Recent wearable devices offer portable monitoring of biopotentials, heart rate, or physical activity, allowing for active management of human health and wellness. Such systems can be inserted in the oral cavity for measuring food intake in regard to controlling eating behavior, directly related to diseases such as hypertension, diabetes, and obesity. However, existing devices using plastic circuit boards and rigid sensors are not ideal for oral insertion. A user-comfortable system for the oral cavity requires an ultrathin, low-profile, and soft electronic platform along with miniaturized sensors. Here, we introduce a stretchable hybrid electronic system that has an exceptionally small form factor, enabling a long-range wireless monitoring of sodium intake. Computational study of flexible mechanics and soft materials provides fundamental aspects of key design factors for a tissue-friendly configuration, incorporating a stretchable circuit and sensor. Analytical calculation and experimental study enables reliable wireless circuitry that accommodates dynamic mechanical stress. Systematic in vitro modeling characterizes the functionality of a sodium sensor in the electronics. In vivo demonstration with human subjects captures the device feasibility for real-time quantification of sodium intake, which can be used to manage hypertension.

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Serverless architectures organized around looselycoupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. In this paper, we explore a completely different part of the application space and examine the feasibility of analytical processing on big data using a serverless architecture. We present Flint, a prototype Spark execution engine that takes advantage of AWS Lambda to provide a pure pay-as-you-go cost model. With Flint, a developer uses PySpark exactly as before, but without needing an actual Spark cluster. We describe the design, implementation, and performance of Flint, along with the challenges associated with serverless analytics.

SummaryViscoelastic properties of cells provide valuable information regarding biological or clinically relevant cellular characteristics. Here, we introduce a new, electronic-based, microfluidic platform—visco-node-pore sensing (visco-NPS)—which quantifies cellular viscoelastic properties under periodic deformation. We measure the storage (G′) and loss (G″) moduli (i.e., elasticity and viscosity, respectively) of cells. By applying a wide range of deformation frequencies, our platform quantifies the frequency dependence of viscoelastic properties. G′ and G″ measurements show that the viscoelastic properties of malignant breast epithelial cells (MCF-7) are distinctly different from those of non-malignant breast epithelial cells (MCF-10A). With its sensitivity, visco-NPS can dissect the individual contributions of different cytoskeletal components to whole-cell mechanical properties. Moreover, visco-NPS can quantify the mechanical transitions of cells as they traverse the cell cycle or are initiated into an epithelial-mesenchymal transition. Visco-NPS identifies viscoelastic characteristics of cell populations, providing a biophysical understanding of cellular behavior and a potential for clinical applications.

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Although Alzheimer’s disease (AD) is the most common neurodegenerative disease, there are still no drugs available to treat or prevent AD effectively. Here, we examined changes in levels of selected proteins implicated in the pathogenesis of AD using plasma samples of control subjects and patients with cognition impairment. To precisely categorize the disease, fifty-six participants were examined with clinical cognitive tests, amyloid positron emission tomography (PET) scan, and white matter hyperintensities scored by magnetic resonance imaging. Plasma cathepsin D levels of the subjects were examined by immunoblotting and enzyme-linked immunosorbent assay (ELISA). Correlation of plasma cathepsin D levels with AD-related factors and clinical characteristics were examined by statistical analysis. By analyzing quantitative immunoblot and ELISA, we found that the plasma level of cathepsin D, a major lysosomal protease, was decreased in the group with amyloid plaque deposition at the brain compared to the control group. The level of plasma cathepsin D was negatively correlated with clinical dementia rating scale sum of boxes (CDR-SB) scores. In addition, our integrated multivariable logistic regression model suggests the high performance of plasma cathepsin D level for discriminating AD from non-AD. These results suggest that the plasma cathepsin D level could be developed as a diagnostic biomarker candidate for AD.

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Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a selfattention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. Experimental results show that our approach significantly improves the accuracy of the translation task than baselines without multi-task learning. In addition, we present an in-depth exploratory analysis on our translated results via topic modeling, uncovering several significant historical events.