The alleviation of salt stress on rice through increasing photosynthetic capacity, maintaining redox homeostasis and regulating soil enzyme activities by Enterobacter sp. JIV1 assisted with putrescine.

The detrimental impact of soil salinization on crop productivity and agricultural economy has garnered significant attention. A rhizosphere bacterium with favorable salt tolerance and plant growth-promoting (PGP) functions was isolated in this work. The bacterium was identified as Enterobacter through 16 S rDNA sequencing analysis and designated as Enterobacter sp. JIV1. Interestingly, the presence of putrescine (Put), which had been shown to contribute in reducing abiotic stress damage to plants, significantly promoted strain JIV1 to generate 1-aminocyclopropane-1-carboxylic (ACC) deaminase, dissolve phosphorus and secrete indole-3-acetic acid (IAA). However, the synergy of plant growth promoting rhizobacteria (PGPR) and Put in improving plant salt resistance has not been extensively studied. In this study, strain JIV1 and exogenous Put effectively mitigated the inhibitory impact of salt stress simulated by 200 mM NaCl on rice (Oryza sativa L.) growth. The chlorophyll accumulation, photosynthetic efficiency and antioxidant capacity of rice were also significantly strengthened. Notably, the combined application of strain JIV1 and Put outperformed individual treatments. Moreover, the co-addition of strain JIV1 and Put increased soil protease and urease activities by 451.97% and 51.70% compared to that of salt treatment group. In general, Put-assisted PGPR JIV1 provides a new perspective on alleviating the salt-induced negative impacts on plants.

Unsupervised Learning of Graph Matching With Mixture of Modes via Discrepancy Minimization.

Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard nature. Recently (deep) learning-based approaches have shown their superiority over the traditional solvers while the methods are almost based on supervised learning which can be expensive or even impractical. We develop a unified unsupervised framework from matching two graphs to multiple graphs, without correspondence ground truth for training. Specifically, a Siamese-style unsupervised learning framework is devised and trained by minimizing the discrepancy of a second-order classic solver and a first-order (differentiable) Sinkhorn net as two branches for matching prediction. The two branches share the same CNN backbone for visual graph matching. Our framework further allows unsupervised learning with graphs from a mixture of modes which is ubiquitous in reality. Specifically, we develop and unify the graduated assignment (GA) strategy for matching two-graph, multi-graph, and graphs from a mixture of modes, whereby two-way constraint and clustering confidence (for mixture case) are modulated by two separate annealing parameters, respectively. Moreover, for partial and outlier matching, an adaptive reweighting technique is developed to suppress the overmatching issue. Experimental results on real-world benchmarks including natural image matching show our unsupervised method performs comparatively and even better against two-graph based supervised approaches.

Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach.

Graph matching aims to establish node correspondence between two graphs, which has been a fundamental problem for its NP-hard nature. One practical consideration is the effective modeling of the affinity function in the presence of noise, such that the mathematically optimal matching result is also physically meaningful. This paper resorts to deep neural networks to learn the node and edge feature, as well as the affinity model for graph matching in an end-to-end fashion. The learning is supervised by combinatorial permutation loss over nodes. Specifically, the parameters belong to convolutional neural networks for image feature extraction, graph neural networks for node embedding that convert the structural (beyond second-order) information into node-wise features that leads to a linear assignment problem, as well as the affinity kernel between two graphs. Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference. Moreover, our network is class-agnostic. Experimental results on extensive benchmarks show its state-of-the-art performance. It bears some generalization capability across categories and datasets, and is capable for robust matching against outliers.

A Portable and Disposable Electrochemical Sensor Utilizing Laser-Scribed Graphene for Rapid SARS-CoV-2 Detection.

The COVID-19 pandemic caused by the virus SARS-CoV-2 was the greatest global threat to human health in the last three years. The most widely used methodologies for the diagnosis of COVID-19 are quantitative reverse transcription polymerase chain reaction (RT-qPCR) and rapid antigen tests (RATs). PCR is time-consuming and requires specialized instrumentation operated by skilled personnel. In contrast, RATs can be used in-home or at point-of-care but are less sensitive, leading to a higher rate of false negative results. In this work, we describe the development of a disposable, electrochemical, and laser-scribed graphene-based biosensor strips for COVID-19 detection that exploits a split-ester bond ligase system (termed 'EsterLigase') for immobilization of a virus-specific nanobody to maintain the out-of-plane orientation of the probe to ensure the efficacy of the probe-target recognition process. An anti-spike VHH E nanobody, genetically fused with the EsterLigase domain, was used as the specific probe for the spike receptor-binding domain (SP-RBD) protein as the target. The recognition between the two was measured by the change in the charge transfer resistance determined by fitting the electrochemical impedance spectroscopy (EIS) spectra. The developed LSG-based biosensor achieved a linear detection range for the SP-RBD from 150 pM to 15 nM with a sensitivity of 0.0866 [log(M)]-1 and a limit of detection (LOD) of 7.68 pM.

Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching.

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's quadratic assignment problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at http://thinklab.sjtu.edu.cn/project/NGM/index.html.

Effects of Fe-Ions Irradiation on the Microstructure and Mechanical Properties of FeCrAl-1.5wt.% ZrC Alloys.

Fe-13Cr-3.5Al-2.0Mo-1.5wt.% ZrC alloy was irradiated by 400 keV Fe+ at 400 °C at different doses ranging from 6.35 × 1014 to 1.27 × 1016 ions/cm2 with a corresponding damage of 1.0-20.0 dpa, respectively, to investigate the effects of different radiation doses on the hardness and microstructure of the reinforced FeCrAl alloys in detail by nanoindentation, transmission electron microscopy (TEM), and atom probe tomography (APT). The results show that the hardness at 1.0 dpa increases from 5.68 to 6.81 GPa, which is 19.9% higher than a non-irradiated specimen. With an increase in dose from 1.0 to 20.0 dpa, the hardness increases from 6.81 to 8.01 GPa, which is an increase of only 17.6%, indicating that the hardness has reached saturation. TEM and APT results show that high-density nano-precipitates and low-density dislocation loops forme in the 1.0 dpa region, compared to the non-irradiated region. Compared with 1.0 dpa region, the density and size of nano-precipitates in the 20.0 dpa region have no significant change, while the density of dislocation loops increases. Irradiation results in a decrease of molybdenum and carbon in the strengthening precipitates (Zr, Mo) (C, N), and the proportionate decrease of molybdenum and carbon is more obvious with the increase in damage.

Meta-analysis of dose selection for budesonide in the treatment of Chinese patients with AECOPD.

OBJECTIVE: To systematically observe the curative efficacy and safety of budesonide inhalation in the treatment of acute exacerbation of chronic obstructive pulmonary disease, and to find a suitable dose of aerosolized budesonide for Chinese patients. METHODS: The meta-analysis study was conducted at Wenjiang District People's Hospital, Chengdu City, Sichuan Province, China from May 2019 to August 2019 and comprised randomised controlled trials of glucocorticoids for acute exacerbation of chronic obstructive pulmonary disease on the databases of the China National Knowledge Infrastructure, Wanfang Medical Network, PubMed, Medline, Embase, Cochrane Library and Google Scholar. Data extraction and quality evaluation of the studies was done and meta-analysis was then performed using RevMan 5.3. RESULTS: There were 25 studies identified that comprised 1959 patients. When the budesonide dose was 6mg/d and the methylprednisolone dose was 40mg/d, no significant difference was found in partial pressure of carbon dioxide and oxygen post-treatment (p>0.05). When the nebulized budesonide dose was <6mg/d, methylprednisolone was more effective than budesonide (p<0.05), while >6mg/d was not significantly more effective (p>0.05). At 4mg/d, the difference in the dyspnoea score post-treatment was significant (p<0.05). No significant difference was found in dyspnoea scores after intravenous glucocorticoid treatment when the dose was greater than or equal to 4mg/d. In terms of adverse reactions, the response rate of blood glucose, blood pressure, excitement, insomnia and stomach discomfort in the intravenous group was higher than that in the nebulised group (p<0.05). Oropharyngeal discomfort in the nebulized group was higher than that the intravenous group (p<0.05). CONCLUSIONS: The optimal dose for the inhalation of budesonide in Chinese patients was between 4mg/d and 6mg/d. The adverse reactions of nebulised budesonide were lower than those of intravenous methylprednisolone.