BackgroundPlants have evolved excellent ability of flexibly regulating the growth of organs to adapt to changing environment, for example, the modulation of lateral root development in response to environmental stresses. Despite of fundamental discovery that some microRNAs are involved in this process, the molecular mechanisms of how these microRNAs work together are still largely unknown.ResultsHere we show that miR390 induced by auxin promotes lateral root growth in rice. However, this promotion can be suppressed by miR393, which is induced by various stresses and ABA (Abscisic Acid). Results that miR393 responded to ABA stronger and earlier than other stresses implied that ABA likely is authentic factor for inducing miR393. The transgenic lines respectively over-expressing miR393 and OsTAS3a (Oryza sativa Trans-Acting Short RNA precursor 3a) displayed opposite phenotypes in lateral root growth. MiR390 was found to be dominantly expressed at lateral root primordia and roots tips while miR393 mainly expressed in the base part of roots at very low level. When miR393 was up-regulated by various stresses, miR390 expression level fell down. However, the risen expression level of miR390 induced by auxin didn’t affect the expression of miR393 and its target OsTIR1 (Transport Inhibitor Response 1). Together with analysis of the two transgenic lines, we provide a model of how the growth of lateral roots in rice is regulated distinctively by the 2 microRNAs.ConclusionWe propose that miR390 induced by auxin triggers the lateral root growth under normal growth conditions, meanwhile miR393 just lurks as a potentially regulative role; Once plants suffer from stresses, miR393 will be induced to negatively regulate miR390-mediated growth of lateral roots in rice.

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With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid the leak of data at the cluster head in innercluster. Furthermore, a mechanism is given to locate the compromised nodes. The analysis shows that the model is robust to many attacks and has a lower communication overhead.

Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets.

One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.

Wireless sensor networks are always deployed in remote and hostile environments to gather sensitive information, in which sensor nodes are apt to encounter some serious leakage of sensitive data. Hence, privacy-preserving is becoming an increasingly important issue in security data aggregation for wireless sensor networks. In this paper, we propose a balance privacy-preserving data aggregation model (BPDA) based on slicing and mixing technology. Compared to fixed or random slicing, BPDA model gives a balance slicing mechanism to ensure that slice can be sent to the nodes which have lower privacy preservation and enhance the privacy-preserving efficacy. Furthermore, according to the influence of the node degree and energy, three different schemes are presented to keep the privacy-preserving data aggregation balance. Theoretical analysis and simulation show that BPDA model demonstrates a good performance in terms of privacy-preserving efficacy and communication overhead and prolongs the lifetime of network.

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Small object detection is one of the research difficulties in object detection, and Feature Pyramid Networks (FPN) is a common feature extractor in deep learning; thus, improving the results of small object detection based on FPN is of great significance in this field. In this paper, SV-FPN is proposed for a small object detection task, which consists of Small Object Feature Enhancement (SOFE) and Variance-guided Region of Interest Fusion (VRoIF). When using FPN as a feature extractor, an SOFE module is designed to enhance the finer-resolution level feature maps from which the small object features are extracted. VRoIF takes the variance of RoI features as the data driver to learn the completeness of several RoI features from different feature layers, which avoids wasting information and introducing noise. Ablation experiments on three public datasets (KITTI, PASCAL VOC 07+12 and MS COCO 2017) demonstrate the effectiveness of SV-FPN, and the mean Average Precision (mAP) of SV-FPN in the three datasets achieves 41.5%, 53.9% and 38.3%, respectively.