Genetic architecture of lodging resistance revealed by genome- wide association study in maize (Zea mays L)

Lodging is one of key factors influencing biomass yield, restricting planting density and reducing mechanical harvesting productivity in maize. Targeted cultivating lodging resistance varieties with screened lines is an eco- nomical and effective approach to improve ability of maize lodging resistance. To accomplish this objective, we performed phenotypic assessment of seven lodging-related traits in a diverse maize population consisting of 290 inbred lines and conducted a genome-wide association study with 201 SSR markers to detected marker-trait as- sociations. Seven lodging-related traits all showed broad phenotypic variations. Through evaluation of stalk push- ing resistance in the field for two years, a number of 32 inbred lines featured with strong lodging resistance were selected out. Correlation analysis indicated that stalk pushing resistance had a significantly positive correlation with third internode diameter and fourth internode diameter and a significantly negative correlation with ear height. Furthermore, a total of 27 and 13 significant associations for lodging-related traits were identified in year 2012 and 2013, respectively. Interestingly, three associations on chromosome 4, 5, and 6 were discovered in both years. Thus, this study provides useful information for understanding genetic architecture of lodging resistance in maize and will benefit maize marker-assistant breeding program with improving lodging resistance

Second-line therapy for patients with steroid-refractory aGVHD: systematic review and meta-analysis of randomized controlled trials

ObjectiveSteroids-refractory (SR) acute graft-versus-host disease (aGVHD) is a life-threatening condition in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), but the optimal second-line therapy still has not been established. We aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the efficacy and safety of different second-line therapy regimens.MethodsLiterature search in MEDLINE, Embase, Cochrane Library and China Biology Medicine databases were performed to retrieve RCTs comparing the efficacy and safety of different therapy regimens for patients with SR aGVHD. Meta-analysis was conducted with Review Manager version 5.3. The primary outcome is the overall response rate (ORR) at day 28. Pooled relative risk (RR) and 95% confidence interval (CI) were calculated with the Mantel-Haenszel method.ResultsEight eligible RCTs were included, involving 1127 patients with SR aGVHD and a broad range of second-line therapy regimens. Meta-analysis of 3 trials investigating the effects of adding mesenchymal stroma cells (MSCs) to other second-line therapy regimens suggested that the addition of MSCs is associated with significantly improvement in ORR at day 28 (RR = 1.15, 95% CI = 1.01–1.32, P = 0.04), especially in patients with severe (grade III–IV or grade C–D) aGVHD (RR = 1.26, 95% CI = 1.04–1.52, P = 0.02) and patients with multiorgan involved (RR = 1.27, 95% CI = 1.05–1.55, P = 0.01). No significant difference was observed betwwen the MSCs group and control group in consideration of overall survival and serious adverse events. Treatment outcomes of the other trials were comprehensively reviewed, ruxolitinib showed significantly higher ORR and complete response rate at day 28, higher durable overall response at day 56 and longer failure-free survival in comparison with other regimens; inolimomab shows similar 1-year therapy success rate but superior long-term overall survial in comparison with anti-thymocyte globulin, other comparisons did not show significant differences in efficacy.ConclusionsAdding MSCs to other second-line therapy regimens is associated with significantly improved ORR, ruxolitinib showed significantly better efficacy outcomes in comparison with other regimens in patients with SR aGVHD. Further well-designed RCTs and integrated studies are required to determine the optimal treatment.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022342487

Comparison of energy efficiency between E and MPS type vertical spindle pulverizer based on the experimental and industrial sampling tests

0.5%–2% gross power generation of coal power plant is consumed by vertical spindle pulverizer (VSP), and it is essential to select a VSP with better operational performance. Simulated studies of lab-scale mills, which show the similar breakage mechanism with VSP, and industrial sampling on VSPs are conducted to compare energy efficiencies of E and MPS type VSPs (with the grinding media of balls and tread rollers, respectively). The classical energy-size reduction model is modified with the addition of particle size in the exponential form to compare the grinding energy efficiency (product fineness for the certain specific energy) of two lab-scale mills. Also, differences in structure and operational parameters of lab-scale mills are considered. For the industrial sampling tests of two VSPs, recorded data and size distribution of sampled materials are preliminarily compared. Product t10 is selected as the bridge to connect the specific grinding energy and size distribution of products. The modified breakage model is combined with the King's equation to compare the energy efficiency on the premise of feed in the same fineness. Comprehensive comparison of the results obtained from both lab-scale and industrial-scale VSPs suggests that the MPS type VSP shows the higher grinding energy efficiency and lower total energy consumption

Role of purinergic signalling in obesity-associated end-organ damage: focus on the effects of natural plant extracts

Obesity has become one of the major public health problems in both the developing and developed countries. Recent studies have suggested that the purinergic signalling is involved in obesity-associated end-organ damage through purine P1 and P2 receptors. In the search for new components for the treatments of obesity, we and other researchers have found much evidence that natural plant extracts may be promising novel therapeutic approaches by modulating purinergic signalling. In this review, we summarize a critical role of purinergic signalling in modulating obesity-associated end-organ damage, such as overhigh appetite, myocardial ischemia, inflammation, atherosclerosis, non-alcoholic fatty liver disease (NAFLD), hepatic steatosis and renal inflammation. Moreover, we focus on the potential roles of several natural plant extracts, including quercetin, resveratrol/trans-resveratrol, caffeine, evodiamine and puerarin, in alleviating obesity-associated end-organ damage via purinergic signalling. We hope that the current knowledge of the potential roles of natural plant extracts in regulating purinergic signalling would provide new ideas for the treatment of obesity and obesity-associated end-organ damage

Formal Verification of Fractional-Order PID Control Systems Using Higher-Order Logic

Fractional-order PID control is a landmark in the development of fractional-order control theory. It can improve the control precision and accuracy of systems and achieve more robust control results. As a theorem-proving formal verification method, it can be applied to an arbitrary system represented by a mathematical model. It is the ideal verification method because it is not subject to limits on state numbers. This paper presents the higher-order logic (HOL) formal verification and modeling of fractional-order PID controller systems. Firstly, a fractional-order PID controller was designed. The accuracy of fractional-order PID control can be supported by simulation, comparing integral-order PID controls. Secondly, the superior property of fractional-order PID control is validated via higher-order logic theorem proofs. An important basic property, the relationship between fractional-order differential calculus and integral-order differential calculus, was analyzed via a higher-order logic theorem proof. Then, the relations between the fractional-order PID controller and integral-order PID controller were verified based on the fractional-order Grünwald–Letnikov definition for higher-order logic theorem proofs. Formalization models of the fractional-order PID controller and the fractional-order closed-loop control system were established. Finally, the stability of the fractional-order control systems was verified based on established formal models and theorems. The results show that the fractional-order PID controllers can be conducive to the control performance of control systems, and the higher-order logic formal verification method can ensure the reliability and security of fractional-order control systems

Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments

Combination Synchronization of Three Identical or Different Nonlinear Complex Hyperchaotic Systems

In this paper, we investigate the combination synchronization of three nonlinear complex hyperchaotic systems: the complex hyperchaotic Lorenz system, the complex hyperchaotic Chen system and the complex hyperchaotic L¨u system. Based on the Lyapunov stability theory, corresponding controllers to achieve combination synchronization among three identical or different nonlinear complex hyperchaotic systems are derived, respectively. Numerical simulations are presented to demonstrate the validity and feasibility of the theoretical analysis

A Deep Learning Optimizer Based on Grünwald–Letnikov Fractional Order Definition

In this paper, a deep learning optimization algorithm is proposed, which is based on the Grünwald–Letnikov (G-L) fractional order definition. An optimizer fractional calculus gradient descent based on the G-L fractional order definition (FCGD_G-L) is designed. Using the short-memory effect of the G-L fractional order definition, the derivation only needs 10 time steps. At the same time, via the transforming formula of the G-L fractional order definition, the Gamma function is eliminated. Thereby, it can achieve the unification of the fractional order and integer order in FCGD_G-L. To prevent the parameters falling into local optimum, a small disturbance is added in the unfolding process. According to the stochastic gradient descent (SGD) and Adam, two optimizers’ fractional calculus stochastic gradient descent based on the G-L definition (FCSGD_G-L), and the fractional calculus Adam based on the G-L definition (FCAdam_G-L), are obtained. These optimizers are validated on two time series prediction tasks. With the analysis of train loss, related experiments show that FCGD_G-L has the faster convergence speed and better convergence accuracy than the conventional integer order optimizer. Because of the fractional order property, the optimizer exhibits stronger robustness and generalization ability. Through the test sets, using the saved optimal model to evaluate, FCGD_G-L also shows a better evaluation effect than the conventional integer order optimizer

Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments