When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals, however, in humans its exact origin and temporal dynamics remain unknown. We report Electrocorticographic (ECoG) recordings in neurosurgical patients and a novel connectivity approach revealing major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis.

Moving object detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition to accomplish such a task when the background is stationary and the foreground is dynamic and relatively small. A fundamental issue with the RPCA is the assumption that the low-rank and sparse components are added at each pixel, whereas in reality, the moving foreground is overlaid on the background. We propose the masked decomposition (i.e. an overlaying model) where each element either belongs to the low-rank or the sparse component, decided by a mask. We introduce the Masked-RPCA (MRPCA) algorithm to recover the mask (hence the sparse object) and the low-rank components simultaneously, via a non-convex formulation. An adapted version of the Douglas-Rachford splitting algorithm is utilized to solve the proposed formulation. Our experiments using real-world video sequences show consistently better performance for both cases of static and dynamic background videos compared to RPCA and its variants based on the additive model. Additionally, we show that utilizing non-convex priors in our formulation leads to improved results without any added complexity compared to a relaxed formulation using convex surrogates and methods based on the additive model.

BackgroundDifferent studies have used different tests to evaluate bond strength of resin cements to root dentin. In this in vitrostudy, three different tests were used to evaluate the bond strength of two resin cements to root dentin using two root dentin irrigation protocols.Material and MethodsNinety-six intact single-rooted teeth were selected for this study. Forty-eight teeth, with a root length of 15mm, were randomly divided into two groups and irrigated with normal saline or 2.5% sodium hypochlorite solutions during root canal preparation, respectively. For each 12 specimens from each group, fiber post #1 was bonded using an etch-and-rinse (Duo-Link) and a self-adhesive (BisCem) resin cement, respectively. After incubation, two specimens were prepared for the push-out test from the middle thirds of the roots. In another 24 teeth, after two 1.5-mm sections were prepared from the middle thirds of the prepared roots, sections of the post were bonded in two subgroups with each of the cements mentioned above and the samples were prepared for the pull-out test. For shear test, the crowns of 48 teeth were cut away, the dentin surfaces were prepared, the two irrigation solutions were used, and the resin cements were bonded. Data collected from the three tests were evaluated by ANOVA, post-hoc Tukey and Weibull tests (α=0.05).ResultsThere were significant differences in the mean bond strength values between the three bond strength tests (P<0.001). Rinsing protocol and cement type resulted in similar variations in the mean bond strength in all tests (P>0.05).ConclusionsUnder the limitations of the present study, the method of the test used had an effect on the recorded bond strength between the resin cement and root dentin. Cement type and irrigation protocol resulted in similar variations with all the tests. Push-out and shear tests exhibited more coherent results. Key words:Bond strength, endodontically treated tooth, fiber post, resin cement, sodium hypochlorite.

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Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and posterior cortical networks, but the degree and timing of their recruitment and dynamics remain unknown. We present a novel deep learning architecture that translates neural signals recorded directly from cortex to an interpretable representational space that can reconstruct speech. We leverage state-of-the-art learnt decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.