135 research outputs found

    Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

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    In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

    Expression characteristics of piRNAs in ovine luteal phase and follicular phase ovaries

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    PIWI-interacting RNAs (piRNAs), as a novel class of small non-coding RNAs that have been shown to be indispensable in germline integrity and stem cell development. However, the expressed characteristics and regulatory roles of piRNAs during different reproductive phases of animals remain unknown. In this study, we investigated the piRNAs expression profiles in ovaries of sheep during the luteal phase (LP) and follicular phase (FP) using the Solexa sequencing technique. A total of 85,219 and 1,27,156 piRNAs tags were identified in ovine ovaries across the two phases. Most expressed piRNAs start with uracil. piRNAs with a length of 24 nt or 27–29 nts accounted for the largest proportion. The obvious ping-pong signature appeared in the FP ovary. The piRNA clusters in the sheep ovary were unevenly distributed on the chromosomes, with high density on Chr 3 and 1. For genome distribution, piRNAs in sheep ovary were mainly derived from intron, CDS, and repeat sequence regions. Compared to the LP ovary, a greater number of expressed piRNA clusters were detected in the FP ovary. Simultaneously, we identified 271 differentially expressed (DE) piRNAs between LP and FP ovaries, with 96 piRNAs upregulated and 175 piRNAs downregulated, respectively. Functional enrichment analysis (GO and KEGG) indicated that their target genes were enriched in reproduction-related pathways including oocyte meiosis, PI3K-Akt, Wnt, and TGF-β signaling pathways. Together, our results highlighted the sequence and expression characteristics of the piRNAs in the sheep ovary, which will help us understand the roles of piRNAs in the ovine estrus cycle

    Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous Small Tail Han sheep (Ovis aries)

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    Background Long non-coding RNAs (lncRNAs) regulate endometrial secretion and uterine volume. However, there is little research on the role of lncRNAs in the uterus of Small Tail Han sheep (FecB++). Herein, RNA-seq was used to comparatively analyze gene expression profiles of uterine tissue between polytocous and monotocous sheep (FecB++) in follicular and luteal phases. Methods To identify lncRNA and mRNA expressed in the uterus, the expression of lncRNA and mRNA in the uterus of Small Tail Han sheep (FecB++) from the polytocous group (n = 6) and the monotocous group (n = 6) using RNA-sequencing and real-time polymerase chain reaction (RT-PCR). Identification of differentially expressed lncRNAs and mRNAs were performed between the two groups and two phases . Gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. LncRNA-mRNA co-expression network was constructed to further analyses the function of related genes. Results In the follicular phase, 473 lncRNAs and 166 mRNAs were differentially expressed in polytocous and monotocous sheep; in the luteal phase, 967 lncRNAs and 505 mRNAs were differentially expressed in polytocous and monotocous sheep. GO and KEGG enrichment analysis showed that the differentially expressed lncRNAs and their target genes are mainly involved in ovarian steroidogenesis, retinol metabolism, the oxytocin signaling pathway, steroid hormone biosynthesis, and the Foxo signaling pathway. Key lncRNAs may regulate reproduction by regulating genes involved in these signaling pathways and biological processes. Specifically, UGT1A1, LHB, TGFB1, TAB1, and RHOA, which are targeted by MSTRG.134747, MSTRG.82376, MSTRG.134749, MSTRG.134751, and MSTRG.134746, may play key regulatory roles. These results offer insight into molecular mechanisms underlying sheep prolificacy

    A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

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    One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO2, Cu2O, Ga2O3, Fe2O3, In2O3, CdO, CeO2, and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area

    Accurate molecular classification of cancer using simple rules

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    <p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p

    Role of MicroRNA in Governing Synaptic Plasticity

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    Although synaptic plasticity in neural circuits is orchestrated by an ocean of genes, molecules, and proteins, the underlying mechanisms remain poorly understood. Recently, it is well acknowledged that miRNA exerts widespread regulation over the translation and degradation of target gene in nervous system. Increasing evidence suggests that quite a few specific miRNAs play important roles in various respects of synaptic plasticity including synaptogenesis, synaptic morphology alteration, and synaptic function modification. More importantly, the miRNA-mediated regulation of synaptic plasticity is not only responsible for synapse development and function but also involved in the pathophysiology of plasticity-related diseases. A review is made here on the function of miRNAs in governing synaptic plasticity, emphasizing the emerging regulatory role of individual miRNAs in synaptic morphological and functional plasticity, as well as their implications in neurological disorders. Understanding of the way in which miRNAs contribute to synaptic plasticity provides rational clues in establishing the novel therapeutic strategy for plasticity-related diseases
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