BackgroundMicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.ResultsA comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.ConclusionsA 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.
The aim of this study was to analyze the relationship between intervertebral disc degeneration and low back pain (LBP). Rat L4/5 disc degeneration model was established by annular puncture using a 0.4 mm needle anteriorly or posteriorly. In both anterior and posterior puncture models, magnetic resonance imaging (MRI) and histological analyses revealed marked disc degeneration 2 weeks after puncture. Cytokine expression was up-regulated in different level in nucleus pulposus (NP) from 3 days after puncture. Pain behavioral tests indicated that the anterior disc puncture did not induce pain behavior changes, whereas the posterior disc puncture resulted in mechanical allodynia from 1 day to 21 days after injury. Besides, cytokine expression was significantly increased in dorsal root ganglion (DRG) at 1 and 2 weeks after posterior puncture, but not after the anterior puncture. These findings indicate the NP of the degenerative disc expresses different levels of inflammatory cytokines, and posterior disc puncture produced mechanical allodynia. Keywords: intervertebral disc degeneration; low back pain; animal model; annulus fibrosus rupture; cytokine Low back pain (LBP) is a significant source of morbidity. Approximately 70% of the population experience LBP during their lives. 1 Although the exact cause of LBP is unclear, degeneration of intervertebral disc (IVD) is thought to drive LBP. 2,3 However, the relationship between intervertebral disc degeneration (IVDD) and LBP remains incompletely understood. Many patients with IVDD do not suffer from LBP. 4 Further investigation is needed to elucidate the relationship between IVDD and LBP.Inflammatory response plays an important role in the process of disc degeneration and LBP. Cytokines, such as tumor necrosis factor alpha (TNF-a), interleukin (IL)-1, and IL-6 are key factors associated with disc degeneration and LBP in human discs. [5][6][7] These inflammatory cytokines are involved in catabolic programs, angiogenesis, and nerve ingrowth in human discs. 8,9 However, the direct relationship between cytokine expression and pain symptoms is still unclear.Animal models are widely used to investigate the mechanisms underlying LBP and develop potential therapies. 10,11 IVD puncture models are popular among researchers, particularly posterior annulus fibrosus (AF) puncture and anterior AF puncture. It was previously shown that posterior lumbar AF puncture could induce pain behavior changes in rats. 12 A recent study further suggested that pain behavior changes might be related to the epidural presence of oozed nucleus pulposus (NP) in the posterior puncture model. 13 In this study, anterior disc puncture did not significantly change spontaneous pain behavior. The essential difference between posterior and anterior disc puncture is the location of the AF rupture. It seems that posterior AF rupture plays an important role in pain behavior changes in the rat disc puncture model.In the current study, we hypothesize that both expression of cytokines and posterior AF ruptu...
Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients’ tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.
The river chief system (RCS) has been innovatively implemented in Wuxi, China since 2007 for the eutrophication control of Tai Lake. In 2016, RCS was eventually promoted throughout China to reinforce river and lake protection. The success of this new river management system is generally attributed to collaboration, accountability, and differentiation effects. This research takes Foshan in the Pearl River Delta region as a case study to examine the feasibility and weaknesses in the implementation of the RCS. Prior to the formal adoption of RCS, a coordinating organization for river improvement undertaking was established in Foshan to overcome fragmentation in water management. Compared with this practice, the new RCS can strengthen the collaboration of administrative authorities and establish a considerably sophisticated and effective management structure. Emphasis on evaluation and accountability mechanisms guarantees that management goals can be achieved. However, similar to the previous one, the new system remains a temporary management practice and its outcomes depend partially on the commitment and capability of each river chief. The imperfect evaluation and accountability mechanism also weaken its long-term effectiveness in improving river water quality. Therefore, some corresponding policy instruments are needed to ensure that RCS can be implemented smoothly.
Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.
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