Bamboo-eating giant panda (Ailuropoda melanoleuca) is an enigmatic species, which possesses a carnivore-like short and simple gastrointestinal tract (GIT). Despite the remarkable studies on giant panda, its diet adaptability status continues to be a matter of debate. To resolve this puzzle, we investigated the functional potential of the giant panda gut microbiome using shotgun metagenomic sequencing of fecal samples. We also compared our data with similar data from other animal species representing herbivores, carnivores, and omnivores from current and earlier studies. We found that the giant panda hosts a bear-like gut microbiota distinct from those of herbivores indicated by the metabolic potential of the microbiome in the gut of giant pandas and other mammals. Furthermore, the relative abundance of genes involved in cellulose- and hemicellulose-digestion, and enrichment of enzymes associated with pathways of amino acid degradation and biosynthetic reactions in giant pandas echoed a carnivore-like microbiome. Most significantly, the enzyme assay of the giant panda's feces indicated the lowest cellulase and xylanase activity among major herbivores, shown by an in-vitro experimental assay of enzyme activity for cellulose and hemicellulose-degradation. All of our results consistently indicate that the giant panda is not specialized to digest cellulose and hemicellulose from its bamboo diet, making the giant panda a good mammalian model to study the unusual link between the gut microbiome and diet. The increased food intake of the giant pandas might be a strategy to compensate for the gut microbiome functions, highlighting a strong need of conservation of the native bamboo forest both in high- and low-altitude ranges to meet the great demand of bamboo diet of giant pandas.

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Future mobile wireless communication networks will be featured as heterogeneity in order to enhance network performance and improve user experience. For better adaption to network challenges over its complexity and vulnerability, cell outage detection technique, a promising intelligent part of selforganizing networks (SON), has drawn considerable attention to deal with unexpected network faults. Our work is devoted to cell outage detection in a two-tier macro-pico network. Based on observation of performance metrics in time domain, we employ a classification algorithm called K-nearest neighbor (KNN) to achieve automatic anomaly detection. With some reasonable assumptions and a LTE-A system simulator, numerical experiments are implemented to demonstrate the efficiency of the proposed algorithm. Finally, localization for anomaly data and performance evaluation are further carried out to validate the classification accuracy.

Heterogeneous networks (HetNets) can increase network capacity through complementing the macro-base-station with low-power nodes, in response to the ongoing exponential growth in data traffic demand. While, unprecedented challenges exist in the planning, optimization, and maintenance in HetNets, especially activities such as cell outage detection and mitigation are labor-intensive and costly. One potential solution to address these issues is to introduce the extensively attracted self-organizing network (SON). This paper is mainly devoted to cell outage detection and compensation methods in two-tier HetNets where macrocell and picocells are coexisted. AK-nearest neighbor (KNN) classification algorithm is employed to detect the cell outage automatically. Consider the breakdown picocell can reload its degraded service to the overlapped macrocell via vertical handover; only the breakdown macrocell executes the performance compensation. Power adjustment on each resource block is carried out via Lagrange optimizing algorithm to compensate the breakdown cell. Through intensive numerical experiments, with the help of our proposal, the outage cells can be successfully detected and performance gain for the outage macrocell can reach 91.4% withα=1/3.

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