Mechanism of Thioesterase-Catalyzed Chain Release in the Biosynthesis of the Polyether Antibiotic Nanchangmycin

SummaryThe polyketide backbone of the polyether ionophore antibiotic nanchangmycin (1) is assembled by a modular polyketide synthase in Streptomyces nanchangensis NS3226. The ACP-bound polyketide is thought to undergo a cascade of oxidative cyclizations to generate the characteristic polyether. Deletion of the glycosyl transferase gene nanG5 resulted in accumulation of the corresponding nanchangmycin aglycone (6). The discrete thioesterase NanE exhibited a nearly 17-fold preference for hydrolysis of 4, the N-acetylcysteamine (SNAC) thioester of nanchangmycin, over 7, the corresponding SNAC derivative of the aglycone, consistent with NanE-catalyzed hydrolysis of ACP-bound nanchangmycin being the final step in the biosynthetic pathway. Site-directed mutagenesis established that Ser96, His261, and Asp120, the proposed components of the NanE catalytic triad, were all essential for thioesterase activity, while Trp97 was shown to influence the preference for polyether over polyketide substrates

Function and Structure of Human Left Fusiform Cortex Are Closely Associated with Perceptual Learning of Faces

SummaryTraining can lead to long-lasting improvement in our perceptual ability, which is referred to as perceptual learning. Unraveling its neural mechanisms has proved difficult. With functional and structural magnetic resonance imaging (MRI), we addressed this issue by searching for the neural correlates of perceptual learning of face views over a long time course. Human subjects were trained to perform a face view discrimination task. Their behavioral performance and MRI signals were measured before, immediately after, and 1 month after training. We found that, across individual subjects, their behavioral learning effects correlated with the stability improvement of spatial activity pattern in the left fusiform cortex immediately after and 1 month after training. We also found that the thickness of the left fusiform cortex before training could predict subjects’ behavioral learning effects. These findings, for the first time, not only suggest that, remarkably, the improved pattern stability contributes to the long-term mechanisms of perceptual learning, but also provide strong and converging evidence for the pivotal role of the left fusiform cortex in adaptive face processing

Random subspace evidence classifier

Although there exist a lot of k-nearest neighbor approaches and their variants, few of them consider how to make use of the information in both the whole feature space and subspaces. In order to address this limitation, we propose a new classifier named as the random subspace evidence classifier (RSEC). Specifically, RSEC first calculates the local hyperplane distance for each class as the evidences not only in the whole feature space, but also in randomly generated feature subspaces. Then, the basic belief assignment is computed according to these distances for the evidences of each class. In the following, all the evidences represented by basic belief assignments are pooled together by the Dempster&#39;s rule. Finally, RSEC assigns the class label to each test sample based on the combined belief assignment. The experiments in the datasets from UCI machine learning repository, artificial data and face image database illustrate that the proposed approach yields lower classification error in average comparing to 7 existing k-nearest neighbor approaches and variants when performing the classification task. In addition, RSEC has good performance in average on the high dimensional data and the minority class of the imbalanced data. (C) 2013 Elsevier B.V. All rights reserved.</p

Cognitive gravitation model for classification on small noisy data

When performing the classification on the high dimensional, the sparse, or the noisy data, many approaches easily lead to the dramatic performance degradation. To deal with this issue from the different perspective, this paper proposes a cognitive gravitation model (CGM) based on both the law of gravitation in physics and the cognitive laws, where the self-information of each sample instead of mass is applied. Subsequently, a new classifier is designed which utilizes CGM to find k nearest neighbors from each class for the query sample and then classifies this query sample to the class whose cognitive gravitation is largest. The cognitive gravitation of the class is defined as the sum of the cognitive gravitation between its each nearest neighbor and the query sample. The advantage of our approach is that it has a firm and simple mathematical basis while it has good classification performance. The conducted experiments on challenging benchmark data sets validate the proposed model and the classification approach. (C) 2013 Elsevier B.V. All rights reserved

Hybrid attention mechanism of feature fusion for medical image segmentation

Abstract Traditional convolution neural networks (CNN) have achieved good performance in multi‐organ segmentation of medical images. Due to the lack of ability to model long‐range dependencies and correlations between image pixels, CNN usually ignores the information of channel dimension. To further improve the performance of multi‐organ segmentation, a hybrid attention mechanism model is proposed. First, a CNN was used to extract multi‐scale feature maps and fed into the Channel Attention Enhancement Module (CAEM) to selectively pay attention to target organs in medical images, and the Transformer encoded tokenized image patches from CNN feature maps as the input sequence to model long‐range dependencies. Second, the decoder upsampled the output from Transformer and fused with the CAEM features in multi‐scale through skip connections. Finally, we introduced a Refinement Module (RM) after the decoder to improve feature correlations of the same organ and the feature discriminability between different organs. The model outperformed on dice coefficient (%) and hd95 on both the synapse multi‐organ segmentation and cardiac diagnosis challenge datasets. The hybrid attention mechanisms exhibited high efficiency and high segmentation accuracy in medical images