Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines

We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.Comment: 11 pages, 13 figure

Pyrolysis gas as a carbon source for biogas production via anaerobic digestion

Carbon is an important resource for anaerobes to enhance biogas production. In this study, the possibility of using simulated pyrolysis gas (SPG) as a carbon source for biogas production was investigated. The effects of stirring speed (SS), gas holding time (GHT), and H2 addition on biomethanation of SPG were evaluated. The diversity and structure of microbial communities were also analyzed under an illumina MiSeq platform. Results indicated that at a GHT of 14 h and an SS at 400 rpm, SPG with up to 64.7% CH4could be bio-upgraded to biogas. Gas–liquid mass transfer is the limitation for SPG biomethanation. For the first time, it has been noticed that the addition of H2 can bioupgrade SPG to high quality biogas (with 91.1% CH4). Methanobacterium was considered as a key factor in all reactors. This study provides an idea and alternative way to convert lignocellulosic biomass and solid organic waste into energy (e.g., pyrolysis was used as a pretreatment to produce pyrolysis gas from biomass, and then, pyrolysis gas was bioupgraded to higher quality biogas via anaerobic digestion)

Staged miRNA re-regulation patterns during reprogramming.

BackgroundMiRNAs often operate in feedback loops with transcription factors and represent a key mechanism for fine-tuning gene expression. In transcription factor-induced reprogramming, miRNAs play a critical role; however, detailed analyses of miRNA expression changes during reprogramming at the level of deep sequencing have not been previously reported.ResultsWe use four factor reprogramming to induce pluripotent stem cells from mouse fibroblasts and isolate FACS-sorted Thy1- and SSEA1+ intermediates and Oct4-GFP+ induced pluripotent stem cells (iPSCs). Small RNAs from these cells, and two partial-iPSC lines, another iPSC line, and mouse embryonic stem cells (mES cells) were deep sequenced. A comprehensive resetting of the miRNA profile occurs during reprogramming; however, analysis of miRNA co-expression patterns yields only a few patterns of change. Dlk1-Dio3 region miRNAs dominate the large pool of miRNAs experiencing small but significant fold changes early in reprogramming. Overexpression of Dlk1-Dio3 miRNAs early in reprogramming reduces reprogramming efficiency, suggesting the observed downregulation of these miRNAs may contribute to reprogramming. As reprogramming progresses, fewer miRNAs show changes in expression, but those changes are generally of greater magnitude.ConclusionsThe broad resetting of the miRNA profile during reprogramming that we observe is due to small changes in gene expression in many miRNAs early in the process, and large changes in only a few miRNAs late in reprogramming. This corresponds with a previously observed transition from a stochastic to a more deterministic signal

Discovery of MK-8318, a Potent and Selective CRTh2 Receptor Antagonist for the Treatment of Asthma

A novel series of tricyclic tetrahydroquinolines were identified as potent and selective CRTh2 receptor antagonists. The agonism and antagonism switch was achieved through structure-based drug design (SBDD) using a CRTh2 receptor homologue model. The challenge of very low exposures in pharmacokinetic studies was overcome by exhaustive medicinal chemistry lead optimization through focused SAR studies on the tricyclic core. Further optimization resulted in the identification of the preclinical candidate 4-(cyclopropyl­((3<i>aS</i>,9<i>R</i>,9<i>aR</i>)-7-fluoro-4-(4-(trifluoromethoxy)­benzoyl)-2,3,3<i>a</i>,4,9,9<i>a</i>-hexahydro-1<i>H</i>-cyclopenta­[<i>b</i>]­quinolin-9-yl)­amino)-4-oxobutanoic acid (<b>15c</b>, <b>MK-8318</b>) with potent and selective CRTh2 antagonist activity and a favorable PK profile suitable for once daily oral dosing for potential treatment of asthma