RESUMEN
To design an algorithm for detecting outliers over streaming data has become an important task in many common applications, arising in areas such as fraud detections, network analysis, environment monitoring and so forth. Due to the fact that real-time data may arrive in the form of streams rather than batches, properties such as concept drift, temporal context, transiency, and uncertainty need to be considered. In addition, data processing needs to be incremental with limited memory resource, and scalable. These facts create big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in an incremental fashion, especially in the streaming environment. To address these problems, we first propose C_KDE_WR, which uses sliding window and kernel function to process the streaming data online, and reports its results demonstrating high throughput on handling real-time streaming data, implemented in a CUDA framework on Graphics Processing Unit (GPU). We also present another algorithm, C_LOF, based on a very popular and effective outlier detection algorithm called Local Outlier Factor (LOF) which unfortunately works only on batched data. Using a novel incremental approach that compensates the drawback of high complexity in LOF, we show how to implement it in a streaming context and to obtain results in a timely manner. Like C_KDE_WR, C_LOF also employs sliding-window and statistical-summary to help making decision based on the data in the current window. It also addresses all those challenges of streaming data as addressed in C_KDE_WR. In addition, we report the comparative evaluation on the accuracy of C_KDE_WR with the state-of-the-art SOD_GPU using Precision, Recall and F-score metrics. Furthermore, a t-test is also performed to demonstrate the significance of the improvement. We further report the testing results of C_LOF on different parameter settings and drew ROC and PR curve with their area under the curve (AUC) and Average Precision (AP) values calculated respectively. Experimental results show that C_LOF can overcome the masquerading problem, which often exists in outlier detection on streaming data. We provide complexity analysis and report experiment results on the accuracy of both C_KDE_WR and C_LOF algorithms in order to evaluate their effectiveness as well as their efficiencies.
RESUMEN
SCOPE: Lactoferrin (Lf) possess a protective potential to liver, but whether it can prevent alcoholic liver injury (ALI) remains unclear. METHODS AND RESULTS: Four groups of male C57BL/6J mice are fed with different diets, namely, AIN-93G diet for control (CON) and ethanol (EtOH) groups, and AIN-93G diet with 0.4% and 4% casein replaced by Lf for low-dose Lf (LLf) and high-dose Lf (HLf) groups, respectively. ALI is induced by giving 20% ethanol ad libitum combined with four "binges". Lf can remarkably decrease EtOH-induced mortality. Lf promotes aldehyde dehydrogenase-2 (ALDH2) expression and suppressing cytochrome P450 2E1 (CYP2E1) overexpression, resulting in the reduced hepatic superoxide and inflammation levels, which ultimately leads to the hepatic injury alleviation. However, HLf increases acetyl-CoA carboxylase and fatty acid synthase protein levels, which suggests that excessive intake may weaken the beneficial effects of Lf. Moreover, LLf increases the relative abundances of Akkermansia and Lactobacillus. Additionally, the study shows that Lf likely exerts action in its digestive product forms rather than intact Lf molecular in normal condition. CONCLUSION: LLf can ameliorate ALI, which is associated with the regulation of hepatic alcohol metabolism and the modulation of gut microbiota. However, excessive Lf intake may result in a diminished benefit.