Objective] To investigate the mechanism of action of Zuogui Pill in treating recurrent spontaneous abortion using network pharmacology. [Methods] Zuogui Pill components and their respective molecular targets were identified from TCMSP, HERB, and PubChem databases. A component-target network was constructed and visualized using Cytoscape 3.10.3. Disease targets related to Recurrent Spontaneous Abortion (RSA) were retrieved from OMIM, DisGeNET, and GeneCards, and intersected with drug targets to identify common targets. A PPI network was constructed via STRING database, followed by GO and KEGG enrichment analyses of shared targets using$\mathbf{R}$software. [Results] A total of$\mathbf{6 3}$active ingredients from Zuogui Pill and 109 RSA-related targets were identified. PPI network analysis revealed 13 key targets. [Conclusion] This study suggests that quercetin is the main active component of Zuogui Pill, acting on targets such as AKT1, IL6, IL1B, TP53, and TNF.

OBJECTIVE: Based on network pharmacology, the mechanism of action of the combination of plum and licorice in treating allergic asthma was explored. METHODS: 134 common targets were identified. Molecular docking confirmed strong binding affinity between the top 10 key targets and active ingredients. RESULTS: Analysis revealed$\mathbf{1 3 4}$common targets between the herb-pair and the disease. Furthermore, molecular docking analysis demonstrated that the top 10 key targets exhibited favorable binding energies with the active ingredients. CONCLUSION:$\boldsymbol{\beta}$-Sitosterol is a primary active ingredient, with TP53 as its direct target. Key therapeutic targets (e.g., STAT3, TP53, IL6, TNF, ESR1, MAPK1, AKT1, MAPK3) suggest the mechanism involves suppressing airway inflammation, modulating immune balance, and alleviating airway remodeling.

Deduplication compresses backup data by identifying and removing duplicate blocks. However, deduplication cannot detect when two blocks are very similar, which opens up opportunities for further data reduction using delta compression. Most existing works find similar blocks by characterizing each block by a set of features and matching similar blocks using coarse-grained super-features. If two blocks share a super-feature, delta compression only needs to store their delta for the new block.

Delta compression attracts many researchers' interest for its high efficiency in eliminating redundant data. It identifies a similar block for the incoming block and stores only the differences between them. The key challenge lies in detecting suitable similar blocks. Existing approaches have their limitations. Hash-based solutions like NTransform miss many similar blocks due to the high similarity detection threshold, while complex-threshold solutions like DeepSketch and Palantir have high computation overhead.

Coflow scheduling is critical to data-parallel applications in data centers. While schemes like Varys can achieve optimal performance, they require a priori information about coflows which is hard to obtain in practice. Existing non-clairvoyant solutions like Aalo generalize least attained service (LAS) scheduling discipline to address this issue. However, they fail to identify the bottleneck flows in a coflow and tend to allocate excessive bandwidth to the non-bottleneck flows, leading to bandwidth wastage and inferior overall performance. To this end, we present Fai that strives to improve the overall coflow performance by accelerating the bottleneck flows without priori knowledge. Fai employs bottleneck-aware scheduling. It adopts loose coordination to update coflow priority and flow rates based on total bytes sent. In addition, Fai detects bottleneck flows based on a flow’s rate and bytes sent, and de-allocates bandwidth for other flows to match the bottleneck rate without affecting the coflow completion time (CCT). The saved bandwidth is then distributed among coflows according to their priority to improve overall performance. Testbed evaluation on a 40-node cluster shows that Fai improves average (P95) CCT by 1.73× (3.43×), compared to Aalo. Large-scale trace-driven simulations also show that Fai outperforms Aalo substantially.