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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. 

Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called group fairness) may inadvertently treat individuals within the same demographic group unfairly. To address this issue, this article introduces a formal definition of within-group fairness that maintains fairness among individuals from within the same group. A pre-processing framework is proposed to meet both inter- and within-group fairness criteria with little compromise in performance. The framework maps the feature vectors of members from different groups to an inter-group fair canonical domain before feeding them into a scoring function. The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness. This framework has been applied to the Adult, COMPAS risk assessment, and Law School datasets, and its performance is demonstrated and compared with two regularization-based methods in achieving inter-group and within-group fairness. 

The inverse problem of inferring clinical gold-standard electrocardiogram (ECG) from photoplethysmogram (PPG) that can be measured by affordable wearable Internet of Healthcare Things (IoHT) devices is a research direction receiving growing attention. It combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis, such as discrete cosine transform (DCT), has limited fidelity for uncommon ECG shapes due to the lack of representative power. To better utilize the data and improve data representation, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL), and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, the proposed joint dictionary learning frameworks extend the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with a variety of PPG and ECG morphologies from two benchmark datasets that cover various age groups and disease types. The results show the proposed frameworks achieve better inference performance than previous methods with average Pearson coefficients being 0.88 using XDJDL and 0.92 using LC-XDJDL, suggesting an encouraging potential for ECG screening using PPG based on the proactively learned PPG-ECG relationship. By enabling the dynamic monitoring and analysis of the health status of an individual, the proposed frameworks contribute to the emerging digital twins paradigm for personalized healthcare. 

Drying of bacterial suspensions is frequently encountered in a plethora of natural and engineering processes. However, the evaporation-driven mechanical instabilities of dense consolidating bacterial suspensions have not been explored heretofore. Here, we report the formation of two different crack patterns of drying suspensions of Escherichia coli ( E. coli ) with distinct motile behaviors. Circular cracks are observed for wild-type E. coli with active swimming, whereas spiral-like cracks form for immotile bacteria. Using the elastic fracture mechanics and the poroelastic theory, we show that the formation of the circular cracks is determined by the tensile nature of the radial drying stress once the cracks are initiated by the local order structure of bacteria due to their collective swimming. Our study demonstrates the link between the microscopic swimming behaviors of individual bacteria and the mechanical instabilities and macroscopic pattern formation of drying bacterial films. The results shed light on the dynamics of active matter in a drying process and provide useful information for understanding various biological processes associated with drying bacterial suspensions. 

Size-selected negatively-charged boron clusters (B n − ) have been found to be planar or quasi-planar in a wide size range. Even though cage structures emerged as the global minimum at B 39 − , the global minimum of B 40 − was in fact planar. Only in the neutral form did the B 40 borospherene become the global minimum. How the structures of larger boron clusters evolve is of immense interest. Here we report the observation of a bilayer B 48 − cluster using photoelectron spectroscopy and first-principles calculations. The photoelectron spectra of B 48 − exhibit two well-resolved features at low binding energies, which are used as electronic signatures to compare with theoretical calculations. Global minimum searches and theoretical calculations indicate that both the B 48 − anion and the B 48 neutral possess a bilayer-type structure with D 2h symmetry. The simulated spectrum of the D 2h B 48 − agrees well with the experimental spectral features, confirming the bilayer global minimum structure. The bilayer B 48 −/0 clusters are found to be highly stable with strong interlayer covalent bonding, revealing a new structural type for size-selected boron clusters. The current study shows the structural diversity of boron nanoclusters and provides experimental evidence for the viability of bilayer borophenes.