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To explore the best method for online detection of blood spot eggs, fiber-optic spectrometer was used to collect the transmission spectra of eggs. Three different kinds of wavebands were extracted using competitive adaptive reweighed sampling (CARS), interval partial least squares (IPLS) and successive projections algorithm (SPA), respectively. The discrimination results of the models established with partial least square discriminant analysis (PLSDA) and support vector machine (SVM) showed that the CARS-screened wavebands had the best modeling results. Through stepwise Bayes discriminant analysis (SBDA) based on the spectral characteristic variables of CARS, five wavelength variables (509, 511, 526, 571 and 599 nm) were determined to be the characteristic variables for the detection of blood spot eggs finally. The detection accuracy of blood spot eggs and normal eggs by Bayes discriminant model using these five variables was 95%. These results show that stepwise Bayes discriminant model can effectively simplify the characteristic wavebands and improve the prediction accuracy, indicating that it can be applied to the real-time online detection of blood spot eggs. Practical Applications This study explore three different kinds of spectral characteristic variables were extracted using CARS, IPLS and SPA, respectively, only five characteristic variables are required in the stepwise Bayes discriminant model. This model can avoid the influence of excessive spectral variables on the accuracy of the model, so it is feasible to apply this model to real-time online detection of blood spot eggs. [ABSTRACT FROM AUTHOR]

Objective: To research the diagnostic performance of clinical potential bone turnover indexes in rheumatoid arthritis (RA) complicated with osteoporosis (OP)., Methods: This study involved 87 RA patients, 48 with OP, and 39 without OP, and 204 non-RA control patients, including those with systemic lupus erythematosus, ankylosing spondylitis, primary Sjogren's syndrome, systemic sclerosis, and healthy patients. The levels of 25-hydroxyvitamin D [25(OH)D], β-crosslaps (β-CROSSL), parathyroid hormone (PTH) were measured by electrochemiluminescence (ECLIA), and the level of bone alkaline phosphatase (BALP) was measured by lectin affinity method., Results: The serum concentration of 25(OH)D in the RA with OP group was significantly lower than the control group ( P <0.01), while the levels of β-CROSSL, BALP in the RA with OP group considerably exceeded those found in the control group ( P <0.01). The levels of β-CROSSL and PTH were significantly higher in RA patients with OP than without OP ( P <0.01), while the level of 25(OH)D was statistically lower than without OP ( P <0.01). An unconditional logistical regression analysis proved an association with low 25(OH)D and elevated β-CROSSL in RA with OP, with 25(OH)D demonstrating greatest diagnostic potential according to the ROC curve., Conclusion: The significantly reduced levels of 25(OH)D and excessive β-CROSSL may indicate a high risk of the secondary osteoporosis in RA patients., (© 2018 by the Association of Clinical Scientists, Inc.)

This research collected the transmission hyper-spectral data of eggs with hyper-spectral imager. Haugh unit value was used as freshness norm. With the help of MATLAB and SAS software combined with stechiometry method, the hyper-spectral data of sample eggs was analyzed and processed. The prediction model of egg freshness was established based on hyper-spectral technology. The research chose the band range from 500 to 1 000 nm as sensitive band. The hyper-spectral data of abnormal samples were removed by using mahalanobis distance. Differential correction was done on hyper-spectral data. After the comparison, there was a high linearity between the second-order differential data of hyper-spectra and haugh unit value. Therefore, this paper conducted a further research on the second-order differential data of hyper-spectra. And it was treated with wavelet denoising, smoothing and standardizing. This paper chose the newly proposed CARS variable selection method to do dimensionality reduction on hyper-spectral data. And thirty-two characteristic parameters were extracted. They were used to establish partial least square prediction model based on all band and multiple regression model based on characteristic parameters on white shell eggs. The correlation coefficients of white shell eggs were 0.88 and 0.93 respectively, and the corresponding mean square errors being 7.565 and 6.44. Inspections were conducted on PLS prediction model based on all band hyper-spectral second-order differential and multiple regression model based on characteristic parameters by using eggs of validation set. The accuracy rates of these two models to discriminate white shell eggs’ freshness and non-freshness were 100% and 88% respectively.

The freshness of egg is an important index to reflect the internal quality. In order to achieve non-destructive detection of freshness, micro fiber spectrometer was used to sample 550～950 nm transmittance spectra of eggs which performed quantitative analysis with haugh unit of eggs. Different pretreatment was combined with partial least squares regression(PLS) and support vector regression(SVR) respectively to find that first derivative combined with SVR predicted better than others through comparison, and it was better to model by SVR than by PLS. In order to improve efficiency and decrease adverse effects of useless information for modeling, the linear dimensionality reduction with principal component analysis (PCA) and the nonlinear dimensionality reduction with locally linear embedding(LLE) were used for the data of first derivative respectively. It indicated that LLE was better than PCA after comparison, and the correlation coefficient of calibration and prediction were 92.2%, 91.1%, and the root mean square error were 7.21, 8.80. The root mean square error of cross validation decreased 0.79.The experimental result illustrated that the nonlinear model of LLE combined with SVR improved predictive performance of egg freshness. It is feasible for the detection of visible/near-infrared spectrum of egg freshness to apply this method.

Due to the harmfulness of melamine to human, the quantitative detection of melamine in egg is very necessary. In the present study, the surface enhanced Raman spectra technology combined with chemometric analysis method was used to conduct melamine quantitative detection in egg white. Firstly, the melamine egg sample could be got by the method of artificial feeding hens usingdifferent feeding formulation. Then the surface enhanced Raman spectra of egg white was determined using portable Raman spectroscopy (Opto Trace RamTracer-200) and Raman enhancement reagents, and the melamine content within the white eggs was measured with gas chromatography mass spectrometry technology. The software of Raman Analyzer was used for baseline correction of Raman spectra. The correlation coefficient method was used to choose 320 spectral variables from the surface enhanced Raman spectroscopy as input variables to establish partial least squares quantitative calibration model . And the peaks-decomposition method was used to establish peaks-decomposition quantitative calibration model. Both models selected 90 and 44 samples respectively as calibration sets and validation sets during model establishment, and both models achieved good prediction effect. The determination coefficient between predicted values of partial least squares quantitative calibration model and measured values of gas chromatography mass spectrometry was 0.856, and root mean square error of prediction was 1.547. The determination coefficient was 0.947 and RMSEP was 0.893 for the peaks-decomposition quantitative calibration model. This study demonstrated that the method can effectively quantitatively detect melamine in eggs. Testing a sample only takes 15 minutes, which can provide a new way for the melamine egg detection.

Objective: To investigate the species composition and distribution of chigger mites on small mammals in flatland area in Menghan, Xishuangbanna of Yunnan Province., Methods: The field investigation was made in a flatland area near Lancangjiang River in Menghan, Xishuangbanna of Yunnan Province. Small mammals were captured with mouse cages and traps. All mites on the hosts were collected and preserved in 70% ethanol. Hoyer's solution was used to mount the chiggers on glass slides. The specimens of the chigger mites on the slides were finally identified into species under microscope. The constituent ratio, infestation rate, mean abundance and mean intensity of chigger mites in different habitats or on different hosts were used to measure the community structure. The species richness and community diversity were analyzed., Results: A total of 233 small mammal hosts were captured (belonging to 2 families, 3 genera and 5 species). 5 763 individuals of chigger mites were identified as 2 subfamilies, 7 genera, and 45 species. Rattus tanezumi (R. flavipectus) was the dominant species among the captured hosts, accounting for 97.4% (227/233). The mite infestation rate, average ectoparasite abundance, and mean mite intensity on R. tanezumi was 56.4% (128/227), 24.7 (5 600/227) and 43.8 (5 600/128), respectively. Leptotrombidium deliense was dominant chigger mite species and account for 57.9% (3 337/5 763), mainly infested R. tanezumi. Compared with indoor and cultivated field habitats, the species richness and community diversity of chigger mites in shrub habitat were higher, and 41 species of chigger mites were collected., Conclusion: The species composition and community structure is relatively simple in the flatland area in Xishuangbanna. L. deliense is the most dominant species of chigger mites and its main host is R. tanezumi.

The detection of the infertile eggs and fertile eggs by the near infrared diffuse reflectance spectra was proposed. Models based on different band regions range, different principal component numbers and the different spectral pre-processing methods were compared and the optimal calibration model was established. The results show that qualitative forecasting model of hatching eggs is established by Mahalanobis Distance, which is with band regions range being 4 119.20-9 881.46 cm(-1), principal component number being 19 and spectral pre-processing method being SNV + first derivative + Norris differential filter. The precision rate of calibration set is 92.5% and that of validation set is 91.67%. The study provides a new way for nondestructive testing of the fertile eggs and infertile eggs prior to incubation.

In order to investigate the area distribution and host selection of Leptotrombidium scutellare in Yunnan Province, a field survey was carried out during 2001 to 2009, based on different geographic location, topography, climate and ecological characteristics. A total of 16 491 L. scutellare were captured from the body surface of 9 838 small mammal hosts of 7 families, 18 genera, and 30 species in 4 orders, accounted for 17.73% (16,491/92,990) of all chigger mites collected. L. scutellare distributed in 12 counties, more in the northwest and south of the Province. Although L. scutellare could parasitize on different small mammal species, most of them were on Eothenomys miletus and Apodemus chevrieri.

Eothenomys miletus were captured in 19 counties of Yunnan Province. The distribution of species-abundance and the species-plots relationship between E. miletus and chigger mites were analyzed by using ecological statistic method. There were totally 40,052 chigger mites collected from the body surface of 1741 E. miletus. 111 species of chigger mites were identified. The species-abundance distribution showed that with the increase of mite individuals, the number of chigger mite species gradually decreased. Most mite species were rare ones. Species-plot relation indicated that with the number of mouse plots (samples of E. miletus) increasing, the number of chigger mite species increased. E. miletus collected quantity up to date still could not reflect the exact species richness of chigger mite.

Objective: To investigate the distribution of Leptotrombidium deliense among different small mammal hosts in some areas of Yunnan province., Methods: A field survey was carried out in some counties of Yunnan province and the small mammal hosts were captured, using mouse cages and traps with baits. Chigger mites on the surface of two auricles were scraped off by a bistoury, and then preserved in 70% ethanol. Every specimen of the chigger mites on the slides was finally identified into species under a microscope. Some conventional statistical methods were adopted to calculate all the collected chigger mite species and the constituent ratios of Leptotrombidium deliense in different areas and on different hosts, together with its prevalence and mean abundance on different hosts., Results: A total of 10 222 small mammal hosts were captured from 19 counties and identified as 11 families, 34 genera and 62 species in 5 orders, and 92 990 individuals of chigger mites were collected from the body surface of these small mammal hosts. All the collected chigger mites were identified as 3 subfamilies, 22 genera, and 225 species. Meanwhile, Leptotrombidium deliensee only accounted for 1.659% of the total. The host specificity of Leptotrombidium deliense was very low and 1544 individuals of Leptotrombidium deliense collected from 8518 small mammal hosts belonged to 6 families, 13 genera and 19 species in 3 orders. Our results showed that Leptotrombidium delienses were mainly collected from Insectivora and Rodentia. Leptotrombidium deliense had long been considered as the dominant species of chigger mites and the main vector of tsutsugamushi disease in Yunnan province of China, but our results seemed not thoroughly supporting this point of view., Conclusion: Traditionally, Leptotrombidium deliense was the dominant species and the main vectors of scrub typhus in Yunnan province. However, based on our results, the above view might be true in some local places and the composition of chigger mites and the main vector of tsutsugamushi disease might be different in regions and habitats in Yunnan province.