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Introduction: Regional dysfunction of cardiac myocardium can result from diverse underlying conditions and cause adverse events even in patients without clinically apparent cardiovascular disease over ejection fraction. Assessment of regional wall motion abnormalities (WMA) currently requires sophisticated imaging that is not feasible for population screening or frequent disease monitoring. Hypothesis: We hypothesized that an AI-enabled ECG model trained on a dataset of qualitative labels could identify and localize wall motion abnormalities with higher accuracy than traditional ECG analysis Methods: In a large academic center, a deep convolutional network was developed with raw 12-lead ECGs and associated reports from 82,424 transthoracic echocardiograms (N = 50,960 patients, age 64 ± 17 years, 54.5% male). 80% of the data was used in model development and the remaining 20% was used for testing. ECGs with ventricular pacing and non-finalized echocardiography reports were excluded. Pre-processing included Kors transformation and beat extraction for input size of 3x300 samples (Figure 1A) and output labels were established by NLP of semi-structured echocardiography reports. Results: ML ECG based models predicted inferior/posterior regional wall motion abnormality with C-statistic of 0.772 (Figure 1B). Physician interpretation of Q-wave ECG pattern was inferior (C-statistic of 0.505). Other segments had similar AUC (apex 0.81, anterior 0.80, lateral 0.75, septal 0.75). Patients with positive ECG screen had +LR 2.70 of having a wall motion abnormality on echocardiography and patients with WMA by echocardiography had decreased survival (LR 0.41, CI: 0.34-0.47, p1500 days (Figure 1C). Conclusion: This study is the first to evaluate the presence and location of regional wall motion abnormalities from inexpensive and noninvasive 12-lead ECG. This approach could be applied for widespread ambulatory screening.

Introduction: It is unclear whether atrial fibrillation (AF) is best identified on intracardiac recordings by varying shape, rapid rate or extent of irregularity. Prioritizing these features may improve device diagnosis of AF. Hypothesis: AF can be separated from organized atrial flutter (AFL) by electrogram shape, independent of the contributions of rate or regularity. Methods: In 86 patients (25 female, age 65±11 years) we trained a convolutional neural network (CNN) to classify AF or AFL from 64 unipolar electrograms of persistent AF recorded for 60 seconds. In cases labelled as AF, we modified inputs by progressively regularizing (a) electrogram shape; (b) rate or (c) regularity in timing, to define which switched the classification to AFL. Results: The CNN provided a c-statistic of 0.95 ± 0.05 to identify AF or AFL in independent test cohorts not used for training, using 10-fold cross validation. Fig A shows AF in which progressive regularization of shape and timing from #1 to #4 flipped CNN classification into AFL in 45%. EGMs with 100% consistent shape and timing were classified by their cycle length (CL=1/rate): ~90% AF for CL < 175 ms, ~80% AFL for CL from 200-280 ms. Fig. B shows sequences simulated from patient-specific EGMs of AF that were classified as AF in 91 ± 12% of cases even if regular with CL of 200-280 ms, showing AF classification based on EGM shape alone. Figure B illustrates some ‘AF pathognomic’ electrogram shapes in red. Conclusions: AF may be identified by specific EGM shape patterns independent of regularity or rate. Regularity in shape and timing contribute ~45% to AFL classification, and adding CL explains up to 80%. Studies are required to study the mechanistic basis and clinical implications of specific AF-electrogram shapes.

Applying classical molecular dynamics simulations, we report the effects of length ( λ) and orientation ( θ) of a line-defect on strength and toughness in defective 2D hexagonal boron nitride. Results reveal the existence of a “transition angle,” θ t = 2.47 °, at which both toughness and strength are insensitive to the finite length of the defect in an infinite domain. For θ θ t, they show the opposite behavior. Examination of the stress-fields shows that θ-dependent variation in stress-localization at the edges of the line-defect and symmetry-breaking of the stress-fields with respect to the defect-axis govern the disparate θ-dependent behavior. For θ θ t, the stress-intensity at the edges is strongly localized at the opposite sides of the line-defect. The stress-intensity increases asymptotically with the increasing defect-length and reduces the strength and toughness of the defective lattice. The stress-localization, however, saturates at a “saturation angle” of around 60 ° for strength and 30 ° for toughness. Additionally, there exists a critical defect-length λ c = 60 A, below which there is a strong θ-dependent variation in elastic interactions between the edges, affecting strength and toughness substantially. For λ > λ c, the elastic interactions saturate and make both strength and toughness insensitive to the change in the length of the defect.

Using density functional theory calculations, we investigate and report the electronic structure basis of symmetry-breaking in atomic structures and variations in extreme mechanical properties of a number of widely used polymers, namely, polyvinylidene fluoride (PVDF), polytetrafluoroethylene, polyethylene, and three structural variants of PVDF. All of these polymers consist of C–C bonds in the backbone and a combination of C–H and C–F bonds attached to the backbone. Our results show that the relative proportion and arrangement of the C–H and C–F bonds, hereafter called the chemical heterogeneity, lead to two distinct sets of structural symmetries and mechanical properties. The set with the lower chemical heterogeneity possesses higher structural symmetry and exhibits higher strength and toughness. Electronic population analysis shows that chemical heterogeneity breaks the structural symmetry and alters deformation induced “electron redistribution patterns” in the C–C backbone. Strong variations in electron redistribution serve as the basis for distinctive mechanical behavior among the polymers: the higher the structural variation and electron redistribution, the lower the bond rupture force and toughness of the polymer. The bond rupture process is initiated by a decrease in the electronic population in the backbone, while the electronic interactions at the C–H and C–F bonds dictate where, how, and which bonds rupture. Furthermore, in the linear regime of mechanical deformation, the force–strain behavior is insensitive to the chemical heterogeneity and controlled by the strong covalent interactions of the C–C bonds. In the nonlinear regime, the chemical heterogeneity dramatically reduces the strength and toughness of the polymer chain.

Nanocages are structurally complex hollow low-dimensional materials that offer unique properties and functions that are inaccessible in bulk materials. They have tremendous potential in revolutionizing a number of fields including drug delivery and nanotechnology, but their applications remain limited primarily due to inadequate understanding of their extreme mechanical properties. Here, using reactive force field-based classical molecular dynamics simulations, we explore the bulk modulus, strength, and toughness modulus for a number of dodecahedral silica nanocages. The results show that, both under hydrostatic tension and compression, the mechanical properties vary nonlinearly with the structural parameters. Also, unlike bulk silica—which shows softening under tension in the nonlinear regime of mechanical deformation—silica nanocage exhibits stiffening at higher deformation that originates from the structural resistance of the nanocage. We show that the surface-area to volume ratio accurately describes the softening and stiffening behavior of the cage. Likewise, under compression, the nanocage shows three distinctive regimes: (i) linear decrease in stress with increasing strain, (ii) constant stress states with increasing strain representing shrinkage of the empty space, and (iii) exponential decrease in stress with increasing strain dominated by strong repulsion. These findings highlight the possibility of attaining a diverse set of mechanical properties from a nanocage by tailoring its structural parameters.

This paper reveals the existence of a critical separation distance ( d c) beyond which the elastic interactions between a pair of monovacancies in graphene or hexagonal boron nitride become inconsequential for the strength and toughness of the defective lattice. This distance is independent of the chirality of the lattice. For any inter-defect distance higher than d c, the lattice behaves mechanically as if there is a single defect. For a distance less than d c, the defect–defect elastic interactions produce distinctive mechanical behavior depending on the orientation ( θ) of the defect pair relative to the loading direction. Both strength and toughness of the lattice containing a pair of “interacting monovacancies (iMVs)” are either higher or smaller than that of the lattice containing a pair of “non-interacting monovacancies (nMVs),” suggesting the existence of a critical orientation angle θ c. For θ < θ c, the smaller the distance between the iMVs, the higher the toughness and strength compared to the lattice containing nMVs, whereas, for θ ≥ θ c, the smaller the separation distance between the iMVs, the smaller the toughness and strength compared to the lattice containing nMVs. The transitional behavior has a negligible dependence on the chirality of the lattice, which indicates that the crystallographic anisotropy has a much weaker influence on toughness and strength compared to the anisotropy induced by the orientation angle itself. These observations underline an important point that the elastic fields emanating from vacancy defects are highly localized and fully contained within a small region of around 1.5 nm radius.