Recurrent infection triggered encephalopathy syndrome in a pediatric patient with RANBP2 mutation and severe acute respiratory syndrome coronavirus 2 infection.

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.

Pressure eye: In-bed contact pressure estimation via contact-less imaging.

Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map. In this work, we present our pressure eye (PEye) approach to estimate contact pressure between a human body and the surface she is lying on with high resolution from vision signals directly. PEye approach could ultimately enable the prediction and early detection of pressure ulcers in bed-bound patients, that currently depends on the use of expensive pressure mats. Our PEye network is configured in a dual encoding shared decoding form to fuse visual cues and some relevant physical parameters in order to reconstruct high resolution pressure maps (PMs). We also present a pixel-wise resampling approach based on Naive Bayes assumption to further enhance the PM regression performance. A percentage of correct sensing (PCS) tailored for sensing estimation accuracy evaluation is also proposed which provides another perspective for performance evaluation under varying error tolerances. We tested our approach via a series of extensive experiments using multimodal sensing technologies to collect data from 102 subjects while lying on a bed. The individual's high resolution contact pressure data could be estimated from their RGB or long wavelength infrared (LWIR) images with 91.8% and 91.2% estimation accuracies in PCSefs0.1 criteria, superior to state-of-the-art methods in the related image regression/translation tasks.

Simultaneously-Collected Multimodal Lying Pose Dataset: Enabling In-Bed Human Pose Monitoring.

Computer vision field has achieved great success in interpreting semantic meanings from images, yet its algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. Among these tasks is in-bed human pose monitoring with significant value in many healthcare applications. In-bed pose monitoring in natural settings involves pose estimation in complete darkness or full occlusion. The lack of publicly available in-bed pose datasets hinders the applicability of many successful human pose estimation algorithms for this task. In this paper, we introduce our Simultaneously-collected multimodal Lying Pose (SLP) dataset, which includes in-bed pose images from 109 participants captured using multiple imaging modalities including RGB, long wave infrared (LWIR), depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under adverse vision conditions. The SLP design is compatible with the mainstream human pose datasets; therefore, the state-of-the-art 2D pose estimation models can be trained effectively with the SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance of these models can be further improved by including additional modalities through the proposed collaborative scheme.

Case Report: Immune reconstitution inflammatory syndrome after hematopoietic stem cell transplantation for severe combined immunodeficiency.

We report a case of immune reconstitution inflammatory syndrome (IRIS) after hematopoietic stem cell transplantation (HSCT). The patient had sever bacillus Calmette-Guerin (BCG) vaccine-caused disseminated infection and had received allogeneic HSCT for X-linked severe combined immunodeficiency disease. After HSCT, complicated by treatment-responding veno-occlusive disease and acute graft-versus-host disease, at the time when immunosuppressants were withdrawn, the patient experienced recurrent fever accompanied by elevated inflammatory indicators. After receiving glucocorticoids and ibuprofen, the patient's condition improved, and a diagnosis with BCG-related IRIS was made.

Privacy-Preserving In-Bed Pose and Posture Tracking on Edge.

In-bed behavior monitoring is commonly needed for bed-bound patient and has long been confined to wearable devices or expensive pressure mapping systems. Meanwhile, vision-based human pose and posture tracking while experiencing a lot of attention/success in the computer vision field has been hindered in terms of usability for in-bed cases, due to huge privacy concerns surrounding this topic. Moreover, the inference models for mainstream pose and posture estimation often require excessive computing resources, impeding their implementation on edge devices. In this paper, we introduce a privacy-preserving in-bed pose and posture tracking system running entirely on an edge device with added functionality to detect stable motion as well as setting user-specific alerts for given poses. We evaluated the estimation accuracy of our system on a series of retrospective infrared (LWIR) images as well as samples from a real-world test environment. Our test results reached over 93.6% estimation accuracy for in-bed poses and achieved over 95.9% accuracy in estimating three in-bed posture categories.

In-Bed Pose Estimation: Deep Learning With Shallow Dataset.

This paper presents a robust human posture and body parts detection method under a specific application scenario known as in-bed pose estimation. Although the human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, the in-bed pose estimation using camera-based vision methods has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation problems. However, the in-bed pose estimation has its own specialized aspects and comes with specific challenges, including the notable differences in lighting conditions throughout the day and having pose distribution different from the common human surveillance viewpoint. In this paper, we demonstrate that these challenges significantly reduce the effectiveness of the existing general purpose pose estimation models. In order to address the lighting variation challenge, the infrared selective (IRS) image acquisition technique is proposed to provide uniform quality data under various lighting conditions. In addition, to deal with the unconventional pose perspective, a 2- end histogram of oriented gradient (HOG) rectification method is presented. The deep learning framework proves to be the most effective model in human pose estimation; however, the lack of large public dataset for in-bed poses prevents us from using a large network from scratch. In this paper, we explored the idea of employing a pre-trained convolutional neural network (CNN) model trained on large public datasets of general human poses and fine-tuning the model using our own shallow (limited in size and different in perspective and color) in-bed IRS dataset. We developed an IRS imaging system and collected IRS image data from several realistic life-size mannequins in a simulated hospital room environment. A pre-trained CNN called convolutional pose machine (CPM) was fine-tuned for in-bed pose estimation by re-training its specific intermediate layers. Using the HOG rectification method, the pose estimation performance of CPM improved significantly by 26.4% in the probability of correct key-point (PCK) criteria at PCK0.1 compared to the model without such rectification. Even testing with only well aligned in-bed pose images, our fine-tuned model still surpassed the traditionally tuned CNN by another 16.6% increase in pose estimation accuracy.

Construction of amorphous TiO2/BiOBr heterojunctions via facets coupling for enhanced photocatalytic activity.

Facets coupled BiOBr with amorphous TiO2 composite photocatalysts are synthesized via an in situ direct growth approach under microwave irradiation. XRD, SEM and HRTEM characterizations indicate that the heterointerface between BiOBr and amorphous TiO2 occurs mainly on the {001} facets of BiOBr. BET and TEM verify that the heterojunctions possess higher specific surface areas and smaller amorphous TiO2 particle size than bare BiOBr and amorphous TiO2, exhibiting the inhibition function of BiOBr on the growth of TiO2 particles. XPS verifies the interaction between the two components. The degradation of methyl orange (MO) and phenol are used as the objective reaction to evaluate the photocatalytic activity of the as-prepared samples. The reaction rate constant of 15% TiO2/BiOBr composite is 3.4 times greater than that of pure BiOBr, which is attributed to its higher surface area, and efficient separation of photo-generated electron-hole pairs between BiOBr and amorphous TiO2.