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Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting), and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective "average event." We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT's capacities on a wave-mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass (1976), which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also \emph{backward in time} to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the \emph{reactive current}. The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions., Comment: 18 pages, 7 figures (main text), 19 pages, 1 figure (supplement). Accepted for publication in the Journal of the Atmospheric Sciences

The long-term stability of the Solar System is an issue of significant scientific and philosophical interest. The mechanism leading to instability is Mercury's eccentricity being pumped up so high that Mercury either collides with Venus or is scattered into the Sun. Previously, only three five-billion-year $N$-body ensembles of the Solar System with thousands of simulations have been run to assess long-term stability. We generate two additional ensembles, each with 2750 members, and make them publicly available at \texttt{https://archive.org/details/@dorianabbot}. We find that accurate Mercury instability statistics can be obtained by (1) including only the Sun and the 8 planets, (2) using a simple Wisdom-Holman scheme without correctors, (3) using a basic representation of general relativity, and (4) using a time step of 3.16 days. By combining our Solar System ensembles with previous ensembles we form a 9,601-member ensemble of ensembles. In this ensemble of ensembles, the logarithm of the frequency of a Mercury instability event increases linearly with time between 1.3 and 5 Gyr, suggesting that a single mechanism is responsible for Mercury instabilities in this time range and that this mechanism becomes more active as time progresses. Our work provides a robust estimate of Mercury instability statistics over the next five billion years, outlines methodologies that may be useful for exoplanet system investigations, and provides two large ensembles of publicly available Solar System integrations that can serve as testbeds for theoretical ideas as well as training sets for artificial intelligence schemes., accepted at ApJ

We present a stable and systematically improvable quantum Monte Carlo (QMC) approach to calculating excited-state energies, which we implement using our fast randomized iteration method for the full configuration interaction problem (FCI-FRI). Unlike previous excited-state quantum Monte Carlo methods, our approach, which is an asymmetric variant of subspace iteration, avoids the use of dot products of random vectors and instead relies upon trial vectors to maintain orthogonality and estimate eigenvalues. By leveraging recent advances, we apply our method to calculate ground- and excited-state energies of strongly correlated molecular systems in large active spaces, including the carbon dimer with 8 electrons in 108 orbitals (8e,108o), an oxo-Mn(salen) transition metal complex (28e,28o), ozone (18e,87o), and butadiene (22e,82o). In the majority of these test cases, our approach yields total excited-state energies that agree with those from state-of-the-art methods -- including heat-bath CI, the density matrix renormalization group approach, and FCIQMC -- to within sub-milliHartree accuracy. In all cases, estimated excitation energies agree to within about 0.1 eV., Comment: 16 pages, 5 figures, 3 tables

To determine whether mitigating the harmful effects of circulating microvesicle-associated inducible nitric oxide (MV-A iNOS) in vivo increases the survival of challenged mice in three different mouse models of sepsis, the ability of anti-MV-A iNOS monoclonal antibodies (mAbs) to rescue challenged mice was assessed using three different mouse models of sepsis. The vivarium of a research laboratory Balb/c mice were challenged with an LD80 dose of either lipopolysaccharide (LPS/endotoxin), TNFα, or MV-A iNOS and then treated at various times after the challenge with saline as control or with an anti-MV-A iNOS mAb as a potential immunotherapeutic to treat sepsis. Each group of mice was checked daily for survivors, and Kaplan–Meier survival curves were constructed. Five different murine anti-MV-A iNOS mAbs from our panel of 24 murine anti-MV-A iNOS mAbs were found to rescue some of the challenged mice. All five murine mAbs were used to genetically engineer humanized anti-MV-A iNOS mAbs by inserting the murine complementarity-determining regions (CDRs) into a human IgG1,kappa scaffold and expressing the humanized mAbs in CHO cells. Three humanized anti-MV-A iNOS mAbs were effective at rescuing mice from sepsis in three different animal models of sepsis. The effectiveness of the treatment was both time- and dose-dependent. Humanized anti-MV-A iNOS rHJ mAb could rescue up to 80% of the challenged animals if administered early and at a high dose. Our conclusions are that MV-A iNOS is a novel therapeutic target to treat sepsis; anti-MV-A iNOS mAbs can mitigate the harmful effects of MV-A iNOS; the neutralizing mAb’s efficacy is both time- and dose-dependent; and a specifically targeted immunotherapeutic for MV-A iNOS could potentially save tens of thousands of lives annually and could result in improved antibiotic stewardship.

Direct computer simulation of intense tropical cyclones (TCs) in weather models is limited by computational expense. Intense TCs are rare and have small‐scale structures, making it difficult to produce large ensembles of storms at high resolution. Further, models often fail to capture the process of rapid intensification, which is a distinguishing feature of many intense TCs. Understanding rapid intensification is especially important in the context of global warming, which may increase the frequency of intense TCs. To better leverage computational resources for the study of rapid intensification, we introduce an action minimization algorithm applied to the Weather Research and Forecasting and WRFPLUS models. Action minimization nudges the model into forming more intense TCs than it otherwise would; it does so via the maximum likelihood path in a stochastic formulation of the model, thereby allowing targeted study of intensification mechanisms. We apply action minimization to simulations of Hurricanes Danny (2015) and Fred (2009) at 6‐km resolution to demonstrate that the algorithm consistently intensifies TCs via physically plausible pathways. We show an approximately tenfold computational savings using action minimization to study the tail of the TC intensification distribution. Further, for Hurricanes Danny and Fred, action minimization produces perturbations that preferentially reduce low‐level shear as compared to upper‐level shear, at least above a threshold of approximately 4 m/s. We also demonstrate that asymmetric, time‐dependent patterns of heating can cause significant TC intensification beyond symmetric, azimuthally averaged heating and find a regime of nonlinear response to asymmetric heating that has not been extensively studied in previous work.

Traditional numerical methods for calculating matrix eigenvalues are prohibitively expensive for high-dimensional problems. Iterative random sparsification methods allow for the estimation of a single dominant eigenvalue at reduced cost by leveraging repeated random sampling and averaging. We present a general approach to extending such methods for the estimation of multiple eigenvalues and demonstrate its performance for several benchmark problems in quantum chemistry., 31 pages, 8 figures

Variational Monte Carlo (VMC) is an approach for computing ground-state wavefunctions that has recently become more powerful due to the introduction of neural network-based wavefunction parametrizations. However, efficiently training neural wavefunctions to converge to an energy minimum remains a difficult problem. In this work, we analyze optimization and sampling methods used in VMC and introduce alterations to improve their performance. First, based on theoretical convergence analysis in a noiseless setting, we motivate a new optimizer that we call the Rayleigh-Gauss-Newton method, which can improve upon gradient descent and natural gradient descent to achieve superlinear convergence at no more than twice the computational cost. Second, in order to realize this favorable comparison in the presence of stochastic noise, we analyze the effect of sampling error on VMC parameter updates and experimentally demonstrate that it can be reduced by the parallel tempering method. In particular, we demonstrate that RGN can be made robust to energy spikes that occur when the sampler moves between metastable regions of configuration space. Finally, putting theory into practice, we apply our enhanced optimization and sampling methods to the transverse-field Ising and XXZ models on large lattices, yielding ground-state energy estimates with remarkably high accuracy after just 200 parameter updates., Comment: 12 pages, 8 figures

Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, Sudden Stratospheric Warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These \emph{optimal forecasts} concretely measure the event's progress. Direct numerical simulation can estimate them in principle, but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are \emph{short} compared to the timescale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass (1976) and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in timescales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology., Comment: 26 pages, 7 figures, major revision after original. Accepted to Monthly Weather Review, American Meteorological Society

One approach to analyzing the dynamics of a physical system is to search for long-lived patterns in its motions. This approach has been particularly successful for molecular dynamics data, where slowly decorrelating patterns can indicate large-scale conformational changes. Detecting such patterns is the central objective of the variational approach to conformational dynamics (VAC), as well as the related methods of time-lagged independent component analysis and Markov state modeling. In VAC, the search for slowly decorrelating patterns is formalized as a variational problem solved by the eigenfunctions of the system's transition operator. VAC computes solutions to this variational problem by optimizing a linear or nonlinear model of the eigenfunctions using time series data. Here, we build on VAC's success by addressing two practical limitations. First, VAC can give poor eigenfunction estimates when the lag time parameter is chosen poorly. Second, VAC can overfit when using flexible parametrizations such as artificial neural networks with insufficient regularization. To address these issues, we propose an extension that we call integrated VAC (IVAC). IVAC integrates over multiple lag times before solving the variational problem, making its results more robust and reproducible than VAC's.

We present three modifications to our recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) and investigate their effects on the method's performance for Ne, H$_2$O, and N$_2$. The initiator approximation, originally developed for full configuration interaction quantum Monte Carlo, significantly reduces statistical error in FCI-FRI when few samples are used in compression operations, enabling its application to larger chemical systems. The semi-stochastic extension, which involves exactly preserving a fixed subset of elements in each compression, improves statistical efficiency in some cases but reduces it in others. We also developed a new approach to sampling excitations that yields consistent improvements in statistical efficiency and reductions in computational cost. We discuss possible strategies based on our findings for improving the performance of stochastic quantum chemistry methods more generally., 13 pages, 5 figures

Dynamical spectral estimation is a well-established numerical approach for estimating eigenvalues and eigenfunctions of the Markov transition operator from trajectory data. Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood. Here we analyze the error of a dynamical spectral estimation method called "the variational approach to conformational dynamics" (VAC). We bound the approximation error and estimation error for VAC estimates. Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy., Comment: 34 pages, 7 figures

We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our new sampler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensemble sampler for the first time to infinite-dimensional function spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our new ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable.

We introduce a family of methods for the full configuration interaction problem in quantum chemistry, based on the fast randomized iteration (FRI) framework [L.-H. Lim and J. Weare, SIAM Rev. 59, 547 (2017)]. These methods, which we term "FCI-FRI," stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11 to 45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4 to 178 times more statistically efficient than the original FCIQMC algorithm., 19 pages, 7 figures

Extreme mesoscale weather, including tropical cyclones, squall lines, and floods, can be enormously damaging and yet challenging to simulate; hence, there is a pressing need for more efficient simulation strategies. Here we present a new rare event sampling algorithm called Quantile Diffusion Monte Carlo (Quantile DMC). Quantile DMC is a simple-to-use algorithm that can sample extreme tail behavior for a wide class of processes. We demonstrate the advantages of Quantile DMC compared to other sampling methods and discuss practical aspects of implementing Quantile DMC. To test the feasibility of Quantile DMC for extreme mesoscale weather, we sample extremely intense realizations of two historical tropical cyclones, 2010 Hurricane Earl and 2015 Hurricane Joaquin. Our results demonstrate Quantile DMC's potential to provide low-variance extreme weather statistics while highlighting the work that is necessary for Quantile DMC to attain greater efficiency in future applications., 18 pages, 9 figures

Background The sepsis pathology remains an enormous medical problem globally because morbidity and mortality remain unacceptably high in septic patients despite intense research efforts. The economic and societal burden of sepsis makes it the most pressing patient care issue in the US and worldwide. Sepsis is a dysregulated immune response normally initiated by an infection. The need for an early, accurate, and reliable biomarker test to detect the onset of sepsis and for a targeted sepsis therapy is widely recognized in the biomedical community. Content This report reviews the published findings relevant to microvesicle-associated inducible nitric oxide synthase (MV-A iNOS) as a novel plasma biomarker for the onset of sepsis, including human clinical studies and animal studies. Plasma iNOS as both a standalone test and 1 of the components of a novel panel of biomarkers to stage the progression of sepsis is presented and discussed in comparison with other biomarkers and other proposed panels of biomarkers for sepsis. Summary The data strongly support the concept that extracellular plasma MV-A iNOS in circulating microvesicles is centrally involved in the initiation of sepsis, and a diagnostic test based on plasma iNOS can serve as an early presymptomatic warning signal for the onset of sepsis. A novel panel of plasma biomarkers comprising iNOS, pro-interleukin-18, pro-interleukin-33, and regenerating protein 1-α is proposed as a multianalyte presymptomatic method to stage the onset of sepsis for improved, prompt, data-driven patient care.

Pemphigus vulgaris (PV) is a life-long, potentially fatal IgG autoantibody-mediated blistering disease targeting mucocutaneous keratinocytes (KCs). PV patients develop pathogenic anti-desmoglein (Dsg) 3 ± 1 and antimitochondrial antibodies (AMA), but it remained unknown whether and how AMA enter KCs and why other cell types are not affected in PV. Therefore, we sought to elucidate mechanisms of cell entry, trafficking, and pathogenic action of AMA in PV. We found that PVIgGs associated with neonatal Fc receptor (FcRn) on the cell membrane, and the PVIgG-FcRn complexes entered KCs and reached mitochondria where they dissociated. The liberated AMA altered mitochondrial membrane potential, respiration, and ATP production and induced cytochrome c release, although the lack or inactivation of FcRn abolished the ability of PVIgG to reach and damage mitochondria and to cause detachment of KCs. The assays of mitochondrial functions and keratinocyte adhesion demonstrated that although the pathobiological effects of AMA on KCs are reversible, they become irreversible, leading to epidermal blistering (acantholysis), when AMA synergize with anti-Dsg antibodies. Thus, it appears that AMA enter a keratinocyte in a complex with FcRn, become liberated from the endosome in the cytosol, and are trafficked to the mitochondria, wherein they trigger pro-apoptotic events leading to shrinkage of basal KCs uniquely expressing FcRn in epidermis. During recovery, KCs extend their cytoplasmic aprons toward neighboring cells, but anti-Dsg antibodies prevent assembly of nascent desmosomes due to steric hindrance, thus rendering acantholysis irreversible. In conclusion, FcRn is a common acceptor protein for internalization of AMA and, perhaps, for PV autoantibodies to other intracellular antigens, and PV is a novel disease paradigm for investigating and elucidating the role of FcRn in this autoimmune disease and possibly other autoimmune diseases.

A panel of monoclonal antibodies (MAbs) to human inducible nitric oxide synthase (hiNOS) has been developed. By isotype analysis of the MAbs cloned from the 24 different positive hybridomas, 13 were determined to be mouse IgG1, two were mouse IgG2a, two were mouse IgG2b, and the seven others were mouse IgM antibodies: all contained kappa light chains. The anti-hiNOS MAbs were initially characterized by ELISA, RIA, Western blot, and immunocytochemistry, and then they were epitope mapped using synthetic peptides and a three-step mapping procedure. In the first step, each of the 24 MAbs was tested by indirect ELISA for binding to 96 overlapping 18-amino acid-long peptides that span the entire 1153-amino acid length of hiNOS. Eight IgG class anti-hiNOS MAbs were found to bind to one of five different peptides. In the second step, a series of amino terminal and carboxyl terminal truncated peptides were synthesized for each of the five peptides to which one or more of the MAbs bound. Each of the eight anti-hiNOS MAbs was found to bind to the truncated peptides with a unique specificity that identified the amino acid segment involved in binding. The third step in the epitope mapping process utilized three series of overlapping 5-, 6-, 7-, 8-, and 9-amino acid-long peptides for each of these segments and identified the exact amino acids of hiNOS involved in antibody binding. Anti-hiNOS MAbs 2A1-F8, 2D2-B2, 21C10-1D10, and 24B10-2C7 were found to be especially useful in different immunoassays.

Acetylcholine mediates cell-to-cell communications in the skin. Human epidermal keratinocytes respond to acetylcholine via two classes of cell-surface receptors, the nicotinic and the muscarinic cholinergic receptors. High affinity muscarinic acetylcholine receptors (mAChR) have been found on keratinocyte cell surfaces at high density. These receptors mediate effects of muscarinic drugs on keratinocyte viability, proliferation, adhesion, lateral migration, and differentiation. In this study, we investigated the molecular structure of keratinocyte mAChR and their location in human epidermis. Polymerase chain reaction amplification of cDNA sequences uniquely present within the third cytoplasmic loop of each subtype demonstrated the expression of the m1, m3, m4, and m5 mAChR subtypes. To visualize these mAChR, we raised rabbit anti-sera to synthetic peptide analogs of the carboxyl terminal regions of each subtype. The antibodies selectively bound to keratinocyte mAChR subtypes in immunoblotting membranes and epidermis, both of which could be abolished by preincubating the anti-serum with the peptide used for immunization. The immunofluorescent staining patterns produced by each antibody in the epidermis suggested that the profile of keratinocyte mAChR changes during epidermal turnover. The semiquantitative analysis of fluorescence revealed that basal cells predominantly expressed m3, prickle cells had equally high levels of m4 and m5, and granular cells mostly possessed m1. Thus, the results of this study demonstrate for the first time the presence of m1, m3, m4, and m5 mAChR in epidermal keratinocytes. Because keratinocytes express a unique combination of mAChR subtypes at each stage of their development in the epidermis, each receptor may regulate a specific cell function. Hence, a single cytotransmitter, acetylcholine, and muscarinic drugs may exert different biologic effects on keratinocytes at different stages of their maturation.

A novel paradigm of keratinocyte (KC) regulation via nicotinic acetylcholine receptors (nAChR) has been discovered in studies of SLURP (secreted mammalian Ly-6/urokinase-type plasminogen activator receptor-related protein)-1 in Mal de Meleda. We cloned human SLURP-1 and produced recombinant protein and the monoclonal antibody 336H12-1A3 that visualized native SLURP-1. SLURP-1 ligated the conventional ligand-binding site of KC nAChR, showing a higher affinity to the [(3)H]nicotine-, compared with the [(3)H]epibatidine-sensitive nAChR. SLURP-1 significantly (p0.05) increased the activities of caspases 3 and 8, and the number of terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling-positive cells. The pro-apoptotic activity of SLURP-1 exceeded that of tumor necrosis factor-alpha, suggesting the involvement of separate pathways. In a series of real-time PCR and in-cell western experiments, SLURP-1 significantly (p0.05) upregulated expression of transglutaminase type I cytokeratin 10, p21, and caspase-3. In the presence of the agonist carbachol, the effects of SLURP-1 on gene expression were augmented, which is in keeping with the notion that SLURP-1 acts as an allosteric agonist at the KC nAChR. Thus, the changes in the cell state induced by SLURP-1 could result from nAChR-mediated effects on the KC gene expression. These results suggest that the biological role of SLURP-1 in the epidermis is to provide fine tuning of the physiologic regulation of KC functions through the cholinergic pathways.

The auction of an object is considered, for the case in which one bidder is better-informed than the others concerning the actual value of the object. An equilibrium point solution of the competitive bidding game is determined; at this equilibrium, the expected revenue of the less-well-informed bidders is zero. The case of an object which can take only values from a discrete set is dealt with as the limit of auctions of continuously-valued objects.