No abstract

Thyroid cancer is the most prevalent malignant tumor of the endocrine organs and accounts for one third of all head and neck tumors. Dysregulation of microRNAs is well‑known to contribute to the development of various cancers, including papillary thyroid carcinoma (PTC), which accounts for 80‑90% of all thyroid cancer cases. The present study aimed to investigate the expression, functional roles of microRNA‑150 (miR‑150) and its direct target gene in PTC. miR‑150 expression in PTC tissues and cell lines was analyzed by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). After transfection with miR‑150 mimics, cell proliferation, migration and invasion was analyzed by MTT and Transwell assays, respectively. Bioinformatics analysis was performed to investigate the potential target genes of miR‑150, which were then confirmed by luciferase reporter assay, RT‑qPCR and western blotting. Functional assays were also applied to investigate the effects of endogenous Rho‑associated protein kinase 1 (ROCK1) in PTC. miR‑150 was demonstrated to be significantly downregulated in PTC tissues and cell lines. In addition, reduced miR‑150 expression was obviously correlated with TNM stage and lymph node metastasis in PTC patients. Restoration of miR‑150 expression significantly inhibited PTC cell proliferation, migration and invasion in vitro. Furthermore, ROCK1 was identified as a direct target gene of miR‑150. Therefore, ROCK1 knockdown may serve tumor suppressive functions in PTC, induced by miR‑150 overexpression. In conclusion, miR‑150 overexpression in PTC may inhibit growth and metastasis of PTC cells. miR‑150/ROCK1‑based targeted therapy may be a potential strategy for the treatment of PTC.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches.

There have been many studies in the literature on social group recognition of crowds of pedestrians. However, most of these studies have approached the problem from a static point of view. A study on the dynamic property of social groups among people over time can provide significant insight into human behaviors and events. Inspired by sociological models of human collective behavior, in this work, we present a framework for characterizing hierarchical social groups based on evolving tracklet interaction network (ETIN) where the tracklets of pedestrians are represented as nodes and the their grouping behaviors are captured by the edges with associated weights. We use non-overlapping snapshots of the interaction network and develop the framework for a unified dynamic group identification and tracklet association. The approach is evaluated quantitatively and qualitatively on videos of pedestrian scenes where manually labeled ground-truth is given. The results of our approach are consistent to human-perceived dynamic social groups of the crowd. The performance analysis of our method shows that the approach is scalable and it provides situational awareness in a real-world scenarios.Index Terms-Dynamic social grouping behavior, pedestrian social groups, tracklet interaction network. 1932-4553

No abstract

No abstract

This paper introduces a novel approach that allows the retrieval of complex images by integrating visual and semantic concepts. The basic idea consists of three aspects. First, we measure the relatedness of semantic and visual concepts and select the visually separable semantic concepts as elements in the proposed image signature representation. Second, we demonstrate the existence of concept co-occurrence patterns. We propose to uncover those underlying patterns by detecting the communities in a network structure. Third, we leverage the visual and semantic correspondence and the co-occurrence patterns to improve the accuracy and efficiency for image retrieval. We perform experiments on two popular datasets that confirm the effectiveness of our approach.

No abstract