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Efficient and accurate prediction of the flow field in turbomachinery is vital for tasks such as optimization and off-design modeling. Deep learning methods offer inspiring tools for flow field prediction when there is sufficient high-fidelity data for training. However, high-fidelity flow fields may be insufficient in practice due to the high computational/experimental cost. In this work, the capabilities of deep learning methods for fusing multi-fidelity flow field data are further explored. A multi-fidelity graph neural network (MFGNN) is proposed. The proposed framework contains two networks for approximating the low-fidelity flow fields and the correlations between the low-fidelity and high-fidelity flow fields, respectively. The data fusion method is validated by the off-design flow field prediction of a turbine. With limited high-fidelity data, MFGNN can accurately predict flow fields and is superior to the graph neural network that only uses high-fidelity data. The effects of low-fidelity dataset size and the extrapolation performance are also explored. With appropriate prior guidance by low-fidelity data, MFGNN can predict unknown flow fields within and beyond the range of high-fidelity training datasets. The proposed deep learning method shows the advantages of high precision and generalizability in addressing the physical field prediction problem. • An MFGNN model is proposed for aerodynamic flow field prediction. • The composite model can be trained by fusing information from different fidelity data. • The flow fields on arbitrary grids can be accurately predicted. • MFGNN may help to relieve the data thirst for network training. [ABSTRACT FROM AUTHOR]

Abstract Elevated CO 2 and use of endophytic microorganisms have been considered as efficient and novel ways to improve phytoextraction efficiency. However, the interactive effects of elevated CO 2 and endophytes on hyperaccumulator is poorly understood. In this study, a hydroponics experiment was conducted to investigate the combined effect of elevated CO 2 (eCO 2 ) and inoculation with endophyte SaMR12 (ES) on the photosynthetic characteristics and cadmium (Cd) accumulation in hyperaccumulator Sedum alfredii. The results showed that eCO 2 × ES interaction promoted the growth of S. alfredii , shoot and root biomass net increment were increased by 264.7 and 392.3%, respectively, as compared with plants grown in ambient CO 2 (aCO 2 ). The interaction of eCO 2 and ES significantly ( P < 0.05) increased chlorophyll content (53.2%), Pn (111.6%), Pn max (59.8%), AQY (65.1%), and Lsp (28.8%), but reduced Gs, Tr, Rd, and Lcp. Increased photosynthetic efficiency was associated with higher activities of rubisco, Ca2+-ATPase, and Mg2+-ATPase, and linked with over-expression of two photosystem related genes (SaPsbS and SaLhcb2). PS II activities were significantly ( P < 0.05) enhanced with Fv/Fm and Φ(II) increased by 12.3 and 13.0%, respectively, compared with plants grown in aCO 2. In addition, the net uptake of Cd in the shoot and root tissue of S. alfredii grown in eCO 2 × ES treatment was increased by 260.7 and 434.9%, respectively, due to increased expression of SaHMA2 and SaCAX2 Cd transporter genes. Our results suggest that eCO 2 × ES can promote the growth of S. alfredii due to increased photosynthetic efficiency, and improve Cd accumulation and showed considerable potential of improving the phytoextraction ability of Cd by S. alfredii. Graphical abstract Unlabelled Image Highlights • Elevated CO 2 improved the colonization of endophytic bacterium SaMR12. • eCO 2 × SaMR12 interaction had positive effects on S. alfredii growth and Cd accumulation. • eCO 2 × SaMR12 interaction increased photosynthetic carbon uptake. • eCO 2 × SaMR12 interaction promoted electron transport and PS II activity. • eCO 2 × SaMR12 interaction upregulated the expressions of heavy metal transporter. [ABSTRACT FROM AUTHOR]

Accurately and rapidly predicting the multi-conditions characteristics of turbines is fundamental for realizing efficient energy conversion and optimal layout schemes. Based on supervised graph learning approaches, this work is dedicated to establishing a fast multidisciplinary prediction model for gas turbines. The research target is a gas turbine blade with complex cooling channels. An Aerodynamic Strength Prediction Graph neural network (ASP-GNN) is proposed to predict the aerodynamic-strength characteristics and temperature field under different boundary conditions. The superiority of our approach is demonstrated by prediction precision and time cost. The generalizability of the network is also investigated by adopting different training set sizes, and the ASP-GNN can achieve satisfactory accuracy with a limited number of training samples. Based on the established model, the effects of various boundary conditions on aerodynamic and strength performance are quantified. The unsteady characteristic of performance and temperature field are also obtained conveniently. The proposed model could serve as a fast analysis approach to aid the design and analysis of turbomachinery. It may relieve the workload of numerical simulations for complex engineering analysis. [ABSTRACT FROM AUTHOR]

The concentration-dependent self-assembly of iodine substituted thienophenanthrene derivative (5,10-DITD) is investigated at the 1-octanic acid/graphite interface using scanning tunneling microscopy. Three kinds of chiral arrangement and transition of 2D molecular assembly mainly driven by halogen bonding is clearly revealed. At high concentration the molecules self-assembled into a honeycomb-like chiral network. Except for the interchain van der Waals forces, this pattern is stabilized by intermolecular continuous C O⋯I⋯S halogen bonds in each zigzag line. At moderate concentration, a chiral kite-like nanoarchitecture are observed, in which the C O⋯I⋯S and I⋯O C halogen bonds, along with the molecule–solvent C O⋯I⋯H halogen bonds are the dominated forces to determine the structural formation. At low concentration, the molecules form a chiral cyclic network resulting from the solvent coadsorption mainly by molecule–molecule C O⋯I⋯S halogen bonds and molecule–solvent C O⋯I⋯H halogen bonds. The density of molecular packing becomes lower with the decreasing of the solution concentration. The solution-concentration dependent self-assembly of thienophenanthrene derivative with iodine and ester chain moieties reveals that the type of intermolecular halogen bond and the number of the co-adsorbing 1-octanic acids by molecule–solvent C O⋯I⋯H halogen bonds determine the formation and transformation of chirality. This research emphasizes the role of different types of halogen (I) bonds in the controllable supramolecular structures and provides an approach for the fabrication of chirality. [ABSTRACT FROM AUTHOR]

Many people suffer from the Fatty Liver disease due to the changes in diet and lifestyle, and the convenient diagnosis of it has attracted many attentions in recent years. The computerized tongue or facial diagnosis as an important diagnostic tool provides a possible way to detect the disease in the daily life. Most of existing approaches only takes a single modality (e.g., tongue or face) into account, although various modalities would contribute complementary information which is beneficial for the improvement of the diagnosis accuracy. To circumvent this issue, a novel multi-modal fusion method is presented in this paper. Particularly, a noninvasive capture device is first used to captured the tongue and facial images, followed by the feature extraction. Our so-called joint discriminative and collaborative representation approach is then proposed to not only reveal the correlation between the tongue and facial images, but also keep the discriminative representation of each class simultaneously. To optimize the proposed method, an efficient algorithm is proposed, obtaining a closed-form solution and greatly reducing the computation. In identification of the Fatty Liver Disease for healthy controls, the proposed multi-modal fusion approach achieves 85.10% in average accuracy and 0.9363 in the area under ROC curve, which obviously outperform the case of using a single modality and some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The actual operation of turbomachinery is inevitably affected by multi-source uncertainties. Such uncertainties are detrimental to the performance and reliability of energy systems. Based on graph learning methods, this work aims to provide a convenient and effective approach for aerodynamic robust optimization of turbomachinery. A radial inflow turbine is taken as the research target and Dual Graph Neural Network (DGNN) regression model is constructed for flow field prediction and performance discrimination. By comparing the accuracy and time consumption, the advantages of DGNN over classical surrogate models and computational fluid dynamics (CFD) are clarified. The proposed model is integrated into uncertainty quantification and aerodynamic robust optimization. The effect of multi-source uncertainties on performance is quantified. The stochastic response of flow fields is also obtained conveniently through DGNN. Robust optimization is performed for power and efficiency, respectively. The power robust optimization improves the power by 1.52% and reduces the standard deviation of power by 15.45%. The efficiency robust optimization achieves an efficiency improvement of 1.76% (increment) and an efficiency standard deviation reduction of 36.82%. The proposed approach is an efficient and competitive choice for uncertainty quantification and robust optimization. The present work contributes to constructing the digital twin of turbomachinery systems. • A new complete approach for aerodynamic robust optimization. • A graph deep learning model is contained for flow fields and performance prediction. • Our model performs better than classical surrogate models. • The effect of multi-source uncertainties is quantified. • The proposed approach can effectively solve aerodynamic robust optimization problems. [ABSTRACT FROM AUTHOR]

We describe a new molecular design, synthesis, and investigation of small molecules that bind to CTG trinucleotide repeats in DNA. 1 H -Pyrrolo[3,2- h ]quinoline-8-amine ( PQA ) has a tricyclic aromatic system with unique non-linear hydrogen-bonding surface complementary to thymine. We have synthesized a series of PQA derivatives with different alkylamino linkers. These PQA s showed binding to pyrimidine bulge DNAs and CNG (N = T and C) repeats depending on the linker structure, while quinoline derivatives lacking the pyrrole ring showed much lower binding affinity. PQA is a useful molecular unit for both CTG and CCG repeat binding. [ABSTRACT FROM AUTHOR]

• A novel measurement scheme for measuring optical properties of complex soft matter systems. • Algorism combining Berreman 4x4 matrix and Monte Carlo approaches enables optical coefficients determination in a facile, fast and low-cost manner. • Data acquisitions of organic solvent, commercial nematic liquid crystal and the emerging ferroelectric nematic liquid crystal are demonstrated. Determination of material and optical properties in structured films and fluids, e.g., refractive index and material thickness, usually requires special optical geometries and instruments. This prevents one from studying optical properties especially in soft matter systems with nonuniform hierarchical and orientational structures. Here we develop a data measurement and analysis scheme by combining Monte Carlo and Berreman 4x4 matrix approaches for obtaining the dispersion of refractive index, material thickness, and the optical anisotropy from a single shot of simple transmission or reflection spectroscopy data at a time scale of seconds. We show the validity of the method by determining Cauchy coefficients and sample thickness of the well-known reference materials of both isotropic toluene solution and anisotropic material of a nematic liquid crystal (4-cyano-4′-pentyl biphenyl). This enables us to further investigate the unknown optical properties of an emerging ferroelectric nematic material, the so-called RM734. [ABSTRACT FROM AUTHOR]

Many vision-based methods for printed label defect detection have been proposed to replace inefficient manual inspection. However, due to the existence of artifacts and noise regions, it usually leads to a large number of misjudgments. Also, since most of the printed labels are non-rigid, they are prone to local deformation, which will cause lots of artifacts after image subtraction. This paper proposes a novel printed label defect detection framework (PLDD), which performs twice gradient matching based on improved cosine similarity measures. The overall idea is based on comparing a golden master (G M) image with test images, thus the G M image is demanded. Specifically, latent defect candidates will be extracted firstly from RGB sub-images for artifact elimination. Mask mechanism is also introduced to eliminate the influence of background gradient features around these defect candidates. Experiments compared with existing methods are conducted with three industrial datasets. The results exhibit that PLDD achieves a high mean F 1 score (0.9702), and only 103 false positives (F P) occurred in 44,628 ground truths. Defects are being detected in real-time with an average time consuming of 0.26362 s. • Propose an effective latent defect candidate extraction algorithm to eliminate artifacts caused by local deformation. • Take advantages of twice gradient matching and masks. • Present an improved cosine similarity measure motivated by the human visual system. • Experimental results on several real-world industrial datasets demonstrate our superiority. [ABSTRACT FROM AUTHOR]

The performance and reliability of turbomachinery directly affect the efficiency and safety of energy conversion systems. A dual graph neural network (DGNN) for turbomachinery flow field reconstruction and performance prediction is proposed, which utilizes flow field data at grid vertices and characterizes the neighborhood relationships of grids through the adjacency matrix. Different from previous work, this work extends deep learning methods to the reconstruction of global turbomachinery fields defined on arbitrary structured/unstructured grids. The flow field reconstruction and performance prediction of low aspect ratio rotors are used to verify the generalizability of DGNN. The proposed method can not only accurately predict performance parameters, but also achieve excellent performance in flow field reconstruction. The superiority of DGNN over the artificial neural network (ANN) in flow field reconstruction is clarified. The optimal GNN operator with the highest accuracy and lowest computation costs is obtained as SAGE. A well-trained DGNN can give the flow field distribution as well as the performance of turbines within 0.05 s. The proposed approach could be a real-time simulation and analysis approach to assist turbomachinery design and optimization. It may provide an efficient solution for establishing the digital twin system of turbomachinery in the future. • A novel DGNN for flow field reconstruction and performance prediction is proposed. • DGNN can realize high accuracy for different regions in turbomachinery. • Flow fields defined on arbitrary structured/unstructured grids can be reconstructed. • DGNN could serve as a real-time simulation and analysis approach for turbomachinery. [ABSTRACT FROM AUTHOR]

New ligands with three-ring system for the recognition of a cytosine bulge and a cytosine–cytosine mismatch were designed and synthesized. The 2-amino-1,10-phenanthroline was selected as a recognition unit among the possible three-ring systems of a parent recognition unit of 2-amino-1,8-naphthyridine. The 3-aminopropanamide of 2-amino-1,10-phenanthroline ( APM ) bound to the cytosine bulge DNA. Other single nucleotide bulges were stabilized by the ligand with much lower efficiency. The dimer APD consisting of two molecules of APM was found to stabilize the C–C mismatch DNA selectively. Structure–activity relationship studies revealed that the APD -binding to the C–C mismatch DNA required both phenanthroline heterocycles in a molecule. [ABSTRACT FROM AUTHOR]

Lithium-rich layered oxides (LLOs) are one of the most promising cathodes for next-generation Li-ion batteries owing to their extraordinary energy density and low cost. However, the anionic redox reactions inevitably destabilize the oxygen framework and lead to oxygen release, which incurs voltage fading and capacity decay. Although less voltage fading can be realized in the cobalt-free iron-substituted materials, they still suffer from severe transition metal (TM) dissolution and poor kinetics. Herein, to ameliorate these drawbacks, we develop a novel eutectic melting salt treatment strategy. By controlling the melt and solidification of a LiF-MgF 2 -CaF 2 ternary salt, the robust fluoride coating layer and functional doping were synchronously conducted in a Co-free Fe-substituted Li-rich cathode Li 1.2 Ni 0.13 Fe 0.13 Mn 0.54 O 2. The outer fluoride layer effectively suppresses the oxygen release and prevents TM ion dissolution, while the inner doping elements improve the Li+ diffusion kinetics and further stabilize the bulk crystal structure. Benefiting from these, the modified cathode exhibits significantly enhanced electrochemical performance, with negligible capacity loss from the 35th to the 120th cycles at 0.2 C, mitigated voltage fading, improved rate capability and better thermal stability as well. [Display omitted] • A LiF-MgF 2 -CaF 2 eutectic melting salt treatment strategy for Li-rich cathodes is reported for the first time. • Coating and doping are synchronously realized in a Co-free Fe-substituted Li-rich cathode Li 1.2 Ni 0.13 Fe 0.13 Mn 0.54 O 2. • Oxygen release is effectively mitigated to achieve the better electrochemical performance of Li 1.2 Ni 0.13 Fe 0.13 Mn 0.54 O 2 cathode. [ABSTRACT FROM AUTHOR]

[Display omitted] • Series of tannin-furanic-SPI foams with excellent fire retardancy were prepared. • H 3 BO 3 , H 3 PO 4 , and montmorillonite were additives to enhance fire performance. • The excellent smoke suppression property was got to the experiment foams. • The outstanding thermal insulation performance was obtained to the series foams. • Tannin foams integrated properties showing promise for building thermal insulation. Owing to foreseable extensive shortages in petrochemical resources, energy consumption, and fire safety concerns, recent scientific research in the construction field has been aimed to develop biomass flame-retardants and thermally-insulating materials. Herein, flame-retardant tannin-furanic-SPI bio-foams with outstanding thermal insulation and smoke suppression performance were developed by exploiting the synergistic effect of additives (boric acid, phosphoric acid, and montmorillonite). Their physical and mechanical properties showed that these tannin-based foams are lightweight (82–122 kg/m3), with an acceptable compression strength (0.2–0.35 MPa), and a low pulverization ratio (5.97–12.06%). Moreover, the morphology, thermal stability, thermal conductivity, and flammability were evaluated by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), thermal conductivity tests, limiting oxygen index (LOI), and cone calorimetry, respectively. These foams exhibited closed cell structures, good thermal stability, and low thermal conductivity. Their flammability results showed that higher LOI values were obtained in the range of 37.33–49.05% and that they had a relatively low peak-heat-release rate (PHRR) and total-smoke-produced (TSP). A possible flame retardancy mechanism was suggested. By comparing them to other commercial construction materials, the modified tannin-furanic-SPI foams with integrated properties, such as flame retardancy, thermal insulation, light-weight, environmental-friendliness, and acceptable mechanical properties, show potential applications as thermal insulation for buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • Series of tannin-furanic-SPI foams with excellent fire performance were prepared. • The SPI improved strength and diminished pulverization ratio markedly of foams. • Lignin greatly upgraded the flame retardancy of tannin-furanic-SPI foams. • New reactions of tannin and SPI formed strong covalent networks was described. Soybean protein isolate (SPI) was tested in this study as a crosslinker and formaldehyde substitute for preparing high biomass content sustainable rigid tannin-furanic-SPI (TFS) and lignin-tannin-furanic-SPI (LTFS) versatile foams. Additionally, flame retardancy was improved by lignin used as a natural fire-retardant. Fourier-transform infrared spectroscopy (FT-IR), coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF-MS), revealed a covalent cross-linking reaction between tannin and SPI. TFS and LTFS foams showed by scanning electron microscopy (SEM) a closed cell structure without any pores. The incorporation of SPI resulted in enhanced mechanical properties and reduced pulverization ratios, improved thermal stability and increased thermal conductivity (approximately 0.042–0.044 W/m K compared with control foam. Furthermore, the TFS foams exhibited outstanding flame retardancy and suppressed smoke generation while undergoing combustion. These results were supported by a higher limiting oxygen index (LOI) value, a lower heat-release rate, and a higher char residue, obtained by LOI and cone calorimetry. The addition of lignin further enhanced the thermal properties and flame retardancy of TFS foams although it decreased their mechanical performance. The TFS and LTFS foams were environmentally friendly, as shown by the low formaldehyde emission measurements. This novel sustainable TFS foam appears to have a good potential for industrial application. [ABSTRACT FROM AUTHOR]

For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However, ERP ODIs suffer from serious geometric distortion and pixel stretching across latitudes, generating massive redundant information at high latitudes. This characteristic poses a huge challenge for the traditional SR methods, which can only obtain the suboptimal ODISR performance. To address this issue, we propose a novel position attention network (PAN) for ODISR in this paper. Specifically, a two-branch structure is introduced, in which the basic enhancement branch (BE) serves to achieve coarse deep feature enhancement for extracted shallow features. Meanwhile, the position attention enhancement branch (PAE) builds a positional attention mechanism to dynamically adjust the contribution of features at different latitudes in the ERP representation according to their positions and stretching degrees, which achieves the enhancement for the differentiated information, suppresses the redundant information, and modulate the deep features with spatial distortion. Subsequently, the features of two branches are fused effectively to achieve the further refinement and adapt the distortion characteristic of ODIs. After that, we exploit a long-term memory module (LM), promoting information interactions and fusions between the branches to enhance the perception of the distortion, aggregating the prior hierarchical features to keep the long-term memory and boosting the ODISR performance. Extensive results demonstrate the state-of-the-art performance and the high efficiency of our PAN in ODISR. [ABSTRACT FROM AUTHOR]

• Novel cellular/non-porous foam structures were prepared via a new molding technique. • All stuffs are renewable and abundant. • Mechanical blowing was employed instead of chemical one for environmental concern. • The physical properties of this structure are outstanding in many senses. • Excellent thermal insulation and friability resistance were obtained. Recently, tannin furanic resin-based foams exhibiting excellent properties have been developed. However, its friability and thermal insulation must be improved prior to industrialization. Therefore, the tannin preconcentrate from wood bark obtained via liquefaction was mixed with furfuryl alcohol in the presence of a catalyst, surfactant, and solvent. This process includes instant in situ formation of a resin matrix, followed by spontaneous molding and curing to yield a distinct cellular/nonporous structure. Further, the cell structure, mechanical strength, friability, thermal insulation, and fire resistance properties of this material were investigated. Results indicated that the developed material displayed a distinguished bilayer structure in the form of a cellular layer crossing the interface into another nonporous layer, as revealed via scanning electron microscopy imaging. In addition, the porosity of the cellular layer was considerably lower than that observed in case of standard tannin foam. Mechanical testing demonstrated a compression strength of 0.183 and > 50 MPa (there is no yield point for the nonporous layer), and internal bond strength of 0.014 and 0.61 MPa for the cellular and nonporous layers, respectively. Rupture was always observed to occur in the cellular layer rather than at the interface. The limiting oxygen index of the cellular layer was approximately 24.51 %, which is comparable to that of the nonporous layer (28.32 %). Based on thermogravimetric analysis (TGA), the overall foam structure can easily decompose into a final residual mass of 2.8 %. Regardless, compared with other tannin foams prepared in the traditional manner by adding a foaming agent, this material exhibited superior friability resistance and thermal insulation, as corroborated from the thermal conductivity measurement (0.0239 W/m K along with an abrasion fraction of 16.49 % after friction, indicating strong durability against abrasion. [ABSTRACT FROM AUTHOR]

Developing highly efficient and cost-effective catalyst with tuned microstructure holds great promise in the reduction of nitroaromatic compounds under mild reaction conditions. Herein, we report a new Co@NC-MF catalyst with a fascinating hierarchical flower-like architecture in situ assembled from uniform Co@NC nanoneedles, which can function as a favorable platform for the efficient reduction of nitroaromatic compounds in the presence of NaBH 4. In addition with the structural advantage, the characterization and experimental results demonstrate the enormous advantage of interfacial synergistic catalysis in enhancing the catalytic performance. The outside electron-rich N-doped carbon layer as Lewis basic sites and the inside Co nanoparticles are responsible for the adsorption of 4-nitrophenol (4-NP) and generation of active hydrogen species, respectively. This work contributes to the construction of well-integrated composites with well-balanced interface synergy to boost the catalytic performance in various heterogeneous reactions. ga1 • Co@NC-MF microflowers in situ assembled from Co@NC nanoneedles were constructed. • Co@NC-MF acts as a favorable platform for the reduction of nitroaromatic compounds. • A synergistic catalysis mechanism is proposed based on systematical analysis. • The structural advantage with rich interfacial synergy boosts the performance. [ABSTRACT FROM AUTHOR]

Root cell wall (RCW) modification is a widespread important defense strategy of plant to cope with trace metals. However, mechanisms underlying its remolding in cadmium (Cd) accumulation are still lacking in hyperaccumulators. In this study, changes of RCW structures and components between nonhyperaccumulating ecotype (NHE) and hyperaccumulating ecotype (HE) of Sedum alfredii were investigated simultaneously. Under 25 μM Cd treatment, RCW thickness of NHE is nearly 2 folds than that of HE and the thickened cell wall of NHE was enriched in low-methylated pectin, leading to more Cd trapped in roots tightly. In the opposite, large amounts of high-methylated pectin were assembled around RCW of HE with Cd supply, in this way, HE S. alfredii decreased its root fixation of Cd and enhanced Cd migration into xylem. TEM and AFM results further confirmed that thickened cell wall was caused by the increased amounts of cellulose and lignin while root tip lignification was resulted from variations of sinapyl (S) and guaiacyl (G) monomers. Overall, thickened cell wall and methylated pectin have synchronicity in spatial location of roots, and their coordination contributed to Cd accumulation in S. alfredii. Image 1 • Two ecotypes of Sedum alfrdii showed different root cell wall (RCW) thickness in response to Cd. • The methylation degree of pectin had opposite trend in NHE and HE S. alfredii. • RCW thickened due to the increased content of cellulose and lignin under Cd stress. • Cd induced the same spatial distribution of thickened cell wall and methylated pectin. • Pectinous RCW thickening contributed more Cd bonded tightly in the NHE than in the HE. Main finding of the work: The spatial synchronization of root cell wall thickenings and pectin methylation regulated Cd accumulation in Sedum alfredii. [ABSTRACT FROM AUTHOR]

The radial transport of cadmium (Cd) is essential for Cd influx in roots. The role of radial transport pathway on the Cd translocation from root to shoot among wheat genotypes are still poorly understood. This study explored the role of apoplastic and symplastic pathway on root Cd uptake and root-to-shoot translocation in Zhenmai 10 (ZM10, high Cd in grains) and Aikang 58 (AK58, low Cd in grains). Under Cd treatment, the deposition of Casparian strips (CSs) and suberin lamellae (SL) initiated closer to the root apex in ZM10 than that in AK58, which resulted in the lower Cd concentration in apoplastic fluid of ZM10. Simultaneously, Cd-induced expression levels of genes related to Cd uptake in roots were significantly higher in AK58 by contrast with ZM10, contributing to the symplastic Cd accumulation in AK58 root. Moreover, the addition of metabolic inhibitor CCCP noticeably decreased the Cd accumulation in root of both genotypes. Intriguingly, compared to ZM10, greater amounts of Cd were sequestrated in the cell walls and vacuoles in roots of AK58, limiting the translocation of Cd from root to shoot. Furthermore, the elevated TaHMA2 expression in ZM10 indicates that ZM10 had a higher capacity of xylem loading Cd than AK58. All of these results herein suggest that the radial transport is significant for Cd accumulation in roots, but it cannot explain the difference in root-to-shoot translocation of Cd in wheat genotypes with contrast Cd accumulation in grains. Image 1 • CSs formation and suberization is more pronounced in ZM10 than AK58. • Long-term Cd exposure mainly affects Cd uptake and transport by symplastic pathway. • AK58 limits more Cd in root rather than transfers it into shoot than ZM10. • Xylem loading of Cd may be decisive for Cd accumulation in shoots and grains. The ability of radial transport, which is of great importance for root Cd accumulation, is not responsible for root-to-shoot translocation of Cd in wheat cultivars. As for high Cd grain accumulation genotype, the content of Cd in ZM10 root was lower, but higher in shoot due to its higher xylem loading ability. In AK58, low Cd grain accumulation genotype, the promoted radial transport led to the higher Cd accumulation in root, which was mainly sequestrated in root vacuolar and cell walls. [ABSTRACT FROM AUTHOR]

The interactive effects of elevated atmospheric CO 2 and nanoparticles (NPs) on the structure and function of soil bacterial community remain unknown. Here we compared the impacts of CeO 2 (nCeO 2) and Cr 2 O 3 (nCr 2 O 3) nanoparticles on the taxonomic compositions and functional attributes of bacterial communities under elevated CO 2 (eCO 2). The stimulated enzyme activities (dehydrogenase, acid phosphatase and urease), increased microbial biomass carbon (MBC), and higher bacterial alpha-diversity were observed under the combined effects of eCO 2 and NPs compared to the single NP treatment, indicating eCO 2 could mitigate the adverse effect of NPs on soil microorganisms. NPs and eCO 2 are important factors influencing the alpha- and beta-diversity (17% and 18% of variations were explained) as well as functional profile (20% and 26% of variations were explained) of bacterial communities. Rising CO 2 level promoted the resilience of NP-resistant bacterial populations, primarily the members of Alphaproteobacteria , Gammaproteobacteria and Bacteroidia , which are also characterized by the fast carbon use capability. Moreover, the significantly (P < 0.05) higher metabolic quotient (q CO 2), reduced available carbon and overrepresented carbon metabolism genes at eCO 2 vs. ambient CO 2 (aCO 2) indicate the acceleration of available carbon turnover in NP-exposed soils. Correlation analysis revealed that mitigation of NPs toxicity by eCO 2 could be attributed to the remarkable decline of bioavailable metals disassociated from NPs and available carbon level, as well as promotion of the rapid carbon-metabolizing microbes. Our study pointed out the positive role of eCO 2 in alleviating the adverse effect of NPs on microbiological soil environment, and results can serve as important basis in establishing guidelines for lowering the ecotoxicity of NPs. Image 1 • Elevated CO 2 accelerated microbial available carbon use in nCr 2 O 3 and nCeO 2 amended soils. • A 180-day exposure to eCO 2 reduced the availability and biotoxicity of nCr 2 O 3 and nCeO 2. • Rising CO 2 promoted the resilience of NP-resistant taxa of α-, γ- proteobacteria and Bacteroidia. • Nanoparticles and eCO 2 significantly altered bacterial taxonomic and functional compositions. Main finding of the work: Elevated CO 2 alleviated the adverse impact of NPs on microbiological soil environment by reducing their solubilization and promoting the rapid carbon-metabolizing microbes. [ABSTRACT FROM AUTHOR]

Glucose-based self-blowing non-isocyanate polyurethanes foams were prepared in our previous work. They showed excellent properties comparable to commercial foams by just using a simple preparation procedure. These foams, nevertheless, present a critical drawback that is their flammability. In the research presented here a natural phenolic compound, condensed tannin, was tested as a flame-retardant to improve the fire resistance of glucose-based NIPU foams. A number of tannin-substituted glucose-based NIPU foams (mimosa tannin replacing glucose at 0%, 25% and 50%, respectively) were prepared and characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), Thermogravimetric analysis (TG), ignition experiment and limiting oxygen index (LOI). Other physical and mechanical properties, such as foaming processes, density, compression strength, etc. were also investigated. The results indicated that the tannin-modified glucose-based NIPU foams (T/G (1/3)-Fs and (T/G (1/1)-Fs)) exhibit smaller mean cell size, improved compression strength and higher density than (T/G (0/4)-Fs). The FT-IR analysis showed that urethane linkages were formed, the chemical structure of glucose-based NIPU foam being nonetheless preserved even if the glucose was partially replaced by the condensed tannin. Thermogravimetric analysis showed that the presence of condensed tannin decreased the thermal stability of the tannin-glucose NIPU foam composites slightly. In addition, ignition experiments were also carried out in which the glucose-based NIPU foams with a condensed tannin showed longer burning time than neat (T/G (0/4)-Fs). Finally, limiting oxygen index (LOI) values from 17.5% to 25.5%, show a higher value with the increasing condensed tannin substitution. • Glucose-Tannin-based NIPU foams were prepared. • Glucose and Tannin coreacted to form mixed NIPU resins. • Tannin increases led to improved fire resistance and longer ignition times. • Tannin increases improved compression, yielded narrower cell size distribution and smaller mean cell size. [ABSTRACT FROM AUTHOR]

Pot-culture experiments were conducted to investigate the potential of microorganism-saponin assisted phytoremediation of cadmium (Cd) and benzo(a)pyrene (B[a]P) co-contaminated soil using Cd-hyperaccumulator Sedum alfredii. Results showed that B[a]P-degrading bacterium (Ochrobactrum intermedium B[a]P-16) inoculation significantly increased root (by 22.1–24.1%) and shoot (by 20.5–23.4%) biomass of S. alfredii , whereas the application of saponin had no effect on the growth of S. alfredii. The saponin solution at 2 g L−1 extracted more Cd and B[a]P than water, saponin enhanced Cd and B[a]P bioavailability in soil and thus promoted their uptake and accumulation in S. alfredii. The activity of B[a]P-16, dehydrogenase and polyphenol oxidase in co-contaminated soil was promoted by growing S. alfredii, and the application of B[a]P-16 and saponins caused a significant (P < 0.05) increase in both enzyme activities. The maximum B[a]P removal rate (82.0%) and Cd phytoextraction rate (19.5%) were obtained by co-application of S. alfredii with B[a]P-16 and saponin. The B[a]P-16 and plant promoted biodegradation were the predominant contributors towards removal of B[a]P from soil. A significant (P < 0.05) synergistic effect of B[a]P-16 and saponin on B[a]P and Cd removal efficiency was observed in this study. It is suggested that planting S. alfredii with application of B[a]P-16 and saponin would be an effective method for phytoremediation of soil co-contaminated with Cd and PAHs. Image 106219 • A novel B[a]P-degrading bacterium Ochrobactrum intermedium B[a]P-16 was identified. • Inoculation with B[a]P-16 promoted the gorwth of S. alfredii. • 2 g L−1 saponin increased Cd and B[a]P solubilization significantly. • Combined use of B[a]P-16 and saponin significantly increased B[a]P and Cd phytoremediation efficiency by S. alfredii. • B[a]P-16 and plant promoted biodegradation played predominant role in removal of B[a]P. [ABSTRACT FROM AUTHOR]

• This work is the first to focus on the cooperation and complementarity of dual asymmetric subnetworks used in the field of thoracic disease classification. • A novel dual asymmetric feature learning network based on the DenseNet and ResNet is proposed. • Two different feature fusion operations, such as feature-level fusion (FLF) and decision-level fusion (DLF) are embedded in our DualCheXNet. • An iterative training strategy is designed to optimize the process of complementary features learning in an alternative way. • The proposed method reaches a new state of the art: the average AUC score for 14 pathologies is 0.823. Recently, deep convolutional neural networks (DCNNs) such as the famous ResNet and DenseNet have achieved significant improvements in the field of automatic analysis of chest X-rays (CXRs). However, we observe that a wider network can combine characteristics from different DCNNs to improve the ability of object recognition compared with the single networks. In this paper, we focus on the cooperation and complementarity of dual asymmetric DCNNs and present a novel dual asymmetric feature learning network named DualCheXNet for multi-label thoracic disease classification in CXRs. Correspondingly, two asymmetric subnetworks based on the ResNet and DenseNet are combined to adaptively capture more discriminative features of different abnormalities from the raw CXRs. Specifically, the proposed method enables two different feature fusion operations, such as feature-level fusion (FLF) and decision-level fusion (DLF), which exactly form the complementary feature learning embedded in DualCheXNet. Moreover, an iterative training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss, and optimize the process of complementary features learning in an alternative way. Extensive experiments on the ChestX-ray14 dataset clearly substantiate the effectiveness of the proposed method as compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

It is essential to guarantee product quality by detecting defects in industrial printed labels. Defect detection based on reference comparison is a common method to achieve this task. However, this method yields poor performance under large deformations of printed labels, due to the lack of accurate registration ability between testing images and reference images. Therefore, it is an urgent task to achieve accurate image registration for printed label defect detection. In this paper, a coarse-to-fine end-to-end registration network, named APPR-Net, is proposed to deal with the large deformation. First, we adopt a strategy of image patch splitting and stitching to improve the scalability of image resolution. Second, we design a four-stream affine transformation module followed by a multi-scale pyramid registration network, where a coarse registration is obtained by the former module and then gradually refined by the later network in a coarse-to-fine manner. Third, we introduce a distortion loss function to constrain the text distortion of the warped image after image registration. Finally, to simulate printed labels with defects and large deformation, we build a synthetic database based on real-world industrial printed labels for performance comparison. The results demonstrate that the proposed APPR-Net significantly outperforms other compared methods. • Propose a coarse-to-fine registration network to deal with large deformation. • Take advantages of image patch splitting and stitching strategy. • Design a four-stream affine transformation module followed by a pyramid network. • Introduce a distortion loss function to constrain text distortions. • Build a synthetic database based on real-world printed labels. [ABSTRACT FROM AUTHOR]

Quercetin (Quer) is a natural flavonoid known for its inhibitory effects against various cancers. However, the mechanism by which Quer inhibits gastric cancer (GC) has not yet been fully elucidated. Ferroptosis, a mode of programmed cell death resulting from lipid peroxidation, is regulated by abnormalities in the antioxidant system and iron metabolism. Through flow cytometry and other detection methods, we found that Quer elevated lipid peroxidation levels in GC cells. Transmission electron microscopy confirmed an increase in ferroptosis in Quer-induced GC. We demonstrated that Quer inhibits SLC1A5 expression. Molecular docking revealed Quer's binding to SLC1A5 at SER-343, SER-345, ILE-423, and THR-460 residues. Using immunofluorescence and other experiments, we found that Quer altered the intracellular ROS levels, antioxidant system protein expression levels, and iron content. Mechanistically, Quer binds to SLC1A5, inhibiting the nuclear translocation of nuclear factor erythroid 2-related factor 2 (NRF2), resulting in decreased xCT/GPX4 expression. Quer/SLC1A5 signaling activated p-Camk2, leading to upregulated p-DRP1 and enhanced ROS release. Additionally, Quer increased the intracellular iron content by inhibiting SLC1A5. These three changes collectively led to ferroptosis in GC cells. In conclusion, Quer targets SLC1A5 in GC cells, inhibiting the NRF2/xCT pathway, activating the p-Camk2/p-DRP1 pathway, and accelerating iron deposition. Ultimately, Quer promotes ferroptosis in GC cells, inhibiting GC progression. Overall, our study reveals that Quer can potentially impede GC progression by targeting SLC1A5, offering novel therapeutic avenues through the modulation of ferroptosis and iron homeostasis. [Display omitted] • Quercetin elevates ferroptosis level in GC cells, inhibiting GC progression. • We identified SLC1A5 as a target of quercetin in GC and predicted poteintial sites. • Quercetin promotes the NRF2/xCT pathway by targeting SLC1A5 to induce ferroptosis. • Quercetin activates the p-Camk2/p-DRP1 axis via SLC1A5 to promote ferroptosis. • Inhibition of SLC1A5 by quercetin caused intracellular iron deposition. [ABSTRACT FROM AUTHOR]