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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.

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.

The time series of precipitation in flood season (May-September) at Wuhan Station, which is set as an example of the kind of time series with chaos characters, is split into two parts: One includes macro climatic timescale period waves that are affected by some relatively steady climatic factors such as astronomical factors (sunspot, etc.), some other known and/or unknown factors, and the other includes micro climatic timescale period waves superimposed on the macro one. The evolutionary modeling (EM), which develops from genetic programming (GP), is supposed to be adept at simulating the former part because it creates the nonlinear ordinary differential equation (NODE) based upon the data series. The natural fractals (NF) are used to simulate the latter part. The final prediction is the sum of results from both methods, thus the model can reflect multi-time scale effects of forcing factors in the climate system. The results of this example for 2002 and 2003 are satisfactory for climatic prediction operation. The NODE can suggest that the data vary with time, which is beneficial to think over short-range climatic analysis and prediction. Comparison in principle between evolutionary modeling and linear modeling indicates that the evolutionary one is a better way to simulate the complex time series with nonlinear characteristics. [PUBLICATION ABSTRACT]

Alcohol use is associated with cognitive impairment and dysregulated inflammation. Oral nitrate may benefit cognitive impairment in aging through altering the oral microbiota. Similarly, the beneficial effects of nitrate on alcohol-induced cognitive decline and the roles of the oral microbiota merit investigation. Here we found that nitrate supplementation effectively mitigated cognitive impairment induced by chronic alcohol exposure in mice, reducing both systemic and neuroinflammation. Furthermore, nitrate restored the dysbiosis of the oral microbiota caused by alcohol consumption. Notably, removing the oral microbiota led to a subsequent loss of the beneficial effects of nitrate. Oral microbiota from donor alcohol use disordered humans who had been taking the nitrate intervention were transplanted into germ-free mice which then showed increased cognitive function and reduced neuroinflammation. Finally, we examined 63 alcohol drinkers with varying levels of cognitive impairment and found that lower concentrations of nitrate metabolism-related bacteria were associated with higher cognitive impairment and lower nitrate levels in plasma. These findings highlight the protective role of nitrate against alcohol-induced cognition impairment and neuroinflammation and suggest that the oral microbiota associated with nitrate metabolism and brain function may form part of a “microbiota-mouth-brain axis”.

Saliva secretion and oral microbiota change in rhythm with our biological clock. Dysbiosis of the oral microbiome and alcohol consumption have a two-way interactive impact, but little is known about whether the oral microbiome undergoes diurnal changes in composition and function during the daytime in patients with alcohol dependence (AD). The impact of alcohol consumption on the diurnal salivary microbiome was examined in a case-control study of 32 AD patients and 21 healthy control (HC) subjects. We tested the changes in microbial composition and individual taxon abundance by 16S rRNA gene sequencing. The present study is the first report showing that alcohol consumption enhanced the richness of the salivary microbiome and lowered the evenness. The composition of the oral microbiota changed significantly in alcohol-dependent patients. Additionally, certain genera were enriched in the AD group, including , , and Cyanobacteria, all of which have pathogenic effects on the host. There is a correlation between liver enzymes and oral microbiota. KEGG function analysis also showed obvious alterations during the daytime. Alcohol drinking influences diurnal changes in the oral microbiota, leading to flora disturbance and related functional impairment. In particular, the diurnal changes of the oral microbiota may open avenues for potential interventions that can relieve the detrimental consequences of AD.

Patients with alcohol dependence (AD) can exhibit gut dysbacteria. Dysbacteria may co-occur with disruptions of circadian rhythmicity of the gut flora, which can aggravate AD. Herein, this study aimed to investigate diurnal oscillations of the gut microbiota in AD patients. Thirty-two patients with AD, based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, and 20 healthy subjects were enrolled in this study. Demographic and clinical data were collected by self-report questionnaires. Fecal samples at 7:00 AM, 11:00 AM, 3:00 PM, and 7:00 PM were collected from each subject. 16S rDNA sequencing was conducted. Wilcoxon and Kruskal-Wallis tests were performed to characterize alterations and oscillations of the gut microbiota. We found that β-diversity of the gut microbiota in AD patients oscillated diurnally compared with healthy subjects (p = 0.01). Additionally, 0.66% of operational taxonomic units oscillated diurnally in AD patients versus 1.68% in healthy subjects. At different taxonomic levels, bacterial abundance oscillated diurnally in both groups, such as Pseudomonas and Prevotella pallens (all p < 0.05). β-diversity of the gut microbiota in AD patients with high daily alcohol consumption, high-level cravings, short AD durations, and mild withdrawal symptoms oscillated diurnally compared with other AD patients (all p < 0.05). The gut microbiota in AD patients exhibits disruptions of diurnal oscillation, which may provide novel insights into mechanisms of AD and the development of therapeutic strategies.