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With advancements in computational molecular modeling and powerful structure search methods, it is now possible to systematically screen crystal structures for small organic molecules. 

Performance improvements obtained by recent principled approaches for pulse rate (PR) estimation from face videos have typically been achieved by adding or modifying certain modules within a reconfigurable system. Yet, evaluations of such remote photoplethysmography (rPPG) are usually performed only at the system level. To better understand each module's contribution and facilitate future research in explainable learning and artificial intelligence for physiological monitoring, this paper conducts a comparative study of video-based, principled PR tracking algorithms, with a focus on challenging fitness scenarios. A review of the progress achieved over the last decade and a half in this field is utilized to construct the major processing modules of a reconfigurable remote pulse rate sensing system. Experiments are conducted on two challenging datasets—an internal collection of 25 videos of two Asian males exercising on stationary-bike, elliptical, and treadmill machines, and 34 videos from a public ECG fitness database of 14 men and 3 women exercising on elliptical and stationary-bike machines. The signal-to-noise ratio (SNR), Pearson's correlation coefficient, error count ratio, error rate, and root mean squared error are used for performance evaluation. The top-performing configuration produces respective values of −0.8 dB, 0.86, 9%, 1.7%, and 3.3 beats per minute (bpm) for the internal dataset and 1.3 dB, 0.77, 28.6%, 6.0%, and 8.1 bpm for the ECG Fitness dataset, achieving significant improvements over alternative configurations. Our results suggest a synergistic effect between pulse color mapping and adaptive motion filtering, as well as the importance of a robust frequency tracking algorithm for PR estimation in low SNR settings. 

Materials exhibiting martensitic phase transitions are essential for applications in shape memory alloys, actuators, and sensors. 

This article presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG). While some smartwatches now allow users to obtain a 30-s ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of cardiac functions. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of cardiac abnormalities. To alleviate the need for continuous user attention and active participation, we design a lightweight neural network that infers ECG from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. We also develop a diagnosis-oriented training strategy to enable the neural network to capture the pathological features of ECG, aiming to increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs). We also leverage model interpretation to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40000 parameters. 

Biological systems, including proteins, employ water-mediated supramolecular interactions to adopt specific conformations for their functions. However, current solid-state supramolecular materials are typically stiff and fail to capture the dynamic behaviors observed in proteins. Here, we present dynamic crystal-hydrates of aliphatic dipeptides with sequence-isomers of leucine (L) and isoleucine (I). These crystals exhibit shallow conformational energy landscapes, with various reconfigurable crystal nano-architectures accessible through small changes in relative humidity and temperature. Specifically, for LI crystals, as water content changes, the solid-state supramolecular architecture rapidly and reversibly transitions between perpendicular and parallel honeycomb nano-architectures, as well as layered van der Waals structures, leading to significant and distinct variations in mechanical and photophysical properties. Our findings demonstrate the potential of leveraging aliphatic hydrophobic domains inspired by protein architectures to create dynamic solid-state materials with context-adaptive properties.