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Additional file 1: Supplementary Figure S1. Mortality rate among PLWH receiving ART in 2010–2019.

Additional file 1: Table S1. Strains, plasmids and primers used in this study. Table S2. qRT-PCR primers used in this study. Table S3. Biological insecticidal activity of wt, Δbfr and ::bfr. Figure S1. LC-MS/MS identification of butenyl-spinosyn. Figure S2. Phylogenetic tree analysis of bacterioferritin. Figure S3. Construction and recombination schematic diagram of pKCcas9dO-sgRNA-UHA-DHA and pOJ260-kasOp*-bfr. Figure S4. Identification of Δbfr and ::bfr. Figure S5. Tricine-SDS-PAGE analysis and 1D-LC-MS/MS identification of heterologously expressed protein Bfr. Figure S6. Butenyl-spinosyn yield curve of the wild-type and mutants. Figure S7. Statistics of total protein and differential protein between the wild-type and mutant strains identified in the quantitative proteome. Figure S8. The expression fold change of bus family proteins between bfr mutant strains and wild-type strain. Figure S9. Verification of the transcription levels of iron homeostasis and oxidative stress-related proteins.

Large-scale Internet VoD services often adopt a hybrid overlay approach using both P2P and CDN to stream video content. When the underlay ISP network interconnection is weak, as the situation is found in China, it is important to provision the CDN bandwidth carefully in different ISP networks. Through measurement studies using real world data (from Tencent Video), it is found that users in large ISP networks can leverage more on P2P service, compared to the case for smaller ISP networks, and this is especially true during heavy load periods. We study systematic strategies for allocating CDN bandwidth given fixed total bandwidth purchasing budget, and the influence of P2P factor. We show that a more optimal strategy would bias towards allocating proportionally more CDN bandwidth to smaller ISP networks. This result from our model and analysis is validated by simulation. This problem has not been considered by past works and has practical implications for improving the operation of real-world VoD service providers. Refereed/Peer-reviewed

Video popularity (measured by view count) over time is an essential reference for both online video providers and users. According to state-of-the-art works, video popularity is useful for system optimization, load generation, video caching, and video recommendation. Thus, deeper understanding of video popularity evolution is very helpful for improving video service quality and providers' operating efficiency. The core question to be explored in this paper is what key factors govern online video popularity evolution? Through collaboration with our industry partner, Tencent Video, we obtain historical data of video view counts over a period of time, and observe their patterns. We then propose a stochastic fluid model, named as EvoModel, which captures two processes giving rise to different evolution patterns of a given video: (a) the information spreading process and (b) the user reaction process. The driving forces for process (a) can be either via recommendation from the system directly, or word-of-mouth; the extent of the spread is governed by the intrinsic popularity of the video. The factor affecting the second process can be modeled by a user reaction rate. These processes together determine different video popularity evolution patterns. We validate our model by fitting the historical data obtained from a real-world system. Furthermore, we discuss the feasibility of estimating model parameters and predicting popularity. Refereed/Peer-reviewed