Identification and core gene-mining of Weighted Gene Co-expression Network Analysis-based co-expression modules related to flood resistance in quinoa seedlings.

BACKGROUND: As an emerging food crop with high nutritional value, quinoa has been favored by consumers in recent years; however, flooding, as an abiotic stress, seriously affects its growth and development. Currently, reports on the molecular mechanisms related to quinoa waterlogging stress responses are lacking; accordingly, the core genes related to these processes were explored via Weighted Gene Co-expression Network Analysis (WGCNA). RESULTS: Based on the transcriptome data, WGCNA was used to construct a co-expression network of weighted genes associated with flooding resistance-associated physiological traits and metabolites. Here, 16 closely related co-expression modules were obtained, and 10 core genes with the highest association with the target traits were mined from the two modules. Functional annotations revealed the biological processes and metabolic pathways involved in waterlogging stress, and four candidates related to flooding resistance, specifically AP2/ERF, MYB, bHLH, and WRKY-family TFs, were also identified. CONCLUSIONS: These results provide clues to the identification of core genes for quinoa underlying quinoa waterlogging stress responses. This could ultimately provide a theoretical foundation for breeding new quinoa varieties with flooding tolerance.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

Defect-rich MoS2/NiS2 nanosheets loaded on SiNWs for efficient and stable photoelectrochemical hydrogen production.

Photoelectrochemical (PEC) reaction with efficient, stable, and cost-effective photocathodes using non-precious metals will be one of the most environmentally friendly technologies for hydrogen (H2) generation under the worldwide pressure for carbon neutrality. Herein, a new 3-dimentional (3D) SiNWs@MoS2/NiS2 photocathode was designed and synthesized. Defect-rich MoS2/NiS2 nanosheets on silicon nanowires (SiNWs) provide more active sites to promote charge transfer and photo-generated electron-hole separation. Meanwhile, the 3D structure of the photocathode provides an effective charge transfer mode and an open channel for rapid H2 release. The SiNWs@MoS2/NiS2 photocathode exhibits the maximum photocurrent density (21.4 mA·cm-2 at 0.9 V vs. RHE), highest H2 production rate (183 µmol·h-1), smallest diffusion resistance (34.7 Ω), and excellent catalytic stability for more than 10 h at pH = 7. Based on density function theory calculation, the MoS2/NiS2 nanosheets are conducive to chemical adsorption of H2 intermediates, which are crucial for the maintenance of the composite photocathode in PEC H2 production.

Enhanced activity of ZnS (111) by N/Cu co-doping: Accelerated degradation of organic pollutants under visible light.

High-efficiency photocatalysts are of great significance for the application of photocatalytic technology in water treatment. In this study, N/Cu co-doped ZnS nanosphere photocatalyst (N/Cu-ZnS) is synthesized by a hydrothermal method for the first time. After doping, the texture of nanosphere becomes loose, the nanometer diameter is reduced, making the specific surface area of catalyst increased from 34.73 to 101.59 m2/g. The characterization results show that more ZnS (111) crystal planes are exposed by N/Cu co-doping; the calculations of density functional theory show that N/Cu co-doping can increase the catalytic activity of the ZnS (111) crystal plane, enhance the adsorption capacity of (111) crystal plane to O2, and promote the generation of •O2-. The energy levels of the introduced impurities can be hybridized with the energy levels of S and Zn at the top of valence band and the bottom of conduction band, which makes the band gap narrower, thus enhancing the absorption of visible light. Compared with pure ZnS, the degradation rates of 2,4-dichlorophenol (2,4-DCP) and tetracycline (TC) by N/Cu-ZnS under visible light (>420 nm) are increased by 83.7 and 51 times, respectively. In this research, a promising photocatalyst for photocatalytic degradation of organic pollutants in wastewater is provided.

Photocatalytic degradation of organic pollutants in water by N-doping ZnS with Zn vacancy: enhancement mechanism of visible light response and electron flow promotion.

In order to improve the visible light response, N-doping ZnS (N-ZnS) nanospheres with Zn vacancy and porous surface were prepared by a simple one-pot hydrothermal method. Characterizations and density functional theory simulations showed excellent visible light response of N-ZnS. N-doping introduced impurity energy levels, which led to orbital hybridization and changed the original dipole moment. The presence of ortho Zn vacancy (O-Znv) can effectively reduce e--h+ recombination and photocorrosion. Furthermore, O-Znv caused lattice distortion (twisted the -S-Zn-N-(O-Znv)-S-Zn-S- chemical bond chain), resulting in "vacancy effect" to accelerate e- flow. Under visible light, the photocatalytic degradation efficiency of tetracycline (TC) and 2,4-dichlorophenol (2,4-DCP) was 90.31% and 60.84%, respectively. TOC degradation efficiency was 31.4% and 25.6%, respectively. Combined with Fukui index and LC-MS methods, it was found that TC and 2,4-DCP were degraded under the constant attack of active substances such as ·OH. This work can provide a reference for the application of catalytic materials in the field of visible light photocatalysis.

Antibacterial and corrosion protection properties of SA-CuZnO@ODA-GO composite in circulating cooling water.

The suitable temperature of circulating cooling water is conducive to the reproduction of bacteria, which will cause corrosion to the pipe and form slime on its surface. Therefore, the sterilization of circulating cooling water is an essential step. In this work, a new type of SA-CuZnO@ODA-GO composite antibacterial material with hydrophobic properties was prepared by ultrasonic treatment method. The composite was characterized and analyzed by SEM, TEM, XPS, XRD, and FT-IR. Then, the CuZnO@ODA-GO@PU hydrophobic and antibacterial coating was prepared. The antibacterial properties and mechanism of the composite were investigated by gram-positive bacteria S. aureus and gram-negative bacteria E. coli. The result shows that the best antibacterial rate of SA-CuZnO@ODA-GO composite antibacterial material is up to 99.10%. As for the SA-CuZnO@ODA-GO@PU composite antibacterial coating, the corrosion resistance of the antibacterial coating is up to 99.99%. The anticorrosion property is due to the hydrophobic modification of the composite, which can insulate the steel sheet from water. Secondly, due to the rigidity of the SA-CuZnO@ODA-GO, it combines with epoxy resin to form a compact structure.

Antibacterial properties of recoverable CuZnO@Fe3O4@GO composites in water treatment.

The growth of bacteria will lead to water quality deterioration and equipment damage. Therefore, it is necessary to control the growth and reproduction of microorganisms in water treatment. A new type of magnetic recoverable CuZnO@Fe3O4@GO composites was prepared by ultrasonic method, and the composites were characterized and analyzed by SEM, TEM, XPS, and other methods. The optimum mass ratio of composites was determined by orthogonal experiment, and the antibacterial properties and mechanism of the composite were investigated by gram-positive bacteria Staphylococcus aureus and gram-negative bacteria Escherichia coli. Finally, the antibacterial properties of the composites in the effluent of the secondary sedimentation tank were researched. It was shown that the optimum mass ratio of the composites was GO:Fe3O4:CuZnO =1:2:3. When the dosage of composites was 180 mg L-1 and the action time was 100 min, the antibacterial rate against S. aureus and E. coli reached more than 99.5%. The composites could destroy the cell structure of two kinds of bacteria, increase the content of active oxygen in bacteria cells, and enhance the leakage rate of protein by more than 9 times in 150 min, thereby causing the death of the bacteria. And the antibacterial rate of the composites in effluent of the secondary sedimentation tank could reach 99%, and the magnetic recovery rate could reach more than 98%. After 5 cycles of use, the antibacterial rate could still exceed 90%.

Antibacterial and anticorrosive properties of CuZnO@RGO waterborne polyurethane coating in circulating cooling water.

In order to control bacterial adhesion and metal corrosion in the circulating cooling water system, it is necessary to prepare a nanocomposite-modified coating with antibacterial and anticorrosive functions. Copper and zinc composite oxide (CuZnO) was synthesized to prepare CuZnO@RGO nanocomposites. The antibacterial mechanism of CuZnO@RGO nanocomposites was investigated using gram-negative bacteria E. coli and gram-positive bacteria S. aureus as the two model microorganisms. The antibacterial properties of CuZnO@RGO nanocomposites on mixed bacteria were researched in the cooling water system. In addition, the CuZnO@RGO waterborne polyurethane (WPU) composite coating (CuZnO@RGO/WPU) was synthesized. The antibacterial performance, hardness, and corrosion inhibition performance of CuZnO@RGO/WPU composite coating in the cooling water system were also investigated. The results showed that after adding CuZnO@RGO nanocomposites to E. coli or S. aureus suspension, the protein leakage after 20 h was 9.3 times or 7.2 times higher than that in the blank experiment. The antibacterial rate of CuZnO@RGO nanocomposites in circulating cooling water reached 99.70% when the mass fraction of RGO was 15%. When the mass fraction of CuZnO@RGO accounting for CuZnO@RGO/WPU composite coating was 2%, the antibacterial rate, hardness, and corrosion inhibition efficiency were 94.35%, 5H, and 93.30%, respectively.

Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

[Comparison of content of curdione, curcumol, germacrone and beta-elemene in different varieties of vinegar backed Rhizoma Curcuma].

OBJECTIVE: To establish a HPLC method for determination of 4 components in different varieties of vinegar backed Rhizoma Curcuma. METHOD: The method was established by using an Elite Hypersil ODS2 column (4.6 mm x 250 mm, 5 microm). The mobile phase comprising acetonitrile (A) and water (B) was used to elute the targets in gradient elution mode. Flow rate and detection wavelength were set at 1 mL x min(-1) and 214 nm, respectively. The column temperature was 25 degrees C and the injection volume was 10 microL. RESULT: All calibration curves showed good linearity with r > 0.999 5. Recoveries measured at three concentrations were in the range of 97.27% - 99.27% with RSD < 3%. CONCLUSION: The validated method is simple, reliable, and successfully applied to determine the contents of the selected compounds in vinegar backed Rhizoma Curcuma. The results of the determination showed that contents of the four components in vinegar backed Curcuma wenyujin were relatively high.

Approximation modeling for the online performance management of distributed computing systems.

A promising method of automating management tasks in computing systems is to formulate them as control or optimization problems in terms of performance metrics. For an online optimization scheme to be of practical value in a distributed setting, however, it must successfully tackle the curses of dimensionality and modeling. This paper develops a hierarchical control framework to solve performance management problems in distributed computing systems operating in a data center. Concepts from approximation theory are used to reduce the computational burden of controlling such large-scale systems. The relevant approximations are made in the construction of the dynamical models to predict system behavior and in the solution of the associated control equations. Using a dynamic resource-provisioning problem as a case study, we show that a computing system managed by the proposed control framework with approximation models realizes profit gains that are, in the best case, within 1% of a controller using an explicit model of the system.

Adaptive sensor placement and boundary estimation for monitoring mass objects.

Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects.