The application of biochar is one of the promising management practices to alleviate soil acidification and improve soil fertility. However, it has been found to reduce the content of ammonium ...nitrogen (NH4+−N) in the soil, which is the most important form of nitrogen (N) for tea tree growth. To investigate the response of soil NH4+−N content to the combined application of biochar and pruned tea plant litter, a pot trial was performed with three treatments: control (CK); biochar (BC); biochar + tea plant litter (BC + L). Soil chemistry properties and ammonification rates were determined, and the microbial community composition was analyzed by high-throughput sequencing. The results showed that the NH4+−N content in BC + L treatment was 1.7–9.5 fold higher than CK and BC treatments after 15 days of application, with no difference in the proportion of ammonia oxidation phyla such as Nitrospirae. The proportion of soil fungus Ascomycota was strongly correlated with the content of soil available nitrogen (p = 0.032), and the relationship was well described by a linear equation (R2 = 0.876, p = 0.01). Further redundancy analysis revealed that soil pH, soil organic carbon (SOC), the ratio of SOC to total nitrogen and the ratio of SOC to alkaline hydrolyzable nitrogen appeared to be important factors influencing the separation of BC + L from CK and BC groups. In summary, the addition of biochar and pruned tea plant litter alters soil properties and may influence the composition of microorganisms with various trophic groups, thus affecting ecosystem function. Our results also highlight the importance of returning pruned materials with biochar application in tea plantation ecosystems.
Cytoplasmic male sterility (CMS) is a widely used trait in angiosperms caused by perturbations in nucleus-mitochondrion interactions that suppress the production of functional pollen. MicroRNAs ...(miRNAs) are small non-coding RNAs that act as regulatory molecules of transcriptional or post-transcriptional gene silencing in plants. The discovery of miRNAs and their possible implications in CMS induction provides clues for the intricacies and complexity of this phenomenon. Previously, we characterized an Ogura-CMS line of turnip (Brassica rapa ssp. rapifera) that displays distinct impaired anther development with defective microspore production and premature tapetum degeneration. In the present study, high-throughput sequencing was employed for a genome-wide investigation of miRNAs. Six small RNA libraries of inflorescences collected from the Ogura-CMS line and its maintainer fertile (MF) line of turnip were constructed. A total of 120 pre-miRNAs corresponding to 89 mature miRNAs were identified, including 87 conversed miRNAs and 33 novel miRNAs. Among these miRNAs, the expression of 10 differentially expressed mature miRNAs originating from 12 pre-miRNAs was shown to have changed by more than two-fold between inflorescences of the Ogura-CMS line and inflorescences of the MF line, including 8 down- and 2 up-regulated miRNAs. The expression profiles of the differentially expressed miRNAs were confirmed by stem-loop quantitative real-time PCR. In addition, to identify the targets of the identified miRNAs, a degradome analysis was performed. A total of 22 targets of 25 miRNAs and 17 targets of 28 miRNAs were identified as being involved in the reproductive development for Ogura-CMS and MF lines of turnip, respectively. Negative correlations of expression patterns between partial miRNAs and their targets were detected. Some of these identified targets, such as squamosa promoter-binding-like transcription factor family proteins, auxin response factors and pentatricopeptide repeat-containing proteins, were previously reported to be involved in reproductive development in plants. Taken together, our results can help improve the understanding of miRNA-mediated regulatory pathways that might be involved in CMS occurrence in turnip.
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's ...service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, signicantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
The massive amounts of monitoring data in network applications bring an urgent need for intelligent operation in large distributed systems. The key problem is precisely detecting anomalies in ...multivariate time series (MTS) monitoring metrics with the awareness of different application scenarios. Unsupervised MTS anomaly detection methods aim at detecting data anomalies from historical MTS without considering the out-of-band information (including user feedback and background information like code deployment status), which leads to poor performance in practice. To take advantage of the out-of-band information, we propose ACVAE, an MTS anomaly detection algorithm through active learning and contrast VAE-based detection models, which simultaneously learns MTS data's normal and anomalous patterns for anomaly detection. We also use a learnable prior to capture system status from the background information. Moreover, we propose a query model for VAE-based methods, which can learn to query labels of the most useful instances to train the detection model. We evaluate our algorithm on three different monitoring situations in eBay's search back-end systems. ACVAE achieves a range F1 score of 0.68~0.96 with only 3% labels, significantly outperforming the best competing methods by 0.18~0.50, and even better than a supervised ensemble method designed by domain experts in eBay.
Today's large datacenters house a massive number of machines, each of which is being closely monitored with multivariate time series (e.g., CPU idle, memory utilization) to ensure service quality. ...Detecting outlier machine instances with multivariate time series is crucial for service management. However, it is a challenging task due to the multiple classes and various shapes, high dimensionality, and lack of labels of multivariate time series. In this article, we propose DOMI, a novel unsupervised model that combines Gaussian mixture VAE with 1D-CNN, to d etect o utlier m achine i nstances. Its core idea is to capture the normal patterns of machine instances by learning their latent representations that consider the shape characteristics, reconstruct input data by the learned representations, and apply reconstruction probabilities to determine outliers. Moreover, DOMI interprets the detected outlier instance based on the reconstruction probability changes of univariate time series. Extensive experiments have been conducted on the dataset collected from 1821 machines with a 1.5-month-period, which are deployed in ByteDance, a top global content service provider. DOMI achieves the best F1-Score of 0.94 and AUC score of 0.99, significantly outperforming the best performing baseline method by 0.08 and 0.03, respectively. Moreover, its interpretation accuracy is up to 0.93.
Traditional Light Detection and Rangings (LiDARs) can quickly collect high-accuracy of three-dimensional (3D) point cloud data at a designated wavelength (i.e., cannot obtain hyperspectral data), ...while the passive hyperspectral imager can collect rich spectral data of ground objects, but are lack of 3D spatial data. This paper presents one innovative study on the design of airborne-oriented supercontinuum laser hyperspectral (SCLaHS) LiDAR with 50 bands covering 400 nm to 900 nm at a spectral resolution of 10 nm and ground sampling distance (GSD) of 0.5 m. The major innovations include (1) development of the high-power narrow-pulse supercontinuum laser source covering 400 nm to 900 nm with 50 bands using multi-core microstructure fibre, all-polarization maintaining fibre and ultra-long cavity structure, (2) a miniaturized aberration correction holographic concave grating spectroscopic and streak tube technique are developed for 50 bands laser echoes detection at high spectral-spatial-temporal resolution and dynamic airborne platform, and (3) the algorithm theoretic basis for SCLaHS LiDAR point cloud data 3D geodetic coordination calculation, including in-flight airborne calibration algorithm. The initial experimental results demonstrated that the designed SCLaHS LiDAR is doable, and a prototype of the (SCLaHS) LiDAR intends to be implemented.
Additive key performance indicators (KPIs) (such as page view (PV), revenue, and error count) with multi-dimensional attributes (such as ISP, Province, and DataCenter) are common and important in ...monitoring metrics in Internet companies. When an anomaly happens to an overall KPI, it is critical but challenging to localize the root cause, which is one (or more) combination of attribute values in multiple dimensions. For example, is the total PV decrease caused by the PV decrease from "Beijing" or "China Mobile in Beijing", or "Beijing and Shanghai"? However, this task is very challenging for two major reasons. First, the PVs of different combinations are interdependent; thus, the PV anomalies at the root cause can cause the changes of many other PVs at different aggregation levels. Second, there could be tens of thousands of combinations to investigate in multi-dimensional attribute space. It is a difficulty to find the root cause from a huge search space. To address the first challenge, our approach HotSpot uses a novel potential score based on the ripple effect for anomaly propagation that we reveal. To address the second challenge, HotSpot adopts the Monte Carlo Tree Search algorithm and a hierarchical pruning strategy. Using the real-world data from a top global search engine, we show that HotSpot achieves a great improvement on effectiveness and robustness, i.e., 95% of all types of root cause cases using HotSpot (compared with only 15% using existing approaches) achieves an F-score over 90%. Operational experiences show that HotSpot can reduce the localization time from more than 1 h in manual efforts to less than 20 s.
Anomaly detection is a crucial task for monitoring various status (i.e., metrics) of entities (e.g., manufacturing systems and Internet services), which are often characterized by multivariate time ...series (MTS). In practice, it's important to precisely detect the anomalies, as well as to interpret the detected anomalies through localizing a group of most anomalous metrics, to further assist the failure troubleshooting. In this paper, we propose InterFusion, an unsupervised method that simultaneously models the inter-metric and temporal dependency for MTS. Its core idea is to model the normal patterns inside MTS data through hierarchical Variational AutoEncoder with two stochastic latent variables, each of which learns low-dimensional inter-metric or temporal embeddings. Furthermore, we propose an MCMC-based method to obtain reasonable embeddings and reconstructions at anomalous parts for MTS anomaly interpretation. Our evaluation experiments are conducted on four real-world datasets from different industrial domains (three existing and one newly published dataset collected through our pilot deployment of InterFusion). InterFusion achieves an average anomaly detection F1-Score higher than 0.94 and anomaly interpretation performance of 0.87, significantly outperforming recent state-of-the-art MTS anomaly detection methods.
PDF
