Fluorescence in situ hybridization mapping of the cystic fibrosis transmembrane conductance regulator (CFTR) gene to 7q31.3

We have used the fluorescence in situ hybridization (FISH) technique to refine the localization of the cystic fibrosis transmembrane conductance regulator (CFTR) gene on human chromosome 7. The result shows that the gene is most likely located within band q31.3.published_or_final_versio

Localization of the human gene encoding the 13.3-kDa subunit of mitochondrial complex III (UQCRB) to 8q22 by in situ hybridization

We have localized the human gene encoding the 13.3-kDa subunit of mitochondrial complex III (UQCRB) to chromosome 8 using both radioactive in situ hybridization and fluorescence in situ hybridization. The additional peak obtained with the former method is attributed to the higher sensitivity of this technique, which results in hybridization of the probe to the less conserved pseudogene. We therefore conclude that the functional gene is most likely located at 8q22.published_or_final_versio

Refined localization of the asparagine synthetase gene (ASNS) to chromosome 7, region q21.3, and characterization of the somatic cell hybrid line 4AF/106/K015

We have mapped the asparagine synthetase gene (ASNS) to 7q21.3 by fluorescence in situ hybridization. While this study refined the localization of the gene, it also revealed a rearrangement in a somatic cell hybrid line which was used in previous ASNS mapping. Using additional probes from other regions of human chromosome 7, we showed that this cell line (4AF/106/KO15) contained a rearranged chromosome 7 in which a segment of the long arm was apparently duplicated and inserted into the short arm. Caution should be used therefore when interpreting data obtained from this cell line for gene mapping studies.published_or_final_versio

Effects of annealing temperature on the characteristics of Ga-doped ZnO film metal-semiconductor-metal ultraviolet photodetectors

published_or_final_versio

Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.Comment: Accepted by ECCV 2020. Code is available at https://github.com/spyflying/LSCM-Refse

Outlier Edge Detection Using Random Graph Generation Models and Applications

Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques.Comment: 14 pages, 5 figures, journal pape

Cold Gas in Massive Galaxies as a Critical Test of Black Hole Feedback Models

Black hole feedback has been widely implemented as the key recipe to quench star formation in massive galaxies in modern semi-analytic models and hydrodynamical simulations. As the theoretical details surrounding the accretion and feedback of black holes continue to be refined, various feedback models have been implemented across simulations, with notable differences in their outcomes. Yet, most of these simulations have successfully reproduced some observations, such as stellar mass function and star formation rate density in the local Universe. We use the recent observation on the change of neutral hydrogen gas mass (including both ${\rm H_2}$ and ${\rm HI}$) with star formation rate of massive central disc galaxies as a critical constraint of black hole feedback models across several simulations. We find that the predictions of IllustrisTNG agree with the observations much better than the other models tested in this work. This favors IllustrisTNG's treatment of active galactic nuclei - where kinetic winds are driven by black holes at low accretion rates - as more plausible amongst those we test. In turn, this also indirectly supports the idea that the massive central disc galaxy population in the local Universe was likely quenched by AGN feedback

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking

Cold Gas in Massive Galaxies as a Critical Test of Black Hole Feedback Models

Black hole feedback has been widely implemented as the key recipe to quench star formation in massive galaxies in modern semi-analytic models and hydrodynamical simulations. As the theoretical details surrounding the accretion and feedback of black holes continue to be refined, various feedback models have been implemented across simulations, with notable differences in their outcomes. Yet, most of these simulations have successfully reproduced some observations, such as stellar mass function and star formation rate density in the local Universe. We use the recent observation on the change of neutral hydrogen gas mass (including both ${\rm H_2}$ and ${\rm HI}$) with star formation rate of massive central disc galaxies as a critical constraint of black hole feedback models across several simulations. We find that the predictions of IllustrisTNG agree with the observations much better than the other models tested in this work. This favors IllustrisTNG's treatment of active galactic nuclei - where kinetic winds are driven by black holes at low accretion rates - as more plausible amongst those we test. In turn, this also indirectly supports the idea that the massive central disc galaxy population in the local Universe was likely quenched by AGN feedback