Gut microbiome plays an essential role in modulating host immune responses. However, little is known about the interaction of microbiota, their metabolites and relevant inflammatory responses in the ...gut. By treating the mice with three different antibiotics (enrofloxacin, vancomycin, and polymixin B sulfate), we aimed to investigate the effects of different antibiotics exposure on gut microbiota, microbial metabolism, inflammation responses in the gut, and most importantly, pinpoint the underlying interactions between them. Although the administration of different antibiotics can lead to different effects on mouse models, the treatment did not affect the average body weight of the mice. A heavier caecum was observed in vancomycin treated mice. Treatment by these three antibiotics significantly up-regulated gene expression of various cytokines in the colon. Enrofloxacin treated mice seemed to have an increased Th1 response in the colon. However, such a difference was not found in mice treated by vancomycin or polymixin B sulfate. Vancomycin treatment induced significant changes in bacterial composition at phylum and family level and decreased richness and diversity at species level. Enrofloxacin treatment only induced changes in composition at family presenting as an increase in
and
and a decrease in
. However, no significant difference was observed after polymixin B sulfate treatment. When compared with the control group, significant metabolic shift was found in the enrofloxacin and vancomycin treated group. The metabolic changes mainly occurred in Valine, leucine, and isoleucine biosynthesis pathway and beta-Alanine metabolism in enrofloxacin treated group. For vancomycin treatment metabolic changes were mainly found in beta-Alanine metabolism and Alanine, aspartate and glutamate metabolism pathway. Moreover, modifications observed in the microbiota compositions were correlated with the metabolite concentrations. For example, concentration of pentadecanoic acid was positively correlated with richness of
and
and negatively correlated with
. This study suggests that the antibiotic-induced changes in gut microbiota might contribute to the inflammation responses through the alternation of metabolic status, providing a novel insight regarding a complex network that integrates the different interactions between gut microbiota, metabolic functions, and immune responses in host.
Road segmentation for all-day outdoor robot navigation is a difficult problem, for the image quality in some time is considerably terrible. In this paper, we propose an effective method to solve this ...problem. For an outdoor image in any time, the road segmentation can be separated into two stages. Firstly, a supervised generative network is trained to map the outdoor images in any time to the images with rich information. Secondly, a semantic segmentation network outputs a binary segmentation result. Our main contributions include: (1) firstly implementing road segmentation for all-day outdoor robot navigation with a low cost; (2) constructing a supervised generative network for domain mapping and (3) building a dataset for road segmentation for the outdoor images in any time. Our method is evaluated on three datasets. The results indicate that our method achieves a comparable performance with the state-of-the-art approaches.
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•Differentiable and generous materials structure descriptor was proposed based on graph attention network (GAT) and graphic atomic cluster.•The GAT descriptor generated from training ...was verified efficient without manually set functional forms and hyperparameters.•The GAT descriptor is contracted, which help avoiding overfitting and reducing computational complexity.
Machine learning has shown great potential in bridging materials structures and properties. However, efficient and general descriptors for materials structures are still missing. This work dedicates to establishing an efficient and self-adaptable materials structure descriptor based on the graph attention network (GAT). In the GAT descriptor generation, the concrete structure is represented by graphic atomic clusters (atoms as nodes, edges as bonds) consisting of one central atom and the surroundings. A multi-head-GAT is applied to gather the short- and long-distance topological information from the graphical atomic clusters to generate dimension-flexible descriptors regardless of atom number; the GAT descriptors are contracted and help avoiding overfitting during the training process theoretically. Further verification of the GAT descriptors is carried out by performing the phase classification, energy prediction, and potential energy surface (PES) prediction task for Al-Cu alloys with complex precipitates and defects. The GAT descriptors adapt themselves for cross tasks without manual intervention and produce accurate predictions that agree with the density functional theory (DFT) results. The GAT descriptors are also testified as differentiable, universal, and precise on structural characterization containing point-, line-, and planar- defects. Further applications will be found for the materials design field demanding accurate structure descriptions.
Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper ...presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the "Relationship Weighted Out" and the "Cut" modules. The "Relationship Weighted Out" module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the "Cut" module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach.
Due to capacitor elements and inductive line impedance in power system, parallel resonance could be triggered in the presence of harmonic currents from nonlinear load, whose frequency may shift ...resulting from intermittent switching of the capacitor devices. So it is difficult for shunt active power filter (SAPF) to damp parallel resonance. This paper investigates parallel resonance detection with square-wave current active injection and selective compensation control with closed-loop regulation of point of common coupling (PCC) voltage for SAPF. The principles of parallel resonance and its frequency detection are analyzed by means of equivalent circuit. Through injecting given square-wave current lasting for 0.5 s, SAPF could fast detect parallel resonance with the help of spectrum analysis of resonance power index. In order to improve power quality of both PCC voltage and grid current, SAPF is controlled to suppress specified harmonic currents from nonlinear load and selectively damp parallel resonance at the same time. In addition, ordinary proportional-integral plus advanced repetitive controller in parallel is used to improve current tracking performance. Experiment test results are provided to verify the validity of proposed detection and control methods.
Mixed
Enterobacter
sp. Z1 and
Klebsiella
sp. Z2 displayed an outstanding ammonia removal capacity than using a single strain. Metabolomics, proteomics, and RNA interference analysis demonstrated that ...the HNAD process was closely related to indole-acetic acid (IAA). Under the cocultured conditions, the excess IAA produced by Z2 could be absorbed by Z1 to compensate for the deficiency of IAA in the cells. IAA directly induced the expression of denitrifying enzymes and further activated the IAA metabolism level, thus greatly improving the nitrogen removal ability of Z1. In turn, nitrate and nitrite induced the expression of key enzymes in the IAA pathways. Moreover, Z1 and Z2 enhanced two IAA metabolic pathways in the process of mixed removal process. The activated hydrolysis-redox pathway in Z1 reduced the oxidative stress level, and the activated decarboxylation pathway in Z2 promoted intracellular energy metabolism, which indirectly promoted the process of HNAD in the system.
Oral squamous cell carcinoma (OSCC) is a prevalent form of malignant tumor, characterized by a persistently high incidence and mortality rate. The extracellular matrix (ECM) plays a crucial role in ...the initiation, progression, and diverse biological behaviors of OSCC, facilitated by mechanisms such as providing structural support, promoting cell migration and invasion, regulating cell morphology, and modulating signal transduction. This study investigated the involvement of ECM-related genes, particularly THBS1, in the prognosis and cellular behavior of OSCC. The analysis of ECM-related gene data from OSCC samples identified 165 differentially expressed genes forming two clusters with distinct prognostic outcomes. Seventeen ECM-related genes showed a significant correlation with survival. Experimental methods were employed to demonstrate the impact of THBS1 on proliferation, migration, invasion, and ECM degradation in OSCC cells. A risk-prediction model utilizing four differentially prognostic genes demonstrated significant predictive value in overall survival. THBS1 exhibited enrichment of the PI3K/AKT pathway, indicating its potential role in modulating OSCC. In conclusion, this study observed and verified that ECM-related genes, particularly THBS1, have the potential to influence the prognosis, biological behavior, and immunotherapy of OSCC. These findings hold significant implications for enhancing survival outcomes and providing guidance for precise treatment of OSCC.
LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous ...driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the ‘L-DIG’ (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.
Antimony (Sb) pollution is a worldwide problem. In some anoxic sites, such as Sb mine drainage and groundwater sediment, the Sb concentration is extremely elevated. Therefore, effective Sb ...remediation strategies are urgently needed. In contrast to microbial aerobic antimonite Sb(III) oxidation, the mechanism of microbial anaerobic Sb(III) oxidation and the effects of nitrate and Fe(II) on the fate of Sb remain unknown. In this study, we discovered the mechanism of anaerobic Sb(III) oxidation coupled with Fe(II) oxidation and denitrification in the facultative anaerobic Sb(III) oxidizer
sp. GW3. We observed the following: (1) under anoxic conditions with nitrate as the electron acceptor, strain GW3 was able to oxidize both Fe(II) and Sb(III) during cultivation; (2) in the presence of Fe(II), nitrate and Sb(III), the anaerobic Sb(III) oxidation rate was remarkably enhanced, and Fe(III)-containing minerals were produced during Fe(II) and Sb(III) oxidation; (3) qRT-PCR, gene knock-out and complementation analyses indicated that the arsenite oxidase gene product AioA plays an important role in anaerobic Sb(III) oxidation, in contrast to aerobic Sb(III) oxidation; and (4) energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and powder X-ray diffraction (XRD) analyses revealed that the microbially produced Fe(III) minerals were an effective chemical oxidant responsible for abiotic anaerobic Sb(III) oxidation, and the generated Sb(V) was adsorbed or coprecipitated on the Fe(III) minerals. This process included biotic and abiotic factors, which efficiently immobilize and remove soluble Sb(III) under anoxic conditions. The findings revealed a significantly novel development for understanding the biogeochemical Sb cycle. Microbial Sb(III) and Fe(II) oxidation coupled with denitrification has great potential for bioremediation in anoxic Sb-contaminated environments.
7-dehydrocholesterol (7-DHC) and cholesterol (CHOL) are biomarkers of Smith-Lemli-Opitz Syndrome (SLOS), a congenital autosomal recessive disorder characterized by elevated 7-DHC level in patients. ...Hair samples have been shown to have great diagnostic and research value, which has long been neglected in the SLOS field. In this study, we sought to investigate the feasibility of using hair for SLOS diagnosis. In the presence of antioxidants (2,6-ditert-butyl-4-methylphenol and triphenylphosphine), hair samples were completely pulverized and extracted by micro-pulverized extraction in alkaline solution or in n-hexane. After microwave-assisted derivatization with N,O-Bis(trimethylsilyl)trifluoroacetamide, the analytes were measured by GC-MS. We found that the limits of determination for 7-DHC and CHOL were 10 ng/mg and 8 ng/mg, respectively. In addition, good linearity was obtained in the range of 50–4000 ng/mg and 30–6000 ng/mg for 7-DHC and CHOL, respectively, which fully meets the requirement for SLOS diagnosis and related research. Finally, by applying the proposed method to real hair samples collected from 14 healthy infants and two suspected SLOS patients, we confirmed the feasibility of hair analysis as a diagnostic tool for SLOS. In conclusion, we present an optimized and validated analytical method for the simultaneous determination of two SLOS biomarkers using human hair.
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