Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy

Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges to prevent chronic diseases planted in long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to freshen chronic disease prevention literacy targeted for times when e-commerce user experience has been assimilated into most people's daily life. Here, retrospective longitudinal query logs and purchase records from millions of online shoppers were accessed, constructing a broad spectrum of lifestyle features covering assorted product categories and buyer personas. Using the lifestyle-related information preceding their first purchases of prescription drugs, we could determine associations between online shoppers' past lifestyle choices and if they suffered from a particular chronic disease. Novel lifestyle risk factors were discovered in two exemplars -- depression and diabetes, most of which showed cognitive congruence with existing healthcare knowledge. Further, such empirical findings could be adopted to locate online shoppers at high risk of chronic diseases with fair accuracy (e.g., [area under the receiver operating characteristic curve] AUC=0.68 for depression and AUC=0.70 for diabetes), closely matching the performance of screening surveys benchmarked against medical diagnosis. Unobtrusive chronic disease surveillance via e-commerce sites may soon meet consenting individuals in the digital space they already inhabit.

An Ultra Lightweight CNN for Low Resource Circuit Component Recognition

In this paper, we present an ultra lightweight system that can effectively recognize different circuit components in an image with very limited training data. Along with the system, we also release the data set we created for the task. A two-stage approach is employed by our system. Selective search was applied to find the location of each circuit component. Based on its result, we crop the original image into smaller pieces. The pieces are then fed to the Convolutional Neural Network (CNN) for classification to identify each circuit component. It is of engineering significance and works well in circuit component recognition in a low resource setting. The accuracy of our system reaches 93.4\%, outperforming the support vector machine (SVM) baseline (75.00%) and the existing state-of-the-art RetinaNet solutions (92.80%).

Construction and Usage of a Human Body Common Coordinate Framework Comprising Clinical, Semantic, and Spatial Ontologies

The National Institutes of Health's (NIH) Human Biomolecular Atlas Program (HuBMAP) aims to create a comprehensive high-resolution atlas of all the cells in the healthy human body. Multiple laboratories across the United States are collecting tissue specimens from different organs of donors who vary in sex, age, and body size. Integrating and harmonizing the data derived from these samples and 'mapping' them into a common three-dimensional (3D) space is a major challenge. The key to making this possible is a 'Common Coordinate Framework' (CCF), which provides a semantically annotated, 3D reference system for the entire body. The CCF enables contributors to HuBMAP to 'register' specimens and datasets within a common spatial reference system, and it supports a standardized way to query and 'explore' data in a spatially and semantically explicit manner. [...] This paper describes the construction and usage of a CCF for the human body and its reference implementation in HuBMAP. The CCF consists of (1) a CCF Clinical Ontology, which provides metadata about the specimen and donor (the 'who'); (2) a CCF Semantic Ontology, which describes 'what' part of the body a sample came from and details anatomical structures, cell types, and biomarkers (ASCT+B); and (3) a CCF Spatial Ontology, which indicates 'where' a tissue sample is located in a 3D coordinate system. An initial version of all three CCF ontologies has been implemented for the first HuBMAP Portal release. It was successfully used by Tissue Mapping Centers to semantically annotate and spatially register 48 kidney and spleen tissue blocks. The blocks can be queried and explored in their clinical, semantic, and spatial context via the CCF user interface in the HuBMAP Portal.