A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units

Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region partition as a prerequisite, resulting in two main pitfalls. Firstly, location-based services necessitate ad-hoc regions for various purposes, requiring multiple ST models with varying scales and zones, which can be costly to support. Secondly, different ST models may produce conflicting outputs, resulting in confusing predictions. In this paper, we propose One4All-ST, a framework that can conduct ST prediction for arbitrary modifiable areal units using only one model. To reduce the cost of getting multi-scale predictions, we design an ST network with hierarchical spatial modeling and scale normalization modules to efficiently and equally learn multi-scale representations. To address prediction inconsistencies across scales, we propose a dynamic programming scheme to solve the formulated optimal combination problem, minimizing predicted error through theoretical analysis. Besides, we suggest using an extended quad-tree to index the optimal combinations for quick response to arbitrary modifiable areal units in practical online scenarios. Extensive experiments on two real-world datasets verify the efficiency and effectiveness of One4All-ST in ST prediction for arbitrary modifiable areal units. The source codes and data of this work are available at https://github.com/uctb/One4All-ST.Comment: Accepted by ICDE 202

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

Mesoporous silica nanoparticles (MSNs) containing vinyl-, propyl-, isobutyl- and phenyl functionalized monolayers were reported. These functionalized MSNs were prepared via molecular self-assembly of organosilanes on the mesoporous supports. The relative surface coverage of the organic monolayers can reach up to 100% (about 5.06 silanes/nm(2)). These monolayer functionalize MSNs were analyzed by a number of techniques including transmission electron microscope, fourier transform infrared spectroscopy, X-ray diffraction pattern, cross-polarized Si(29) MAS NMR spectroscopy, and nitrogen sorption measurement. The main elements (i.e., the number of absorbed water, the reactivity of organosilanes, and the stereochemistry of organosilane) that greatly affected the surface coverage and the quality of the organic functionalized monolayers on MSNs were fully discussed. The results show that the proper amount of physically absorbed water, the use of high active trichlorosilanes, and the functional groups with less steric hindrance are essential to generate MSNs with high surface coverage of monolayers

RETRACTED: LncRNA BLACAT1 Accelerates Non-small Cell Lung Cancer Through Up-Regulating the Activation of Sonic Hedgehog Pathway

Recently, increasing evidence has displayed that lncRNAs can exhibit crucial function in cancer progression, including lung cancer. LncRNA bladder cancer-associated transcript 1 (BLACAT1) is reported to participate in various cancers. The aim of our current study was to investigate the function of BLACAT1 in non-small cell lung cancer progression and study the functional pathway. Here, we reported BLACAT1 was significantly up-regulated in lung cancer tissues in comparison to the adjacent normal tissues, which suggested BLACAT1 might act as an oncogene in lung cancer. Then, A549 and PC9 cells were infected with BLACAT1 overexpression plasmid and shRNA. As shown, we proved up-regulation of BLACAT1 greatly induced the growth of non-small cell lung cancer cells. Reversely, knockdown of BLACAT1 reduced A549 and PC9 cell proliferation, migration and invasion. Sonic hedgehog (shh) signaling is able to exert a significant role in carcinogenesis, including lung cancer. Currently, we proved that up-regulation of BLACAT1 activated shh signaling pathway, via inducing shh, Gli-1 and Smo expression. shh pathway inhibitor GANT-61 reversed the effect of overexpression of BLACAT1 on non-small cell lung cancer. Moreover, we manifested that loss of BLACAT1 remarkably reduced the in vivo growth and metastasis of A549 cells via enhancing infiltrating CD3+ T cells. In conclusion, our research revealed a critical role of BLACAT1 in the modulation of non-small cell lung cancer via modulating shh pathway

Design of the Firstâ inâ Class, Highly Potent Irreversible Inhibitor Targeting the Meninâ MLL Proteinâ Protein Interaction

The structureâ based design of Mâ 525 as the firstâ inâ class, highly potent, irreversible smallâ molecule inhibitor of the meninâ MLL interaction is presented. Mâ 525 targets cellular menin protein at subâ nanomolar concentrations and achieves low nanomolar potencies in cell growth inhibition and in the suppression of MLLâ regulated gene expression in MLL leukemia cells. Mâ 525 demonstrates high cellular specificity over nonâ MLL leukemia cells and is more than 30 times more potent than its corresponding reversible inhibitors. Mass spectrometric analysis and coâ crystal structure of Mâ 525 in complex with menin firmly establish its mode of action. A single administration of Mâ 525 effectively suppresses MLLâ regulated gene expression in tumor tissue. An efficient procedure was developed to synthesize Mâ 525. This study demonstrates that irreversible inhibition of menin may be a promising therapeutic strategy for MLL leukemia.Irreversible inhibitor Mâ 525 targets the meninâ MLL interaction. It is demonstrated that irreversible inhibition of menin is a promising therapeutic strategy for the treatment of MLL leukemia and may have advantages over reversible inhibitors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141532/1/anie201711828.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141532/2/anie201711828-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141532/3/anie201711828_am.pd

Design of the Firstâ inâ Class, Highly Potent Irreversible Inhibitor Targeting the Meninâ MLL Proteinâ Protein Interaction

The structureâ based design of Mâ 525 as the firstâ inâ class, highly potent, irreversible smallâ molecule inhibitor of the meninâ MLL interaction is presented. Mâ 525 targets cellular menin protein at subâ nanomolar concentrations and achieves low nanomolar potencies in cell growth inhibition and in the suppression of MLLâ regulated gene expression in MLL leukemia cells. Mâ 525 demonstrates high cellular specificity over nonâ MLL leukemia cells and is more than 30 times more potent than its corresponding reversible inhibitors. Mass spectrometric analysis and coâ crystal structure of Mâ 525 in complex with menin firmly establish its mode of action. A single administration of Mâ 525 effectively suppresses MLLâ regulated gene expression in tumor tissue. An efficient procedure was developed to synthesize Mâ 525. This study demonstrates that irreversible inhibition of menin may be a promising therapeutic strategy for MLL leukemia.Der irreversible Inhibitor Mâ 525 greift an der Meninâ MLLâ Wechselwirkung an. Die irreversible Inhibition von Menin erweist sich als vielversprechende Strategie für die Behandlung von MLLâ Leukämie, mit möglichen Vorteilen gegenüber dem Einsatz reversibler Inhibitoren.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141701/1/ange201711828_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141701/2/ange201711828.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141701/3/ange201711828-sup-0001-misc_information.pd