Introduction:Acute necrotizing encephalopathy(ANE),a fatal subtype of infection-triggered encephalopathy syndrome(ITES),can be triggered by many systemic infections.RANBP2 gene mutations were associated with recurrent...Introduction:Acute necrotizing encephalopathy(ANE),a fatal subtype of infection-triggered encephalopathy syndrome(ITES),can be triggered by many systemic infections.RANBP2 gene mutations were associated with recurrent ANE.Case presentation:Here we report a 1-year-old girl with recurrent ITES and RANBP2 mutation.She was diagnosed with influenza-associated encephalopathy and made a full recovery on the first episode.After severe acute respiratory syndrome coronavirus 2 infection,the patient presented with seizures and deteriorating mental status.Brain magnetic resonance imaging revealed necrotic lesions in bilateral thalami and pons.Methylprednisolone,immunoglobulin,and interleukin 6 inhibitors were administered.Her consciousness level was improved at discharge.Nineteen cases of 2019 coronavirus disease-related ANE have been reported,of which 22.2%of patients died and 61.1%had neurologic disabilities.RANBP2 gene mutation was found in five patients,two of whom developed recurrent ITES.Conclusion:Patients with RANBP2 mutations are at risk for recurrent ITES,may develop ANE,and have a poor prognosis after relapse.展开更多
Estimating 3D human pose from 2D images in real world contexts remains a challenge,characterized by unique data constraints.Large general datasets of motion-captured 3D adult human poses paired with 2D images exist,bu...Estimating 3D human pose from 2D images in real world contexts remains a challenge,characterized by unique data constraints.Large general datasets of motion-captured 3D adult human poses paired with 2D images exist,but in many application settings,collection of further motion-captured data is impossible,precluding a straightforward fine-tuning approach to adaptation.We present a method for improving 3D pose estimation transfer learning to domains where there are only depth camera images available as supervision.Our heuristic weakly supervised 3D human pose(HW-HuP)estimation method learns partial pose priors from general 3D human pose datasets and employs weak supervision with depth data to guide learning in an optimization and regression cycle.We show that HW-HuP meaningfully improves upon state-of-the-art models in the adult in-bed setting,as well as on large scale public 3D human pose datasets,under comparable supervision conditions.Our model code and data are publicly available at https://github.com/ostadabbas/hw-hup.A significantly expanded version of this paper,with supplementary material,is available as a preprint on arXiv at https://arxiv.org/abs/2105.10996.展开更多
文摘Introduction:Acute necrotizing encephalopathy(ANE),a fatal subtype of infection-triggered encephalopathy syndrome(ITES),can be triggered by many systemic infections.RANBP2 gene mutations were associated with recurrent ANE.Case presentation:Here we report a 1-year-old girl with recurrent ITES and RANBP2 mutation.She was diagnosed with influenza-associated encephalopathy and made a full recovery on the first episode.After severe acute respiratory syndrome coronavirus 2 infection,the patient presented with seizures and deteriorating mental status.Brain magnetic resonance imaging revealed necrotic lesions in bilateral thalami and pons.Methylprednisolone,immunoglobulin,and interleukin 6 inhibitors were administered.Her consciousness level was improved at discharge.Nineteen cases of 2019 coronavirus disease-related ANE have been reported,of which 22.2%of patients died and 61.1%had neurologic disabilities.RANBP2 gene mutation was found in five patients,two of whom developed recurrent ITES.Conclusion:Patients with RANBP2 mutations are at risk for recurrent ITES,may develop ANE,and have a poor prognosis after relapse.
基金supported by the National Science Foundation under Grant No.1755695.
文摘Estimating 3D human pose from 2D images in real world contexts remains a challenge,characterized by unique data constraints.Large general datasets of motion-captured 3D adult human poses paired with 2D images exist,but in many application settings,collection of further motion-captured data is impossible,precluding a straightforward fine-tuning approach to adaptation.We present a method for improving 3D pose estimation transfer learning to domains where there are only depth camera images available as supervision.Our heuristic weakly supervised 3D human pose(HW-HuP)estimation method learns partial pose priors from general 3D human pose datasets and employs weak supervision with depth data to guide learning in an optimization and regression cycle.We show that HW-HuP meaningfully improves upon state-of-the-art models in the adult in-bed setting,as well as on large scale public 3D human pose datasets,under comparable supervision conditions.Our model code and data are publicly available at https://github.com/ostadabbas/hw-hup.A significantly expanded version of this paper,with supplementary material,is available as a preprint on arXiv at https://arxiv.org/abs/2105.10996.
