Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of microwave absorption performance of porous carbon induced by Ce (CO3) OH

In recent years, electromagnetic pollution has become more and more serious, resulting in a very negative impact on people’s health. Therefore, it is important to develop efficient microwave absorbers to reduce electromagnetic pollution. Here, we construct a novel absorbing material of the polymer gel-derived porous carbon decorated by rare earth compounds (Ce (CO3) OH). When the thickness is 2.2 mm, the composite exhibits excellent microwave absorption performance with the optimal RLmin value and EAB reached up to −47.67 dB and 5.52 GHz, respectively, covering the Ku band. The high-efficiency microwave absorption is mainly attributed to the synergistic effect of dipole polarization, defect polarization and interfacial polarization. This work not only provides a new view for designing superior absorber materials, but also lay a foundation for their real applications.
The submandibular and sublingual glands maintain oral microbial homeostasis through multiple antimicrobial proteins

Introduction Oral microbial homeostasis is a key factor affecting oral health, and saliva plays a significant role in maintaining oral microbial homeostasis. The submandibular gland (SMG) and sublingual gland (SLG) together produce the most saliva at rest. Organic ingredients, including antimicrobial proteins, are rich and distinctive and depend on the type of acinar cells in the SMG and SLG. However, the functions of the SMG and SLG in maintaining oral microbial homeostasis have been difficult to identify and distinguish, given their unique anatomical structures Methods In this study, we independently removed either the SMG or SLG from mouse models. SMGs were aseptically removed in three mice in the SMG-removal group, and SLGs were aseptically removed in three mice in the SLG-removal group. Three mice from the sham-operated group were only anesthetized and incised the skin. After one month, we analyzed their oral microbiome through 16S rRNA sequencing. And then, we analyzed each gland using proteomics and single-cell RNA sequencing. Results Our study revealed that the microbiome balance was significantly disturbed, with decreased bacterial richness, diversity, and uniformity in the groups with the SMG or SLG removed compared with the sham-operated group. We identified eight secreted proteins in the SMG and two in the SLG that could be involved in maintaining oral microbial homeostasis. Finally, we identified multiple types of cells in the SMG and SLG (including serous acinar, mucinous acinar, ductal epithelial, mesenchymal, and immune cells) that express potential microbiota homeostasis regulatory proteins. Our results suggest that both the SMG and SLG play crucial roles in maintaining oral microbial homeostasis via excretion. Furthermore, the contribution of the SMG in maintaining oral microbial homeostasis appears to be superior to that of the SLG. These findings also revealed the possible antimicrobial function of gland secreta. Discussion Our results suggest that control of oral microbial dysbiosis is necessary when the secretory function of the SMG or SLG is impaired. Our study could be the basis for further research on the prevention of oral diseases caused by microbial dysbiosis.
Latitude variations of soil bacterial community diversity and composition in three typical forests of temperate, northeastern of China

Soil bacteria are a crucial component of forest soil biodiversity and play important functions in numerous ecosystem processes. Hence, studying the variation of diversity and composition of soil bacteria between latitude gradients and the driving factors responsible for these differences is important for understanding the changes of soil bacteria. We used Illumina MiSeq sequencing of bacterial 16S rRNA to investigate the distribution pattern and driving factors of bacterial diversity and composition in temperate forest soils at three different latitudes in northeast China, with samples taken at low, middle and high latitude. Each sample area was located at a distance of 1,200 km. Our results indicate that the soil bacterial diversity decreased with increasing latitude. Members of the phyla Acidobacteria and Proteobacteria were the dominant in all investigated soils, the highest relative abundances of these phyla were: Acidobacteria and Proteobacteria in high-latitude forest, Rokubacteria and Actinobacteria in low-latitude forest. The dominant bacterial genera in the three different latitude forests were Candidatus_Solibacter, Bryobacter, Roseiarcus and Granulicella. Mean average temperature, soil pH and total nitrogen content were the key environmental factors shaping the soil bacterial diversity and composition in different latitudes of these temperate forests. The results of this study contribute to a deeper understanding and better predictions the latitudinal pattern of soil biodiversity.
Sugarcane mosaic virus orchestrates the lactate fermentation pathway to support its successful infection

Viruses often establish their own infection by altering host metabolism. How viruses co-opt plant metabolism to support their successful infection remains an open question. Here, we used untargeted metabolomics to reveal that lactate accumulates immediately before and after robust sugarcane mosaic virus (SCMV) infection. Induction of lactate-involved anaerobic glycolysis is beneficial to SCMV infection. The enzyme activity and transcriptional levels of lactate dehydrogenase (LDH) were up-regulated by SCMV infection, and LDH is essential for robust SCMV infection. Moreover, LDH relocates in viral replicase complexes (VRCs) by interacting with SCMV-encoded 6K2 protein, a key protein responsible for inducing VRCs. Additionally, lactate could promote SCMV infection by suppressing plant defense responses. Taken together, we have revealed a viral strategy to manipulate host metabolism to support replication compartment but also depress the defense response during the process of infection.
Waist-to-height-ratio is associated with sustained hypertension in children and adolescents with high office blood pressure

Background Waist-to-height-ratio (WHtR) has been proposed as another indicator for cardiometabolic risk factors including hypertension. Normally, hypertension can be diagnosed in the office setting by detecting high blood pressure for three occasions. However, patients with high office blood pressure may not exhibit high blood pressure outside the office. Ambulatory blood pressure monitoring (ABPM) is a procedure to measure blood pressure over 24-h. Sustained hypertension is characterized as hypertension detected by both office measurement and ABPM. This study aimed to evaluate the performance of WHtR in the diagnosis of sustained hypertension in patients with high office blood pressure. Materials and methods Demographic data, height, body weight, body mass index (BMI), and waist circumference were retrospectively reviewed in children and adolescents who underwent ABPM due to persistently high office blood pressure. Patients were separated into two groups: a sustained hypertension group and a normal ABPM group. BMI was adjusted to z-score using the WHO Anthroplus software. WHtR was calculated by the formula: waist circumference (cm)/height (m). The performances of different parameters were analyzed using the receiver operating characteristic (ROC) curve and multivariate logistic regression. Results Sixty patients (63% male) with a mean age of 12.9 ± 3.7 years had persistently high office blood pressure. Twenty-nine (48.3%) had high ambulatory blood pressure parameters so-called “sustained hypertension. ” The sustained hypertension group had a higher mean BMI z-score (2.32 vs. 1.31, p = 0.01) and a higher mean WHtR (57.7 vs. 49.2 cm/m, p < 0.001) than those of the normal ABPM group. For the diagnosis of sustained hypertension, the ROC analysis revealed that WHtR had a greater area under the ROC curve (AUC) than that of BMI z-score (0.772 vs. 0.723). WHtR remained associated with sustained hypertension (OR 1.2, 95% CI 1.022–1.408, p = 0.026) after adjusting for age, gender, and BMI z-score. Conclusions Apart from being a more user-friendly metric, WHtR tended to outperform BMI z-score in predicting sustained hypertension in children and adolescents with persistently high office blood pressure.
Uncovering the molecular mechanisms between heart failure and end-stage renal disease via a bioinformatics study

Background: Heart failure (HF) is not only a common complication in patients with end-stage renal disease (ESRD) but also a major cause of death. Although clinical studies have shown that there is a close relationship between them, the mechanism of its occurrence is unclear. The aim of this study is to explore the molecular mechanisms between HF and ESRD through comprehensive bioinformatics analysis, providing a new perspective on the crosstalk between these two diseases. Methods: The HF and ESRD datasets were downloaded from the Gene Expression Omnibus (GEO) database; we identified and analyzed common differentially expressed genes (DEGs). First, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVA) were applied to explore the potential biological functions and construct protein−protein interaction (PPI) networks. Also, four algorithms, namely, random forest (RF), Boruta algorithm, logical regression of the selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE), were used to identify the candidate genes. Subsequently, the diagnostic efficacy of hub genes for HF and ESRD was evaluated using eXtreme Gradient Boosting (XGBoost) algorithm. CIBERSORT was used to analyze the infiltration of immune cells. Thereafter, we predicted target microRNAs (miRNAs) using databases (miRTarBase, TarBase, and ENOCRI), and transcription factors (TFs) were identified using the ChEA3 database. Cytoscape software was applied to construct mRNA−miRNA−TF regulatory networks. Finally, the Drug Signatures Database (DSigDB) was used to identify potential drug candidates. Results: A total of 68 common DEGs were identified. The enrichment analysis results suggest that immune response and inflammatory factors may be common features of the pathophysiology of HF and ESRD. A total of four hub genes (BCL6, CCL5, CNN1, and PCNT) were validated using RF, LASSO, Boruta, and SVM-RFE algorithms. Their AUC values were all greater than 0.8. Immune infiltration analysis showed that immune cells such as macrophages, neutrophils, and NK cells were altered in HF myocardial tissue, while neutrophils were significantly correlated with all four hub genes. Finally, 11 target miRNAs and 10 TFs were obtained, and miRNA−mRNA−TF regulatory network construction was performed. In addition, 10 gene-targeted drugs were discovered. Conclusion: Our study revealed important crosstalk between HF and ESRD. These common pathways and pivotal genes may provide new ideas for further clinical treatment and experimental studies.
Systems biology of asphalt pollutants and their human molecular targets

More than 90% of all the roads in the United States are covered with asphalt, despite hundreds of scientific studies demonstrating the detrimental effect of asphalt on human health. Asphalt is a complex mixture of thousands of compounds. Here, we not only review studies of the effects of asphalt on human health, but go a step further by taking a novel view of these health effects from a systems biology perspective. In particular, we propose an analogy to protein-protein interaction networks, which can be within species and across species when looking at host-pathogen interactions. While in the former, all nodes are of the same type (e.g., human proteins), in the latter nodes can be of different types, such as human proteins and pathogen proteins. To build a corresponding network of interactions between different nodes for asphalt, we retrieved the literature studying the molecular targets of identified components in asphalt and their corresponding cellular biomarkers. Using this approach, we show that a complex trans pollutant-human target network appears in which multiple health effects can be triggered through interactions of multiple pollutant molecules with multiple human targets. We envision that the insights gained from this analysis may assist future efforts at regulating the use of asphalt.
A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images

Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.
Sexual identity fluidity, identity management stress, and depression among sexual minority adolescents

Introduction Sexual identity is mutable and evolving, particularly during adolescence. Sexual identity fluidity could be stressful for some adolescents and may differ by birth-sex. Evidence suggests chronic stress can lead to negative mental health outcomes. However, it is unknown if these two processes (stress and depression) differ by sexual identity fluidity. Methods This paper studied time-sequential associations between identity management stress and depression over time by sexual identity fluidity, in a national longitudinal data from sexual minority adolescents (SMA) aged 14–17 years using a multigroup autoregressive cross-lagged model (n = 1077). Results In the sample, 40% of SMA reported at least one change in sexual identity over 18-month period. Greater number of cisgender females reported sexual identity fluidity compared to their male counterparts (46.9% vs. 26.6%). A temporal cross-lagged effect was reported between depression and identity management stress among cisgender females who reported fluidity in sexual identity; and no cross-lagged effect was reported among those females who did not report fluidity. However, among cisgender male sample depression predicted subsequent identity management stress, irrespective of their change sexual identity fluidity status. Conclusion Public health programs and practice must be responsive to the sexual identity fluidity processes among adolescents, with particular attention to minority stress and depression. In addition, our results indicate that sexual identity development and fluidity processes differ between cisgender females and males; and the nuances associated with these processes of change need further investigation.
Identification and virtual screening of novel anti-inflammatory peptides from broccoli fermented by Lactobacillus strains

Lactobacillus strains fermentation of broccoli as a good source of bioactive peptides has not been fully elucidated. In this work, the peptide composition of broccoli fermented by L. plantarum A3 and L. rhamnosus ATCC7469 was analyzed by peptidomics to study the protein digestion patterns after fermentation by different strains. Results showed that water-soluble proteins such as rubisco were abundant sources of peptides, which triggered the sustained release of peptides as the main target of hydrolysis. In addition, 17 novel anti-inflammatory peptides were identified by virtual screening. Among them, SIWYGPDRP had the strongest ability to inhibit the release of NO from inflammatory cells at a concentration of 25 μM with an inhibition rate of 52.32 ± 1.48%. RFR and KASFAFAGL had the strongest inhibitory effects on the secretion of TNF-α and IL-6, respectively. At a concentration of 25 μM, the corresponding inhibition rates were 74.61 ± 1.68% and 29.84 ± 0.63%, respectively. Molecular docking results showed that 17 peptides formed hydrogen bonds and hydrophobic interactions with inducible nitric oxide synthase (iNOS). This study is conducive to the high-value utilization of broccoli and reduction of the antibiotic use.
DGTR: Dynamic graph transformer for rumor detection

Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of the structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for the graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between the dynamic graph nodes and the temporal long-range dependence between the temporal snapshots by employing a self-attention mechanism. In addition, the CLS token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model when compared to the state of the art.
Characterization of the lncRNA-miRNA-mRNA regulatory network to reveal potential functional competing endogenous RNAs in traumatic brain injury

Traumatic brain injury (TBI) is one of the most common acute central nervous system injury diseases. Given the medical and socio-economic burdens of TBI patients, the pathogenesis in TBI and the latent intervention targets needed to be further illuminated. Long non-coding RNAs (lncRNAs) had been revealed to play a vital role in the regulation of pathogenesis after TBI. However, the mutual communication and adjustment of lncRNA associated competing for endogenous RNA (ceRNA) networks in TBI have not been explored to date. In this study, we systematically sequenced the whole transcriptome of lncRNAs, miRNAs, and mRNAs between sham and TBI groups and a total of 939 differentially expressed (DE) lncRNAs, 46 DE miRNAs, and 1,951 DE mRNAs were obtained. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein interaction relationship analyses were conducted for DE mRNAs to identify hub DE genes in TBI. Based on the criteria of bioinformatics prediction, the lncRNA associated ceRNA network covering 201 lncRNAs, 22 miRNAs, and 79 mRNAs was constructed. This study provides a novel perspective on the molecular mechanism of lncRNA in TBI and identifies certain lncRNAs as potential therapeutic targets against TBI.
Rapid detection of SARS-CoV-2: The gradual boom of lateral flow immunoassay

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is still in an epidemic situation, which poses a serious threat to the safety of people and property. Rapid diagnosis and isolation of infected individuals are one of the important methods to control virus transmission. Existing lateral flow immunoassay techniques have the advantages of rapid, sensitive, and easy operation, and some new options have emerged with the continuous development of nanotechnology. Such as lateral flow immunoassay test strips based on colorimetric-fluorescent dual-mode and gold nanoparticles, Surface Enhanced Raman Scattering, etc., these technologies have played an important role in the rapid diagnosis of COVID-19. In this paper, we summarize the current research progress of lateral flow immunoassay in the field of Severe Acute Respiratory Syndrome Coronavirus 2 infection diagnosis, analyze the performance of Severe Acute Respiratory Syndrome Coronavirus 2 lateral flow immunoassay products, review the advantages and limitations of different detection methods and markers, and then explore the competitive CRISPR-based nucleic acid chromatography detection method. This method combines the advantages of gene editing and lateral flow immunoassay and can achieve rapid and highly sensitive lateral flow immunoassay detection of target nucleic acids, which is expected to be the most representative method for community and clinical point-of-care testing. We hope that researchers will be inspired by this review and strive to solve the problems in the design of highly sensitive targets, the selection of detection methods, and the enhancement of CRISPR technology, to truly achieve rapid, sensitive, convenient, and specific detection of novel coronaviruses, thus promoting the development of novel coronavirus diagnosis and contributing our modest contribution to the world’s fight against epidemics.
A deep ensemble learning method for single finger-vein identification

Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.