Quantitative analysis of the spatial diversity of Moraceae in China

Changes in distribution patterns of economically essential forest species under global change are urgently needed in the scientific forecast, and large-scale spatial modeling is a crucial tool. Using diversity pattern indicators and other data obtained through geographic information systems (GIS) and spatial data on Moraceae species obtained from published data, we quantitatively studied the spatial diversity patterns of genera in the Moraceae in China. The results revealed that the patch richness, diversity index, and total shape index of the genera with multiple species were significantly higher than those of the monotypic genera. Monotypic genera had no spatial diversity and no distribution in patterns of spatial diversity. Maclura had the most concentrated spatial distribution and the lowest distribution area among the Moraceae in China. The number of patches and the total area were the smallest, while the most significant patch index was the highest. Maclura had no spatial diversity. Streblus had the highest patch abundance compared to other genera with fewer species. Streblus had the smallest number of patches and total area of distribution, the lowest spatial distribution, and a small total shape index, indicating its concentrated distribution. The values of the Shannon’s Diversity Index (SHDI) and Simpson’s Diversity Index (SIDI) were the highest, and the spatial distribution was the most diverse among the genera with fewer species. The patch type of Streblus had a more considerable value than other genera, but the number of patches was small, and the total shape index was low. Streblus was primarily distributed in the south of Yunnan, western Guangxi, the west and central parts of Hainan, and southern Guangdong. Most of these areas were mountainous. The temperature decreased with elevation, providing diverse environmental conditions for the narrow-stem genus. Among the Moraceae in China, the spatial distribution of Ficus was the most diverse, with the highest number of patches, patch types, total shape index, SHDI, and SIDI values. The spatial diversity of Ficus could be used as a protected area for Moraceae in China

Low-Pass Parabolic FFT Filter for Airborne and Satellite Lidar Signal Processing

In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing

Low-Pass Parabolic FFT Filter for Airborne and Satellite Lidar Signal Processing

In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing

Additional file 1 of Estrogen receptor β deficiency impairs gut microbiota: a possible mechanism of IBD-induced anxiety-like behavior

Additional file 1: Table S1. Scoring system for histological changes in the colon. Table S2. The sequences of primers used in this study. Figure S1. ERβ deficiency did not influence the sensorimotor function, memory function, or social interactions in mice following induced experimental colitis. (A) Nest score in the nest building test was performed among the four groups to detect the sensorimotor functions. (B) Spatial memory was assessed by percentage of spontaneous alterations in the Y maze test. (C) Recognition memory was detected by the discrimination index in the novel object recognition test. (D, E) Time spent in each chamber and time spent in sniffing a novel mouse or novel object were used to test the sociability in the social approach period (D). Social recognition was evaluated by the time spent in each chamber and time sniffing familiar mouse or novel mouse in social novelty period (E). Data are presented as mean ± SEM. Statistical comparisons were performed by two-way ANOVA or paired t-test for the three-chamber test. n = 8/group. *P < 0.05, **P < 0.01. Figure S2. Fecal microbiota of WT and ERβ−/− mice under baseline and inflammatory states at the class and order levels. (A) Bar graph of bacterial abundance at the class level. (B) Relative abundances of substantially changed bacterial taxa at the class level. (C) Bar graph of bacterial abundances at the order level. (D) Relative abundances of substantially changed bacterial taxa at the order level. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 9/group, except for n = 8 in the WT DSS group. *P < 0.05. Figure S3. ERβ deficiency does not influence gut microbiota composition in adult female mice. (A) Community richness calculated by observed OTUs. (B, C) Principal coordinates analysis of microbial unweighted UniFrac compositional differences (B), quantified by UniFrac distance (C) between WT and ERβ−/− female mice. (D) Taxonomic cladogram obtained using LEfSe analysis. (E–G) Bar graph of bacterial abundances at the phylum (E), family (F), and genus (G) levels. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 5/group. Figure S4. Tight junctions in WT and ERβ−/− mice under the baseline and inflammatory states on day 5 post-DSS treatment. (A) Representative images of immunofluorescence staining for tight junction proteins (occludin and ZO-1) in the distal colon of WT and ERβ−/− mice under homeostatic conditions and 5 days following DSS treatment. Scale bar = 100 μm. (B–C) Quantitative real-time PCR analysis of mRNA expressions of occludin and ZO-1 in whole colon tissues of WT and ERβ−/− male mice under homeostatic conditions and 5 days following DSS treatment. n = 7-8/group. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01. Figure S5. ERβ deficiency aggravated the development of DSS-induced colitis on day 10 after initial DSS exposure. (A, B) Mice were sacrificed on day 10 after DSS treatment to measure the colon length. n = 7/group. (C) Histology of distal colon tissues collected at day 10 was examined by hematoxylin and eosin (HE) and Alcian Blue Periodic Acid Schiff (AB-PAS) staining. Scale bars = 100 μm. (D–G) Composite score of histopathology (inflammation, ulceration, and crypt damage scores). n = 7/group. *P < 0.05, **P < 0.01, ***P < 0.001. Figure S6. Tight junctions in WT and ERβ−/− mice under baseline and inflammatory states on day 10 post-DSS treatment. (A) Tight junctions and villi in the colonic epithelium were examined under an electron microscope (scale bar = 2 or 1 μm as indicated in figure), and representative images of immunofluorescence staining (scale bars = 100 μm) of tight junction proteins (occludin and ZO-1) in the distal colon of WT and ERβ−/− mice under homeostasis conditions and day 10 following DSS treatment. (B–C) Quantitative real-time PCR analysis of mRNA expressions of occludin and ZO-1 in whole colon tissues of WT and ERβ−/− male mice under homeostatic conditions and 10 days following DSS treatment. n = 9/group. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01. Figure S7. ERβ deficiency did not significantly influence the neuroinflammation status compared with WT mice after DSS treatment. (A, B) Diagrams, representative images (A), and quantitative analysis (B) of Iba1-positive cells in mPFC. (C, D) Diagrams, representative images (C), and quantitative analysis (D) of Iba1-positive cells in the amygdala. (E, F) Diagrams, representative images (E), and quantitative analysis (F) of Iba1-positive cells in the ventral hippocampus (including CA1, DG, and CA3 areas). (G, H) Diagrams, representative images (G), and quantitative analysis (H) of Iba1-positive cells in the dorsal hippocampus (including CA1, DG, and CA3 areas). Scale bars = 200 μm for lower magnification, and 100 μm for the higher magnification. n = 4/group. Data are presented as mean ± SEM. Statistical comparisons were performed using two-way ANOVA. *P < 0.05, **P < 0.01. Figure S8. mRNA expression levels of hypothalamic neuropeptides and hierarchical clustering of the 934 overlapping genes. (A) Hierarchical clustering heatmap of several hypothalamic neuropeptide gene expression profiles (Crh, Sst, Npy, Agrp, Vip, Avp, Gal, Oxt, and Trh) of WT and ERβ−/− mice under homeostasis conditions and treatment with DSS. n = 3/group. (B) The gene expression profile of the overlapping genes in hypothalamus of WT and ERβ−/− mice under the homeostasis conditions and DSS treatment. Figure S9. Fecal microbiota of SiHo WT, SiHo ERβ−/−, CoHo WT, and CoHo ERβ−/− mice before DSS treatment. (A) UniFrac distances showing microbiota compositional differences among SiHo WT, SiHo ERβ−/−, CoHo WT and CoHo ERβ−/− mice. (B) Taxonomic cladogram obtained using LEfSe analysis. (C) Relative abundances of substantially changed bacterial taxa at the genus level. (D) Relative abundances of substantially changed bacterial taxa at the family level. Data are presented as boxplots. Statistical comparisons were performed using the non-parametric Wilcoxon rank sum test. n = 9/group, except for n = 8 for the CoHo WT group. *P < 0.05, **P < 0.01, ***P < 0.001. Supplemental materials and methods