do modelo quanto nas estatísticas residuais e fornece estimativas de covariáveis que estão entre os extremos relativos fornecidos por outros modelos. CAR1 e OLST demonstram um desempenho quase idêntico e substancialmente melhor que o OLS, que apresenta o pior desempenho, e o DRM. Conclusão: para dados observacionais de caso único, DRM, CAR1, OLST e 2LRI são responsáveis pela correlação seriada que normalmente está presente em dados de caso único de maneira um pouco diferentes sob suposições um pouco diversas, e todos têm um desempenho melhor que o OLS. Implicações dessas descobertas para estudos de caso único observacionais, quase-experimentais e experimentais são discutidas.

Validación de una escala para evaluar las motivaciones para estudiar Estomatología (MEEC-7) en Cuba / Validation of a scale to evaluate motivational reasons to study dentistry in Cuba (MEEC-7)

Introducción: Las motivaciones para elegir las carreras universitarias determinan en buena medida el desempeño profesional, de allí la necesidad de contar con instrumentos válidos y confiables para su estudio. Objetivo: Validar una escala para evaluar las motivaciones para estudiar Estomatología en alumnos cubanos. Métodos: Estudio de tipo instrumental, transversal y multicéntrico, que incluyó estudiantes de nueve universidades cubanas. A partir de un instrumento en español validado en estudiantes latinoamericanos de medicina, se realizó un análisis factorial exploratorio por mínimos cuadrados no ponderados. Luego se realizó un análisis factorial confirmatorio y se midió la consistencia interna con el alpha de Cronbach. Resultados: Se incluyó a 1324 participantes, de los cuales el 66,8 por ciento fueron mujeres y la media de la edad fue 21,2 ± 1,8 años. Sobre la base de una matriz de correlaciones, la prueba de Bartlett arrojó indicadores significativos (p < 0,05) y el índice KMO fue superior a 0,8. La varianza explicada fue superior al 50 por ciento y el análisis paralelo sugirió solo 2 factores. De la escala inicial, el análisis factorial sugirió eliminar los ítems 4 y 5 (factor 1), 9 y 12 (factor 2) y el ítem 1, por lo que el modelo quedó conformado por 7 ítems, 3 para el factor 1 y 4 para el factor 2. El ajuste e índices fueron adecuados, lo que demostró validez de constructo. Conclusión: La escala de motivaciones para estudiar Estomatología demostró ser válida y confiable, y está conformada por dos dominios que denotan aspectos sociales y económicos(AU)

Introduction: The motivations for the choice of university studies determine professional performance to a considerable extent. Hence the need for valid, reliable tools to evaluate them. Objective: Validate a scale to evaluate motivational reasons to study dentistry among Cuban students. Methods: An instrumental cross-sectional multicenter study was conducted which included students from nine Cuban universities. Based on a tool in Spanish validated in Latin American medical students, exploratory factor analysis was performed by unweighted least squares. Confirmatory factor analysis was then carried out, and internal consistency was measured with Cronbach's alpha. Results: A total 1 324 participants were included, of whom 66.8 percent were women; mean age was 21.2 ± 1.8 years. Based on a correlation matrix, Bartlett's test yielded significant indicators (p < 0.05), and the KMO index was above 0.8. Explained variance was above 50 percent, and parallel analysis suggested only two factors. Factor analysis suggested to remove the following items from the initial scale: 4 and 5 (factor 1), 9 and 12 (factor 2) and 1, as a result of which the model would consist of 7 items: 3 for factor 1 and 4 for factor 2. The adjustment and the indices were appropriate, which showed construct validity. Conclusion: The scale for motivations to study dentistry was found to be valid and reliable. It consists of two domains denoting social and economic aspects(AU)

Exploration of emulsifying material basis of Angelicae Sinensis Radix volatile oil based on partial least squares and hydrophile-lipophile balance value / 中国中药杂志

This study explores the emulsifying material basis of Angelicae Sinensis Radix volatile oil (ASRVO) based on partial least squares (PLS) method and hydrophile-lipophile balance (HLB) value.The turbidity of ASRVO emulsion samples from Gansu,Yunnan,and Qinghai was determined and the chemical components in the emulsion were analyzed by GC-MS.The PLS model was established with the chemical components as the independent variable and the turbidity as the dependent variable and evaluated with indexes R~2X and R~2Y.The chemical components which were in positive correlation with the turbidity were selected and the HLB values were calculated to determine the emulsification material basis of ASRVO.The PLS models for the 81 emulsion samples had high R~2X and R~2Y values,which showed good fitting ability.Seven chemical components,2-methoxy-4-vinylphenol,trans-ligustilide,3-butylidene-1(3H)-isobenzofuranone,dodecane,1-methyl-4-(1-methylethylidene)-cyclohexene,trans-beta-ocimene,and decane,had positive correlation with turbidity.Particularly,the HLB value of 2-methoxy-4-vinylphenol was 4.4,which was the HLB range of surfactants to be emulsifiers and 2-methoxy-4-vinylphenol was positively correlated with turbidity of the ASRVO emulsion samples from the main producing area.Therefore,2-methoxy-4-vinylphenol was the emulsifying material basis of ASRVO.The selected emulsifying substances can lay a foundation for exploring the emulsification mechanism and demulsification solution of ASRVO.

Rapid Qualitative and Quantitative Analysis of Caffeine and Sodium Benzoate in Annaca by Infrared Spectroscopy / 法医学杂志

Objective To establish an infrared spectroscopic method for the rapid qualitative and quantitative analysis of caffeine and sodium benzoate in Annaka samples. Methods Qualitative and quantitative modeling samples were prepared by mixing high-purity caffeine and sodium benzoate. The characteristic absorption peaks of caffeine and sodium benzoate in Annaka samples were determined by analyzing the infrared spectra of the mixed samples. The quantitative model of infrared spectra was established by partial least squares （PLS）. Results By analyzing the infrared spectra of 17 mixed samples of caffeine and sodium benzoate （the purity of caffeine ranges from 10% to 80%）, the characteristic absorption peaks for caffeine were determined to be 1 698, 1 650, 1 237, 972, 743, and 609 cm-1. The characteristic absorption peaks for sodium benzoate were 1 596, 1 548, 1 406, 845, 708 and 679 cm-1. When the detection of all characteristic absorption peaks was the positive identification criteria, the positive detection rate of caffeine and sodium benzoate in 48 seized Annaka samples was 100%. The linear range of PLS quantitative model for caffeine was 10%-80%, the coefficient of determination （ R2） was 99.9%, the root mean square error of cross validation （RMSECV） was 0.68%, and the root mean square error of prediction （RMSEP） was 0.91%; the linear range of PLS quantitative model for sodium benzoate was 20%-90%, the R2 was 99.9%, the RMSECV was 0.91% and the RMSEP was 1.11%. The results of paired sample t test showed that the differences between the results of high performance liquid chromatography method and infrared spectroscopy method had no statistical significance. The established infrared quantitative method was used to analyze 48 seized Annaka samples, the purity of caffeine was 27.6%-63.1%, and that of sodium benzoate was 36.9%-72.3%. Conclusion The rapid qualitative and quantitative analysis of caffeine and sodium benzoate in Annaka samples by infrared spectroscopy method could improve identification efficiency and reduce determination cost.

Identification of Armeniacae Semen Amarum and Persicae Semen from different origins based on near infrared hyperspectral imaging technology / 中国中药杂志

In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.

Identification of active components of Dendrobium inhibit growth of gastric cancer cells based on component-activity relationship / 中国中药杂志

Three cancer cell lines including gastric cancer SGC-7901, HGC-27, and MGC-803 cells were employed to evaluate the bioactivity of seven Dendrobium species. Simultaneously, these Dendrobium species were assessed with UPLC-Q-TOF-MS, and 504 common peaks were found. Based on the hypothesis that biological effects varied with differences in components, multivariate relevance analysis for chemical component-activity relationship of Dendrobium, including grey relation(GRA) and partial least squares(PLS) analysis were performed to evaluate the contribution of each identified component. The target peaks were identified by standards toge-ther with databases of Dendrobium, Nature Chemistry, MassBank, etc. Finally, four active components, including 3,5,9-trihydroxy-23-methylergosta-7,22-dien-6-one, diacylglycerol(14∶1/22∶6/0∶0), pipercitine, and 22-tricosenoic acid, might have negative effect on the growth of gastric cancer cells.

Criticalquality attribute assessment of big brand traditional Chinese medicine: visualization method for quality control of Ginkgo Leaves Tablets based on spatial distribution uniformity / 中国中药杂志

Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.

Critical quality attribute assessment of big brand traditional Chinese medicine: study on NIR field detection method of chemical properties of moisture in Tongren Niuhuang Qingxin Pills / 中国中药杂志

For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine / 中国中药杂志

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Can the Granulometric Soil Fractions Attenuate the Radiation Differently from the Whole Soil?

Abstract The purpose of this research was to discriminate soil fractions using mineralogical and elemental analyses and to show those fractions that present greater contribution to the soil mass attenuation coefficient (μ) as well as their partial cross-sections for photoelectric absorption (PA), coherent scattering (CS) and incoherent scattering (IS). Soil samples from different places of Brazil classified as Yellow Argisol, Yellow Latosol and Gray Argisol were submitted to elemental and mineralogical analyses through energy dispersive X-ray fluorescence (EDXRF) and Rietveld Method with X-ray diffraction data (RM-XRD). The mixture rule was utilized to calculate μ of each soil. The EDXRF analysis showed as predominant elements Si, Al, Fe and Ti oxides. The highest contents were Si (914.3 to 981.3 g kg-1) in the sand fractions, Al (507.9 to 543.7 g kg-1) and Fe (32.5 to 76.7 g kg-1) in the clay fractions, and Ti (18.0 to 59.0 g kg-1) in the silt fractions. The RM-XRD allowed identifying that the sand fractions are predominantly made of quartz (913.3 to 995.0 g kg-1), while the clay greatest portion is made of kaolinite (465.0 to 660.6 g kg-1) and halloysite (169.0 to 385.0 g kg-1). The main effect responsible for μ was IS (50 to 61.4%) followed by PA (28 to 40.1%) and CS (9.9 to 10.6%). By using the principal component analysis (PC-1: 57.5% and PC-2: 20.9%), the samples were differentiated through the discrimination between physical, chemical and mineralogical properties. The results obtained suggest that general information about the radiation interaction in soils can be obtained through the elemental and mineralogical analyses of their fractions.

Age determination in subadults using histological analysis of bones / Determinación de la edad en subadultos usando análisis histológico de los huesos

Abstract Introduction: According to the literature, the amount of osteons has been suggested as a good proxy to determine the age of death in adults. However in subadults research has not been carried out yet. Objective: To determine the accuracy of the histomorphometric technique predicting the age at death in subadults using bone remains. Methodology: The information of static histomorphometric parameters from about 120 iliac bones retrieved from the exhumed remains of subadults whose age at death was known was taken from the Granada collection. In order to predict the age at death we performed a step by step linear regression to estimate the fittest model. Results: The most closely and significantly associated biopsy findings with age were: the osteon count, the internal cortical width, and the trabecular bone volume. Pearson's correlation index indicated a weak linear association among these variables. To assess the accuracy of the model we used a coefficient of determination with a 0.32 value. 32% of the age variation in the subadults was explained by the three variables. Conclusion: This regression model explains a percentage of the total age variation in the subadult population. However this model is not enough to determine the age at death.

Resumen Introducción: La capacidad de predicción de las osteonas para determinar la edad de muerte de los individuos ha sido descrito en la literatura científica. No obstante, no se ha determinado dicha capacidad en individuos subadultos. Objetivo: Determinar la eficacia de lo parámetros histomorfometricos en población subadulta. Metodología: Se realizaron biopsias de hueso ilíaco en los restos de 120 subadultos, de la Colección Osteológica de Granada, con edad conocida en el momento de la muerte. Para establecer la capacidad de predicción se utilizó el R2 obtenido a partir de regresión lineal múltiple. Resultados: Las variables con mayor nivel predictivo y significativo para la estimación de la edad fueron: recuento de osteonas tipo 2 de la cortical interna y externa, y el volumen óseo trabecular; En la evaluación del modelo, se obtuvo un coeficiente de determinación de 0.32, es decir, el 32% de la variación en la edad de los subadultos se explica por el modelo. Sin embargo, se evidenció diferencias en la capacidad de predicción por sexo. Conclusión: Este modelo de regresión explica un porcentaje sustancial de la varianza de la edad de los individuos en la muestra. No obstante, no es suficiente para garantizar una adecuada predicción de la edad al momento de muerte de los individuos subdultos.

Análisis psicométrico del Body Shape Questionnaire en universitarios mexicanos / Psychometric Analysis of the Body Shape Questionnaire in Mexican University Students

RESUMEN Objetivo: Analizar las propiedades psicométricas, estructura interna y relación con indicadores antropométricos del Body Shape Questionnaire (BSQ) en universitarios mexicanos, partiendo de un enfoque de la invarianza de medición. Métodos: Se realizó un estudio instrumental, orientado a la evaluación de las propiedades psicométricas, validez y fiabilidad, del BSQ. Se realizó análisis de invarianza de la medición por el método de estimación mínimos cuadrados ponderados con varianza ajustada y correlaciones policóricas, previa evaluación de diferentes modelos de medición del BSQ en cada grupo. Las puntuaciones de la versión final se correlacionaron con indicadores antropométricos mediante el coeficiente de correlación de Pearson. Resultados: En el análisis dimensional, todos los modelos previos del BSQ presentan índices de ajuste favorables, aunque aquellos de un solo factor presente son los que tienen evidencia más robusta. Se aceptó la invarianza configural, lo que indica que la estructura unidimensional es común a varones y mujeres. Sin embargo, las cargas factoriales de 16 ítems fueron estadísticamente diferentes entre los grupos, por lo que se descartaron y se obtuvo una versión de 18 ítems (BSQ-18), que se considera invariante respecto al sexo. Además, hay relación directa entre las puntuaciones de la versión del BSQ-18 y el índice de masa corporal, la circunferencia de cintura y el porcentaje de grasa. En cuanto a la fiabilidad, se hallaron indicadores satisfactorios. Conclusiones: El BSQ-18 es aplicable tanto a varones como a mujeres y tiene indicadores de fiabilidad elevados que posibilitan su uso en entornos clínicos para la evaluación en el abordaje de trastornos de la conducta alimentaria y obesidad en jóvenes universitarios.

ABSTRACT Objective: To analyse the psychometric properties, internal structure, and relationship with anthropometric indicators of the Body Shape Questionnaire (BSQ) among Mexican university students according to the measurement invariance approach. Methods: An instrumental study was carried out to assess the psychometric properties, validity, and reliability of the BSQ. The analysis of the measurement invariance was performed using the Least Squares Estimation, and weighted by adjusted variance and polychoric correlations after assessing different measurement models for BSQ in each group. The scores of the final version were correlated with anthropometric indicators by the Pearson correlation coefficient. Results: As regards the dimensional analysis, all of the previous models for BSQ have favourable adjustment rates, although those with a single factor show more robust evidence. The configural invariance was accepted; suggesting that the one-dimensional structure is common for both men and women. However, 16-item factorial loadings were statistically different between the groups. Hence, they were discarded and an 18-item version (BSQ-18) was obtained, which is considered invariant as regards gender. In addition, there is a direct relationship between the scores of the BSQ-18 version and the body mass index, waist circumference, and fat percentage. Satisfactory indicators were found as regards stability. Conclusions: The BSQ-18 can be used with men and women, and has high reliability indicators to be conducted in clinical settings to assess eating disorders and obesity among university students