Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

Investigating the behavior of diffusion models for accelerating electronic structure calculations.

We present an investigation of diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for de novo molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ∼10× lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a >2× speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field.

The genetic landscape of antibiotic sensitivity in Staphylococcus aureus.

Despite the critical importance of essential genes, systems-level investigations of their contribution to antibiotic sensitivity have been limited. Using CRISPR Adaptation-mediated Library Manufacturing (CALM), we generated ultra-dense CRISPR interference (CRISPRi) libraries in methicillin-sensitive and -resistant strains of Staphylococcus aureus, which allowed us to quantify gene fitness on a global scale across ten clinically relevant antibiotics. This led to the identification of a comprehensive set of known and novel biological processes modulating bacterial fitness in the antibiotics. Notably, we found that essential genes from diverse processes dominated antibiotic-gene interactions, including a large number of synergistic interactions between bactericidal antibiotics and processes such as cell wall synthesis/cell division (CC), DNA replication/DNA recombination (DD), protein export, and coenzyme A biosynthesis. Simultaneous genetic perturbations of diverse CC and DD processes aggravated bacterial fitness, revealing a widespread synergy between the two highly coordinated processes. In contrast, perturbation of transcriptional, translational, and select energy processes antagonized the effects of bactericidal antibiotics. Finally, we show that small molecule inhibitors recapitulated synergistic antibiotic-gene interactions, providing a rational foundation for developing novel combinatorial antimicrobial therapies.

Cardiovascular Risk Factors and Echocardiographic Findings in a Predominantly Black Population with Rheumatoid Arthritis and Heart Failure.

Among White rheumatoid arthritis (RA) cohorts, heart failure with preserved ejection fraction (HFpEF) is the most prevalent type of heart failure (HF). We aimed to assess the type of HF affecting Black RA patients. 64 patients with RA-HF were compared to age-, sex-, and race-matched RA patients without HF. Left ventricular ejection fraction (LVEF), wall motion abnormalities, left ventricle (LV) mass, and wall thickness were reviewed. 87.3% were Black, 84.4% were women, with a mean age of 69.6 ± 1.38 (± SEM) and BMI (kg/m 2) 29.6 ± 1.07. RA-HF patients had higher rates of hypertension (HTN), chronic kidney disease, and atrial fibrillation. 66.7% had ≥3 cardiovascular risk factors compared to RA patients without HF. 2D-echocardiograms of RA-HF revealed that 62.3% had LVEF ≥50%, 37% had diastolic dysfunction, and 43.1% had wall motion abnormalities. LV mass and relative wall thickness measurements indicated LV eccentric remodeling. The odds ratio for HF was 4.7 (1.5-14.53 CI), p<0.01, among RA-HTN group and 3.5 (1.091-11.7 CI) p<0.01 among smokers. In our predominantly Black RA-HF patients, HFpEF was the most common type of HF. HTN was associated with the highest OR for HF. Eccentric hypertrophic remodeling, a known poor prognostic indicator for cardiovascular events, was found. Further studies are required to confirm our findings.

Extreme Metastability of Diamond and its Transformation to the BC8 Post-Diamond Phase of Carbon.

Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synthesis and recovery of the theoretically predicted post-diamond BC8 phase remains elusive. Through quantum-accurate multimillion atom molecular dynamics (MD) simulations, we have uncovered the extreme metastability of diamond at very high pressures, significantly exceeding its range of thermodynamic stability. We predict the post-diamond BC8 phase to be experimentally accessible only within a narrow high pressure-temperature region of the carbon phase diagram. The diamond to BC8 transformation proceeds through premelting followed by BC8 nucleation and growth in the metastable carbon liquid. We propose a double-shock compression pathway for BC8 synthesis, which is currently being explored in experiments at the National Ignition Facility.

CathAI: fully automated coronary angiography interpretation and stenosis estimation.

Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.

A Rare Case of a Panic Attack Inducing a Junctional Escape Rhythm on ECG.

We present a unique and rare case of a young female patient who presented with complaints typical of her prior panic attacks and was found to have a junctional escape rhythm on ECG. Upon resolution of her symptoms, a repeat ECG demonstrated a return to normal sinus rhythm. Given that alternative etiologies had been ruled out, it was postulated that her panic attack induced a transient junctional escape rhythm.

Rett-like Phenotypes in HNRNPH2-Related Neurodevelopmental Disorder.

Rett Syndrome (RTT) is a neurodevelopmental disorder with a prevalence of 1:10,000 to 15,000 females worldwide. Classic Rett Syndrome presents in early childhood with a period of developmental regression, loss of purposeful hand skills along with hand stereotypies, gait abnormalities, and loss of acquired speech. Atypical RTT is diagnosed when a child shows some but not all the phenotypes of classic RTT, along with additional supporting criteria. Over 95% of classic RTT cases are attributed to pathogenic variants in Methyl-CpG Binding Protein 2 (MECP2), though additional genes have been implicated in other RTT cases, particularly those with the atypical RTT clinical picture. Other genetic etiologies have emerged with similar clinical characteristics to RTT Syndrome. Our team has characterized HNRNPH2-related neurodevelopmental disorder (HNRNPH2-RNDD) in 33 individuals associated with de novo pathogenic missense variants in the X-linked HNRNPH2 gene, characterized by developmental delay, intellectual disability, seizures, autistic-like features, and motor abnormalities. We sought to further characterize RTT clinical features in this group of individuals by using caregiver report. Twenty-six caregivers completed electronic surveys, with only 3 individuals having previously received an atypical RTT diagnosis, and no individuals with a typical RTT diagnosis. Caregivers reported a high number of behaviors and/or phenotypes consistent with RTT, including the major criteria of the syndrome, such as regression of developmental skills and abnormal gait. Based on the survey results, 12 individuals could meet the diagnostic clinical criteria for atypical RTT Syndrome. In summary, individuals with HNRNPH2-RNDD exhibit clinical characteristics that overlap with those of RTT, and therefore, HNRNPH2-RNDD, should be considered on the differential diagnosis list with this clinical picture.

Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8+ T cell responses post SARS-CoV-2 infection.

T cells are a critical component of the response to SARS-CoV-2, but their kinetics after infection and vaccination are insufficiently understood. Using "spheromer" peptide-MHC multimer reagents, we analyzed healthy subjects receiving two doses of the Pfizer/BioNTech BNT162b2 vaccine. Vaccination resulted in robust spike-specific T cell responses for the dominant CD4+ (HLA-DRB1∗15:01/S191) and CD8+ (HLA-A∗02/S691) T cell epitopes. Antigen-specific CD4+ and CD8+ T cell responses were asynchronous, with the peak CD4+ T cell responses occurring 1 week post the second vaccination (boost), whereas CD8+ T cells peaked 2 weeks later. These peripheral T cell responses were elevated compared with COVID-19 patients. We also found that previous SARS-CoV-2 infection resulted in decreased CD8+ T cell activation and expansion, suggesting that previous infection can influence the T cell response to vaccination.

Heterogeneity in IgG-CD16 signaling in infectious disease outcomes.

In this review, we discuss how IgG antibodies can modulate inflammatory signaling during viral infections with a focus on CD16a-mediated functions. We describe the structural heterogeneity of IgG antibody ligands, including subclass and glycosylation that impact binding by and downstream activity of CD16a, as well as the heterogeneity of CD16a itself, including allele and expression density. While inflammation is a mechanism required for immune homeostasis and resolution of acute infections, we focus here on two infectious diseases that are driven by pathogenic inflammatory responses during infection. Specifically, we review and discuss the evolving body of literature showing that afucosylated IgG immune complex signaling through CD16a contributes to the overwhelming inflammatory response that is central to the pathogenesis of severe forms of dengue disease and coronavirus disease 2019 (COVID-19).