Tighter sum uncertainty relations via metric-adjusted skew information

In this paper, we first provide three general norm inequalities, which are used to give new uncertainty relations of any finite observables and quantum channels via metric-adjusted skew information. The results are applicable to its special cases as Wigner-Yanase-Dyson skew information. In quantifying the uncertainty of channels, we discuss two types of lower bounds and compare the tightness between them, meanwhile, a tight lower bound is given. The uncertainty relations obtained by us are stronger than the existing ones. To illustrate our results, we give several specific examples.Comment: 17 pages, 4 figure

Tighter monogamy relations in multiparty quantum systems

We investigate tight monogamy relations of multiparty quantum entanglement for any quantum state in this paper. First, we obtain a class of lower bounds for multiparty quantum systems which improve the previous results. Next, we establish a class of tighter monogamy relations in tripartite quantum systems by means of the new inequality. Furthermore, we generalize this relations to multiparty quantum systems. And then we prove the lower bounds we obtained are larger than the existing ones. Detailed examples are provided at last.Comment: 11 pages, 2 figure

Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

In the past decade, the heterogeneous information network (HIN) has become an important methodology for modern recommender systems. To fully leverage its power, manually designed network templates, i.e., meta-structures, are introduced to filter out semantic-aware information. The hand-crafted meta-structure rely on intense expert knowledge, which is both laborious and data-dependent. On the other hand, the number of meta-structures grows exponentially with its size and the number of node types, which prohibits brute-force search. To address these challenges, we propose Genetic Meta-Structure Search (GEMS) to automatically optimize meta-structure designs for recommendation on HINs. Specifically, GEMS adopts a parallel genetic algorithm to search meaningful meta-structures for recommendation, and designs dedicated rules and a meta-structure predictor to efficiently explore the search space. Finally, we propose an attention based multi-view graph convolutional network module to dynamically fuse information from different meta-structures. Extensive experiments on three real-world datasets suggest the effectiveness of GEMS, which consistently outperforms all baseline methods in HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation metrics. More importantly, we conduct an in-depth analysis on the identified meta-structures, which sheds light on the HIN based recommender system design.Comment: Published in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20

Huntingtin forms toxic NH2-terminal fragment complexes that are promoted by the age-dependent decrease in proteasome activity

Although NH2-terminal mutant huntingtin (htt) fragments cause neurological disorders in Huntington's disease (HD), it is unclear how toxic htt fragments are generated and contribute to the disease process. Here, we report that complex NH2-terminal mutant htt fragments smaller than the first 508 amino acids were generated in htt-transfected cells and HD knockin mouse brains. These fragments constituted neuronal nuclear inclusions and appeared before neurological symptoms. The accumulation and aggregation of these htt fragments were associated with an age-dependent decrease in proteasome activity and were promoted by inhibition of proteasome activity. These results suggest that decreased proteasome activity contributes to late onset htt toxicity and that restoring the ability to remove NH2-terminal fragments will provide a more effective therapy for HD than inhibiting their production

Genome-wide identification and expression analysis of 3-ketoacyl-CoA synthase gene family in rice (Oryza sativa L.) under cadmium stress

3-Ketoacyl-CoA synthase (KCS) is the key rate-limiting enzyme for the synthesis of very long-chain fatty acids (VLCFAs) in plants, which determines the carbon chain length of VLCFAs. However, a comprehensive study of KCSs in Oryza sativa has not been reported yet. In this study, we identified 22 OsKCS genes in rice, which are unevenly distributed on nine chromosomes. The OsKCS gene family is divided into six subclasses. Many cis-acting elements related to plant growth, light, hormone, and stress response were enriched in the promoters of OsKCS genes. Gene duplication played a crucial role in the expansion of the OsKCS gene family and underwent a strong purifying selection. Quantitative Real-time polymerase chain reaction (qRT-PCR) results revealed that most KCS genes are constitutively expressed. We also revealed that KCS genes responded differently to exogenous cadmium stress in japonica and indica background, and the KCS genes with higher expression in leaves and seeds may have functions under cadmium stress. This study provides a basis for further understanding the functions of KCS genes and the biosynthesis of VLCFA in rice

A cross-cultural study of domestic luminous environment in the United Kingdom and Japan

Abstract not available

Description of Scheffersomyces henanensis sp. nov., a new D-xylose-fermenting yeast species isolated from rotten wood.

Two strains of a D-xylose-fermenting yeast species were isolated from rotten wood samples collected from the Baotianman Nature Reserve in Henan Province, central China. These strains formed hat-shaped ascospores in conjugated and deliquescent asci. Multilocus phylogenetic analysis that included the nearly complete small subunit (SSU), the internal transcribed spacer (ITS) region and the D1/D2 domain of the large subunit (LSU) rRNA genes, as well as RNA polymerase II largest subunit (RPB1) gene demonstrated that the two strains represent a novel yeast species closely related to Scheffersomyces segobiensis. A sequence comparison of xylose reductase (XYL1) gene, which was recently recommended for rapid identification of cryptic species in the Scheffersomyces clade, revealed a significant sequence divergence of 25 nucleotides between the novel strains and their closest relative S. segobiensis, supporting their classification as a distinct species. Furthermore, these new strains can be clearly distinguished from S. segobiensis by a number of morphological and physiological characteristics. Therefore, a novel yeast species, Scheffersomyces henanensis sp. nov., is proposed to accommodate these strains. The type strain is BY-41T ( =  CICC 1974T  =  CBS 12475T)

Getting Back on Track: Understanding COVID-19 Impact on Urban Mobility and Segregation with Location Service Data

Understanding the impact of COVID-19 on urban life rhythms is crucial for accelerating the return-to-normal progress and envisioning more resilient and inclusive cities. While previous studies either depended on small-scale surveys or focused on the response to initial lockdowns, this paper uses large-scale location service data to systematically analyze the urban mobility behavior changes across three distinct phases of the pandemic, i.e., pre-pandemic, lockdown, and reopen. Our analyses reveal two typical patterns that govern the mobility behavior changes in most urban venues: daily life-centered urban venues go through smaller mobility drops during the lockdown and more rapid recovery after reopening, while work-centered urban venues suffer from more significant mobility drops that are likely to persist even after reopening. Such mobility behavior changes exert deeper impacts on the underlying social fabric, where the level of mobility reduction is positively correlated with the experienced segregation at that urban venue. Therefore, urban venues undergoing more mobility reduction are also more filled with people from homogeneous socio-demographic backgrounds. Moreover, mobility behavior changes display significant heterogeneity across geographical regions, which can be largely explained by the partisan inclination at the state level. Our study shows the vast potential of location service data in deriving a timely and comprehensive understanding of the social dynamic in urban space, which is valuable for informing the gradual transition back to the normal lifestyle in a “post-pandemic era”