Crystal structure of E. coli arginyl-tRNA synthetase and ligand binding studies revealed key residues in arginine recognition

The arginyl-tRNA synthetase (ArgRS) catalyzes the esterification reaction between L-arginine and its cognate tRNA(Arg). Previously reported structures of ArgRS shed considerable light on the tRNA recognition mechanism, while the aspect of amino acid binding in ArgRS remains largely unexplored. Here we report the first crystal structure of E. coli ArgRS (eArgRS) complexed with L-arginine, and a series of mutational studies using isothermal titration calorimetry (ITC). Combined with previously reported work on ArgRS, our results elucidated the structural and functional roles of a series of important residues in the active site, which furthered our understanding of this unique enzyme. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13238-013-0012-1) contains supplementary material, which is available to authorized users

Photocleavage of the Polypeptide Backbone by 2-Nitrophenylalanine

SummaryPhotocleavage of the polypeptide backbone is potentially a powerful and general method to activate or deactivate functional peptides and proteins with high spatial and temporal resolution. Here we show that 2-nitrophenylalanine is able to photochemically cleave the polypeptide backbone by an unusual cinnoline-forming reaction. This unnatural amino acid was genetically encoded in E. coli, and protein containing 2-nitrophenylalanine was expressed and site-specifically photocleaved

FreMAE: Fourier Transform Meets Masked Autoencoders for Medical Image Segmentation

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMAE for self-supervised pre-training for medical image segmentation. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the high-level semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our proposed FreMAE over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMAE could consistently bring considerable improvements to the model performance. To the best our knowledge, this is the first attempt towards MIM with Fourier Transform in medical image segmentation

Med-Tuning: Exploring Parameter-Efficient Transfer Learning for Medical Volumetric Segmentation

Deep learning based medical volumetric segmentation methods either train the model from scratch or follow the standard "pre-training then finetuning" paradigm. Although finetuning a well pre-trained model on downstream tasks can harness its representation power, the standard full finetuning is costly in terms of computation and memory footprint. In this paper, we present the first study on parameter-efficient transfer learning for medical volumetric segmentation and propose a novel framework named Med-Tuning based on intra-stage feature enhancement and inter-stage feature interaction. Given a large-scale pre-trained model on 2D natural images, our method can exploit both the multi-scale spatial feature representations and temporal correlations along image slices, which are crucial for accurate medical volumetric segmentation. Extensive experiments on three benchmark datasets (including CT and MRI) show that our method can achieve better results than previous state-of-the-art parameter-efficient transfer learning methods and full finetuning for the segmentation task, with much less tuned parameter costs. Compared to full finetuning, our method reduces the finetuned model parameters by up to 4x, with even better segmentation performance

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019.Comment: Accepted by WACV 202

A comparative study on the ecological footprint of living consumption in northwest ethnic regions: 1980–2018

This paper focuses on the northwest region, which is related to China’s overall ecological security and ethnic stability. This paper selects the neighboring regions of Dingxi City, Gannan Tibetan Autonomous Prefecture and Linxia Hui Autonomous Prefecture as the starting point, deeply and systematically analyzes the impact of different lifestyles on the environment. Using environmental economics, ecological economics, environmental sociology and other related theories, ecological footprint were used to investigate different lifestyles’ impact to environment. Neural network were also used to carry out multi-perspective environmental impact research from the spatial scale and time scale. The research finds that Dingxi, Gannan and Linxia’s different mode of production has led to different lifestyle, and results in different impact on environment. The governments of the three places should take actions to promote ecological civilization and encourage the establishment of an ecologically-friendly and environmentally-friendly way of life so as to reduce the impact on the ecological environment and realize regional sustainable development