Reducing large spectral datasets to parsimonious representations of wavelengths is of value for efficient storage and easing analysis, in addition to the potential to use a simpler and cheaper spectrophotometer. This study evaluated the potential of calibrating visible and near infrared (vis‐NIR) spectra to total nitrogen (N), total carbon (C), organic C and inorganic C in soil on a 15‐ha farm, with the aim of comparing several wavelength reduction algorithms and rates in terms of model prediction accuracy. We explored the uninformative variables elimination (UVE), UVE coupled with successive projections algorithm (SPA) and two uniform‐interval wavelength reduction approaches (UWR‐I and UWR‐II) with successive wavelength reduction rates (WRRs) of 2, 5, 10, 20, 50, 100, 200, 500 and 1000. The standard normal variate (SNV)‐transformed absorbance spectra of soil samples recorded from 400 to 2499 nm at 1‐nm intervals were used. The calibration sets were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation. Prediction results showed that UVE can reduce wavelength variables significantly while retaining good model prediction accuracy. The UVE‐SPA produced only three or four wavelengths, with which PLSR models achieved competitive prediction performance, compared with those based on all 2100 wavelengths, with coefficient of determination (R2) of 0.91, 0.89, 0.91 and 0.53 and residual prediction deviation (RPD) of 3.53, 2.95, 3.27 and 1.53 for soil total N, total C, organic C and inorganic C, respectively. The UWR tests showed that PLSR models responded insensitively to various WRRs from 2 to 100. The models calibrated for the 100‐nm interval spectra (21 remaining wavelengths) performed almost as well as those for the 1‐nm interval spectra. Although these findings might be valid only at the farm scale, it is recommended that the proposed wavelength reduction algorithms for more soil types and soils originated from larger areas should be examined.

No abstract

Mammals that live in cold climates endure months of exposure to low temperature in the winter. The incidence of respiratory diseases has increased. The goal of this study was to investigate the effects of chronic cold stress on lung inflammatory networks, apoptosis, and mitochondrial function via Yorkshire pig models, as well as the ameliorative effect of glucose as energy supplements. Here, two trials were conducted (chronic cold stress and glucose supplementation). The results showed that chronic cold stress induced obvious inflammatory cell infiltration in the lungs and damaged the lung tissue structure. Compared with the Y-Con group, the expression of toll-like receptor 4 (TLR4), myeloid differentiation primary response 88 (MyD88), high mobility group box 1 (HMGB1), nucleotide-binding domain, and leucine-rich repeat protein 3 (NLRP3), IL-1β, IL-2, IL-6, and IFN-γ in the lungs of the Y-CS group was enhanced by chronic cold stress (p < 0.05). Moreover, chronic cold stress promoted the expression of the Bax and Mfn2 in lungs of Y-CS group (p < 0.05). Interestingly, dietary glucose supplementation significantly reduced inflammatory cell infiltration in the lungs. Moreover, glucose supplementation inhibited the expression of TLR4, MyD88, HMGB1, NLRP3, IL-1β, IL-2, IL-6, IFN-γ, and Bax during chronic cold stress. In conclusion, chronic cold stress promoted inflammatory networks, apoptosis, and mitochondrial fusion in the lungs. Dietary glucose supplementation inhibited the inflammatory network during chronic cold stress.

No abstract

. Instrumental measurement of soil properties are affected by several factors including soil texture. The classification of soil texture can help improve the accuracy of calibration models for soil measurement. In the study, the effect of soil particle size on the selection of preprocessing methods for principle component analysis (PCA) of soil classification was examined. Total 178 soil samples with five textures were collected from Silsoe Farm, Cranfield, England. After dried and ground, soil samples sieved by a 2mm sieve were named by Mixed Group. A Vis/NIR spectrophotometer (LabSpec2500, spectral range 350-2200nm, ASD) was used for spectral scanning of soil samples. After that, all samples were sieved by a 1mm sieve and divided into two groups: one with particle size less than 1mm named by Thin Group and another with particle size between 1mm and 2mm named by Thick Group. Preprocessing methods of moving average with segment size of 5(MA5), standard normal variation (SNV) and 1st Savitzky-Golay derivatives with smoothing points of 3(Der1(3)) were examined. PCA was applied to evaluate the discriminative capacity of MA5, MA5+SNV and MA5+Der1(3). The score plots of 1st~2nd and 2nd~3rd PCs show that MA5+Der1(3) is the best preprocessing method not only for Thick Group and Thin Group, but also for Mixed Group. MA5+SNV is suitable for Thick Group and Thin Group but does not perform well for Mixed Group. Only MA5 does not perform well in any of three groups. The study suggests that pre-processing with 1st derivative is an essential step for soil classification with various particle sizes using Vis/NIR spectroscopy.