Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.

Environmental remediation by employing visible-light-active semiconductor heterostructures provides effective solutions for handling emerging contaminants by a much greener and lower cost approach compared with other methods. This report demonstrates that the in situ growth of nanosized single-crystal-like defective anatase TiO mesocrystals (DTMCs) on g-C N nanosheets (NSs) can produce a 3D/2D DTMC/g-C N NS heterostructure with the two components held together by chemical bonds to form tight interfaces. This nanostructured heterostructure displayed remarkably improved photocatalytic activity toward the removal of the model pollutants Methyl Orange (MO) and Cr under visible-light irradiation in comparison with the pristine DTMC and g-C N NS components, which suggests that both the oxidation and reduction abilities of the DTMC/g-C N NSs were simultaneously enhanced after fabrication. On the basis of the results of a systematic characterization, a reasonable mechanism for the photocatalytic activity based on a direct Z-scheme heterojunction is proposed and further verified by the measurement of OH. This novel Z-scheme heterojunction endows the heterostructure with improved photogenerated electron/hole pair separation and a strong redox ability for the efficient degradation of wastewater pollutants. This work will be useful for the design and fabrication of direct Z-scheme heterostructured photocatalysts with novel architectures for applications in energy conversion and environmental remediation.

The polycomb group (PcG) proteins are key epigenetic regulators in stem cell maintenance. PcG proteins have been thought to act through one of two polycomb repressive complexes (PRCs), but more recent biochemical analyses have challenged this model in the identification of noncanonical PRC1 (nc-PRC1) complexes characterized by the presence of Rybp or Yaf2 in place of the canonical Chromobox proteins. However, the biological significance of these nc-PRC1s and the potential mechanisms by which they mediate gene repression are largely unknown. Here, we explore the functional consequences of Yaf2 disruption on stem cell regulation. We show that deletion of Yaf2 results in compromised proliferation and abnormal differentiation of mouse embryonic stem cells (mESCs). Genome-wide profiling indicates Yaf2 functions primarily as a transcriptional repressor, particularly impacting genes associated with ectoderm cell fate in a manner distinct from Rybp. We confirm that Yaf2 assembles into a noncanonical PRC complex, with deletion analysis identifying the region encompassing amino acid residues 102-150 as required for this assembly. Furthermore, we identified serine 166 as a Yaf2 phosphorylation site, and we demonstrate that mutation of this site to alanine (S166A) compromises Ring1B-mediated H2A monoubiquitination and in turn its ability to repress target gene expression. We therefore propose that Yaf2 and its phosphorylation status serve as dual regulators to maintain the pluripotent state in mESCs.

Background. Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods. There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results. The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion. The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.

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Abstract. In the humid and semi-humid regions of China, tree-ring-width (TRW) chronologies offer limited moisture-related climatic information. To gather additional climatic information, it would be interesting to explore the potential of the intra-annul tree-ring-width indices (i.e., the earlywood width, EWW, and latewood width, LWW). To achieve this purpose, TRW, EWW, and LWW were measured from the tree-ring samples of Pinus tabuliformis originating from the semi-humid eastern Qinling Mountains, central China. Standard (STD) and signal-free (SSF) chronologies of all parameters were created using these detrending methods including (1) negative exponential functions combined with linear regression with negative (or zero) slope (NELR), (2) cubic smoothing splines with a 50 % frequency cutoff at 67 % of the series length (SP67), and (3) age-dependent splines with an initial stiffness of 50 years (SPA50). The results showed that EWW chronologies were significantly negatively correlated with temperature but positively correlated with precipitation and soil moisture conditions during the current early-growing season. By contrast, LWW and TRW chronologies had weaker relationships with these climatic factors. The strongest climatic signal was detected for the EWW STD chronology detrended with the NELR method, explaining 50 % of the variance in the May–July self-calibrated Palmer Drought Severity Index (MJJ scPDSI) during the instrumental period 1953–2005. Based on this relationship, the MJJ scPDSI was reconstructed back to 1868 using a linear regression function. The reconstruction was validated by comparison with other hydroclimatic reconstructions and historical document records from adjacent regions. Our results highlight the potential of intra-annual tree-ring indices for reconstructing seasonal hydroclimatic variations in humid and semi-humid regions of China. Furthermore, our reconstruction exhibits a strong in-phase relationship with a newly proposed East Asian summer monsoon index (EASMI) before the 1940s on the decadal and longer timescales, which may be due to the positive response of the local precipitation to EASMI. Nonetheless, the cause for the weakened relationship after the 1940s is complex, and cannot be solely attributed to the changing impacts of precipitation and temperature.

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time.