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Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline.

Obesity, and obesity-associated conditions such as hypertension, chronic kidney disease, type 2 diabetes, and cardiovascular disease, are important risk factors for severe Coronavirus disease-2019 (COVID-19). The common denominator is metaflammation, a portmanteau of metabolism and inflammation, which is characterized by chronically elevated levels of leptin and pro-inflammatory cytokines. These induce the “Suppressor Of Cytokine Signaling 1 and 3” (SOCS1/3), which deactivates the leptin receptor and also other SOCS1/3 sensitive cytokine receptors in immune cells, impairing the type I and III interferon early responses. By also upregulating SOCS1/3, Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 adds a significant boost to this. The ensuing consequence is a delayed but over-reactive immune response, characterized by high-grade inflammation (e.g., cytokine storm), endothelial damage, and hypercoagulation, thus leading to severe COVID-19. Superimposing an acute disturbance, such as a SARS-CoV-2 infection, on metaflammation severely tests resilience. In the long run, metaflammation causes the “typical western” conditions associated with metabolic syndrome. Severe COVID-19 and other serious infectious diseases can be added to the list of its short-term consequences. Therefore, preventive measures should include not only vaccination and the well-established actions intended to avoid infection, but also dietary and lifestyle interventions aimed at improving body composition and preventing or reversing metaflammation. Sin financiación 6.706 JCR (2021) Q1, 15/90 Nutrition & Dietetics 1.287 SJR (2021) Q1, 19/338 Food Science No data IDR 2021 UEM

The coronavirus disease 2019 (COVID-19) pandemic is a global health threat with particular risk for severe disease and death in older adults and in adults with age-related metabolic and cardiovascular disease. Recent advances in the science of ageing have highlighted how ageing pathways control not only lifespan but also healthspan, the healthy years of life. Here, we discuss the ageing immune system and its ability to respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We specifically focus on the intersect of severe COVID-19 and immunosenescence to highlight pathways that may be determinant for the risk of complications and death following infection with SARS-CoV-2. New or adapted therapeutics that target ageing-associated pathways may be important tools to reduce the burden of death and long-term disability caused by this pandemic. Proposed interventions aimed at immunosenescence could enhance immune function not only in the elderly but in susceptible younger individuals as well, ultimately improving complications of severe COVID-19 for all ages.

How the human immune system reacts prior to a cancer diagnosis is largely unknown. Here we analyze a longitudinal cohort of 133 healthy individuals (18 to over 90 years old), 28 of whom developed cancers over nine years. We find the global abundance of a broad spectrum of cytokines (26 out of 32 assayed) surges 2-3 years before cancer diagnosis but not with other conditions (such as inflammatory or cardiovascular diseases). These cytokine surges precede cancer diagnosis only in those 80 years or older, suggesting age-specific inflammatory effects. Meanwhile, in early-stage (but not late-stage) tissues across 10 cancer types, aging elevates cytokine transcriptional activity, which coincides with an age-dependent expansion of the inflammatory macrophage population. We further find that macrophage is a major producer of cytokines in cancer tissues, irrespective of cancer types. These results suggest that cancer associated macrophage is a major source of peripheral inflammatory molecules and may mobilize immunity well before a clinical diagnosis in the elderly. Citation Format: Guangbo Chen, Hong Zheng, Yael Rosenberg-Hasson, Saron Araya, Cindy Padilla, Kavita Sarin, Cornelia L. Dekker, Philip Grant, Holden T. Maecker, David Furman, Shai Shen-Orr, Purvesh Khatri, Mark M. Davis. Cytokines surge in the blood years prior to a cancer diagnosis in elderly individuals [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr PR002.

Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual’s immune age. Here, we use multiple ‘omics’ technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a highdimensional trajectory of immune aging (IMM-AGE) that describes a person’s immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk. REPORTING SUMMARY. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.

Despite being largely preventable, cardiovascular disease (CVD) is still the leading cause of death globally. Recent studies suggest that the immune system, particularly a form of systemic chronic inflammation (SCI), is involved in the mechanisms leading to CVD; thus, targeting SCI may help prevent or delay the onset of CVD. In a recent placebo-controlled randomized clinical trial, an oat product providing 3 g of β-Glucan improved cholesterol low-density lipoprotein (LDL) levels and lowered cardiovascular risk in adults with borderline high cholesterol. Here, we conducted a secondary measurement of the serum samples to test whether the oat product has the potential to reduce SCI and improve other clinical outcomes related to healthy aging. We investigated the effects of the oat product on a novel metric for SCI called Inflammatory Age® (iAge®), derived from the Stanford 1000 Immunomes Project. The iAge® predicts multimorbidity, frailty, immune decline, premature cardiovascular aging, and all-cause mortality on a personalized level. A beneficial effect of the oat product was observed in subjects with elevated levels of iAge® at baseline (>49.6 iAge® years) as early as two weeks post-treatment. The rice control group did not show any significant change in iAge®. Interestingly, the effects of the oat product on iAge® were largely driven by a decrease in the Eotaxin-1 protein, an aging-related chemokine, independent of a person’s gender, body mass index, or chronological age. Thus, we describe a novel anti-SCI role for oats that could have a major impact on functional, preventative, and personalized medicine.

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes. From the blood immunome of 1,001 individuals aged 8–96 years, the authors used deep learning to develop an inflammatory clock of aging (iAge) that tracks with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The main contributor to iAge is the chemokine CXCL9, which is shown to control endothelial cell senescence and function.

Significance Chronic inflammation contributes to morbidity and mortality in aging, but whether similar mechanisms underlie dysfunction in infection-associated chronic inflammation is unclear. Using a multicohort systems immunology approach, we identified signatures of immune dysfunction that are shared in aging and chronic viral infections, namely HIV and hepatitis C virus. We show that these shared dysfunctions persist despite viral clearance, and we describe the changes in functional coordination that occur during viral eradication. Finally, we highlight a partial restoration in interferon-α sensitivity across all major immune cell lineages as viral load drops. Our findings suggest a broad and persistent functional remodeling and deterioration of the human immune system despite removal of a chronic pathogenic burden that shares features of chronic inflammation in aging., Chronic inflammation is thought to be a major cause of morbidity and mortality in aging, but whether similar mechanisms underlie dysfunction in infection-associated chronic inflammation is unclear. Here, we profiled the immune proteome, and cellular composition and signaling states in a cohort of aging individuals versus a set of HIV patients on long-term antiretroviral therapy therapy or hepatitis C virus (HCV) patients before and after sofosbuvir treatment. We found shared alterations in aging-associated and infection-associated chronic inflammation including T cell memory inflation, up-regulation of intracellular signaling pathways of inflammation, and diminished sensitivity to cytokines in lymphocytes and myeloid cells. In the HIV cohort, these dysregulations were evident despite viral suppression for over 10 y. Viral clearance in the HCV cohort partially restored cellular sensitivity to interferon-α, but many immune system alterations persisted for at least 1 y posttreatment. Our findings indicate that in the HIV and HCV cohorts, a broad remodeling and degradation of the immune system can persist for a year or more, even after the removal or drastic reduction of the pathogen load and that this shares some features of chronic inflammation in aging.

Molecular dynamics (MD) simulations provide an important link between theories and experiments. While ab initio methods can be prohibitively costly, the ReaxFF force field has facilitated in silico studies of chemical reactivity in complex, condensed-phase systems. However, the relatively poor energy conservation in ReaxFF MD has either limited the applicability to short time scales, in cases where energy propagation is important, or has required a continuous coupling of the system to a heat bath. In this study, we reveal the root cause of the unsatisfactory energy conservation, and offer a straightforward solution. The new scheme results in orders of magnitude improvement in energy conservation, numerical stability, and accuracy of ReaxFF force fields, compared to the previous state-of-the-art, at no additional cost. We anticipate that these improvements will open new avenues of research for more accurate reactive simulations in complex systems on long time scales.

Four established ReaxFF force fields, trained on biochemical systems, have been systematically benchmarked on 20 proteinogenic amino acids and 11 dipeptides. The force fields were compared with respect to geometries, energetics, and atomic charges of conformers for the amino acids. To assess the performance with respect to reactivity, the condensation reactions for the formation of dipeptides were investigated by calculating the reaction energetics and pathways. We found systematic errors in the torsion angles for the amino acids, with deviations over 100°, and a generally incorrect account of relative energies for amino acid conformers. In describing the reactivity, only one of the force fields could reproduce the reaction energies of amino acid condensations quantitatively. All four force fields predict unphysical mechanisms for these reactions, involving highly unstable intermediate structures, proton transfers involving aliphatic protons, and even five-coordinate carbon atoms. The corresponding energy landscapes exhibit fluctuations on small length scales and artificial minima.

BACKGROUND The COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life. OBJECTIVE With increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19. METHODS Over 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend. RESULTS The most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations. CONCLUSIONS Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations.

Background The COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life. Objective With increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19. Methods Over 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend. Results The most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations. Conclusions Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations.

The coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome (SARS)-CoV-2, continues to burden medical institutions around the world by increasing total hospitalization and Intensive Care Unit (ICU) admissions1–9 A better understanding of symptoms, comorbidities and medication used for preexisting conditions in patients with COVID-19 could help healthcare workers identify patients at increased risk of developing more severe disease10,11. Here, we have used self-reported data (symptoms, medications and comorbidities) from more than 3 million users from the COVID-19 Symptom Tracker app12 to identify previously reported and novel features predictive of patients being admitted in a hospital setting. Despite previously reported association between age and more severe disease phenotypes13–18, we found that patient’s age, sex and ethnic group were minimally predictive when compared to patient’s symptoms and comorbidities. The most important variables selected by our predictive algorithm were fever, the use of immunosuppressant medication, mobility aid, shortness of breath and fatigue. It is anticipated that early administration of preventative measures in COVID-19 positive patients (COVID+) who exhibit a high risk of hospitalization signature may prevent severe disease progression.

In recent work, we have presented a new ReaxFF formulation with a superior conservation of energy in reactive molecular dynamics simulations. The key ingredient in the approach involved the tapering of bond order and bond distance discontinuities using Hermite polynomials. This Communication extends the previous formulation by alleviating additional sources of numerical instability in the original formalism. These "numerical pathologies" are rooted in the counting of lone-pair electrons, the sum of bond orders between atoms that form a valence angle, and the definition of a torsional potential. Based on a theoretical analysis, new functions that mitigate these limitations are designed and validated. The extent of their transferability with previous parameterizations is discussed. The new enhancements provide further gains in numerical stability to facilitate exploration of reactive energy landscapes.

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Pentaerythritol tetranitrate (PETN) finds many uses in the energetic materials community. Due to the recent availability of erythritol, erythritol tetranitrate (ETN) can now be readily synthesized. Accordingly, its complete characterization, especially its stability, is of great interest. This work examines the thermal decomposition of ETN, both through experimental and computational methods. In addition to kinetic parameters, decomposition products were examined to elucidate its decomposition pathway. It is found that ETN begins its decomposition sequence by a unimolecular homolytic cleavage of the internal and external O–NO2 bonds, while the competing HONO elimination reaction is largely suppressed. The global activation energy for decomposition is found to be 104.3 kJ/mol with a pre-exponential factor of 3.72 × 109 s–1. Despite the ability to exist in a molten state, ETN has a lower thermal stability than its counterpart PETN.

Systems-biology approaches in immunology take various forms, but here we review strategies for measuring a broad swath of immunological functions as a means of discovering previously unknown relationships and phenomena and as a powerful way of understanding the immune system as a whole. This approach has rejuvenated the field of vaccine development and has fostered hope that new ways will be found to combat infectious diseases that have proven refractory to classical approaches. Systems immunology also presents an important new strategy for understanding human immunity directly, taking advantage of the many ways the immune system of humans can be manipulated.

Nickel oxide-based materials have attracted significant interest for a variety of energy conversion applications although many of their structures remain unresolved. In this study, Density Functional Theory+U (DFT+U) and hybrid DFT calculations are used to analyze the properties of crystalline nickel oxyhydroxide (β-NiOOH) with hydrogen (H) vacancies. Hydrogen vacancies are found to lower the band gap without creating states inside the band gap. Inter-layer crossing is a possible transport pathway, while intra-layer transport is inhibited. Bulk modulus is not influenced by H vacancies in the crystal. β-NiOOH with H vacancies exhibits good electronic properties, essential for solid electrolytes and anodes in solid oxide fuel cells.

Despite clear differences in immune system responses and in the prevalence of autoimmune diseases between males and females, there is little understanding of the processes involved. In this study, we identified a gene signature of immature-like neutrophils, characterized by the overexpression of genes encoding for several granule-containing proteins, which was found at higher levels (up to 3-fold) in young (20–30 y old) but not older (60 to >89 y old) males compared with females. Functional and phenotypic characterization of peripheral blood neutrophils revealed more mature and responsive neutrophils in young females, which also exhibited an elevated capacity in neutrophil extracellular trap formation at baseline and upon microbial or sterile autoimmune stimuli. The expression levels of the immature-like neutrophil signature increased linearly with pregnancy, an immune state of increased susceptibility to certain infections. Using mass cytometry, we also find increased frequencies of immature forms of neutrophils in the blood of women during late pregnancy. Thus, our findings show novel sex differences in innate immunity and identify a common neutrophil signature in males and in pregnant women.

The mechanochemical effects of 5 nm spherical heterogeneities on the reactivity of shocked crystalline erythritol tetranitrate (ETN), an emerging improvised explosive, are revealed for the first time. Reactive molecular dynamics simulations in conjunction with the symmetric plate-impact technique, are employed to characterize different stages of the process, including shock–void interaction, void collapse, enhanced reactivity, and subsequent molecular decomposition. The formation of supersonic nanojets from the proximal void surface is observed to greatly enhance local heating following impact of the nanojets on the distant void surface. It is demonstrated that the nanovoid collapse closely matches a Rayleigh-type hydrodynamic bubble collapse, including a spherical-to-crescent shape transformation. The chemical decomposition mechanism of condensed phase ETN is analyzed and is shown to follow a unimolecular path. The molecular decomposition of a defect containing crystal occurs with significantly higher ra...

A novel universal mechanism for the ejection of intact, neutral molecules from thin films into the gas phase initiated by high intensity ultrashort laser pulses is described. The proposed mechanism is substantiated by detailed reactive molecular dynamics simulations. In the present study, 2,4,6-trinitrotoluene (TNT) and cryogenic benzene are used as thin film targets. According to the proposed mechanism, the laser pulse, absorbed by the substrate, forms a hot plasma plume which in turn generates a shock wave that moves across the deposited thin film. The simulations indicate that the shock wave propagates through the thin film without dissipation and eventually ejects molecules from the free surface. It is revealed that the proposed mechanism is feasible only in a limited range of shock velocities. The results compare well qualitatively with recent experimental findings of femtosecond, nonresonant, laser-induced desorption. Simple experimental measurements are outlined to validate the proposed mechanism.

The development of a reactive force field (ReaxFF formalism) for hydrazoic acid (HN3), a highly sensitive liquid energetic material, is reported. The force field accurately reproduces results of density functional theory (DFT) calculations. The quality and performance of the force field are examined by detailed comparison with DFT calculations related to uni, bi, and trimolecular thermal decomposition routes. Reactive molecular dynamics (RMD) simulations are performed to reveal the initial chemical events governing the detonation chemistry of liquid HN3. The outcome of these simulations compares very well with recent results of tight-binding DFT molecular dynamics and thermodynamic calculations. On the basis of our RMD simulations, predictions were made for the activation energies and volumes in a broad range of temperatures and initial material compressions.

Nickel hydroxide phases are common in several energy conversion devices including battery electrochemical cell electrodes. These materials have a unique layered structure that may facilitate hydrogen transfer between oxygen sites and allow using this material also for proton conducting fuel cells. In order to assess this functionality, we use Density Functional Theory+U together with the Nudged Elastic Band method to calculate minimum energy diffusion paths and hydrogen vacancy formation energies of different crystal phases of NiOOH including β-NiOOH, β-Ni(OH)2 and α-Ni(OH)2. We follow several diffusion paths and mechanisms in several phases, both across layers and through them. We pin down the reason for efficient diffusion laterally on a layer and explain why diffusion through a layer is impossible. Our results suggest that hydrogen transfer may be possible for the β-NiOOH phase with hydrogen added interstitially and transferred along the layers. This study significantly advances our understanding of diffusion in an uncommonly structured material.

The performance of interFoam (a widely-used solver within the popular open source CFD package OpenFOAM) in simulating the propagation of a nonlinear (stream function solution) regular wave is investigated in this work, with the aim of systematically documenting its accuracy. It is demonstrated that over time there is a tendency for surface elevations to increase, wiggles to appear in the free surface, and crest velocities to become (severely) overestimated. It is shown that increasing the temporal and spatial resolution can mitigate these undesirable effects, but that a relatively small Courant number is required. It is further demonstrated that the choice of discretization schemes and solver settings (often treated as a "black box" by users) can have a major impact on the results. This impact is documented, and it is shown that obtaining a "diffusive balance" is crucial to accurately propagate a surface wave over long distances without requiring exceedingly high temporal and spatial resolutions. Finally, the new code isoAdvector is compared to interFoam, which is demonstrated to produce comparably accurate results, while maintaining a sharper surface. It is hoped that the systematic documentation of the performance of the interFoam solver will enable its more accurate and optimal use, as well as increase awareness of potential shortcomings, by CFD researchers interested in the general CFD simulation of free surface waves., 18 pages and 23 figures

ObjectivesThis study sought to determine whether a simple score combining indexes of right ventricular (RV) function and right atrial (RA) size would offer good discrimination of outcome in patients with pulmonary arterial hypertension (PAH).BackgroundIdentifying a simple score of outcome could simplify risk stratification of patients with PAH and potentially lead to improved tailored monitoring or therapy.MethodsWe recruited patients from both Stanford University (derivation cohort) and VU University Medical Center (validation cohort). The composite endpoint for the study was death or lung transplantation. A Cox proportional hazard with bootstrap CI adjustment model was used to determine independent correlates of death or transplantation. A predictive score was developed using the beta coefficients of the multivariable models.ResultsFor the derivation cohort (n = 95), the majority of patients were female (79%), average age was 43 ± 11 years, mean pulmonary arterial pressure was 54 ± 14 mm Hg, and pulmonary vascular resistance index was 25 ± 12 Wood units × m2. Over an average follow-up of 5 years, the composite endpoint occurred in 34 patients, including 26 deaths and 8 patients requiring lung transplant. On multivariable analysis, RV systolic dysfunction grade (hazard ratio [HR]: 3.4 per grade; 95% confidence interval [CI]: 2.0 to 7.8; p < 0.001), severe RA enlargement (HR: 3.0; 95% CI: 1.3 to 8.1; p = 0.009), and systemic blood pressure

Generalized immune activation during HIV infection is associated with an increased risk of cardiovascular disease, neurocognitive disease, osteoporosis, metabolic disorders, and physical frailty. The mechanisms driving this immune activation are poorly understood, particularly for individuals effectively treated with antiretroviral medications. We hypothesized that viral characteristics such as sequence diversity may play a role in driving HIV-associated immune activation. We therefore sequenced proviral DNA isolated from peripheral blood mononuclear cells from HIV-infected individuals on fully suppressive antiretroviral therapy. We performed phylogenetic analyses, calculated viral diversity and divergence in the env and pol genes, and determined coreceptor tropism and the frequency of drug resistance mutations. Comprehensive immune profiling included quantification of immune cell subsets, plasma cytokine levels, and intracellular signaling responses in T cells, B cells, and monocytes. These antiretroviral therapy-treated HIV-infected individuals exhibited a wide range of diversity and divergence in both env and pol genes. However, proviral diversity and divergence in env and pol , coreceptor tropism, and the level of drug resistance did not significantly correlate with markers of immune activation. A clinical history of virologic failure was also not significantly associated with levels of immune activation, indicating that a history of virologic failure does not inexorably lead to increased immune activation as long as suppressive antiretroviral medications are provided. Overall, this study demonstrates that latent viral diversity is unlikely to be a major driver of persistent HIV-associated immune activation. IMPORTANCE Chronic immune activation, which is associated with cardiovascular disease, neurologic disease, and early aging, is likely to be a major driver of morbidity and mortality in HIV-infected individuals. Although treatment of HIV with antiretroviral medications decreases the level of immune activation, levels do not return to normal. The factors driving this persistent immune activation, particularly during effective treatment, are poorly understood. In this study, we investigated whether characteristics of the latent, integrated HIV provirus that persists during treatment are associated with immune activation. We found no relationship between latent viral characteristics and immune activation in treated individuals, indicating that qualities of the provirus are unlikely to be a major driver of persistent inflammation. We also found that individuals who had previously failed treatment but were currently effectively treated did not have significantly increased levels of immune activation, providing hope that past treatment failures do not have a lifelong “legacy” impact.

Activation energy for the decomposition of explosives is a crucial parameter of performance. The dramatic suppression of activation energy in condensed phase decomposition of nitroaromatic explosives has been an unresolved issue for over a decade. We rationalize the reduction in activation energy as a result of a mechanistic change from unimolecular decomposition in the gas phase to a series of radical bimolecular reactions in the condensed phase. This is in contrast to other classes of explosives, such as nitramines and nitrate esters, whose decomposition proceeds via unimolecular reactions both in the gas and in the condensed phase. The thermal decomposition of a model nitroaromatic explosive, 2,4,6-trinitrotoluene (TNT), is presented as a prime example. Electronic structure and reactive molecular dynamics (ReaxFF-lg) calculations enable to directly probe the condensed phase chemistry under extreme conditions of temperature and pressure, identifying the key bimolecular radical reactions responsible for the low activation route. This study elucidates the origin of the difference between the activation energies in the gas phase (∼62 kcal/mol) and the condensed phase (∼35 kcal/mol) of TNT and identifies the corresponding universal principle. On the basis of these findings, the different reactivities of nitro-based organic explosives are rationalized as an interplay between uni- and bimolecular processes.

Elderly humans show decreased humoral immunity to pathogens and vaccines, yet the effects of aging on B cells are not fully known. Chronic viral infection by cytomegalovirus (CMV) is implicated as a driver of clonal T cell proliferations in some aging humans, but whether CMV or Epstein-Barr virus (EBV) infection contributes to alterations in the B cell repertoire with age is unclear. We have used high-throughput DNA sequencing of immunoglobulin heavy chain (IGH) gene rearrangements to study the B cell receptor repertoires over two successive years in 27 individuals ranging in age from 20 to 89 years. Some features of the B cell repertoire remain stable with age, but elderly subjects show increased numbers of B cells with long CDR3 regions, a trend toward accumulation of more highly mutated IgM and IgG immunoglobulin genes, and persistent clonal B cell populations in the blood. Seropositivity for CMV or EBV infection alters B cell repertoires, regardless of the individual's age: EBV infection correlates with the presence of persistent clonal B cell expansions, while CMV infection correlates with the proportion of highly mutated antibody genes. These findings isolate effects of aging from those of chronic viral infection on B cell repertoires, and provide a baseline for understanding human B cell responses to vaccination or infectious stimuli.

A study of genome-wide gene expression in major depressive disorder (MDD) was undertaken in a large population-based sample to determine whether altered expression levels of genes and pathways could provide insights into biological mechanisms that are relevant to this disorder. Gene expression studies have the potential to detect changes that may be because of differences in common or rare genomic sequence variation, environmental factors or their interaction. We recruited a European ancestry sample of 463 individuals with recurrent MDD and 459 controls, obtained self-report and semi-structured interview data about psychiatric and medical history and other environmental variables, sequenced RNA from whole blood and genotyped a genome-wide panel of common single-nucleotide polymorphisms. We used analytical methods to identify MDD-related genes and pathways using all of these sources of information. In analyses of association between MDD and expression levels of 13 857 single autosomal genes, accounting for multiple technical, physiological and environmental covariates, a significant excess of low P-values was observed, but there was no significant single-gene association after genome-wide correction. Pathway-based analyses of expression data detected significant association of MDD with increased expression of genes in the interferon α/β signaling pathway. This finding could not be explained by potentially confounding diseases and medications (including antidepressants) or by computationally estimated proportions of white blood cell types. Although cause-effect relationships cannot be determined from these data, the results support the hypothesis that altered immune signaling has a role in the pathogenesis, manifestation, and/or the persistence and progression of MDD.

The reaction kinetics of the thermal decomposition of hot, dense liquid TNT was studied from first-principles-based ReaxFF multiscale reactive dynamics simulation strategy. The decomposition process was followed starting from the initial liquid phase, decomposition to radicals, continuing through formation of carbon-clusters products, and finally to formation of the stable gaseous products. The activation energy of the initial endothermic decomposition rate and the subsequent exothermic reactions were determined as a function of density. Analysis of fragments production in different densities and temperatures is presented. We find that unimolecular C–N bond scission dominates at the lower densities (producing NO2), whereas dimer formation and decomposition to TNT derivatives and smaller gaseous fragments prevails at higher compressions. At higher densities, enhanced carbon-clustering is observed, while the initial gaseous fragments formation is suppressed. Increasing the temperature speeds up the producti...

System-wide approaches are now being applied to study vaccine responses, whose mechanisms of action, and failure, are not well understood. These works have repeatedly shown vaccine response to be an orchestrated process involving multiple arms of immunity most noticeable sensing and innate components. Prediction of vaccine responses based on system-wide measures is achievable, but challenges remain for robust population wide predictions based only on pre-vaccination measures, especially in partially efficacious vaccines such as influenza. This is especially true in older adults, who are often less responsive to vaccination and exhibit high level of variation compared to young in many components of immunity. Despite this increase in variation, most of the studies on aging use group averages of immune phenotypes to model immune system behavior. Using systems approaches, it is possible to exploit this variation to form distinguishable clusters of phenotypes within and across individuals to discover underlying immune states.

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.

Advances in technology and data analysis have made it possible to take a new look at human immunology. These advances run the gamut from systems biology approaches, which are likely in the vanguard of how we can start “to put the pieces together” of immune function, to a deeper understanding of specific diseases and vaccines and the immune repertoire. In our own experience, we have also found that asking simple questions about human immunity has often given us very surprising answers, causing a rethink of established dogma. Thus, we have developed a new perspective on the nature of the αβ TCR repertoire and also the likely role of T-cell repertoire (TCR) cross-reactivity in generating T memory independent of specific antigen interactions. These findings show that human immunology is not just a necessary step for “translating” basic immunology to treat diseases or develop better vaccines, but is also an important complement to the inbred mouse model.