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Objective Charcot–Marie–Tooth disease (CMT) reduces health‐related quality of life (QOL), especially in children. Defining QOL in pediatric CMT can help physicians monitor disease burden clinically and in trials. We identified items pertaining to QOL in children with CMT and conducted validation studies to develop a pediatric CMT‐specific QOL outcome measure (pCMT‐QOL). Methods Development and validation of the pCMT‐QOL patient‐reported outcome measure were iterative, involving identifying relevant domains, item pool generation, prospective pilot testing and clinical assessments, structured focus‐group interviews, and psychometric testing. Testing was conducted in children with CMT seen at participating sites from the USA, United Kingdom, and Australia. Results We conducted systematic literature reviews and analysis of generic QOL measures to identify 6 domains relevant to QOL in children with CMT. Sixty items corresponding to those domains were developed de novo, or identified from literature review and CMT‐specific modification of items from the pediatric Neuro‐QOL measures. The draft version underwent prospective feasibility and face content validity assessments to develop a working version of the pCMT‐QOL measure. From 2010 to 2016, the pCMT‐QOL working version was administered to 398 children aged 8 to 18 years seen at the participating study sites of the Inherited Neuropathies Consortium. The resulting data underwent rigorous psychometric analysis, including factor analysis, test–retest reliability, internal consistency, convergent validity, item response theory analysis, and longitudinal analysis, to develop the final pCMT‐QOL patient‐reported outcome measure. Interpretation The pCMT‐QOL patient‐reported outcome measure is a reliable, valid, and sensitive measure of health‐related QOL for children with CMT. ANN NEUROL 2021;89:369–379

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We introduce SDM-NET, a deep generative neural network which produces structured deformable meshes. Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respects the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by deforming the box. The architecture of SDM-NET is that of a two-level variational autoencoder (VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring the coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, benefiting shape interpolation and other subsequent modeling tasks.

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose a level of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.