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The existing research demonstrates that the link prediction algorithm which based on community similarity has better prediction performance than that of other node similarity-based methods, and it is more suitable for predicting the probability of the missing links between node-pairs with far distance. However, the disadvantage of these community similarity-based methods is the resolution of prediction accuracy is very low, which resulting in the existence probability of the missing links between node-pairs within a community or between a specific pair of communities is the same. In addition, the link prediction algorithms which based on multi-resolution community division can calculate the existence probability of missing links under different resolutions, but the relevance between communities had not taken into account, which makes it difficult to predict the existence probability of target links if the number of interconnections between communities is small. Combining the advantages of these two algorithms, we propose a more realistic link prediction model which based on a novel quasi-local community relevance index under multi-resolution community division. The performance of our algorithms is demonstrated by comparing with other well-known methods on two kinds of networks in different scales. The experiment results indicate that our approaches are very competitive.

Link prediction, whose primitive aim lies in remodeling or inferring link formations in complex networks, has been accepted as a fundamental study in understanding interactions between specific node pairs. To overcome the shortcomings of sparsity and intricacy in networks, an iterative method is proposed to comprehensively describe links in both local and global perspectives. From defining a truncated similarity matrix, local inherited properties in the network are maintained. With the refined similarity, a graph-regularized low-rank representation is provided to simultaneously preserve local and global structure information. Then, the representation is optimized to accurately predict link interactions in the network. Compared with the state-of-arts on real-world networks, the competitive experimental results demonstrate that our method is capable of effectively delineating interactions in multiple networks.

Polianthes tuberosa is a popular ornamental plant. Its floral scent volatiles mainly consist of terpenes and benzenoids that emit a charming fragrance. However, our understanding of the molecular mechanism responsible for the floral scent of P. tuberosa is limited. Using transcriptome sequencing and de novo assembly, a total of 228,706,703 high-quality reads were obtained, which resulted in the identification of 96,906 unigenes (SRA Accession Number SRP126470, TSA Acc. No. GGEA00000000). Approximately 41.85% of the unigenes were functionally annotated using public databases. A total of 4,694 differentially expressed genes (DEGs)were discovered during flowering. Gas chromatography-mass spectrometry analysis revealed that the majority of the volatiles comprised benzenoids and small amounts of terpenes. Homology analysis identified 13 and 17 candidate genes associated with terpene and benzenoid biosynthesis, respectively. Among these, PtTPS1, PtDAHPSs, PtPAL1, and PtBCMT2 might play important roles in regulating the formation of floral volatiles. The data generated by transcriptome sequencing provide a critical resource for exploring concrete characteristics as well as for supporting functional genomics studies. The results of the present study also lay the foundation for the elucidation of the molecular mechanism underlying the regulation of floral scents in monocots.

Magnetic fabrics indicate that the Xiaoban shear zone underwent low-grade hydrothermal alteration in the Mesozoic (~ 180Ma). Samples studied come from altered and unaltered units from three mine levels. (1) Susceptibility is mainly due to paramagnetic minerals such as biotite and pyrite. The reduction of mean susceptibility is due to decrease in Fe content and change of magnetic mineral size, as well as increase of diamagnetic minerals. (2) Anisotropy (P) decreases after hydrothermal alteration. Its tendency to change is the same as that of the mean susceptibility. (3) The AMS ellipsoid does not substantially change its shape after hydrothermal alteration. An oblate sphere is maintained. The minimum principal susceptibility axis (K3) falls close to the plane which is perpendicular to the schistosity for all samples. However, the maximum principal susceptibility axis is subject to large changes. The results indicate that new magnetic minerals are distributed on the existing schistosity plane, without a preferred orientation.

La zona de deformación de Xiaoban experimentó alteración hidrotermal a temperaturas moderadas en el Mesozoico (180 Ma). Las muestras estudiadas corresponden a unidades alteradas e inalteradas provenientes de tres niveles en la mina. Después de un estudio sistemático de las fábricas de rocas magnéticas encontramos que: 1) La susceptibilidad se debe principal- mente a la presencia de minerales paramagnéticos como la biotita y la pirita. La reducción de la susceptibilidad promedio se debe a la disminución de hierro y al cambio del tamaño del mineral magnético, así como al incremento de minerales diamagnéticos. 2) Decrecimiento de la anisotropía (P) después de la alteración hidrotermal. La tendencia al cambio es la misma que la de la susceptibilidad promedio. 3) El elipsoide de AMS no cambia sustancialmente su forma después de la alteración hidrotermal, y se mantiene una esfera oblada. El eje menor de susceptibilidad axial principal (K3) cae cerca del plano perpendicular al plano de equistosidad de todas las muestras. Sin embargo, el eje mayor de susceptibilidad axial principal está sujeto a grandes cambios. Los resultados indican que los nuevos minerales magnéticos están distribuidos en el plano existente de equistosidad, pero no tienen una orientación preferencial. doi: https://doi.org/10.22201/igeof.00167169p.2003.42.1.362

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.