RESUMEN
BACKGROUND: Parathyroid carcinoma (PC), often misdiagnosed as a parathyroid adenoma (PA), is prone to local relapse due to the initial surgery being restricted to parathyroid lesions instead of en bloc resection of parathyroid lesions with negative incision margins. However, it is very challenging to distinguish PC from PA preoperatively; hence, this study investigated an effective biomarker for increasing accuracy in PC diagnosis. METHOD: First, the differentially expressed circular RNAs between three PC tissues and three PA tissues were screened by high-throughput circular RNA sequencing, and the expression of hsa_circ_0005729 was verified by qRT-PCR in 14 patients with PC and 40 patients with PA. Secondly, the receiver operating characteristic curve and the area under the curve (AUC) were used to analyze the diagnostic efficiency of hsa_circ_0005729 in PC by combining with laboratory data. Thirdly, RNF138mRNA, the corresponding linear transcript of hsa_circ_0005729, was measured, and the relationship between hsa_circ_0005729 and RNF138 mRNA was analyzed in patients with PA and patients with PC. RESULTS: Hsa_circ_0005729 expression was significantly higher in patients with PC than in patients with PA. Serum calcium (P = 0.045), alkaline phosphatase (ALP) (P = 0.048), and creatinine levels (P = 0.036) were significantly higher in patients with PC than in patients with PA. The AUC increased to 0.86 when hsa_circ_0005729 combined with serum calcium, creatinine, and ALP. In addition, hsa_circ_0005729 was positively correlated with RNF138 mRNA in patients with PA but not in patients with PC. CONCLUSION: The novel circular RNA hsa_circ_0005729 was found to have a higher expression in patients with PC, indicating its usefulness for distinguishing PC from PA.
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A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.
RESUMEN
A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observed matrix is a superposition of the two. The matrix is assumed noisy, with unknown and possibly non-stationary noise statistics. The Bayesian framework infers an approximate representation for the noise statistics while simultaneously inferring the low-rank and sparse-outlier contributions; the model is robust to a broad range of noise levels, without having to change model hyperparameter settings. In addition, the Bayesian framework allows exploitation of additional structure in the matrix. For example, in video applications each row (or column) corresponds to a video frame, and we introduce a Markov dependency between consecutive rows in the matrix (corresponding to consecutive frames in the video). The properties of this Markov process are also inferred based on the observed matrix, while simultaneously denoising and recovering the low-rank and sparse components. We compare the Bayesian model to a state-of-the-art optimization-based implementation of robust PCA; considering several examples, we demonstrate competitive performance of the proposed model.