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Direct air capture (DAC) of CO2 is considered one of the most promising carbon capture methods to reduce ambient CO2 concentration for climate change mitigation. Biochar represents an attractive adsorbent for DAC because it is environmentally friendly and cost-effective. In this work, KOH-activated bamboo biochar shows the CO2 capture capacity of 3.49 mmol g−1 (25 °C, 1 bar), and it is used as an adsorbent to perform direct air capture using a fixed-bed reactor with an ambient CO2 capture capacity of up to 51.74 μmol g−1. Furthermore, the effect of relative humidity on CO2 adsorption by biochar was investigated and exhibited promising stability under 2.7% relative humidity. However, the biochar activity for CO2 capture is reduced to 63.88% under 67.1% humidity after 50 cycles.

Biomass gasification is a promising technology to produce renewable syngas used for energy and chemical applications. However, biomass gasification has challenges of low process energy efficiency, low syngas production with low H2/CO ratio and the sintering of biomass ash which limit the deployment of the technology. This work investigated the influence of in-situ generated heat from CaO–CO2 on cellulose CO2 gasification using a fixed bed reactor, thermogravimetric analysis-Fourier transform infrared spectroscopy (TGA-FTIR) and differential scanning calorimetry (DSC). Experimental results indicate an approximate 20 °C temperature difference in the fix-bed reactor between cellulose CO2 gasification with the energy compensation of CaO carbonation (denoted auto-thermal biomass gasification) and conventional CO2 gasification of cellulose after the power of external furnaces were turned off. Around 5 times H2/CO molar ratio is obtained after switching off the power in the auto-thermal biomass gasification compared with conventional gasification. The gas yield enhances significantly from 0.29 g g−1 cellulose to 0.56 g g−1 cellulose when CaO/cellulose mass ratio increases from 0 to 5. Furthermore, the TGA-FTIR results demonstrate the feasibility of adopting energy compensation of CaO carbonation to reduce the gasification temperature. DSC analysis also proves that the released heat from the CaO–CO2 reaction reduces the required energy for cellulose degradation.

Additional file 1: Table S1. Length of exons and introns for genes in Bidens CP genomes. Table S2. Relative Synonymous Codon Usage (RSCU) of Bidens CP genomes. Table S3A. Number of different SSR types detected in three Bidens species. Table S3B. Frequency of identified SSR motifs in different repeat class types. Table S4. The nucleotide diversity of Genes. Table S5. Design primers for Bidens species. Table S6. In silico PCR analysis of the Bidens markers. Table S7. Bidens species from NCBI. Table S8. Sequencing raw data information of Bidens. Fig. S1. ITS1 phylogenetic tree constructed by the maximum parsimony of Bidens. Fig. S2. ITS2 phylogenetic tree constructed by the maximum parsimony of Bidens. Fig. S3. Gene map of B. bipinnata chloroplast genome. Fig. S4. Gene map of B. alba var. radiata chloroplast genome. Fig. S5A. Sliding window analysis based on the Bidens CP genomes. Fig. S5B. The nucleotide diversity (Pi) values of the cp genomes: Pi values of coding genes and Pi values of IGS.

Graphical abstract, A key to tackling the coronavirus disease 2019 (COVID-19) pandemic is to understand how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manages to outsmart host antiviral defense mechanisms. Stress granules (SGs), which are assembled during viral infection and function to sequester host and viral mRNAs and proteins, are part of the antiviral responses. Here, we show that the SARS-CoV-2 nucleocapsid (N) protein, an RNA binding protein essential for viral production, interacted with Ras-GTPase-Activating protein SH3-domain-binding protein (G3BP) and disrupted SG assembly, both of which require intrinsically disordered region1 (IDR1) in N protein. The N protein partitioned into SGs through liquid-liquid phase separation with G3BP, and blocked the interaction of G3BP1 with other SG-related proteins. Moreover, the N protein domains important for phase separation with G3BP and SG disassembly were required for SARS-CoV-2 viral production. We propose that N protein-mediated SG disassembly is crucial for SARS-CoV-2 production.

Thermo-chemical conversion of plastics provides an economic flexible and environmentally friendly method to recycle waste plastics, and generates valuable materials, such as carbon nanotubes (CNTs) and syngas. The development of catalysts is a key challenge for improving the quantity and quality of CNTs. In this study, spherical catalysts loaded with Ni were studied to control CNTs growth using waste plastic as the feedstock. Three parameters were studied, including catalytic temperature, Ni content and plastics/catalysts ratio. A fixed two-stage reactor was used for thermal pyrolysis of plastic waste and the materials were characterized with various methods including scanning electronic microscopy (SEM), temperature programme oxidation (TPO) and X-ray diffraction (XRD). The results showed that different contents of Ni loaded on sphere results in the formation of metal particles with various sizes, thus leading to CNTs production with different quantity and quality. In addition, an optimal catalytic temperature at 800 °C is suggested for CNTs formation with the Ni/sphere catalyst, as the catalyst might not be activated at 600 °C and 700 °C.

Based on the split-stream rushing principle and multi-unit coupling theory, a new type of coupled muffler for diesel engine was designed, and the experimental study on the acoustic performance and exhaust resistance performance was completed. The results show that the growth rate of insertion loss of the coupled muffler within 200 Hz is 34.01% compared to the original muffler. The average fuel consumption reduction rate of the diesel engine with the coupled muffler is 19.16% lower than that of the original muffler. Therefore, the coupled muffler can achieve the comprehensive goal of good acoustic performance and low exhaust resistance.

Reactive oxygen species (ROS) are a group of oxygen-containing free radicals and peroxygenic compounds, which are important substances in the maintenance of normal physiological functions of the body. Excessive ROS destroy the REDOX balance of the body and cause oxidative damage. Ionizing radiation (IR) is an important source of exogenous ROS and an important pathway for endogenous ROS production in the body. The continuous activation and increase in endogenous and exogenous ROS destroys the body’s antioxidant system and stimulates the production of more ROS to form a cascade of amplified inflammatory responses, which ultimately lead to cell death. Pyroptosis is a programmed death process involved in natural immunity, and closely related to the ROS level of the body. Whether pyroptosis is related to IR is still unknown. The present paper reported the effects of IR and pyroptosis on the production and development of ROS to clarify the relationship between ROS, IR and pyroptosis and provide novel ideas for radiation damage, a potential direction for pyroptosis, and an updated connotation for oxidative stress.

Wound infections with methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) are particularly difficult to treat and present a great challenge to clinicians. Nanoemulsions (NE) are novel oil-in-water emulsions formulated from soybean oil, water, solvent, and surfactants such as benzalkonium chloride (BZK). An optimal ratio of those components produces nanometer-sized particles with the positive-charged surfactant at their oil-water interface. We sought to investigate antimicrobial NE as a novel treatment to address wounds co-infected by MRSA and VRE. Swine split-thickness skin wounds were first infected with MRSA and/or VRE, then treated with the nanoemulsion formulation (X-1735) or placebo controls. Bacterial viability after treatment were determined by nutrient agar plates for total, MRSA-specific, and VRE-specific loads. In addition, inflammation indexes were scored by histopathology. When VRE infected wounds were treated with X-1735, they contained 10

The hypoxic nature of solid tumors has severely negative effects on oxygen-based photodynamic therapy. In this study, we used porous Pt nanoparticles as a catalase (CAT) nanozyme, the second near-infrared (NIR-II) region photothermal transition agents (PTAs), and carriers of photosensitizer chlorin e6 (Ce6) to synthesize a composite nanosystem Pt-Ce6. In this system, Pt-Ce6 can continuously and stably decompose H2O2 into oxygen, thereby alleviating tumor hypoxia and improving the effect of photodynamic therapy (PDT). With 650 nm illumination, the reactive oxygen species (ROS) produced by Ce6 will decrease the mitochondrial membrane potential (MMP, ΔΨm) to release cytochrome c (Cyt-c) from the mitochondria into the cytoplasm, eventually leading to mitochondrial-mediated cellular apoptosis during the PDT process. In addition, Pt-Ce6 has good photothermal stability and high photothermal conversion efficiency (52.62%) in the NIR-II region. In U14 tumor-bearing mice, Pt-Ce6 completely suppressed tumor growth and recurrence under laser irradiation. Thus the nanocomposite shows excellent PDT/photothermal therapy (PTT) synergistic performance in vitro and in vivo.

In order to analyze the flow field characteristics of the split-stream rushing muffler, a theoretical model describing the velocity of the split streams is established and verified by the tracer test. For this new-principle muffler, the acoustic performance and the relationship between the velocity drop of the airflow and the pressure field are analyzed, also the structure optimization of the muffler is carried out based on the orthogonal test. Finally, a new muffler is fabricated based on the designing theory of this type of muffler for a prototype of diesel engine, and the comparative analyses are conducted compared with its original muffler. The results show that the establishment and analysis of the theoretical model for velocity during the split-streams rushing process are correct. In the frequency range of 0–1000 Hz, the average transmission loss of split-stream rushing muffler is better than that of the original muffler. While the speed of airflow is reduced by split-streams rushing, a certain pressure loss is caused at the same time, which is about 50% of total pressure loss of the muffler, and the average fluid resistance coefficient of the split-stream rushing process is 0.91. Compared to the original muffler of the sample engine, the average insertion loss of the optimized new muffler is increased by 61.2%. At inlet air velocity of 30 m/s, the pressure loss is reduced by 16.8%. The results provide a potential for practical engineering application of this new split-stream rushing muffler in future. © 2020 Institute of Noise Control Engineering

The hedgehog (HH) signaling pathway is one of the key pathways that is indispensable for many developmental processes and postnatal tissue homeostasis. Dysregulated HH signaling could lead to developmental disorders and tumorigenesis in a variety of tissues via inherited or sporadic mutation, gene overexpression, and crosstalk with other signaling pathways. Recently, accumulating evidence has shown that HH signaling is targeted by viruses to facilitate viral transcription, immune evasion, and uncontrolled growth, leading to effective viral replication and pathogenesis. In this study, we will summarize recent advances in functional interaction between HH signaling and different types of viruses, particularly focusing on the pathological role of HH signaling in viral infections and related diseases.

Additional file 3: Fig. S1. Analysis of simple sequence repeats (SSRs) in the cp genomes of three Amomum species. Fig. S2. Frequency of identified SSR motifs in different repeat class types. Fig. S3. Repeat sequences of three Amomum species. F, P, R, and C indicate the repeat types F (forward), P (palindrome), R (reverse), and C (complement), respectively. Fig. S4. ML tree based on ccsA sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S5. ML tree based on trnC-GCA_petN sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S6. ML tree based on ndhB_rps7 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S7. ML tree based on psaI_ycf4 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S8. ML tree based on rpl20 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S9. ML tree based on rpl33 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S10. ML tree based on rps3 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S11. ML tree based on rpoA sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S12. ML tree based on rps4 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S13. ML tree based on ndhD_1 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S14. ML tree based on ndhD_2 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S15. ML tree based on ITS sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S16. ML tree based on ITS2 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S17. ML tree based on psbA-trnH sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S18. ML tree based on matK sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S19. ML tree based on rbcL sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S20. 3D-structure for the represented positively-selected genes with 1 substitution. Fig. S21. Phylogenetic tree constructed by NJ based on complete chloroplast genome. Numbers under the nodes indicate bootstrap probabilities. Our sequenced samples are marked in red. Species from different genuses are marked in different color on the nodes.

Additional file 2: Table S1.. Number of different SSR types detected in three Amomum species. Table S3. Species information of Ka/Ks. Table S5. ITS2 haplotypes of A. villosum and A. longiligulare from Sanger sequencing. Table S6. ITS2 reads of A. villosum and A. longiligulare from NGS data. Table S7. The primers and conditions for PCR.

Additional file 3: Fig. S1. Analysis of simple sequence repeats (SSRs) in the cp genomes of three Amomum species. Fig. S2. Frequency of identified SSR motifs in different repeat class types. Fig. S3. Repeat sequences of three Amomum species. F, P, R, and C indicate the repeat types F (forward), P (palindrome), R (reverse), and C (complement), respectively. Fig. S4. ML tree based on ccsA sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S5. ML tree based on trnC-GCA_petN sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S6. ML tree based on ndhB_rps7 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S7. ML tree based on psaI_ycf4 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S8. ML tree based on rpl20 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S9. ML tree based on rpl33 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S10. ML tree based on rps3 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S11. ML tree based on rpoA sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S12. ML tree based on rps4 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S13. ML tree based on ndhD_1 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S14. ML tree based on ndhD_2 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S15. ML tree based on ITS sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S16. ML tree based on ITS2 sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S17. ML tree based on psbA-trnH sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S18. ML tree based on matK sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S19. ML tree based on rbcL sequences of Amomum species. This Bootstrap consensus tree was constructed by K2P model with 1000 bootstrap replicates. Numbers under the nodes indicate bootstrap probabilities. The cut off value for the condensed tree was 50%. Fig. S20. 3D-structure for the represented positively-selected genes with 1 substitution. Fig. S21. Phylogenetic tree constructed by NJ based on complete chloroplast genome. Numbers under the nodes indicate bootstrap probabilities. Our sequenced samples are marked in red. Species from different genuses are marked in different color on the nodes.

Additional file 2: Table S1.. Number of different SSR types detected in three Amomum species. Table S3. Species information of Ka/Ks. Table S5. ITS2 haplotypes of A. villosum and A. longiligulare from Sanger sequencing. Table S6. ITS2 reads of A. villosum and A. longiligulare from NGS data. Table S7. The primers and conditions for PCR.

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Background: Subthreshold depression (SD) is considered to be the precursor stage of major depression, which is correlated with functional impairment and increased suicide rate. Although there are multiple therapies for the treatment of SD, the comparison and efficacy of various methods has yet to be evaluated. This study aimed to evaluate the efficacy of different therapies by performing a Bayesian network meta-analysis. Methods: We searched eight databases on April 3, 2021. Center for Epidemiologic Studies Depression Scale (CES-D), Beck Depression Inventory scale (BDI), the Patient Health Questionnaire-9 (PHQ-9), and the Kessler Screening Scale for Psychological Distress (K-6) were used as efficacy outcomes. This Bayesian network meta-analysis used a fixed-effects model. Findings: Twenty-one randomized controlled trials involving 5,048 participants were included in this study. The results suggested that electroacupuncture (MD −12.00, 95% CrI −15.00, −10.00), conventional acupuncture plus wheat-grain moxibustion (MD −9.70, 95% CrI −14.00, −5.30), and the Chinese traditional peripateticism pill plus group counseling (MD −9.00, 95% CrI −11.00, −6.70) had better efficacy than the control group (CG) in improving CES-D. For BDI outcome, bright light therapy (MD −9.70, 95% CrI −13.00, −6.00), behavioral activation program (MD −5.70, 95% CrI −6.10, −5.40), and dim light therapy (MD −6.30, 95% CrI −10.00, −2.20) were better than the CG. Tai chi (MD −3.00, 95% CrI −4.00, −2.00) was better than CG for PHQ-9 outcomes. Telephone-based cognitive behavioral treatment (MD −2.50 95% CrI −2.70, −2.30) was better than the CG for K-6 scores. Conclusion: Our results suggest that electroacupuncture or bright light therapy appear to be the better choices in the treatment of SD. This study provide new insights into clinical treatment selection and may aid the development of guidelines for the management of SD., Graphical Abstract Graphical abstract of the network meta-analysis. Note: CES-D, Center for Epidemiologic Studies Depression Scale; BDI, Beck Depression Inventory Scale; PHQ-9, the nine-Item Patient Health Questionnaire; K6, Kessler Screening Scale for Psychological Distress; SUCRA, surface area under the cumulative ranking curves.

Urbanization and the ecological resource environment are mutually influential and interdependent, and their coordinated development is the key to the sustainable development of urbanizatio...

The use of multi-mode remote sensing data for biomass prediction is of potential value to aid planting management and yield maximization. In this study, an advanced biomass estimation approach for sugarcane fields is proposed based on multi-source remote sensing data. Since feature interpretability in agricultural data mining is significant, a feature extraction method of deep archetypal analysis (DAA) that has good model interpretability is introduced and aided by principal component analysis (PCA) for feature mining from the multi-mode multispectral and light detection and ranging (LiDAR) remote sensing data pertaining to sugarcane. In addition, an integrated regression model integrating random forest regression, support vector regression, K-nearest neighbor regression and deep network regression is developed after feature extraction by DAA to precisely predict biomass of sugarcane. In this study, the biomass prediction performance achieved using the proposed integrated learning approach is found to be predominantly better than that achieved by using conventional linear methods in all the time periods of plant growth. Of more significance, according to model interpretability of DAA, only a small set of informative features maintaining their physical meanings (four informative spectral indices and four key LiDAR metrics) can be extracted which eliminates the redundancy of multi-mode data and plays a vital role in accurate biomass prediction. Therefore, the findings in this study provide hands-on experience to planters with indications of the key or informative spectral or LiDAR metrics relevant to the biomass to adjust the corresponding planting management design.

Emerging evidence witnesses the association of air pollution exposure with sleep disorders or the risk of obstructive sleep apnea (OSA); however, the results are not consistent. OSA patients with or without a low arousal threshold (LAT) have different pathology and therapeutic schemes. No study has evaluated the potential diverse effects of air pollution on the phenotypes of OSA. The current study aimed to evaluate the associations of short-term and long-term exposure to air pollution with sleep-disordered measures and OSA phenotypes. This cross-sectional study consisted of 4634 participants from a sleep center in Taipei from January 2015 to April 2019. The personal exposure to ambient PM

Background Highly pathogenic H5N1 influenza viruses remain a pandemic risk to the world population. Although vaccines are the best solution to prevent this threat, a more effective vaccine for H5 strains of influenza has yet to be developed. All existing vaccines target only serum antibody against influenza as the primary outcome, while mucosal immunity has not been addressed. To address these shortcomings we have used an effective mucosal adjuvant system to produce a prototype vaccine that provides antibody, cellular and mucosal immunity to multiple serotypes of H5. Methods Plant-derived recombinant H5 (rH5) antigen was mixed with a novel nanoemulsion NE01 adjuvant. The rH5-NE01 vaccine was administered intranasally to CD-1 mice and ferrets. Immunogenicity of this immunization was evaluated through rH5-specific antibody and cellular immune responses. Hemagglutination inhibition (HI) and virus neutralization (VN) assays were performed. Protection against H5N1 virus challenge was evaluated in ferrets. Results Intranasal immunization with rH5-NE01vaccine induced high titers (>106) of rH5-specific IgG in mice. In mice and ferrets this vaccine also achieved titers of ≥40 for both HI and VN. Additionally, the levels of rH5-specific IgA were significantly increased in bronchial secretions in these animals. The rH5-NE01 vaccine enhanced rH5-specific cellular immune responses including IFN-γ and IL-17. Ten-day survival post challenge was 100% in ferrets that received rH5-NE01compared to 12.5% in the PBS group. Furthermore, this vaccine prevented weight loss and increases in body temperature after H5N1 challenge as compared to the controls. Moreover, H5N1 virus in nasal wash of rH5-NE01-vaccinated ferrets was significantly decreased compared to controls. Conclusion Intranasal immunization with rH5 antigen formulated with NE01 adjuvant elicited strong, broad and balanced immune responses that effectively protect against H5N1 influenza virus infection in the ferret model. The ease of formulation of rH5-NE01 makes this novel combination a promising mucosal vaccine candidate for pandemic influenza.

Reverse water–gas shift reaction using renewable H2 is a promising route for CO2 upgrade, however, it is restricted by the equilibrium. The chemical looping reverse water–gas shift reaction using oxygen carriers (i.e. Fe) has proven a more effective CO2 utilization process to produce CO. However, CO2 with high purity is needed to obtain concentrated CO. Herein, we propose a calcium chemical dual looping using one-pot sol–gel synthesized Ca-Fe dual functional materials (DFMs). The CO2 in the exhaust gas (∼10% CO2) can be captured and transformed into carbonates and then in-situ converted into CO through continuous chemical looping in H2 atmosphere. This process avoids CO2 enrichment, storage and transportation and simultaneously realizes efficient CO2 conversion. The Ca-Fe DFMs possessed significantly improved catalytic efficiency (enhanced real-time CO generation rate) compared to CaO. It is found that Ni1Fe9-CaO could optimally achieve 11.3 mmol gDFM−1 CO yield, 82.5% CO2 conversion and 99.9% CO selectivity at 650 °C. Notably, Ni1Fe9-CaO displayed high CO2 conversion (>80%) and CO selectivity (>99.9%) during the cycle tests and possessed enhanced stability in relation to CO yield after 10 cycles (20.9% and 35.5% decrease for Ni1Fe9-CaO and CaO, respectively). Herein, Ca2Fe2O5 plays two roles: acting as an oxygen carrier for in-situ chemical looping to produce CO and a thermally stable physical barrier to retard the sintering of CaO. It is noted that Fe-related species could be reduced into the metallic state at the end of hydrogenation, resulting in CO formation in the following CO2 capture process.

Additional file 8: Figure S5. Compartmental conversion among chromosomes. A. The frequency of compartmental transition between 18 chromosomes in the control group of venom gland compared with the 3d group of venom gland. B. The frequency of compartmental transition between 18 chromosomes in muscle compared with the 3d group of venom gland.

Additional file 6: Figure S1. Analysis of gene expression of different time and tissues. A. Scatter plot showing gene expression between the control group and 3d group in venom gland. The axes represent normalized RNA-seq log2FoldChange. Red and black dots denote genes whose expression changed significantly and grey dots denote genes whose expression was unchanged. B. The PCA analysis of 6 tissues in B. multicinctus.

Additional file 10: Figure S7–S8. Heatmap and distribution of histone marks around TSS.

We experimentally demonstrate a highly nonlinear organic-silicon hybrid slot waveguide (HN-OSSW), whose nonlinear coefficient reaches to 1.55 × 106 W−1km−1 with the waveguide length of 2mm.

Additional file 7: Figure S2–S4. Chromosomal interactions among control/ 3d group of venom gland and muscle. A. Comparison of intra-chromosomal interactions between MACs and MICs. B. Comparison of inter-chromosomal interactions between MICs and MICs-MACs. C. Comparison of inter-chromosomal interactions between MACs and MICs-MACs. D. Comparison of inter-chromosomal interactions between MACs and MICs.

Visual Grounding (VG) is a task of locating a specific object in an image semantically matching a given linguistic expression. The mapping of the linguistic and visual contents and the understanding of diverse linguistic expressions are the two challenges of this task. The performance of visual grounding is consistently improved by deep visual features in the last few years. While deep visual features contain rich information, they could also be noisy, biased and easily over-fitted. In contrast, symbolic features are discrete, easy to map and usually less noisy. In this work, we propose a novel modular network learning to match both the object's symbolic features and conventional visual features with the linguistic information. Moreover, the Residual Attention Parser is designed to alleviate the difficulty of understanding diverse expressions. Our model achieves competitive performance on three popular datasets of VG. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-003), and the MOE AcRF Tier-1 research grants: RG28/18 (S), RG22/19 (S) and RG95/20.

Additional file 9: Figure S6. Relationship between histone proteins and genes. A. The relationship between transition of A/B Compartment and histone modifications. B. The Pearson correlation coefficients of H3ac and genes. C. The Pearson correlation coefficients of H3K27me3 and genes.

Chronic inflammatory pain is a severe clinical symptom that aggravates the life quality of patients and places a huge economic burden on individuals and society. As one complementary and alternative therapy, electroacupuncture (EA) is widely used in clinical practice to treat chronic inflammatory pain based on its safety and efficacy. Previous studies have revealed the potential role of adenosine, neuropeptides, and inflammatory factors in EA analgesia in various pain models, but the identity of some of the signaling pathways involved remain unknown. In the present study, we explored whether neuregulin1 (NRG1)-ErbB4 signaling is involved in EA analgesia in inflammatory pain. Repeated EA treatment at the acupoints Zusanli (ST36) and Sanyinjiao (SP6) for 3 consecutive days remarkably attenuated mechanical allodynia and thermal hyperalgesia in complete Freund’s adjuvant (CFA)-treated mice, with an increased expression of NRG1 in spinal cord (SC). We found that ErbB4 kinase participated in both the EA and NRG1 mediated analgesic effects on inflammatory pain by pharmacological inhibition or genetic ablation ErbB4 in vivo. Intriguingly, the mice with conditional knockout of ErbB4 from PV+ interneurons in SC showed abnormal basal mechanical threshold. Meanwhile, NRG1 treatment could not relieve tactile allodynia in PV-Erbb4–/– mice or AAV-PV-Erbb4–/– mice after CFA injection. These experimental results suggest that regulating NRG1-ErbB4 signaling in SC could reduce pain hypersensitivity and contribute to EA analgesia in inflammatory pain.

In order to meet the demand of the rapid economic growth for power load, this paper proposes a 500kV substation planning method considering the interconnection structure of 220kV substation. Firstly, the interconnection structure between 220 kV substations is analyzed; secondly, based on the interconnection structure between 220 kV substations and further considering the access of 220 kV high-voltage power supply, a mathematical model of 500 kV substation planning considering the interconnection structure of 220 kV substation is constructed; furthermore, the improved weighted Voronoi diagram algorithm and the low-voltage side line network loss and investment cost are adopted The method of station location optimization is used to solve the planning model of 500kV substation. Finally, the scientificalness and practicability of the proposed method in the planning of 500kV substation is verified by an example.

Prenatal infection during pregnancy increases the risk for developing neuropsychiatric disorders such as schizophrenia. This is linked to an inflammatory microglial phenotype in the offspring induced by maternal immune activation (MIA). Microglia are crucial for brain development and maintenance of neuronal niches, however, whether and how their activation is involved in the regulation of neurodevelopment remains unclear. Here, we used a MIA rodent model in which polyinosinic: polycytidylic acid (poly (I:C)) was injected into pregnant mice. We found fewer parvalbumin positive (PV+) cells and impaired GABAergic transmission in the dentate gyrus (DG), accompanied by schizophrenia-like behavior in the adult offspring. Minocycline, a potent inhibitor of microglia activation, successfully prevented the above-mentioned deficits in the offspring. Furthermore, by using microglia-specific arginase 1 (Arg1) ablation as well as overexpression in DG, we identified a critical role of Arg1 in microglia activation to protect against poly (I:C) imparted neuropathology and altered behavior in offspring. Taken together, our results highlight that Arg1-mediated alternative activation of microglia are potential therapeutic targets for psychiatric disorders induced by MIA.

Biofilm processes have a better nitrogen removal ability than traditional activated sludge at low temperatures (

Airflow velocity is a dominant factor of influencing exhaust resistance and turbulence noise in exhaust muffler of internal combustion engine. In this paper, a new principle of reducing the airflow velocity, based on split-stream rushing in muffler, was proposed. The main idea of this principle is to split the exhaust airflow into two streams first, and then lead them rushing when meeting from two opposite-located holes so that achieve the result of reducing the exhaust airflow velocity through the muffler. The basic structure of the split-stream rushing muffler unit was presented and its acoustic performance was analyzed by using the acoustic wave theory. Finally, the numerical simulation and experimental way were used to verify the internal sound field and velocity changes in the new muffler unit. The results show that the proposed muffler unit can effectively reduce the exhaust airflow velocity through the silencer and exhaust backpressure.

The objective of this study was to explore nitrogen removal, especially effluent dissolved organic nitrogen (DON) composition, relative genes and microbial community structures with four individual nitrogen sources at 5 °C. Results show that effluent DON did not have dependent relationship with the TN removal rate (urea > ammonia chloride > L -Alanine > D -Alanine). With the same influent TN, the highest effluent DON was formed with urea; the lowest DON was fed with ammonia chloride. The main DON composition was the product of cell metabolism excluding urea, rather than the original substrate. Glutamic acid synthesizing process was of great importance to DON accumulation at 5 °C. The nitrogen source type was important to the diversity and heterogeneity of the nitrogen removal genes. Bacterial population structure using redundancy analysis (RDA) showed Simplicispira occupied a higher abundance remarkably in the reactors feeding with urea, and Dyadobacter occupied higher feeding with l -Alanine.

Silicon photonic integrated devices used for nonlinear optical signal processing play a key role in ultrafast switching, computing, and modern optical communications. However, current devices suffer from limited operation speeds and low conversion efficiencies due to the intrinsically low nonlinear index of silicon. In this paper, we experimentally demonstrate enhanced optical nonlinearity in a silicon–organic hybrid slot waveguide consisting of an ultranarrow slot waveguide coated with a highly nonlinear organic material. The fabricated slot area is as narrow as 45 nm, which is, to the best of our knowledge, the narrowest slot width that has been experimentally reported in silicon slot waveguides. The nonlinear coefficient of the proposed device with a length of 3 mm is measured to be up to 1.43 × 10 6 W − 1 km − 1 . Based on the nanostructure design, the conversion efficiencies of degenerate four-wave mixing showed enhancements of more than 12 dB and 5 dB compared to those measured for an identical device without the organic material and a silicon strip waveguide, respectively. As a proof of concept, all-optical canonical logic units based on the prepared device with two inputs at 40 Gb/s are analyzed. The obtained logic results showed clear temporal waveforms and wide-open eye diagrams with error-free performance, illustrating that our device has great potential for use in high-speed all-optical signal processing and high-performance computing in the nodes and terminals of optical networks.

This study aimed to construct a multimorbidity index among Chinese older adults. Participants aged 65-84 years (n=11,757) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Fourteen self-reported chronic conditions were assessed at baseline. Outcome was all-cause mortality within five-year follow-up. We used restrictive association rules mining to identify the patterns of multiple chronic conditions associated with mortality. The weights of conditions and disease combinations were assigned using logistic regression adjusted by age and sex in training set. Multimorbidity index (MI) with individual diseases and multimorbidity index incorporating disease combinations (MIDC) were developed. We compared the performance of MI and MIDC with condition count and XGBoost algorithm in the validation set. There were no significant differences of c-statistics between condition count (0.687) and MI (0.692) or MIDC (0.689). The c-statistic of XGBoost algorithm (0.675) was the lowest among all models. The Integrated Discrimination Improvement (IDI) and categorical Net Reclassification Index (NRI) for MI (IDI: 0.01, P < 0.001; NRI: 0.01, P = 0.127), MIDC (IDI: 0.004, p = 0.002; NRI: 0.02, P = 0.033), and XGBoost model (IDI: 0.02, P < 0.001; NRI: 0.03, P = 0.004) were significantly positive compared with condition count. However, no significant differences for IDI and NRI were observed between MI and MIDC. Among Chinese older adults, weighted multimorbidity index with individual disease can better predict five-year mortality risk over condition count. There was little improvement in the predictive performance of the index after considering the joint effects of disease combinations.

It is very significant to monitor livestock foraging behavior accurately and in real-time to further improve pasture management and livestock welfare. Although various algorithms have been developed to identify and classify animals' foraging behavior, it still has room to be improved in generality and function. In this study, a representative acoustic dataset generated by typical bodyweight sheep when grazing on various grasses and subsequent ruminating was created. Then an algorithm for the identification and classification of foraging behavior was proposed based on feature extraction technique and deep learning. Specifically, all prominent fragments in the acoustic signal were identified as events by the identification algorithm, and the events were classified as noise, chew, bite, chew-bite, or ruminating behavior through the classification model. The effect of each parameter on the identification algorithm was analyzed, and an optimal set of parameters was derived. As a result, the accuracy of 96.13% was achieved by the identification algorithm with the optimal parameters. Meanwhile, the performances of three common deep network models, including deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), were compared. The results showed that the RNN, CNN, and DNN model's accuracy were 93.17%, 92.53%, and 79.43%, respectively. The RNN model had a stronger classification capacity than the CNN model since it involved the inter-dependent information of adjacent events. The CNN model achieved a superior classification performance than the DNN model because the log-scaled Mel-spectrogram representation of the event waveform was more effective than the waveform itself. The algorithm proposed in this study could be well applied to identify and classify all foraging behaviors of typical weight sheep foraging freely on various grasses in the future.

Aim: To develop NB-201, a nanoemulsion compound, as a novel microbicidal agent against methicillin-resistant Staphylococcus aureus (MRSA) infection, which is a common threat to public health but with limited therapeutic options. Materials & methods: NB-201 was tested in in vitro and in vivo murine and porcine models infected with MRSA. Results: Topical treatment of MRSA-infected wounds with NB-201 significantly decreased bacterial load and had no toxic effects on healthy skin tissues. NB-201 attenuated neutrophil sequestration in MRSA-infected wounds and inhibited epidermal and deep dermal inflammation. The levels of proinflammatory cytokines were reduced in NB-201-treated MRSA-infected wounds. Conclusion: NB-201 can greatly reduce inflammation characteristic of infected wounds and has antimicrobial activity that effectively kills MRSA regardless of the genetic basis of antibiotic resistance.

Background Vaccination is the most effective approach to prevent infection with highly pathogenic avian influenza (HPAI). Adjuvants are often used to induce effective immune responses and overcome the immunological weakness of recombinant HPAI antigens. Given the logistical challenges of immunization to HPAI during pandemic situations, vaccines administered via the intramuscular (I.M.) route would be of value. Methods A new formulation of nanoemulsion adjuvant (NE02) suitable for I.M. vaccination was developed. This NE02 was evaluated alone and in combination with CpG to develop H5 immune responses in mouse and ferret models. Measures of recombinant H5 (rH5) specific immunity evaluated included serum IgG and IgG subclasses, bronchoalveolar lavage fluid IgA, and cytokines. The activation of NF-kB was also analyzed. The efficacy of the vaccine was assessed by performing hemagglutination inhibition (HAI), virus neutralization (VN) assays, and viral challenges in ferrets. Results I.M. vaccination with rH5-NE02 significantly increased rH5-specific IgG and protected ferrets in the viral challenge model providing complete protection and sterile immunity in all animals tested. Combining NE02 and CpG produced accelerated antibody responses and this was accompanied by an elevation of IFN-γ and IL-17 responses and the downregulation of IL-5. The combination also caused a synergistic effect on NF-kB activation. In immunized ferrets after viral challenge, the rH5-NE02 + CpG vaccine via I.M. achieved at least 75% and 88% seroconversion of HAI and VN antibody responses, respectively, and improved body temperature stabilization and weight loss over NE02 alone. Conclusions The I.M. injection of NE02 adjuvanted rH5 elicits strong and broad immune responses against H5 antigens and effectively protects animals from lethal H5 challenge. Combining this adjuvant with CpG enhanced immune responses and provided improvements in outcomes to viral challenge in ferrets. The results suggest that combinations of adjuvants may be useful to enhance H5 immune responses and improve protection against influenza infection.

The emergence of immunocompromising diseases such as HIV/AIDS or other immunosuppressive medical conditions have opened an opportunity for fungal infections to afflict patients globally. An increase antifungal drug resistant fungi have posed a serious threat to patients. Combining these circumstances with a limited variety of antifungal drugs available to treat patients has left us in a situation where we need to develop new therapeutic approaches that are less prone to development of resistance by pathogenic fungi. In this study we present the utilization of the nanoemulsion NB-201 to control human pathogenic fungi. We found that the NB-201 exhibited in vitro activity against C. albicans, including both planktonic growth and biofilms. Furthermore, treatments with NB-201 significantly reduced the fungal burden at the infection site and presented enhanced healing process after subcutaneous infections by multidrug resistant C. albicans in a murine host system. NB-201 also exhibited in vitro growth inhibition activity against other fungal pathogens, including Cryptococcus spp, Aspergillus fumigatus, and Mucorales. Due to the nature of the activity of this nanoemulsion, there is a minimized chance of drug resistance to develop, thus presents a novel treatment to control fungal wound or skin infections.

Thermo-chemical conversion is a promising technology for the recycle of waste plastics, as it can produce high-value products such as carbon nanotubes (CNTs) and hydrogen. However, the low yield of CNTs is one of the challenges. In this work, the addition of Mn (0 wt.%, 1 wt.%, 5 wt.%, and 10 wt.%) to Fe-based catalyst to improve the production of CNTs has been investigated. Results show that the increase of Mn content from 0 wt.% to 10 wt.% significantly promotes CNTs yield formed on the catalyst from 23.4 wt.% to 32.9 wt.%. The results show that Fe-particles in the fresh catalysts are between 10−25 nm. And the addition of Mn in the Fe-based catalyst enhanced the metal-support interactions and the dispersion of metal particles, thus leading to the improved catalytic performance in relation to filamentous carbon growth. In addition, the graphitization of CNTs is promoted with the increase of Mn content. Overall, in terms of the quantity and quality of the produced CNTs, 5 wt.% of Mn in Fe-based catalyst shows the best catalytic performance, due to the further increase of Mn content from 5 wt.% to 10 wt.% led to a dramatic decrease of purity by 10 wt.%.

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.

Although many breast and lung cancers overexpress human epidermal growth factor receptor-2 (HER-2), no methods currently exist for effective and early detection of HER-2-positive cancers. To address this issue, we designed and synthesized dendrimer-based novel nano-imaging agents that contain gold nanoparticles (AuNPs) and gadolinium (Gd), conjugated with the humanized anti-HER-2 antibody (Herceptin). Generation 5 (G5) polyamidoamine (PAMAM) dendrimers were selected as the backbone for the nano-imaging agents due to their unique size, high ratio of surface functional groups and bio-functionality. We modified G5 PAMAM dendrimer surface with PEG and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelators to encapsulate AuNPs and complex Gd. These dendrimer entrapped AuNPs were further conjugated with Herceptin through copper-catalyzed azide- alkyne click reaction to construct the nano-imaging agent Au-G5-Gd-Herceptin. The targeted nano-imaging agent bound selectively to HER-2 overexpressing cell lines, with subsequent internalization into the cells. More importantly, non-targeted nano-imaging agent neither bound nor internalized into cells overexpressing HER-2. These results suggest that our approach could provide a platform to develop nano-diagnostic agents or nano-therapeutic agents for early detection and treatment of HER-2-positive cancers.

Metal–organic frameworks (MOFs) usually have micropores smaller than 2 nm, which may restrict their applications in some cases. Hierarchical-pore MOFs (H-MOFs) are a new family of MOF materials, possessing both micro- and mesopores to address this problem. Here we demonstrate a competitive coordination strategy for the synthesis of H-MOF nanostructures, such as two-dimensional (2D) H-MOF nanosheets and H-MOF nanocubes, evolving through an etching process tuned by a competitive ligand. The as-synthesized 2D H-MOF nanosheets can serve as a substrate to in situ immobilize Pd nanoparticles to achieve a surfactant-free Pd catalyst, by means of a simple soaking method of Pd2+ precursors. Combined with the unique structure and gas adsorption capacity of H-MOF-5, the Pd-H-MOF-5 catalyst exhibits superior catalytic performance.

In this paper, we investigate limit cycles induced by threshold nonlinearity of piecewise linear (PWL) differential systems, which are node-focus type or node-center type with the focus or the center being virtual or boundary. To get the number and stability of limit cycles, we adopt a new displacement function with a better configuration than usual. For a given parameter subregion, we exhibit the exact number or the minimum number of limit cycles. In particular, sufficient conditions are established ensuring that there are exactly two limit cycles. When the focus is boundary, we not only show that the maximum number is two, but also verify that the exact number is zero, one or two by varying parameter subregions. Finally, the exact number as well as the stability are obtained in different parameter regions for the PWL differential systems of node-center type.

Online social networks like twitter are self-organized systems with emergent behaviors from the individual interactions. Predicting the users’ behaviors like posting, replying or retweeting is one of the most important issue. In the paper, we present a method to model the twitter users’ posting behavior with the other users’ response. We also contrast the simulation precision with the model that does not involve the other users’ response. We make use of a stochastic model with Markov chain to predict a specific user’s next tweeting behavior. From the result by contrasting the simulation data and the real data, we demonstrate that the engineering method is able to predict individual posting behaviors based on time windows. In addition, the proposed model involving other users’ response is better than that only considering posting time sequences.