Rabbit Anti-Human TNN (internal) Polyclonal antibody

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Obsessive tendencies, language deficits, and social deficits are the hallmarks of ASD. Poor information integration across functional networks of the brain may be the root cause of these deficiencies. Functional network connectivity (FNC) assessment among six brain networks, viz. anterior default mode network, posterior default mode network, two frontoparietal networks, basal ganglia network (BGN) and salience network (SN) have been examined here in this paper. To understand the development trajectory of ASD, functional magnetic resonance imaging (fMRI) dataset of autistic children, adolescents and adults are considered. The SN and BGN of participants with ASD had the most FNC differences with typically developing cohorts. The SN had hyperconnectivity with the rest five networks for autistic children while hypoconnectivity were observed for adolescents and adults. The two lateralized fronto-parietal network had no connectivity for the two initial developmental stages however for adults with ASD they had stronger functional connectivity.

Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers. TNN is demonstrated to be a convex surrogate of rank. However, it tends to over-penalize large singular values and thus usually results in biased solutions. To handle this issue, we propose a new definition of tensor logarithmic norm (TLN) as the nonconvex surrogate of rank, which can decrease the penalization on larger singular values and increase that on smaller ones simultaneously to preserve the low-rank structure of a tensor. Then, the strategy of tensor factorization is combined into the minimization of TLN to improve computational performance. To handle impulsive scenarios, we propose a nonconvex l(p)-ball projection scheme with 0 < p < 1 instead of the conventional convex scheme with p = 1, which enhances the robustness against outliers. By incorporating the TLN minimization and the l(p)-ball projection, we finally propose two low-rank recovery algorithms, whose resulting optimization problems are efficiently solved by the alternating direction method of multipliers (ADMM) with convergence guarantees. The proposed algorithms are applied to the synthetic data recovery and image and video restorations in real-world. Experimental results demonstrate the superior performance of the proposed methods over several state-of-the-art algorithms in terms of tensor recovery accuracy and computational efficiency.