115 research outputs found
BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation
Single-stage multi-person human pose estimation (MPPE) methods have shown
great performance improvements, but existing methods fail to disentangle
features by individual instances under crowded scenes. In this paper, we
propose a bounding box-level instance representation learning called BoIR,
which simultaneously solves instance detection, instance disentanglement, and
instance-keypoint association problems. Our new instance embedding loss
provides a learning signal on the entire area of the image with bounding box
annotations, achieving globally consistent and disentangled instance
representation. Our method exploits multi-task learning of bottom-up keypoint
estimation, bounding box regression, and contrastive instance embedding
learning, without additional computational cost during inference. BoIR is
effective for crowded scenes, outperforming state-of-the-art on COCO val (0.8
AP), COCO test-dev (0.5 AP), CrowdPose (4.9 AP), and OCHuman (3.5 AP). Code
will be available at https://github.com/uyoung-jeong/BoIRComment: Accepted to BMVC 2023, 19 pages including the appendix, 6 figures, 7
table
Detection of Acidic Pharmaceutical Compounds Using Virus-Based Molecularly Imprinted Polymers
Molecularly imprinted polymers (MIPs) have proven to be particularly effective chemical probes for the molecular recognition of proteins, DNA, and viruses. Here, we started from a filamentous bacteriophage to synthesize a multi-functionalized MIP for detecting the acidic pharmaceutic clofibric acid (CA) as a chemical pollutant. Adsorption and quartz crystal microbalance with dissipation monitoring experiments showed that the phage-functionalized MIP had a good binding affinity for CA, compared with the non-imprinted polymer and MIP. In addition, the reusability of the phage-functionalized MIP was demonstrated for at least five repeated cycles, without significant loss in the binding activity. The results indicate that the exposed amino acids of the phage, together with the polymer matrix, create functional binding cavities that provide higher affinity to acidic pharmaceutical compounds
A Successful Primary Percutaneous Coronary Intervention Twelve Days After a Cabrol Composite Graft Operation in Marfan Syndrome
The Cabrol procedure is one of several techniques used for re-implantation of a coronary artery. After replacement of the ascending aorta and aortic valve using a composite graft, second Dacron tube grafts are used for anastomosis between the ascending aortic graft and the coronary arteries. Ostial stenosis is one of the complications associated with the Cabrol operation. However, there have been no reported cases of acute thrombosis of a Cabrol graft. Here we report a case with acute ST elevation myocardial infarction due to thrombotic total occlusion of a right Cabrol graft-to-right coronary artery (RCA) twelve days after surgery in a patient with Marfan syndrome. He was successfully treated with primary percutaneous coronary intervention (PCI)
Long-term efficacy, safety and immunogenicity in patients with rheumatoid arthritis continuing on an etanercept biosimilar (LBEC0101) or switching from reference etanercept to LBEC0101: an open-label extension of a phase III multicentre, randomised, double-blind, parallel-group study
Background
To evaluate the long-term efficacy, safety and immunogenicity of continuing LBEC0101; the etanercept (ETN) biosimilar; or switching from the ETN reference product (RP) to LBEC0101 in patients with rheumatoid arthritis (RA).
Methods
This multicentre, single-arm, open-label extension study enrolled patients who had completed a 52-week randomised, double-blind, parallel phase III trial of LBEC0101 vs ETN-RP. Patients treated with ETN-RP during the randomised controlled trial switched to LBEC0101; those treated with LBEC0101 continued to receive LBEC0101 in this study. LBEC0101 (50 mg) was administered subcutaneously once per week for 48 weeks with a stable dose of methotrexate. Efficacy, safety and immunogenicity of LBEC0101 were assessed up to week 100.
Results
A total of 148 patients entered this extension study (70 in the maintenance group and 78 in the switch group). The 28-joint disease activity scores (DAS28)-erythrocyte sedimentation rate (ESR) were maintained in both groups from week 52 to week 100 (from 3.068 to 3.103 in the maintenance group vs. from 3.161 to 3.079 in the switch group). ACR response rates at week 100 for the maintenance vs. switch groups were 79.7% vs. 83.3% for ACR20, 65.2% vs. 66.7% for ACR50 and 44.9% vs. 42.3% for ACR70. The incidence of adverse events and the proportion of patients with newly developed antidrug antibodies were similar in the maintenance and switch groups (70.0% and 70.5%, 1.4% and 1.3%, respectively).
Conclusions
Administration of LBEC0101 showed sustained efficacy and acceptable safety in patients with RA after continued therapy or after switching from ETN-RP to LBEC0101.
Trial registration
ClinicalTrials.gov, NCT02715908. Registered 22 March 2016.This extension study was funded by LG Chem, Ltd. (formerly, LG Life Sciences, Ltd), Mochida Pharmaceutical Co., Ltd. and Korea Health Industry Development Institute
Electrochemical Catalysts for Green Hydrogen Energy
Developing clean and renewable energy resources has become one of the world's most important challenges, given the double burden of energy scarcity and environmental pollution. For sustainable energy conversion and storage, efficient electrocatalysts play a pivotal role in important energy-related reactions, including oxygen reduction, oxygen evolution, and hydrogen evolution. To satisfy practical requirements, the catalysts need to demonstrate high performance, durability, and acceptable cost. These are primary considerations when designing and preparing various new electrocatalysts. Among the research programs being actively conducted around the world, some promising recent results suggest strong potential alternatives to current expensive noble metal-based catalysts. This review summarizes recent technical advances in the preparation of efficient electrocatalysts
BoIR: Box-Supervised Instance Representation for Multi Person Pose Estimation
Single-stage multi-person human pose estimation (MPPE) methods have shown great performance improvements, but existing methods fail to disentangle features by individual instances under crowded scenes. In this paper, we propose a bounding box-level instance representation learning called BoIR, which simultaneously solves instance detection, instance disentanglement, and instance-keypoint association problems. Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation. Our method exploits multi-task learning of bottom-up keypoint estimation, bounding box regression, and contrastive instance embedding learning, without additional computational cost during inference. BoIR is effective for crowded scenes, outperforming state-of-the-art on COCO val (0.8 AP), COCO test-dev (0.5 AP), CrowdPose (4.9 AP), and OCHuman (3.5 AP). Code will be available at https://github.com/uyoung-jeong/BoI
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