Hsa_circ_0005729 enhances accuracy in diagnosing parathyroid carcinoma.

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.

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

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.

Bayesian robust principal component analysis.

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.