S100A9 blockade prevents lipopolysaccharide-induced lung injury via suppressing the NLRP3 pathway.

BACKGROUND: S100 calcium binding protein A9 (S100A9) is a pro-inflammatory alarmin associated with several inflammation-related diseases. However, the role of S100A9 in lung injury in sepsis has not been fully investigated. Therefore, the present study aimed to determine the role of S100A9 in a lipopolysaccharide (LPS)-induced lung injury murine model and its underlying molecular mechanisms. METHODS: LPS was utilized to induce sepsis and lung injury in C57BL/6 or NOD-like receptor family pyrin domain containing 3 (NLRP3)-/- mice. To investigate the effects of S100A9 blockade, mice were treated with a specific inhibitor of S100A9. Subsequently, lung injury and inflammation were evaluated by histology and enzyme­linked immunosorbent assay (ELISA), respectively. Furthermore, western blot analysis and RT-qPCR were carried out to investigate the molecular mechanisms underlying the effects of S100A9. RESULTS: S100A9 was upregulated in the lung tissues of LPS-treated mice. However, inhibition of S100A9 alleviated LPS-induced lung injury. Additionally, S100A9 blockade also attenuated the inflammatory responses and apoptosis in the lungs of LPS-challenged mice. Furthermore, the increased expression of NLRP3 was also suppressed by S100A9 blockade, while S100A9 blockade had no effect on NLRP3-/- mice. In vitro, S100A9 downregulation mitigated LPS-induced inflammation. Interestingly, these effects were blunted by NLRP3 overexpression. CONCLUSION: The results of the current study suggested that inhibition of S100A9 could protect against LPS-induced lung injury via inhibiting the NLRP3 pathway. Therefore, S100A9 blockade could be considered as a novel therapeutic strategy for lung injury in sepsis.

Associations between I/D polymorphism in the ACE gene and lung cancer: an updated systematic review and a meta-analysis.

BACKGROUND: We evaluated the association between the I/D polymorphism in the ACE gene and lung cancer risk by performing a meta-analysis. METHODS: The heterogeneity in the study was tested using the Cochran χ2-based Q statistic test and I2 test, and then the random ratio or fixed effect was utilized to merge the odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the strength of the association between ACE polymorphisms and susceptibility to lung cancer. Sensitivity analysis was also performed. Using funnel plot and Begg's rank test, we investigated the publication bias. All statistical analyses were performed using Stata 12.0 and RevMan 5.3. RESULTS: A total of 4307 participants (2181 patients; 2126 controls) were included in the 12 case-control studies. No significant association was found between the ACE I/D polymorphism and lung cancer risk (II vs. ID + DD: OR = 1.22, 95% CI = 0.89-1.68; II + ID vs. DD: OR = 1.21, 95% CI = 0.90-1.63; I vs. D: OR = 1.15, 95% CI = 0.95-1.39). In the subgroup analysis by ethnicity, no significant association between the ACE I/D polymorphism and lung cancer risk was found among Asian and Caucasian populations for the comparisons of II vs. ID + DD, II + ID vs. DD, and I vs. D genetic models. CONCLUSION: The ACE I/D polymorphism is not associated with the risk of lung cancer.

A stepwise method for estimating optical properties of two-layer turbid media from spatial-frequency domain reflectance.

This research was conducted to estimate the optical absorption and reduced scattering coefficients of two-layer turbid media using a stepwise method from the spatial-frequency domain reflectance generated by Monte Carlo (MC) simulation. The stepwise method's feasibility for optical property estimations was first investigated by comparing the reflectance generated by the diffusion model and MC simulation for one-layer and two-layer turbid media. The results showed that, with proper frequency selection, the one-layer model could be used for estimating the optical properties of the first layer of the two-layer turbid media. A sample-based calibration method was proposed for calibrating discrepancies of the reflectance between the diffusion model and MC simulation. This significantly improved the parameter estimation accuracy. Results showed that the stepwise method's parameter estimation efficacy and accuracy were much better than that for the one-step method. This was especially true when estimating the absorption coefficient. Absolute error contour maps were generated in order to determine the constraining conditions for the first-layer thickness. It was found that, when each layer's optical properties are within the range of 0.005 mm-1 ≤ µa ≤ 0.04 mm-1 and 0.69 mm-1 ≤ µs'≤ 2.2 mm-1, the first-layer's minimum thickness-for which the first layer's optical properties could be accurately estimated-could be as small as 0.2 mm. Further, the first layer's maximum thickness could not exceed 2.0 mm, in order to have acceptable estimations of the optical properties of the second layer.

Prediction of canned black bean texture (Phaseolus vulgaris L.) from intact dry seeds using visible/near infrared spectroscopy and hyperspectral imaging data.

BACKGROUND: Texture is a major quality parameter for the acceptability of canned whole beans. Prior knowledge of this quality trait before processing would be useful to guide variety development by bean breeders and optimize handling protocols by processors. The objective of this study was to evaluate and compare the predictive power of visible and near infrared reflectance spectroscopy (visible/NIRS, 400-2498 nm) and hyperspectral imaging (HYPERS, 400-1000 nm) techniques for predicting texture of canned black beans from intact dry seeds. Black beans were grown in Michigan (USA) over three field seasons. The samples exhibited phenotypic variability for canned bean texture due to genetic variability and processing practice. Spectral preprocessing methods (i.e. smoothing, first and second derivatives, continuous wavelet transform, and two-band ratios), coupled with a feature selection method, were tested for optimizing the prediction accuracy in both techniques based on partial least squares regression (PLSR) models. RESULTS: Visible/NIRS and HYPERS were effective in predicting texture of canned beans using intact dry seeds, as indicated by their correlation coefficients for prediction (Rpred ) and standard errors of prediction (SEP). Visible/NIRS was superior (Rpred = 0.546-0.923, SEP = 7.5-1.9 kg 100 g-1 ) to HYPERS (Rpred = 0.401-0.883, SEP = 7.6-2.4 kg 100 g-1 ), which is likely due to the wider wavelength range collected in visible/NIRS. However, a significant improvement was reached in both techniques when the two-band ratios preprocessing method was applied to the data, reducing SEP by at least 10.4% and 16.2% for visible/NIRS and HYPERS, respectively. Moreover, results from using the combination of the three-season data sets based on the two-band ratios showed that visible/NIRS (Rpred = 0.886, SEP = 4.0 kg 100 g-1 ) and HYPERS (Rpred = 0.844, SEP = 4.6 kg 100 g-1 ) models were consistently successful in predicting texture over a wide range of measurements. CONCLUSION: Visible/NIRS and HYPERS have great potential for predicting the texture of canned beans; the robustness of the models is impacted by genotypic diversity, planting year and phenotypic variability for canned bean texture used for model building, and hence, robust models can be built based on data sets with high phenotypic diversity in textural properties, and periodically maintained and updated with new data. © 2017 Society of Chemical Industry.

Finite element simulation of light transfer in turbid media under structured illumination.

The spatial-frequency domain (SFD) imaging technique allows us to estimate the optical properties of biological tissues in a wide field of view. The technique is, however, prone to error in measurement because the two crucial assumptions used for deriving the analytical solution to the diffusion approximation cannot be met perfectly in practical applications. This research mainly focused on modeling light transfer in turbid media under the normal incidence of structured illumination using the finite element method (FEM). Finite element simulations were performed for 50 simulation samples with different combinations of optical absorption and scattering coefficients for varying spatial frequencies, and the results were then compared with the analytical method and Monte Carlo simulation. Relationships between diffuse reflectance and dimensionless absorption and dimensionless scattering coefficients were investigated. The results indicated that the FEM provided reasonable results for diffuse reflectance, compared with the analytical method. Both the FEM and the analytical method overestimated the reflectance for µtr/fx values of greater than 2 and underestimated the reflectance for µtr/fx values of smaller than 2. Larger values of µs'/µa yielded better diffuse reflectance estimations than did those of smaller than 10. The reflectance increased nonlinearly with the dimensionless scattering, whereas the reflectance decreased linearly with the dimensionless absorption. It was also observed that diffuse reflectance was relatively stable and insensitive to µs' when the dimensionless scattering was larger than 50. Overall results demonstrate that the FEM is effective for modeling light transfer in turbid media and can be used to explore the effects of crucial parameters for the SFD imaging technique.

Finite element modeling of light propagation in turbid media under illumination of a continuous-wave beam.

Spatially resolved spectroscopy provides a means for measuring the optical properties of biological tissues, based on analytical solutions to diffusion approximation for semi-infinite media under the normal illumination of an infinitely small light beam. The method is, however, prone to error in measurement because the actual boundary condition and light beam often deviate from that used in deriving the analytical solutions. It is therefore important to quantify the effect of different boundary conditions and light beams on spatially resolved diffuse reflectance in order to improve the measurement accuracy of the technique. This research was aimed at using finite element method (FEM) to model light propagation in turbid media, subjected to normal illumination by a continuous-wave beam of infinitely small or finite size. Three types of boundary conditions [i.e., partial current (PCBC), extrapolated (EBC), and zero (ZBC)] were evaluated and compared against Monte Carlo (MC) simulations, since MC could provide accurate fluence rate and diffuse reflectance. The effect of beam size was also investigated. Overall results showed that FEM provided results as accurate as those of the analytical method when an appropriate boundary condition was applied. ZBC did not give satisfactory results in most cases. FEM-PCBC yielded a better fluence rate at the boundary than did FEM-EBC, while they were almost identical in predicting diffuse reflectance. Results further showed that FEM coupled with EBC effectively simulated spatially resolved diffuse reflectance under the illumination of a finite size beam. A large beam introduced more error, especially within the region of illumination. Research also confirmed an earlier finding that a light beam of less than 1 mm diameter should be used for estimation of optical parameters. FEM is effective for modeling light propagation in biological tissues and can be used for improving the optical property measurement by the spatially resolved technique.

Gram-Schmidt orthonormalization for retrieval of amplitude images under sinusoidal patterns of illumination.

Structured illumination using sinusoidal patterns has been used for optical imaging of biological tissues in biomedical research, and of horticultural products in food quality evaluation. Implementation of structured-illumination imaging relies on retrieval of amplitude images, which is conventionally achieved by a phase-shifting technique that requires collecting a minimum of three phase-shifted images. In this study, we have proposed Gram-Schmidt orthonormalization (GSO) to retrieve amplitude component (AC) images using only two phase-shifted images. We have proposed two forms of GSO implementation, and prior to GSO processing, we eliminated the direct component (DC) background by subtracting a DC image we recovered using a spiral phase function (SPF) in the Fourier space. We demonstrated the GSO methods through numerical simulations and application examples of detection of bruise defects in apples by structured-illumination reflectance imaging (SIRI). GSO performed comparably to conventional three-phase-based demodulation. It is simple, fast and effective for amplitude retrieval and requires no prior phase information, which could facilitate fast implementation of structured-illumination imaging.

Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging.

Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers.

Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical properties of biological materials.

This paper reports on the optimization and assessment of a hyperspectral imaging-based spatially-resolved system for determination of the optical properties of biological materials over the wavelengths of 500-1,000 nm. Twelve model samples covering a wide range of absorption and reduced scattering coefficients were created to validate the hyperspectral imaging system, and their true values of absorption and reduced scattering coefficients were determined and then cross-validated using three commonly used methods (i.e., transmittance, integrating sphere, and empirical equation). Light beam and source-detector distance were optimized through Monte Carlo simulations and experiments for the model samples. The optimal light beam should be of Gaussian type with the diameter of less than 1 mm, and the optimal minimum and maximum source-detector distance should be 1.5 mm and 10-20 mean free paths, respectively. The optimized hyperspectral imaging-based spatially-resolved system achieved good estimation of the optical parameters.

Assessment of the optical properties of peaches with fungal infection using spatially-resolved diffuse reflectance technique and their relationships with tissue structural and biochemical properties.

This research measured the optical absorption (µa) and reduced scattering (µs') properties in peaches during quality deterioration, and determine the relationships of the optical parameters with select structural and biochemical parameters. Spatially resolved reflectance was measured for healthy and fungal infected peaches, followed by physical (the size and tissue color), structural [membrane permeability and SEM], and biochemical (Vc, soluble sugar, titratable acid, chlorophyll, total phenolic content) measurements. Both µa and µs' were correlated well with the cellulosic structural and biochemical parameters of peaches, and they had the best correlations with those quality parameters at 675 nm. The correlation of µs' with membrane permeability was the highest from -0.962-0.743, while µa had the best correlation with the chlorophyll content at 675 nm which is an indicator of plant maturation and senescence. These findings would be useful for further development of an effective optical technique for early disease detection of peach fruit.

Quantification of the optical properties of two-layer turbid materials using a hyperspectral imaging-based spatially-resolved technique.

Recent research has shown that a hyperspectral imaging-based spatially-resolved technique is useful for determining the optical properties of homogenous fruits and food products. To better characterize fruit properties and quality attributes, it is desirable to consider fruit to be composed of two homogeneous layers of skin and flesh. This research was aimed at developing a nondestructive method to determine the absorption and scattering properties of two-layer turbid materials with the characteristics of fruit. An inverse algorithm along with the sensitivity coefficient analysis for a two-layer diffusion model was developed for the extraction of optical properties from the spatially-resolved diffuse reflectance data acquired using a hyperspectral imaging system. The diffusion model and the inverse algorithm were validated with Monte Carlo simulations and experimental measurements from solid model samples of known optical properties. The average errors of determining two and four optical parameters were 6.8% and 15.3%, respectively, for Monte Carlo reflectance data. The optical properties of the first or top layer of the model samples were determined with errors of less than 23.0% for the absorption coefficient and 18.4% for the reduced scattering coefficient. The inverse algorithm did not give acceptable estimations for the second or lower layer of the model samples. While the hyperspectral imaging-based spatially-resolved technique has the potential to measure the optical properties of two-layer turbid materials like fruits and food products, further improvements are needed in determining the optical properties of the second layer.

Prognostic value of lncRNA ROR expression in various cancers: a meta-analysis.

Background: There is a dispute on the prognostic value of long non-coding RNA regulator of reprogramming (lncRNA ROR) in cancers. The purpose of the present study was to evaluate the prognostic significance of lncRNA ROR expression in human cancers. Methods: PubMed, Embase, and Cochrane Library were searched to look for relevant studies. The meta-analyses of prognostic and clinicopathological parameters (CPs) were conducted. Results: A total of ten studies were finally included into the meta-analysis. High lncRNA ROR expression was significantly associated with shorter overall survival (hazard ratio [HR] = 2.88, 95% confidence interval [CI] = 2.16-3.84, P<0.01) and disease-free survival (HR = 3.25, 95% CI = 2.30-4.60, P<0.01) compared with low lncRNA ROR expression. Besides, high lncRNA ROR expression was obviously related to more advanced clinical stage (P<0.01), earlier tumor metastasis (P=0.02), lymph node metastasis (P<0.01), and vascular invasion (P<0.01) compared with low lncRNA ROR expression. However, there was no significant correlation between lncRNA ROR expression and other CPs, including age (P=0.18), gender (P=0.33), tumor size (P=0.25), or tumor differentiation (P=0.13). Conclusion: High lncRNA ROR expression was associated with worse prognosis in cancers. LncRNA ROR expression could serve as an unfavorable prognostic factor in various cancers.

Measurement of the absorption and scattering properties of turbid liquid foods using hyperspectral imaging.

A hyperspectral imaging system in line scanning mode was used for measuring the absorption and scattering properties of turbid food materials over the visible and near-infrared region of 530-900 nm. An instrumental calibration procedure was developed to compensate for the nonuniform instrument response of the imaging system. A nonlinear curve-fitting algorithm for a steady-state diffusion theory model was proposed to determine absorption (mua) and reduced scattering coefficients (mu's) from the spatially resolved hyperspectral reflectance profiles. The hyperspectral imaging system provided good measurement of mua and mu's for the simulation samples made of Intralipid scattering material and three absorbers (blue dye, green dye, and black ink) with average fitting errors of 16% and 11%, respectively. The optical properties of the fruit and vegetable juices and milks were determined. Values of the absorption and reduced scattering coefficient at 600 nm were highly correlated to the fat content of the milk samples with the correlation coefficient of 0.995 and 0.998, respectively. Compared to time-resolved and frequency-domain techniques, the hyperspectral imaging technique provides a faster and simpler means for measuring the optical properties of turbid food and agricultural products.

Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy.

Visible and near-infrared spectra in interactance mode were acquired for intact and sliced beet samples, using two portable spectrometers for the spectral regions of 400-1100 nm and 900-1600 nm, respectively. Sucrose prediction models for intact and sliced beets were developed and then validated. The spectrometer for 400-1100 nm was able to predict the sucrose content with correlations of prediction (rp) of 0.80 and 0.88 and standard errors of prediction (SEPs) of 0.89% and 0.70%, for intact beets and beet slices, respectively. The spectrometer for 900-1600 nm had rp values of 0.74 and 0.88 and SEPs of 1.02% and 0.69% for intact beets and beet slices. These results showed the feasibility of using the portable spectrometer to predict the sucrose content of beet slices. Using simple correlation analysis, the study also identified important wavelengths that had strong correlation with the sucrose content.

Hyperspectral diffuse reflectance imaging for rapid, noncontact measurement of the optical properties of turbid materials.

We present a method and technique of using hyperspectral diffuse reflectance for rapid determination of the optical properties of turbid media. A hyperspectral imaging system in line scanning mode was used to acquire spatial diffuse reflectance profiles from liquid phantoms made up of absorbing dyes and fat emulsion scatterers over the spectral range of 450-1000 nm instantaneously. The hyperspectral reflectance data were analyzed by using a steady-state diffusion approximation model for semi-infinite homogeneous media. A calibration procedure was developed to compensate the nonuniform instrument response of the imaging system, and a curve-fitting algorithm was used to extract absorption and reduced scattering coefficients (mua and mus', respectively) for the phantoms in the wavelength range from 530 to 900 nm. The hyperspectral imaging system gave good measures of mua and mus' for the phantoms with average fitting errors of 12% and 7%, respectively. The hyperspectral imaging technique is fast, noncontact, and easy to use, which makes it especially suitable for measurement of the optical properties of turbid liquid and solid foods.