Search

The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1‐palmitoyl‐2‐palmitoleoyl‐GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging‐related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health. [ABSTRACT FROM AUTHOR]

To create a deep learning (DL) model that can accurately detect and classify three distinct types of rat dorsal root ganglion neurons: normal, segmental chromatolysis, and central chromatolysis. The DL model has the potential to improve the efficiency and precision of neuron classification in research related to spinal injuries and diseases. Methods: H&E slide images were divided into an internal training set (80%) and a test set (20%). The training dataset was labeled by two pathologists using pre-defined grades. Using this dataset, a two-component DL model was developed with the first component being a convolutional neural network (CNN) that was trained to detect the region of interest (ROI) and the second component being another CNN used for classification. Results: A total of 240 lumbar dorsal root ganglion (DRG) pathology slide images from rats were analyzed. The internal testing results showed an accuracy of 93.13%, and the external dataset testing demonstrated an accuracy of 93.44%. Conclusion: The DL model demonstrated a level of agreement comparable to that of pathologists in detecting and classifying normal and segmental chromatolysis neurons, although its agreement was slightly lower for central chromatolysis neurons. Significance: DL in improving the accuracy and efficiency of pathological analysis suggests that it may have a role in enhancing medical decision-making. [ABSTRACT FROM AUTHOR]

Periodontitis development is strongly associated with the succession of the oral microbiome. However, the knowledge about the succession of the oral microbiome in the development of periodontitis remains insufficient. In the present study, an analysis was conducted on the succession of tongue back, the saliva (Sal) microbiome, and gingival crevicular fluid (GCF) from healthy individuals and patients with mild (CPL), moderate (CPM), severe chronic (CPH), and generalized aggressive periodontitis (GAgP). The composition and structure of the oral microbiome gradually changed with the increasing severity of periodontitis, among which GCF showed the highest correlation with periodontitis. With an ecological preference, pathogens in the mouth varied with the development of periodontitis. In healthy and CPL patients, Sal‐derived microorganisms accounted for a large proportion of GCF but exhibited a decrease in patients with CPM, CPH, and GAgP. Permutation and time course sequencing analysis revealed that a variety of microorganisms changed with the severity of periodontitis. A majority of these microorganisms are closely related to clinical periodontal indices. Ecological analysis suggested that the composition of oral microbial communities at different stages of periodontitis is controlled by random processes. The comparison of microbial interaction networks demonstrated that a series of key microorganisms drive oral health to severe periodontitis. In this study, the relationship between the succession process of the oral microbiota and the development of periodontitis was revealed. [ABSTRACT FROM AUTHOR]

We investigated the relationship between parental conflict and adolescent academic burnout, along with the mediating role of the parent–child relationship and the moderating role of teacher support. We surveyed 886 first-year secondary vocational school students in Foshan City using four scales: the Adolescent Student Burnout Inventory, the Children's Perception of Interparental Conflict Scale, the Parent–Child Closeness Scale, and the Students' Perception of Teachers' Supporting Behavior Questionnaire. The results showed that adolescent academic burnout was directly and positively associated with parental conflict, and that the parent–child relationship partially mediated this relationship. Teacher support moderated the effect of parental conflict and affected adolescent academic burnout indirectly through parent–child relationships. Thus, parents should try to create an emotionally secure family environment for children and cultivate high-quality parent–child relationships, to prevent or intervene in academic burnout among adolescents. Additionally, schools should consider the role of teacher support in the improvement of parent–child relationships among students. [ABSTRACT FROM AUTHOR]

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications. [ABSTRACT FROM AUTHOR]

In the context of extensive disciplinary integration, researchers worldwide have increasingly focused on musical ability. However, despite the wide range of available music ability tests, there remains a dearth of validated tests applicable to China. The Music Ear Test (MET) is a validated scale that has been reported to be potentially suitable for cross-cultural distribution in a Chinese sample. However, no formal translation and cross-cultural reliability/validity tests have been conducted for the Chinese population in any of the studies using the Music Ear Test. This study aims to assess the factor structure, convergence, predictiveness, and validity of the Chinese version of the MET, based on a large sample of Chinese participants (n≥1235). Furthermore, we seek to determine whether variables such as music training level, response pattern, and demographic data such as gender and age have intervening effects on the results. In doing so, we aim to provide clear indications of musical aptitude and expertise by validating an existing instrument, the Music Ear Test, and provide a valid method for further understanding the musical abilities of the Chinese sample. [ABSTRACT FROM AUTHOR]

Granulite facies rocks provide important keys to evaluating collisional metamorphism in orogenic belts. The mafic granulites of Baoding in the Fuping Complex of the North China Craton occur within the Trans-North China Orogen (TNCO), a major Paleoproterozoic collisional orogen. Here, we present results from detailed investigations on newly discovered garnet pyroxenite, garnet two-pyroxene granulite, and garnet-bearing-plagioclase amphibolite using petrographic, mineralogical, geochemical, and zircon U-Pb dating methods. Our results show that the Fuping Complex metamorphic evolution in this study evolved in four stages: prograde (M1), high-pressure granulite facies (M2), granulite facies (M3), and retrograde (M4) stages. The mineral assemblage of the prograde stage (M1) consists of Amp + Pl + Q within garnet cores. The mineral assemblage of high-pressure granulite facies at the peak stage (M2) consists of Gt + Cpx + Pl + Q ± Amp, forming the garnet pyroxenite. The granulite facies stage M3 is characterized by the occurrence of orthopyroxene, with a mineral assemblage of Gt + Cpx + Opx + Amp+ Pl + Q. The early retrograde stage M4-1 includes clinopyroxenes scattered inside amphiboles, following the breakdown of garnet and clinopyroxene. The mineral assemblage of this stage comprises Amp + Pl + Q + Ilm ± Cpx. Later, in the late retrograde stage M4-2, the composition of amphiboles changed to actinolite, and epidote and chlorite started to appear in the matrix. Traditional geothermobarometry yielded P-T conditions of 700~706 °C and 6.0~6.2 kbar for prograde stage M1, 854~920 °C and 13.0~13.8 kbar for high-pressure granulite facies stage M2, 912~939 °C and 8.1~9.9 kbar for M3, 661~784 °C and 3.1~4.4 kbar for M4-1, and 637~638 °C, 1.1~1.3 kbar for M4-2, along a clockwise P-T path with a nearly isothermal decompression (ITD) and slight heating. Zircon LA-ICP-MS U-Pb dating constrains the timing of the high-pressure granulite facies metamorphic event to be between 1.83 and 1.86 Ga. Geochemical features suggest that the protoliths of the high-pressure granulites may have formed in an island arc environment within a convergent margin setting. Together with results from previous studies, our data suggest that the ~1.85 Ga metamorphic age recorded in the Fuping Complex represents a regional metamorphism in the TNCO, associated with the subduction–collision and assembly of the Eastern and Western Blocks of the NCC. [ABSTRACT FROM AUTHOR]

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The detection of infrared dim and small targets in complex backgrounds is very challenging because of the low signal-to-noise ratio of targets and the drastic change in background. Low-rank sparse decomposition based on the structural characteristics of infrared images has attracted the attention of many scholars because of its good interpretability. In order to improve the sensitivity of sliding window size, insufficient utilization of time series information, and inaccurate tensor rank in existing methods, a four-dimensional tensor model based on superpixel segmentation and statistical clustering is proposed for infrared dim and small target detection (ISTD). First, the idea of superpixel segmentation is proposed to eliminate the dependence of the algorithm on selection of the sliding window size. Second, based on the improved structure tensor theory, the image pixels are statistically clustered into three types: corner region, flat region, and edge region, and are assigned different weights to reduce the influence of background edges. Next, in order to better use spatiotemporal correlation, a Four-Dimensional Fully-Connected Tensor Network (4D-FCTN) model is proposed in which 3D patches with the same feature types are rearranged into the same group to form a four-dimensional tensor. Finally, the FCTN decomposition method is used to decompose the clustered tensor into low-dimensional tensors, with the alternating direction multiplier method (ADMM) used to decompose the low-rank background part and sparse target part. We validated our model across various datasets, employing cutting-edge methodologies to assess its effectiveness in terms of detection precision and reduction of background interference. A comparative analysis corroborated the superiority of our proposed approach over prevailing techniques. [ABSTRACT FROM AUTHOR]

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Poly(lactic acid) (PLA) and Poly(butylene succinate) (PBS) were chosen as raw materials and melt blended by a twin screw extruder and pelletized; then, the pellets were extruded into filaments; after that, various PBS/PLA blending samples were prepared by Fused Deposition Molding (FDM) 3D printing technology using the filaments obtained and the effect of the dosage of PBS on technological properties of 3D-printed specimens was investigated. For comparison, the PLA specimen was also prepared by FDM printing. The tensile strength, tensile modulus, thermal stability, and hydrophilicity became poorer with increasing the dosage of PBS, while the flexural strength, flexural modulus, impact strength, and crystallinity increased first and then decreased. The blend containing 10% PBS (10% PBS/PLA) had the greatest flexural strength of 60.12 MPa, tensile modulus of 2360.04 MPa, impact strength of 89.39 kJ/m2, and crystallinity of 7.4%, which were increased by 54.65%, 61.04%, 14.78%, and 51.02% compared to those of printed PLA, respectively; this blend also absorbed the least water than any other specimen when immersed in water. Different from the transparent PLA filament, 10% PBS/PLA filament presented a milky white appearance. The printed 10% PBS/PLA specimen had a smooth surface, while the surface of the printed PLA was rough. All the results indicated that the printed 10% PBS/PLA specimen had good comprehensive properties, including improved mechanical properties, crystallization performance, and surface quality than PLA, as well as proper wettability and water absorption. The prominent conclusion achieved in this work was that 10% PBS/PLA should be an ideal candidate for biodegradable feedstock among all the PBS/PLA blends for FDM 3D printing. [ABSTRACT FROM AUTHOR]

Human-caused nitrogen (N) deposition is a global environmental issue that can change community composition, functions, and ecosystem services. N deposition affects plants, soil, and microorganisms regionally and is linked to ecosystem, soil, and climate factors. We examined the effects of six N addition levels (0, 2.34 g, 4.67, 9.34,18.68, and 37.35 g N m−2 yr−1) on aboveground vegetation, surface soil properties, and microbial community. Alterations in microbial communities in response to N addition were monitored using 16S rRNA (16S ribosomal ribonucleic acid, where S donates a sedimentation coefficient) and ITS (internal transcribed spacer) regions for bacterial and fungal communities, respectively. N addition positively affected aboveground vegetation traits, such as biomass and community weighted mean of leaf nitrogen. N addition also limited phosphorus (P) availability and altered the microbial community assembly process from random processes to deterministic processes. The microbial community diversity and composition, however, were not sensitive to N addition. Partial least squares structural equation models showed that the composition of bacterial communities was mainly driven by the composition of plant communities and total nitrogen, while the composition of fungal communities was driven by soil pH and community weighted mean of leaf nitrogen. Taken together, the results of this research improved our understanding of the response of grassland ecosystems to N deposition and provided a theoretical basis for grassland utilization and management under N deposition. [ABSTRACT FROM AUTHOR]

In the field of landmark detection based on deep learning, most of the research utilise convolutional neural networks to represent landmarks, and rarely adopt Transformer to represent and encode landmarks. Meanwhile, many works focus on modifying the network structure to improve network performance, and there is little research on the distribution of landmarks. In this article,the authors propose an unsupervised model to extract landmarks of objects in images. First, Transformer structure is combined with the convolutional neural network structure to represent and encode the landmarks; next, positive and negative sample pairs between landmarks are constructed, so that the semantically consistent landmarks on the image are pulled closer in the feature space and the semantically inconsistent landmarks are pushed farther in the feature space; then the authors concentrate their attention on the most active points to distinguish the landmarks of an object from the background; finally, based on the new contrastive loss, the network reconstructs the image by the landmarks of the object that are continuously learnt during training. Experiments show that the proposed model achieves better performance than other unsupervised methods on the CelebA, Annotated Facial Landmarks in the Wild, 300W datasets. [ABSTRACT FROM AUTHOR]

Wood flour–poly(butylene succinate)–poly(lactic acid) biocomposite samples were prepared by fused-deposition-molding 3D-printing technology, and modifications with glycerol and a silane coupling agent (KH550) were carried out. The samples were then hydrothermally aged. Modification with glycerol and KH550 enhanced the hydrophilicity of the samples and increased their tensile strength. Hydrothermal aging clearly whitened the surfaces of all the samples and made them more hydrophobic. Meanwhile, their tensile properties and thermal stability became poor; a higher hydrothermal aging temperature affected the mechanical properties more negatively. The modified samples turned out to be more resistant to the hydrothermal aging, and modification with KH550 could improve the anti-hydrothermal aging properties of the samples better than that with glycerol, where the tensile properties and the cross-sectional morphologies of the fractured specimens were concerned. Generally, the effects of hydrothermal aging temperature on the physico-mechanical properties of the printed specimens were greater than those by hydrothermal aging time. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model. [ABSTRACT FROM AUTHOR]