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While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality.This study proposes PROS, an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-off between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) efficiently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing efficiency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath.We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high fidelity signal. It boosts the power efficiency of the wearable device by more than 1200\% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals. 

Inspired by a new coded computation algorithm for invertible functions, we propose Coded-InvNet a new approach to design resilient prediction serving systems that can gracefully handle stragglers or node failures. Coded-InvNet leverages recent findings in the deep learning literature such as invertible neural networks, Manifold Mixup, and domain translation algorithms, identifying interesting research directions that span across machine learning and systems. Our experimental results show that Coded-InvNet can outperform existing approaches, especially when the compute resource overhead is as low as 10%. For instance, without knowing which of the ten workers is going to fail, our algorithm can design a backup task so that it can correctly recover the missing prediction result with an accuracy of 85.9%, significantly outperforming the previous SOTA by 32.5%. 

Nucleic acid biosensing technologies have the capability to provide valuable information in applications ranging from medical diagnostics to environmental sensing. The unique properties of plasmonic metallic nanoparticles have been used for sensing purposes and among them, plasmonic sensors based on surface-enhanced Raman scattering (SERS) offer the advantages of sensitive and muliplexed detection owing to the narrow bandwidth of their characteristic Raman spectral features. This paper describes current applications that employ the unique SERS-based inverse molecular sentinel (iMS) nanobiosensors developed in our laboratory. Herein, we demonstrate the use of label-free iMS nanoprobes for detecting specific nucleic acid biomarkers in a wide variety of applications from cancer diagnostics to genetic monitoring for plant biology in renewable biofuel research.

 

Genetic mutants defective in stimulus‐induced Ca²⁺increases have been gradually isolated, allowing the identification of cell‐surface sensors/receptors, such as the osmosensor OSCA1. However, determining the Ca²⁺‐signaling specificity to various stimuli in these mutants remains a challenge. For instance, less is known about the exact selectivity between osmotic and ionic stresses in theosca1mutant.

Here, we have developed a method to distinguish the osmotic and ionic effects by analyzing Ca²⁺increases, and demonstrated thatosca1is impaired primarily in Ca²⁺increases induced by the osmotic but not ionic stress.

We recorded Ca²⁺increases induced by sorbitol (osmotic effect, OE) and NaCl/CaCl₂(OE + ionic effect, IE) inArabidopsiswild‐type andosca1seedlings. We assumed the NaCl/CaCl₂total effect (TE) = OE + IE, then developed procedures for Ca²⁺imaging, image analysis and mathematic fitting/modeling, and foundosca1defects mainly in OE.

The osmotic specificity ofosca1suggests that osmotic and ionic perceptions are independent. The precise estimation of these two stress effects is applicable not only to new Ca²⁺‐signaling mutants with distinct stimulus specificity but also the complex Ca²⁺signaling crosstalk among multiple concurrent stresses that occur naturally, and will enable us to specifically fine tune multiple signal pathways to improve crop yields.