Pain in rheumatoid arthritis (RA) joints is the most significant symptom which is a multifactor process that may link to peripheral joint inflammation, disease progression, structural change, and the dysregulation of the central nervous system. In clinical practices, a ternate Chinese medicine (e.g. PAEONIAE RADIX ALBA/ 白芍, CHUANXIONG RHIZOMA/ 川芎, and CORYDALIS RHIZOMA/ 延胡索) can alleviate pain significantly. However, due to the complexity of multi-constituent, multi-compound, multi-target, and multi-regulation, the mechanism is not clear. In this study, targeting the RA pain pathways, bioinformatics and network pharmacology analysis were deployed to explore the pain alleviation mechanism. As a result, this study delivered a candidate mechanism network: 6 bioactive compounds of the ternate Chinese medicines inhibit 24 target proteins (e.g. IL4/6/1B, NFKB1/2, MAPK8, et a1) which also further regulate 14 downstream proteins (e.g., CXCLS, CCL2, TRAF6, et al.). The mechanism network was also validated with the RNA transcript microarray data with samples of a normal control group, joint pain group, and RA group.

Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods that addresses pairwise envy by the removal of at most one good. In the worst case, each pair of agents might require the (hypothetical) removal of a different good, resulting in a weak aggregate guarantee. We study allocations that are nearly envy-free in aggregate, and define a novel fairness notion based on information withholding. Under this notion, an agent can withhold (or hide) some of the goods in its bundle and reveal the remaining goods to the other agents. We observe that in practice, envy-freeness can be achieved by withholding only a small number of goods overall. We show that finding allocations that withhold an optimal number of goods is computationally hard even for highly restricted classes of valuations. In contrast to the worst-case results, our experiments on synthetic and real-world preference data show that existing algorithms for finding EF1 allocations withhold a close-to-optimal amount of information.