MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (i.e. atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.Comment: 10 pages including referenc

Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety.

O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology

N-Terminal Deletion of Peptide:N-Glycanase Results in Enhanced Deglycosylation Activity

Peptide:N-glycanase catalyzes the detachment of N-linked glycan chains from glycopeptides or glycoproteins by hydrolyzing the β-aspartylglucosaminyl bond. Peptide:N-glycanase in yeast binds to Rad23p through its N-terminus. In this study, the complex formed between Peptide:N-glycanase and Rad23p was found to exhibit enhanced deglycosylation activity, which suggests an important role for this enzyme in the misfolded glycoprotein degradation pathway in vivo. To investigate the role of this enzyme in this pathway, we made stepwise deletions of the N-terminal helices of peptide:N-glycanase. Enzymatic analysis of the deletion mutants showed that deletion of the N-terminal H1 helix (Png1p-ΔH1) enhanced the deglycosylation activity of N-glycanase towards denatured glycoproteins. In addition, this mutant exhibited high deglycosylation activity towards native glycoproteins. Dynamic simulations of the wild type and N-terminal H1 deletion mutant implied that Png1p-ΔH1 is more flexible than wild type Png1p. The efficient deglycosylation of Png1p-ΔH1 towards native and non-native glycoproteins offers a potential biotechnological application

Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study

Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. To accomplish this, we introduce a challenging new long-tailed chest X-ray benchmark to facilitate research on developing long-tailed learning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning methods on this new benchmark, analyzing which aspects of these methods are most beneficial for long-tailed medical image classification and summarizing insights for future algorithm design. The datasets, trained models, and code are available at https://github.com/VITA-Group/LongTailCXR.Comment: DALI 2022 (MICCAI workshop

Growth mechanisms of multiscale, mound-like surface structures on titanium by femtosecond laser processing

Femtosecond laser surface processing (FLSP) can be used to functionalize many surfaces, imparting specialized properties such as increased broadband optical absorption or superhydrophobicity/- hydrophilicity. In this study, the subsurface microstructure of a series of mound-like FLSP structures formed on commercially pure titanium using five combinations of laser fluence and cumulative pulse counts was studied. Using a dual beam Scanning Electron Microscope with a Focused Ion Beam, the subsurface microstructure for each FLSP structure type was revealed by cross-sectioning. The microstructure of the mounds formed using the lowest fluence value consists of the original Ti grains. This is evidence that preferential laser ablation is the primary formation mechanism. However, the underlying microstructure of mounds produced using higher fluence values was composed of a distinct smaller-grained a-Ti region adjacent to the original larger Ti grains remaining deeper beneath the surface. This layer was attributed to resolidification of molten Ti from the hydrodynamic Marangoni effect driven fluid flow of molten Ti, which is the result of the femtosecond pulse interaction with the material

Growth mechanisms of multiscale, mound-like surface structures on titanium by femtosecond laser processing

Femtosecond laser surface processing (FLSP) can be used to functionalize many surfaces, imparting specialized properties such as increased broadband optical absorption or superhydrophobicity/- hydrophilicity. In this study, the subsurface microstructure of a series of mound-like FLSP structures formed on commercially pure titanium using five combinations of laser fluence and cumulative pulse counts was studied. Using a dual beam Scanning Electron Microscope with a Focused Ion Beam, the subsurface microstructure for each FLSP structure type was revealed by cross-sectioning. The microstructure of the mounds formed using the lowest fluence value consists of the original Ti grains. This is evidence that preferential laser ablation is the primary formation mechanism. However, the underlying microstructure of mounds produced using higher fluence values was composed of a distinct smaller-grained a-Ti region adjacent to the original larger Ti grains remaining deeper beneath the surface. This layer was attributed to resolidification of molten Ti from the hydrodynamic Marangoni effect driven fluid flow of molten Ti, which is the result of the femtosecond pulse interaction with the material

Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety

O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology

Sequential Reassortments Underlie Diverse Influenza H7N9 Genotypes in China

Initial genetic characterizations have suggested that the influenza A (H7N9) viruses responsible for the current outbreak in China are novel reassortants. However, little is known about the pathways of their evolution and, in particular, the generation of diverse viral genotypes. Here we report an in-depth evolutionary analysis of whole-genome sequence data of 45 H7N9 and 42 H9N2 viruses isolated from humans, poultry, and wild birds during recent influenza surveillance efforts in China. Our analysis shows that the H7N9 viruses were generated by at least two steps of sequential reassortments involving distinct H9N2 donor viruses in different hosts. The first reassortment likely occurred in wild birds and the second in domestic birds in east China in early 2012. Our study identifies the pathways for the generation of diverse H7N9 genotypes in China and highlights the importance of monitoring multiple sources for effective surveillance of potential influenza outbreaks.National Natural Science Foundation (China) (31125016)National Natural Science Foundation (China) (31371338)National Center for Biotechnology Information (U.S.) (Major National Earmark Project for Infectious Diseases, 2013ZX10004611-002)National Basic Research Program of China (973 Program)National Basic Research Program of China (973 Program, grant, 2009CB918503)National Science and Technology Major Projects (2012ZX10004214001002)Jiangsu Sheng (China) (Priority Academic Program Development of Jiangsu Higher Education Institutions)National Natural Science Foundation (China) (31100950)MIT International Science and Technology Initiative