Search

Abstract Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

Abstract Superparamagnetic iron oxide nanoparticles (SPIONs) are characterized by their exceptional susceptibility and relaxivity at ultra-low field (ULF) regimes, make them a promising contrast agent (CA) for ULF MRI. Despite their distinct advantages, the translation of these properties into clinically valuable image contrast in ULF MRI remains underexplored. In this study, we investigate the use of SPIONs to generate in vivo MRI contrast at 6.5 mT within the organs and vascular system of rodents. This investigation includes comprehensive SPION characterization and phantom imaging experiments to validate the utility of SPIONs to produce positive image contrast and to facilitate phase-sensitive imaging at ULF. Optimized balanced steady-state free precession (bSSFP) and spoiled gradient echo (SPGR) MRI sequences are used to generate in vivo contrast by leveraging the distinctive properties of SPIONs at ULF. Imaging studies in rodents reveal positive organ contrast attainable in magnitude images, and MRI phase maps can be used to visualize the vascular system. This work demonstrates the effectiveness of SPIONs in enhancing preclinical organ and vascular imaging at ULF; it bridges the gap between the study of the distinctive physical properties of SPIONs and the demonstration of in vivo image contrast.

Abstract Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

Abstract Magnetic resonance imaging is a widespread clinical tool for the detection of soft tissue morphology and pathology. However, the clinical deployment of magnetic resonance imaging scanners is ultimately limited by size, cost, and space constraints. Here, we discuss the design and performance of a low-field single-sided magnetic resonance sensor intended for point-of-care evaluation of skeletal muscle in vivo. The 11 kg sensor has a penetration depth of >8 mm, which allows for an accurate analysis of muscle tissue and can avoid signal from more proximal layers, including subcutaneous adipose tissue. Low operational power and shielding requirements are achieved through the design of a permanent magnet array and surface transceiver coil. The sensor can acquire high signal-to-noise measurements in minutes, making it practical as a point-of-care tool for many quantitative diagnostic measurements, including T2 relaxometry. In this work, we present the in vitro and human in vivo performance of the device for muscle tissue evaluation.

The discovery of novel experimental techniques often lags behind contemporary theoretical understanding. In particular, it can be difficult to establish appropriate measurement protocols without analytic descriptions of the underlying system-of-interest. Here we propose a statistical learning framework that avoids the need for such descriptions for ergodic systems. We validate this framework by using Monte Carlo simulation and deep neural networks to learn a mapping between nuclear magnetic resonance spectra acquired on a novel low-field instrument and proton exchange rates in ethanol-water mixtures. We found that trained networks exhibited normalized-root-mean-square errors of less than 1 % for exchange rates under 150 s−1 but performed poorly for rates above this range. This differential performance occurred because low-field measurements are indistinguishable from one another for fast exchange. Nonetheless, where a discoverable relationship between indirect measurements and emergent dynamics exists, we demonstrate the possibility of approximating it in an efficient, data-driven manner.

Abstract Chimeric Antigen Receptor (CAR) T-cell therapy is a highly effective treatment for B-cell malignancies but limited in use due to clinically significant hyperinflammatory toxicities. Understanding the pathophysiologic mechanisms which mediate these toxicities can help identify novel management strategies. Here we report a novel in vitro model of the macrophage-endothelial interface to study the effects of CAR T-cell-induced cytokine storm. Using this model, we demonstrate that macrophage-mediated inflammation is regulated by endothelial cell activity. Furthermore, endothelial inflammation occurs independently of macrophages following exposure to CAR T-cell products and the induced endothelial inflammation potentiates macrophage-mediated inflammatory signaling, leading to a hyperinflammatory environment. While corticosteroids, the current gold standard of care, attenuate the resulting macrophage inflammatory signaling, the endothelial activity remains refractory to this treatment strategy. Utilizing a network model, coupled to in vitro secretion profiling, we identified STAT3 programming as critical in regulating this endothelial behavior. Lastly, we demonstrate how targeting STAT3 activity can abrogate endothelial inflammation and attenuate this otherwise hyperinflammatory environment. Our results demonstrate that endothelial cells play a central role in the pathophysiology of CAR T-cell toxicities and targeting the mechanisms driving the endothelial response can guide future clinical management.

Diabetes is a major global health issue. We evaluated compliance to laboratory-based management guidelines for diabetes (type 1 and 2), essential for effective treatment and reducing diabetes-related morbidity and mortality. Our study utilized South Africa's National Health Laboratory Services (NHLS) data, focusing on patients from birth to age 80 years who underwent initial diabetes laboratory testing between January 1, 2012-January 1, 2016. Patients were categorized into type 1 (

Hyperpolarization chemistry based on reversible exchange of parahydrogen, also known as Signal Amplification By Reversible Exchange (SABRE), is a particularly simple approach to attain high levels of nuclear spin hyperpolarization, which can enhance NMR and MRI signals by many orders of magnitude. SABRE has received significant attention in the scientific community since its inception because of its relative experimental simplicity and its broad applicability to a wide range of molecules, however, in vivo detection of molecular probes hyperpolarized by SABRE has remained elusive. Here we describe a first demonstration of SABRE-hyperpolarized contrast detected in vivo, specifically using hyperpolarized [1–13C]pyruvate. Biocompatible formulations of hyperpolarized [1–13C]pyruvate in, both, methanol-water, and ethanol-water mixtures followed by dilution with saline and catalyst filtration were prepared and injected into healthy Sprague Dawley and Wistar rats. Effective hyperpolarization-catalyst removal was performed with silica filters without major losses in hyperpolarization. Metabolic conversion of pyruvate to lactate, alanine, and bicarbonate was detected in vivo. Pyruvate-hydrate was also observed as a minor byproduct. Measurements were performed on the liver and kidney at 4.7 T via time-resolved spectroscopy and chemical-shift-resolved MRI. In addition, whole-body metabolic measurements were obtained using a cryogen-free 1.5 T MRI system, illustrating the utility of combining lower-cost MRI systems with simple, low-cost hyperpolarization chemistry to develop safe and scalable molecular imaging.

Abstract Background Animal movement data are regularly used to infer foraging behaviour and relationships to environmental characteristics, often to help identify critical habitat. To characterize foraging, movement models make a set of assumptions rooted in theory, for example, time spent foraging in an area increases with higher prey density. Methods We assessed the validity of these assumptions by associating horizontal movement and diving of satellite-telemetered ringed seals (Pusa hispida)—an opportunistic predator—in Hudson Bay, Canada, to modelled prey data and environmental proxies. Results Modelled prey biomass data performed better than their environmental proxies (e.g., sea surface temperature) for explaining seal movement; however movement was not related to foraging effort. Counter to theory, seals appeared to forage more in areas with relatively lower prey diversity and biomass, potentially due to reduced foraging efficiency in those areas. Conclusions Our study highlights the need to validate movement analyses with prey data to effectively estimate the relationship between prey availability and foraging behaviour.

Abstract Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidelity, high-coverage calculations (e.g., an existing database of GGA calculations) in a robust and scalable manner. We then use legacy PBE(+U) GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions, and discuss practical considerations for its implementation.

Abstract Background Direct-acting antivirals (DAAs) are highly effective in achieving sustained virologic response among those with chronic hepatitis C virus (HCV) infection. Quality of life (QOL) benefits for an HCV-infected population with high numbers of people who inject drugs and people living with HIV (PLHIV) in Eastern Europe have not been explored. We estimated such benefits for Ukraine. Methods Using data from a demonstration study of 12-week DAA conducted in Kyiv, we compared self-reported QOL as captured with the MOS-SF20 at study entry and 12 weeks after treatment completion (week 24). We calculated domain scores for health perception, physical, role and social functioning, mental health and pain to at entry and week 24, stratified by HIV status. Results Among the 857 patients included in the final analysis, health perception was the domain that showed the largest change, with an improvement of 85.7% between entry and week 24. The improvement was larger among those who were HIV negative (104.4%) than among those living with HIV (69.9%). Other domains that showed significant and meaningful improvements were physical functioning, which improved from 80.5 (95% CI 78.9–82.1) at study entry to 89.4 (88.1–90.7) at 24 weeks, role functioning (64.5 [62.3–66.8] to 86.5 [84.9–88.2]), social functioning (74.2 [72.1–76.2] to 84.8 [83.2–86.5]) and bodily pain (70.1 [68.2–72.0] to 89.8 [88.5–91.1]). Across all domains, QOL improvements among PLHIV were more modest than among HIV-negative participants. Conclusion QOL improved substantially across all domains between study entry and week 24. Changes over the study period were smaller among PLHIV.

Background White matter hyperintensity (WMH) on magnetic resonance imaging (MRI) of the brain is associated with vascular cognitive impairment, cardiovascular disease, and stroke. We hypothesized that portable magnetic resonance imaging (pMRI) could successfully identify WMHs and facilitate doing so in an unconventional setting. Methods and Results In a retrospective cohort of patients with both a conventional 1.5 Tesla MRI and pMRI, we report Cohen's kappa (κ) to measure agreement for detection of moderate to severe WMH (Fazekas ≥2). In a subsequent prospective observational study, we enrolled adult patients with a vascular risk factor being evaluated in the emergency department for a nonstroke complaint and measured WMH using pMRI. In the retrospective cohort, we included 33 patients, identifying 16 (49.5%) with WMH on conventional MRI. Between 2 raters evaluating pMRI, the interrater agreement on WMH was strong (κ=0.81), and between 1 rater for conventional MRI and the 2 raters for pMRI, intermodality agreement was moderate (κ=0.66, 0.60). In the prospective cohort we enrolled 91 individuals (mean age, 62.6 years; 53.9% men; 73.6% with hypertension), of which 58.2% had WMHs on pMRI. Among 37 Black and Hispanic individuals, the Area Deprivation Index was higher (versus White, 51.8±12.9 versus 37.9±11.9; P

Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

Abstract Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5–30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.

Abstract Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ = 0.87; continuous, ICC = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07–40.04, p = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11–2.49, p = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.

Objectives To examine differences in rural community children’s moderate-to-vigorous physical activity (MVPA) and participation in out-of-school activities from fall 2019 to fall 2020 and explore enacted PA opportunity modifications post initial COVID-19 disruption.Design Mixed methods study using the validated Youth Activity Profile (YAP), administrator reports and stakeholder surveys and semistructured interviews.Setting Children and community stakeholders from one rural US Great Plains community in the state of Nebraska were recruited.Participants Third through fifth graders in fall 2019 (n=144) and fall 2020 (n=174) reported MVPA and participation in out-of-school activities using the YAP. School administrators reported weekly physical education (PE) and recess minutes. Community stakeholders reported pandemic-related changes in community social structures in semistructured interviews (n=4) and surveys (n=19).Results Average daily MVPA minutes increased from 2019 to 2020 (75.0 vs 81.3, SE=1.6, p

Conventional magnetic resonance imaging (MRI) operates at a high magnetic field strength and requires a strict access-controlled environment, making MRI often inaccessible. Here, the authors present a portable low-field MRI device that detects intracerebral hemorrhage with high accuracy.

Objective Test whether Sudarshan Kriya Yoga (SKY) was non-inferior to cognitive processing therapy (CPT) for treating symptoms of post-traumatic stress disorder (PTSD) among veterans via a parallel randomised controlled non-inferiority trial.Setting Outpatient Veterans Affairs healthcare centre.Participants 85 veterans (75 men, 61% white, mean age 56.9) with symptoms of PTSD participated between October 2015 and March 2020: 59 participants completed the study.Interventions SKY emphasises breathing routines and was delivered in group format in a 15-hour workshop followed by two 1-hour sessions per week for 5 weeks. CPT is an individual psychotherapy which emphasises shifting cognitive appraisals and was delivered in two 1-hour sessions per week for 6 weeks.Measures The primary outcome measure was the PTSD Checklist-Civilian Version (PCL-C). The secondary measures were the Beck Depression Inventory-II (BDI-II) and Positive and Negative Affect Scale (PANAS).Results Mean PCL-C at baseline was 56.5 (±12.6). Intent-to-treat analyses showed that PCL-C scores were reduced at 6 weeks (end of treatment) relative to baseline (SKY, −5.6, d=0.41, n=41: CPT, −6.8, d=0.58, n=44). The between-treatment difference in change scores was within the non-inferiority margin of 10 points (−1.2, 95% CI −5.7 to 3.3), suggesting SKY was not inferior to CPT. SKY was also non-inferior at 1-month (CPT–SKY: −2.1, 95% CI −6.9 to 2.8) and 1-year (CPT–SKY: −1.8, 95% CI −6.6 to 2.9) assessments. SKY was also non-inferior to CPT on the BDI-II and PANAS at end of treatment and 1 month, but SKY was inferior to CPT on both BDI-II and PANAS at 1 year. Dropout rates were similar (SKY, 27%, CPT, 34%: OR=1.36, 95% CI 0.51 to 3.62, p=0.54).Conclusions SKY may be non-inferior to CPT for treating symptoms of PTSD and merits further consideration as a treatment for PTSD.Trial registration number NCT02366403.

Abstract Recent years have seen a resurgence of interest in inexpensive low magnetic field (

Abstract Root phenotyping provides critical information to plant breeders for developing varieties with improved drought tolerance, greater root biomass, and greater nutrient use efficiency. Phenotyping roots in the natural environment is important for understanding the effect of the soil environment on root genotypic expressions. The goal of this work was to design and test a field‐scale mobile low‐field magnetic resonance imaging (LF‐MRI) Rhizotron that produces actionable root phenotyping data. We demonstrated this novel technology for root visualization and quantification using a LF‐MRI Rhizotron operating at 47 mT with two soil types. The LF‐MRI Rhizotron weights 453 kg, with a height of 90 cm, a diameter of 28 cm and an imaging field of view of 28 cm × 28 cm. The unit was operated in a Belk clay (Entic Hapluderts) and Weswood silt loam (Udifluventic Halustepts) generating 2‐D and 3‐D image data sets. The 2‐D image data had a collection time of 16.5 min per image at an image resolution of 2.2 mm per pixel. The 3‐D data had a collection time of 13 h per image with a 2.2 × 2.2 × 2.2 mm voxel resolution. Low‐field magnetic resonance imaging worked well for visualizing roots in moderate to high clay soils, demonstrating the potential for this technology; however, the broad application of this platform is hampered due to the prohibitively long scanning time to obtain 3‐D images. By increasing the field strength, and therefore the signal‐to‐noise ratio, faster scan times can enable a more useful system for root phenotyping.

Recent statistical studies have suggested a relationship between increased harmonic surprise and music preference. Conclusive behavioral evidence to establish this relationship is still lacking. We set out to address this gap through a behavioral study using computer-generated stimuli designed to differ only in contrastive and absolute harmonic surprise. We produced the stimuli with both experimental control and ecological validity in mind by engaging the help of studio musicians. The stimuli were rated for preference by 84 participants (44 female, 40 male) between 18 to 65 years old. Participants rated items featuring moderately increased absolute and contrastive surprise significantly higher than items with lower harmonic surprise. This effect applied only to levels of surprise within a range typically found in popular music, however. Excessive surprises did not yield an increase in preference. We discuss different mechanisms of consistency and how they may mediate the selection of neural strategies leading to preference formation. These findings provide evidence of a causal behavioral relationship between harmonic surprise and music preference.

Neuroimaging is a critical component of triage and treatment for patients who present with neuropathology. Magnetic resonance imaging and non-contrast computed tomography are the gold standard for diagnosis and prognostication of patients with acute brain injuries. However, these modalities require intra-hospital transport to strict, access-controlled environments, which puts critically ill patients at risk for complications and secondary injuries. A novel, portable MRI (pMRI) device that can be deployed at the patient's bedside provides a needed solution. In a dual-center investigation, Yale New Haven Hospital has obtained regular neuroimaging on patients using the pMRI as part of routine clinical care in the Emergency Department and Intensive Care Unit (ICU) since August of 2020. Massachusetts General Hospital has begun using pMRI in the Neuroscience Intensive Care Unit since January 2021. This technology has expanded the population of patients who can receive MRI imaging by increasing accessibility and timeliness for scan completion by eliminating the need for transport and increasing the potential for serial monitoring. Here we describe our methods for screening, coordinating, and executing pMRI exams and provide further detail on how to scan specific patient populations.

Accurate and effective local data collection systems are needed to inform community change on youth health behaviors such as physical activity (PA). Systematic methods are particularly important for understanding PA behaviors that may be influenced by individual, interpersonal, organizational, and regional factors. The purpose of this study was to describe a protocol for coordinating community stakeholders to implement an online youth PA surveillance instrument. The research team collaborated with local health departments (LHDs) from two rural communities to coordinate schools in implementing school-wide youth PA surveillance. A data sharing agreement was established between all partners. School administrators and teachers attended in-person training sessions for an online PA survey and how to use the data. Following the training, students were provided individualized logins to complete the survey once a semester over a two-year academic period. Across both communities, 23 teachers and administrators attended the training sessions that were facilitated by the LHDs and research team. In Year 1 (Y1), a total of 465 3rd through 6th grade students were enrolled in the participating schools (community 1 = 227; community 2 = 238). Survey response rates ranged from 86.1% to 95.4% completion, depending on the community and semester. In Year 2 (Y2), a total of 501 3rd through 6th grade students were enrolled (community 1 = 260; community 2 = 241). Response rates ranged from 86.3% to 89.6% in the fall term. A protocol for coordinating LHD and community stakeholders was an effective strategy for implementing population-level youth PA surveillance with high levels of reach.

Abstract Background This retrospective consecutive case series study was performed to determinate the survival rate and implant stability of short (7 mm length) dental implants with an electrowetted hydrophilic surface that were in function from 1 to 7 years. Methods A retrospective chart review identified and evaluated 86 consecutively placed 7-mm-long dental implants (ProActive, Neoss Ltd., Harrogate, England) in 75 patients. Analysis was performed for implant survival as well as implant stability, as measured by insertion torque (IT) and resonance frequency analysis (RFA). Results Clinical follow-ups were performed from 1.0 to 7.0 years after implant placement (mean 4.0 ± 2.1 years). Two implants failed prior to loading resulting in a 5-year cumulative survival rate (CSR) of 97.7%. An additional late failure occurred at 60 months post-loading for a 7-year CSR of 94.8%. Mean insertion torque was 30.1 ± 7.4 Ncm and mean RFA at insertion was 73.6 ± 8.1 ISQ. Follow-up RFA measurements suggested that the achieved primary stability was maintained throughout the healing phase. Conclusion The present study demonstrates that treatment with short implants can be a predictable treatment option with high survival rate in sites with limited available bone.

Although the ability of marine mammals to lower heart rates for extended periods when diving is well documented, it is unclear whether marine mammals have electrophysiological adaptations that extend beyond overall bradycardia. We analyzed electrocardiographic data from 50 species of terrestrial mammals and 19 species of marine mammals to determine whether the electrical activity of the heart differs between these two groups of mammals. We also tested whether physiological state (i.e., anesthetized or conscious) affects electrocardiogram (ECG) parameters. Analyses of ECG waveform morphology (heart rate, P-wave duration, and PQ, PR, QRS, and QT intervals) revealed allometric relationships between body mass and all ECG intervals (as well as heart rate) for both groups of mammals and specific differences in ECG parameters between marine mammals and their terrestrial counterparts. Model outputs indicated that marine mammals had 19% longer P-waves, 24% longer QRS intervals, and 21% shorter QT intervals. In other words, marine mammals had slower atrial and ventricular depolarization, and faster ventricular repolarization than terrestrial mammals. Heart rates and PR intervals were not significantly different between marine and terrestrial mammals, and physiological state did not significantly affect any ECG parameter. On average, ECG interval durations of marine and terrestrial mammals scaled with body mass to the power of 0.21 (range: 0.19–0.23) rather than the expected 0.25—while heart rate scaled with body mass to the power of –0.22 and was greater than the widely accepted –0.25 derived from fractal geometry. Our findings show clear differences between the hearts of terrestrial and marine mammals in terms of cardiac timing that extend beyond diving bradycardia. They also highlight the importance of considering special adaptations (such as breath-hold diving) when analyzing allometric relationships.