Extracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity

The Pichia pastoris transmembrane protein GT1 is a glycerol transporter and relieves the repression of glycerol on AOX1 expression

Promoter of alcohol oxidase I (PAOX1) is the most efficient promoter involved in the regulation of recombinant protein expression in Pichia pastoris (P. pastoris). PAOX1 is tightly repressed by the presence of glycerol in the culture medium; thus, glycerol must be exhausted before methanol can be taken up by P. pastoris and the expression of the heterologous protein can be induced. In this study, a candidate glycerol transporter (GT1, GeneID: 8197545) was identified, and its role was confirmed by further studies (e.g. bioinformatics analysis, heterologous complementation in Schizosaccharomyces pombe (S. pombe)). When GT1 is co-expressed with enhanced green fluorescent protein (EGFP), it localizes to the membrane and S. pombe carrying gt1 but not the wild-type strain can grow on medium containing glycerol as the sole carbon source. The present study is the first to report that AOX1 in the X-33gt1 mutant can achieve constitutive expression in medium containing glycerol; thus, knocking down gt1 can eliminate the glycerol repression of PAOX1 in P. pastoris. These results suggest that the glycerol transporter may participate in the process of PAOX1 inhibition in glycerol medium

SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity

To address the challenge of increasing network size, researchers have developed sparse models through network pruning. However, maintaining model accuracy while achieving significant speedups on general computing devices remains an open problem. In this paper, we present a novel mobile inference acceleration framework SparseByteNN, which leverages fine-grained kernel sparsity to achieve real-time execution as well as high accuracy. Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning. It designs multiple sparse patterns for different operators. Combined with our proposed whole network rearrangement strategy, the schema achieves a high compression rate and high precision at the same time. (b) Inference engine co-optimized with the sparse pattern. The conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for ARM and WebAssembly. Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet-v1 outperform strong dense baselines on the efficiency-accuracy curve. Experimental results on Qualcomm 855 show that for 30% sparse MobileNet-v1, SparseByteNN achieves 1.27x speedup over the dense version and 1.29x speedup over the state-of-the-art sparse inference engine MNN with a slight accuracy drop of 0.224%. The source code of SparseByteNN will be available at https://github.com/lswzjuer/SparseByteN

Interferon-γ-Induced Intestinal Epithelial Barrier Dysfunction by NF-κB/HIF-1α Pathway

Interferon-? (IFN-?) plays an important role in intestinal barrier dysfunction. However, the mechanisms are not fully understood. As hypoxia-inducible factor-1 (HIF-1) is a critical determinant response to hypoxia and inflammation, which has been shown to be deleterious to intestinal barrier function, we hypothesized that IFN-? induces loss of barrier function through the regulation of HIF-1α activation and function. In this study, we detected the expressions of HIF-1α and tight junction proteins in IFN-?-treated T84 intestinal epithelial cell line. IFN-? led to an increase of HIF-1α expression in time- and dose-dependent manners but did not change the expression of HIF-1?. The IFN-?-induced increase in HIF-1α was associated with an activation of NF-?B. Treatment with the NF-?B inhibitor, pyrolidinedithiocarbamate (PDTC), significantly suppressed the activation of NF-?B and the expression of HIF-1α. In addition, IFN-? also increased intestinal epithelial permeability and depletion of tight junction proteins; inhibition of NF-?B or HIF-1α prevented the increase in intestinal permeability and alteration in tight junction protein expressions. Interestingly, we demonstrated that a significant portion of IFN-? activation NF-kB and modulation tight junction expression is mediated through HIF-1α. Taken together, this study suggested that IFN-? induced the loss of epithelial barrier function and disruption of tight junction proteins, by upregulation of HIF-1α expression through NF-?B pathway.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140108/1/jir.2013.0044.pd

Solution-Processed Efficient Nanocrystal Solar Cells Based on CdTe and CdS Nanocrystals

Solution-processed CdTe nanocrystals solar cells have attracted much attention due to their low cost, low material consumption, and potential for roll-to-roll production. Among all kinds of semiconductor materials, CdS exhibits the lowest lattice mismatch with CdTe, which permits high junction quality and high device performance. In this study, high quality CdS nanocrystals were prepared by a non-injection technique with tetraethylthiuram disufide and 2,2′-dithiobisbenzothiazole as the stabilizers. Based on the CdTe and CdS nanocrystals, devices with the architecture of ITO/ZnO/CdS/CdTe/MoOx/Au were fabricated successfully by a solution process under ambient condition. The effects of annealing conditions, film thickness, and detailed device structure on the CdTe/CdS nanocrystal solar cells were investigated and discussed in detail. We demonstrate that high junction quality can be obtained by using CdS nanocrystal thin film compared to traditional CdS film via chemical bath deposition (CBD). The best device had short circuit current density (Jsc), open circuit voltage (Voc) and fill factor (FF) of 17.26 mA/cm2, 0.56 V, and 52.84%, respectively, resulting in a power conversion efficiency (PCE) of 5.14%, which is significantly higher than that reported using CBD CdS as the window layer. This work provides important suggestions for the further improvement of efficiency in CdTe nanocrystal solar cells