The scaling exponent of the monthly mean temperatures at different time periods from 1909–2014 for the three stations.

<p>The scaling exponent of the monthly mean temperatures at different time periods from 1909–2014 for the three stations.</p

Annual mean temperature(units:°C) time series from 1909–2014 at (a) Harbin; (b) Changchun; (c) Shenyang.

<p>Annual mean temperature(units:°C) time series from 1909–2014 at (a) Harbin; (b) Changchun; (c) Shenyang.</p

Warming rate of the monthly mean temperatures (units:°C/100a) from 1909–2014 at (a) Harbin; (b) Changchun; (c) Shenyang.

Warming rate of the monthly mean temperatures (units:°C/100a) from 1909–2014 at (a) Harbin; (b) Changchun; (c) Shenyang.</p

The long-range correlation and evolution law of centennial-scale temperatures in Northeast China

<div><p>This paper applies the detrended fluctuation analysis (DFA) method to investigate the long-range correlation of monthly mean temperatures from three typical measurement stations at Harbin, Changchun, and Shenyang in Northeast China from 1909 to 2014. The results reveal the memory characteristics of the climate system in this region. By comparing the temperatures from different time periods and investigating the variations of its scaling exponents at the three stations during these different time periods, we found that the monthly mean temperature has long-range correlation, which indicates that the temperature in Northeast China has long-term memory and good predictability. The monthly time series of temperatures over the past 106 years also shows good long-range correlation characteristics. These characteristics are also obviously observed in the annual mean temperature time series. Finally, we separated the centennial-length temperature time series into two time periods. These results reveal that the long-range correlations at the Harbin station over these two time periods have large variations, whereas no obvious variations are observed at the other two stations. This indicates that warming affects the regional climate system’s predictability differently at different time periods. The research results can provide a quantitative reference point for regional climate predictability assessment and future climate model evaluation.</p></div

The scaling exponent of the monthly mean temperature from 1909–2014 at the three stations: (a) Harbin; (b) Changchun; (c) Shenyang.

<p>The scaling exponent of the monthly mean temperature from 1909–2014 at the three stations: (a) Harbin; (b) Changchun; (c) Shenyang.</p

The location of the three stations.

<p>The location of the three stations.</p

The scaling rate of the monthly mean temperatures during 1909–2014 at the three stations: (a) Harbin; (b) Changchun; (c) Shenyang.

<p>The scaling rate of the monthly mean temperatures during 1909–2014 at the three stations: (a) Harbin; (b) Changchun; (c) Shenyang.</p

The long-range correlation of the annual mean temperatures from 1909–2014 for the stations at (a) Harbin; (b) Changchun; (c) Shenyang.

<p>The long-range correlation of the annual mean temperatures from 1909–2014 for the stations at (a) Harbin; (b) Changchun; (c) Shenyang.</p

Data_Sheet_1_Advances and perspectives on perylenequinone biosynthesis.docx

Under illumination, the fungal secondary metabolites, perylenequinones (PQs) react with molecular oxygen to generate reactive oxygen species (ROS), which, in excess can damage cellular macromolecules and trigger apoptosis. Based on this property, PQs have been widely used as photosensitizers and applied in pharmaceuticals, which has stimulated research into the discovery of new PQs and the elucidation of their biosynthetic pathways. The PQs-associated literature covering from April 1967 to September 2022 is reviewed in three sections: (1) the sources, structural diversity, and biological activities of microbial PQs; (2) elucidation of PQ biosynthetic pathways, associated genes, and mechanisms of regulation; and (3) advances in pathway engineering and future potential strategies to modify cellular metabolism and improve PQ production.</p

Supplementary Fig. S1-S8 from LIN28/<i>let-7</i>/PD-L1 Pathway as a Target for Cancer Immunotherapy

Figure S1.Inverse correlation between let-7e and PD-L1 expression. Figure S2.Let-7 suppresses PD-L1 expression. Figure S3.LIN28 mediates PD-L1 expression and does not directly promote PD-L1 transcription. Figure S4.C1632 suppresses PD-L1 expression in mouse cancer cells. Figure S5.C1632 suppresses PD-L1 expression and this inhibitory effect is rescued by LIN28 overexpression. Figure S6.LIN28 inhibitor suppresses cell growth in a variety of cancer cell lines. Figure S7.Depletion of PD-L1 by shRNA suppresses the proliferation of TUBO cancer cells. Figure S8.LIN28/let-7 axis regulates PD-L1 expression of THP-1 macrophages.</p