Model diagnostics for all death events. by Xin Zhang (35492)

“…Results from the dual-pollutants model demonstrated that the effect of PM on the risk of RD mortality remained significant and slightly increased in magnitude. Moreover, composite pollutants exhibited a higher risk effect, reaching its peak after one week; however, there was a decrease in single-day cumulative effects as more pollutant types were included. …”

Model diagnostics for female death events. by Xin Zhang (35492)

“…Results from the dual-pollutants model demonstrated that the effect of PM on the risk of RD mortality remained significant and slightly increased in magnitude. Moreover, composite pollutants exhibited a higher risk effect, reaching its peak after one week; however, there was a decrease in single-day cumulative effects as more pollutant types were included. …”

Model diagnostics for male death events. by Xin Zhang (35492)

“…Results from the dual-pollutants model demonstrated that the effect of PM on the risk of RD mortality remained significant and slightly increased in magnitude. Moreover, composite pollutants exhibited a higher risk effect, reaching its peak after one week; however, there was a decrease in single-day cumulative effects as more pollutant types were included. …”

Acoustic Startle at 28 dpf. by Morgan Barnes (7876373)

“…(C) There is a significant decrease of PPI in the 48+ and 72 + fish (p < 0.0001). …”

PERCOM, an unbiased and easily-accessible protein-centric analysis workflow. by Brian Foo (19273132)

Downregulation of CD36 impaired phagocytic capacity and is associated with a proinflammatory phenotype of <i>Mst1</i> deficiency macrophages. by Jianyang Li (461703)

Alternative representation of daunorubicin fluorescence testing of novel inhibitors. by Lauren A. McCormick (20540638)

“…Note that for compound 78, the significance denotes a significant decrease in fluorescence.…”

Fig S1. by Leonardo Santos (477304)

“…Data are presented as mean ± SD (n = 4 per group). Statistical significance was determined using an unpaired t-test. …”

PCAIs disrupt actin filaments in NCI-H23 cells. by Matthew D. Gregory (19929096)

<i>japonicum</i> infection induces MST1 activation. by Jianyang Li (461703)

Lectin microarray analysis of T/F and chronic HIV-1. by Yiying Zhang (198443)

PCAIs suppress and disrupt the levels of vinculin punctates in NCI-H23 cells. by Matthew D. Gregory (19929096)

Mst1 knockout or suppressing its kinase activity increases the production of inflammatory mediators by activating the NF-κB pathway. by Jianyang Li (461703)

MST1was downregulated in macrophages from fibrotic livers of <i>S</i>. <i>japonicum</i>-infected mice and SEA-treated RAW264.7 cells. by Jianyang Li (461703)

PCAIs induce apoptosis in NCI-H23 cells. by Matthew D. Gregory (19929096)

Absolute β convergence results. by Ke Liu (121889)

“…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”

Conditional β convergence results. by Ke Liu (121889)

“…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”

Markov transition probability matrix (k = 4). by Ke Liu (121889)

“…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”

Markov transition probability matrix (k = 4). by Ke Liu (121889)

“…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”

Regression results. by Ke Liu (121889)

“…Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. …”