Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target-dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.
Autistic spectral disorder (ASD) is a prevalent neurodevelopmental disease that affects multiple brain regions. Both clinical and animal studies have revealed the possible involvement of the cerebellum in ASD pathology. In this study, we generated a rodent ASD model through a single prenatal administration of valproic acid (VPA) into pregnant mice, followed by cerebellar morphological and functional studies of the offspring. Behavioral studies showed that VPA exposure led to retardation of critical motor reflexes in juveniles and impaired learning in a tone-conditioned complex motor task in adults. These behavioral phenotypes were associated with premature migration and excess apoptosis of the granular cell (GC) precursor in the cerebellar cortex during the early postnatal period, and the decreased cell density and impaired dendritic arborization of the Purkinje neurons. On acute cerebellar slices, suppressed synaptic transmission of the Purkinje cells were reported in the VPA-treated mice. In summary, converging evidence from anatomical, electrophysiological and behavioral abnormalities in the VPA-treated mice suggest cerebellar pathology in ASD and indicate the potential values of motor dysfunction in the early diagnosis of ASD.
With the advent of the powerful editing software and sophisticated digital cameras, it is now possible to manipulate images. Copy-move is one of the most common methods for image manipulation. Several methods have been proposed to detect and locate the tampered regions, while many methods failed when the copied region undergone some geometric transformations before being pasted, because of the de-synchronization in the searching procedure. This paper presents an efficient technique for detecting the copy-move forgery under geometric transforms. Firstly, the forged image is divided into overlapping circular blocks, and Polar Complex Exponential Transform (PCET) is employed to each block to extract the invariant features, thus, the PCET kernels represent each block. Secondly, the Approximate Nearest Neighbor (ANN) Searching Problem is used for identifying the potential similar blocks by means of locality sensitive hashing (LSH). In order to make the algorithm more robust, morphological operations are applied to remove the wrong similar blocks. Experimental results show that our proposed technique is robust to geometric transformations with low computational complexity.
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