Three novel α-L-iduronidase mutations in 10 unrelated Chinese mucopolysaccharidosis type I families

Mucopolysaccharidosis type I (MPS I) arises from a deficiency in the α-L-iduronidase (IDUA) enzyme. Although the clinical spectrum in MPS I patients is continuous, it was possible to recognize 3 phenotypes reflecting the severity of symptoms, viz., the Hurler, Scheie and Hurler/Scheie syndromes. In this study, 10 unrelated Chinese MPS I families (nine Hurler and one Hurler/Scheie) were investigated, and 16 mutant alleles were identified. Three novel mutations in IDUA genes, one missense p.R363H (c.1088G > A) and two splice-site mutations (c.1190-1G > A and c.792+1G > T), were found. Notably, 45% (nine out of 20) and 30% (six out of 20) of the mutant alleles in the 10 families studied were c.1190-1G > A and c.792+1G > T, respectively. The novel missense mutation p.R363H was transiently expressed in CHO cells, and showed retention of 2.3% IDUA activity. Neither p.W402X nor p.Q70X associated with the Hurler phenotype, or even p.R89Q associated with the Scheie phenotype, was found in this group. Finally, it was noted that the Chinese MPS I patients proved to be characterized with a unique set of IDUA gene mutations, not only entirely different from those encountered among Europeans and Americans, but also apparently not even the same as those found in other Asian countries

RepVGG:Making VGG-style ConvNets Great Again

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG.Comment: CVPR 202

Microscopic characteristics of magnetorheological fluids subjected to magnetic fields

© 2020 Elsevier B.V. With the aim of studying the microscopic characteristics of a magnetorheological fluid (MRF) in a magnetic field, the theoretical analyses of the particles dynamics in a magnetic field are presented, and a model for the particle motion is proposed. Based on these analyses, a three-dimensional numerical simulation of the microstructure of MRFs in different magnetic fields is performed. Furthermore, the microstructures of the MRFs are investigated using industrial computed tomography (CT) imaging. The numerical simulation and industrial CT results indicate that the chain structure of the same MRF becomes more apparent as the magnetic field strength increases, and in the same external magnetic field, this chain structure also becomes more apparent with an increase in the particle volume fraction. The lengths of particle chains in different magnetic fields are also captured in the industrial CT experiments. When the magnetic field strength is 12 mT, the particle chains of the MRF with a particle volume fraction of 30% reach more than 10 mm in length, which bridge the inner diameter of the container, and the dense clusters-like structure is formed, the clusters-like structure becomes denser with an increase in magnetic field. Moreover, the particle chain lengths of MRF with high particle volume fractions increase sharply with the magnetic field. The experiments demonstrated that the industrial CT is an efficient method to study the microstructures of MRFs by providing particle distributions of MRFs more clearly and intuitively

Exon-intron boundary inhibits m

Regional bias of N6-methyladenosine (m6A) mRNA modification avoiding splice site region, calls for an open hypothesis whether exon-intron boundary could affect m6A deposition. By deep learning modeling, we find that exon- intron boundary represses a proportion (12% to 34%) of m6A deposition at adjacent exons (~100 nt to splice site). Experiments validate that m6A signal increases once the host gene does not undergo pre-mRNA splicing to produce the same mRNA. Inhibited m6A sites have higher m6A enhancers and lower m6A silencers locally and show high heterogeneity at different exons genome- widely, with only a small proportion (12% to 15%) of exons showing strong inhibition, enabling more stable mRNAs and flexible protein coding. m6A is majorly responsible for why mRNAs with more exons be more stable. Exon junction complex (EJC) only partially contributes to this exon-intron boundary m6A inhibition in some short internal exons, highlighting additional factors yet to be identified