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"Feng, Meng"
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Mass production of bulk artificial nacre with excellent mechanical properties
Various methods have been exploited to replicate nacre features into artificial structural materials with impressive structural and mechanical similarity. However, it is still very challenging to produce nacre-mimetics in three-dimensional bulk form, especially for further scale-up. Herein, we demonstrate that large-sized, three-dimensional bulk artificial nacre with comprehensive mimicry of the hierarchical structures and the toughening mechanisms of natural nacre can be facilely fabricated via a bottom-up assembly process based on laminating pre-fabricated two-dimensional nacre-mimetic films. By optimizing the hierarchical architecture from molecular level to macroscopic level, the mechanical performance of the artificial nacre is superior to that of natural nacre and many engineering materials. This bottom-up strategy has no size restriction or fundamental barrier for further scale-up, and can be easily extended to other material systems, opening an avenue for mass production of high-performance bulk nacre-mimetic structural materials in an efficient and cost-effective way for practical applications.
Artificial materials that replicate the mechanical properties of nacre represent important structural materials, but are difficult to produce in bulk. Here, the authors exploit the bottom-up assembly of 2D nacre-mimetic films to fabricate 3D bulk artificial nacre with an optimized architecture and excellent mechanical properties.
Journal Article
Epidemiology characteristics of human coronaviruses in patients with respiratory infection symptoms and phylogenetic analysis of HCoV-OC43 during 2010-2015 in Guangzhou
Human coronavirus (HCoV) is one of the most common causes of respiratory tract infection throughout the world. To investigate the epidemiological and genetic variation of HCoV in Guangzhou, south China, we collected totally 13048 throat and nasal swab specimens from adults and children with fever and acute upper respiratory infection symptoms in Gunazhou, south China between July 2010 and June 2015, and the epidemiological features of HCoV and its species were studied. Specimens were screened for HCoV by real-time RT-PCR, and 7 other common respiratory viruses were tested simultaneously by PCR or real-time PCR. HCoV was detected in 294 cases (2.25%) of the 13048 samples, with most of them inpatients (251 cases, 85.4% of HCoV positive cases) and young children not in nursery (53.06%, 156 out of 294 HCoV positive cases). Four HCoVs, as OC43, 229E, NL63 and HKU1 were detected prevalent during 2010-2015 in Guangzhou, and among the HCoV positive cases, 60.20% were OC43, 16.67% were 229E, 14.97% were NL63 and 7.82% were HKU1. The month distribution showed that totally HCoV was prevalent in winter, but differences existed in different species. The 5 year distribution of HCoV showed a peak-valley distribution trend, with the detection rate higher in 2011 and 2013 whereas lower in 2010, 2012 and 2014. The age distribution revealed that children (especially those <3 years old) and old people (>50 years) were both high risk groups to be infected by HCoV. Of the 294 HCoV positive patients, 34.69% (101 cases) were co-infected by other common respiratory viruses, and influenza virus was the most common co-infecting virus (30/101, 29.70%). Fifteen HCoV-OC43 positive samples of 2013-2014 were selected for S gene sequencing and phylogenetic analysis, and the results showed that the 15 strains could be divided into 2 clusters in the phylogenetic tree, 12 strains of which formed a separate cluster that was closer to genotype G found in Malaysia. It was revealed for the first time that genotype B and genotype G of HCoV-OC43 co-circulated and the newly defined genotype G was epidemic as a dominant genotype during 2013-2014 in Guanzhou, south China.
Journal Article
Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm
With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system’s adaptive and real-time response capabilities.
Journal Article
Interactions Between Commensal Microbiota and Mucosal Immunity in Teleost Fish During Viral Infection With SVCV
The mucosa of vertebrates is a particularly complex but dynamic environment in which the host constantly interacts with trillions of commensal microorganisms and pathogens. Although the internal and external mucosal microbiomes with immune defense of mammals have been well investigated, the relationship between mucosal microbes and their host’s immune responses has not been systematically understood in the early vertebrates. In this study, we compared the composition and distribution of mucosal microbiota in common carp ( Cyprinus carpio ), and found that there were significant differences of microbiota between in the internal (gut) and external mucosal (buccal mucosa, gills and skin) tissues. Next, we successfully constructed an infection model with spring viremia of carp virus (SVCV). Specifically, following viral infection, the immune and antiviral related genes showed different up-regulation in all selected mucosal tissues while significant morphological changes were only found in external tissues including buccal mucosa, gills and skin. Using 16S rRNA gene sequence, we revealed that the abundance of Proteobacteria in mucosal tissues including buccal mucosa, gills and gut showed increased trend after viral infection, whereas the abundance of Fusobacteria significantly decreased in gut. In addition, the loss of dominant commensal microorganisms and increased colonization of opportunistic bacteria were discovered in the mucosal surfaces indicating that a secondary bacterial infection might occur in these mucosal tissues after viral infection. Overall, our results firstly point out the distribution of internal and external mucosal microbiota and analyze the changes of mucosal microbiota in common carp after SVCV infection, which may indicated that the potential role of mucosal microbiota in the antiviral process in early vertebrates.
Journal Article
Grid-Based Crime Prediction Using Geographical Features
Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.
Journal Article
miR156-SPL modules regulate induction of somatic embryogenesis in citrus callus
Overexpression of csi-miR156a enhances the somatic embryogenesis capability of citrus through downstream repression of CsSPL3 and CsSPL14.
Abstract
miR156 is a highly conserved plant miRNA and has been extensively studied because of its versatile roles in plant development. Here, we report a novel role of miR156 in regulating somatic embryogenesis (SE) in citrus, one of the most widely cultivated fruit crops in the world. SE is an important means of in vitro regeneration, but over the course of long-term sub-culturing there is always a decline in the SE potential of the preserved citrus embryogenic callus, and this represents a key obstacle for citrus biotechnology. In this study, the SE competence of citrus callus of wild kumquat (Fortunella hindsii) was significantly enhanced by either overexpression of csi-miR156a or by individual knock-down of the two target genes, CsSPL3 and CsSPL14, indicating that the effect of miR156-SPL modules was established during the initial phases of SE induction. Biological processes that might promote SE in response to miR156 overexpression were explored using RNA-seq, and mainly included hormone signaling pathways, stress responses, DNA methylation, and the cell cycle. CsAKIN10 was identified as interacting protein of CsSPL14. Our results provide insights into the regulatory pathway through which miR156-SPL modules enhance the SE potential of citrus callus, and provide a theoretical basis for improvement of plant SE competence.
Journal Article
Early Activation of Myeloid-Derived Suppressor Cells Participate in Sepsis-Induced Immune Suppression via PD-L1/PD-1 Axis
Myeloid derived suppressor cells (MDSCs) have been reported to keep elevating during sepsis. The current study was performed to investigate the immunosuppressive effect of MDSCs and their subsets with the underlying mechanisms.
The immunosuppressive status was manifested by the apoptosis of splenocytes, quantity of T cells and PD-1 expression. The dynamics of quantity and PD-L1 level of MDSCs and the subsets were determined over time. The subset of MDSCs with high PD-L1 level was co-cultured with T cells to observe the suppressive effect.
Abdominal abscess was observed after 7 days post-sepsis. Five biomarkers related to organ functions were all significantly higher in the CLP group. The survival rate was consistent with the middle grade severity of sepsis model. Apoptosis of splenocytes increased over time during sepsis; CD4 + T cell decreased from day 1 post-sepsis; CD8+ T cells significantly reduced at day 7. The PD-1 expression in spleen was upregulated from an early stage of sepsis, and negatively related with the quantity of T cells. MDSCs were low at day 1 post-sepsis, but increased to a high level later; the dynamics of PMN-MDSC was similar to MDSCs. PD-L1 on MDSCs was highest at day 1 post-sepsis; PMN-MDSC was the main subset expressing PD-L1. The PMN-MDSC with high PD-L1 expression level extracted on day 1 after surgery from CLP mice significantly inhibited the proliferation of T cells.
Sepsis-induced immunosuppression is initiated from a very early stage, a high expression level of PD-L1 on MDSCs and the main subset, PMN-MDSC might play a critical role suppressive role on T cells through PD-L1/PD-1 axis.
Journal Article
LncRNA SLCO4A1-AS1 suppresses lung cancer progression by sequestering the TOX4-NTSR1 signaling axis
Background
Metastasis is a multistep process involving the migration and invasion of cancer cells and is a hallmark of cancer malignancy. Long non-coding RNAs (lncRNAs) play critical roles in the regulation of metastasis. This study aims to elucidate the role of the lncRNA solute carrier organic anion transporter family member 4A1-antisense 1 (SLCO4A1-AS1) in metastasis and its underlying regulatory mechanisms.
Methods
A comprehensive analysis of the Gene Expression Omnibus (GEO) database were used to identify metastasis-associated lncRNAs. Transwell migration and invasion assays, and a tail vein-injection mouse model were used to assess the migration and invasion of cancer cells in vitro and in vivo, respectively. High-throughput screening methods, including MASS Spectrometry and RNA sequencing (RNA-seq), were used to identify the downstream targets of SLCO4A1-AS1. Reverse transcription quantitative polymerase chain reaction (RT-qPCR), western blotting, RNA pull-down, RNA immunoprecipitation (RIP), fluorescence in situ hybridization (FISH), and chromatin immunoprecipitation (ChIp) assays were conducted to identify and validate the underlying regulatory mechanisms of SLCO4A1-AS1.
Results
SLCO4A1-AS1 reduced cancer cell migration and invasion by disrupting cytoskeleton filaments, and was associated with longer overall survival in patients with lung adenocarcinoma. SLCO4A1-AS1 directly interacted with the DNA-binding protein, TOX High Mobility Group Box Family Member 4 (TOX4), to inhibit TOX4-induced migration and invasion. Furthermore, RNA-seq revealed that neurotensin receptor 1 (NTSR1) is a novel and convergent downstream target of SLCO4A1-AS1 and TOX4. Mechanistically, SLCO4A1-AS1 functions as a decoy of TOX4 by interrupting its interaction with the
NTSR1
promoter and preventing
NTSR1
transcription. Functionally,
NTSR1
promotes cancer cell migration and invasion through cytoskeletal remodeling, and knockdown of NTSR1 significantly inhibits TOX4-induced migration and invasion.
Conclusion
These findings demonstrated that SLCO4A1-AS1 antagonizes TOX4/NTSR1 signaling, underscoring its pivotal role in lung cancer cell migration and invasion. These findings hold promise for the development of novel therapeutic strategies targeting the SLCO4A1-AS1/TOX4/NTSR1 axis as a potential avenue for effective therapeutic intervention in lung cancer.
Journal Article
Explore the interaction between root metabolism and rhizosphere microbiota during the growth of Angelica sinensis
Angelica sinensis is a medicinal plant widely used to treat multiple diseases in Asia and Europe, which contains numerous active components with therapeutic value. The interaction between root and rhizosphere microorganisms is crucial for the growth and quality formation of medicinal plants. But the micro-plant-metabolite regulation patterns for A. sinensis remain largely undetermined. Here, we collected roots and rhizosphere soils from A. sinensis in seedling stage (M) and picking stage (G), respectively cultivated for one year and two years, generated metabolite for roots, microbiota data for rhizospheres, and conducted a comprehensive analysis. Changes in metabolic and microbial communities of A.sinensis over growth were distinct. The composition of rhizosphere microbes in G was dominated by proteobacteria, which had a strong correlation with the synthesis of organic acids, while in M was dominated by Actinobacteria, which had a strong correlation with the synthesis of phthalide and other organoheterocyclic compounds, flavonoids, amines, and fatty acid. Additionally, co-occurrence network analysis identified that Arthrobacter was found to be strongly correlated with the accumulation of senkyunolide A and n-butylidenephthalide. JGI 0001001.H03 was found to be strongly correlated with the accumulation of chlorogenic acid. Based on rhizosphere microorganisms, this study investigated the correlation between root metabolism and rhizosphere microbiota of A. sinensis at different growth stages in traditional geoherb region, which could provide references for exploring the quality formation mechanism of A. sinensis in the future.
Journal Article