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Key message We report a transcriptome assembly and expression profiles from RNA-Seq data and identify genes responsible for culm gall formation in Zizania latifolia induced by Ustilago esculenta . The smut fungus Ustilago esculenta can induce culm gall in Zizania latifolia , which is used as a vegetable in Asian countries. However, the underlying molecular mechanism of culm gall formation is still unclear. To characterize the processes underlying this host-fungus association, we performed transcriptomic and expression profiling analyses of culms from Z. latifolia infected by the fungus U. esculenta . Transcriptomic analysis detected U. esculenta induced differential expression of 19,033 and 17,669 genes in Jiaobai (JB) and Huijiao (HJ) type of gall, respectively. Additionally, to detect the potential gall inducing genes, expression profiles of infected culms collected at −7, 1 and 10 DAS of culm gall development were  analyzed. Compared to control, we detected 8089 genes (4389 up-regulated, 3700 down-regulated) and 5251 genes (3121 up-regulated, 2130 down-regulated) were differentially expressed in JB and HJ, respectively. And we identified 376 host and 187 fungal candidate genes that showed stage-specific expression pattern, which are  possibly responsible for gall formation at the initial and later phases, respectively. Our results indicated that cytokinins play more prominent roles in regulating gall formation than do auxins. Together, our work provides general implications for the understanding of gene regulatory networks for culm gall development in Z. latifolia , and potential targets for genetic manipulation to improve the future yield   of  this crop.

In this work, ovalbumin-stabilized gold nanoclusters (OVA-Au NCs) fluorescent nanoprobes were synthesized by microwave heating and applied to detect picric acid (PA). The nanoprobes emitted red fluorescence with the maximum fluorescence peak of 680 nm under the excitation wavelength of 350 nm, and the Stokes shifts could be up to 330 nm which could effectively eliminate the interference of resonance scattering light. Compared with hydrothermal method, the synthesis method was simple and fast, and only took 50 s. Due to the absorption peak of PA overlapped with the emission peak of OVA-Au NCs in a large range, PA could selectively quench the fluorescence of OVA-Au NCs based on the inner filter effect (IFE) and a quick response time (1 min). Therefore, a new and sensitive method for PA monitoring was established. Under the optimal conditions, the concentration of PA demonstrated a satisfactory linear correlation with the fluorescence quenching degree Δ F / F 0 of the sensing system in the range of 20–240 µmol/L with the detection limit of 6.4 µmol/L. The proposed method is simple, fast, accurate, and easy to realize real-time monitoring.

Periodic mould level fluctuation (MLF) during slab casting is a bottleneck for upgrading the surface quality and casting speed especially for hypoperitectic (HP) or ultralow carbon steels. The uneven growth of the initially solidified shell is verified to be one of the important inducements to MLF due to related unsteady bulging in the secondary cooling zone. It is shown that the solidification mode of steels and the contraction behavior can be modified through chemical composition optimization within given composition limits. For high strength low alloy (HSLA) steels, the actual peritectic points calculated by Thermo-Calc software may change remarkably with the slight variations of alloying element contents. Accordingly, the narrow limit of chemical composition of HP steels through optimization is proven to be one of the effective factors to control the popular MLF phenomenon during slab casting.

The spatial distribution of employment is very important to the study of regional structure. We made an analysis on the spatial characteristics of employment density in Northeast China based on the regional density equation and spatial statistical methods with GIS and Geoda. The results showed that the employment centers concentrate along the coast, the local convergence trend becomes more and more obvious from the northern to the southern part, and the multi-center pattern of employment has been formed in the Northeast Region of China, which is like an inverted letter Y. From the year 2004 to 2008, the concentration of the employment density in the Northeast China continued to be strengthened. However, different local employment centers presented different patterns of growth. The results in this study can support the decision-making for local development, and the model and methods used in this paper may provide reference for relative studies.

The rapid progress of cloud technology has attracted a growing number of video providers to consider deploying their streaming services onto cloud platform for more cost-effective, scalable and reliable performance. In this paper, we utilize Markov decision process model to formulate the dynamic deployment of cloud-based video services over multiple geographically distributed datacenters. We focus on maximizing the average profits for the video service provider over a long run and introduce an average performance criteria which reflects the cost and user experience jointly. We develop an optimal algorithm based on the sensitivity analysis and sample-based policy iteration to obtain the optimal video placement and request dispatching strategy. We demonstrate the optimality of our algorithm with theoretical proof and specify the practical feasibility of our algorithm. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee users’ quality of experience (QoE).

It is known to all that mobile ad hoc network (MANET) is more vulnerable to all sorts of malicious attacks which affects the reliability of data transmission because the network has the characteristics of wireless, multi-hop, etc. We put forward novel approach of distributed & adaptive trust metrics for MANET in this paper. Firstly, the method calculates the communication trust by using the number of data packets between nodes, and predicts the trust based on the trend of this value, and calculates the comprehensive trust by considering the history trust with the predict value; then calculates the energy trust based on the residual energy of nodes and the direct trust based on the communication trust and energy trust. Secondly, the method calculates the recommendation trust based on the recommendation reliability and the recommendation familiarity; adopts the adaptive weighting, and calculates the integrate direct trust by considering the direct trust with recommendation trust. Thirdly, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. The feature of the proposed method is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation experiments and tests of the practical applications of MANET show that the proposed approach can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these problems. We also designed training rules for the improved particle swarm optimization to train fuzzy neural network, and the hybrid algorithm is applied to solve the path planning problem of intelligent driving vehicles. The efficiency and practicability of the algorithm are proved by experiments.

Recent emerge of dielectric nanoparticle chains featuring subwavelength topological states has opened unprecedented avenues for light. Here, we demonstrate a mechanical analogy of zigzag nanoparticle chain that supports vibrational and rotational localizations in the form of subwavelength topological edge states at extremely low frequency (near zero). We elaborate analytical methodology to thoroughly analyze the wave dynamics in the near zero-frequency (NZF) regime. Due to weak rotational couplings, we find that motion can be efficiently confined on the boundaries of the chains. Interestingly, the vibration-rotation coupled property enables the granular chain for exotic NZF waves with spreading rotation inside the chain but localized vibration on the boundaries. We characterize the propagation properties of elastic waves in the chain, and exhibit the fingerprints of topological edge states on the boundaries. Our study provides the possibilities for vibration control techniques using granular media at extremely low frequency.

New-onset atrial fibrillation (NOAF) increases the risk of embolism and sudden death in critically ill patients; however, limited data exist attempting to identify modifiable risk factors and predict the incidence of NOAF. We aimed to investigate the risk factors for NOAF and develop an optimized clinical prediction model based on machine learning algorithms. Data from patients admitted to the intensive care unit (ICU) of the Affiliated Hospital of Nanjing University of Chinese Medicine from August 2019 to January 2022 were retrospectively analyzed. LASSO regression and Random Forest (RF) algorithms were used to screen predictive variables. Logistic Regression, RF, Gradient Boosting and Support Vector Machine models were constructed to evaluate the recognition ability of different machine learning algorithms. The confusion matrix and calibration curve were used to assess the degree of accuracy of the four models. Decision curve analysis (DCA) was conducted to evaluate the utility of the model in decision-making. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were also calculated to evaluate the performance of the models. The learning curves of the four models were plotted to evaluate the precision of different models. The SHapley Additive exPlanations (SHAP) was used to explain the supreme-performing model. In total, 417 patients were enrolled in the study, and 333 patients were allocated to the training group and 84 to the validation group. The baseline characteristic distributions were similar between the two groups. Age, heart rate, mean arterial pressure, activated partial thromboplastin time, and brain natriuretic peptide were revealed as independent predictors of NOAF by LASSO regression and the RF algorithm. The RF model had the best performance, with the area under the receiver operator characteristic curve (AUROC) of 0.758, the area under the precision-recall curve (AUPRC) of 0.524, and accuracy of 0.735 in the training set, paralleled by AUROC of 0.796, AUPRC of 0.686, and accuracy of 0.702 in the validation set. The confusion matrix and calibration curves showed that RF had the best performance. DCAs also showed that the RF model provided the highest net benefit in the clinical setting. The NRI results showed that the RF significantly improved reclassification ability compared to the baseline model (NRI = 0.38). The IDI results further demonstrated a moderate improvement in discrimination ability for the RF (IDI = 0.033) compared to the baseline. The learning curves revealed that RF also showed superior performance. SHAP could be used visualized individual NOAF risk predicted by the model. The RF model exhibited the best performance in predicting NOAF in critically ill patients and has the potential to help clinicians identify high-risk patients and guide clinical decision making.

In the application environment having dense distribution of marginal wireless sensor network (WSN), the data transmission process will generate a large number of conflicts, which will result in loss of transmission data and increase of transmission delay. The multi-path data transmission method can effectively solve the problem of large data loss and transmission delay caused by collisions. A new approach of multi-path reliable transmission for application of marginal WSN (named RCB-MRT) is proposed in this paper. It adopts redundancy mechanism to realize the reliability of data transmission, and uses concurrent woven multi-path technology to improve the transmission efficiency of data packets. Firstly, it divides the data packets that the sensor node needs to transmit into several sub-packets with data redundancy, and then forwards the sub-packets to the aggregation node through multi-path by the intermediate nodes of marginal environment. The results of our experimental tests show that the proposed multi-path reliable transmission method can effectively reduce data packet loss rate, reduce transmission delay and increase network lifetime. The method is very useful for the applications of marginal wireless sensor network.