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IntroductionThe gut microbiota, T cell subsets, and cytokines participate in tuberculosis (TB) pathogenesis. To date, the mechanisms by which these factors interactively promote TB development at different time points remain largely unclear. In the context of this study, We looked into the microorganisms in the digestive tract, T cell types, and cytokines related to tuberculosis.MethodsAccording to QIIME2, we analyzed 16SrDNA sequencing of the gut microbiome on the Illumina MiSeq. Enzyme-linked immunosorbent assay was used to measure the concentrations of cytokines.ResultsWe showed the presence of 26 identifiable differential microbiomes in the gut and 44 metabolic pathways between healthy controls and the different time points in the development of TB in patients. Five bacterial genera (Bacteroides, Bifidobacterium, Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4/CD8, whereas three bacterial taxa (Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4. Three bacterial taxa (Faecalibacterium, Ruminococcus, and Dorea) were most closely associated with IL-4. Ruminococcus was most closely associated with IL-2 and IL-10.ConclusionDiverse microorganisms, subsets of T cells, and cytokines, exhibiting varying relative abundances and structural compositions, were observed in both healthy controls and patients throughout distinct phases of tuberculosis. Gaining insight into the function of the gut microbiome, T cell subsets, and cytokines may help modulate therapeutic strategies for TB.

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.

Seedling emergence is an essential event in the life cycle of plants. Most invasive plants have an advantage in population colonization over native congeners. However, differential seedling emergence between invasive plants and native congeners, especially their mechanisms, have rarely been explored. In this study, we show that the seedlings of invasive Phytolacca americana emerge faster compared to native P. acinosa. Genome-wide transcriptomes of initially germinated seeds versus seedlings at 4 days after germination (DAG) suggested that differentially expressed genes (DEGs) in the photosynthesis-antenna proteins pathway were up-regulated in both P. americana and P. acinosa, while DEGs in starch and sucrose metabolism were significantly down-regulated in P. americana. Gene expression analysis indicated that photosynthesis-related DEGs reached their highest level at 3 DAG in P. americana, while they peaked at 4 DAG in P. acinosa. We also identified one β-amylase gene in P. americana (PameAMYB) that showed the highest expression at 1 DAG, and two β-amylase genes in P. acinosa that expressed lower than PameAMYB at 0 and 1 DAG. Enzymatic activity of β-amylases also suggested that P. americana had the highest activity at 1 DAG, which was earlier than P. acinosa (at 4 DAG). Soluble sugars, the main source of energy for seedling emergence, were showed higher in P. americana than in P. acinosa, and reached the highest at 4 DAG that positively affected by photosynthesis. These results indicate that the rapid seedling emergence of invasive P. americana benefited from the high soluble sugar content produced by starch metabolism and photosynthesis. Altogether, this work contributes to our fundamental knowledge on physiological and molecular mechanisms for plant invasion success.

Abstract Objective Triglyceride glucose index (TyG index) has been recommended as an alternative indicator of insulin resistance. However, the association between TyG and regression from prediabetes to normoglycemia remains to be elucidated. Methods This retrospective cohort study involved 25,248 subjects with prediabetes at baseline conducted from 2010 to 2016. A Cox proportional hazard regression model was designed to evaluate the role of TyG in identifying people at converting from prediabetes to normoglycemia. Cox proportional hazards regression with cubic spline functions and smooth curve fitting was used to dig out the nonlinear relationship between them. Detailed evaluations for TyG were also performed using sensitivity and subgroup analyse. Results Among the included prediabetes subjects (n = 25,248), the mean age was 49.27 ± 13.84 years old, and 16,701 (66.15%) were male. The mean TyG was 8.83 ± 0.60. The median follow-up time was 2.96 ± 0.90 years. 11,499 (45.54%) individuals had a final diagnosis of normoglycemia. After adjusting for covariates, TyG was negatively affecting the results of glucose status conversion in prediabetes people (HR 0.895, 95% CI 0.863, 0.928). There was a nonlinear connection between TyG and normoglycemia in prediabetes people, and the inflection point was 8.88. The effect sizes (HR) on the left and right sides of the inflection point were 0.99 (0.93, 1.05) and 0.79 (0.74, 0.85), respectively. Sensitivity analysis confirmed the robustness of these results. Subgroup analysis showed that TyG was more strongly associated with incident glucose status conversion in male, BMI ≥ 25. In contrast, there was a weaker relationship in those with female, BMI

Early childhood caries (ECC) is a public healthcare concern that greatly reduces the quality of life of young children. As a leading factor of ECC, cariogenic biofilms are composed of acidogenic/aciduric pathogens and extracellular polysaccharides (EPSs), creating an acidic and protected microenvironment. Antimicrobial photodynamic therapy (aPDT) is a noninvasive, painless, and efficient therapeutic approach that is suitable for treating ECC. However, due to the hyperfine structure of cariogenic biofilms, most photosensitizers (PSs) could not access and penetrate deeply in biofilms, which dramatically hamper their efficiency in the clinic. Herein, bioresponsive nanoparticle loaded with chlorin e6 (MPP-Ce6) is developed, which largely increases the penetration depth (by over 75%) and retention (by over 100%) of PS in the biofilm compared with free Ce6. Furthermore, MPP-Ce6-mediated aPDT not only kills the bacteria in preformed biofilms but also inhibits multispecies biofilm formation. A rampant caries model is established to mimic ECC in vivo, where the population of cariogenic bacteria is decreased to 10% after MPP-Ce6-mediated aPDT. Importantly, the number and severity of carious lesions are efficiently reduced via Keyes’ scoring and micro-CT analysis. This simple but effective strategy can serve as a promising approach for daily oral hygiene in preventing ECC.

ABSTRACT Caries are chronic infections in which the cariogenic biofilm plays a critical role in disease occurrence and progression. Photodynamic therapy (PDT) is a new effective treatment that is receiving wide attention in the antibacterial field, but it can lead to the upregulation of heat shock proteins (HSPs), which enhances bacterial resistance. Herein, we incorporated HSP inhibitors with PDT to evaluate the effect on Streptococcus mutans, Streptococcus sobrinus, and Streptococcus sanguinis under planktonic conditions and on cariogenic biofilms. Additionally, a model of caries was established in 2-week-old rats, and anticaries properties were evaluated by Keyes’ scoring. Importantly, the combination of HSP inhibitors and PDT had outstanding efficiency in inhibiting the growth of tested Streptococcus strains and the formation of either monomicrobial or multispecies biofilms in vitro. In addition, the quantity of colonized streptococci and the severity of carious lesions were also distinctly suppressed in vivo. Overall, the synergistic application of HSP inhibitors and PDT has promising potential in the prevention and treatment of dental caries. IMPORTANCE Effective therapies for the prevention and control of caries are urgently needed. Cariogenic streptococci play a key role in the occurrence and progression of caries. Recently, photodynamic therapy has been demonstrated to have good antibacterial efficiency, but it can cause a heat shock response in bacteria, which may weaken its practical effects. We indicate here an effective therapeutic strategy of combining heat shock protein inhibitors and photodynamic therapy, which shows excellent inhibition toward three dominant streptococci related to caries and suppression of carious progression in a rat model. Further development for clinical application is promising.

With the continuous development of modern remote sensing satellite technology, high-resolution (HR) remote sensing image data have gradually become widely used. However, due to the vastness of areas that need to be monitored and the difficulty in obtaining HR images, most monitoring projects still rely on low-resolution (LR) data for the regions being monitored. The emergence of remote sensing image super-resolution (SR) reconstruction technology effectively compensates for the lack of original HR images. This paper proposes an Improved Enhanced Super-Resolution Generative Adversarial Network (IESRGAN) based on an enhanced U-Net structure for a 4× scale detail reconstruction of LR images using NaSC-TG2 remote sensing images. In this method, in-depth research has been performed and consequent improvements have been made to the generator and discriminator within the GAN network. Specifically, before introducing Residual-in-Residual Dense Blocks (RRDB), in the proposed method, input images are subjected to reflective padding to enhance edge information. Meanwhile, a U-Net structure is adopted for the discriminator, incorporating spectral normalization to focus on semantic and structural changes between real and fake images, thereby improving generated image quality and GAN performance. To evaluate the effectiveness and generalization ability of our proposed model, experiments were conducted on multiple real-world remote sensing image datasets. Experimental results demonstrate that IESRGAN exhibits strong generalization capabilities while delivering outstanding performance in terms of PSNR, SSIM, and LPIPS image evaluation metrics.

Spectral unmixing (SU) is a significant preprocessing task for handling hyperspectral images (HSI), but its process is affected by nonlinearity and spectral variability (SV). Currently, SV is considered within the framework of linear mixing models (LMM), which ignores the nonlinear effects in the scene. To address that issue, we consider the effects of SV on SU while investigating the nonlinear effects of hyperspectral images. Furthermore, an augmented generalized bilinear model is proposed to address spectral variability (abbreviated AGBM-SV). First, AGBM-SV adopts a generalized bilinear model (GBM) as the basic framework to address the nonlinear effects caused by second-order scattering. Secondly, scaling factors and spectral variability dictionaries are introduced to model the variability issues caused by the illumination conditions, material intrinsic variability, and other environmental factors. Then, a data-driven learning strategy is employed to set sparse and orthogonal bases for the abundance and spectral variability dictionaries according to the distribution characteristics of real materials. Finally, the alternating direction method of multipliers (ADMM) optimization method is used to split and solve the objective function, enabling the AGBM-SV algorithm to estimate the abundance and learn the spectral variability dictionary more effectively. The experimental results demonstrate the comparative superiority of the AGBM-SV method in both qualitative and quantitative perspectives, which can effectively solve the problem of spectral variability in nonlinear mixing scenes and to improve unmixing accuracy.

Abstract Background Phytolaccaceae species in China are not only ornamental plants but also perennial herbs that are closely related to human health. However, both large-scale full-length cDNA sequencing and reference gene validation of Phytolaccaceae members are still lacking. Therefore, single-molecule real-time sequencing technology was employed to generate full-length transcriptome in invasive Phytolacca americana and non-invasive exotic P. icosandra. Based on the transcriptome data, RT-qPCR was employed to evaluate the gene expression stability in the two plant species and another indigenous congener P. acinosa. Results Total of 19.96 Gb and 19.75 Gb clean reads of P. americana and P. icosandra were generated, including 200,857 and 208,865 full length non-chimeric (FLNC) reads, respectively. Transcript clustering analysis of FLNC reads identified 89,082 and 98,448 consensus isoforms, including 86,989 and 96,764 high-quality ones. After removing redundant reads, 46,369 and 50,220 transcripts were obtained. Based on structure analysis, total 1675 and 1908 alternative splicing variants, 25,641 and 31,800 simple sequence repeats (SSR) as well as 34,971 and 36,841 complete coding sequences were detected separately. Furthermore, 3574 and 3833 lncRNA were predicted and 41,676 and 45,050 transcripts were annotated respectively. Subsequently, seven reference genes in the two plant species and a native species P. acinosa were selected and evaluated by RT-qPCR for gene expression analysis. When tested in different tissues (leaves, stems, roots and flowers), 18S rRNA showed the highest stability in P. americana, whether infested by Spodoptera litura or not. EF2 had the most stable expression in P. icosandra, while EF1-α was the most appropriate one when attacked by S. litura. EF1-α showed the highest stability in P.acinosa, whereas GAPDH was recommended when infested by S. litura. Moreover, EF1-α was the most stable one among the three plant species whenever germinating seeds or flowers only were considered. Conclusion Full-length transcriptome of P. americana and P. icosandra were produced individually. Based on the transcriptome data, the expression stability of seven candidate reference genes under different experimental conditions was evaluated. These results would facilitate further exploration of functional and comparative genomic studies in Phytolaccaceae and provide insights into invasion success of P. americana.

The mapping relationship between social status and horizontal space (left/right) in Chinese culture has a long history. In order to explore the representation pattern of horizontal spatial metaphor of social status in Chinese culture, this study introduced two direct measurements, implicit relational assessment procedure (IRAP) paradigm, and spatial placement task to evaluate the mapping of social status to horizontal space. A total of 144 Chinese undergraduates participated in the research, wherein they were asked to place certain words indicating social status in either left or right box before or after the IRAP computer test. The results from the two measurements consistently showed that the mode of HIGH SOCIAL STATUS-LEFT and LOW SOCIAL STATUS-RIGHT (HLLR) had an advantage over HIGH SOCIAL STATUS-RIGHT and LOW SOCIAL STATUS-LEFT (HRLL), implying that the representation pattern of horizontal spatial metaphor of social status for the Chinese is HIGH SOCIAL STATUS-LEFT and LOW SOCIAL STATUS-RIGHT. However, the result convergence of the two measurements was not high, which suggests that embodiment effect has multiple characteristics and new specific experimental paradigms should be created to measure it.

Since the hardware limitations of satellite sensors, the spatial resolution of multispectral (MS) images is still not consistent with the panchromatic (PAN) images. It is especially important to obtain the MS images with high spatial resolution in the field of remote sensing image fusion. In order to obtain the MS images with high spatial and spectral resolutions, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed in this paper. Firstly, a weighted local spatial frequency-based (WLSF) fusion method is utilized to fuse all the bands of a MS image to generate an intensity component IC. In accordance with an image matting model, IC is taken as the original α channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (SS), large-scale (LS) and basic images by weighted mean curvature filter (WMCF) and Gaussian filter (GF). The multi-scale morphological detail measure (MSMDM) value is used as the inputs of the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model. With the MSMDM-guided PAC-PCNN model, the basic image and IC are effectively fused. The fused image as well as the LS and SS images are linearly combined together to construct the last α channel. Finally, in accordance with an image matting model, a foreground image, a background image and the last α channel are reconstructed to acquire the final fused image. The experimental results on four image pairs show that the proposed method achieves superior results in terms of subjective and objective evaluations. In particular, the proposed method can fuse MS and PAN images with different spatial and spectral resolutions in a higher operational efficiency, which is an effective means to obtain higher spatial and spectral resolution images.

Hyperspectral image classification technology is important for the application of hyperspectral technology. Support vector machines (SVMs) work well in supervised classifications of hyperspectral images; however, they still have some shortcomings, and their use of a parallel decision plane makes it difficult to conform to real hyperspectral data distribution. The improved nonparallel support vector machine based on SVMs, i.e., the bias constraint additional empirical risk minimization nonparallel support vector machine (BC-AERM-NSVM), has improved classification accuracy compared its predecessor. However, BC-AERM-NSVMs have a more complicated solution problem than SVMs, and if the dataset is too large, the training speed is significantly reduced. To solve this problem, this paper proposes a least squares algorithm, i.e., the least square bias constraint additional empirical risk minimization nonparallel support vector machine (LS-BC-AERM-NSVM). The dual problem of the LS-BC-AERM-NSVM is an unconstrained convex quadratic programming problem, so its solution speed is greatly improved. Experiments on hyperspectral image data demonstrate that the LS-BC-AERM-NSVM displays a vast improvement in terms of solution speed compared with the BC-AERM-NSVM and achieves good classification accuracy.

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution.

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance.

Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability (δ) in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and δ , which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).

Objective. Compared with other insulin analogues, insulin detemir induces less weight gain. This study investigated whether this effect was achieved by influencing the hypothalamic appetite regulators neuropeptide Y (NPY) and galanin (GAL). Methods. Type 2 diabetic rat models were established with a high-fat diet and intraperitoneal injection of STZ. All rats were divided into NC, DM, DM+DE and DM+GLA groups. Glycemic levels of all study groups were checked at study onset and after 4 weeks of insulin treatment. Food intake and body weight were monitored during treatment. After 4 weeks, the hypothalamus of rats was examined for NPY and GAL mRNA and protein expression. Results. After 4 weeks of treatment, compared with the DM+GLA group, the DM+DE group exhibited less food intake (P