Dynamical alterations of brain function and gut microbiome in weight loss

ObjectiveIntermittent energy restriction (IER) is an effective weight loss strategy. However, little is known about the dynamic effects of IER on the brain-gut-microbiome axis.MethodsIn this study, a total of 25 obese individuals successfully lost weight after a 2-month IER intervention. FMRI was used to determine the activity of brain regions. Metagenomic sequencing was performed to identify differentially abundant gut microbes and pathways in from fecal samples.ResultsOur results showed that IER longitudinally reduced the activity of obese-related brain regions at different timepoints, including the inferior frontal orbital gyrus in the cognitive control circuit, the putamen in the emotion and learning circuit, and the anterior cingulate cortex in the sensory circuit. IER longitudinally reduced E. coli abundance across multiple timepoints while elevating the abundance of obesity-related Faecalibacterium prausnitzii, Parabacteroides distasonis, and Bacterokles uniformis. Correlation analysis revealed longitudinally correlations between gut bacteria abundance alterations and brain activity changes.ConclusionsThere was dynamical alteration of BGM axis (the communication of E. coli with specific brain regions) during the weight loss under the IER

Unimodal model-based inter mode decision for high efficiency video coding

In this paper, a fast inter mode decision algorithm, called the unimodal model-based inter mode decision (UMIMD), is proposed for the latest video coding standard, the high-efficiency video coding. Through extensive simulations, it has been observed that a unimodal model (i.e., with only one global minimum value) can be established among the size of different prediction unit (PU) modes and their resulted rate-distortion (RD) costs for each quad-tree partitioned coding tree unit (CTU). To guarantee the unimodality and further search the optimal operating point over this function for each CTU, all the PU modes need to be first classified into 11 mode classes according to their sizes. These classes are then properly ordered and sequentially checked according to the class index, from small to large so that the optimal mode can be early identified by checking when the RD cost starts to arise. In addition, an effective instant SKIP mode termination scheme is developed by simply checking the SKIP mode against a pre-determined threshold to further reduce the computational complexity. The extensive simulation results have shown that the proposed UMIMD algorithm is able to individually achieve a significant reduction on computational complexity at the encoder by 61.9% and 64.2% on average while incurring only 1.7% and 2.1% increment on the total Bjontegaard delta bit rate (BDBR) for the low delay and random access test conditions, compared with the exhaustive mode decision in the HEVC. Moreover, the experimental results have further demonstrated that the proposed UMIMD algorithm outperforms multiple state-of-the-art methods.Published versio