General audio tagging with ensembling convolutional neural network and statistical features

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample re-weight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Our codes are available at https://github.com/Cocoxili/DCASE2018Task2/.Comment: Submitted to ICASS

SoC-Cluster as an Edge Server: an Application-driven Measurement Study

Huge electricity consumption is a severe issue for edge data centers. To this end, we propose a new form of edge server, namely SoC-Cluster, that orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC-Cluster server that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server has been commercialized successfully and deployed in large scale on edge clouds. The current dominant workload on those deployed SoC-Clusters is cloud gaming, as mobile SoCs can seamlessly run native mobile games. The primary goal of this work is to demystify whether SoC-Cluster can efficiently serve more general-purpose, edge-typical workloads. Therefore, we built a benchmark suite that leverages state-of-the-art libraries for two killer edge workloads, i.e., video transcoding and deep learning inference. The benchmark comprehensively reports the performance, power consumption, and other application-specific metrics. We then performed a thorough measurement study and directly compared SoC-Cluster with traditional edge servers (with Intel CPU and NVIDIA GPU) with respect to physical size, electricity, and billing. The results reveal the advantages of SoC-Cluster, especially its high energy efficiency and the ability to proportionally scale energy consumption with various incoming loads, as well as its limitations. The results also provide insightful implications and valuable guidance to further improve SoC-Cluster and land it in broader edge scenarios

Mechanistic Insight Into the Interaction Between Helicobacter pylori Urease Subunit α and Its Molecular Chaperone Hsp60

Helicobacter pylori is the etiologic agent in a variety of gastroduodenal diseases. As its key pathogenic factors, both urease and Hsp60 play important roles in the pathogenesis of H. pylori. Previous studies have suggested that there is close relationship between urease and Hsp60, which implied that Hsp60 may act as a chaperone in urease stabilization and assembly. However, how these two proteins interact remains unclear. In this study, the impact of Hsp60 on urease activity of H. pylori lysate was first detected to confirm the interaction between urease and Hsp60. Pull-down assays further indicated that Hsp60 could bind to UreA subunit but not UreB. Then, the 3D structure of Hsp60 was modeled using I-TASSER to simulate the binding complex with UreA by molecular docking. The results showed that UreA is a perfect fit for the cavity of Hsp60. Analysis of the resulting model demonstrated that at least seven residues of UreA, located on two interfaces, participate in the interaction. Site-directed mutagenesis of these potential residues showed reduced affinity with Hsp60 than the wild type UreA through surface plasmon resonance (SPR) experiments, and D68 appears to have an important role in the affinity. Further analysis also showed that mutation of E25 and K26 caused a more rapid association and dissociation than with wild UreA, implying that they have roles in stabilizing the interaction complex. These affinity comparisons suggested that the interfaces predicted by molecular docking are credible. Our study indicated a direct interaction between Hsp60 and urease and revealed the binding interfaces and key residues involved in the interaction. These results provide further evidence for the chaperone activity of Hsp60 toward urease and lay a foundation to better understand the maturation mechanism of urease in H. pylori

Streptococcus suis Sequence Type 7 Outbreak, Sichuan, China

An outbreak of Streptococcus suis serotype 2 emerged in the summer of 2005 in Sichuan Province, and sporadic infections occurred in 4 additional provinces of China. In total, 99 S. suis strains were isolated and analyzed in this study: 88 isolates from human patients and 11 from diseased pigs. We defined 98 of 99 isolates as pulse type I by using pulsed-field gel electrophoresis analysis of SmaI-digested chromosomal DNA. Furthermore, multilocus sequence typing classified 97 of 98 members of the pulse type I in the same sequence type (ST), ST-7. Isolates of ST-7 were more toxic to peripheral blood mononuclear cells than ST-1 strains. S. suis ST-7, the causative agent, was a single-locus variant of ST-1 with increased virulence. These findings strongly suggest that ST-7 is an emerging, highly virulent S. suis clone that caused the largest S. suis outbreak ever described