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Smartwatch and fitness band wearable consumer electronics can passively measure pulse rate from the wrist using photoplethysmography (PPG). Identification of pulse irregularity or variability from these data has the potential to identify atrial fibrillation or atrial flutter (AF, collectively). The rapidly expanding consumer base of these devices allows for detection of undiagnosed AF at scale. The Apple Heart Study is a prospective, single arm pragmatic study that has enrolled 419,093 participants (NCT03335800). The primary objective is to measure the proportion of participants with an irregular pulse detected by the Apple Watch (Apple Inc, Cupertino, CA) with AF on subsequent ambulatory ECG patch monitoring. The secondary objectives are to: 1) characterize the concordance of pulse irregularity notification episodes from the Apple Watch with simultaneously recorded ambulatory ECGs; 2) estimate the rate of initial contact with a health care provider within 3 months after notification of pulse irregularity. The study is conducted virtually, with screening, consent and data collection performed electronically from within an accompanying smartphone app. Study visits are performed by telehealth study physicians via video chat through the app, and ambulatory ECG patches are mailed to the participants. The results of this trial will provide initial evidence for the ability of a smartwatch algorithm to identify pulse irregularity and variability which may reflect previously unknown AF. The Apple Heart Study will help provide a foundation for how wearable technology can inform the clinical approach to AF identification and screening.

BackgroundCardiovascular and arrhythmic events have been reported in hospitalized COVID-19 patients. However, arrhythmia manifestations and treatment strategies used in these patients have not been well-described. We sought to better understand the cardiac arrhythmic manifestations and treatment strategies in hospitalized COVID-19 patients through a worldwide cross-sectional survey.MethodsThe Heart Rhythm Society (HRS) sent an online survey (via SurveyMonkey) to electrophysiology (EP) professionals (physicians, scientists, and allied professionals) across the globe. The survey was active from March 27 to April 13, 2020.ResultsA total of 1197 respondents completed the survey with 50% of respondents from outside the USA, representing 76 countries and 6 continents. Of respondents, 905 (76%) reported having COVID-19-positive patients in their hospital. Atrial fibrillation was the most commonly reported tachyarrhythmia whereas severe sinus bradycardia and complete heart block were the most common bradyarrhythmias. Ventricular tachycardia/ventricular fibrillation arrest and pulseless electrical activity were reported by 4.8% and 5.6% of respondents, respectively. There were 140 of 631 (22.2%) respondents who reported using anticoagulation therapy in all COVID-19-positive patients who did not otherwise have an indication. One hundred fifty-five of 498 (31%) reported regular use of hydroxychloroquine/chloroquine (HCQ) + azithromycin (AZM); concomitant use of AZM was more common in the USA. Sixty of 489 respondents (12.3%) reported having to discontinue therapy with HCQ + AZM due to significant QTc prolongation and 20 (4.1%) reported cases of Torsade de Pointes in patients on HCQ/chloroquine and AZM. Amiodarone was the most common antiarrhythmic drug used for ventricular arrhythmia management.ConclusionsIn this global survey of > 1100 EP professionals regarding hospitalized COVID-19 patients, a variety of arrhythmic manifestations were observed, ranging from benign to potentially life-threatening. Observed adverse events related to use of HCQ + AZM included prolonged QTc requiring drug discontinuation as well as Torsade de Pointes. Large prospective studies to better define arrhythmic manifestations as well as the safety of treatment strategies in COVID-19 patients are warranted.

A bstract Holographic entanglement entropy was recently recast in terms of Riemannian flows or ‘bit threads’. We consider the Lorentzian analog to reformulate the ‘complexity=volume’ conjecture using Lorentzian flows — timelike vector fields whose minimum flux through a boundary subregion is equal to the volume of the homologous maximal bulk Cauchy slice. By the nesting of Lorentzian flows, holographic complexity is shown to obey a number of properties. Particularly, the rate of complexity is bounded below by conditional complexity , describing a multi-step optimization with intermediate and final target states. We provide multiple explicit geometric realizations of Lorentzian flows in AdS backgrounds, including their time-dependence and behavior near the singularity in a black hole interior. Conceptually, discretized flows are interpreted as Lorentzian threads or ‘gatelines’. Upon selecting a reference state, complexity thence counts the minimum number of gatelines needed to prepare a target state described by a tensor network discretizing the maximal volume slice, matching its quantum information theoretic definition. We point out that suboptimal tensor networks are important to fully characterize the state, leading us to propose a refined notion of complexity as an ensemble average. The bulk symplectic potential provides a specific ‘canonical’ thread configuration characterizing perturbations around arbitrary CFT states. Consistency of this solution requires the bulk satisfy the linearized Einstein’s equations, which are shown to be equivalent to the holographic first law of complexity, thereby advocating for a principle of ‘spacetime complexity’. Lastly, we argue Lorentzian threads provide a notion of emergent time. This article is an expanded and detailed version of [1], including several new results.

The central nervous system of humans and other animals modulates spinal cord activity to achieve several locomotion behaviors. Previous neuromechanical models investigated the modulation of human gait changing selected parameters belonging to CPGs (Central Pattern Generators) feedforward oscillatory structures or to feedback reflex circuits. CPG-based models could replicate slow and fast walking by changing only the oscillation’s properties. On the other hand, reflex-based models could achieve different behaviors through optimizations of large dimensional parameter spaces. However, they could not effectively identify individual key reflex parameters responsible for gait characteristics’ modulation. This study investigates which reflex parameters modulate the gait characteristics through neuromechanical simulations. A recently developed reflex-based model is used to perform optimizations with different target behaviors on speed, step length, and step duration to analyze the correlation between reflex parameters and their influence on these gait characteristics. We identified nine key parameters that may affect the target speed ranging from slow to fast walking (0.48 and 1.71 m/s) as well as a large range of step lengths (0.43 and 0.88 m) and step duration (0.51, 0.98 s). The findings show that specific reflexes during stance significantly affect step length regulation, mainly given by positive force feedback of the ankle plantarflexors’ group. On the other hand, stretch reflexes active during swing of iliopsoas and gluteus maximus regulate all the gait characteristics under analysis. Additionally, the results show that the hamstrings’ group’s stretch reflex during the landing phase is responsible for modulating the step length and step duration. Additional validation studies in simulations demonstrated that the modulation of identified reflexes is sufficient to regulate the investigated gait characteristics. Thus, this study provides an overview of possible reflexes involved in modulating speed, step length, and step duration of human gaits.

A bstract Consistent coupling of quantum and classical degrees of freedom exists so long as there is both diffusion of the classical degrees of freedom and decoherence of the quantum system. In this paper, we derive the Newtonian limit of such classical-quantum (CQ) theories of gravity. Our results are obtained both via the gauge fixing of the recently proposed path integral theory of CQ general relativity and via the CQ master equation approach. In each case, we find the same weak field dynamics. We find that the New-tonian potential diffuses by an amount lower bounded by the decoherence rate into mass eigenstates. We also present our results as an unraveled system of stochastic differential equations for the trajectory of the hybrid classical-quantum state and provide a series of kernels for constructing figures of merit, which can be used to rule out part of the parameter space of classical-quantum theories of gravity by experimentally testing it via the decoherence-diffusion trade-off. We compare and contrast the weak field limit to previous models of classical Newtonian gravity coupled to quantum systems. Here, we find that the Newtonian potential and quantum state change in lock-step, with the flow of time being stochastic.

Using a smartphone app, the investigators recruited 419,297 participants to be monitored for irregular pulses. Patterns suggesting atrial fibrillation were detected in 2161 participants who then received ECG monitoring devices to be worn for 7 days to confirm the presence or absence of atrial fibrillation.

Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodings in functional MRI during narrative listening. Linguistic features of word unpredictability (surprisal) and contextual importance (saliency) were derived from the GPT-2 applied to the text of a 12-min narrative. Segments of variable duration (from 15 to 90 s) defined the context for the next word, resulting in different sets of neural predictors for functional MRI signals recorded in 27 healthy listeners of the narrative. GPT-2 surprisal, estimating word prediction errors from the artificial network, significantly explained the neural data in superior and middle temporal gyri (bilaterally), in anterior and posterior cingulate cortices, and in the left prefrontal cortex. GPT-2 saliency, weighing the importance of context words, significantly explained the neural data for longer segments in left superior and middle temporal gyri. These results add novel support to the use of DL tools in the search for neural encodings in functional MRI. A DL language model like the GPT-2 may feature useful data about neural processes subserving language comprehension in humans, including next-word context-related prediction.

Lymphatic dissemination from the primary tumor is a major mechanism by which breast cancer cells access the systemic circulation, resulting in distant metastasis and mortality. Numerous studies link activation of hypoxia-inducible factor 1 (HIF-1) with tumor angiogenesis, metastasis, and patient mortality. However, the role of HIF-1 in lymphatic dissemination is poorly understood. In this study, we show that HIF-1 promotes lymphatic metastasis of breast cancer by direct transactivation of the gene encoding platelet-derived growth factor B (PDGF-B), which has proliferative and chemotactic effects on lymphatic endothelial cells. Lymphangiogenesis and lymphatic metastasis in mice bearing human breast cancer orthografts were blocked by administration of the HIF-1 inhibitor digoxin or the tyrosine kinase inhibitor imatinib. Immunohistochemical analysis of human breast cancer biopsies demonstrated colocalization of HIF-1α and PDGF-B, which were correlated with lymphatic vessel area and histological grade. Taken together, these data provide experimental support for breast cancer clinical trials targeting HIF-1 and PDGF-B.