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Ranking the nodes' ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.

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In some plants, exposure to stress can induce a memory response, which appears to play an important role in adaptation to recurrent stress environments. However, whether rice exhibits drought stress memory and the molecular mechanisms that might underlie this process have remained unclear. Here, we ensured that rice drought memory was established after cycles of mild drought and re-watering treatment, and studied gene expression by whole-transcriptome strand-specific RNA sequencing (ssRNA-seq). We detected 6,885 transcripts and 238 lncRNAs involved in the drought memory response, grouped into 16 distinct patterns. Notably, the identified genes of dosage memory generally did not respond to the initial drought treatment. Our results demonstrate that stress memory can be developed in rice under appropriate water deficient stress, and lncRNA, DNA methylation and endogenous phytohormones (especially abscisic acid) participate in rice short-term drought memory, possibly acting as memory factors to activate drought-related memory transcripts in pathways such as photosynthesis and proline biosynthesis, to respond to the subsequent stresses.

Aim  Fossil pollen spectra from lake sediments on the Tibetan Plateau have been used for qualitative climate reconstruction, but no modern pollen–climate calibration set based on lake sediments is available to infer past climate quantitatively. This study aims to develop such a dataset and apply it to fossil data. Location  The Tibetan Plateau, between 30 and 40° N and 87 and 103° E. Methods  We collected surface sediments from 112 lakes and analysed them palynologically. The lakes span a wide range of mean annual precipitation (Pann; 31–1022 mm), mean annual temperature (Tann; −6.5 to 1 °C), and mean July temperature (TJuly; 2.6–19.7 °C). Redundancy analysis showed that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Transfer functions for Pann, Tann and TJuly were developed with weighted averaging partial least squares. Model performance was assessed by leave‐one‐out cross‐validation. Results  The root mean square errors of prediction (RMSEP) were 104 mm (Pann), 1.18 °C (Tann) and 1.17 °C (TJuly). The RMSEPs, when expressed as percentages of the gradient sampled, were 10.6% (Pann), 15.7% (Tann) and 11.9% (TJuly). These low values indicate the good performance of our models. An application of the models to fossil pollen spectra covering the last c. 50 kyr yielded realistic results for Luanhaizi Lake in the Qilian Mountains on the north‐eastern Tibetan Plateau (modern Pann 480 mm; Tann−1 °C). Tann and Pann values similar to present ones were reconstructed for late Marine Isotope Stage 3, with minimum values for the Last Glacial Maximum (c. 300 mm and 2 °C below present), and maximum values for the early Holocene (c. 70 mm and 0.5 °C greater than present). Main conclusions  The modern pollen–climate calibration set will potentially be useful for quantitative climate reconstructions from lake‐sediment pollen spectra from the Tibetan Plateau, an area of considerable climatic and biogeographical importance.