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BackgroundAcute lymphoblastic leukemia (ALL) is the most common childhood malignancy. Caring for children with ALL is challenging for parents. A mobile health (mHealth) supportive care intervention was developed to meet parents’ needs. ObjectiveThis study aims to evaluate the potential effectiveness of this mHealth supportive care intervention on emotional distress, social support, care burden, uncertainty in illness, quality of life, and knowledge. MethodsWe conducted a quasi-experimental pre- and postdesign study from June 2015 to January 2016. In total, 101 parents were enrolled in the study, with 50 in the observation group and 51 in the intervention group. Parents in the observation group received the standard health education and were observed for 3 months. Parents in the intervention group received the mHealth supportive care intervention, in addition to the standard health education. The intervention consisted of 2 parts—an Android smartphone app “Care Assistant (CA)” and a WeChat Official Account. The CA with 8 modules (Personal Information, Treatment Tracking, Family Care, Financial and Social Assistance, Knowledge Center, Self- Assessment Questionnaires, Interactive Platform, and Reminders) was the main intervention tool, whereas the WeChat Official Account was supplementary to update information and realize interaction between parents and health care providers. Data of parents’ social support, anxiety, depression, care burden, uncertainty in illness, quality of life, their existing knowledge of ALL and care, and knowledge need were collected before and after the 3-month study period in both groups. For the intervention group, parents’ experience of receiving the intervention was also collected through individual interviews. ResultsOverall, 43 parents in the observation group and 49 in the intervention group completed the study. Results found that the intervention reduced parents’ anxiety (Dint(Post-Pre)=−7.0 [SD 13.1], Dobs(Post-Pre)=−0.4 [SD 15.8], t90=−2.200, P=.03) and uncertainty in illness (Dint(Post-Pre)=−25.0 [SD 8.2], Dobs(Post-Pre)=−19.8 [SD 10.1], t90=−2.761, P=.01), improved parents’ social function (Dint(Post-Pre)=9.0 [SD 32.8], Dobs(Post-Pre)=−7.5 [SD 30.3], t90=2.494, P=.01), increased parents’ knowledge of ALL and care (Dint(Post-Pre)=28.4 [SD 12.4], Dobs(Post-Pre)=17.2 [SD 11.9], t90=4.407, P

Non-neovascular or dry age-related macular degeneration (AMD) is a multi-factorial disease with degeneration of the aging retinal-pigmented epithelium (RPE). Lysosomes play a crucial role in RPE health via phagocytosis and autophagy, which are regulated by transcription factor EB/E3 (TFEB/E3). Here, we find that increased AKT2 inhibits PGC-1α to downregulate SIRT5, which we identify as an AKT2 binding partner. Crosstalk between SIRT5 and AKT2 facilitates TFEB-dependent lysosomal function in the RPE. AKT2/SIRT5/TFEB pathway inhibition in the RPE induced lysosome/autophagy signaling abnormalities, disrupted mitochondrial function and induced release of debris contributing to drusen. Accordingly, AKT2 overexpression in the RPE caused a dry AMD-like phenotype in aging Akt2 KI mice, as evident from decline in retinal function. Importantly, we show that induced pluripotent stem cell-derived RPE encoding the major risk variant associated with AMD (complement factor H; CFH Y402H) express increased AKT2, impairing TFEB/TFE3-dependent lysosomal function. Collectively, these findings suggest that targeting the AKT2/SIRT5/TFEB pathway may be an effective therapy to delay the progression of dry AMD.

The combination of reverse engineering (RE) with additive manufacturing (AM) is widely used for solid freeform fabrication and overcomes limitations with the current capabilities of designers and CAD packages that are restricted to classical model shapes. The shape flexibility offered by RE typically leads to the generated CAD models being represented in point cloud formats or stereolithography files. One resulting challenge is the difficulty with tolerance specification for the RE-generated models, especially for freeform shapes. Geometric metrology for the AM-produced parts to determine their geometric conformation can also be involved. Furthermore, conventional process analyses cannot be performed directly after RE because of the complex geometric data structure. We propose to address all these issues with a new tolerance specification and process analysis framework based on volumetric data analysis. The tolerance zone constructed under our framework, and the corresponding geometric measurements, are consistent with the profile tolerance in geometric dimensioning and tolerancing standards. Reverse engineering's process capability is assessed under our methodology via a parametric bootstrap procedure in the size-and-shape space. The performance and utility of the proposed framework are validated via a process capability study on RE-generated CAD models of multiple, additively manufactured freeform objects. [ABSTRACT FROM AUTHOR]

Tolerances are critical to the product's design, manufacturing, and quality. However, tolerances are often overlooked in a reverse engineering (RE) process for industrial applications, especially in legacy parts or spare parts remanufacturing. Ignoring tolerances could either unnecessarily call for high precision remanufacturing processes or make the reproduced parts unqualified. Additive manufacturing (AM) techniques are used in remanufacturing applications because of their ability to manufacturing intricated parts. Due to its layer-by-layer fabrication nature, the metrology for AM-created parts is drastically different when compared to traditional feature-generation processes. In this study, we first propose a novel way to classify manufacturing processes based on whether they directly identify or generate features, which could profoundly affect their metrology tools. Next, a systematic geometric inspection and tolerance estimation methodology for the RE system is proposed. A set of tools is developed to extract various geometric dimensional values from the point clouds based on their tolerancing types. Moreover, based on the domain knowledge in production process design and planning, methods are developed to estimate empirical tolerances from a small batch of legacy parts. Comparisons of empirical tolerances of real machined parts to their designed tolerances are presented to evaluate the performance of the proposed framework. [ABSTRACT FROM AUTHOR]

Three-dimensional (3-D) point cloud data are increasingly being used to describe a wide range of physical objects in detail, corresponding to customized and flexible shape designs. The advent of a new generation of optical sensors has simplified and reduced the costs of acquiring 3-D data in near-real-time. However, the variation of the acquired point clouds, and methods to describe them, create bottlenecks in manufacturing practices such as Reverse Engineering (RE) and metrology in additive manufacturing. We address this issue by developing an automated variance modeling algorithm that utilizes a physical object's local geometric descriptors and Bayesian Extreme Learning Machines (BELMs). Our proposed ensemble and residual BELM-variants are trained by a scanning history that is composed of multiple scans of other, distinct objects. The specific scanning history is selected by a new empirical Kullback–Leibler divergence we developed to identify objects that are geometrically similar to an object of interest. A case study of our algorithm on additively manufactured products demonstrates its capability to model the variance of point cloud data for arbitrary freeform shapes based on a scanning history involving simpler, and distinct, shapes. Our algorithm has utility for measuring the process capability of 3-D scanning for RE processes. [ABSTRACT FROM AUTHOR]

Reverse Engineering (RE) has been widely used to extract geometric design information from a physical product for reproduction or redesign purposes. A scan of an object is often implemented to (re-)construct the computer-aided design model. However, this model is most likely an inaccurate representation of the original design, due to the existing uncertainties in each part and the scanning process. This randomness can result in shrinking the original tolerance region or even yielding asymmetric tolerance regions, which can call for unnecessarily high precision reproduction. In this article, we first propose an algorithm to generate the mean configuration based on the data clouds collected from several scans and multiple parts (if applicable). A Bayesian model with prior knowledge of production processes and scanners is specified to model the statistical properties of the mean configuration. Its marginal posterior outperforms single-scan models with lower variances, concentrating around the physical object or initial design. Furthermore, we propose a bi-objective optimization model to address RE process planning questions regarding the required number of scans and parts to achieve target accuracy requirements. Simulations and industrial case studies, including both unique freeform objects and mechanical parts, are conducted to illustrate and evaluate the performances of proposed methods. [ABSTRACT FROM AUTHOR]