A quick search method for audio signals based on a piecewise linear representation of feature trajectories

This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experiment results indicate significant improvements in search speed. For example the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 seconds from a 200-hour audio database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and Language Processin

Dynamic-segmentation-based feature dimension reduction for quick audio/video searching

We propose a new feature dimension reduction method for multimedia search. The main technique in the method is dynamic segmentation that partitions sequential feature trajectories dynamically. While dynamic segmentation reduces the average dimensionality and accelerates the search, it requires huge amount of calculation. Thus, our method quickly executes suboptimal partitioning of the trajectories by using the discreteness of dimension changes. This guarantees the optimal amount of calculation to derive the suboptimal partitioning under the condition that the dimension monotonously increases as the segment length increases. The experiment shows that our method is over 10 times faster than a straightforward dynamic segmentation method. 1

Dual Strands of Pre-miR-149 Inhibit Cancer Cell Migration and Invasion through Targeting FOXM1 in Renal Cell Carcinoma

Our recent studies revealed that dual strands of certain pre-microRNAs, e.g., pre-miR-144, pre-miR-145, and pre-miR-150, act as antitumor microRNAs (miRNAs) in several cancers. The involvement of passenger strands of miRNAs in cancer pathogenesis is a novel concept in miRNA research. The analysis of a miRNA expression signature in clear cell renal cell carcinoma (ccRCC) has revealed that the guide strand of pre-miR-149 is significantly downregulated in cancer tissues. The aims of this study were to investigate the functional significance of miR-149’s guide strand (miR-149-5p) and passenger strand (miR-149-3p), and to identify the oncogenic genes regulated by these miRNAs in ccRCC cells. The ectopic expression of these miRNAs significantly inhibited cancer cell migration and invasion in ccRCC cells. Forkhead box protein M1 (FOXM1) was directly regulated by miR-149-5p and miR-149-3p in ccRCC cells. Knockdown studies using si-FOXM1 showed that the expression of FOXM1 enhanced RCC cell aggressiveness. Interestingly, the analysis of a large number of patients in the The Cancer Genome Atlas (TCGA) database (n = 260) demonstrated that patients with high FOXM1 expression had significantly shorter survival than did those with low FOXM1 expression (p = 1.5 × 10-6). Taken together, dual strands of pre-miR-149 (miR-149-5p and miR-149-3p) acted as antitumor miRNAs through the targeting of FOXM1 in ccRCC cells

Fibrocystic breast disease with pleomorphic calcifications and segmental distribution: A case report

Fibrocystic breast disease is the most common benign condition and is important for differentiating breast cancer. We present the case of a 27-year-old female patient with pleomorphic calcifications and segmental distribution on mammography, which was highly suggestive of breast cancer; however, the pathological findings were fibrocystic disease. Although fibrocystic breast disease does not require treatment, appropriate follow-up is necessary after assessing the risk of breast cancer